Emotional Intelligence Predicts Life Skills, but Not As Well As Personality and Cognitive Abilities, Personality and Individual Differences, 39, 1135 – 1145)

ARE THE CLAIMS FOR EMOTIONAL INTELLIGENCE

JUSTIFIED?

EMOTIONAL INTELLIGENCE PREDICTS LIFE SKILLS,

BUT NOT AS WELL AS PERSONALITY AND

COGNITIVE ABILITIES.

VENETA A. BASTIAN

DEPARTMENT OF PSYCHOLOGY

UNIVERSITY OF ADELAIDE

DECEMBER, 2005

TABLE OF CONTENTS

CHAPTER 1: THE CONCEPT OF INTELLIGENCE...... 20

1.1 OVERVIEW OF THIS THESIS ...... 20

1.2 COGNITIVE INTELLIGENCE THEORIES ...... 21

1.3 NON-COGNITIVE INTELLIGENCE THEORIES ...... 22

1.3.1 Social Intelligence ...... 24

1.3.2 Personal Intelligence...... 27

1.3.3 Practical Intelligence ...... 28

1.3.4 Emotional Intelligence...... 30

1.3.4.1 Goleman’s Original (1995) Model of Emotional Intelligence .... 34

1.3.4.2 Goleman’s Workplace (1998) Model of Emotional Intelligence 36

1.3.4.3 Goleman’s Claims About the Predictive Nature of Emotional

Intelligence...... 37

1.3.4.4 Criticisms of Goleman’s models ...... 42

CHAPTER 2: SCIENTIFIC THEORIES OF EMOTIONAL INTELLIGENCE .

...... 46

2.1 CRITICISMS ABOUT THE LABELLING OF “EMOTIONAL INTELLIGENCE” ...... 46

2.2 ‘ABILITY’ VERSUS ‘MIXED’ MODELS OF EMOTIONAL INTELLIGENCE ...... 47

2.3 THE MEASUREMENT OF EMOTIONAL INTELLIGENCE...... 48

2.3.1 Ability Emotional Intelligence Measures ...... 49

2.3.2 Self-report Emotional Intelligence Measures ...... 57

2.3.3 Observer-rater Emotional Intelligence measures ...... 59

2.3.4 Relationships between Emotional Intelligence Measures...... 60

2.4 RELATIONSHIP OF EMOTIONAL INTELLIGENCE WITH INTELLIGENCE AND

PERSONALITY...... 62

2.5 ISSUES AFFECTING THE PREDICTIVE VALIDITY OF EMOTIONAL

INTELLIGENCE...... 70

2.6 ALEXITHYMIA ...... 72

2.7 MAYER AND SALOVEY’S MODEL OF EMOTIONAL INTELLIGENCE ...... 73

2.7.1 Perception of Emotion...... 76

2.7.2 Utilisation of Emotion ...... 77

2.7.3 Understanding of Emotion ...... 79

2.7.4 Management of Emotion ...... 81

2.7.5 Trait Meta Mood Scale (TMMS) ...... 83

2.7.6 Multifactor Emotional Intelligence Scale (MEIS) and the Mayer,

Salovey and Caruso Emotional Intelligence Test (MSCEIT)...... 87

2.7.7 Assessing Emotions Scale (AES)...... 92

2.8 BAR-ON’S MODEL OF EMOTIONAL INTELLIGENCE...... 100

2.8.1 Emotional Quotient Inventory (EQ-i)...... 103

2.9 PETRIDES AND FURNHAM’S FRAMEWORK FOR EMOTIONAL INTELLIGENCE ....

...... 110

2.9.1 The Trait Emotional Intelligence Questionnaire (TEIQue)...... 112

2.10 CONCLUSION ...... 113

CHAPTER 3: THE PREDICTIVE NATURE OF EMOTIONAL

INTELLIGENCE FOR ‘LIFE SKILLS AND OUTCOMES’ AND

DEMOGRAPHIC VARIABLES...... 116

3.1 ACADEMIC ACHIEVEMENT AND EMOTIONAL INTELLIGENCE ...... 117 3

3.2 LIFE SATISFACTION...... 124

3.2.1 Life Satisfaction and Emotional Intelligence ...... 127

3.3 COPING ...... 132

3.3.1 Coping and Emotional Intelligence ...... 137

3.4 PROBLEM-SOLVING ...... 142

3.4.1 Problem Solving and Emotional Intelligence ...... 145

3.5 ANXIETY...... 146

3.5.1 Anxiety and Emotional Intelligence...... 151

3.6 GENDER AND EMOTIONAL INTELLIGENCE ...... 152

3.7 AGING AND EMOTIONAL INTELLIGENCE ...... 156

3.8 CONCLUSION ...... 159

CHAPTER 4: STUDY 1: UNIVERSITY SAMPLE...... 161

4.1 HYPOTHESES...... 161

4.2 METHOD ...... 163

4.2.1 Participants...... 163

4.2.2 Materials...... 164

4.2.2.1 Emotional Intelligence...... 164

4.2.2.2 Cognitive Abilities...... 170

4.2.2.3 Personality ...... 171

4.2.2.4 Life Skills...... 172

4.2.3 Procedure ...... 177

4.3 RESULTS ...... 178

4.3.1 Descriptive Statistics...... 178

4.3.2 Factor Analysis of Measures ...... 180 4

4.3.2.1 Trait Meta Mood Scale ...... 180

4.3.2.2 Assessing Emotions Scale ...... 181

4.3.2.3 Mayer, Salovey and Caruso Emotional Intelligence Test ...... 181

4.3.2.4 NEO Personality Inventory (Revised) ...... 181

4.3.2.5 Satisfaction With Life Scale ...... 181

4.3.2.6 The COPE...... 182

4.3.2.7 Problem Solving Inventory...... 182

4.3.2.8 Anxious Thoughts Inventory ...... 182

4.3.3 Factor Analysis of Emotional Intelligence...... 182

4.3.4 Reduction of ‘Life Skills’ variables...... 183

4.3.5 Gender Differences...... 184

4.3.5.1 Gender Differences in Emotional Intelligence...... 184

4.3.5.2 Gender Differences in Cognitive Abilities, Personality and Life

Skills ...... 185

4.3.6 Correlations between Emotional Intelligence, Cognitive Abilities,

Personality and Life Skills...... 186

4.3.6.1 Correlations Between Emotional Intelligence Measures ...... 188

4.3.6.2 Correlations Between Life Skills...... 189

4.3.6.3 Correlations Between Emotional Intelligence, Cognitive

Abilities, Personality...... 190

4.3.6.4 Correlations Between Emotional Intelligence, Cognitive

Abilities, Personality and Life Skills ...... 191

4.3.7 The Dimensionality of Emotional Intelligence, Cognitive Abilities and

Personality...... 192

4.3.8 Prediction of Life skills by Emotional Intelligence, Cognitive Abilities

and Personality...... 193

4.4 DISCUSSION ...... 197

CHAPTER 5: STUDY 2 – OLDER COMMUNITY SAMPLE ...... 200

5.1 HYPOTHESES...... 200

5.2 METHOD ...... 202

5.2.1 Participants...... 202

5.2.2 Materials...... 203

5.2.3 Procedure ...... 203

5.3 RESULTS ...... 205

5.3.1 Descriptive Statistics...... 205

5.3.2 Factor Analysis of Measures ...... 206

5.3.2.1 Trait Meta Mood Scale ...... 207

5.3.2.2 Assessing Emotions Scale ...... 207

5.3.2.3 Mayer, Salovey and Caruso Emotional Intelligence Test ...... 207

5.3.2.4 NEO Personality Inventory (Revised) ...... 208

5.3.2.5 Satisfaction With Life Scale ...... 208

5.3.2.6 The COPE...... 208

5.3.2.7 Problem Solving Inventory...... 208

5.3.2.8 Anxious Thoughts Inventory ...... 209

5.3.3 Factor Analysis of Emotional Intelligence variables ...... 209

5.3.4 Reduction of ‘Life Skills’ variables...... 209

5.3.5 Gender differences...... 210

5.3.5.1 Gender Differences in Emotional Intelligence...... 210 6

5.3.5.2 Gender Differences in Cognitive Abilities, Personality and Life

Skills ...... 211

5.3.6 Correlations between Emotional Intelligence, Cognitive Abilities,

Personality and Life Skills ...... 213

5.3.6.1 Correlations Between Emotional Intelligence Measures ...... 215

5.3.6.2 Correlations Between Life Skills...... 216

5.3.6.3 Correlations Between Emotional Intelligence, Cognitive

Abilities and Personality...... 216

5.3.6.4 Correlations Between Emotional Intelligence, Cognitive

Abilities, Personality and Life Skills ...... 217

5.3.7 The Dimensionality of Emotional Intelligence, Cognitive Abilities and

Personality...... 218

5.3.8 Prediction of Life Skills by Emotional Intelligence, Cognitive Abilities

and Personality...... 219

5.3.9 Age-related Differences in Emotional Intelligence...... 222

5.4 DISCUSSION ...... 223

CHAPTER 6: CONCLUSIONS ...... 226

6.1 DISCUSSION OF RESULTS FROM STUDY 1 (UNIVERSITY SAMPLE) AND STUDY 2

(OLDER COMMUNITY SAMPLE)...... 227

6.1.1 The Relationship Between Emotional Intelligence Measures...... 227

6.1.2 Gender Differences in Emotional Intelligence...... 228

6.1.3 Age Related Changes in Emotional Intelligence ...... 229

6.1.4 The Relationship Between Emotional Intelligence, Cognitive Abilities

and Personality...... 231 7

6.1.5 Relationship Between EI and Life Skills...... 234

6.1.6 Incremental Predictive Validity of Emotional Intelligence ...... 237

6.2 CONTRIBUTION OF RESULTS TO THE FIELD OF EMOTIONAL INTELLIGENCE239

6.3 LIMITATIONS OF STUDIES...... 240

6.4 SUMMARY AND FUTURE DIRECTIONS...... 244

6.5 CONCLUSION ...... 250

REFERENCES...... 251

APPENDIX A...... 277

APPENDIX B ...... 278

APPENDIX C ...... 279

APPENDIX D...... 280

APPENDIX E...... 288

APPENDIX F...... 295

APPENDIX G...... 296

APPENDIX H...... 304

FIGURES

Figure 1: The Ability Emotional Intelligence Model...... 76

Figure 2: Bar-On’s Emotional Intelligence Theory...... 102

Figure 3: The Measures Used in This Study...... 164

TABLES

Table 1: The Correlations Between EI and Cognitive Abilities ...... 64

Table 2: The Correlations Between EI and Personality ...... 68

Table 3: Correlations Between TMMS and Predictive Criteria ...... 85

Table 4: Correlations Between MEIS and MSCEIT and Predictive Criteria...... 90

Table 5: Correlations Between AES and Predictive Criteria...... 97

Table 6: Correlations Between EQ-i and Predictive Criteria ...... 108

Table 7 : Correlations Between EI and Academic Achievement...... 122

Table 8: Correlations Between EI and Life Satisfaction...... 130

Table 9: Relationships Between Gender and EI...... 154

Table 10: Relations Between Age and EI ...... 158

Table 11: The Tasks Associated with Each of the Four Branches of the MSCEIT. .168

Table 12: Descriptive Statistics for the Measures Assessed in Study 1...... 178

Table 13: The Gender Differences in Emotional Intelligence (Study 1)...... 185

Table 14: The Gender Differences in Cognitive Abilities, Personality and ‘Life

Skills’ (Study 1)...... 186

Table 15: The Correlations Between All Measures and Subscales...... 187

Table 16: Hierarchical Regression of Life Skills on Emotional Intelligence (Step

1) and NEO Personality Inventory (Revised), Raven’s Advanced

Progressive Matrices and Phonetic Word Association Task (Step 2)

(Study 1)...... 194

Table 17: Hierarchical Regression of Satisfaction With Life Scale on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Trait Meta Mood Scale subscales (Step 2)

(Study 1)...... 195

Table 18: Hierarchical Regression of Life Skills on NEO Personality Inventory

(Revised), Raven’s Advanced Progressive Matrices and Phonetic

Word Association Task (Step 1) and the Additional R2 (Variance

Explained) Contribution of Emotional Intelligence (Step 2) (Study 1). ..196

Table 19: Descriptive Statistics for the Measures Assessed in Study 2...... 205

Table 20: Gender Differences in Emotional Intelligence (Study 2)...... 211

Table 21: The Gender Differences in Cognitive Abilities, Personality and ‘Life

Skills’ (Study 2)...... 212

Table 22: The Correlations Between All Measures and Subscales (Study 2)...... 214

Table 23: Hierarchical Regression of Life Skills on Emotional Intelligence (Step

1) and NEO Personality Inventory (Revised), Raven’s Advanced

Progressive Matrices and Phonetic Word Association Task (Step 2)

(Study 2)...... 220

Table 24: Hierarchical Regression of Life Skills on NEO Personality Inventory

(Revised), Raven’s Advanced Progressive Matrices and Phonetic

Word Association Task (Step 1) and the Additional R2 (Variance

Explained) Contribution of Emotional Intelligence (Step 2) (Study 2). ..221

Table 25: Age-related Differences in Emotional Intelligence and Cognitive

Abilities Between the Two Studies...... 223

Table D1: The Correlations Between the 15 Cope Scales (Study 1) ...... 280

Table D2: The Correlations Between the 15 COPE Subscales, Cognitive Abilities

and Personality (Study 1)...... 281

Table D3: The Correlations Between the 15 COPE Subscales, Cognitive Abilities

and Personality (Study 1)...... 282

Table D4: The Correlations Between the 15 COPE Subscales and ‘Life Skills’

(Study 1)...... 283

Table D5: The Correlations Between the Problem Solving Inventory Subscales

(Study 1)...... 284

Table D6: The Correlations Between the Problem Solving Inventory Subscales

and Emotional Intelligence (Study 1)...... 285

Table D7: The Correlations Between the Problem Solving Inventory Subscales,

Cognitive Abilities and Personality (Study 1)...... 286

Table D8: The Correlations Between the Problem Solving Inventory Subscales

and ‘Life Skills’ (Study 1)...... 287

Table E1: Hierarchical Regression of Tertiary Entrance Rank on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 1)...... 288

Table E2: Table 33: Hierarchical Regression of Satisfaction With Life Scale on

NEO Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 1)......

...... 289

Table E3: Hierarchical Regression of Problem Focused Coping on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 1)...... 290

Table E4: Hierarchical Regression of Emotion Focused Coping on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 1)...... 291

Table E5: Hierarchical Regression of Avoidance Coping on NEO Personality

Inventory (Revised) Subscales, Raven’s Advanced Progressive

Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step

1) and on Each Emotional Intelligence Measure Individually (Step 2)

(Study 1)...... 292

Table E6: Hierarchical Regression of Problem Solving Inventory (Total) on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 1)...... 293

Table E7: Hierarchical Regression of Anxious Thoughts Inventory on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 1)...... 294

Table G1: The Correlations Between the 15 COPE Subscales (Study 2) ...... 296

Table G2: The Correlations Between the 15 COPE Subscales and Emotional

Intelligence (Study 2)...... 297

Table G3: The Correlations Between the 15 COPE Subscales, Cognitive Abilities

and Personality (Study 2)...... 298

Table G4: The Correlations Between the 15 COPE Subscales and ‘Life Skills’

(Study 2)...... 299

Table G5: The Correlations Between the Problem Solving Inventory Subscales

(Study 2)...... 300

Table G6: The correlations Between the Problem Solving Inventory Subscales

and Emotional Intelligence (Study 2)...... 301

Table G7: The Correlations Between the Problem Solving Inventory Subscales,

Cognitive Abilities and Personality (Study 2)...... 302

Table G8: The Correlations Between the Problem Solving Inventory Subscales

and ‘Life Skills’ (Study 2)...... 303

Table H1: Hierarchical Regression of Satisfaction With Life Scale on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 2)...... 304

Table H2: Hierarchical Regression of Problem Focused Coping on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 2)...... 305

Table H3: Hierarchical Regression of Emotion Focused Coping on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 2)...... 306

Table H4: Hierarchical Regression of Avoidance Coping on NEO Personality

Inventory (Revised) Subscales, Raven’s Advanced Progressive

Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step

1) and on Each Emotional Intelligence Measure Individually (Step 2)

(Study 2)...... 307

Table H5: Hierarchical Regression of Problem Solving Inventory (Total) on

NEO Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 2)...... 308

Table H6: Hierarchical Regression of Anxious Thoughts Inventory on NEO

Personality Inventory (Revised) Subscales, Raven’s Advanced

Progressive Matrices (RAPM) and Phonetic Word Association Task

(PWAT) (Step 1) and on Each Emotional Intelligence Measure

Individually (Step 2) (Study 2)...... 309

ABSTRACT

Emotional Intelligence (EI) is held to explain how emotions may function to advance life goals, with the basic proposition being that individuals monitor and discriminate emotions within themselves and others to solve problems. A number of different theories of EI have been proposed and consequently there is still controversy about the best way in which to conceptualise and measure EI. It is, nonetheless, agreed that the relevance of EI is largely dependent on it being able to predict significant life outcomes. Academic achievement, life satisfaction, coping, problem-solving ability and anxiety are variables that have relevance in academic, occupational and interpersonal situations. The relationship between these variables and EI was assessed in two diverse populations (University sample: N=246; mean age=19.9; Older community sample: N=212; mean age=51.6). The magnitude and direction of findings in both studies were found to be remarkably similar. As expected, older adults (community sample) were found to score significantly higher on EI than younger adults (University sample). Few gender differences in

EI, however, were apparent, but those that were significantly favoured females.

Previously identified relationships suggesting that self-report EI measures are moderately-to-highly correlated to personality, whereas ability EI is reasonably distinct from other constructs, were also upheld. Inconsistent with previous research, however, differential associations between EI and verbal and abstract reasoning ability were not observed. Fitting theoretical expectations, in both studies EI was low-to-moderately correlated with higher life satisfaction, problem and emotion focused coping and perceived problem solving ability and with lower avoidance coping and anxiety. However, the correlations for academic 16

achievement were not significant. These correlations were found to be higher for self-report EI than they were ability EI, perhaps due to method variance with the life skills. Nevertheless, despite these low-to-moderate correlations, hierarchical regression analyses controlling for personality and cognitive abilities revealed that the incremental predictive validity of EI was 7% at most. This finding was found for all life skills regardless of the EI measure involved. This raises some implications for the field of EI and highlights that personality and cognitive abilities should be taken into account when making assertions about EI’s predictive power.

STATEMENT

This thesis contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text.

I give consent to this copy of my thesis, when deposited in the University library, being available for loan and photocopying.

Signed:

Dated:

ACKNOWLEDGEMENTS

This thesis would not have been possible without the assistance of the numerous individuals who volunteered to be participants in these studies. I am also grateful to Multi-Health Systems International who generously provided me with free access to the MSCEIT for the purpose of this research.

I would also like to thank my two supervisors for all their assistance. To Professor Ted Nettelbeck who challenged me in so many ways, but who has been such an enormous influence in my life, both personally and professionally. It has been a privilege working with you. To Dr. Nick Burns for his guidance in explaining various statistical analyses and his quiet patience in doing so and also for his careful nudges at the end that inspired me to prove that I really could do this. I also appreciate the support of my work supervisor, Dr Brett McLindin, who understood better than most the challenge of attempting to work full time while also completing a PhD and who did everything he could to make sure I succeeded.

Thanks also for the support of my family – my parents, Peter and Jocelyn, and my brothers, John and Bruce - for sharing in my triumphs, encouraging me through the more difficult times and graciously looking past the times when I was preoccupied, stressed or tired (and especially those horrible moments when I was a combination of all three). Special thanks to my friends Jo Abbott, Janette Warwick and Taryn Elliott who, through the bonds of common experience, instantly understood the parts of doing a PhD that no one ever tells you about and for which no one will ever understand unless they do one themselves. Your support over the last few years has been invaluable.

But most of all, thank you to Peter. For his love and his support. For picking me up when I fell down. For believing in me when I sometimes didn't believe in myself. For forgiving me for how hard this all was. I couldn't have done this if it wasn't for you. I'll make it up to you now I promise. Ti Amo.

CHAPTER 1: THE CONCEPT OF INTELLIGENCE

1.1 Overview of this Thesis

This thesis attempted to elucidate the nature of the Emotional Intelligence

(EI) construct by examining its predictive validity for a number of life skills

(academic achievement, life satisfaction, coping ability, problem solving ability and anxiety). In particular attempts were made to determine the incremental predictive validity of various EI measures for these life skills over and above the contribution of assessments of personality and cognitive abilities. This was examined in two large scale studies on diverse populations (a university sample and an older, wider community sample), in order to obtain some degree of generalisability of the results.

The following thesis will first provide a basis for the development of the field of EI by examining the historical emergence of cognitive intelligence theories and the generation of non-cognitive intelligences, including EI (Chapter 1). A discussion of the more scientific theories of EI, including a discussion of current popular measures of EI and some of the preliminary results that these have generated will be presented in Chapter 2. Chapter 3 will form the basis for establishing why the particular life skills examined in this thesis were chosen, as well as an assessment of how EI is related to gender and age. Chapter 4 (University sample) and Chapter 5 (older community sample) provide an account of the two studies that formed the basis of this thesis. Finally, Chapter 6 provides an overarching summary of results, as well as a discussion of the limitations of the research and possible future directions that were generated from these findings.

1.2 Cognitive Intelligence Theories

To date, numerous theories of human intelligence have been proposed, although despite over a century of research, there is still considerable debate about how best to describe intelligence and only limited understanding about its biological basis. Roberts, Zeidner and Matthews (2001) have described general intelligence as being indicative of “a person’s overall capacity for adaptation through effective cognition and information processing” (p. 197). However, different conceptions have viewed intelligence as a general “competence of the mind” or as a collection of higher order abilities (Roberts et al., 2001; p. 197). Efforts to better understand the various psychometrically defined abilities that are widely thought to contribute to general intelligence have resulted in the division of such abilities into broad categories (Roberts et al., 2001). This has given rise to three major classes of intelligence models: the two-factor model (e.g., Spearman, 1927); the multiple abilities model (e.g., Gardner, 1983; Guilford, 1958; Thurstone, 1938) and the hierarchical model (e.g., Horn & Cattell, 1966; Carroll, 1993).

Carroll’s (1993) three-stratum model of cognitive abilities, which maps all known and hypothesised abilities, is widely accepted as being the most complete psychometric description of intelligence currently available (Deary, 2000). The hierarchical nature of this model (in which narrowly defined primary abilities are positioned on the lowest stratum with broader abilities on the second level and an overarching general intelligence on the upper level), typifies a trend in intelligence theories that has suggested the prominence of a single general ability factor. This general ability factor is typically referred to as g, following Spearman’s lead and

has widely been accepted as representing an important mental ability, which is involved in all forms of mental performance.

Nevertheless, although a hierarchical design has become the most popular way of conceptualising human intelligence, an increasing number of researchers have criticised such a structure, arguing that this formation places too much emphasis on g and those abilities that are crucial to academic performance (Pfeiffer,

2001). Furthermore, most intelligence theories have typically focused on cognitive aspects, excluding other processes, such as mood and emotion, based on the assumption that emotions can be disruptive influences on rational, cognitive ways of thinking (Salovey & Mayer, 1994). In doing so, such cognitive models neglect other mental abilities that have also been found to be important to behaving intelligently, such as the ability to read social cues (Pfeiffer, 2001). An emphasis on these types of skills has lead to the development of a number of ‘non-cognitive’ intelligences, as discussed in the following sections.

1.3 Non-Cognitive Intelligence Theories

Research interest in non-cognitive intelligence theories has been sparked by the realisation that academic intelligence, as operationalised by IQ tests, cannot account for more than about 25% of variance in educational achievement and accounts for even less of successful post-school achievements. Researchers have often noted a disparity between individuals who may be classified as academically bright, but whose skills have not always conferred an advantage in non-academic situations, and individuals who, although being less ‘academically gifted’, have succeeded in other areas in life beyond these expectations (Epstein, 1998; Salovey,

Mayer & Caruso, 2002).

As discussed in Section 1.2, to a large extent the focus of intelligence theories has been primarily on cognitive abilities, with only a minimal emphasis on non-cognitive processes. However, Thorndike (1920) was one of the first to deviate from this theme with his theory of intelligence, which included a ‘social intelligence’ component (see Section 1.3.1). Wechsler (1940) also emphasised the importance to significant life achievements of what he termed “non-intellective factors”. He acknowledged that broad general intelligence could be defined in terms beyond scores from IQ tests and suggested that intelligent behaviour should be evaluated as something requiring more than pure intellectual ability. Such views were based on observations that individuals with identical IQs may differ in their ability to cope effectively with their environment and also on the statistical finding that IQ tests account for only a relatively small amount of variance in outcome measures for important real-world achievements. Wechsler’s suggestion was, therefore, that the remaining variance might be accounted for by affective, personal and social influences, such as persistence, curiosity, drive, will and conscientiousness, which facilitate ‘intelligent behaviour’. Conversely, Wechsler proposed that other affective characteristics, such as anxiety, emotional insecurity and impulsivity, may inhibit such functions.

However, interest in the concept of social intelligence waned considerably following these formative efforts, mainly due to difficulties in accurately defining and measuring the construct (Cronbach, 1960). Interest in non-cognitive intelligences was not renewed until the 1980s when work by Gardner (1983) on multiple intelligences challenged the predominant importance of conventional ideas about cognitive intelligence. This attention led to speculation about intelligences with a greater focus on emotional and personal components, such as

personal intelligence (see Section 1.3.2), practical intelligence (see Section 1.3.3) and emotional intelligence (see Section 1.3.4), as well as renewed investigations into the social intelligence construct (see Section 1.3.1) in order to provide a fuller understanding of human intelligence. The conceptual base to these various constructs appears to be similar, with the prime focus being to discover

“component[s] of effective living” (Jones & Day, 1997; p. 489).

1.3.1 Social Intelligence

It is commonly, but according to Landy (in press) incorrectly, assumed that the term Social Intelligence was introduced by Thorndike in 1920. In fact, it would appear that the concept was actually mentioned earlier by John Dewey (1909) and later by Herbert Lull (1911) (both cited by Landy, in press) in their writings about morality and public education. However, Dewey and Lull’s stance on social intelligence was more focused on revising school curriculum and attempting to engage the student in socially current issues and as such involved the comprehension of social behaviour and norms. This is in contrast to the proposition of social intelligence as an attribute that was suggested by Thorndike in 1920.

Thorndike (1920) proposed the division of intelligence into three components, with social intelligence, the third division, described as the ability to understand and manage people and to act wisely in social situations, based on one’s own and other’s perceived internal states, motives and behaviours.

Thorndike’s definition suggests both knowledge and behavioural components (i.e.,

‘managing’ and ‘acting’), in the sense that it is one thing to know what to do, but another thing to actually do it. Landy (in press), however, has argued that

Thorndike’s comments on social intelligence and the division of intelligence were

more directed towards cautioning against narrow measurements of intelligence to a non-scientific audience (via Harper’s Weekly) and highlighting that different environments presented different challenges that required mental ability than a theory of intelligence per se.

In the 1930s, research on social intelligence attempted to identify the mechanisms and accuracy through which people made social judgements. Vernon

(1933), for example, saw social intelligence described as an individual’s ability to get along with others, to have knowledge of social matters, to be at ease in society and to have insight into the moods and personality of strangers. The 1950s, however, saw the domain of social intelligence divide into two traditions: an intelligence perspective, which investigated the skills involved in person perception; and a social psychological perspective, which was concerned with the social aspects of this person perception (Mayer & Geher, 1996).

However, developments in the social intelligence field were (and still are) impeded by difficulties in adequately defining and measuring the construct. The problems in defining social intelligence research have been due to a lack of consensus about how to define the social constructs that may be involved. The lack of progress in social intelligence research has also been due to difficulties in constructing standardised measures of real-life social situations that are able to assess actual social competencies and also in determining suitable external criteria against which measures of social intelligence may be validated (Jones & Day; 1997;

Ford & Tisak, 1983). Additionally, social intelligence measures have been characterised by an inability to be discriminated from assessments of general intelligence, with most such measures loading heavily on indices of verbal intelligence, all of which has lead to poor measurement and interpretation (Ford &

Tisak, 1983; Jones & Day, 1997; Kihlstrom & Cantor, 2000; Marlowe, 1986). For instance, the George Washington Test, the first social intelligence measure, proved unreliable as an assessment of social intelligence given that almost every attempt failed to adequately demonstrate independence from abstract intelligence (with correlations between the two being upwards of r = .60) (Landy, in press). One of the difficulties with using paper and pencil tests to assess a construct such as social intelligence is that such items cannot adequately assess the ‘behavioural’ aspect of social intelligence. Faced with such problems, interest in social intelligence waned considerably, particularly following Cronbach’s (1960) conclusion that the social intelligence construct was moribund.

Consequently, it has only been relatively recently that concerted attempts to define and measure social intelligence more adequately, such as those by Marlowe

(1986), Cantor and Kihlstrom (1987) and Wong, Day, Maxwell and Meara (1995), have been renewed. The common theme amongst social intelligence theories appears to be the view that “people are reflective, thinking beings and their behaviour can be understood in terms of the ways that they actively seek to engage in their social environment and pursue desired outcomes in the important domains of their lives” (Zirkel, 2000; p. 3). Achieving this involves interpersonal connections with others in order to socially interact with them effectively, which requires being sensitive to and insightful of social cues in order to evaluate and utilise this social information in some way. Furthermore, because individuals are presumed to be knowledgeable about themselves and their social world, most social intelligence theories assume that individuals actively use this knowledge to manage their emotions and direct their behaviour toward desired outcomes (Zirkel, 2000). It is therefore assumed that people can be best understood by the evaluation of the

adaptive and purposive aspects of their behaviour, based on the assumption that individuals actively try to understand the world around them and, in doing so, they modify their behaviour to achieve this. Social intelligence theories also typically assume that behaviour is socially contextualised, in that all behaviour occurs within contexts that place socially defined meanings on actions (Zirkel,

2000).

Nevertheless, despite the noted problems in defining and measuring the social intelligence construct, the field has emphasised that different environments require the use of different forms of mental ability for success. Social intelligence has also helped define other forms of non-cognitive intelligence, such as practical and emotional intelligence, in the way in which these later constructs are defined and assessed. These intelligences will be discussed further in the following sections.

1.3.2 Personal Intelligence

Gardner (1983) includes within his theory of multiple intelligences two non- cognitive intelligences that have been termed the ‘personal intelligences’. ‘Personal intelligences’ is a collective term that refers to the emotional aspects that influence an individual’s mental functioning and encompasses ‘intrapersonal’ and

‘interpersonal’ intelligences. Specifically, intrapersonal intelligence refers to an individual’s ability to access and understand one’s own thoughts and feelings and involves the labelling and encoding of these feelings in order to guide behaviour.

Interpersonal intelligence, however, emphasises the ability to make distinctions about the moods, temperaments, motivations and intentions of another person and potentially to act upon such knowledge (Gardner, 1983). Knowledge gained in this

sense may be utilised in different ways in interpersonal situations, such as: organising groups in order to initiate and coordinate the efforts of a number of people; negotiating solutions to prevent and resolve any arising conflicts; analysing social realms in order to detect and be insightful about other people’s feelings, motives and concerns; and personally connecting with others in a way which allows the recognition of other’s feelings and concern such that an appropriate response can be made (Hatch, 1997).

Gardner (1983) has emphasised the importance of the personal intelligences, suggesting that they come into play with almost every interaction, given that “it is the unusual individual who does not try to deploy his understanding of the personal realm in order to improve his own well-being or his relationship to the community” (p. 241). Furthermore, despite the slightly differing focus of these two intelligences, both are inextricably combined because the ability to make inferences based on the observation of other’s behaviour is dependent on the understanding of these factors in oneself. The concept behind the personal intelligences has proven to be fundamental in defining EI.

1.3.3 Practical Intelligence

The notion of practical intelligence came into favour based on observations that although g is a relatively consistent predictor of performance, there are limitations to the reliability of such predictions, particularly in practical situations outside of academic settings (Sternberg & Hudlund, 2002). Although a concise definition has not yet been achieved, it is commonly held that practical intelligence encompasses the abilities one needs to deal successfully with and solve situations and problems that are encountered in everyday life (Fredrickson, 1986; Wagner,

2000). Such skills have been commonly characterised in the US as ‘street smarts’ and involve “knowing how” rather than “knowing that” (Sternberg, Wagner,

Williams & Horvath, 1995; Sternberg & Hudland, 2002).

It has been claimed that much of this knowledge is tacit. Tacit knowledge has been defined by Sternberg and his various colleagues as “knowledge that is not explicitly taught or even verbalised, but is necessary for an individual to thrive in an environment” (p. 35; Sternberg, Okagaki & Jackson, 1990). Tacit knowledge is often disorganised and context specific and therefore is not usually directly taught or articulated but rather, is learnt through experience (Wagner & Sternberg, 1986;

Sternberg & Hudland, 2002). Sternberg (1999) has, however, emphasised that, although tacit knowledge is positively related to experience, it is the act of profiting from experience, rather than the experience per se that results in higher levels of practical intelligence.

Tacit knowledge is primarily assessed by analysing the responses that individuals generate to deal with practical situations or problems (Sternberg &

Hudland, 2002). For example, current tacit knowledge measures (i.e., Tacit

Knowledge Inventory for Managers, Tacit Knowledge Inventory for Sales and the

Tacit Knowledge Inventory for Military Leaders; Sternberg, Grigorenko & Bundy.,

2000) involve scenario-based examples, in which the test taker rates the quality of various proposed courses of action. The scoring of such measures is typically on the basis of degree of conformity with experts in that particular field, which is in contrast to traditional intelligence measures but similar to the way in which some

EI measures are assessed (see Section 2.3.1) (Sternberg & Hudland, 2002). It has been suggested that this form of measurement is less problematic for practical intelligence than it is for emotional intelligence given that there are more definite

criteria for establishing experts in the practical intelligence field (Austin &

Saklofske, 2005).

Studies conducted using these measures have determined that they are able to predict real-world criteria (such as management or military leadership experience, sales experience, salary, performance ratings, e.g., Sternberg et al., 2000;

Sternberg & Hudland, 2002; Wagner & Sternberg, 1985; higher education attainment, citation rate, number of publications, e.g., Sternberg et al., 1990;

Sternberg & Hudland, 2002; Williams, Blithe, White, Li, Gardner & Sternberg, 2002) just as well, if not better, than IQ measures. Practical intelligence measures have furthermore been found to be relatively independent from intelligence and other selection measures and therefore it is claimed they are able to explain aspects of performance that are unable to be adequately explained by measures of IQ

(Bowman, Markham & Roberts, 2002; Sternberg et al., 1995; Sternberg et al., 2000;

Sternberg & Hudland, 2002). Bowman et al. (2002), however, have cautioned that establishing the veracity and generalisability of these results is difficult, given that some studies have not been published and that typically the assessed samples have generally been small (often fewer than 50 participants). Nevertheless, practical intelligence, as do the other forms of non-cognitive intelligences, appears to be a useful construct for complementing analytical forms of intelligence in defining intelligent behaviour.

1.3.4 Emotional Intelligence

EI has recently emerged as another non-academic intelligence to help explain life differences independent of cognitive abilities. Previously it was thought that emotions are disruptive to rational, cognitive ways of thinking (Salovey &

Mayer, 1994). It has only been relatively recently that there has been recognition that emotions are not necessarily incompatible with cognitive pursuits. It is now considered possible that moods and emotions have important intellectual implications because they often influence the way in which individuals interpret and react to information (Salovey & Mayer, 1994). Schwarz (1990) has even suggested that some sources of information may actually be ignored if salient mood states are present.

Emotions stem from our evolutionary history where they acted as an internal guidance system and were therefore important in survival (LeDoux, 1996).

Emotions are psychological and physiological events that are experienced in relation to internal thoughts or to an object, person or event, which invokes a general state of readiness of action for survival (Masters & McShane, 2003; Frijda,

2000). As such, emotions are considered responses to events, which coordinate perceptual, experiential, cognitive and psychological subsystems into coherent experiences about moods and emotions (Mayer, Caruso & Salovey, 1999; Salovey &

Mayer, 1994; Frijda, 2000). On the assumption that emotions can fulfil some psychological function to guide thought and action, Frijda (1988) has described emotions as central to human functioning. Furthermore, Salovey and Mayer (1994) have suggested that emotions may form an important link between personality, which constitutes differences in the ways in which people interact with the world, and intelligence, which shapes the accuracy, efficiency and success of the processing mechanisms with which people interact with the world.

EI has been conceptualised as a broad, umbrella construct to explain how emotions allow more ‘intelligent thinking’ and the ways in which individuals may think intelligently about emotions. The underlying notion is that individuals

monitor and discriminate emotions, within themselves and in others, in order to solve problems. If emotions can act as a source of information, it is likely that this information is processed in a similar manner to all other forms of information

(Schwarz, 1990). Salovey and Mayer (1994) have therefore suggested that, analogous to other types of mental information, individuals differ in the extent to which they are skilled at processing emotional information, with some individuals being “better” at these skills than others. Females, for example, have typically been found to score higher on EI measures than do males (e.g., Brackett, Mayer &

Warner, 2003; Ciarrochi, Chan & Bajgar, 2001a; Kafetsios, 2004; Mayer et al., 1999;

Schutte, Malouff, Hall, Haggerty, Cooper, Golden & Dornheim, 1998; as discussed further in Section 3.6).As such, Mayer and Salovey (1997) have suggested that EI may explain some of the discrepancies in everyday performance among individuals who are otherwise intellectually equal.

Although the precursors to EI were conceptualised within the theories of

Social Intelligence and Personal Intelligences, the term was in fact referenced as early as 1966 (Leuner, 1966) and one of the first definitive references to EI was made in 1986 in an unpublished dissertation by Dr. Wayne Payne. He distinguished EI from more cognitive forms of intelligence, by describing EI as:

“The facts, meanings, truths, relationships, etc., [of emotional intelligence]

are those that exist in the realm of emotion. Thus, feelings are facts…The

meanings are felt meanings; the truths are emotional truths; the

relationships are interpersonal relationships. And the problems we solve are

emotional problems, that is, problems in the way we feel”. (Payne, 1986. p.

165; as cited by Mayer, 2001).

However, according to Mayer (2001), this definition is not clear because it does not adequately consider what is meant by a “felt meaning”, nor does it explain what kind of a truth an “emotional truth” might be, or sufficiently explain the statement that “feelings are facts”. Salovey and Mayer (1990) were nevertheless the first to attempt to locate EI within a sustainable scientific theory.

Although Mayer and Salovey have not generally credited themselves with inventing the term ‘emotional intelligence’, their publications in the field have nevertheless been influential and have, for the most part, formed the basis for much of the academic thought and research conducted thus far (as will be discussed in Chapter 2). However, although Mayer and Salovey have been leaders in EI research, Goleman (1995) has been largely responsible for the widespread popularisation of the term “Emotional Intelligence” through the publication of his books (Emotional intelligence: Why it can matter more than IQ [1995] and Emotional intelligence in the workplace [1998]), both of which were written primarily for the

‘lay-educated’ market. Although not a scientific researcher himself at the time of these publications, Goleman has moulded his own overarching theory of EI based on drawing together his observations and conclusions from the work of other researchers. Nevertheless, Goleman’s work has been so captivating that since the publication of these books, his views have been readily accepted in community sectors (e.g., schools, business organisations, leadership training etc.), which has subsequently helped to generate recent academic-based research in the field.

While Goleman’s theory of EI can hardly be considered ‘scientific’ - nor indeed was it the first such theory of EI - it is nevertheless the most popular and certainly the most widely known and therefore serves as a useful basis for the examination of more scientific theories of EI (as will be discussed in Chapter 2). A

discussion of Goleman’s theory of EI and the claims he has made, follow, although it should be noted that many of Goleman’s claims have not been adequately substantiated.

1.3.4.1 Goleman’s Original (1995) Model of Emotional Intelligence

The basis of Goleman’s EI model is his belief that omnibus IQ is ineffective at predicting ‘life success’ after the effects of schooling, innate potential and opportunity have been taken into account. In particular, Goleman has argued that

IQ contributes only approximately 20% of the variance necessary to predict ‘life success’, with the implication being that the remaining 80% of variance in success is at least predominantly accounted for by EI. Given this apparent disparity, Goleman reasons that the presence of EI components is what explains why some individuals with lower IQ may end up being as successful as individuals who have higher IQ or, conversely, the absence of EI may explain why individuals with high IQ do not reach the potential expected of them.

Goleman views EI as a ‘meta-ability’ which may either facilitate or interfere with one’s capabilities in other areas. Goleman (1995) has therefore argued that the extent to which individuals are motivated by their feelings, and their ability to use emotions to enhance thinking, planning, pursuing goals and solving problems, is what enables individuals to extend the limits of their innate mental abilities.

However, whereas Salovey and Mayer’s EI model (as discussed further in

Section 2.7) only emphasises the cognitive aspect of emotions in the ability to perceive, utilise, understand and manage emotions, Goleman’s definition includes not only these, but a number of personality and social factors as well.

Consequently, Goleman’s view of EI is expansive and appears to include just about all personal characteristics other than IQ that constitute some form of ‘success’.

Goleman’s (1995) model of EI consists of five competencies: Self-Awareness,

Self Regulation, Motivation, Empathy and Social Skills. According to Goleman, Self-

Awareness refers to an individual’s recognition of one’s own emotions, self worth and capabilities. This is said to encourage self-reflectiveness on the internal states, which is an important precursor to developing further emotional competencies.

Self-Regulation involves managing one’s emotions, being flexible and taking responsibility for one’s performance and behaviour. Goleman holds that the path to emotional well-being is through the ability to manage emotions in oneself in order to keep distressing emotions in check and also to ensure that no emotion – whether positive or negative – is allowed to become too ‘out of control’ by being either too extreme or too persistent. Motivation refers to applying initiative and maintaining optimism in order to meet goals, which is considered to be an important part of academic and occupational success. Empathy indicates personally connecting with the emotions of others and requires appropriately reading, developing and understanding another individual’s feelings. Being aware of and open to one’s feelings may facilitate perceiving emotions in others. As the majority of interpersonal communication is non-verbal, Goleman suggests that emotional empathy towards others allows the detection of slight nuances of non-verbal communication, which is intrinsically important in the formation and maintenance of interpersonal relationships. Finally, Social Skills, an area that is closely related to empathy, concerns the interaction with others in order to induce desired responses, a factor which relies on the skills of listening, conflict management, collaboration and leadership. Goleman’s view is that managing the emotion of others, which he

suggests is akin to ‘people skills’, or learnt ways of dealing with people, occurs through the process of ‘mood transfer’ from a ‘dominant’ partner (either through a power imbalance or through their more forceful expression of emotion) to the more

‘passive’ partner. Goleman suggests that aligning moods in this way generates feelings of rapport and influences the attunement of individuals to each other, which assists in the formation and maintenance of interpersonal relationships.

In addition to these competencies, Goleman’s (1995) EI model also encompasses a vast array of qualities that includes: self-control, zeal, persistence, assertiveness, motivation, mood regulation, empathy, optimism, hope, self- confidence, impulse control and to delay gratification, handle anxiety and stress and to conduct successful interpersonal interactions. Furthermore, Goleman (1995) proposed that having high EI qualities is the exemplar of how people ‘should’ behave, by suggesting that EI is akin to having “character” (p. 285), “moral values”

(p. 286) and being a “decent human being” (p. 263). However, the inclusion of so many aspects has led to questions over whether all these may be integrated as a theory of EI (discussed further in Section 1.3.4.4). Matthews, Zeidner and Roberts

(2002), have further argued that, although many of these aspects are worthwhile qualities, they are representative of cultural norms rather than scientific principles.

1.3.4.2 Goleman’s Workplace (1998) Model of Emotional Intelligence

Goleman (1998) has since extended his definition of EI and has adapted it towards primarily predicting personal effectiveness at work and overall organisational success. This model includes the constructs of: self-assessment, self- confidence, self-control, emotional awareness, empathy, conscientiousness, trustworthiness, optimism, initiative, commitment, innovation, adaptability,

dynamic thinking, motivation and achievement drive. This model also includes a leadership aspect, which encompasses: communication skills, influence, the ability to develop and manage others, conflict management, collaboration and cooperation skills and also political awareness.

1.3.4.3 Goleman’s Claims About the Predictive Nature of Emotional Intelligence

The wide impact of Goleman’s model of EI rests on his strong assertions about the importance of EI in almost all aspects of everyday life. Goleman (1995;

1998), for instance, has implied that EI is vital to interpersonal relationships, health and academic and workplace ‘success’. In doing so he has made a number of statements as to what EI is able to predict. As evidence for these claims he has cited a number of (albeit often inadequately referenced) studies from which he has generated his ideas.

In terms of interpersonal relationships, Goleman has suggested that being aware of another’s emotions and, more particularly, being able to manage the emotions of others, is fundamental to the formation and maintenance of successful relationships. Goleman (1995) has also suggested that individuals who lack the ability to read, interpret and respond appropriately to others often misinterpret others intentions resulting in a feeling of a lack of control in social situations, which usually ends up with them being ‘socially rejected’. As a consequence of such reactions, these individuals often feel frustrated, depressed, lonely, anxious, apathetic and powerless, which have negative ramifications for psychological health and well-being and in other life areas.

For example, Goleman has suggested that socially rejected children who feel like they do not ‘belong’ are more likely to drop out of school because of such

difficulties, which has an obvious impact on academic achievement. Furthermore,

Goleman has suggested that learning is impeded by negative emotions, such as anxiety, anger, hopelessness, helplessness and depression, arguing that being engaged in a negative state dominates attention so that it cannot be used in other pursuits. In contrast, positive emotions, such as optimism or hope, assist in maintaining motivation in the pursuit of goals, thereby contributing to success.

Goleman has therefore also suggested that optimism and hope constitute part of EI, presumably because individuals who are better able to regulate their emotions are better able to generate and maintain these states.

Goleman also considers impulse control, a process inherent to academic and other types of success, to be a component of EI. Impulse control is likened to managing emotions and Goleman argues that the ability to suppress impulsive desires in order to rationally identify the best course of action will lead to better emotional decisions. In a similar vein, Goleman (1995), based on the marshmallow studies by Mischel in the 1960s, has also attributed motivation and the ability to postpone gratification – aspects which are important to academic achievement - as being a part of EI. In this study, adolescents, who as 4 – 5 year old children were able to delay eating a marshmallow for 15 minutes contingent on the promise that they would receive two marshmallows if they waited, were found to be more successful in life than those with poorer control. As adolescents, these children were found to be more socially competent, personally effective, self assertive, less likely to regress under stress and better able to cope with frustrations. They were also found to be more self-reliant, confident, trustworthy, dependable, more likely to embrace challenges and also more likely to still delay gratification in order to achieve their goals. Such children were also found to be more academically

competent, with higher scores on their SAT tests than those who immediately ate the marshmallow.

As a consequence of these findings, Goleman has argued that the ability to delay gratification, even though it is independent of IQ, is an important contributor to intellectual achievement and potential. Furthermore, Goleman appears to view this study as evidence that an individual’s level of EI determines how effectively they are able to use their other mental capacities. However, such arguments overlook the possibility that the emotions or qualities necessary to deal with such situations are social constructions, or that the parents who taught their children the art of delaying gratification at the age of four years are likely to still be in their children’s lives at 16 years when the subsequent testing was taking place. Hence, the likelihood that such parents would have passed on further valuable lessons to their child in the interim should be taken into account, which was not done in this instance (Epstein, 1998).

Goleman has also argued that EI is crucial to workplace success. Goleman

(1995; 1998) has suggested that individuals with higher EI are better able to motivate themselves, display initiative, coordinate team work, regulate time and work commitments, to empathise, encourage cooperation, minimise conflicts and are better at creating ad hoc networks, all of which are said to facilitate workplace activities. In particular, Goleman has claimed that the key to successfully working in groups, as is often required in workplace settings, is the group’s social harmony.

Although it is apparent that a group can be no more efficient than the sum total of its combined skills, the effectiveness of groups may be hampered by the group dynamics if this results in an inadequate sharing of talents. This may arise if some individuals are too eager to contribute to the project and, in their eagerness,

attempt to control and dominate the direction of the group, leaving other members unable to participate adequately. Alternatively, success may be lowered if a large portion of the group is unable, or unwilling, to contribute to the group in order to complete the task. Goleman (1995) has therefore argued that the difference between groups that are effective and those that are not is the EI of the people involved; stating that groups that have a high overall level of EI are better at interacting with others and this facilitates a whole range of work activities.

Goleman (1998) has also argued that EI becomes increasingly important the higher one is promoted in an organisation, as differences in technical skills come to be viewed as of only limited importance, while people’s ability to manage others (a component of EI) is deemed more relevant. Goleman has therefore suggested that technical and cognitive skills become ‘threshold variables’ the higher up in an organisation’s hierarchy someone is, because they are essential requirements for everyone in that job. As a consequence, Goleman has argued these abilities lose their ‘power’ as competencies for distinguishing outstanding from average performers. Goleman has therefore suggested that although IQ might predict what occupation an individual may attain, and is thus a strong predictor of success among the general population, within a particular profession, IQ’s predictive power will diminish when comparisons are made between individuals in the same job. Goleman therefore suggests that in this instance it is EI that becomes more important. Goleman (1998) has even claimed that EI predicts approximately two- thirds of success at work and has concluded that “compared to IQ and expertise, emotional competence mattered twice as much” (p. 31).

However, Goleman’s prediction and the way in which he derived such conclusions has been criticised as being of limited scientific validity as it was based

merely on a comparison of qualities mentioned in job advertisements; i.e., counting the number of purely cognitive factors and the purely emotional factors that appeared in various job advertisements and comparing these two figures. Mayer

(1999) has pointed out that this approach merely assesses words that have made it into a job description which, he argues, cannot in any way be related to actual job performance. Mayer (1999) has, furthermore, argued that such a finding is largely unsurprising, given the magnitude of Goleman’s (1998) definition of EI (which encompasses 25 broad socio-emotional skills).

Goleman (1995) has also claimed that EI is important to health, based on a number of studies that have indicated that negative health effects (such as heart attacks, cancer and other diseases) are a consequence of prolonged negative emotions. Prolonged negative emotions, he has suggested, are a consequence of poorer ability at managing these emotions when they occur. Therefore conversely the ability to manage negative emotions it is suggested will result in the lower incidence of such diseases.

However, while unregulated and constant emotions may indeed contribute to the development of such diseases, Goleman’s stance in this regard ignores the fact that emotions – whether they are positive or negative – have purpose and are important to everyday experience. Although it is ideal to limit negative emotions while simultaneously maximising positive emotions, Salovey and Mayer (1990)

(see Section 2.7) have emphasised that EI is not about ‘ignoring’ unwanted emotions but about managing them, if desired, to minimise their effects. Goleman, however, appears to be unusually preoccupied with the concept that negative emotions are intrinsically aversive and should be quelled at all times. In fact,

Goleman seems to view that all emotions, particularly extreme emotions, should be

‘controlled’ so that they may be used ‘appropriately’. In doing so Goleman places far less value than academic researchers on the role or the inherent value of feelings and therefore fails to acknowledge the function of emotions in life changes.

1.3.4.4 Criticisms of Goleman’s models

Although, Goleman has popularised the EI construct and has been important in drawing attention to the limited applicability of academic intelligence for success, there are limitations to the usefulness of his EI model. For instance,

Goleman’s EI theory has been criticised for his failure to adequately define EI. As a consequence, his theory includes so many different qualities that are potentially important to some level of life success that it has rendered the definition unwieldy and untestable (Epstein, 1988). Furthermore, many of the components included in

Goleman’s theory are more closely aligned to attitudes, beliefs and traits, which have already been extensively examined within psychology, than they are with the nature of emotion and intelligence (Pfeiffer, 2001). Although it is acknowledged that many of these theoretical components may predict ‘success’, what is disputed is that all of these variables are worthy of inclusion within an EI framework.

Including all these variables in one model, Pfeiffer (2001) has suggested, stretches the boundaries of what can be legitimately conceptualised as an intelligence.

In order for Goleman’s definition of EI to be deemed scientifically viable, it must be able to be demonstrated that all components of the theory are positively correlated with each other and that these may be conceptualised as part of a single overarching ability composed of semi-independent factors (Epstein, 1998).

However, the broad nature of Goleman’s EI models limits the scientific investigation of the theory simply because it is difficult to comprehensively assess

all proposed concepts (Epstein, 1998). As such, little corroborative evidence for formulating Goleman’s often diverse variables into an overarching coherent theory under any recognised definition of intelligence has been obtained (Pfeiffer, 2001).

Mayer and Cobb (2000) have also suggested that the components in

Goleman’s EI theory must be classified as ‘highly important’ to be worthy of includsion. Mayer, Salovey and Caruso (2000a), however, have documented that many of the variables that Goleman has claimed are correlated with EI, have been shown in personality research to not be substantially related to each other. Mayer et al. (2000a) have further argued that Goleman fails to acknowledge that some of these traits may actually be detrimental in some circumstances. For example, being too optimistic (a component of Goleman’s theory) in hoping for change to occur spontaneously, may lead someone to ignore actions that may otherwise help rectify a situation. Thus, possessing such qualities, but not being able to adequately recognise their appropriateness in certain situations in order to act accordingly, may also be disadvantageous.

Goleman (Cherniss & Goleman, 2001), however, has disputed suggestions that his EI model is not well-defined. Instead he has claimed that his original intention in writing about EI was to explore the concept of EI as a “groundbreaking conception of intelligence, rather than to systematically articulate an EI model”

(p. 20). However, as detailed above, Goleman has made a number of (still) largely unsupported claims about the prediction of EI in a number of life areas. He has even gone so far as to suggest that “at best, IQ contributes about 20% to the factors that determine life success, which leaves 80% to other factors” (p. 34), from which he goes on to hint that EI constitutes the majority, if not all, of this remaining variance.

These claims go beyond anything that had been written about EI at the time and have even to date not been adequately scientifically validated. Mayer and

Salovey have expressed surprise at Goleman’s suggestion that EI is substantially more important than intelligence, given that intelligence has long been considered an important predictor of academic, and in some cases, professional success. Based on previous research, for EI to out-predict intelligence it would mean that EI would have to exhibit correlations at levels higher than r = .45 with the proposed criterion.

This would seem highly improbable, given that no other variable studied in a century of psychological research has been shown to make such a large contribution (Mayer, Salovey & Caruso, 2000b). Mayer and Salovey (1997) have further argued that a single personality factor is only able to account for a relatively small proportion of life outcomes. Mayer (1999) has therefore cautiously advised that the magnitude of any prediction from EI about life outcomes could only be in the order of predictions able to be made by other important personality variables

(i.e., somewhere between 2% to 25% of explained variance). It would thus seem that, although cognitive intelligence may not be able to account for all predictive variance, it is unlikely that EI, in its place, will be able to do so either.

Cherniss and Goleman (2001), however, have argued that Goleman’s (1995) statement that EI “can be as powerful and at times more powerful than IQ” in predicting life success has been misinterpreted and subsequently misrepresented by Mayer, Salovey and colleagues (p. 34). According to Cherniss and Goleman

(2001), Mayer and colleagues have wrongly assumed that Goleman’s assertion, referred to success in all areas. Goleman (Cherniss & Goleman, 2001) has since claimed that he was in fact referring to the areas in life for which IQ’s prediction is not as strong, such as health or marital success.

Unfortunately, however, the popularity of Goleman’s books within the wider community has meant that Goleman’s many claims about the importance of

EI have often been taken as fact. Goleman’s ideas appear intrinsically appealing because they suggest that success in life may be achieved regardless of IQ level.

Additionally, Goleman has portrayed EI as the answer to all of society’s evils, arguing that an escalating incidence of random violence, family breakdowns, teen pregnancy, child abuse and neglect, suicide, eating disorders and substance abuse, among others, are a result of a general decline in EI. Goleman views that an increasing number of individuals are receiving inadequate ‘EI training’, which fails to equip them to manage the involuntary responses that arise in relation to threatening situations. Thus, he has suggested, more and more people are acting out primitive emotional impulses when they feel fear, hate, anxiety, resentment, grief and depression. However, the scientific evidence to support Goleman’s many assertions is lacking.

Nevertheless, Goleman must be credited with popularising the concept of

EI. The publication of Goleman’s best selling book has since spurred on the publication of a number of other ‘lay-educated’ books on the concept of EI (e.g.,

Cooper & Sawaf, 1997; Goleman, 1998; Gottman, 1997; Lencioni, Bradberry &

Greaves, 2005; Segal, 1997) and these have resulted in both print and other media interest. This material has helped generate much of the recent prolific research interest in the field, which has generated a number of different EI theories, each of which provides somewhat different theoretical frameworks and perspectives. A discussion of the three major scientific theories of EI is presented in Chapter 2.

CHAPTER 2: SCIENTIFIC THEORIES OF EMOTIONAL

INTELLIGENCE

Since the publication of Goleman’s (1995; 1998) influential books on EI (see

Chapter 1), interest in EI within the scientific community has substantially increased. Cherniss and Goleman (2001) have since claimed that the “EI model seems to be emerging as an influential framework in Psychology” (p. 15) and have further suggested that EI has relevance in the areas of developmental, educational, clinical, social, industrial, organisational and health psychology. Increased interest in EI has, however, somewhat clouded research into the nature of the construct due to the influx of numerous scientific and popular components for proposed inclusion. There has thus been some confusion within the field of EI over which of the multitude of qualities thought to be contained in the concept actually represents the true nature of EI and the best way in which it may then be measured

(Mayer & Salovey, 1997; Roberts et al., 2001).

Currently, there are several models of EI, which provide somewhat different theoretical frameworks and perspectives. A discussion of the three major scientific frameworks and the EI measures derived from these models, by Salovey and

Mayer (1990; Mayer & Salovey, 1997), Bar-On (1997) and Petrides and Furnham

(2000; 2001; 2002), follows below. First, a number of issues concerning EI are raised.

2.1 Criticisms about the labelling of “Emotional Intelligence”

Some criticisms have been made about the use of the term “Emotional

Intelligence”, based on the argument that combining the two terms is contradictory

(Salovey & Mayer, 1990). Emotions have long been held to be disruptive interruptions to mental activity and thus it may be viewed that connecting the word ‘emotion’ to ‘intelligence’ is contradictory to the underlying assumption that important or unique abilities are not associated with emotions (see Salovey &

Mayer, 1990; Mayer & Salovey, 1993). Epstein (1998), for instance, has suggested that pairing ‘emotion’ and ‘intelligence’ together (a previously ‘contentious’ area with a ‘non-contentious’ area) deliberately creates controversy and confusion because it assumes that emotions ‘have’ intelligence. Epstein (1998) instead views that “emotions are not a way of thinking, but a consequence of preconscious, automatic thinking” (p. 8).

In response, Mayer and Salovey (1993) have argued that although they could have labelled the concept ‘Emotional Competence’, they preferred the term

‘Emotional Intelligence’, because the use of the word ‘intelligence’ implied that the process referred to a mental aptitude and they have conceptualised EI as such (i.e., as an ‘intelligence’, see Section 2.7). Therefore, in making such a distinction they wished to convey the sense that intellectual problems may either contain or require emotional information in order to assist in making rational decisions. However, this approach has been criticised by Roberts et al. (2001) who have argued that the use of ‘intelligence’ is perhaps inaccurate given that EI has yet to be adequately located within a model of intelligence.

2.2 ‘Ability’ versus ‘Mixed’ models of Emotional Intelligence

Due to continued disagreements over which variables should be included in the EI framework, conceptualisations of EI have been divided into two broad categories that distinguish the different approaches in this regard. These have been

described by Mayer et al. (1999) as “ability models” and “mixed models” although,

“self-report” and “trait” models of EI have also come to be viewed as acceptable terms for mixed model conceptualisations.

Mayer, Salovey and colleagues have defined EI as an ‘ability’, emphasising the cognitive processing of affective information and their EI model (as discussed in Section 2.7) is focused entirely on the specific interaction between emotion and thought. In contrast, mixed models of EI, as characterised by Goleman (1995; 1998, as discussed in Chapter 1), Bar-On (1997, as discussed in Section 2.8) and Cooper and Sawaf (1997, not discussed here), have typically included some aspects contained in ‘ability’ models but have also included personality, motivational factors and affective dispositions (Mayer et al., 1999; Roberts et al., 2001). Mixed models of EI have been widely criticised as over-inclusive because they incorporate multiple variables that, while possibly being important to life success, extend conceptually beyond what is typically invoked by the terms ‘emotion’,

‘intelligence’ and ‘EI’ (Mayer, 1999).

2.3 The Measurement of Emotional Intelligence.

The way in which EI is conceptualised largely determines the way in which it is evaluated. The three methods currently available for evaluating EI are ‘ability’ measures (an objective assessment, see Section 2.3.1), ‘self-report’ measures (see

Section 2.3.2) and ‘observer-rater’ measures (both subjective assessments, see

Section 2.3.3). There is some contention over the appropriateness and effectiveness of each of these methods, fuelled largely by differing opinions on what elements should comprise EI and on a number of methodological difficulties in determining the validity of the information obtained with each approach.

Questions over how effective any form of EI assessment can actually be have also been raised. Gold and Concar (1996) have argued that emotional skills, since they operate within dynamic environments, are changeable and relative in a way that IQ is not. Consequently, they have argued that since emotional response is largely embedded within the social context in which it occurs, an individual’s ability to be emotionally intelligent will likely vary according to whether the situations involve people with whom a close relationship has been formed versus those who are strangers. They have therefore raised doubts about whether EI measures can detect and adequately measure some of the more subtle components postulated as comprising EI. Ciarrochi, Chan, Caputi and Roberts (2001b) have also suggested that if EI cannot be adequately measured then it might be necessary to

“admit that it might not exist as a meaningful scientific construct” (p. 25 - 26).

2.3.1 Ability Emotional Intelligence Measures

The model of EI developed by Mayer, Salovey and colleagues (see

Section 2.7) proposes that EI is a mental ability. As with other types of mental abilities, they suggest that individuals differ in their effective level of emotional processing. Ability models of EI are therefore typically assessed by objective maximal performance measures, similar to the way in which cognitive intelligence is evaluated. Such measures (e.g., Multifactor Emotional Intelligence Scale [MEIS] and Mayer, Salovey and Caruso Emotional Intelligence Test [MSCEIT]; see

Section 2.7.6) require solving emotional problems for which there are defined

‘correct’ and ‘incorrect’ answers. This form of testing putatively provides a quantifiable indication of an individual’s actual EI abilities, as opposed to simply asking an individual what they believe their emotional abilities to be. It has been

argued that the nature of such measures reduces the likelihood of participants

‘faking’ a good performance and also eliminates the need for the individual to have insight into their own EI. These two factors may impact on the quality of the results that are obtained and have been identified as fundamental problems in self-report measures (see Section 3.1.2) (Ciarroch, Chan & Caputi., 2000). In light of these advantages, ability measures of EI are considered to be the most reliable form of measurement because they appear to be more empirically based than self-report EI measures (Mayer et al., 1999; Mayer & Salovey, 1997; Roberts et al., 2001).

However, the problem with ability EI measures is the difficulty in determining the ‘correctness’ of answers to such problems so that these measures can be scored. Measures of cognitive intelligence consist of items which are based on a formal rule system which provides clear justification for the ‘correctness’ of a particular answer (Roberts et al., 2001). Roberts et al. (2001) have therefore argued that if EI is to be considered an ‘intelligence’, a similar set of rules must be derived and upheld for the scoring of these measures. Difficulties with this requirement arise because the correctness of emotional responses may be viewed as largely subjective and are therefore dependent on individual judgement, something that makes assessments by objective scoring criteria somewhat difficult (Furnham &

Petrides, 2003; Mayer et al., 1999). Roberts et al. (2001) have consequently suggested that within the emotional domain it “may be inappropriate to insist that the test items should have rigid unequivocal right and wrong answers” (p. 226).

Mayer et al. (1999) have suggested three alternative scoring methods

(consensus scoring, expert scoring and target scoring) for establishing the correctness of answers for ability EI measures. Consensus scoring works on the

basis that observations obtained from a large number of people can be combined and averaged in order to produce a reliable measure of general opinion. In this sense the most common answer endorsed by a group is deemed to be the most appropriate and is therefore considered the ‘correct’ response. This approach, it is argued, reduces the subjectiveness of any given individual’s opinion. Scoring is therefore dependent on how often the test-taker endorses the group consensus. For example, if .21 of the group sample advocated a particular answer as correct, a participant selecting the same answer would receive a score incremented by .21.

Expert scoring relies on ‘emotion experts’ (e.g., psychiatrists, psychologists, philosophers, emotion researchers) to judge what they believe to be the most appropriate response. It is presumed that such experts will have more emotional and behavioural knowledge to assist them in making their decisions than the typical layperson and will thus be more likely to know the ‘correct’ answer (Mayer et al., 1999). As with consensus scoring, test-takers receive credit based on the level of correspondence of their answers with those provided by these experts.

Finally, target scoring has been proposed whereby a target individual is asked to describe their feelings about a particular experienced event. An external observer (the test taker) must then attempt to determine the emotions that the target individual was likely to have felt in that scenario. These results are then compared in order to determine the level of congruency between the two. It is assumed that the target individual will have more information about their own mood states than will external observers and therefore the responses provided by the target individuals are deemed to be the ‘correct’ answers (Mayer & Geher,

1996). This assumption, however, is challenged by observations that target individuals often alter their reports of emotional content in order to appear more

socially desirable or report incorrect emotions simply because they are unable to determine accurately or express the complex emotions that they are experiencing

(Mayer & Geher, 1996). Additionally, target scoring has only limited functionality within the field of EI because it is only relevant to emotion-identification tasks, rather than to higher-level EI components and has thus not been widely used

(Roberts et al., 2001).

Mayer et al. (1999) have claimed that in general there is a great deal of similarity between these three methods, reporting correlations ranging between r = .16 and r = .95 between them, with at least half of all correlations being above r = .52, based on the use of two experts. Mayer et al. (2000a) have argued that the similarity between these three scoring techniques enables the designation of some answers as more plausible (i.e., ‘more correct’) than others. Nevertheless, despite the similarity between these three scoring techniques, Mayer et al. (1999) have recommended the use of consensus scoring. This is based on evidence that suggests that targets may sometimes minimise or inaccurately report their feelings, or the possibility that experts may be merely providing an estimate of what they believe to be group opinion anyway, while the pooling of responses into large normative samples appears to produce fairly reliable judgements and will eliminate individual biases.

However, Roberts et al. (2001) have questioned Mayer et al.’s (1999) claim that there is congruency between the scoring procedures. In a sample of US Air

Force trainees (N = 704), they failed to find adequate convergence between consensus and expert scoring, with correlations between the two ranging from r = .00 to r = .74, with an average of only r = .26. These results differed markedly from those obtained by Mayer et al. (1999) and are, Roberts et al. (2001) have

claimed, far smaller than would reasonably be expected for intelligence type measures. Roberts et al. (2001) have further found that the different scoring methods produced different results when gender was considered. For instance, male participants tended to score higher when expert scoring was used, while female participants scored higher when consensus scoring was used. Additionally, they found differences in relation to ethnicity, with no observable differences between majority and minority groups found when consensus scoring was utilised but with differences, favouring majority groups, found when expert scoring was used. According to Roberts et al. (2001) such differences were sufficiently large that there are grounds for suggesting that one of the scoring methods should be discarded.

Roberts et al. (2001) have suggested that the nature of the individuals used to derive the correct answers, in both the consensus and expert groups, may have affected the results. MacCann, Roberts, Matthews and Zeidner (2004) have pointed out that there are no particular criteria to determine who is an ‘expert’ in EI.

Matthews et al. (2002) have therefore suggested that it is likely that an ‘expert’s’ judgement in one field may differ from that of an ‘expert’ in another relevant field.

Roberts et al. (2001) have noted that when developing the expert opinion for the

MEIS there were only two ‘experts’, who were both white, well-educated males, which is hardly a representative sample. Roberts et al. (2001) have further suggested that there is no doubt that experts are biased by their social and cultural experiences and therefore the potential differences in opinion that may be apparent in minority groups may have been overlooked in developing the scoring methodology for the MEIS. Roberts et al. (2001) have thus argued that responses on emotional based tasks between two groups that differ fundamentally on age,

gender, ethnic and cultural status are unlikely to be easily comparable.

Consequently, participants who are demographically similar to the experts will be more likely to score higher under the expert scoring criteria.

Roberts et al. (2001) have also argued that a similar bias may have influenced the results obtained for consensus scoring. In the case of the normative consensus group used in the MEIS, 67% of the sample were female, which may explain why female participants tended to score higher on the MEIS than male participants when this form of scoring was utilised. Mayer et al. (1999), however, have argued, on the basis of analysis of one MEIS subtest, that gender differences found in favour of females on the MEIS were not caused by differences in male/female criteria, although as only one assessment was used this result is hardly definitive.

Furthermore, Roberts et al. (2001) have argued that if there are no defined

‘correct’ answers, as appears to be the case in regards to emotional criteria, then consensus will be of no validity. Additionally, Matthews et al. (2002) have argued that one of the difficulties with consensus scoring is that it is likely to be actually measuring what they have termed ‘emotional stupidity’, rather than EI. They have suggested that given that items with graduated difficulty are required in order to obtain a comparable level of reliability across the full range of abilities, consensus scoring is likely to be inaccurate in distinguishing ‘emotional geniuses’ from the average emotionally intelligent person. As only a limited number of people are likely to be able to correctly answer difficult test items, the majority of individuals will incorrectly favour other responses, which, by the very definition of consensus scoring, will then become the ‘correct’ answer. Highly emotionally intelligent individuals when taking the test could be expected to nominate the actual correct

answer (but the ‘consensually incorrect’ answer) and will therefore score lower than their ‘true’ level of ability. Given these difficulties it is likely that consensus scoring will only be appropriate for simple emotional problems (e.g., recognising emotions from faces), than it would for more complex problems (e.g., identifying subtleties of social interaction), which only individuals with higher EI will be likely to solve ‘correctly’ (Austin & Saklofske, 2005; Matthews et al., 2002). Answers generated by consensus scoring may therefore be more accurately reflecting social conformity than emotional competence (Matthews, Emo, Roberts & Zeidner, in press). Nevertheless, Legree, Psotka, Tremble and Bourne (2005), have pointed out that a particular benefit of consensus scoring is that it allows for the assessment of knowledge domains for which there are no defined or easily identified ‘experts’, which is of benefit in areas that are important for daily success beyond traditional cognitive assessments, as in the case of EI.

On the basis of some of Roberts et al.’s (2001) comments, Mayer, Salovey,

Caruso and Sitarenios (2001; 2003) have attempted to address some of these concerns with expert scoring by expanding the expert sample used in the MSCEIT.

Instead of only 2 experts, they have created an aggregate of responses from 21 experts from the International Research on Emotions Committee, an academic society for affective science. Scoring was on the basis of the proportion of experts who gave the same answer. A comparison of answers between the consensus scores of a general sample (N = 2112) and between the consensus scores of this expert sample indicated a high degree of congruency (r = .91; Mayer et al., 2003).

Experts, however, were found to be more reliable judges in substantially researched emotions areas where clear criteria for answers have already been established. This has led Mayer et al. (2003) to suggest that if such reliabilities are

confirmed, the use of expert scores should become the scoring method of choice.

However, the issue of whether scores from this new sample produces a gender bias has not yet been evaluated.

Matthews et al. (2002) have acknowledged that such results indicate a significant improvement in construct validity for the MSCEIT compared with the

MEIS. Nonetheless, they still remain cautious about these findings. In particular, they have questioned the homogeneity of the expert sample, given that scant details were provided by Mayer et al. (2001; 2003). Information on these experts suggests that they were 10 males and 11 females, with a mean age of approximately

40 years. However, no information on ethnicity is provided other than to suggest that they came from “Western Countries”. This has lead Matthews et al. (2002) to wonder whether the majority of them were white, middle-class, highly-educated

Westerners and, therefore, perhaps unable to accurately reflect the opinions of minority groups. Data were also not available on the extent of inter-expert agreement in the judgements made. Matthews et al. (2002) have therefore suggested it is impossible to know whether all members have sufficient expertise in the many domains of emotion to make appropriate judgements about circumstances that are outside of their individual area of expertise. Furthermore, given the relatively large pool of experts, Matthews et al. (2002) have suggested that, as with consensus scoring, these experts may have been tapping a cultural consensus, rather than any special expertise.

Wilhelm (2005) has also questioned the reliability of scores generated by the

MSCEIT given that based on only eight tasks, seven ‘abilities’ (four branch scores, two area scores and a general EI score) are obtained. As a consequence, an individual that scores badly on a particular task will receive poor assessments in all

subsequent scores. Wilhelm has argued that there are therefore not enough tasks

(currently two) to sufficiently assess each of the four emotion branches. He has instead suggested that at least four tasks that have been demonstrated to assess each ability should be used – an issue which should be examined in future developments of ability EI measures.

2.3.2 Self-report Emotional Intelligence Measures

In contrast, mixed models of EI (e.g., Goleman: Section 1.3.4.1; Bar-On:

Section 2.8), which are typically regarded as comprising a dispositional tendency similar to personality, are primarily assessed by self-report measures. Such measures (e.g., Trait Meta Mood Scale [TMMS]: Section 2.7.5, Assessing Emotions

Scale [AES]: Section 2.7.7 and the Emotional Quotient Inventory [EQ-i]:

Section 2.8.1) essentially seek an assessment of an individual’s typical level of functioning by asking individuals to rate the extent to which a series of descriptive statements are indicative of themselves.

Self-report measures assume that individuals are the best judges of themselves and their own behaviour and therefore self-report assessments provides appropriate evaluation. However, there are grounds for challenging these assumptions. First, self-report measures assess an individual’s perception of their skill, rather than actual competency in those domains. Mayer et al., (2000a) have suggested that such assessments of EI are akin to asking someone how fast they think they can type rather than quantifiably assessing this ability. Consequently, as with target-scoring assessments (see Section 2.3.1), the accuracy of self-report measures is reliant on an individual’s level of insightfulness into their own behaviour and ability, which may be distorted or even unavailable to conscious

interpretation. Petrides and Furnham (2000), for instance have suggested that self- reported estimates of EI are often biased, with respondents typically rating themselves as above average in ability. Secondly, such measures rely on an individual’s honesty when reporting their behaviours, and thus self-report EI measures are susceptible to a social desirability bias (Mayer et al., 2000a; Geher,

Warner & Brown, 2001; Malouff & Schutte, 2001; Roberts et al., 2001;).

In fact, research has indicated that self-report assessments of ability rarely reflect actual ability. For example, Mabe and West (1982), in a meta-analytic investigation of 55 studies, determined that correlations between self-report evaluations of intelligence and actual intelligence measures are only moderate

(r = .34). Roberts et al. (2001) have also reported that in relation to intelligence, self- report measures account for less than 10% of actual intelligence score variance.

Based on such evidence, Roberts et al. (2001) and Wilhelm (2005) have considered it reasonable to expect that correlations of a similar magnitude would exist between self-report and ability measures of EI. Given this disparity, Wilhelm (2005) has cautioned that it is not appropriate to use self-report assessments as proxies for ability EI.

Self-report measures of EI have also been found to have moderate-to-high correlations with a number of other well-established psychological constructs, particularly with personality (e.g., Bar-On, 1997; Ciarrochi, et al., 2000; Dawda &

Hart, 2000; Newsome, Day & Catano, 2000). It is possible that these correlations may reflect the nature of self-report EI test construction, which is similar to the way in which personality itself is assessed, or, as is more generally thought, it may be due to the relative reliance that mixed models of EI place on pre-established personality traits (Palmer, Manocha, Gignac & Stough, 2003).

Given the relatively high correlations between self-report EI measures and personality scales, questions have been raised as to whether EI models relying on such measures are really assessing anything beyond already heavily researched psychological constructs (Davies, Stankov & Roberts, 1998; Matthews et al., 2002).

Consequently, although substantial correlations have been found between self- report measures of EI and theoretically relevant criteria (as discussed in more detail in Sections 2.7.5, 2.7.7 and 2.8.1), it is uncertain whether these are substantive relationships or simply arise because these measures tap personality traits known to predict these criteria (Matthews et al., 2002; Newsome et al., 2000; Petrides &

Furnham, 2000; Palmer, Donaldson, Stough, 2002). Nevertheless, it has also been acknowledged that self-report assessments have some benefit in that they can provide useful insight into internal processes and experiences that are not measurable by performance based assessment (Neubauer & Freudenthaler, 2005).

2.3.3 Observer-rater Emotional Intelligence measures

Attempts have been made to address some of the criticisms concerning self- report EI tests with the use of observer ratings, obtained from a close associate of the person, as a comparison to increase the reliability of the obtained information

(Malouff & Schutte, 2001). Observer rating scales are typically structured in a similar way to self-report EI measures but they provide information about how an individual is perceived by others. Malouff and Schutte (2001) in a study of 45 individuals have found self-report and observer rating assessments of EI (by an acquaintance of at least three months duration) to be moderately correlated (r = .31) suggesting that they are assessing similar constructs, although this level of association is hardly strong.

Observer rating EI measures, however, suffer from similar problems to self- report EI measures in that they rely on ratings of behaviour, which may be incorrect due to corruption by the observer’s own biases, or insufficient knowledge about the other individual. This is particularly likely, given that mental abilities are generally private and not always observable and therefore external raters are likely to be less accurate at judging these types of abilities in others, than individuals would with their own ratings (Mayer et al., 2000a). Mayer, Salovey and Caruso

(2000c) have therefore argued that such measures should really only be used for observable behaviours and not for the evaluation of mental abilities.

2.3.4 Relationships between Emotional Intelligence Measures

Generally, self-report EI measures have been found to be moderately-to- highly correlated with each other. For example, Schutte et al. (1998) have found the

TMMS and the AES to be moderately-to-highly correlated (TMMS subscales, as described in Section 2.7.5: Attention: r = .63; Clarity: r = .52; Repair: r = .68; N = 48).

Further, Brackett and Mayer (2003) and Kohan and Mazmanian (2003; as cited by

Bar-On, 2004) have both found the total scores of the AES and the EQ-i to be moderately correlated (r = .43, N = 207; r = .66; N = 103, respectively).

However, correlations between ability and self-report measures of EI have typically been found to be much lower. For example, Brackett and Mayer (2003) have reported low correlations between the total scores of the MSCEIT and the AES

(r = .18; N = 207). Low correlations have also been found between the MSCEIT

(Total) and TMMS by Lopes, Salovey and Straus (2003) (Attention: r = .01; Clarity: r = .04; Repair: r = .15; N = 103) and Warwick and Nettelbeck (2004) (Attention: r = .18; Clarity: r = .18; Repair: r = .00; N = 84). Additionally, low-to-moderate

correlations have been found between the Total scores of the MSCEIT and the EQ-i

(e.g., Brackett & Mayer, 2003; r = .21; N = 207; O’Connor & Little, 2003; r = .34;

N = 90).

It is apparent from these results that self-report and ability measures of EI share little variance and will therefore yield different measurements of the same person (Brackett & Mayer, 2003; Lopes et al., 2003). Petrides and Furnham (2001) have suggested that this is unsurprising given the differing way in which the models associated with these measures have been defined and the subsequently different ways in which each model is typically measured. Namely, self-report EI measures assess typical behaviour tendencies, whereas ability EI measures assess actual abilities, which are two different things. As a result, Petrides and Furnham

(2000) have argued against the trend of validating tentative EI measures against each other, claiming that such a practice does not assist in clarifying what each questionnaire actually measures. They have argued that particularly with self- report EI measures, this will only serve to falsely inflate the associated correlations, considering that most of these measures attempt to assess relatively similar semantic content.

2.4 Relationship of Emotional Intelligence with Intelligence1 and

Personality

For EI to be considered an ‘intelligence’, it must be demonstrated to be moderately, but not too highly, associated with measures of academic intelligence

(Mayer et al., 1999; Van der Zee, Thijs & Schakel, 2002). However, despite EI being generally viewed to fit within the broad framework of human cognitive abilities, relatively few studies have actually found a clear relationship between EI and cognitive abilities.

For example, as shown in Table 1 on page 64, the correlations between ability EI and assessments of general intelligence have generally been found to be moderate (e.g., Roberts et al., 2001; Schulte, Ree & Carretta, 2004). In contrast, the correlations between self-report assessments of EI and mental ability have typically been low, regardless of the types of measurements used (e.g., Derksen, Kramer &

Katzko, 2002; Jae, 1997; as cited by Bar-On, 2004; Pallazza & Bar-On, 1995; as cited by Bar-On, 1997). To provide a more general indication, a recent meta-analysis by

1 The Cattell-Horn-Carroll model (see Horn & Noll, 1994) represents intelligence as a large number of primary mental abilities, which yield about 10 broad general abilities, which in turn yield a general ability factor at the third order of analysis. Assessments of ‘intelligence’ only using one facet of cognitive ability (as has been typical in much of EI research) can hardly be considered representative and labelling such assessments as measures of ‘intelligence’ is deemed to be misleading. Describing such assessments as ‘ability’ would perhaps be more appropriate in these instances. Nevertheless, given that the term ‘intelligence’ has been so widely used and become commonly accepted in the EI literature, this will be used in the thesis throughout when referring to other researchers’ work. The research generated by this thesis, however, will use the term ‘ability’ in recognition of the fact that only two facets of intelligence were assessed (as discussed further in Chapter 4; see also Footnote 6).

Van Rooy and Viwesvaran (2004) using 19 studies which reported correlations between EI and general mental ability, demonstrated that the two constructs were moderately correlated (r = .22). However, when this meta-analysis was performed only on ability and self-report EI measures separately, it was found that the correlations between general mental ability and ability EI were moderate (r = .33), but were low for self-report EI measures (r = .09). Furthermore, Van Rooy and

Viwesvaran (2004) have demonstrated that the incremental validity of EI over general mental ability is a nominal .02, whereas the incremental validity of general mental ability over EI was a more substantial .31. Van Rooy and Viwesvaran (2004) have consequently stated that the claims for the predictive validity of EI over cognitive ability are not able to be substantiated.

Nevertheless, investigations of relationships with different cognitive abilities, have revealed that although self-report EI always exhibits near zero correlations (e.g., Warwick & Nettelbeck, 2004), ability EI is more closely associated with crystallised ability than it is with fluid ability. For example, ability

EI has been found to be low-to-moderately correlated with assessments of verbal ability (e.g., David, 2002; Lopes et al., 2003; Mayer et al., 1999), but to have near zero correlations with non-verbal ability (e.g., Ciarrochi et al., 2000; Chan, 2003;

Warwick & Nettelbeck, 2004).

Table 1: The Correlations Between EI and Cognitive Abilities

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n r P f i ; U o 1 M

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s I e i lli n y y an f e c th th e E r r i i

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N 84 259 354 120 84

s s n ent io h t g ud

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The lack of substantial correlations between abstract reasoning measures and EI (particularly ability EI) is not consistent with the assumption that EI is an

‘intelligence’ as intelligences should be moderately correlated with other forms of intelligences (see Section 2.7.6). That (ability) EI appears to only be related to some intelligence measures and not others raises questions about whether EI can be considered a true form of ‘intelligence’. Ciarrochi et al. (2000), however, have suggested that the ability to correctly apply a verbal label to emotions would reasonably be more related to verbal intelligence than it would to performance intelligence, which may explain some of this disparity.

Nevertheless, despite the suggestion that EI exists as part of the cognitive intelligence framework, a considerable number of studies have found consistent relationships between EI (particularly self-report) and personality, most of which have proved to be stronger than the findings obtained for intelligence. Van Rooy and Viwesvaren (2004) in a meta-analysis of EI and personality measures have found low-to-moderate correlations between various measures of EI and the Big

Five (Neuroticism: r = -.33; Extraversion: r = .34; Openness: r = 23; Agreeableness: r = .23; Conscientiousness: r = .31). These results suggest that the distinction between

EI and personality may not be ideal. However, Van Rooy and Viwesvaran (2004) in their meta-analysis did also determine that EI in general adds significant incremental validity of .17 (Neuroticism), .14 (Extraversion), .29 (Openness), .18

(Agreeableness) and .06 (Conscientiousness) over the Big Five, although the Big Five did not display incremental validity over EI. They have thus suggested that EI may be considered a better predictor of performance than personality.

This finding does, however, appear to be due mainly to the differing ways in which theorists have conceptualised EI. Only proponents of ability EI models

(i.e., Mayer, Salovey and colleagues; see Section 2.7) have strongly promoted the notion of EI as an intelligence, and the ability measures derived from this theory have been the ones to correlate most strongly with intelligence assessments. In contrast, mixed models of EI tend to favour personality type components and the self-report EI measures derived from these models are also typically assessed in a similar fashion to personality measures, where the level of agreement with a series of statements is sought. It is therefore perhaps unsurprising that self-report EI measures derived from these theories have been shown to have quite high correlations with valid personality scales, whereas associations between personality and ability EI have generally been low, as shown in Table 2.

Table 2: The Correlations Between EI and Personality ) e g

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n n n n n :

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3 2 2 2 s s s s s ersi sness t .5 ; A

s s s s s v u 4; 6 = . = . a = . = 32; = . = . = . en o = .3 4 i i r r r r r r r r r t t sne sne sne sne sne c

: : s : : : : : : x n u u u u u r = . = e n n n n n n n o o o o o ce ce E i i i i i r r

o o o o o o o ; : : en en C si si si 5 si si si

n n nsci d d ent ent ent ent ent 0 o o er er er er er er o i i v v v v v v s s 27; C = . a a a a a a

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m r r E E E E E E In In :

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e e y r al al o c c t S S

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o o o r E E ; e i o T T T t N N

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80 07 07 02 54 00 d 1 90 2 2 1 3 1 n a I n E o i t

a een s s s s s s s

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0 : 1 : 0 2 ; . : : : : . . p .1 tr tr 4 ss

02 = . . e ess 3 n n = - = - = - o o = Re r

ness ness ness ness r r r ;

: : . = . = : enn enn s s -C -C : 3 : 3 9 3 1 r r en en en en s s n p p y y 2 lf lf 2 1 2 1 e e : p p : p p o e e et et O O n S S i i

O O O O = . = . sn = . = . sn = .

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s s s s r v o t e e e e = . = . a = . = . = . = . = - i en en = - ness x t i i r n n n n r r 16; 04; r r r r r r t c s s c s s E

: : s s : : : : : :

x us en u u u u r ; n n n n = . n = . o n n n o o o o ce ce E i i i i i

o o o o o o o o ; r r t t t t 18 . en en C C 0 si si si si si si : : nt nsci n n n n

e d d e e e e 1 o i er er er er er er i i i i on on = - c c c c c C v v v v v v i i 34; 24; s s s s s = . r s s a a a a a a . . n d r r r r r n r n r n r n : r t t t t t t o = n : depen depen = x x x x x x m a r Co Co r Co Co

ave ave C

m E E E E E E is In In : :

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c ; ; ; ; ; ; tr tr ; i ; i 0 7 2 7 8 2 3 x x t . 1 8 15; 32; 41; 27; 30; ic ess ess 4 1 2 2 0 0 o 2 1 t . . . . . E E ...... r

- o : : = = en = = en = r u = - l l = - = - r r = - = - r r = - r r b b u = - = - = les les : : : : : : r r r r r r Ne r r s s s s s s a a

: : : : : : Ne : : eea eea c c

s: r r : m m m m m m bs bs n is is is is is is enes enes enes enes enes enes su Ag Ag c c ness c c ness c c

i i i i i i n bl bl bl bl bl bl ; ; tio Su Su e ed ed 3 9 ot ot ot ot ot ot ea ea ea ea ea ea n s 2 r r r r r r .1 n nd nd

u u u u u u i i tte o = . = r Ne Ne A Agre 16 PF M Agre Ne 16 PF Ne Agre M Agre C Ne r Ne Agre Agre

r y

r r r r o t o o o o t c t t t a c c c n y t i F l ve Fa Fa Fa y a n t e e e i

n I s o r al e s o Fiv Fiv Fiv r t on e c ur O O O a rs P E E E as e F O N N N P ; ; ; E ve ) ) ) Me i l l l F 16 a a a ; ; N t t t ) ) l o l o o O a a E t t (T (T (T o o y y y y y N r r r r r T T T ; T T I I I o o o o o ( ( t t t t t E E E S S n n n n n MS C C C I I e e e e e MS Inv TM ME Inv MS Inv ME Inv MS Inv

03 80 02 04 1 84 1 90 1 7 84

n n e o es t i ud t a

s s s s s St rce ed ent ent ent ent ent pul nees ege ege ege ege ege ege St t i i ll ll ll ll ll ll r Fo a ud ud ud ud ud n o o o o o o r C St C St C St C St C St U Ai T C

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t i n d L 03) a Po

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On the basis of these results it is apparent that self-report EI, in particular, is moderately-to-highly correlated to Neuroticism (negatively) and Extraversion

(positively), traits which have often been linked to emotional disposition. EI, however, appears to be reasonably distinct from Openness, Agreeableness and

Conscientiousness (but with correlations usually being positive and low-to- moderate, where found) (Matthews et al., 2002).

In sum, these results suggests that, perhaps due to the way the models of EI have been conceptualised, ability measures of EI are more highly correlated with assessments of cognitive abilities than they are with personality, while self-report

EI measures are more highly correlated to personality than they are to cognitive abilities.

2.5 Issues Affecting the Predictive Validity of Emotional Intelligence.

Once EI has been defined and operationalised, its usefulness as a construct is dependent on its predictive validity. A number of studies assessing the predictive validity of EI have been conducted (as discussed in Sections 2.7.5, 2.7.6,

2.7.7, 2.8.1 and 3.8.1, which detail the various EI measures). MacCann et al. (2004), however, have argued that there are problems in definitively determining the extent of these predictions given that the criterion used to assess this “often shares conceptual overlap with the predictor” (p. 646). Therefore, for EI to be considered useful its incremental validity, to predict outcomes after the effects of other factors, such as personality and cognitive abilities have been adequately controlled, must be ascertained.

Many studies, however, have not comprehensively partialled out the effects of such compounding factors when evaluating the performance of EI or, if they

have, generally only personality or cognitive abilities, but typically not both, have been controlled. This requirement is particularly important when using self-report

EI measures, given the relatively high associations of these measures with personality factors (see Section 2.4). Thus, while self-report EI measures have been found in a number of studies to correlate with theoretically relevant criteria, there is doubt concerning the validity of such relationships, given the similarly high correlations of these EI measures with personality. It has consequently been suggested that these relationships exist simply because the EI measures are tapping into the personality traits that are themselves known to predict these theoretically relevant criteria (Mayer et al., 1999; Newsome et al., 2000; Petrides & Furnham,

2000; Palmer et al., 2002).

Based on such concerns, Petrides, Frederickson and Furnham (2004) have argued that the currently available evidence for EI (both self-report and ability measures of EI) indicates that the effects of EI are not “as strong or pervasive as some theoretical accounts would suggest” (p. 289). Furthermore, Petrides et al.

(2004) have pointed out that most findings have only been in relation to mixed EI models and have noted that even these effects have only been small-to-moderate.

As a result, Petrides et al. (2004) have suggested that the importance of research in the area of EI should not “be judged according to the resultant effect sizes, but rather according to the extent to which it elucidates the nature of the construct”

(p. 289).

A further difficulty in evaluating the importance of the EI construct is the issue of the causal role of EI. While a number of studies have indicated that EI is related to several important behaviours, Ciarrochi et al. (2001b) have argued that these studies have not necessarily ascertained whether it is actually EI that is

causing the behaviour. For example, while EI may be associated with high-quality friendships, Ciarrochi et al. (2001b) have questioned whether it is EI that leads to the occurrence of higher-quality friendships or whether high-quality friendships lead to higher levels of EI. Ciarrochi et al. (2001b) have proposed that the most effective way to evaluate this relationship is to ‘train’ individuals to be more emotionally intelligent and to then investigate their performance following this training. However, they have cautioned that such research is likely to be limited in that training people in this way tends to teach them a variety of skills and thus it cannot be accurately determined whether the improvements are due to EI or these other skills.

2.6 Alexithymia

Before discussing the popular theories of EI, the concept of Alexithymia, an aspect that has been found to be inversely related to EI, will be examined.

Alexithymia (derived from the Greek to literally mean “lack of words for emotions”) refers to a specific disturbance in the cognitive processing of emotions, such that emotions remain undifferentiated and poorly regulated (Parker, Taylor &

Bagby, 1993). Alexithymic individuals consequently experience difficulty in identifying, describing and verbalising their (and often also other’s) feelings and difficulty in distinguishing between the feelings and somatic sensations that are associated with emotional arousal, as well as a tendency towards externally orientated thinking and a limited imaginal capacity (Martinez-Sanchez, Ato-Garcia

& Ortiz-Soria, 2003; Taylor, Bagby & Parker, 1991).

Alexithymia has been found to be moderately-to-highly and negatively correlated to EI in a number of studies. For example, Schutte et al. (1998) found

scores on the Assessing Emotions Scale (AES) to be highly correlated to the 26-item

Toronto Alexithymia Scale (TAS; r = -.65; N = 25). Dawda and Hart (2000) also found scores on the 20-item TAS to be low-to-moderately and negatively related to the EQ-i (Males: Intrapersonal: r = -.53; Interpersonal: r = -.38; Adaptability: r = -.39;

Stress Management: r = -.37; General Mood: r = -.27; Females: Intrapersonal: r = -.57;

Interpersonal: r = -.40; Adaptability: r = -.47; Stress Management: r = -.33; General Mood: r = -.40; N = 234). Additionally, Parker, Taylor and Bagby (2001) have found moderate-to-high correlations between the EQ-i and the 20-item TAS (Intrapersonal: r = -.66; Interpersonal: r = -.54; Adaptability: r = -.62; Stress Management: r = -.47;

General Mood scores not available).

A discussion of the current major EI theories and frameworks, follows.

2.7 Mayer and Salovey’s Model of Emotional Intelligence

The most influential scientific EI model is that of Mayer and Salovey. Mayer,

Salovey and colleagues (e.g., Salovey & Mayer, 1990; Mayer & Salovey, 1993; 1997;

Mayer et al., 1999; Mayer et al., 2000a) have been firm advocates for defining EI as an “ability”, in which the cognitive aspects of emotions are considered. More particularly, Mayer and Salovey’s EI model attempts to distinguish the specific mental abilities that are associated with recognising and dealing with emotions from characteristic patterns of behaviour, such as personality, talents and dispositions that are often associated with ‘mixed’ EI models (as discussed in

Chapter 1 and Section 2.8). Based on the conception of EI as an ability, Mayer,

Salovey and colleagues have viewed EI as having similar psychometric properties and structures as those defining ‘general intelligence’ and have, in fact, claimed that the MEIS (and by extension the MSCEIT because it is based on the same

principles) meets the standards of validity and reliability that are demanded of traditional cognitive measures.

Mayer et al. (1999) have argued that for EI to be considered a legitimate scientific construct, it must meet the three criteria (‘conceptual’, ‘correlational’ and

‘developmental’) that are demanded of new intelligences. The first criterion specifies that EI must be able to be operationalised as a set of (emotion-related) abilities that may be defined in terms of mental performance, rather than a person’s preferred way of behaving. Mayer et al. (1999) have argued that this has been achieved via their ability EI model in which they contend that EI may be conceptualised as a set of four emotional abilities (Salovey & Mayer, 1990; Mayer &

Salovey, 1997). Mayer et al. (1999) further assert that objective EI measures are able to operationalise EI as an ability given that ‘better’ answers on such measures are able to be distinguished from ‘worse’ answers.

The second criterion specifies that EI should be correlated with closely related abilities, but should also be distinct enough to ensure unique variance, suggesting that a new type of ‘intelligence’ is being assessed. Intelligences should be moderately correlated with one another (which allows for a moderate amount of differences in intelligence levels within the same person), but correlations should not be so high as to be unable to provide new information (Mayer & Salovey, 1997).

Thus, EI should theoretically exhibit moderate correlations when assessed against measures of cognitive abilities. Some evidence for this proposition has been found.

For example, some moderate correlations between ability EI and general intelligence and also verbal ability have been found, although interestingly not with abstract reasoning (see Section 2.4), suggesting that EI is perhaps only related

to some cognitive abilities. It is therefore somewhat questionable how well EI meets this intelligence requirement.

Finally, EI should demonstrate incremental development trends with age and experience. As evidence for this Mayer et al. (1999), for example, have found EI scores to be higher for an adult population than they were for an adolescent population for all scoring methods [Consensus: F(1, 713) = 23.8, p = <.001; Expert:

F(1, 709) = 22.3, p = <.001; Target: F(1, 718) = 8.0 p = <.01] from which they have concluded that EI increases with age and experience. Further evidence of increases in EI with age are discussed further in Section 3.7. Roberts et al. (2001), however, have argued that these result are based on cross-sectional studies and therefore are only indicative of average group differences, rather than an accurate assessment of developmental changes.

Salovey and Mayer (1990) defined EI as “a form of social intelligence that involves the ability to monitor one’s own and others’ emotions, to discriminate among them, and to use this information to guide one’s thinking and actions”

(p. 189). Mayer and Salovey (1997) have subsequently expanded this definition and have described EI as “the ability to perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge and to reflectively regulate emotions so as to promote emotional and intellectual growth” (p. 10). Mayer and Salovey’s (1997) model of EI places these EI components into four ‘branches’ (viz Perception, Utilisation, Understanding and

Management of emotions), each of which subsumes four abilities that differ in complexity. These four branches are viewed as being arranged in a developmental hierarchy from relatively basic psychological processes (namely Perception and

Utilisation of emotions) to more complex and integrated processes (namely

Understanding and Management of emotions), as shown in Figure 1.

Figure 1: The Ability Emotional Intelligence Model

2.7.1 Perception of Emotion

The first branch of Mayer and Salovey’s model is Perception of emotions, which concerns the accuracy with which individuals are able to attend, appraise and express their own emotional states. This involves the individual being aware of their emotions in order to monitor them so as to differentiate between them and to then express these emotions adequately (Mayer & Salovey, 1997; Salovey & Mayer,

1990; Salovey & Mayer, 1994; Salovey, Bedell, Detweiler & Mayer, 2000; Salovey,

Hsee & Mayer, 1993). Given that emotions signal an underlying ‘need’, for example, that feelings of sadness signify loss having access to one’s own emotional states is necessary to avoid being blinded by those emotions and also means that this information is more readily available to the individual to be used constructively (Cobb & Mayer, 2000). Furthermore, individuals who are more accurate at perceiving their own emotions are better able to respond to these emotions, to their surrounding environment and are also better able to express these emotions to others (Salovey & Mayer, 1990; Salovey et al., 2000).

Salovey and Mayer (1990) have stated that from an evolutionary standpoint, it is important that people are able to perceive the emotions of others. The identification of emotions in other individuals occurs largely through the evaluation of non-verbal cues, which constitutes the majority of interpersonal communication. The recognition of emotional content has also been related to the concept of empathy, the ability to understand and experience the feelings or emotions of a person in need. Empathy is thus viewed as being fundamental in developing and maintaining social support and positive interpersonal relationships

(Salovey & Mayer, 1990). Expressing emotions is also an integral part of interpersonal relationships because it stimulates and enhances emotional connection and encourages a deeper understanding of the other person.

Ambivalence in the expression of emotions may therefore be detrimental to the development of interpersonal relationships (George, 2000).

Furthermore, Mayer and Salovey (1997) have suggested that once these aptitudes have developed, there is also the potential for emotionally-relevant content to be perceived in other objects, such as architecture, artwork, music and other inanimate objects.

2.7.2 Utilisation of Emotion

The second branch in the model, Utilisation of emotions, describes the way in which emotional events may assist with intellectual processing (Mayer &

Salovey, 1997). Once information about emotions has been obtained through the accurate appraisal of emotions (see Section 2.7.1), it may be harnessed for a number of means.

Emotions or knowledge about emotions are important for the generation of particular emotional states, which may be relevant for certain situations or in directing one’s attention to important information. Emotions may also be generated in order for certain situations to be better understood, a process which is fundamental to empathy.

Alternatively, emotions may be generated in order to meet the needs of differing circumstances. For instance, an individual may achieve a higher degree of success if they experience particular emotions in particular situations, as evidenced by findings that positive moods tend to make positive outcomes more likely, whereas negative moods tend to make negative outcomes more likely (Salovey &

Mayer, 1990; Salovey et al., 2000). Thus, feelings of happiness during a job interview, for example, are likely to produce a more positive outcome to the individual than feelings of despondency in the same situation. Similarly, abilities such as problem-solving may be enhanced or disadvantaged by the presence of differing emotions. For example, happiness may facilitate inductive reasoning, integrative thinking and creativity, whereas sad moods may focus attention to detail and may therefore be useful for developing deliberate strategies (Salovey &

Mayer, 1990; Salovey et al., 2000). Consequently, if the current emotion that is being experienced is not conducive to that circumstance it may be beneficial to the individual to generate a different emotion through cognitive or behavioural strategies (Mayer & Salovey, 1997).

Additionally, emotions may enhance motivation and may thus assist individuals to persist in the face of challenges. For example, focusing on negative outcomes may induce a state of fear and a desire to perform well in order to avoid this outcome eventuating. Conversely, focusing on a positive outcome may

enhance perseverance in order to fulfil a particular challenge (Salovey & Mayer,

1990; Salovey et al., 2000).

Emotions may also redirect and prioritise thinking by interrupting current thought processes and redirecting attention to important information or more pressing issues. This is achieved, primarily, by anticipating how one would feel if particular events occurred (Salovey & Mayer, 1990). As such, recognising and using one’s emotions may assist in making judgments, finalising choices, and in planning

(Mayer & Salovey, 1997). Emotions may be further used as a guide to identify various contributing factors to a problem, which may assist in the resolution of that problem. Changes in emotions may also assist in altering memory organisation so that cognitive material is better integrated, causing otherwise diverse ideas to be seen as related (Salovey & Mayer, 1990). In doing so, this expands the range of options available to the individual by opening up multiple perspectives for consideration. This in turn can enhance problem solving ability and an individual’s ability to generate potential solutions (Mayer & Salovey, 1997; Salovey et al., 2000).

2.7.3 Understanding of Emotion

The third branch of the model is Understanding (or Knowledge) of emotions, which concerns the ability to understand the causes and determinants of emotions, to understand the relationships between differing emotions, such as which emotions are similar and what messages they convey, to understand how emotions evolve over time and also involves the ability to reason with these emotions (Mayer

& Salovey, 1997; Salovey & Mayer, 1990).

Understanding emotions involves having an understanding of what each emotion ‘signals’ in order to determine what is the motivating purpose behind that

emotion. For example, feelings of ‘sadness’ can reflect a sense of ‘loss’, which may motivate people to either grieve and accept this loss or to try to reconnect or recapture the loss (Cobb & Mayer, 2000; Salovey et al., 2000). Therefore, having an understanding of the relationships that emotions signal can assist in the understanding of one’s underlying wants, thereby allowing these wants to be fulfilled. This knowledge may then be used as a basis for understanding and appreciating the behaviour in both oneself and in others. Furthermore, understanding the meanings that are behind emotions and recognising that emotions often form the backdrop for messages that we send to others can assist in determining which emotions and the way in which they are expressed are the most appropriate for certain situations (Mayer & Salovey, 1997).

Understanding emotions also involves recognising the similarities between emotions. While there are six identified universal emotions (i.e., happiness, anger, sadness, surprise, fear and disgust; Ekman, Friesen & Ellsworth, 1972), there are numerous other emotions that constitute subgroups of these six, but which represent different degrees of intensity. For example, ‘rage’, ‘irritation’ and

‘annoyance’ are all terms associated with anger. Understanding the associations between emotions provides a greater understanding of emotions and the way in which they work (Salovey et al., 2000).

Additionally, understanding emotions involves appreciating and recognising that emotions may be combined in subtle ways in a process known as

“emotional blends”. For example, ‘awe’ may be seen as a combination of ‘fear’ and

‘surprise’, or ‘remorse’ may be seen as a mixture of ‘guilt’ and ‘regret’. Similarly, emotional knowledge involves understanding that emotions may exist in contradictory states, such as the simultaneous feelings of love and hate (Mayer &

Salovey, 1997; Salovey et al., 2000). Finally, the understanding of emotions concerns appreciating and recognising transitions in emotions, such as how ‘shame’ can quickly turn to ‘rage’, or how ‘contempt’ may turn into ‘disgust’ (Salovey et al.,

2000). Recognising the way in which emotions change may lead to reasoning about the underlying causes of the progression of emotions, which may in turn allow a greater understanding of emotions and the ways in which they motivate behaviour

(Mayer & Salovey, 1997).

2.7.4 Management of Emotion

The fourth branch of the model is Management (or Regulation) of emotion, which involves the conscious regulation of emotions for personal growth and may involve the regulation of emotions in oneself (i.e., mood management) and in others (Mayer & Salovey, 1997). Such a skill is dependent on being able to correctly monitor, discriminate and label one’s feelings, as well as believing and being able to modify and improve these feelings (Brackett & Mayer, 2004). It has been suggested that skill at regulating the emotions of oneself and others may assist in alleviating stress and may be useful in social adaptation and problem solving

(Matthews et al., 2002).

The regulation of emotions is an attempt to influence which emotions are felt, at what particular time they are felt and how these emotions are experienced or expressed (Gross, 1998). Although individuals typically strive to maintain positive moods while simultaneously avoiding unpleasant moods, Mayer and

Salovey (1997) have suggested that true mood management requires experiencing all emotions. For instance, Mayer and Salovey (1997) have suggested that learning about emotions, which is necessary for utilising and understanding them, can only

truly occur if individuals attend to their feelings, whether they are positive or negative. Attending to one’s feelings in this way provides the option for individuals to engage reflectively or detach from that emotion depending on it’s judged informativeness. It is, however, argued that individuals who are better able to regulate their emotions are more likely to experience positive emotions because they are more likely to be effective at changing their emotions should negative emotions arise. Therefore management of emotions may be best thought of as allowing emotions, both positive and negative, to be experienced although not necessarily expressed. Further to this, Gross (1998) has suggested that the regulation of emotions may be harmful if it means that emotions, which may be construed as ‘negative’, such as sadness or anger, are not expressed at times when these emotions are in fact appropriate.

Related to the management of emotion in the self, is the management of emotions in others. Managing the emotions of others may assist in influencing how the other person feels and the decisions that they may make. In this sense, managing the emotions of others may be seen to be manipulative. It is, however, argued that truly emotionally intelligent individuals are able to influence the emotions of others in a way that is not manipulative and that will therefore not destroy interpersonal trust and relationships (Mayer & Salovey, 1997).

Given the perspective of EI representing an ‘ability’, Salovey and Mayer (1990) and

Mayer and Salovey (1997) have proposed that differences exist in the extent to which individuals are skilled at certain EI functions. As such, Mayer and Salovey

(1997) have suggested that highly emotionally intelligent individuals will be able to master more abilities and at a faster pace than less emotionally intelligent

individuals and that these differences should be measurable. Preliminary attempts by Mayer, Salovey and colleagues to assess EI have been the Trait Meta Mood Scale

(TMMS) (see Section 2.7.5) and the Multifactor Emotional Intelligence Test (MEIS)

(see Section 2.7.6).

2.7.5 Trait Meta Mood Scale (TMMS)

One of the first attempts to measure EI was the development of the TMMS

(Salovey, Mayer, Goldman, Turvey & Palfai, 1995). Overall, scores on the TMMS are thought to represent stable differences in the way in which individuals respond to their moods and is thus claimed to assess core capabilities that may characterise emotionally intelligent individuals. The TMMS is a 30-item, self-report measure that assesses the degree to which individuals attend to their emotions and mood states (Attention), the clarity with which they are able to experience their feelings and distinguish between their emotional experiences (Clarity) and the extent to which they engage in repair of negative moods and maintain positive moods

(Repair). Salovey et al. (1995) have reported acceptable reliability for each of the subscales (α = .86, .88 and .82, respectively) and have suggested that the measure demonstrates evidence of convergent and discriminant validity.

Martinez-Pons (1997) has identified a “sequential dependency” between the subscales of the TMMS, in the sense that capabilities captured in some subscales are dependent upon the development of skills in other subscales (p. 6). For example, clarity of experience of emotions in order to differentiate between them is first reliant on attention to emotions, whereas being able to repair emotions effectively is dependent on being clear about the type of emotions that are being expressed. However, the correlations found between the TMMS subscales in a

number of studies (Attention: Clarity: r =.12; Repair: r = .17; Clarity: Repair: r = .39;

N = 134, Goldman, Kraemer & Salovey, 1996; Attention: Clarity: r =.26; Repair: r = .23; Clarity: Repair: r = .47; N = 108, Martinez-Pons, 1997; Attention: Clarity: r =-

.11; Repair: r = -.12; Clarity: Repair: r = .44; N = 152, Salovey et al., 1995) have been found to be low-to-moderate, which suggests some degree of conceptual distinctiveness between the subscales.

Unlike other self-report EI measures which have a heavy emphasis on personality variables, the TMMS was developed as a result of work focused on the reflective processes that accompany mood states (e.g., Mayer & Gaschke, 1988;

Mayer & Stevens, 1994; Mayer, DiPaulo & Salovey, 1990). The majority of work on the TMMS therefore concerns various aspects of mood management, such as recovering from negative moods and reducing rumination, which functions in coping (as will be discussed further in Section 3.3.1).

As shown in Table 3, higher scores on the TMMS have also been low-to- moderately associated with a number of emotional adjustment variables, such as lowered depression, but higher optimism, empathy, self-esteem and satisfaction with interpersonal relationships and life in general (Fernández-Berrocal, Extremera

& Ramos, 2004; Martinez-Pons, 1997; Salovey, Stroud, Woolery & Epel, 2002).

Scores on the TMMS have also been found to be moderately correlated with life satisfaction in a number of studies (e.g., Fernández-Berrocal et al., 2004; Martinez-

Pons, 1997; Palmer et al., 2002; as discussed in Section 3.2.1) and low-to-moderately and negatively related to anxiety (e.g., Goldman et al, 1996; Salovey et al., 2002; as discussed in Section 3.5.1). TMMS scores have also been low-to-moderately and positively related to a number of health related outcomes and psychological adjustment, although the available correlations in some instances have been low

(e.g., Extremera & Fernandez-Berrocal, 2002; Golman et al, 1996; Salovey et al.,

2002).

Table 3: Correlations Between TMMS and Predictive Criteria ) ge pa t x ne d

nue . .

. . . t

6 5 .

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Repa Repa Repa ; ; ; ; r

8 0 ; ; ; 6 6 2 4 4 3 4 2 9 . . 4 0 3 0 . . . 3 1 3 = - = . = - = - = . = . = . = - = - r r r r r r r r r : : : : : : : : : y y y y t t y y y y t y i i i t t t t t t i i i i i i r r r r r r r r r a a a a a a a a a Cl Cl Cl

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These studies have demonstrated a variety of empirical findings associated with the TMMS. Nevertheless, it has been argued that although the psychometric properties of the TMMS are reasonable, its use is limited because its factor structure has only three dimensions and therefore does not represent all emotional competencies outlined in Mayer and Salovey’s ability model (Martinez-Pons, 1997;

Salovey et al., 2000). Thus, although central elements of EI, such as the awareness and management of one’s emotions, are satisfactorily addressed, aspects such as the awareness and management of other’s emotions are not (Martinez-Pons, 1997).

It should also be noted that although the TMMS is not heavily based on personality traits, this measure is a self-report assessment. This genre of tests has been heavily criticised due to scepticism that this type of measurement can truly assess the EI of a respondent, given that it does not allow individuals to demonstrate their emotional competencies (Salovey et al., 2000) (as was discussed in Section 2.3.2).

2.7.6 Multifactor Emotional Intelligence Scale (MEIS) and the Mayer, Salovey

and Caruso Emotional Intelligence Test (MSCEIT)

Although they developed the TMMS, Mayer, Salovey and colleagues have more recently viewed EI as an ability or an ‘intelligence’, which they believe cannot be adequately assessed by self-report measures, but rather requires the use of performance based measures (e.g., the MEIS or the MSCEIT; see Sections 2.3.1 and 2.7). The original ability EI measure was the MEIS, which has since been superseded by the MSCEIT, based largely on the MEIS containing too many items to be extensively used in research settings. Both the MEIS and the MSCEIT provide four subscale scores, corresponding to the four branches (i.e., Perception, Utilisation, 87

Understanding and Management of emotions) specified in Mayer and Salovey’s

(1997) EI model (see Section 2.7), as well as a Total EI score. Mayer, Salovey and

Caruso (2000d) have stated that the total EI score provides an adequate assessment of EI performance but that the subscale scores allow a more detailed examination of the skills and abilities that quantify EI.

The MEIS and the MSCEIT are relatively similar, with both consisting of eight tasks (two tasks for each branch) of emotional based problems (as discussed further in Section 4.3.2.3). According to the MSCEIT test manual (Mayer et al.,

2000d), both the MSCEIT and the MEIS have been shown to exhibit good reliability

(ranging between α = .73 and α = .87 for the branch scores and between α = .52 and

α = .85 for the task scores). Mayer et al. (2000d) have also reported good measurement properties for both the MEIS and the MSCEIT. They have contended that there is strong evidence for discriminant validity for both these measures, given that in general both the MEIS and the MSCEIT have been found in a number of studies to correlate sufficiently with expected outcome variables. Findings for the MEIS are assumed to be upheld for the MSCEIT considering they are based on similar theoretical underpinnings, from which it may be assumed from intelligence research that newer measures will correlate with older measures (Mayer et al.,

2000d; Roberts et al., 2001). Neubauer and Freudenthaler (2005), however, has noted that there are some differences between early and later versions of this measure (such as in the expert sample used and the types of tasks assessed) and thus it should not be assumed that the conceptual underpinnings are indeed similar.

As shown in Table 4 on page 90, relevant findings in relation to the MEIS and the MSCEIT have included, for example, low-to-moderate and correlations

between the MEIS and higher self-reported empathy (Ciarrochi et al; 2000; Mayer et al., 1999) and higher self-esteem (Ciarrochi et al. 2000; Lopes et al., 2003). A number of studies (e.g., Ciarrochi et al., 2000; Mayer et al., 1999; Schutte, Lopez &

Malouff, 2000) have also found low-to-moderate positive correlations between the

MEIS and MSCEIT and assessments of life satisfaction (as discussed further in

Section 3.2.1).

Higher MEIS scores have also been found to be low-to-moderately associated with better interpersonal relationship indices in a number of studies, such as better relationship quality and the incidence of more positive relationships and less negative relationships and more parental warmth (e.g., Brackett, Warner &

Bosco, 2005; Ciarrochi et al., 2000; Lopes et al., 2003; Lopes, Brackett, Nezlek,

Schutz, Sellin & Salovey, 2004; Mayer et al., 1999).

Higher scores on the MEIS have also been found to be associated with higher pro-social behaviour and with less negative social behaviours. For example, higher MEIS scores have been found to be positively associated with higher pro- social behaviour ratings by teachers and lower peer assessed ratings of aggression in school children (Rubin, 1999). In contrast, lower scores on the MEIS and MSCEIT have been found to be associated with higher self-reports of violent and trouble prone behaviour, such as social deviance, even after controlling for personality and verbal abilities (Brackett & Mayer, 2003), although variable results in relation to drug, alcohol and cigarette use have been found in different studies. For example, some correlations have been near zero (e.g., Brackett & Mayer, 2003; Trinidad &

Johnson, 2002), while others have been low-to-moderate (e.g., Brackett, et al., 2003).

Table 4: Correlations Between MEIS and MSCEIT and Predictive Criteria :

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2.7.7 Assessing Emotions Scale (AES)

Another measure based on the ability definition of EI has been developed by Schutte et al. (1998). This measure has been referred to in different papers as the

Emotional Intelligence Scale or the Assessing Emotions Scale (AES)2 and is based on Salovey and Mayer’s original (1990) model of EI (Appraisal and Expression,

Utilisation and Regulation of emotions). Schutte et al. (1998) have specified that they chose this model (as opposed to Mayer and Salovey’s 1997 EI model) because they believed it lent itself more readily to “conceptualising the various dimensions of an individual’s current state of emotional development” and further were of the opinion that most EI dimensions in other models could be readily integrated into this framework (p. 171).

The original version of the AES started out with 62 items, from which

Schutte et al. (1998) extracted a four-factor solution. However, they decided that this solution was uninterpretable and chose to only retain a general factor, which encompassed only 33 of the original 62 items. Of the total number of remaining items (33), 13 items measure the Appraisal and Expression of emotion, 10 items measure the Utilisation of emotions and 10 items measure the Regulation of emotions.

There has been, however, some contention over the appropriateness of this one factor solution. Petrides and Furnham (2000) assert that it is difficult for factors to be clearly identified because the scale is based on what they believe to be an under representation of test items. They have argued that if Salovey and Mayer’s

2Personal communication (2nd March, 2004) with Dr. Nicola Schutte has confirmed that she prefers the title ‘Assessing Emotions Scale’ for this measure, so this will be used throughout. 92

(1990) three domain EI model was successfully sampled these three factors should have been recovered in the subsequent analysis. Consequently, they have argued that the AES is biased towards a unifactoral solution and therefore it cannot be assumed that it is measuring a general EI factor. Saklofske, Austin and Minski

(2003) have also criticised the apparent homogeneity of the scale, arguing that most items in this scale are positively loaded with 91% of items being phrased in the same direction. It is likely that compliant responding will confound individual differences on this measure.

In a confirmatory factor analysis of this measure using data obtained from

260 University students, Petrides and Furnham (2000) identified a four-factor solution, which they labelled: Optimism/Mood Regulation, Appraisal of Emotions,

Social Skills and Utilisation of Emotions. They do, nevertheless, concede that they may have overestimated the number of factors obtained, suggesting that the last factor, in particular, may not emerge in other datasets. Petrides and Furnham

(2000) have also claimed that even after the four factors have been extracted (40.4% of the total variance) from the AES, a large amount of unaccounted error variance remains, which they consider to be contributing to the factor solution being somewhat unstable. Sjöberg (2001), in a later investigation of this measure, has also found the first factor to account for only 25% of the total variance, providing support for this view.

This issue, however, has been further confounded by evidence from a number of different researchers who have found variable factor results. For example, Ciarrochi, et al. (2001a) have found that, while the AES may perhaps be best represented by a four-factor model (although they believe the internal consistency of the Utilisation factor is too low and should be avoided), they have

also determined that a satisfactory fit could be obtained for the original one-factor model. Saklofske, et al. (2003), in another factor analytic revision of this measure

(after first re-writing the three items that were reversed scored to ensure that all items were loaded in the same direction), have also determined that either a one or a four factor solution could be extracted with 23% and 40% of the variance being explained for each model, respectively. In particular, Saklofske et al. (2003) found that a general EI factor was super-ordinate over the four factors. These results suggest that either a one or four factor solution for this measure may be equally appropriate.

Nevertheless, despite their disagreements with the measure, Petrides and

Furnham (2000) do note that the scale has good face validity, as well as showing evidence of construct, predictive and discriminant validities. The AES has also been shown to have good internal reliability (α = .87 and α = .90 in two adult samples) and test-retest scores (r = .78) (Schutte et al., 1998). An internal consistency reliability of α = .84 has also been found for an adolescent sample (Ciarrochi et al.,

2001a).

Additionally, low-to-moderate correlations have been found between the

AES and a number of relevant and expected factors. For example, as shown in

Table 5 on page 97, higher scores on the AES have been low-to-moderately associated with higher self-esteem (e.g., Ciarrochi et al., 2001a; Schutte, Malouff,

Simunek, McKenley & Hollander, 2002) and with a number of positive behaviours, such as more altruism, courtesy, sportsmanship, conscientiousness and civic virtue

(Charbonneau & Nicol, 2002) and have also been moderately related to academic achievement (e.g., Schutte et al., 1998; as discussed further in Section 3.1).

Higher AES scores have also been found to be positively associated with a number of mental health issues, such as with lowered suicidal ideation, hopelessness and hassles (Ciarrochi, Deane & Anderson, 2002) and with depression

(Ciarrochi et al., 2002; Saklofske et al., 2003; Schutte et al., 1998). The AES has also been found to be low-to-moderately associated with lowered trait anxiety (e.g.,

Ciarrochi et al., 2001a; as discussed further in Section 3.5.1). Similarly, higher AES has been found to be moderately related to higher levels of happiness, although this association was low when personality (NEO Five Factor Inventory) was taken into account (Saklofske et al., 2003). Higher AES scores have been moderately associated with higher levels of optimism and thus less pessimism and with less impulsivity (r = -.39; N = 56) (Schutte et al., 1998). Additionally, higher scores on the AES have been moderately correlated to higher life satisfaction (e.g., Saklofske et al., 2003; Schutte et al., 1998, as discussed further in Section 3.2.1).

Schutte, Malouff, Bobik, Coston, Greeson, Jedlicka, Rhodes and Wendorf

(2001) have also found higher AES scores to be moderately-to-highly correlated to a number of positive relationship behaviours. For example, having improved self- monitoring (understanding the emotions of others and their relationship to the environment and adjusting self-presentation accordingly), higher cooperation with others (an element important in building and maintaining relationships), higher emotional involvement with others and to having better social skills, although the sample sizes in these studies were small (Schutte et al., 2001). Scores on the AES have also been found to be positively associated with components of interpersonal relationships, for example, with more positive social support amongst friends, parents, extended family and siblings (Ciarrochi et al., 2001); with higher marital satisfaction (Schutte et al., 2001) and with lower social, family and romantic

loneliness (Saklofske et al., 2003). However, the magnitude of the loneliness correlations were all found to be much lower when personality (NEO Five Factor

Inventory) was taken into account (Saklofske et al., 2003).

Moreover, Schutte et al. (2001) have found that individuals are typically more attracted to prospective partners who display higher levels of EI. For example, a group of 37 university students reading descriptions of individuals, which emphasised different levels of EI abilities, indicated that individuals who were described as being adept at perceiving and managing emotions in the self and in others were considered the most desirable prospective partner. Individuals who were able to perceive and manage emotions in either the self or others, but not both, were considered less desirable [t(51) = 10.71, p < .0001], while individuals who were able to neither perceive or manage emotions in themselves or others were given the lowest desirability rankings [t(51) = 6.65, p < .0001].

Table 5: Correlations Between AES and Predictive Criteria ) e

l g

. a ; a d i 5 2 e c p 1

7 d . t So n x

= . ; e t = - r 6; r

n :

0 Ex s

; r ed = 43 es: = . e 3 u h 2 r N t

ssl n : i a t = . 1; p O i 3 H r h .

t on :

i s . = nsh (c t 57: 4 r w n

2 : e -. ma s on s = r i = . t t e r r r Par

a h

: r t ; : e e O Sp u

t ; op h

ness ) r o t r

= .26 i 1 4

ess C vi r . ; l

5 w c .2 : e 8 t s = p 0 vi n

d = . 24 i e r n r C : = . e =

Ho m : i

; y e ; r r s 0 N

v 2 1

: e l 2 t s F 4 o . t r 9; g . v 5 u = . n 7 gs n i udy . . = - r

7 Co bl r l I = on

St 41

s: ;

r a . : = ;

Si 5 :

n n 7 ; am 2 N o

= - io 6 ; t ng snes ti 3 r 1 = . ot u

ri = . or : ea o r o .4 p i = .

n t d : t p o : Em = r I

ni u l ; :

en 1 r a sm

i ssi 8 s y 1 : y l d l e mo i 4 a 3 r d ls - i c f p nsc i mi c trui il u l e o = . o e k D C Su Al Fa S r St S N = S

a p n i r so u sh

sues r o n n i e is o o v i h ti rp a r t

a l h te l e n ite re b

i eem eem e e e t Cr t v v v s s al hea ti ti e ti e

- - f f si si si s l l

o o o p e e s i

ment r s

s h a s d d p d p d p d d n n n n eri o an a a an an a viou t ti a S S S S S S a h l Cri AE AE AE re AE AE AE be e v i

c 1: 2: i d y y een n

N d d

red u u P

302 134 131 131 St 50; St 47 Betw 24 a 77

n s s

d t t a n ol n n

S e e 9 ar tio d ud ud e a AE

y ts ts ts ts an St St e e e u l l n n n n ge o o 8 p 18 eg eg eg o o een l l l lle o l l l ar h h o o o w c c t C Stude

C Stude 13 – S C Stude Ye S Co Stude e B

s d y d d n n orf e

o , , a d i nl f f 02 e an an nd t ) n ka e P n n a n , c

uf uf 20 r i a a Ke a

n o o , ) o e u a 002 l l o dl 0 a 1a) a a rrel d W e 0 th e 2) st Ch Ch 0 D (2 Mc , , , der 0 n , J 0 o n u i i i n , 2 M M n 00 k , , a n o h h h Co n A a ( C s : e e (2 s i ,

t t r e ll ne 5 so bo k l (2 ar e 1) r u e o e rroc rroc rroc d od a l e bi put jg c hut hut a a a m 00 n b h h a o 2 a Sc B Gr R (

Ci A C Ni Ci C Ci Ba Sc Si & Ho T

; 4 1

. c i t = - n r a

, m 3 1 . Ro

; 9 = - 2 r

: y = -. t

r .

4 : ali . s 1 n 5 . o 1 s nes = - r i = . l r

r :

ne pe : y r

y r o Lo lit lit y a l a i n n o m o s a lling f s r F r o

r ; t 3 pe r n 3 r pe .

o o . o f y C l

= - g . e g f r 9 n

v : 1 i . s lli t lin s l c o e o r e = - r p t r lin nt

s n e o o n c re o

, ; ess:

9 3 7 2 n L 2 5; c i l 3 4 3 38 1 . . . . . 5 4 a - i .

nel c = - = - = . = - = - = o o r r r L r r S r = r

n o i r

n n

s ity s ite m s s v m i i e sio sio Cr s s s l ines m e e lin u mis r r p i e t p p p ssi n e e e p o imp d d o ha l d d d d d p d d n an a an an an an an S S S S S S S AE AE AE AE AE AE AE

N 56 27 27 38 354 354 354

n tio a l ts ts ts ts ts ts ts e e e e e e e u n n n n n n n p eg eg eg eg eg eg eg l l l l l l l o l l l l l l l o o o o o o o C Stude C Stude C Stude C Stude C Stude C Stude C Stude

) , n 3 f an i

0 , n P 8 st 0 uf r e y 9 2 t u o o 9 l r ( ld a e A 1 th , g ki u g M Go im , , ns A ske e f t er he Ha o p n Mi r ll, o d hut o n

Sc Ha C Do Sakl a

Nevertheless, despite the apparent predictive nature of their measure,

Schutte et al. (1998) have acknowledged that as with other self-report measures, their scale is susceptible to ‘faking good’. Thus while they advocate that the scale may have value in assessing individuals who want an indication of their own EI for self improvement purposes, they warn against using this measure for discriminatory purposes, such as in job selection. Furthermore, as with other self- report measures of EI (see Section 3.4), the AES appears to be moderately-to-highly correlated to measures of personality.

To counteract some of the problems associated with self-report EI measures,

Malouff and Schutte (2001) have developed an observer rating measure of EI, based on their self-report scale [the AES] (see Section 2.3.3). Malouff and Schutte (2001) have reported that this new scale has good internal consistency (α = .98 and α = .92 in two different samples) and have determined that using the average scores of two observers rather than just one observer, leads to more reliable assessments (inter- rater reliabilities of α = .73 for a single rater versus α = .84 for two-raters). Using this measure, Malouff and Schutte (2001) have demonstrated that scores are low-to- moderately correlated with a number of self reported indices of academic achievement, such as self-reported college adjustments (r = .35), which indicates emotional success, and to cumulative grade point average (r = .55), which indicates academic success, in University students. Additionally, this measure has been found to be related to supervisor ratings of interns’ practicum performance, which provides an indication of how well they may perform in their future occupation (r

= .54). Adjustment to college, cumulative grades and supervisor evaluations are all factors which may be seen as relevant to the success of a college student and thus

Malouff and Schutte (2001) have concluded that similar to cognitive intelligence, EI may have some place in predicting educational and occupational success.

However, as with self-report EI measures, observer-rater measures also suffer from a number of methodological problems. Malouff and Schutte (2001) have noted that reliability and validity of the ratings are susceptible to bias in the sense that ratings may vary according to the knowledge and motivation of the rater. Additionally, Mayer et al., (2000a) (as discussed in Section 2.3.3) have suggested that the level of information that an external observer may be able to provide about another individual is questionable. Nevertheless, Malouff and

Schutte (2001) have concluded that the reliability and validity evidence obtained suggest that this observer rating measure of EI has the potential to be useful in research, perhaps in combination with scores obtained from self-report EI assessments.

2.8 Bar-On’s model of Emotional Intelligence

Bar-On’s (1997) model of EI is based on work that he has conducted since

1980 when he first questioned why some individuals were more successful than others at obtaining and maintaining better psychological well-being and life success. Interest in this idea prompted Bar-On to study the factors that were thought to determine success and his work has been promoted, since Goleman’s

(1995) EI publication, as a theory of EI. Bar-On (1997) has defined EI as “an array of non-cognitive capabilities, competencies and skills that influence one’s ability to succeed in coping with environmental demands and pressures” (p. 14). However, although Bar-On has claimed that his model of EI concerns “non-cognitive” capabilities, his account is sometimes inconsistent with this. Specifically, Bar-On

100

(1997) views EI as being able to use emotional information successfully, such as in the ability to be aware of, understand, express and relate to one’s own and other’s emotions and the ability to deal with strong emotions, control one’s impulses and to adapt to change in order to solve problems of a personal or social nature. Bar-On

(1997) has consequently suggested that EI can predict success because it reflects how a person is able to use emotional knowledge to deal with their immediate situation.

Bar-On’s (1997) approach to EI is multi-factorial and extends the number of emotional factors beyond those identified in Mayer and Salovey’s (1997) model to

15 facets. These components fit into five subscales, namely: Intrapersonal (the extent to which individuals are in touch with their own emotions, their self-confidence and their degree of self-satisfaction), Interpersonal (how well individuals interact with and understand other people), Adaptability (how well individuals successfully solve problems and cope with demands), General Mood (an individual’s level of happiness and optimism) and Stress Management (the extent to which individuals are able to withstand stress).

The 15 facets in Bar-On’s (1997) EI theory and the categories which they fall under are outlined in Figure 2.

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Figure 2: Bar-On’s Emotional Intelligence Theory

However, following criticisms of this model, Bar-On (2000) has since adapted his EI framework and now considers the Self-Actualisation, Independent,

Social Responsibility, Optimism and Happiness sub-components to be a ‘facilitators’ of

EI, rather than constituent components.

Criticisms have also been made about the terminology which Bar-On uses and the theoretical basis of his model. For example, Petrides and Furnham (2001) have pointed out that although Bar-On (1997) liberally uses the terms ‘ability’ and

‘intelligence’ in the technical manual of his EI measure (the EQ-i; see Section 2.8.1) based on this model, the scales and items actually encompassed by this measure refer more to self-perceived traits and dispositions than they do to cognitive ability.

Anderson (1996) has also questioned the legitimacy of implying that a skill (if ‘skill’ is taken, as it is in most intelligence concepts to be decision making ability) may be

‘non-cognitive’, which seems to be somewhat contradictory. Neubauer and

Freudenthaler (2005), have also argued that although it is recognised by intelligence researchers that other traits may contribute to success, those characteristics should not necessarily be classified as intelligence components.

Labelling abilities, traits and emotional and non-emotional components as

102

emotional intelligence, they suggest, confuses the boundaries that are necessary to adequately define the construct.

Additionally, as discussed previously (see Section 2.7), Mayer, Salovey and their various colleagues have strongly disagreed with the conception of EI as anything but a cognitive ability. They have consequently argued that the inclusion by Bar-On (1997) of so many non-ability traits (e.g., global, personal and social functioning) in his EI model only serves to over conceptualise the idea and to undermine the utility of the term “Emotional Intelligence” as a scientific construct

(Mayer & Salovey, 1997). Furthermore, Matthews et al., (2002) have noted that there is surprisingly little theoretical rationale for Bar-On’s (1997) EI model that is not expressly linked to his EI measure (as discussed in Section 2.8.1).

2.8.1 Emotional Quotient Inventory (EQ-i).

Based on his model of EI, Bar-On (1997) has developed a measure of EI, the

‘EQ-i. Bar-On (1997) claims that EQ-i assesses the emotional, personal and social aspects of non-cognitive intelligence, with the assumption that non-cognitive intelligence is capable of predicting success in various aspects of life. The EQ-i is a

133-item self-report inventory, based on the five subscales defined in Bar-On’s EI model. Participants indicate their level of agreement with each statement based on a 5-point Likert scale.

Bar-On (1997) has reported the internal consistency of the EQ-i to be high, ranging from α = .69 (social responsibility) to α = .86 (self-regard), with an average of α = .76, as well as high test-retest reliability over one-month (α = .85) and over four-month (α = .75). Dawda and Hart (2000) have also credited the EQ-i with

103

having good structural properties and item homogeneity, which they claim is not affected by response styles or biases.

As with Goleman, Bar-On (1997) has made a number of claims about what his EI measure is able to predict. In particular he has suggested that the EQ-i is able to distinguish successful from unsuccessful individuals and may be useful for applications in educational, corporate, clinical, medical and research settings. For instance, in the educational realm, it has been suggested that the EQ-i may be able to identify students with emotional problems or those who are unable to cope with scholastic demands and who are therefore at risk of dropping out of school. It is further suggested that the EQ-i may aid in vocational guidance based on assessments of individual emotional characteristics and how effective these may be in relation to certain work environments. In corporate environments, Bar-On (1997) has suggested that the EQ-i may be useful for screening job applicants or for evaluating the well-being and functioning of current employees, particularly during times of organisational change and restructuring. In clinical settings, Bar-On

(1997) has suggested that the EQ-i may be useful in determining a patient’s potential for emotional health and to evaluate their current level of psychological well-being. Knowing this information, it is suggested, could help to guide treatment process by highlighting the areas needed for further development and the potential success of further treatment. Finally, in the medical field, it has been suggested that the EQ-i may be beneficial in evaluating how patients may be coping with serious illnesses in order to identify emotional skills that might be helpful in the overall patient treatment program.

Empirical results with this measure, however, are limited and are largely based on unpublished studies cited by Bar-On himself. For example, scores on the

104

EQ-i have been found to be able to distinguish between successful and unsuccessful US Air Force recruits (those who met or exceeded their annual requirement quotas versus those that were unable to meet at least 80%) for Total

EQ-i (t = 2.8, p = .01) as well as most of the facet scores (with the exception of

Empathy, Interpersonal Relationships, Social Responsibility, Reality Testing and Impulse

Control; Handley, 1997; as cited by Bar-On, 1997). According to Bar-On (2004), regression analyses on these data indicated that at least 28% of the variance in the performance of these recruiters could be accounted for by the EQ-i. Bar-On,

Handley and Fund (in press; as cited by Bar-On, 2004) have similarly found Israeli soldiers who scored higher on the EQ-i to receive higher performance rankings from their superiors in terms of a number of military and interpersonal factors than soldiers with lower EQ-i scores [t= 5.18, p < .01; N = 335].

Additionally, (as discussed further in Section 3.1), EQ-i scores have been found to distinguish between individuals who perceive themselves to be (Bar-On,

1997) and individuals that are more academically successful (Swart, 1996, as cited by Bar-On, 1997) from individuals who are not. In both instances, the more academically successful individuals had the higher EQ-i. Reiff, Hatzes, Bramel and

Gibbon (2001) have further found that scores on the EQ-i are able to distinguish between students with and without a learning disability (as will be discussed further in Section 3.1).

Furthermore, as shown in Table 6 on page 108, higher scores on the EQ-i have been found to be low-to-moderately correlated with psychological health variables, such as depression (Bar-On, 1997; Dawda & Hart, 2000) and life satisfaction (e.g., Kirkcaldy, 1995; as cited by Bar-On, 1997; as discussed further in

Section 3.2.1). EQ-i scores have also been found to predict ‘psychological wellness’,

105

based on a comparison between soldiers discharged from the Israeli military for psychiatric reasons, those who had identifiable psychiatric disturbances that did not interfere with their active service and a normal comparative sample (Bar-On et al, in press; as cited by Bar-On, 2004). Individuals who had never been diagnosed with psychiatric illnesses were found to score significantly higher on EQ-i than those who had received a psychiatric discharge [t = 4.89, p < .01] and those that had noted psychiatric problems but who had still continued to serve [t = 3.54, p < .01].

There was, however, not a significant difference between individuals who were discharged compared to those who had a psychiatric profile [t = 1.25, ns]. The EQ-i has also been reported as being generally moderately and negatively correlated with indices of poorer physical health, such as the severity and frequency of physical symptoms and increases in somatic symptoms when under stress (Bar-On,

1997; Dawda & Hart, 2000).

Moreover, it has been claimed the scores on the EQ-i are able to distinguish between individuals that are likely to be successful at completing substance abuse programs; prisoners from non-prisoners; and abusers from non-abusers. For instance, Flett (as cited by Bar-On, 1997) has found that individuals with higher

EQ-i scores are more likely to be successful at completing a substance abuse rehabilitation program, with results being significant for Total EQ-i scores

[t(67) = 2.39; p =.01] and most facets (with the exception of Self Actualisation,

Interpersonal Relationship, Social Responsibility, Impulse Control and Happiness. Higher scores on the EQ-i have also been found to be mildly related to the lower incidence of negative behaviours, such as drug, alcohol and cigarette use and social deviance

(Brackett & Mayer, 2003). Additionally, Pallazza and Bar-On (1995; as cited by Bar-

On, 1997) has demonstrated that non-prisoners score significantly higher than the

106

prisoners on Total EQ-i score [t(190) = -3.42; p = .00] and most of the subscale scores

(with the exception of Independence, Problem Solving and Flexibility). Similarly,

Winters, Clift and Dutton (2004) have found individuals convicted of domestic violence to score one standard deviation below the mean on Total EQ-i score

(Average mean: 100±15; Batterers mean: 85.16±17.16) and were also found to score significantly lower than the general public on all EQ-i subscales (p < .05).

107

Table 6: Correlations Between EQ-i and Predictive Criteria : : ) y y e

t t : : i i

g l l i i ty ty 62 . i i 57 b b pa l l .

i i t ta ta

b b = -

; . ; x g g p p = - 4 7 4 7 ta ta a a r

r in in 6 2 3 6 p p

. . . . ne d a a g g Ad d Ad d

n n ; ; = - = - = - = - e a a Ad Ad 1 an 2 an

r r r r ; ; 0 2 : r : r . . : : : : 9 5 nu 33 . d d d d i 00 ts ts 0 2 t . . o o o o . - = - = - o o o o ce ce n = = r r o = - = - r r : : Fa Fa c M M M l M l

r r ( l l l l ; ; n n : : a a a a 5 7 l l na e e na 8 4 e e . . so so na na w w er er t t = - = - p p Gener Gener Gener Gener rso rso

r r

r r e e ; ; ; ; e e : : 0 9 8 5 i i t t

rp rp g be g be 4 2 2 4 n n r . . . . I I te te

; ; n n I I 3 = - = - = - 7 = - EQ- EQ-

l l ngin ngin . ; ; 3 6

r r r r . . . a a a a 6 0 : : : : 85 2 5 r r . . . 46 : : = - = - . nt nt nt nt Tot Tot e e e e

ts ts r r : : = - = - = - : : x = - x m m m m ce ce l l r r r e e

r : : ge ge ge ge

Fa Fa d d l l d na na

d ; ; na na na na so so n na na an In In 56 66 er er a . . y y so so t t p p 0 54 er a er a . 0 ri ri = - = - r r p p - a a r r = = .

ve ve r r ress Ma ress Ma ress Ma ress Ma : : Int Int i i e e r r

- -

: : St St St St Int s Int s S S n n ; ; ; ;

Q Q l l : e : e 7 2 9 2 l l s a a s ee ee E E 4 3 2 5 . . l l . . le le a a w w ob ob ma ma t t l l = - = - = - = - r Ma r Fe r Tot Tot G be G be Ma r Fe

or m m

s o o t t

m p p g n n le m m o o in i i y y s s ob S S

es es pr

l lf-Rat pr pr ety ety a e e n n c i i Se i

s D D N N g n y k k ( ( h io ec ec th th p B B er (Zun al al t ( (

e e of n n y o o on h h

Cri c i i i s s s al al n e) s c c e i i es es e u s s r r r q y y p p p e

Scal e e e

) ) s a d d fr t

ph i ph

d st st on y) y) i i d d d d d d r r er n o n o t ssi t t i an a an an an a e k L k L

n n r r c c e e e e p mplain v v C o e EQ-i c EQ-i In EQ-i De

EQ-i Ch EQ-i Ch EQ-i In v i t c i

N red 34 34 2 58 48

34 66 2 P d

n s s s s s t t t t t a

an an n n n n n n i n n e e e e e o - ic ic a a i r r t c c e e Q i i

r r m m E ers A A Af Af pula n h h h h ege Stud ege Stud ege Stud ege Stud o ege Stud t t een l l l l l s r r l l l l l ut ut i o o o o o r w t C So C No P

No C So C C e B s n o

i t t r Po a r el Ha 7)

rr d n (199 Co a Autho : 0) e 6 l b a

Bar-On

Dawda (200 T

108

e: s 46; . - U e = r rett : a y g t i i C

bil

; . a

0 t 6 2 ed . 2 t ap . r = - o

= - Ad r p

; r e e 9 : s r 2 d t . o o U o l

. = - o n h r M : ed o es l l t c r

a ra r o n e Al c por s ; e 4 r erso le 2 Gen

. t a ; o c 3 erp = - t 3

n . bs n r I u : es = - e r ; S . r 4 o 1 : 4 . Us t sc 2 . g n - e

u le = - = a m r c e r Dr :

l g : : bs a ) ) l l n na a a a t t Su o o

erso 9 13. p (T (T . .3 -i -i - tra ress M = Q Q n I St E r = E r

y it s pen o r

P s ( e s e

e n s e o en c i l

r o illnes i an i c abu i ite ale) v e v r c c t e i t Cr n Sc d hia ta al ess i mes yc bs c o u en o v s ps s d i s d d d u an an and an b

i i i i - - - - A r o EQ f EQ EQ EQ N

7 6 7 44 20 45 20

d se n e t rs ents ents u c e b i i tio a v d

l n o sal e Stud e Stud S u i pula o es co eg eg el l l l l l a sp o o f sra M o C I C

d r

e d an y

y Po ) an l r

s M t d s f e d tho n 004) pr u 2 Cli , Han , n ( A s tt a i e ( ) er on k t On d t - t n n ac i u 2003 W Du

Bar F Br (

109

Based on such results, Bar-On (2004) has stated that the EQ-i is a valid measure of emotionally and socially intelligent behaviour and has further claimed that the EQ-i has a predictive validity higher than any other EI measure.

Nevertheless, the EQ-i appears to have a strong conceptual relationship with personality, as demonstrated by a number of studies that have found high correlations between the EQ-i and various personality measures (as discussed in

Section 2.4). Matthews et al. (2002) have also argued that the validity of much of the available data on the EQ-i is questionable, given that Bar-On has never attempted to demonstrate the incremental validity of the EQ-i after controlling for personality.

2.9 Petrides and Furnham’s framework for Emotional Intelligence

While Mayer et al. (1999) have distinguished between ‘mixed’ (signifying models with an emphasis on personality components) and ‘ability’ models of EI

(those with a more cognitive basis), Petrides and Furnham (2000; 2001; 2002) have advanced a conceptual differentiation for EI, based not on the models themselves, but on the assessment of such models. In doing so they have argued that regardless of the theory it is the type of measurement that defines the outcome as this directly influences the conceptualisation of the construct (even if the theoretical domains are similar), the hypotheses that may be tested and thus the results and the conclusions that may be drawn. Considering such differences, Petrides and

Furnham have suggested that the two main forms of measuring EI (currently ability and self-report) should be seen as theoretically distinct and they have thus proposed the division of EI into “ability EI” and “trait EI”.

‘Ability EI’ (originally labelled “information processing” by Petrides &

Furnham, 2001) has been defined as the actual ability to recognise, process and

110

utilise emotion-laden information. Given the skill component of this form of EI, it is suggested that this should be examined in relation to psychometric intelligence.

Alternatively, ‘Trait EI’ has been defined as an individual’s behavioural dispositions and their self-perceptions about their ability to recognise process and utilise emotion-laden information. This definition includes various dispositions that typically fall under the personality domain and also under social and personal intelligences. Consequently, Petrides and Furnham (2001) have placed the concepts captured by ‘trait’ EI on the lower levels of the personality hierarchy. Based on the differing conceptualisation of each EI model, Petrides and Furnham (2001; 2002) have suggested that ‘trait’ EI models should be assessed via ‘self-report’ questionnaires, whereas ‘ability’ EI models should be assessed by maximum performance tests. Petrides and Furnham (2002) have further warned that in light of these differences, it should not be expected that both types of measurements will be related, or will produce entirely similar results because each type of measurement essentially assesses different constructs.

However, despite their labelling of these two constructs as ‘trait’ and

‘ability’ EI, Petrides and Furnham (2001) have proposed a refinement in naming based on the argument that the concept of an ‘ability emotional intelligence’ is redundant. More specifically, Petrides and Furnham (2001) regard the term ‘trait’ as akin to ‘dispositions’, a concept which should distinguish it from ‘cognitive ability’ and therefore they view the labelling of this type of EI as ‘trait EI’ as a useful identifier. However, they view that since intelligence is commonly seen as an ‘ability’ and not a ‘trait’, that the pairing of the terms ‘ability’ and ‘EI’ to specify the more cognitive approach to EI is unnecessary. Instead they have suggested that

‘emotional self-efficacy’ be used in place of ‘trait EI’ and the term ‘cognitive

111

emotional ability’ to be used instead of ‘ability EI’. Nevertheless, Petrides and

Furnham (2001) have suggested that until this further clarification is necessary the terms ‘trait’ and ‘ability’ EI should be retained for the present in order to be consistent with previous literature.

2.9.1 The Trait Emotional Intelligence Questionnaire (TEIQue)

Petrides and Furnham (2003) have also recently developed a trait measure of EI that they have labelled the Trait Emotional Intelligence Questionnaire

(TEIQue). The full-form version of this measure is a 144 item self-report questionnaire, based on a 7-point Likert scale, although a short form of this test, containing only 30 items, has also been developed. The TEIQue is claimed to assess a highly reliable global trait EI and consists of 9 subscales (Adaptability,

Assertiveness, Emotion Perception, Emotion Regulation, Empathy, Impulsiveness,

Relationship Skills, Social Competence and Stress Management). This measure, however, has been somewhat criticised for the fact that many of the test items have been derived from existing scales, such as the Emotional Empathy scale, the

Toronto Alexithymia scale and other self-report assessments of EI (Wilhelm, 2005).

Preliminary results with this measure have indicated that higher TEIQue scores (higher EI) are highly and positively associated with happiness (r = .70;

N = 88, Furnham & Petrides, 2003). Regression analyses have further indicated that

TEIQue scores are able to explain over 50% of the total variance in happiness, with positive relationships existing even after personality and g were controlled.

Additionally, scores on the TEIQue have been found to be differentially related to academic achievement in different subjects (Petrides et al., 2004; as discussed

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further in Section 3.1) and have also been found to be positively related to some forms of coping (Petrides & Furnham, 2003; as discussed further in Section 3.3.1).

2.10 Conclusion

The nature of EI as it presently stands is somewhat confusing and, as yet, the existence of a cohesive EI framework has not been adequately demonstrated

(Petrides & Furnham, 2000). A number of different EI models have been proposed, although Ciarrochi, et al. (2000) have noted that despite the varied definitions of EI, these definitions are generally complementary, rather than contradictory.

Nevertheless, while all current EI models can be recognised as having some merit, it is still uncertain how these various models can be redefined into a singular and more complete formulation. Indeed, it would seem that in actual fact the term

‘emotional intelligence’ refers to multiple constructs, some of which may not actually constitute a form of intelligence (Roberts, Schulze, Zeidner & Matthews,

2005). However, that a universally excepted definition of EI has not been established is not all that surprising given that the field is still relatively new.

Difficulties in the assessment of EI have also arisen and appear to be based on the lack of agreement over an adequate conceptual definition of EI, which has lead to the development of a number of different ways to assess EI and measures claiming to do so. Of some concern is that the current measures of EI are differentially related to cognitive intelligence and personality, depending largely on whether they are based on an ability or mixed EI model conception. There are, however, also differences in this regard amongst the various self-report scales. For example, although self-report EI measures are generally closely related and share similarly high correlations with personality measures, the two leading self-report

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EI measures currently available (the AES and the EQ-i) appear to show different patterns of associations with personality (Matthews et al., 2002). Additionally, there are issues with the construct validity of EI measures, given that ability and self- report EI measures fail to converge on a common construct (Matthews et al., 2002).

Many substantial claims about the predictive nature of EI have been made

(for example, by Bar-On, 1997; Cooper & Sawaf, 1997; Goleman, 1995; 1998). These claims have suggested that EI is related to aspects that are important for academic and occupational success, emotional well-being, positive inter-personal relationships and improved physical health. Empirical research, however, is only just beginning to ascertain the veracity of these assertions. Of the currently available research, the predictive validity of EI measures thus far has been rather unimpressive, with correlations between EI measures and expected criteria being typically only in the order of between r = .10 and r = .30 (Matthews et al., 2002).

Petrides and Furnham (2000) have further argued that it is unclear whether the current EI models and measures are able to provide any incremental validity over and above the sum of their components.

On the basis of available evidence, it would appear that self-report EI measures are more predictive of relevant criteria than are ability EI measures.

However, this is questionable, given that much of this association appears to be based on the redundancy of these measures with existing personality scales, because personality could be expected to also correlate with many of these variables. Surprisingly, the predictive validity of EI measures when pertinent factors, such as general intelligence and personality have been taken into account has, up until recently, not been extensively studied. Studies that have done so (e.g.,

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Brackett & Mayer, 2003; Saklofske et al., 2003) have suggested that the incremental validity of EI in these cases is small.

In summary, there is as yet no consensus about the nature of EI and how to measure it, but it is realised that the importance of EI is dependent on it’s predictive incremental validity. To extend the current knowledge of EI, the relationship between measures of EI and a number of variables that may be important to ‘life success’ (i.e., academic achievement, life satisfaction, coping ability, problem solving ability and anxiety) as well as between EI and gender and age were investigated. A rationale for examining the extent of the predictive nature of EI for these variables is presented in Chapter 3. This chapter will provide a basis for the investigations in Chapters 4 and 5.

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CHAPTER 3: THE PREDICTIVE NATURE OF EMOTIONAL

INTELLIGENCE FOR ‘LIFE SKILLS AND OUTCOMES’ AND

DEMOGRAPHIC VARIABLES

Proponents of mixed models of EI (e.g., Bar-On, 1997, Cooper & Sawaf,

1997; Goleman, 1995; 1998) have typically been the most vocal in making claims about the predictive promise of EI and what EI means in terms of ‘life success’ (see

Section 1.3.4.1and Section 2.8). These models have generally appeared to assume that just about any variable other than IQ that has been found to show propensity towards predicting some degree of success (i.e., higher income, more frequent promotions, higher academic achievement, higher tertiary attainment, more satisfying interpersonal relationships and better physical and psychological health) is representative of EI. The field of EI, however, is still relatively new (see

Chapter 2) and thus many of these claims have not been substantiated.

Furthermore, many such claims appear to be unrealistic and to extend beyond what could be reasonably attributed to the EI construct.

Nevertheless, it is apparent that the relevance of the EI concept is dependent on its predictive validity. As discussed in Chapter 2, work in this area is currently being undertaken. On the basis of this research, EI has been found, among other things, to be positively correlated with social network size and quality (Ciarrochi et al., 2001a); positive relations with others, perceived parental support and fewer negative interactions with close friends (Lopes et al., 2003); pro-social behaviour, parental warmth and positive peer and family relations (Mayer et al., 1999); more optimism (Schutte et al., 1998); higher empathic perspective taking and self-

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monitoring in social situations, higher social skills and higher marital satisfaction

(Schutte et al., 2001). Additionally, negative correlations have been reported with illegal drug and alcohol use, deviant behaviour and poor relationships with friends

(Brackett et al., 2004), unauthorised absences and exclusions from school (Petrides et al., 2004) and depression (Dawda & Hart, 2000; Schutte et al., 1998).

Complementing and extending the findings of other researchers, other variables that may be relevant to general ‘life success’ are aptitudes and achievements, such as academic achievement, life satisfaction, coping ability, problem solving ability and level of anxiety. These aptitudes have relatively broad applicability and thus competencies in these ‘life skills’ may be necessary for the attainment of ‘success’ in different life domains within academic, occupational and interpersonal realms. In order to provide a basis for suggesting how EI may be related to these constructs, which were further investigated in two studies (as described in Chapters 4 and 5), which form the basis of this thesis, a brief theoretical evaluation of these ‘life skills’, together with any relevant EI research, follows.

3.1 Academic Achievement and Emotional Intelligence

A number of studies have attempted to investigate the relationship between

EI and various assessments of academic achievement, although most studies of this kind have involved Grade Point Average (GPA). EI could be expected to be related to academic achievement given that EI, as an intelligence, is expected to be related to general intelligence (see Section 2.3.4), which has in turn been found to be predictive of academic achievement. O’Connor and Little (2003) have argued that, conceptually, it would seem more likely that ability measures of EI, because they

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are based on a cognitive framework, would better predict academic achievement than would self-report EI measures. Nevertheless, Saklofske et al. (2003) have also suggested that emotional and social competence in dealing with an academic environment could be expected to contribute to overall academic achievement and thus it could be expected that self-report EI measures will also show reasonable associations with measures of academic achievement.

However, as shown in Table 7 on page 122, results from studies on the relationship between academic achievement and EI (regardless of the type of measure) have been mixed, with there being just as many studies reporting significant differences as those that have not, although most correlations have been generally low. For example, in relation to self-report EI measures, Schutte et al.

(1998) have found a moderate correlation between college GPA and the AES, while

Newsome et al. (2000), O’Connor and Little (2003) and Parker, Summerfeldt,

Hogan and Majeski (2004) have all found low correlations between college GPA and the EQ-i. Brackett and Mayer (2003) have also reported very low correlations between GPA and scores on the EQ-i and the AES when personality (NEO PI-R) and verbal SAT scores were and were not controlled. Interestingly, however, although Parker et al. (2004) found low-to-moderate correlations between the EQ-i and college GPAs, the correlations between the high school GPAs of these same students were near zero.

Generally low correlations have also been found between academic achievement and ability EI. For example, Brackett et al. (2003) and O’Connor and

Little (2003) have found near zero correlations between college GPA and the

MSCEIT. Similarly, low results between college GPA and the MSCEIT have also

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been found by Brackett and Mayer (2003) regardless of whether personality (NEO

Personality Inventory) and verbal SAT scores were or were not controlled.

Several studies have also attempted to determine the relationship between

EI and other forms of academic assessments, such as SAT scores. The obtained results have tended to be as varied as those for GPA scores, but correlations have generally been low. Schutte et al. (1998), for example, did not find a relationship between the AES and verbal and mathematical SAT scores in college students.

Brackett and Mayer (2003) have similarly found self-report assessments of EI (EQ-i and AES) to be non-significantly correlated to verbal SAT scores. Ability EI measures seem to be more moderately correlated with verbal SAT scores, with moderate results being reported by Brackett and Mayer (2003), Brackett et al. (2003) and Lopes et al. (2003) in relation to verbal SAT scores and the MSCEIT.

Additionally, Bar-On (2004) has claimed that based on a latent variable path model of these results, at least 17% of scholastic performance over and above cognitive intelligence may be explained by the EQ-i.

Bar-On (1997) and Parker et al. (2004) have further reported that EQ-i scores are able to distinguish between academically successful and unsuccessful students.

For example, Swart, 1996 (as cited by Bar-On, 1997) (N = 448) has found that more academically ‘successful’ college students (categorised as GPAs 1 standard deviation above the mean) had significantly higher total EQ-i scores [t = 1.94 ; p = .05] than academically unsuccessful students (GPAs 1 standard deviation below the mean). However, only the Stress Tolerance, Reality-Testing, Problem Solving, Self-

Actualisation and Optimism subscales demonstrated significant differences (p < .05) in this regard.

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Additionally, Parker et al. (2004) (N = 372) in an investigation of college students (in which ‘successful’ students were classified as those with GPAs of 80% or better and ‘unsuccessful’ students were those with GPAs of 59% or lower) found similar results. In this study, the more successful students were found to score significantly higher than the less successful students on three out of the four short version EQ-i subscales [Intrapersonal: F(1, 127) = 30.43, p <.001; Stress Management:

F(1, 127) = 32.44, p <.001; Adaptability: F(1, 127) = 89.45, p <.001]; (results for the

Interpersonal subscale not significant and General Mood not assessed on the short form EQ-i measure).

It has also been suggested that EI may be of particular benefit to the academic achievement of certain academically vulnerable groups, such as individuals with learning disabilities or lower general intelligence (e.g., Reiff et al.,

2001; Petrides et al., 2004). These individuals, it is suggested, are more likely to experience stress and emotional difficulties in academic situations and are thus more likely to need to draw on non-academic capabilities, like EI, to cope.

For instance, Hatzes (1996; unpublished dissertation cited by Reiff et al.,

2001) conducted interviews with 20 individuals with learning disabilities, 10 of whom had been dismissed from university on academic grounds and 10 who had successfully graduated. Both groups were matched in terms of demographic variables and IQ, but individuals who continued at university were found to be better able to manage their emotions, persist in the face of difficulties, to empathise more with others and were reported to have a more positive explanatory style.

Additionally, Reiff et al. (2001) compared 128 students with (N = 54) and without

(N = 74) learning disabilities on EI. They found that those with learning disabilities

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had significantly lower scores than regular students on two EQ-i composites [Stress

Management: F(1, 126) = 8.76, p = .004; Adaptability: F(1, 126) = 6.00, p = .016].

Petrides et al. (2004) have also assessed the influence of EI on the academic achievement of individuals with different levels of cognitive ability (N = 901 Year

11 high school students, with grades ranging from A to G). They reported that scores on the TEIQue moderated the relationship between intelligence and academic achievement, with the effect maintained after controlling for personality

(Eysenck Personality Questionnaire-Revised) for lower IQ students (Grades D to G) up to approximately 1 SD above the mean. In particular, they found that the

TEIQue was significantly, but differentially associated across a range of academic subjects. Within the lower IQ group, individuals who also had high self-reported EI were found to score better in English and overall General Certificate of Secondary

Education performance. Negligible results, however, were found for maths or science performance, regardless of the IQs within the sample.

Based on these results, Petrides et al. (2004) have concluded that higher self- reported EI may act as a ‘stabilising influence’ during assessments and have suggested that EI will likely have more of an effect where the demands of the situation outweigh an individual’s resources. Thus, compared to high IQ students

(Grades A to C), lower IQ students will be more likely to need to draw on non- cognitive abilities, such as EI, to compensate for their lower intellectual ability in academic settings.

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Table 7 : Correlations Between EI and Academic Achievement

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Nevertheless, it is apparent from this collection of results that a clear understanding of the relationship between EI and academic intelligence has as yet not been achieved. Most studies, regardless of the type of EI assessment, have typically found low correlations with assorted indices of academic achievement.

Further, most of these correlations drop to near-zero once other variables, such as

SAT scores, are taken into account. Nevertheless, despite this, EI has also been found in a number of studies to be able to differentiate successfully between individuals who may be classified as academically successful from those that are less academically successful. In particular, the obtained results appear to suggest that EI may be of greater importance to less academically successful individuals who may make greater use of their EI skills to compensate for deficiencies in other areas. It is possible, therefore, that EI is a ‘threshold’ variable, which is more relevant to certain populations than it is to others.

3.2 Life Satisfaction

Subjective well-being is an individual’s cognitive and affective evaluation of their own life as based on that individual’s chosen criteria (Diener, Emmons, Larsen

& Griffin, 1985). It is commonly agreed that subjective well-being consists of three aspects; a cognitive component (i.e., the intellectual evaluation of one’s life as a whole or on the basis of specific life domains) and two affective components (i.e., an individual’s emotional reactions to events) labelled as ‘positive’ or ‘negative’ affect (Alfonso, Allison, Rader & Gorman, 1996; Andrews & Robinson, 1991; Diener et al., 1985). As the name suggests, positive affect encompasses pleasant emotions, such as joy and happiness, whereas negative affect consists of unpleasant emotions, such as fear and sadness (Emmons, 1986). According to Diener, Sandvik and Pavot

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(1991), judgements of well-being are based primarily on the experience of positive affect, with the frequency of such feelings being more important than the level of intensity, given that the occurrence of intense positive affect is generally rare.

Nevertheless, the majority of people report positive affect most of the time and thus, on the whole, most people are generally happy (Diener, Lucas & Oishi, 2002).

A number of attempts have been made to determine which variables, if any, may influence subjective well-being. Results have generally suggested that although a number of variables may factor into an individual’s level of life satisfaction, the magnitude of these associations are typically small. Additionally, often there are just as many studies that have not reported significant differences as those that have and thus the nature of such effects is difficult to categorise (Diener,

1984). Diener has thus suggested that it is likely to be unrealistic to expect that subjective well-being can be adequately accounted for by a small number of variables, given the large number of factors that could potentially influence it.

Gender differences in subjective well-being, where found, have generally been minimal. Similarly, no clear differences across ages have been apparent.

However, difficulties with accurately assessing age effects arise because individuals of different ages are likely to have varying opinions on what constitutes a

‘satisfying life’. Life satisfaction in this sense may only be evaluated on the basis of global assessments rather than on the comparison of different life domains (Hong &

Giannakopoulous, 1994). Cohort effects may also factor into assessments when cross-sectional rather than longitudinal studies are undertaken, whereby differences may not be related so much to age, but rather to the different social and cultural values expressed by different generations as a consequence of living in different historical times (Diener, 1984; Hong & Giannakopoulous, 1994). It is

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further argued that increasing similarity between the daily lives of males and females through the more increasing presence of females in the work force and the greater involvement of males in the home may account for gender similarities in life satisfaction (Hong & Giannakopoulous, 1994).

Demographic variables, such as marital status, education and income have also been investigated in relation to life satisfaction. Being married (or co- habitating), for example, has been found to be one of the most reliable predictors of life satisfaction, even after the effects of education, income and occupational status have been controlled (Glen & Weaver, 1981). Diener, Gohm, Suh and Oishi (2000) have suggested that the increase in satisfaction found among married individuals may be due either to the happiness that stems from the love and companionship that are offered in long-term relationships or from the social approval that is typically derived from marriage. The influence of education on life satisfaction has been found to be marginal, appearing to be relevant only in terms of its effect on other variables, such as income (Diener, 1984). Both within and between countries, income has been found to be positively related to subjective well-being, with wealthier individuals being typically happier than poorer individuals. However, the influence of income has also been found to be relative. Thus, although wealthier individuals are typically happier, this level of happiness does not rise in response to overall income rises (Diener, 1984). It therefore appears that income may only result in higher subjective well-being where poverty means basic needs are not being adequately fulfilled (Diener, Diener & Diener, 1995).

Traits such as personality or attitudes have also yielded similarly minimal results, although extraverted individuals have been found to have slightly higher life satisfaction than introverts (Diener, 1984). Culture, however, has been found to

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have some influence on the way in which individuals make subjective well-being judgements (Suh, Diener, Oishi & Triandis, 1998). For instance, in a study of over

6780 individuals from a number of countries, those from individualistic cultures

(i.e., Western cultures) were found to rely more heavily on emotional experiences

(accounting for 76% of variance in well-being) than they did on cultural norms

(what they considered to be a “culturally ideal person”) (12% of variance) when making subjective well-being judgements. In contrast, individuals from collectivist cultures (i.e., Eastern cultures) relied to the same extent on emotions (39% of variance) and cultural norms (40% of variance) when making these judgements.

This finding is perhaps not unduly surprising given that individualistic cultures tend to define the self primarily in terms of an individual’s internal thoughts and feelings, whereas the opposite is true of collectivist cultures. Nevertheless, these results suggest that emotional experiences influence subjective well-being judgements and this therefore provides a basis for expecting that EI and life satisfaction may be linked (as discussed further in Section 3.2.1).

3.2.1 Life Satisfaction and Emotional Intelligence

It has been suggested that individuals with higher EI, who are thus better at understanding and regulating their emotions, have a better understanding of the environment around them and their emotional reaction to it and are therefore able to adjust to those events accordingly. It is argued that such individuals as a result are better able to resist any threats to positive self-esteem that may otherwise depress negative mood and are consequently better able to enhance and maintain positive well-being. Evidence for this comes from a number of studies that have shown EI to be moderately correlated with higher characteristic positive mood

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(e.g., AES: r = .55; N = 40; Schutte et al., 2002) and (as was discussed in Chapter 2), low-to-moderately with lower depression, with higher self-esteem and more optimism.

A number of studies have furthermore found higher EI to be low-to- moderately and positively correlated to life satisfaction, regardless of the measures used. For example, as shown in Table 8 on page 130, in relation to ability EI measures, Mayer at al. (1999) have reported weak correlations between the MEIS and an un-named life satisfaction measure that assessed satisfaction with one’s relationships, academic status and career. Ciarrochi et al. (2000) have also found low correlations between the MEIS and this life satisfaction measure, even after controlling for personality (NEO PI-R) and cognitive abilities (Raven’s Standard

Progressive Matrices). However, using the MSCEIT and the Satisfaction With Life

Scale (SWLS), Schutte et al. (2000) found a high correlation, although Brackett and

Mayer (2003) in contrast have reported non-significant correlations with these same measures.

Low-to-moderations correlations between EI and life satisfaction have also been found in relation to self-report EI measures. For example, Brackett and Mayer

(2003) have found a low correlation between the AES and the SWLS, while

Saklofske et al. (2003) found low-to-moderate correlations between a modified version of the AES and the Temporal Satisfaction With Life Scale. The magnitude of

Saklofske et al’s correlations, however, was found to drop appreciably after controlling for the effects of personality (NEO Five Factor Inventory) in these correlations. Moderate correlations have also been found between the EQ-i and measures of life satisfaction, for example, the SWLS (e.g., Brackett & Mayer, 2003)

128

and the Kirkcaldy’s Quality of Life measure (Kirkcaldy, 1995; as cited by Bar-On,

1997).

Additionally, Martinez-Pons (1997) has found all subscales of the TMMS to be moderately correlated to an un-named life satisfaction questionnaire adapted from his earlier work, while Fernadez-Berrocal et al. (2004) and Palmer et al. (2002) have both found the SWLS to be low-to-moderately correlated with the Clarity and

Repair subscales of the TMMS, but with near-zero correlations reported for the

Attention subscale. Palmer et al. (2002) have furthermore found Clarity to add a statistically significant increase in the prediction of life satisfaction (approximately

5.5%), over and above the contribution of positive and negative affect.

Finally, Gannon and Ranzijn (2005) have found all subscales of the

Swinburne University Emotional Intelligence Test to be low-to-moderately correlated to the SWLS. However, despite these significant correlations and personality (NEO Five Factor Inventory) and a number of demographic variables

(e.g., marital status and income) accounting for a substantial amount of variance

(34.2%) in life satisfaction, Gannon and Ranzijn (2005) found that EI only accounted for a further 1.3% (p < 0.05) of variance in life satisfaction over and above these variables. The effects of intelligence (Australian Council of Educational Research

B40 Advanced Test) were not taken into account in this instance, because IQ did not significantly correlate with life satisfaction. This finding raises some questions as to the incremental predictive validity of EI for theoretically relevant criteria after controlling for other predictive variables.

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Table 8: Correlations Between EI and Life Satisfaction : n

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The sum of these obtained results on EI and life satisfaction tends to suggest that being aware of and being able to manage one’s emotions leads to generally higher life satisfaction. It is presumed that individuals who are better able to deal with any negative emotions that may arise are better able to reduce their potential impact and their subsequent possible effect on happiness and well-being and therefore have higher characteristic positive mood and life satisfaction.

3.3 Coping

According to Lazarus (1976), stress occurs when the demands placed on a particular individual exceed that person’s psychological resources. In order to reduce the negative cognitive, behavioural, emotional and physical responses generated by stress, coping responses must be implemented (Roger, Jarvis &

Najarian, 1993; Somerfield & McCrae, 2000; Stone, Greenberg, Kennedy-Moore &

Newman, 1991). Coping has been defined by Lazarus and Folkman (1984) as the

“constantly changing cognitive and behavioural efforts to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of the person” (p. 141). Although it is apparent that coping is to a certain extent intrinsic to the individual, with individuals having somewhat prescribed ways of coping, coping is largely adaptive and changes according to the contextual demands of the situation and the individual’s subjective appraisal of that situation

(Lazarus, 1993; Roger et al., 1993; Stone et al., 1991). Lazarus and Folkman (1984) have therefore emphasised both situational coping, based on the relationship between the person and the environment; and dispositional coping, which refers to the consistent coping responses displayed by an individual across different times.

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Although there are a number of ways to classify coping responses, most approaches have distinguished between Problem Focused Coping (sometimes called Active Coping) and Emotion Focused Coping (sometimes called Passive

Coping) (Lazarus & Folkman, 1984). The addition of Avoidance Coping has also been suggested (Endler & Parker, 1990). Problem focused coping concerns developing specific strategies to confront and actively reduce or eliminate the source of stress, such as defining the problem, generating and weighing alternate solutions and developing and following a plan of action. Emotion focused coping, however, does not change the actual situation, but rather involves the regulation of emotional responses caused by the stressful encounter, for example, by changing the way the stressful relationship is attended to or by changing the relational meaning of the situation. Avoidance coping describes strategies, such as wishful thinking, distancing, denial and substance abuse, which avoid and do not attempt to deal with either the problem or the emotional consequences of the stressful situation.

Intrinsic to the issue of coping is the question of coping ‘effectiveness’

(Zeidner & Saklofske, 1996). Ideally, coping should lead to permanent problem resolution achieved via the removal of the source of stress, as well as the successful reduction and management of the emotional impact that was caused by the stressful situation (Pearlin & Schooler, 1978). Most models of coping have typically assumed a hierarchy of coping processes, with processes viewed as being either

‘adaptive’ or ‘maladaptive’ or essentially as more or less ‘superior’ than others

(Zeidner & Saklofske, 1996).

For instance, problem focused coping has typically been viewed to be the most effective type of coping strategy, largely because such strategies are focused

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on actively changing the problematic situation. Opinions on the effectiveness of emotion focused coping are somewhat mixed. In general, emotion focused coping has been viewed to be less effective than problem focused coping largely because such coping processes do not actually change the situation but instead focus on dealing with any adversive emotions that may have arisen (Lazarus & Folkman,

1984). This stance in regards to emotion focused coping, however, seems somewhat contradictory to the recent theorising that higher EI is associated with better coping

(as discussed further in Section 3.3.1). It is, nonetheless, recognised that dealing with the emotions that may have been generated by a stressful situation is critical to shaping both short and long term adjustment to certain events (Zeidner &

Saklofske, 1996). In contrast, to problem and emotion focused coping, avoidance coping is rarely viewed as an effective coping strategy. Although some avoidance coping techniques may provide temporary relief from the impact of the stressor, rarely do such strategies assist the individual in the long-term.

Lazarus (1993), however, has cautioned against creating such a hierarchy of responses based on preconceived notions over the inherent ‘effectiveness’ of different processes, arguing that there are no universal criteria from which these judgements can be made. Instead, he has argued that coping effectiveness must be evaluated in terms of the specific situational context (i.e., the degree or nature of the stressor) or the personal characteristics of the individual involved (i.e., their personality and their beliefs in their coping resources and abilities). Different coping strategies may consequently be more or less effective for different individuals or for the same individual but under different circumstances. Zeidner and Saklofske (1996) have also argued that a coping process might be considered successful when judged from one criterion, but not from another, or the resolution

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of one coping task may create a stressful event in another area. Therefore, successful adaptive coping may best be thought of as the use of different coping strategies in different ways, in order to determine which technique is most successful in managing the stress, rather than resolutely sticking to the same limited coping response when faced with differing stressors (Zeidner & Saklofske,

1996).

It would appear that different situations or circumstances are more or less suited to particular types of coping strategies. For example, an individual’s perceived level of control over a situation is likely to influence the type of coping strategies that will be employed (Aldwin, 1991). Problem focused coping is said to be more effective in controllable situations, where active problem solving steps are likely to be successful (e.g., appraising that increased studying may enhance exam performance, Matthews et al., 2002). If the situation is unlikely to be under the individual’s control and thus cannot be easily solved, feelings of frustration may arise if problem focused coping is employed with unsuccessful results. In contrast, emotion focused coping, because it involves dealing with the emotions generated by the situation, is likely to be more adaptive in uncontrollable situations where the emotions generated by the event, rather than the event itself, are more likely to be resolved (Terry & Hynes, 1998) (e.g., following the diagnosis of a terminal illness,

Matthews, et al., 2002). Similarly, Pearlin and Schooler (1978) have suggested that emotion focused coping may be more effective in unclear situations where the best way to deal with the problem is not directly apparent.

Additionally, avoidance coping, although typically viewed as maladaptive, may be useful for dealing with the stressors in some situations; for example, coping with the early stages of stress at a time when resources may be insufficient to cope

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more constructively (Lazarus & Folkman, 1984). Avoidance may also be effective in dealing with brief stressors, such as those associated with noise, pain or uncomfortable procedures (Suls & Fletcher, 1985). Avoidance in such situations, it is argued, may give the individual a ‘psychological break’ from the constant pressure of a particularly stressful event. Such coping is, however, never useful for long-term coping, because revisiting the problem again after avoidance has occurred will only serve to renew the anxiety (Lazarus & Folkman, 1984).

Lazarus and Folkman (1987) have further suggested that the use of a particular coping strategy is moderated by what may be at stake in the encounter.

For example, situations that are a threat to self-esteem typically evoke the use of either confrontative or avoidance coping, whereas situations that are not seen as a threat to self-esteem are typically characterised by the greater use of problem solving and by seeking social support.

Lazarus and Folkman (1984) have, however, specified that most coping responses typically involve numerous different coping strategies to be successful, employing both problem and emotion focused coping to deal with the source of stress and the emotional consequences of that stress. For instance, the use of problem focused coping may decrease the presence of a threat, thereby making it appear less threatening, and thus reducing the emotional distress caused by the stressful events. Emotion focused coping, in turn, may assist problem focused coping by removing some of the stressful emotions that detract from problem solving efforts.

However, regardless of these circumstances, what is firmly recognised is that coping is moderated by the resources available to the individual and that individual’s perceived ability at successfully using these resources to cope

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effectively (Lazarus & Folkman, 1984). EI has recently come to be viewed as a vital resource available to individuals that may assist in the ability to cope successfully

(e.g., Salovey, Bedell, Detweiler & Mayer, 1999). Thus, (as discussed further in

Section 3.3.1) individuals with a higher EI may be better at coping with stressful situations.

3.3.1 Coping and Emotional Intelligence

A great deal of theoretical supposition on the link between EI and coping has been made, although only some of this has been empirically examined. Stress has been described as the experience of negative emotions that are generated by the perception of a danger, threat or challenge (Salski & Cartwright, 2003). One of the main functions of emotions (as described in Section 2.7.2) is to draw an individual’s attention to potential threats in order for them to be dealt with adaptively so as to protect the individual from harm. Additionally, certain moods may facilitate the types of thought process that are necessary to deal effectively with a particular situation. As such, Zeidner, Matthews and Roberts (in press; as cited by Matthews et al., in press) have suggested that individuals with EI are better at: making use of effective and flexible coping strategies; avoiding stressful encounters through personal insight; making more constructive perceptions and situational appraisals to find opportunities for personal growth; adaptively regulating and repairing emotions; and having richer coping resources such that they are better able to regulate and reduce the negative emotions generated by stress. Conversely, poorer perception and clarity of emotional experience appears to reduce the ability to manage emotional experience, resulting in poorer adaptability and poorer control in stressful situations and thus more stress (Gohm, Corser, Dalsky, 2005).

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Salovey et al. (1999) have also proposed that EI is linked to coping through its association with three processes (rumination, social support and the disclosure of trauma) that are related to coping skills. Rumination may be thought of as the repetitive focusing of thought on one’s negative symptoms of distress, characterised by anxiety and depression. Individuals who ruminate tend to focus excessively on the perception and appraisal of their mood states without actually attempting to regulate them to relieve or resolve conflict, a process which is contrary to the notion of EI.

Clarity of emotion appears to be particularly important in this process, while

Attention to mood seems to have a negative effect. For example, using the TMMS

Salovey et al. (1995) found individuals with higher Clarity of emotions to be more likely to exhibit reductions in negative mood over time following a negative mood induction [F(18, 567) = 1.92, p = .05; N = 78], while individuals with lower skills tended to continue ruminating (z = 1.67, p <.05, one tailed), although this is a marginal result. Scores for Attention and Repair were not significant and were not reported. Salovey et al., (2002) have also found higher mood Repair to be moderately associated with less rumination (assessed with the Response Style

Questionnaire) after a negative mood induction (r = -.56; N = 48), with Attention being found to be mildly correlated with more rumination (r = .26) although the results for Clarity were low (r = .03). It would thus appear that individuals who ruminate are unable to differentiate between their emotions effectively, which affects their ability to repair negative emotions when they arise. That Attention to emotion has been found to be associated with the higher incidence of rumination suggests that simply attending to feelings may not be sufficient to actually change

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them or alternatively that excessively focusing on negative moods may lead to feelings of besiegement (Gohm et al., 2005; Salovey et al, 1995).

Furnham, Petrides, Sisterson and Baluch (2003) have found repressive individuals to score significantly higher than anxious individuals on EI. In this study, university students (N = 259) were divided into four groups according to their level of defensiveness and anxiety, based on scores above or below the mean on the Taylor Manifest Anxiety Scale and the Marlowe-Crowne Social Desirability

Scale (i.e., 1. Truly low anxiety: Low on defensiveness and anxiety; 2. Non- defensive/high anxiety: Low on defensiveness and high on anxiety; 3.

Defensive/high anxiety: High on defensiveness and anxiety; 4. Repressors: High on defensiveness and low on anxiety). Out of these four groups, repressors were found to score most similarly to the truly low anxious group and were found to have the highest self-report EI scores (as assessed by the AES) [F(3, 259) = 3.07; p < 0.05].

Pennebaker (1997) has also suggested that repressing negative thoughts actually serves to keep that thought in cognitive awareness as the individual attempts to determine whether the act of repression has been successful.

Confronting and disclosing emotional difficulties, even anonymously, appears to release the physiological burden that inhibition imposes and thus seems to be beneficial in improving an individual’s physical and mental health by enhancing immunological functioning, decreasing physical symptoms and depression and thereby resulting in fewer health centre visits.

Having social support has further been found to be associated with less rumination as individuals naturally turn to others to make sense of their emotional experiences. Salovey et al. (1999) have suggested that social support is associated

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with better coping by acting as an ‘emotional buffer’ against stressful events. They have also argued that individuals with higher EI are more likely to have and make use of larger support networks by virtue of their EI skills, which assists them in building and maintaining interpersonal relationships. Additionally, Salovey et al.

(1999) have suggested that emotionally intelligent individuals are more likely to make use of the social networks that they do have, because they are more likely to appreciate the emotional benefits associated with sharing and using social support as a means of coping.

A further benefit of emotional disclosure is that it is likely to aid in progressively re-organising traumatic information, thereby allowing individuals to adapt to the situation and move on (Salovey et al., 1999). It has been proposed that individuals with higher EI will be more successful at this process given that they are better able to understand their emotional experiences and the exact cause of their emotional distress and are, in turn, better able to manage their emotions to repair negative moods. Hunt and Evans (2004), for instance, have found individuals with higher self-reported EI (assessed by the Nottingham Emotional

Intelligence Scale) to be moderately associated with the reporting of fewer incidences of trauma related symptoms (r = -.34; N = 414) than individuals with lower EI.

Finally, it has been argued that EI, because it concerns an individual’s ability to engage with rather than avoid emotions and because EI functions to regulate negative emotions that can impede problem solving attempts, should be associated with the selection and use of more adaptive coping strategies to meet the demands of stressful situations (Salovey et al., 1999; Salovey et al., 2002). Salovey et al. (2002), for example, have found higher EI to be moderately associated with the use of

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more active and less passive coping. In a study involving 60 adults, in which an

‘active’ and a ‘passive’ global coping construct were created from the COPE measure (Carver, Scheier & Weintraub, 1989), they found higher mood Repair to be associated with the perception of stressors as less threatening (r = -.35) and with less trait and state passive coping (r = -.31 and r = -.34, respectively). The correlations between the other TMMS subscales and active and passive coping, however, were not significant (and not reported) (Salovey et al., 2002). A follow up study by Salovey et al. (2002) (N = 48) using the same methodology also found higher mood Repair and Clarity of emotions to be low-to-moderately associated with the use of more active coping (r = .44 and r = .23, respectively). However, the correlations between Attention and active coping and between the TMMS subscales and passive coping were all non-significant (and not reported). Additionally, Hunt and Evans (2004) (N = 414) have found individuals with higher EI to be more likely to engage in a monitoring (processing) coping style (r = .14), than a blunting

(avoidance) style (r = -.17), although the correlations in both instances were weak.

On the basis of these results, EI appears to be low-to-moderately and positively correlated with better coping. This process appears to be somewhat indirect in that EI offers individuals advantages in relevant areas (such as assisting the individual to ruminate less or by enabling better and more social support networks) that in turn offer coping advantages. It has also been proposed that individuals with higher EI have significant advantages in that they are better able to recognise and make use of more adaptive coping strategies and further that they are better at avoiding stressful situations in the first place. However, the mechanisms for how such individuals achieve these advantages has not been explicitly detailed.

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3.4 Problem-Solving

Every individual in the course of his or her daily life is bombarded with a multitude of decisions, hassles, constantly changing situations and major life events that require continual attention and problem solving in order to be dealt with effectively (Heppner & Lee, 2002). Problems may be thought of as situations for which no effective response is immediately available and thus problem solving is the process by which individuals attempt to find and apply effective responses to deal with such situations (D’Zurilla & Goldfried, 1971).

Early problem solving research focused on examining how individuals solved pre-defined laboratory-based problems, such as puzzles and anagrams.

These types of problems, it could be argued, differ substantially from the types and complexity of problems that are typically encountered in everyday life (Heppner,

Hibel, Neal, Weinstein & Rabinowitz, 1982). Heppner et al. (1982) have thus suggested that it is likely an individual’s full problem solving ability cannot be adequately assessed from these laboratory-based scenarios. Nevertheless, it has been argued that a pivotal part in dealing with problems is an individual’s appraisal of their own problem solving skills, something which is much more easily measurable (Heppner & Lee, 2002). Although self-appraised problem solving may not accord with assessments of problem solving skills through solving actual problems, it is presumed that the way an individual evaluates their problem solving skills influences the way in which they think, feel and behave during the problem solving process, which will therefore have an influence on how that individual problem solves (Larson, Piersel, Imao & Allen, 1990; MacNair & Elliott,

1992).

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The Problem Solving Inventory (PSI) developed by Heppner and Petersen

(1982) has been one of the most widely used instruments in this area. This measure assesses perceived confidence in one’s problem solving ability (Problem Solving

Confidence), the degree to which an individual either approaches or avoids problems (Approach-Avoidance) and an individual’s perceived emotional and behavioural control over personal problem solving abilities (Personal Control).

Investigations using this measure in a number of studies have determined that there are substantial differences between self-appraised effective and ineffective problem solvers on a wide range of dimensions3.

For example, individuals who perceive themselves to be more effective problem solvers have been found to be more insightful [F(1, 32) = 6.4, p = <.05] and to have a clearer understanding of the problem [F(1, 32) = 8.4, p = <.05], than ineffective problem solvers which enables such individuals to solve problems more effectively. More effective problem solvers have also been found to be more persistent [F(1, 32) = 29.2, p = <.05] and motivated to solve their problems [F(1, 32)

= 19.1, p = <.05], to be less impulsive [F(1, 32) = 6.4, p = <.05], to be less avoidant of their problems [F(1, 32) = 15.4, p = <.05], more willing to delay gratification

[F(1, 32) = 7.5, p = <.05], to have higher expectations at succeeding in problem solving situations [F(1, 32) = 10.7, p = <.05] and to brainstorm more [F(1, 32) = 15.9, p = <.05] than less effective problem solvers (Heppner et al., 1982; N = 34).

Additionally, more effective problem solvers have rated themselves as being more

3 Low scores on the PSI are actually indicative of perceived effective problem solving ability and thus a positive correlation with, for example, anxiety indicates that a self-perceived effective problem solver is less anxious.

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intuitive [F(1, 32) = 4.6, p = <.05], cautious [F(1, 32) = 7.2, p = <.05], serious

[F(1, 32) = 12.4, p = <.05] and systematic in making decisions [F(1, 32) = 18.9, p = <.05] (Heppner et al., 1982; N = 34) and to also enjoy cognitive activities more than less effective perceived problem solvers [F(1, 51) = 31.2, p = <.001] (Heppner,

Reeder & Larson, 1983; N = 52). Higher self-appraised problem solvers have also been found to have significantly higher self-esteem [F(4, 45) = 13.63, p = <.0001;

N = 52, Heppner et al., 1983], to have a more external locus of control [r = .48;

N = 206; r = .34; N = 237; Larson et al., 1990] and to be less likely to suffer depression (r = .44; N = 206; r = .25; N = 237; Larson et al., 1990).

Higher perceived problem solving has also been associated with more positive coping behaviours. For example, Larson et al. (1990) have found higher scores on the PSI to be moderately associated with the use of more positive coping

(as assessed by the Coping Strategies Inventory; r = -.54; N = 206; r = -.50; N = 237) and with less negative coping (r = .33; N = 206; r = .25; N = 237). Additionally,

MacNair and Elliott (1992) have found individuals with higher perceived problem solving skills to report the use of more problem focused coping responses [F(1,

365) = 11.45, p < .001] and fewer emotion focused strategies [F(1, 365) = 7.51, p = .001] (N = 141), as assessed by the Ways of Coping Questionnaire. MacNair and

Elliott (1992) have suggested that this tendency for self-perceived ineffective problem solvers to use more emotion focused coping is perhaps due to their inability to regulate their emotions under stress effectively, whereas self perceived effective problem solvers make use of more problem focused strategies as they typically approach, rather than avoid, their problems.

This review of results suggests that effective and ineffective perceived problem solvers differ on a number of cognitive and emotional components.

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Heppner and colleagues (e.g., Heppner et al., 1983) have suggested that, as a consequence of these differences, self-appraised effective problem solvers are more effective at solving their problems and thus experience less distress. It is possible that individuals who are better at regulating the distressing emotions generated by problematic situations are better able to implement more effective problem solving behaviours as they are not impaired by these negative emotions. EI may thus be critical for developing and maintaining the skills that are crucial for perceived and actual problem solving (as discussed further in Section 3.4.1).

3.4.1 Problem Solving and Emotional Intelligence

To my knowledge, there has been only one study on the association between EI and problem solving ability or on an individual’s perceptions of their problem solving ability (Schutte, Schuettpelz & Malouff, 2001). In this study, participants (N = 38) with higher EI solved more anagram problems than less emotionally intelligent individuals with this performance maintained even after encountering performance frustrations. However, Mayer and Salovey (1997;

Salovey & Mayer, 1990) in their definitions of EI have projected EI as being about using the information obtained from emotional resources to solve everyday problems (see Section 2.7.2). If emotional information can guide cognition, then problem-solving skills should be positively related to EI, particularly ability EI conceptions, and consequently an association between the two is expected.

Additionally, on the basis of the relationship between problem solving and coping (as discussed in Section 3.3 and Section 3.4), which suggests that better problem solving ability is related to better coping ability and the corresponding relationship between higher EI and better coping (see Section 3.3.1), it could be

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expected that higher EI will be associated with better actual and perceived problem solving ability.

3.5 Anxiety

Different researchers have viewed anxiety as a stimulus for certain behaviours, as a response, a learnt drive or a personality variable (Shedletsky &

Endler, 1974). It is suggested that anxiety may arise as a result of a threat to one’s well-being, self-esteem or identity. Anxiety is therefore associated with the arousal of the autonomic nervous system, which acts as a defence mechanism to protect the individual from harm (Spielberger, 1966).

Probably the most influential classification of anxiety has been the division of anxiety into ‘trait’ and ‘state’ components, based in early work by Cattell and

Scheier (1958; as cited by Cattell, 1966). Trait anxiety has been defined as a stable characteristic that signifies the propensity for an individual to experience anxiety in any given situation. Conversely, state anxiety has been described as a transitory state of tension, apprehension and activation of the nervous system that fluctuates from moment to moment according to the presence of environmental or internal challenges (Morris, Davis & Hutchings, 1981; Spielberger, 1966). There are, therefore, stable differences between individuals in the extent to which they are likely to experience anxiety in any given situation, as well as within individual differences that fluctuate as a result of internal and external stimulation

(Spielberger, 1966).

Endler and Parker have developed a similar framework to this for investigating anxiety, known as the Interactional Model of Anxiety, which specifically focuses on the interaction between the individual and the

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circumstances that induced the anxiety situation (Endler, 1983). As with Cattell and

Scheier’s conception of anxiety, this theory identifies both an individual’s predisposition to experiencing anxiety (trait anxiety) and an individual’s actual response to a particular situation (state anxiety) (Endler, Crooks & Parker, 1992).

State and trait anxiety, however, are both viewed to be multidimensional constructs. State anxiety is suggested to have two components: Cognitive Worry

Reactions, which involves the presence of self-ruminating thoughts that focus on worry, inadequacies and potential failures; and Autonomic Emotional Reactions, which describes the symptoms of the autonomic nervous system, such as the experience of sweaty palms, rapid heart rate or a dry mouth (Endler et al., 1992).

Trait anxiety is divided into three situational domains in which anxiety may be evoked that have been described as: Interpersonal, Physical Danger and Ambiguous

(Endler, 1983).

The basis of the Interactional Model of Anxiety is the presumption that state anxiety is a function of the interaction of a particular dimension of trait anxiety with a congruent situational threat (labelled the ‘Differential Hypothesis’) (Endler,

Edwards, Vitelli & Parker, 1989; Endler et al., 1992). Specifically, it is argued that if an individual is high on a particular situational dimension of trait anxiety, that individual, when exposed to that particular situational threat, will experience higher levels of state anxiety than that which would be experienced by an individual with low trait anxiety for the same situational threat. However, reactions to situations are largely context dependent and will vary as a function of the individual’s perception of that particular situation. It is therefore argued that different situations, or the same situation at different times, may produce differing levels of anxiety in a particular individual (Endler, 1983).

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Worry and anxiety have been found to be moderately-to-highly correlated with each other (e.g., State Trait Anxiety Inventory [STAI] and Penn State Worry

Questionnaire: r = .66; STAI and Worry Domains Questionnaire: r = .58; N = 82,

Davey, 1994; STAI and Student Worry Scale: r = .57; N = 105; Davey, Hampton,

Farrell & Davidson, 1992) and the two are generally considered to be similar constructs. For instance, O’Neill (1985) has suggested that worrying and anxiety are functionally the same, with worrying being merely a cognitive manifestation of anxiety. Mathews (1990) has similarly suggested that worrying is a causal by- product of anxiety because worrying tends to result from the predisposition to attend to threatening cues, which is a feature of anxiety. Borkovec (1985) has further suggested that much of the experience of anxiety is generated by the worry thought process.

Worry is a common psychological phenomenon that is typically associated with real-life triggers of anticipated threats that may be either present or future- orientated and which concerns problems that are either real or probable, as opposed to imaginary or remote (Tallis, Davey & Capuzzo, 1994). Even though most individuals appreciate that such beliefs are illogical, worriers have been found to view worrying as effective in decreasing the likelihood that a feared outcome will eventuate. In this sense, worrying is often seen to be a legitimate problem solving technique given that the continual rehearsal of possible events appears to allow the individual to find ways to avoid such a negative outcome from occurring. Simultaneously, such a process acts to ‘desensitise’ the individual to the threat, thereby increasing tolerance should the outcome eventuate (Borkovec,

1985). Mathews (1990) has also agreed that worrying is attempted problem solving to avoid aversive or threatening events, but has suggested that worrying actually

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represents unsuccessful problem solving attempts. Mathews (1990) has argued that, although worrying may appear to represent problem solving activities, it is unlikely that worrying will be successful in resolving a problem as consistent worrying tends to lead to continuous rumination and the perception that the danger is constantly present. Davey (1994) has also suggested that the continued focus on negative emotions in this way will likely diminish an individual’s attempts at problem solving and simultaneously lead to greater levels of emotional discomfort. Thus, Borkovec (1985) has suggested that the functional effect of worrying is that it maintains anxiety.

Consistent with this view, worrying has been found to be a relatively inefficient problem solving technique. Worry and anxiety have both been found to be related to poor problem solving confidence, which may lead to the implementation of ineffective coping strategies. For example, Davey (1994) using the PSI has reported worrying to be moderately related to lowered Problem Solving

Confidence (Penn State Worry Questionnaire: r = .58; Worry Domains

Questionnaire: r = .47; N = 82) and perceived Problem Solving Control (Penn State

Worry Questionnaire: r = .49; Worry Domains Questionnaire: r = .45; N = 82).

Similarly, Davey et al. (1992) have found both STAI trait anxiety and worry to be moderately correlated to poor PSI Problem Solving Confidence using the PSI

(Anxiety: r = .49; Worry: r = .30; N = 105) and poor Personal Control (Anxiety: r = .40; Worry: r = .55; N = 105), while Larson et al. (1990) have found higher perceived problem solving to be moderately associated with less STAI trait anxiety

(r = .51; N = 206 and r = .46; N = 237). Additionally, Borkovec (1985) has reported that although worriers were good at defining problems, they were poor at actually generating successful solutions and utilising effective coping responses. This

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disparity, Nezu (1986) has suggested, is due to worriers’ general intolerance of the uncertainty that is often found in problem solving situations, such that they feel the need to redefine the problem constantly in an attempt to remove the associated ambiguity.

In order to reduce anxiety and manage stress, adaptive resources such as coping (as discussed in Section 3.3) may be implemented. Poor coping skills are likely to do little to reduce anxiety; hence, anxious individuals are more likely to engage in rumination and avoidance and other poorer coping responses, a concept that has been found to be negatively related to EI (see Section 3.3.1) (Deisinger,

Cassisi & Whitaker, 1996). For example, Blankstein, Flett and Johnston (1992)

(N = 125) have also found both STAI trait anxiety and worry (as assessed by the

Reactions to Test Scale) to be moderately correlated with the use of more avoidance coping (Ways of Coping Questionnaire; r = .39; r = .38, respectively) and also with

STAI state anxiety and worry (r = .32; r = .38, respectively). Additionally, Davey et al. (1992) (N = 105) have found worry and anxiety have been found to be moderately correlated to avoidance coping strategies (Health and Daily Living

Form) (STAI: r = .39; Student Worry Scale: r = .44) and, conversely, unrelated to active cognitive (Anxiety: r = -.14; Worry: r = .05) and behavioural strategies

(Anxiety: r = -.16; Worry: r = .15). Similarly, Larson et al. (1990) have found more positive coping (assessed by the Coping Strategies Inventory) to be moderately correlated with less state anxiety (r = -.42; N = 206; r = -.34; N = 237) and trait anxiety (r = -.53; N = 206; r = -.40; N = 237) as assessed by the STAI. They have furthermore, found negative coping to be moderately associated with more state anxiety (r = .42; N = 206; r = .43; N =237) and trait anxiety (r = .47; N = 206; r = .49;

N = 237). Thus it would appear, individuals who believe that they are able to cope

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effectively and therefore exercise control over their environment and any potential threats are less likely to be troubled by anxiety.

3.5.1 Anxiety and Emotional Intelligence

The basis for the proposed association between higher EI and lower anxiety lies primarily on the hypothesised and identified relationships between EI and better coping (as discussed in Section 3.3.1) and the corresponding correlations between higher coping and lower anxiety (see Sections 3.3 and 3.5). Such literature has suggested that individuals with higher EI are better able to manage and cope with daily life hassles by being better able to regulate and manage their emotions.

Better coping has further been related to lower anxiety.

However, anxiety and worry have not been extensively investigated in relation to EI before. Of the limited research on EI and anxiety, EI has, as expected, been found to be generally negatively correlated to anxiety. For example, Head

(2002) and David (2002) (both cited by Brackett and Salovey, 2004) have both reported low correlations between the MSCEIT (Total) and STAI (r = -.29 and r = -

.31, respectively). In relation to self-report EI measures, Salovey et al. (2002) have found social anxiety (as assessed by the Social Anxiety subscale of the Self-

Conscious Scale) to be low-to-moderately and negatively correlated with the Clarity

(r = -.30) and Repair (r = -.37) subscales of the TMMS, although the Attention subscale was low, but positively correlated (r = .11; N = 108). Ciarrochi et al. (2001a) have also found a four factor version of the AES to be low-to-moderately correlated with lower trait anxiety as assessed by the STAI (Regulate Self: r = -.49; Regulate

Others: r = -.33; Perceive Emotions: r = -.21; Utilise Emotions: r = -.14; N = 131 13 – 15 year olds). Additionally, Tsaousis and Nikolaou (2005) found scores on their Traits

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Emotional Intelligence Questionnaire to be low-to-moderately and negatively correlated with the anxiety subscale of the General Health Questionnaire

(Perception: r = -.20; Control: r = -.42; Utilisation: r = -.27; Understanding: r = -.17;

N = 365). Furthermore, Furnham et al. (2003) have found highly anxious individuals to have the lowest scores on EI (AES) in comparison to repressive or low anxiety individuals in a sample of 259 university students (as was discussed in

Section 3.3.1).

3.6 Gender and Emotional Intelligence

It is commonly held that females, by being more emotionally responsive and by experiencing and expressing emotions more intensely, are much more

‘emotional’ than males, (Barrett, Lane, Sechrest & Schwartz, 2000). Gender differences in emotions, if they are indeed found, have been generally in the stereotypic direction, with females tending to experience and express higher levels of fear, anxiety, depression, guilt and happiness, whereas males tend to feel more anger (Barrett et al., 2000). Based on such views, it has been suggested that females will also be more ‘emotionally intelligent’ than males. A number of studies have supported this view, although not all studies have found significant gender differences, or at least not for all EI components. Those that have, however, have typically found gender differences to favour females.

For example, as shown in Table 9 on page 154, in relation to ability EI measures, Mayer et al. (1999) have found females to score significantly higher than males on the MEIS, regardless of the type of scoring procedure employed.

Ciarrochi et al. (2000) have also found females to score significantly higher than males on all subscales of the MEIS. Additionally, Kafetsios (2004) has found that

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females scored significantly higher than males on the Perception branch of the

MSCEIT, although the results for all other subscales were not statistically significant. Brackett et al. (2003) and Brackett et al. (2005) have further both found females to score significantly higher than males on Total MSCEIT score (subscale scores not reported).

In relation to self-report EI measures, both Reiff et al. (2001) and Parker et al.

(2001) have found females to score significantly higher than males on the

Interpersonal subscale of the EQ-i, although no other differences were found significant. Dawda and Hart (2000) have also reported no significant gender differences on the EQ-i from a population of college students. Additionally, Schutte et al. (1998) on a University student population and Charbonneau and Nicol (2002) on a sample of school children have both reported significant differences favouring females with the AES. Furthermore, using a four factor version of the AES,

Ciarrochi et al. (2001a) has reported females to score significantly higher than males on three out of the four subscales (Perception, Utilisation and Regulation of Other’s

Emotions), with only the results in relation to Regulating One’s Own Emotions not being significant. In another study of a four factor AES solution, Saklofske et al.

(2003) have similarly found females to score significantly higher than males on the

Appraisal of emotions and the Social Skills subscale, although interestingly, males were found to score significantly higher than females on the Utilisation of emotions subscale, but no significant difference on the Optimism/Mood Regulation factor.

Additionally, Hunt and Evans (2004) found males to score significantly higher than females on the Nottingham Emotional Intelligence Scale [F = 40.62, p < .001;

N = 414].

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Table 9: Relationships Between Gender and EI

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Although not all studies investigating EI and gender differences have found gender differences – at least not for all components of EI – those that do have typically found females to score higher than males on both self-report and ability assessments of EI, providing some support for the suggestion of gender differences in EI favouring females.

3.7 Aging and Emotional Intelligence

An increasing amount of research has noted a number of positive changes in emotional experience with longitudinal age. Increasing age during adulthood, for example, has been found to correspond to more positive emotional experience, more positive and less negative affect, higher salience of emotion, improved emotional control and better emotional regulation (see Carstensen, Pasupathi,

Mayr & Nesselroade, 2000; Turk-Charles, Mather & Carstensen, 2003 for a review).

Carstensen (1993, 1995) and colleagues have proposed the Socioemotional

Selectivity Theory to account for such findings. This theory contends that humans are motivated by two trajectories: a knowledge trajectory that seeks the acquisition of new information necessary for future educational and occupational success; and an emotion trajectory that motivates the acquisition of emotional satisfaction and meaning (Carstensen et al., 2000). Younger adults, it is argued, are motivated by the knowledge trajectory, almost to the detriment of emotional components, whereas individuals in late adulthood, increasingly conscious of their own mortality, are said to have a shift in their motivational goals, such that a preference for emotionally meaningful experiences results.

As part of the criteria for defining EI as an intelligence (see Section 2.7), EI should increase with age and experience. Roberts et al. (2005) have identified four

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questions that are of prime interest in the assessment of the development trajectory of EI, namely: Are there any age-related differences in EI and in which direction?

Are age-related differences in EI related to age-related differences in cognitive abilities? What does EI predict across the life span? Are there gender differences in

EI that change across the life span?

To date only some of these issues have been considered. Age-related differences in EI have been found in a number of studies, typically favouring older adults. For example, as shown in Table 10 over the page, in relation to ability EI measures, Mayer et al. (1999) have found adults (N = 503; aged 17 – 70 years) to have significantly higher scores on the MEIS for all scoring methods than did an adolescent sample (N = 229; aged 12 – 26 years). Kafetsios (2004) has also found significant age differences favouring older adults on three out of four subscales of the MSCEIT, in a sample of 239 community members aged 19 – 66. However, contrary to these results, Day and Carroll (2004) in a sample of 246 university students aged 17 – 54 found only one significant, but weak, age related difference, which actually favoured younger individuals (r = -.14). They do, however, believe that these differences may have been due to disproportionate sampling across the ages.

Significant age related differences in EI favouring older adults have also been found with the EQ-i. For example, Bar-On (1997) has reported significant increases in EI with age from early adulthood to middle age, with individuals aged

40 – 49 having significantly higher total scores than individuals aged 20 – 29.

Derksen et al. (2002) have also found EQ-i scores to increase with age in a community sample of 873 individuals aged 19 – 84 years. Scores were found to generally peak at 35 – 44 years of age and to decline thereafter, although the rate of

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increase and decline with age varied somewhat according to the component scale.

This increase in EI was found to contrast with the corresponding decline in general cognitive ability (as assessed by the General Adult Mental Ability Scale) in the same sample. Derksen et al. (2002) have therefore concluded that different psychological factors must underlie EI and general intelligence.

Table 10: Relations Between Age and EI

Table 10: Relationships Between Age and EI

Author Population N Assessment Result

Mayer, Caruso and Community 503 MEIS Consensus: F(1, 713) = 23.8,

Salovey (1999) Sample. p < 0.001; Expert:

(Adolescents: 12 – F(1, 709) = 22.3, p < 0.001;

16; Adults: 17 – 70) Target: F(1, 718) = 8. 00,

p < 0.001

Kafetsios (2004) University and 239 MSCEIT Perception: ns and not

Adult Community reported’; Utilisation:

Sample (Aged 19 – F(3, 160) = 4.13, p < 0.01;

66) Understanding:

[F(3, 158) = 5.62, p < 0.001;

Management: F(3, 162) = 5.42,

p < 0.001

Day and Carroll College Student 246 MSCEIT Perception: r = -.14; Utilisation:

(2004) (Aged 17 – 54)s r = -.04; Understanding:

r = .01; Management: r = .04.

Bar-On (1997) Adult Community EQ-i F = 46.3, p < 0.001

Sample (Aged 20 –

49)

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Based on these studies, it is possible that EI increases with age during middle adulthood. It should, however, be noted that these studies have been cross- sectional, not longitudinal and therefore it is possible that differences in EI in these instances may have been due to cohort, rather than age related effects. Having EI increase in this way, however, suggests that EI reflects a set of acquired skills rather than intrinsically innate abilities and thus it may be possible for at least some aspects of EI to be ‘taught’ and ‘learnt’ in some way.

3.8 Conclusion

The importance of EI as a new psychological construct depends on its predictive ability. Research has begun to explore the importance of EI for a number of variables. Of importance in this thesis is the predictive validity of EI for what have been termed ‘life skills’ – essentially skills that may be important in a multitude of life situations, such as occupational, educational and interpersonal domains.

The life skills of interest here are: academic achievement, life satisfaction, coping ability, problem solving ability and level of anxiety, only some of which have been extensively investigated in relation to EI before. For example, fairly consistent low-to-moderate relationships have been found between EI and life satisfaction. Mixed results, however, have been reported for academic assessment regardless of the EI measure or assessment used, with correlations ranging from near zero to moderate. Preliminary results have indicated that EI is low-to- moderately and positively correlated with coping and negatively with anxiety. The relationship between EI and problem solving ability, or perceptions of problem solving ability, however, has not been examined before. Nevertheless, it could be

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expected that EI would be positively related to problem solving ability based on the relationship between EI and coping and coping and problem solving ability.

Additionally, research on the influence of demographic variables on EI has suggested that females tend to score higher than males on all assessments of EI and that older individuals tend to score higher than younger individuals on EI.

This chapter forms the theoretical foundation for considering relationships between EI and these life skills and demographic variables. An evaluation of the association between these variables and EI was conducted in two diverse samples – a younger University sample of convenience (see Chapter 4) and an older wider community sample recruited by advertisement (see Chapter 5). These two studies form the basis of this thesis.

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CHAPTER 4: STUDY 1: UNIVERSITY SAMPLE4

To investigate the reliability and validity of a number of the current EI measures and the EI construct as a whole, this study investigated the relationships between EI measured by both self-report and ability scales, cognitive abilities, personality and a number of ‘life skills’ theorised to be related to EI (i.e., academic success, life satisfaction, coping ability, perceived problem solving ability and level of anxiety). The theoretical justification for the examination of these particular life skills was developed in Chapter 3. In particular, this study tested whether EI predicts real life outcomes in terms of these life skills, after controlling for the influence of personality and cognitive abilities.

4.1 Hypotheses

This study investigated a number of hypotheses, as follows:

1. Based on previous research that has suggested that females are more

sensitive to emotional considerations than males and findings that females

typically score higher than males on measures of EI (see Section 3.6),

females will have significantly higher EI scores than males on all EI

measures (TMMS, AES and MSCEIT).

4 A shorter version of this chapter has been published in a peer review journal (Bastian, V. A., Burns, N., & Nettelbeck, T. (2005) Emotional Intelligence predicts life skills, but not as well as personality and cognitive abilities, Personality and Individual Differences, 39, 1135 – 1145). A version of these results together with those detailed in Chapter 5 has also been accepted for publication as a book chapter [Burns, N., Bastian, V. A., & Nettelbeck, T. (in press) Emotional intelligence: More than personality and cognitive ability? Science of Emotional Intelligence: Known and Unknowns. R. D. Roberts, M. Zeidner & G. Matthews (Eds.)]. 161

2. Based on previous research that has suggested that self-report EI measures

and personality share some degree of method and/or trait variance (see

Section 2.4), self-report EI measures (the TMMS and the AES) will be

more highly correlated with personality than with cognitive abilities.

Additionally, given that ability EI is conceptualised as an ‘intelligence’ and

all intelligences should be theoretically related (see Sections 2.3.1 and 2.4),

ability EI (the MSCEIT) will be more highly correlated with cognitive

abilities than with personality.

3. Consistent with previous research (see Section 2.4) it is predicted that

higher EI will be related to higher Extraversion, Openness,

Agreeableness, and Conscientiousness and with lower Neuroticism.

4. Based on previous research (see Section 2.4), it is expected that EI will be

more highly correlated with verbal ability (Phonetic Word Association

Task) than with abstract reasoning (Raven’s Advanced Progressive

Matrices).

5. Based on previous research (see Sections 3.1), higher EI will be correlated

with higher academic achievement.

6. Based on previous research (see Sections 3.2.1), higher EI will be correlated

with higher life satisfaction.

7. Based on previous research (see Sections 3.3.1), higher EI will be correlated

with better coping ability.

8. Given the positive relationship between coping and problem-solving ability

(see Sections 3.3 and 3.4) and the corresponding positive relationship

between coping and EI (see Section 3.3.1), higher EI will be correlated with

higher perceived problem solving ability.

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9. Given the negative relationship between coping and anxiety (see

Sections 3.3 and 3.5) and the corresponding positive relationship between

coping and EI (see Section 3.3.1), higher EI will be correlated with lower

anxiety.

10. Considering EI should show significant effects independent of other

constructs, EI will account for substantial variance in life skills, after

controlling for the effects of cognitive abilities and personality.

4.2 Method

4.2.1 Participants

Participants were 246 individuals (69 males; 177 females) ranging in age from 16 – 39 years (M = 19.9; SD = 4.2). These individuals were predominantly first-year Psychology university students (N = 219) who participated in order to obtain course credit. Recent school graduates were targeted in order to get access to an independent measure of academic achievement (Tertiary Entrance Rank; TER).

Several other individuals (N = 27; primarily University students from other years) also volunteered to participate in the study for their own interest.

The majority of participants were single (94.0%), with only 2.0% married,

2.8% in a defacto relationship and 1.2% divorced. Most participants (91.9%) were still completing university. A further 6.9% of participants had completed an undergraduate degree and 1.2% of participants had secondary school education only. Of those participants who had been, or were currently at university, 35.8% were enrolled through the Faculty of Humanities and Social Sciences, 29.3% from the Faculty of Science, 13.4% from the Faculty of Health Sciences and the remaining

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21.5% were from the Faculty of Engineering, Computer and Mathematical Sciences and the Faculty of the Professions.

4.2.2 Materials

Participants were administered a battery of 10 measures in order to assess

EI, cognitive abilities, personality and self-reported life skills (academic success, life satisfaction, coping ability, perceived problem solving ability and anxiety), as shown below in Figure 3 and discussed in the following sections. Demographic details for each participant were also obtained (see Appendix A). These measures were chosen because they were readily available, widely used and suitable for an Australian sample.

Figure 3: The Measures Used in This Study.

4.2.2.1 Emotional Intelligence

EI was assessed by the Trait Meta Mood Scale, the Assessing Emotions Scale and the Mayer, Salovey and Caruso Emotional Intelligence Test.

4.2.2.1.1 Trait Meta Mood Scale (TMMS)

The TMMS is a 30-item self-report measure developed by Salovey et al.

(1995) to assess core features of emotionally intelligent individuals. This measure consists of three subscales (Attention, Clarity and Repair), which Salovey et al. (1995)

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have suggested are fundamental to the self-regulatory domain of EI. These subscales assess the degree of Attention that individuals devote to their feelings (13- items, e.g., “I pay a lot of attention to how I feel”), the Clarity with which they can discriminate amongst differing emotions (11-items, e.g., “Sometimes I can’t tell what my feelings are”) and the propensity for individuals to Repair and regulate either positive and negative emotions (6-items, e.g., “I try to think good thoughts no matter how badly I feel”).

Participants indicate their level of agreement with each statement based on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Scores are calculated by summing the individual values for each subscale (where items 2, 3, 4,

5, 9, 11, 14, 16, 17, 18, 19, 22, 23, 27 and 29 are reversed scored), with scores on the

Attention subscale ranging from 13 - 65; on the Clarity subscale from 11 - 55; and the

Repair subscale from 6 - 30. Higher scores on each subscale are indicative of higher ability in that area.

The internal reliability of this scale has been reported as high, with values of

α = .86, α = .87 and α = .82 for the Attention, Clarity and Repair components, respectively (Salovey et al., 1995).

4.2.2.1.2 Assessing Emotions Scale (AES)

The AES is a 33-item self-report measure of EI, developed by Schutte et al.

(1998), based on Salovey and Mayer’s (1990) model of EI. Items in this measure assess the Appraisal and Expression of emotion (13-items, e.g., “I am aware of my emotions as I experience them”), the Utilisation of emotions (10-items, e.g., “When I am in a positive mood, solving problems is easy for me”) and the Regulation of

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emotions (10-items, e.g., “When I experience a positive emotion, I know how to make it last”).

Participants indicate their level of agreement with each statement based on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Only a Total score is derived, which is calculated by summing the individual values for each item (where items 5, 28 and 33 are reversed scored) and thus scores may range from 33 – 165. Higher scores indicate higher EI.

Schutte et al. (1998) have reported the internal reliability of this scale to be

α = .87 and α = .90 in two different samples and a two-week, test-retest reliability to be α = .78.

4.2.2.1.3 Mayer, Salovey and Caruso Emotional Intelligence Test

(MSCEIT)5

The MSCEIT (Version 2.0) is the research version of an ‘ability’ type measure of EI developed by Mayer et al. (2000d) – based on the MEIS (Mayer et al.,

1999) in order to measure actual performance at solving emotional problems, rather than a self-reported interpretation of emotional skills. The MSCEIT consists of 141- questions presented in a multiple-choice format with either four or five alternate answers. This measure consists of eight tasks (‘Faces’, ‘Facilitation’, ‘Changes’,

‘Emotion Management’, ‘Pictures’, ‘Synesthesia’, ‘Blends’ and ‘Emotions in

Relationships’), which produces four subscale scores (Perception, Utilisation,

5 I am grateful to Multi-Health Systems International who allowed me free access to the MSCEIT for the purposes of this research in exchange for assisting in norming a recently developed measure that they were publishing. 166

Understanding and Management of emotions) and a Total EI score (sum of subscales).

Derived from the branches of Mayer and Salovey’s EI model (see

Section 2.7), the Perception of emotions scale assesses the ability to recognise emotions in oneself, others and in stories, art, music and other ‘inanimate’ objects.

The Utilisation of emotions scale assesses the ability to generate, use and feel emotions and the ability to use emotions in mental processes. The Understanding of emotions scale assesses the ability to understand the changing and dynamic nature of emotions in order to reason with them and thus enhance understanding within the self and between others. The Management of emotions scale assesses the ability to be open to feelings in order to modify them in oneself and others so as to enhance personal understanding and growth. The Total score is simply a measure of ‘Overall Emotional Intelligence’ and is derived from the four subscale scores by summing these scores. A description of the tasks that are associated with each of these branches is presented in Table 11 over the page.

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Table 11: The Tasks Associated with Each of the Four Branches of the MSCEIT.

Table 11: The Tasks Associated with Each of the Four Branches of the MSCEIT. MSCEIT Branch MSCEIT Task Description of Task Perception ‘Faces’ Assesses the perception of emotion by asking individuals to identify how a person feels, based on a picture of facial expression. ‘Pictures’ Involves determining the emotion expressed in ‘inanimate’ objects, such as scenery or artwork. Utilisation ‘Synesthesia’ Assesses the ability to reason with mood, by comparing different emotions to different sensations, such as light or colour. ‘Facilitation’ Assesses knowledge about the ways in which moods interact and support our thinking and reasoning. Understanding ‘Changes’ Assesses knowledge about how conflicting emotions may be felt in certain situations and how emotions may change from one situation to another. ‘Blends’ Assesses the ability to connect certain emotions with certain situations. Management ‘Emotion Assesses the effectiveness of alternative actions to Management’ achieve a particular result in circumstances where regulation of emotions is required. ‘Emotions in Involves evaluating the effectiveness of different Relationships’ actions in achieving a particular outcome, as applied to situations involving other people.

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Administration of the MSCEIT was via the internet using the MSCEIT V.2

Computer Program for WindowsTM. Items were presented on a computer screen and participants indicated their answers by clicking on the appropriate radio button. Responses were collected in a central database maintained by Multi-Health

Systems International and were electronically scored and then returned by an administrator.

Scoring was by the ‘consensus’ scoring method, whereby participant scores were compared to those from a normative sample of 1794 individuals established by Mayer et al. (2000d). Scores were calculated by determining how consistent the participant’s score was with the normative sample group, such that if a participant’s response agreed with responses from .21 of the standardised group on a particular item, the participant received a score of .21 for that item. All items for each task were summed to obtain a cluster score for that task; branch scores were then calculated by summing the scores for each task applicable to that branch and from summing these branch scores a Total score was obtained.

Total scores for the MSCEIT are reported as normed standard scores and are standardised, similar to traditional intelligence scales, with a mean of 100 and a standard deviation of 15. Consistent with this scheme, scores above 115 are interpreted as indicative of ‘enhanced EI’, scores between 85 and 115 indicate

‘average EI’ and scores below 85 indicate that ‘EI needs development’. Thus, higher scores are indicative of higher EI.

The Total score and branch scores of the MSCEIT have been reported to have high reliability. The reliability scores for the Perception, Utilisation,

Understanding, Management and Total score have been reported by Mayer et al.

(2000d) as α = .87, α = .76, α = .73, α = .82 and α = .90, respectively.

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4.2.2.2 Cognitive Abilities6

Cognitive abilities were assessed by Raven’s Advanced Progressive Matrices and a

Phonetic Word Association Task.

4.2.2.2.1 Raven’s Advanced Progressive Matrices (RAPM)

The RAPM (Set II) is a 36-item non-verbal abstract reasoning task widely regarded as assessing fluid abstract reasoning ability (Raven, Court & Raven, 1993).

Each item consists of a pattern represented in a 3 x 3 matrix (with between one and five figural elements), with the bottom right piece missing. The participant must choose, from eight alternate answers, which piece will fit into the missing area to complete the pattern. Participants have 40 minutes to complete the test. Scores may range between 0 and 36, with higher scores (number of correct responses) indicative of better performance.

Internal reliability statistics have been reported in the RAPM manual as ranging between α = .83 and α = .87, based on a number of studies. Test-retest reliabilities after six to eight weeks have been reported to be r = .91 for adults.

4.2.2.2.2 Phonetic Word Association Test (PWAT)

The PWAT, taken from the Australian Council for Educational Research

Shorthand Aptitude Test (Brownless & Dunn, 1958) is a written test of crystallised

6 Despite the term ‘intelligence’ being commonly used in EI literature (see Footnote 2), here assessments of cognitive abilities were restricted to Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT). These measures were moderately correlated (r = .42), but it is recognised that this combination is not sufficient to define intelligence. Consequently, the RAPM outcome is here referred to as “abstract reasoning ability”; and the PWAT as “verbal ability”. 170

verbal ability. Participants are presented with 50 words that are spelt

‘phonetically’, which the participant is required to spell correctly (e.g., Item:

“bowkay”; Answer: “bouquet”). Participants have 10 minutes to complete the task.

Scores may range between 0 and 50, with higher scores (number of correct responses) indicative of higher performance.

4.2.2.3 Personality

Personality was assessed by the NEO Personality Inventory (Revised).

4.2.2.3.1 Revised NEO Personality Inventory (NEO PI-R)

The NEO PI-R, developed by Costa and McCrae (1992), is a 240-item self- report measure of five major domains of personality (48 questions each)

(Neuroticism: e.g., “I am not a worrier”, Extraversion: e.g., “I really like most people I meet”, Openness: e.g., “How I feel about things is important to me”, Agreeableness: e.g., “Some people think I’m selfish and egotistical” and Conscientiousness: “I try to perform all the tasks assigned to me conscientiously”) and the six facets that define each of these domains (Neuroticism: Anxiety, Angry Hostility, Depression, Self-

Consciousness, Impulsiveness, Vulnerability; Extraversion: Warmth,

Gregariousness, Assertiveness, Activity, Excitement-Seeking, Positive Emotions;

Openness: Fantasy, Aesthetics, Feelings, Actions, Ideas, Values; Agreeableness: Trust,

Straightforwardness, Altruism, Compliance, Modesty, Tender-Mindedness;

Conscientiousness: Competence, Order, Dutifulness, Achievement Striving, Self-

Discipline, Deliberation) (Costa & McCrae, 1992).

Participants indicate their level of agreement with each item based on a 5- point Likert scale (1 = Strongly Agree to 5 = Strongly Disagree). Domain scores are calculated by summing the individual values for each trait and then each domain 171

subscale. Scores for each domain may therefore range between 48 and 240. Higher scores on each domain indicate higher congruence with those personality traits.

The NEO PI-R professional manual reports acceptable internal reliability statistics of α = .92, α = .89, α = .87, α = .86 and α = .90 for the Neuroticism,

Extraversion, Openness, Agreeableness and Conscientiousness domains, respectively.

4.2.2.4 Life Skills

Self-reported assessment of five life skills (academic achievement, life satisfaction, coping, problem solving and anxiety) were assessed by various measures, as follows.

4.2.2.4.1 Tertiary Entrance Rank (TER)

As an independent measure of academic success, self-reported TER scores were provided by all participants who were recent school graduates (within the previous year). In Australia, all students who complete the final year of schooling

(Year 12) requirements receive a TER (a percentile ranking relative to other students), based on their school performance. A TER is used for admission of the vast majority of students into tertiary education programs and is standard across all Universities and other tertiary institutions throughout Australia.

Tertiary entrance points are calculated for all Year 12 subjects and all subjects are scaled to ensure cross-subject comparability. For University entrance, the tertiary entrance points for a student’s best five full-year (or equivalent) subjects create a ‘University aggregate’ (ranging between 61 and 99.96). For TAFE

(vocational training) selection, TAFE selection TERS (below 60) are calculated from the tertiary entrance points of a student’s best three full-year (or equivalent) subjects. 172

4.2.2.4.2 Satisfaction With Life Scale (SWLS)

The SWLS is a 5-item self-report measure of general life satisfaction, developed by Diener et al. (1985). This measure was developed to represent the assessment of life satisfaction as a whole, rather than for the measurement of specific satisfaction domains (e.g., “In most ways my life is close to my ideal”). This method of assessment allows individuals to integrate and weigh different domains in whichever way they chose.

Participants indicate their level of agreement with each item based on a 7- point Likert scale (1 = Strongly Disagree to 7 = Strongly Agree). A total score is calculated by summing the individual scores for each item. Scores may therefore range between 5 and 35, with higher scores indicating higher life satisfaction.

Diener et al. (1985) reported both adequate internal reliability statistics

(α = .87) and an adequate two-month test-retest correlation coefficient (r = .82).

4.2.2.4.3 The COPE

The COPE is a 60-item self-report multidimensional coping inventory developed by Carver et al. (1989), used to assess situational coping (responses to a specific situation) or dispositional coping (typical responses to stressors), or both.

In order to objectively compare the coping ability of participants, only dispositional coping was assessed in this study.

The COPE consists of 15 subscales (Active Coping; e.g., “I take direct action to get around the problem”, Planning, e.g., “I think about how I might best handle the problem”; Seeking Instrumental Social Support, e.g., “I try to get advice from someone about what to do”; Seeking Emotional Social Support, e.g., “I talk to someone about how I feel”; Suppression of Competing Activities, e.g., “I keep myself

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from getting distracted by other thoughts or activities”; Turning to Religion, e.g., “I try to find comfort in my religion”; Positive Reinterpretation and Growth, e.g., “I look for something good in what is happening”; Restraint Coping, e.g., “I make sure not to make matters worse by acting too soon”; Acceptance, e.g., “I learn to live with it”;

Focus on and Venting of Emotions, e.g., “I let my feelings out”; Denial, e.g., “I act as though it hasn’t even happened”; Mental Disengagement, e.g., “I turn to work or other substitute activities to take my mind off things”; Behavioural Disengagement, e.g., “I admit to myself that I can’t deal with it and give up trying”; Alcohol/Drug

Use, e.g., “I try to lose myself for a while by drinking alcohol or taking drugs” and

Humour, e.g., “I laugh about the situation”) that reflect different coping mechanisms (four items per subscale).

Participants indicate how often they perform a particular coping behaviour in response to typical stressful events based on a 4-point Likert scale (1 = ‘I don’t do this at all’ to 4 = ‘I do this a lot’). Subscale scores are calculated by summing the individual values from each item for each subscale and thus subscale scores may range between 4 and 16. For each subscale, higher scores indicate higher incidence of using that type of coping mechanism to deal with typical stressors.

Carver et al. (1989) have reported that the internal reliability statistics of the

COPE scales all exceeded α = .60, which they state is “acceptably high” (p. 271).

Scores on the COPE have also been reported as relatively stable at 6-week test- retest, with reliabilities on subscales for a sample of students ranging between r = .42 and r = .89; and 8-week test-retest reliabilities on another sample of students that ranged between r = .46 and r = .86 for the different subscales.

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4.2.2.4.4 Problem Solving Inventory (PSI)

The PSI is a 32-item self-report measure developed by Heppner and

Petersen (1982) that assesses perceived problem solving skills and behaviours. The

PSI consists of three subscales that are designed to assess an individual’s Problem

Solving Confidence and self-assurance while engaging in problem solving behaviour

(11-items, e.g., “I am usually able to think up creative and effective alternatives to solve a problem”); their Approach-Avoidance Style, or their tendency to either approach or avoid different problem solving activities (16-items, e.g., “After I have solved a problem, I do not analyse what went right or what went wrong”) and their

Personal Control or beliefs about the degree to which they are in control of their emotions and behaviours while problem solving (5-items, e.g., “When my first efforts to solve a problem fail, I become uneasy about my ability to handle the situation”).

Participants indicate their level of agreement with each statement based on a 6-point Likert scale (1 = Strongly Agree to 6 = Strongly Disagree). Scores are calculated by summing the individual scores for each subscale (where items 1, 2, 3,

4, 10, 12, 13, 14, 16, 20, 23, 24, 27, 29, 31 are reversed scored). Scores on the Problem

Solving Confidence subscale may range from 11 - 66; on the Approach Avoidance Style subscale from 16 – 96; and the Personal Control subscale from 5 - 30. A Total score may also be calculated by summing the subscale scores together, with scores ranging from 32 - 192. Lower scores indicate more effective perceived problem solving ability.

Internal reliability statistics for the PSI have been reported by Heppner and

Petersen (1982) as α = .85, α = .84, α = .72 and α = .90 for Problem Solving Confidence,

Approach-Avoidance Style, Personal Control and Total score, respectively. Two-week 175

test-retest coefficient reliabilities on undergraduate students were reported by

Heppner and Petersen (1982) as r = .85, r = .88, r = .83 and r = .89 for the subscale and total scores, respectively.

4.2.2.4.5 Anxious Thoughts Inventory (ATI)

The ATI is a 22-item self-report measure of ‘generalised worry’, developed by Wells (1994), which assesses three dimensions of worry: Social Worry, Health

Worry and Meta-Worry. Social Worry (9 items. e.g., “I worry about not being able to cope in life as adequately as others seem to”) and Health Worry (6 items, e.g., “I have thoughts about being seriously ill”) indicate different content dimensions, whereas Meta-Worry (7 items, e.g., “I take disappointments so keenly that I can’t put them out of my mind”) indicates both content and processes dimensions of worry, which essentially reflect a pre-occupation with thinking negatively about one’s own worry.

Participants indicate their level of agreement with each item based on a 4- point Likert scale (1 = Almost Never to 4 = Almost Always). To provide an overarching analysis of ‘Anxiety’ as a whole, only a Total score was calculated and used in this study. This was obtained by summing the individual scores for each item. Scores could therefore range from 22 - 88. Higher scores indicate higher anxiety.

Internal reliability coefficients for the three subscales were reported by

Wells (1994) as α = .84 for Social Worry, α = .81 for Health Worry and α = .75 for

Meta-Worry, although statistics for Total score were not provided. Six-week test- retest reliability was found to be r = .76 for Social Worry, r = .84 for Health Worry, r = .77 for Meta-Worry and r = .80 for Total score.

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4.2.3 Procedure

Participants were recruited via posters advertising a study on “Emotional

Intelligence”. Testing was conducted in one three-hour session in groups of up to six people. Participants were free to have a break during this time (except during the timed tasks).

Each participant was given an envelope that contained the response sheets for each measure, coded by number, as well as all relevant question booklets.

Before commencing, each participant was provided with an information sheet detailing the study (Appendix B) and a consent form (Appendix C), which they were requested to read and sign. Once the consent form had been signed and collected, participants were given a brief run down of the materials to familiarise them with the procedure.

Participants were first administered the two time-limited tasks as a group.

However, before commencing these tasks, participants were instructed that if they finished early they could continue with the un-timed tasks in any order they chose.

The PWAT (10-minute limit) was presented first, with participants instructed that they were to be presented with 50 words spelt ‘phonetically’ that they were required to spell correctly. Two practice examples were given to ensure that everyone understood what was required. Participants were given a warning at the eight-minute mark of the approaching ‘time is up’. Answer sheets were collected and then the RAPM (40-minute limit) was administered. In order to explain the procedure, one practice example (from RAPM Set I) was held up to the group to indicate the location of the ‘missing’ piece and the eight alternate answers with participants instructed that they had to identify the pattern in the matrix and

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determine which piece from the eight alternate answers would complete the pattern. Once each participant indicated that they understood the concept, timing commenced with participants given a warning at the 30-minute mark of the approaching ‘time is up’.

After all tasks were completed, each participant’s responses were checked to ensure that all questions had been answered, at which time they were free to leave.

All measures were hand scored and then rechecked for accuracy.

4.3 Results

4.3.1 Descriptive Statistics

Descriptive statistics for all measures in this study are presented in Table 12.

Table 12: Descriptive Statistics for the Measures Assessed in Study 1.

Measure Variable Na Range Mean±SD Reliabilityc

Trait Meta Mood Scale Attention 246 28-68 49.7±6.5 .84

Clarity 246 15-55 37.2±6.6 .86

Repair 246 6-30 21.6±4.3 .83

Assessing Emotions Scale 239 78-157 123.8±12.5 .89

Mayer, Salovey and Caruso Perception 246 42-123 102.5±14.7 .91

Emotional Intelligence Test Utilisation 246 42-127 99.5±13.2 .68

Understanding 246 55-124 103.7±11.5 .45

Management 246 51-124 95.5±11.6 .49

Total 246 33-119 99.6±12.6 .83

Raven’s Advanced Progressive Matrices 246 2-36 23.5±7.0 -

Phonetic Word Association Task 246 4-49 36.1±9.0 -

(continued next page)

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Measure Variable Na Range Mean±SD Reliabilityc

NEO Personality Inventory Neuroticism 246 46-164 98.2±23.8 .76

(Revised) Extraversion 246 50-163 118.6±21.2 .79

Openness 246 39-168 125.4±19.5 .74

Agreeableness 246 57-164 117.5±18.9 .76

Conscientiousness 246 43-172 108.6±23.0 .87

Tertiary Entrance Rankb 185 50-100 86.2±10.7 -

Satisfaction With Life Scale 226 6-34 24.3±5.6 .85

The Cope Active Coping 204 4-16 10.8±2.5 .39

Planning 204 4-16 11.2±2.5 .25

Instrumental Social Support 204 4-16 11.5±2.9 .49

Emotional Social Support 204 3-16 11.1±3.3 .53

Suppression of Activities 204 3-16 9.2±2.2 .29

Turning to Religion 204 2-16 7.2±4.3 .84

Positive Reinterpretation 204 4-16 11.8±2.7 .78

Restraint Coping 204 2-15 9.2±2.3 .37

Acceptance 204 2-16 10.6±2.8 .76

Focus/Venting of Emotions 204 2-16 9.6±3.4 .83

Denial 204 2 – 14 5.6±2.2 .75

Mental Disengagement 204 3 – 16 9.4±2.5 .51

Behavioural Disengagement 204 2 – 15 5.9±2.0 .72

Alcohol/Drug Use 204 2 – 16 5.2±2.7 .97

Humour 204 2 – 19 8.5±3.4 .90

Problem Solving Inventory Problem Solving Confidence 204 11-60 28.4±7.7 .88

Approach Avoidance Style 204 20-91 47.4±10.4 .86

Personal Control 204 6-29 17.2±4.4 .73

Total 204 48-177 93.0±18.8 .89

Anxious Thoughts Inventory (Total) 106 23-80 47.3±11.2 .91 a = Variable Ns are recorded due to some measures being added partway through the testing. b = Older participants did not have useable TERs c = Cronbach’s alpha for internal consistency

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4.3.2 Factor Analysis of Measures

Factor analyses of all measures with defined subscales were conducted in order to confirm that the structure present in this sample was consistent with that proposed by the developers of these scales. Factor analyses were conducted using the principal axis factor extraction method with a varimax rotation of extracted factors. The number of factors extracted was decided on the basis of eigenvalues over unity and on visual inspection of the scree plot. The factor structure of each measure was also confirmed by using a maximum likelihood extraction with the indicated number of factors. The solution was then statistically evaluated to determine goodness-of-fit.

4.3.2.1 Trait Meta Mood Scale

A three or four factor solution was found to be adequate for the TMMS.

Goodness-of-fit statistics on both solutions, suggested that the four-factor solution

[χ2(321) = 541.7; p < .001] (45.23% of explained variance) provided a slightly better fit than the three-factor solution [χ2(348) = 832.6; p < .001] (39.87%); however, there was not much difference between the two7. Given this, and considering that the three-factor solution is consistent with that of the developers of this measure, who identified three factors (Attention, Clarity and Repair of emotions), the three-factor solution was retained.

7 The large sample size involved makes the interpretation of the associated p values somewhat problematic so consequently the χ2/df ratio was used to determine the goodness-of-fit.

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4.3.2.2 Assessing Emotions Scale

Although Schutte et al. (1998) have favoured a one-factor solution of the

AES, other researchers have suggested a four factor solution may be better suited, although these same researchers have also acknowledged that a one-factor solution is adequate (see Section 2.7.7). The factor analysis of this measure provides support for both a one or four factor solution. Goodness-of-fit statistics indicated that a single factor solution [χ2(495) = 1314.93; p < .001] (21.97%) or a four-factor solution

[χ2(402) = 690.18; p < .001] (35.61%) fit just as well. Considering that a one factor solution is consistent with the recommendations of the developers and this has also been confirmed as acceptable by other researchers, this solution was retained here.

4.3.2.3 Mayer, Salovey and Caruso Emotional Intelligence Test

Assessment of the MSCEIT was via the internet with responses sent directly to the publisher for scoring. Responses from individual items were therefore not available for factor analysis.

4.3.2.4 NEO Personality Inventory (Revised)

A five-factor solution [χ2(295) = 526.4; p < .001] (53.09%) was confirmed for the NEO PI-R, which is consistent with the five factors (Neuroticism, Extraversion,

Openness, Agreeableness and Conscientiousness) identified by the developers.

4.3.2.5 Satisfaction With Life Scale

A one-factor solution [χ2(5) = 2.2; p = .82] (55.75%), consistent with that of the developers, was found for the SWLS.

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4.3.2.6 The COPE

In this study, only eight factors [χ2(1318) = 1759.3; p < .001] (49.86%) were strongly identifiable from the COPE, which is contrary to the 15 factors (listed in

Section 4.2.2.4.3) that were found by the developers. Based on item content, these eight factors appeared to represent: Communication, Turning to Religion,

Alcohol/Drug Use, Denial, Humour, Focus on and Venting of Emotions, Concentration, and a somewhat non-descript General Coping factor.

4.3.2.7 Problem Solving Inventory

Consistent with the developers of the PSI, who identified three factors

(Problem Solving Confidence, Approach Avoidance Style and Personal Control), three factors consistent with these [χ2(374) = 528.63; p < .001] (41.52%) were also identified in this study.

4.3.2.8 Anxious Thoughts Inventory

For the ATI, a three-factor solution [χ2(168) = 311.7; p < .001] (51.13%) was found, which confirmed the three factors (Social Worry, Health Worry and Meta-

Worry) reported by the developers.

4.3.3 Factor Analysis of Emotional Intelligence

A factor analysis of the EI measures used in this study was also conducted to investigate the nature of the construct. From the three EI measures used, there were eight EI variables (Attention, Clarity, Repair, AES Total, Perception, Utilisation,

Understanding and Management).

A principal axis factor analysis with a varimax rotation of the EI subscales indicated a two-factor solution (41.78%). The TMMS and AES subscales (both self-

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report EI measures) were found to load solely on one factor (subsequently named

“self-report EI”) and the subscales of the MSCEIT (the ability EI measure) loaded solely on the other (named “ability EI”). These results confirmed the factor structure of EI assessed in this study.

Although, based on these findings, arguments could be made for reducing the various EI subscales into only two factors (“Self-Report EI” and “Ability EI”), the EI variables were deemed to be the variables of most interest in this study. It was thus decided to retain the defined EI subscales, but to use the results from this factor analysis to describe the nature of EI generally.

4.3.4 Reduction of ‘Life Skills’ variables

Taking into account the various subscales of the assorted ‘life skills’ measures would result in 23 different life outcome variables (including the Total scores for the PSI and the COPE). It was therefore decided that converting all these variables into a smaller number of scores would allow for more meaningful analyses. The simplest way of doing this was to represent the various ‘life skills’ by their Total score.

It was, however, deemed to be not sensible to talk about ‘better’ coping as a general construct, given that the COPE investigated some coping behaviours that were obviously more maladaptive than others (e.g., Alcohol/Drug Use versus

Planning). A three-factor solution of coping, which was representative of the most popular coping classification (i.e., problem focused, emotion focused and avoidance coping) was therefore generated.

This was achieved by first conducting a maximum likelihood extraction factor analysis with a direct oblimin rotation and limiting the number of extracted

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factors to three. These results indicated that the three extracted factors were only minimally correlated. Consequently, it was decided that, to be consistent with the previous factor analyses, a maximum likelihood extraction with a varimax rotation should be tried. However, conducting these analyses using the individual test items produced a nonsensical result, although factor analyses using the developer’s prescribed 15-factor subscale scores produced factors with subscale loadings consistent with these theoretical expectations (Problem focused coping: Active,

Planning, Suppression of Activities, Positive Reinterpretation and Growth, Restraint

Coping, Acceptance and Humour; Emotion focused coping: Instrumental Social

Support, Emotional Social Support and Focus on/Venting of Emotions; Avoidance coping: Denial, Mental Disengagement, Behavioural Disengagement and Alcohol/Drug

Use), with only the Turning to Religion subscale not included in these factors.

In summary, seven life skills components were therefore derived (i.e., academic achievement, life satisfaction, problem focused coping, emotion focused coping, avoidance coping ability, perceived problem solving ability and anxiety).

Results from analyses using subscales defined by test developers and that were actually utilised in this study but not reported in the body of the thesis (i.e., from the COPE and the PSI) are presented in the attached Appendices (Appendix D).

4.3.5 Gender Differences

4.3.5.1 Gender Differences in Emotional Intelligence

Previous literature (see Section 3.6) has indicated that females tend to score higher on measures of EI than do males. Independent sample t-tests examined whether this was apparent in this study (Hypothesis 1). These statistics, along with the effect sizes for each evaluation, are presented in Table 13. For consistency when

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calculating effect sizes, a positive effect size indicates that females scored higher on this variable.

Table 13: The Gender Differences in Emotional Intelligence (Study 1).

Measure Variable ♀ EI scores ♂ EI scores da Independent Samples t-test

Trait Meta Mood Scale Attention 50.3±6.0 48.1±7.4 + .34 t(244) = 2.38, p = .01

Clarity 36.9±6.3 37.8±7.3 - .13 t(244) = .89, p = .38

Repair 21.7±4.2 21.3±4.4 + .08 t(244) = .60, p = .55

Assessing Emotions Scale 124.5±11.7 122.1±14.5 + .19 t(237) = 1.33, p = .18

Mayer, Salovey and Caruso Perception 103.5±11.7 100.1±16.6 + .23 t(244) = 1.61, p = .11

Emotional Intelligence Test Utilisation 100.0±12.7 98.2±14.3 + .14 t(244) = .99, p = .32

Understanding 103.8±11.1 103.2±12.6 + .05 t(244) = .39, p = .70

Management 96.2±11.2 93.6±12.2 + .23 t(244) = 1.63, p = .11

a = Cohen’s effect size

The effect sizes for all gender differences were relatively small, but indicated that females scored higher than males on most EI measures. However, contrary to earlier studies and therefore not generally supporting Hypotheses 1

(that females will have significantly higher EI scores than males), only one EI gender difference, which favoured females, was found to be statistically significant

(TMMS Attention: t(244) = 2.38; p = .01, 2-tailed; d = .34).

4.3.5.2 Gender Differences in Cognitive Abilities, Personality and Life Skills

Independent sample t-tests also examined whether there were any gender differences in the other domains assessed. Results from these t-tests, as well as the associated effect sizes, are presented in Table 14.

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Table 14: The Gender Differences in Cognitive Abilities, Personality and ‘Life Skills’ (Study 1).

Measure Variable ♀ ♂ da Independent scores scores Samples t-test

Cognitive Abilities RAPM (Matrices) 22.9±7.0 24.9±6.6 - .28 t(244) = 1.94, p = .05

PWAT (Phonetics) 35.9±8.9 36.6±9.1 - .08 t(244) = .53, p = .60

NEO Personality Inventory Neuroticism 99.8±23.8 94.0±23.5 + .24 t(244) = 1.71, p = .09

Extraversion 120.0±20.7 114.9±22.1 + .25 t(244) = .1.73, p = .09

Openness 126.0±18.8 124.0±21.3 + .11 t(244) = .74, p = .46

Agreeableness 120.2±17.9 110.5±19.8 + .52 t(244) = 3.68, p < .001

Conscientiousness 109.9±23.2 105.4±22.4 + .20 t(244) = 1.38, p = .17

Tertiary Entrance Rank 86.54±10.58 85.18±11.15 +.04 t(183) = 0.76, p = .45

Satisfaction With Life Scale 24.78±5.69 22.88±6.65 +.13 t(224) = 2.11, p = .04

The COPEb Problem Focused -.05±0.90 .20±0.97 -.15 t(202) = 1.64, p = .10

Emotion Focused .10±0.95 -.30±1.06 -.20 t(202) = 2.44, p = .02

Avoidance -.03±0.93 .12±0.70 -.10 t(202) = 1.01, p = .32

Problem Solving Inventory Total 93.0±18.8 92.8±19.1 + .01 t(202) = .08, p = .94

Anxious Thoughts Inventory (Total) 47.9±10.9 44.9±12.7 + .26 t(104) = 1.07, p = .29

a = Cohen’s effect size b = Based on regression residuals generated from factor analysis.

These results indicate that there were very few gender differences in this

sample on the variables assessed. Females scored significantly higher than males

on Agreeableness and life satisfaction (SWLS) and the Emotion Focused coping factor,

while males were found to score significantly higher than females on RAPM. No

other variables exhibited significant gender differences.

4.3.6 Correlations between Emotional Intelligence, Cognitive Abilities,

Personality and Life Skills.

Correlations (available data using pairwise correlations) between all

measures (EI, cognitive abilities, personality and life skills) are shown in Table 15.

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The reliability of these data were confirmed by comparing the results obtained from listwise correlations, which revealed markedly similar outcomes.

Table 15: The Correlations Between All Measures and Subscales 2 1

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4.3.6.1 Correlations Between Emotional Intelligence Measures

Within each measure, the subscales of the TMMS and the MSCEIT were moderately correlated with each other (TMMS average: r = .32; MSCEIT average: r = .37).

As expected, considering they are both self-report EI measures and may exhibit method variance, the AES was found to be moderately-to-highly correlated with all TMMS subscales (Attention: r = .46; Clarity: r = .47; Repair: r = .62).

Additionally, consistent with previous findings (see Section 2.4), the MSCEIT (an ability EI measure) exhibited only low correlations with the AES (ranging between r = .03 and r = .26) and generally low correlations with the TMMS (ranging between r = .02 and r = .35 for all subscales). These results suggest that the nature of self- report ability EI assessments are so different that they are essentially assessing different constructs (see Section 2.9) and also highlights that individuals are probably not particularly skilled at perceiving their own EI ability (see also 4.3.7, below).

Using LISREL 8.54 (Jöreskog & Sörbom, 2003), subscales from TMMS and

MSCEIT were fitted to a two-correlated-factors model where TMMS subscales defined a self-report EI factor and MSCEIT subscales defined an ability EI factor. This model provided poor fit; chi-square(13) = 33.9, p < .001; Root Mean Square Error of

Approximation (RMSEA) = .081 (Confidence Interval [CI]90 = .048,.110); Consistent

Akaikie Information Criterion (CAIC) = 131.5. However, allowing the Understanding and Management subscales of MSCEIT to have secondary loadings on the self-report

EI factor produced a good fitting model, with all loadings statistically significant; chi- square(11) = 16.2, p = .13; RMSEA = .044 (CI90 = .000, .086); CAIC = 126.8. A new model including AES loading on the self-report EI factor fitted less well and the 188

loading of MSCEIT Understanding on the self-report EI factor was not statistically differe nt from zero. Dropping this path from the model improved the fit; chi- square(18) = 37.9, p = .004; RMSEA = .067 (CI90 = .037, .097); CAIC = 155.0, although the estimated correlation between the two EI factors was only r = .19. These analyses suggested some overlap between TMMS and MSCEIT but that it is possible to achieve relative independence between self-report and ability measures.

4.3.6.2 Correlations Between Life Skills

Problem focused coping was found to be moderately correlated to higher perceived problem solving (r = -.38) and with less anxiety (r = -.19), while avoidance coping was found to be associated with poorer perceived problem solving (r = .49) and with higher anxiety (r = .19). Emotion focused coping, however, was found to be not related to either problem solving ability or anxiety.

Higher perceived problem solving ability was further found to be moderately correlated with lower anxiety (r = .29). These results provide some support for previous findings that indicate that better coping ability is associated with better problem solving ability and both are associated with lower anxiety (see

Sections 3.3, 3.4 and 3.5).

Additionally, higher TER was found to be positively and moderately related to life satisfaction (r = .29) and with better perceived problem solving (r = -.38), but was not significantly correlated with anxiety (r = -.06). TER was only moderately related with less avoidance coping (r = -.35), but was not significantly correlated with the other forms of coping. Life satisfaction was further found to be low-to- moderately correlated with higher problem focused (r = .20) and emotion focused

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coping (r = .18) and better perceived problem solving (r = -.37), but with less avoidance coping (r = -.29) and with less anxiety (r = -.46).

4.3.6.3 Correlations Between Emotional Intelligence, Cognitive Abilities, Personality

Personality was found to be generally moderately correlated with the self- report EI measures (Self-report EI factor: ranging between r = .31 and r = .52), although correlations with ability EI (Ability EI factor: ranging between r = -.02 and r = -.23) were typically found to be low. In contrast, the correlations between the cogniti ve abilities measures and ability EI were low-to-moderate (Ability EI factor:

RAPM: r = .27; PWAT: r = .26), whereas the correlations between cognitive abilities and self-report EI measures were generally not significant (Self-report EI factor:

RAPM: r = .08; PWAT: r = .08).

From these results it is also apparent that the magnitude of correlations between self-report EI and personality were higher than those obtained between self-report EI and cognitive abilities. The opposite was observed for ability EI, with the correlations between ability EI and cognitive abilities being higher than those obtained for ability EI and personality. This provides support for Hypothesis 2

(that self-report EI measures will be more highly correlated to personality than to cogniti ve abilities, while ability EI measures will be more highly correlated to cognitive abilities than to personality) and previous research in this area (see

Section 2.4).

Nevertheless, even though the extent of correlations between EI and personality were higher for self-report EI measures than they were for ability EI measures, the general trend from these results suggested that higher EI is associated with higher Extraversion, Openness, Agreeableness and

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Conscientiousness, but with lower Neuroticism. This is consistent with findings from previous research (see Section 2.4) and therefore supports Hypothesis 3 (that there would be low-to-moderate associations between EI and personality in these directions). However, the magnitude of correlations between the two cognitive abilities assessed here was found to be similar when compared across each EI subscale. Therefore, previous results (see Section 2.4) and Hypothesis 4 (that EI will be more highly correlated with verbal abilities than it will with cognitive abilities) were not supported here.

4.3.6.4 Correlations Between Emotional Intelligence, Cognitive Abilities, Personality and Life Skills

In the section that follows, correlations are reported between various measures of EI and several outcome measures. Before discussing these results it should, of course, be noted, that correlations indicate relationships but do not establish causality.

The correlations between life skills and EI were found to be higher for self- report EI measures than they were for ability EI, with correlations between self- report EI all being low-to-moderate, whereas those between ability EI were all low.

The general trend from these correlations has suggested that higher levels of EI are low-to-moderately correlated to higher life satisfaction (Self-report EI factor: r = .51;

Ability EI factor: r = .14), problem focused coping (Self-report EI factor: r = .40; Ability

EI factor: r = -.04), emotion focused coping (Self-report EI factor: r = .22; Ability EI factor: r = .04) and better perceived problem solving ability (Self-report EI factor: r = -

.55; Ability EI factor: r = -.04), and with lower avoidance coping (Self-report EI factor: r = -.27; Ability EI factor: r = -.13) and anxiety (Self-report EI factor: r = -.25; Ability EI

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factor: r = -.22). However, the correlations between EI and academic achievement were non-significant for all EI measures (Self-report EI factor: r = .07; Ability EI factor: r = .10), with the exception of the Understanding and Management subscales of the

MSCEIT. These results provided support for Hypotheses 6 – 9 (that EI would be low-to-moderately correlated with life satisfaction, coping, problem solving and anxiety), but not for Hypothesis 5 (that EI would be low-to-moderately correlated with academic success).

These correlations further suggest that personality is more highly correlated to the various life skills variables than are cognitive abilities. It is possible that this distinction, in light of the fact that self-report EI measures have also been found to be moderately correlated to personality but not to cognitive abilities (see

Section 4.3.6.3), may explain the findings that self-report measures are more associated with life skills than are ability EI measures (i.e., method variance).

4.3.7 The Dimensionality of Emotional Intelligence, Cognitive Abilities and

Personality8

A set of measurement models on the main constructs, using LISREL 8.54, was examined. First, a proxy measure for IQ was calculated as the average of the z- scores for RAPM and PWAT. Next, two basic models were compared; a trait model and a method model. For the trait model, all EI measures defined an EI factor and the five NEO PI-R measures together with IQ defined a second factor. For the method model, all self-report measures (TMMS, AES, NEO PI-R) defined a self-

8 I wish to acknowledge the assistance of Dr. Nicholas Burns in the Structural Equation

Modelling components of this thesis.

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report factor and all ability measures (MSCEIT and IQ) defined an ability factor.

Neither model fitted the data well, although fit for the method model was better

(CAIC = 540.9 compared to 747.4). The next set of models defined four factors: EI self-report and EI ability, as above, a factor defined by Neuroticism, Agreeableness and Conscientiousness (NAC), and a second personality factor defined by

Extraversion, Openness and IQ (EOIQ). This second factor is consistent with empirical results that suggest that these personality constructs can influence intellig ence (see Matthews, Deary & Whiteman, 2003). By allowing secondary loadings of variables across factors for EI, as above, fixing the correlation between the two EI factors as zero, and the correlation between EI self-report and EOIQ as

1.0, model fit was improved over the basic model (CAIC = 538.1 and 561.4, respectively). However, none of these models provided good fit to the data and the method factor provided a better fit than a trait factor, suggesting that the outcome was highly dependent on measurement procedures. Moreover, while retaining separate traits for EI and personality, overall fit would be improved by collapsing these constructs. Taken together, the results suggest substantial overlap between EI and personality measures.

4.3.8 Prediction of Life skills by Emotional Intelligence, Cognitive Abilities and Personality.

In order to determine the extent of variance attributable to each EI measure for each life skills measure, hierarchical regression analyses were conducted. For each regression model a life skill was the dependent variable, with an EI measure entered as an independent variable in the first step and then personality and cognitive abilities entered in together as a second step. A summary of these results

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indicating the R2 (variance explained) contribution of Step 1 and Step 2 for each EI measure and life skill is shown in Table 16.

Table 16: Hierarchical Regression of Life Skills on Emotional Intelligence (Step 1) and NEO Personality Inventory (Revised), Raven’s Advanced Progressive Matrices and Phonetic Word Association Task (Step 2) (Study 1).

TMMS AES MSCEIT Self Report Ability EI EI Factor Factor

Regression Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step TER .01 .33* .00 .35* .13* .24* .01 .34* .01 .34*

SWLS .31* .09* .23* .15 .05* .31* .26* .13* .02* .33*

PFC .27* .09* .22* .11* .03 .27* .24* .10* .00 .29*

EFC .10* .10* .08* .11* .04 .13* .08* .11* .00 .16*

AC .13* .18* .07* .22* .06* .24* .09* .20* .02* .27*

PSI .21* .42* .30* .35* .08* .56* .32* .33* .00 .63*

ATI .18* .39* .05* .52* .11* .52* .07* .50* .06* .55*

NB: TER=Tertiary Entrance Rank; SWLS=Satisfaction With Life Scale; PFC=Problem Focused Coping; EFC=Emotion Focused Coping; AC=Avoidance Coping; PSI=Problem Solving Inventory; ATI=Anxious Thoughts Inventory; TMMS=Trait Meta Mood Scale; AES=Assessing Emotions Scale; MSCEIT=Mayer, Salovey and Caruso Emotional Intelligence Test; Self-report and Ability EI indicate the two EI factors derived from a factor analysis of all EI measures. *p < .001

It is apparent from these results that the EI measures clearly have good predictive validity for the life skills and outcomes assessed when EI is entered into the regression equation first. However, the theoretical issue of importance here is

EI’s incremental criterion validity after controlling for the effects of personality and cognitive abilities. This is best tested by entering personality and cognitive abilities into the regression equation first.

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In this instance, for each regression model a life skill was the dependent variable, with personality, cognitive abilities and EI entered as independent variables in steps (Step 1 = personality and cognitive abilities; Step 2 = EI measure).

For ease of presentation, the full regression statistics for only one regression analysis (TMMS and SWLS) is presented here in Table 17 as an example. This indicates the contribution to the life skill (life satisfaction) that is made by personality and cognitive abilities (Step 1) and the individual variance attributable to the different EI measures (TMMS) represented by the R2 Change (Step 2). A summary table for all the remaining life skills and EI measures to allow more direct comparisons is presented in Table 18. The full details of all these regression analyses are available in Appendix E.

Table 17: Hierarchical Regression of Satisfaction With Life Scale on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Trait Meta Mood Scale subscales (Step 2) (Study 1).

Regression Step Predictor β R2 change Variables

Step 1 Neuroticism -.29*

Extraversion .31*

Openness -.02

Agreeableness .08

Conscientiousness .10

RAPM (Matrices) .16*

PWAT (Phonetics) -.04 .35; F(7, 218) = 16.7; p<.001

Step 2 Attention .10

Clarity .13*

Repair .19* .05; F(3, 215) = 6.0; p<.001

*p<.001

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Table 18: Hierarchical Regression of Life Skills on NEO Personality Inventory (Revised), Raven’s Advanced Progressive Matrices and Phonetic Word Association Task (Step 1) and the Additional R2 (Variance Explained) Contribution of Emotional Intelligence (Step 2) (Study 1).

Regression Step Step 1 Step 2

TMMS AES MSCEIT Self-report EI Ability EI Factor factor Tertiary Entrance Rank .35* .00 .00 .03 .00 .00

Satisfaction With Life Scale .35* .05* .03 .01 .05* .01*

Problem Focused Coping .29* .07* .05* .02 .06* .00

Emotion Focused Coping .16* .05* .01 .01 .03* .00

Avoidance Coping .29* .02 .00 .01 .00 .01

Problem Solving Inventory .63* .00 .02* .01* .01* .00

Anxious Thoughts Inventory .57* .01 .01 .06* .00 .04*

NB: TMMS=Trait Meta Mood Scale; AES=Assessing Emotions Scale; MSCEIT=Mayer, Salovey and Caruso Emotional Intelligence Test; Self-report and Ability EI indicate the two EI factors derived from a factor analysis of all EI measures. *p < .001

These results indicated that, despite low-to-moderate correlations between

EI and most life skills, most of the variance that is attributable to life skills is accounted for by personality and cognitive abilities, with the contributions from the various EI measures being, at most, 7% (problem focused coping and TMMS).

The limited extent of these contributions was consistent across all EI measures and across all life skills. Thus, although these results have found some contributions of

EI over and above personality and cognitive abilities (Hypothesis 10), in general the incremental predictive validity of EI can hardly be considered to have been of appreciable practical potential.

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4.4 Discussion

The results from this study have supported previous research that has suggested that self-report EI exhibits moderate correlations with personality assessments, but low correlations with cognitive abilities. Conversely, ability EI was found to be reasonably distinct from both cognitive abilities and personality, although these correlations were higher for cognitive abilities than they were for personality. Further supporting previous findings of the relationships between EI, cognitive abilities and personality, EI (mainly self-report) was found here to be low-to-moderately correlated to higher Extraversion, Openness, Agreeableness and

Conscientiousness and with lower Neuroticism. However, although it is appreciated that the correlations between cognitive abilities were generally higher for ability EI than they were for self-report EI, contrary to some previous findings a differential association between EI and verbal abilities and abstract reasoning abilities was not observed when the correlations between the two cognitive abilities were compared on a per EI subscale basis. That is the magnitude of correlations for verbal ability and abstract reasoning ability were relatively similar, which is contrary to other studies that have tended to find EI to be more correlated to verbal abilities than to abstract reasoning ability.

It should, however, be noted that the scoring of the MSCEIT was on the basis of consensus norms generated from the ratings of individuals from another country (North America). The validity of using these norms on the current

(Australian) population can therefore not be guaranteed. Nonetheless, consensus scores calculated directly from these data correlated rs = .64, .67, .64 and .68 for

Perceiving, Using, Understanding and Management branch scores, as provided by

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MultiHealth Systems International (Burns, Bastian & Nettelbeck, in press). These moderately strong correlations therefore suggest some confidence in current data.

In relation to the life skills assessed in this study, all the life skills were more highly correlated to the two self-report EI measures than they were to the ability EI measure. These results indicated that higher EI is low-to-moderately correlated with higher life satisfaction, problem focused coping, emotion focused coping and perceived problem solving ability, as well as with lower avoidance coping and anxiety, although the correlations between EI and academic achievement were generally insignificant. These results provide support for previous research on life satisfaction, coping and anxiety (see Sections 3.2.1, 3.3.1 and 3.5.1). Additionally, although insignificant correlations were found between EI measures and academic achievement, this was not entirely inconsistent with previous findings because different studies have differentially found moderate or non-significant correlations.

Problem solving has not been extensively examined in relation to EI, but the results here were consistent with theoretical expectations.

However, despite these results, hierarchical regression analyses that took into account the effects of personality and cognitive abilities when evaluating the extent to which EI is able to predict these life skills, found that the incremental predictive validity of EI for the life skills was minimal (7% or less). These results were apparent regardless of the EI measure involved and were similar across all life skills.

It is possible that the narrow age and intellectual range assessed here

(primarily 18 – 20 year-old current or former University students) may have affected the results. As such, it is possible that EI is a ‘threshold’ variable, of relevance to a less highly selected sample than that assessed in this study. More

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variability in age and intellectual range may provide a better indication of the predictive nature of EI. An investigation that included these considerations would also allow an evaluation of any age-related changes in EI. A follow-up study, examining the same variables and using the same measures for comparative purposes, was therefore conducted on an older (40 – 68 year old) wider community sample. This study is described in Chapter 5.

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CHAPTER 5: STUDY 2 – OLDER COMMUNITY SAMPLE9

Results in the first study (see Chapter 4) may have been due to the relatively narrow age and intellectual ranges of those participants. This second study, therefore, attempted to replicate the findings using the same measures and procedures, but by sampling from the wider community to test the veracity of these results. Specific attempts were made to obtain a more diverse population, in terms of age and intelligence, than that sampled in the first study. An older sample was also chosen in order to determine whether there were any age differences in EI and its predictive validity with age.

5.1 Hypotheses

The same hypotheses tested in Study 1 (see Section 4.1) were again examined here, with the exception of the hypotheses concerning academic achievement as this was not relevant in this sample and the addition of one hypothesis that investigated the possibility of age related changes in EI.

These hypotheses were as follows:

1. Based on previous research that has suggested that females are more

sensitive to emotional considerations than males and findings that females

typically score higher than males on measures of EI (see Section 3.6),

9 A version of these results and those from Chapter 4 are presented in a forthcoming book chapter [Burns, N., Bastian, V. A., & Nettelbeck, T. (in press) Emotional intelligence: More than personality and cognitive ability? Science of Emotional Intelligence: Known and Unknowns. R. D. Roberts, M. Zeidner & G. Matthews (Eds.)]. 200

females will have significantly higher EI scores than males on all EI

measures (TMMS, AES and MSCEIT).

2. Based on previous research that has suggested that self-report EI measures

and personality share some degree of method and/or trait variance (see

Section 2.4), self-report EI measures (the TMMS and the AES) will be

more highly correlated with personality than with cognitive abilities.

Additionally, given that ability EI is conceptualised as an ‘intelligence’ and

all intelligences should be theoretically related (see Sections 2.3.1 and 2.4),

ability EI (the MSCEIT) will be more highly correlated with cognitive

abilities than with personality.

3. Consistent with previous research (see Section 2.4) it is predicted that

higher EI will be related to higher Extraversion, Openness,

Agreeableness, and Conscientiousness and with lower Neuroticism.

4. Based on previous research (see Section 2.4), it is expected that EI will be

more highly correlated with verbal ability (Phonetic Word Association

Task) than with abstract reasoning (Raven’s Advanced Progressive

Matrices).

5. Based on previous research (see Sections 3.2.1), higher EI will be correlated

with higher life satisfaction.

6. Based on previous research (see Sections 3.3.1), higher EI will be correlated

with better coping ability.

7. Given the positive relationship between coping and problem-solving ability

(see Sections 3.3 and 3.4) and the corresponding positive relationship

between coping and EI (see Section 3.3.1), higher EI will be correlated with

higher perceived problem solving ability.

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8. Given the negative relationship between coping and anxiety (see

Sections 3.3 and 3.5) and the corresponding positive relationship between

coping and EI (see Section 3.3.1), higher EI will be correlated with lower

anxiety.

9. Considering EI should show significant effects independent of other

constructs, EI will account for substantial variance in life skills, after

controlling for the effects of cognitive abilities and personality.

5.2 Method

5.2.1 Participants

Participants were 212 individuals (61 males; 151 females) ranging in age from 40 - 68 years (M = 51.6; SD = 7.35). The lower limit of 40 years was set in order to provide an adequate age comparison between the predominantly 18 – 20 year old participants sampled in the first study (see Chapter 4).

All participants were members of the wider community who volunteered to participate in the study for their own interest. As an incentive, all participants who completed the study were entered into a draw to win one of 15 double movie passes, which was drawn at the completion of the data collection.

Of the participants in this sample, 23.1% had a secondary school education,

17.5% had completed some studies at university, 36.8% had completed undergraduate degrees and 22.2% also had postgraduate degree qualifications.

Approximately half of the participants in the sample were married (48.6%), while

7.1% were in defacto relationships, 2.9% were divorced or separated, 1.9% were widowed and 12.3% were single.

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5.2.2 Materials

Participants completed the same battery of measures (TMMS, AES, MSCEIT,

RAPM, PWAT, NEO PI-R, SWLS, COPE, PSI and ATI) that were utilised in the first study. Specific details about the measures involved are available in Chapter 4

(Section 4.2.2).

Demographic information (see Appendix A) for each participant was also obtained, with the exception of TER because this system of academic measurement was not applicable to these older participants.

5.2.3 Procedure

Participants were recruited via advertisements for a study on “Emotional

Intelligence”. Advertisements requesting participants were placed in a number of local newspapers, ‘Neighbourhood Watch’ newsletters and other Council newsletters that are regularly delivered to surburban homes. Requests for volunteers were also published in a University of Adelaide newspaper available to members of the wider University community and relevant information was circulated in an email newsletter delivered to staff and students at the University of

Adelaide.

To reduce inconvenience that these older participants (most of whom worked full-time and/or had children) may have had when attending a 3-hour session for testing, participants were given the option of attending one such session or having all non-timed paper and pencil measures posted to them. Most participants (N = 209) elected to have the measures posted to them. This was done on the understanding that they would attend a testing session (of approximately 1

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½ hour s duration at the University at a later date). This process resulted in some degree of participant attrition (N = 24) (not included in the participant statistics because the specific demographic details are not known).

Each participant was posted out an envelope that contained an information sheet (Appendix B), a consent form (Appendix C) and the response sheets for all non-timed paper and pencil tests, which were coded by participant number.

Participants were also provided with written instructions (Appendix F) about how to complete the tests and they were notified that they were free to contact the researcher at any time if there were any difficulties. Measures that were not posted to part icipants were the two cognitive ability measures (PWAT and RAPM) because these were timed and the MSCEIT, which required computer administration. Participants were asked to contact the researcher after all measures had been completed so that a testing session to complete the remaining tasks could be arranged. Participants were contacted by the researcher if no contact had been made approximately 2 - 4 weeks after the tests had been posted to determine if there were any problems and to arrange the final testing session.

Testing for the remaining tasks was conducted at the University either individually or in groups of up to four participants. A small number of participants

(N = 9) had the second testing session conducted in their own home, mainly due to health/transportation issues that prevented them from attending the University.

Participants were administered the two timed tasks as a group (PWAT =

10 minutes; RAPM = 40 minutes). The PWAT was presented first, followed by the

RAPM as per the procedure detailed in Section 4.2.3. Before commencing the two timed tasks, participants were instructed that if they finished early they could continue with the MSCEIT and were shown what was involved.

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At the testing session, the responses from the non-timed tasks were checked for completeness. All measures were hand scored and then rechecked for accuracy.

5.3 Results

5.3.1 Descriptive Statistics

Descriptive statistics for all measures in this study are presented in Table 19.

Table 19: Descriptive Statistics for the Measures Assessed in Study 2.

Measure Variable Na Range Mean±SD Reliabilityb

Trait Meta Mood Scale Attention 211 20-67 49.4±7.4 .84

Clarity 211 24-58 42.5±6.3 .73

Repair 211 8-30 23.4±3.8 .83

Assessing Emotions Scale 210 88-161 126.7±12.7 .85

Mayer, Salovey and Caruso Perception 212 43-123 102.3±15.9 .92

Emotional Intelligence Test Utilisation 212 44-125 102.4±12.7 .66

Understanding 212 51-124 105.8±11.3 .48

Management 212 61-122 103.3±11.2 .53

Total 212 58-124 103.7±14.2 .83

Raven’s Advanced Progressive Matrices 212 0-34 18.9±6.7 -

Phonetic Word Association Task 210 9-50 42.5±7.3 -

NEO Personality Inventory Neuroticism 211 11-157 79.7±26.1 .89 (Revised) Extraversion 211 58-158 110.9±20.1 .76

Openness 211 72-172 129.4±18.9 .77

Agreeableness 211 85-173 128.5±16.1 .72

Conscientiousness 211 63-170 121.4±21.6 .96

(continued next page)

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Measure Variable Na Range Mean±SD Reliabilityb

Satisfaction With Life Scale 208 5-37 23.5±7.3 .88

The Cope Active Coping 205 6-16 12.5±2.3 .48

Planning 205 7-17 13.1±2.4 .07

Instrumental Social Support 205 4-16 11.4±2.9 .56

Emotional Social Support 205 4-16 11.3±3.3 .54

Suppression of Activities 205 4-15 10.2±2.2 .26

Turning to Religion 205 4-16 7.1±4.2 .83

Positive Reinterpretation 205 4-16 12.3±2.5 .78

Restraint Coping 205 5-16 10.4±2.4 .70

Acceptance 205 6-16 11.4±2.4 .69

Focus/Venting of Emotions 205 4-16 9.8±2.9 .81

Denial 205 4-11 5.3±1.7 .54

Mental Disengagement 205 4-14 8.4±2.2 .39

Behavioural Disengagement 205 4-14 6.0±2.0 .56

Alcohol/Drug Use 205 4-16 5.4±2.3 .91

Humour 205 4-16 8.6±3.1 .90

Problem Solving Inventory Problem Solving Confidence 205 11-55 25.6±8.1 .88 Approach Avoidance Style 205 17-75 40.8±10.9 .86

Personal Control 205 5-27 15.2±4.9 .73

Total 205 33-143 81.7±20.3 .91

Anxious Thoughts Inventory (Total) 204 22-79 43.4±12.0 .92

a = Variable Ns are recorded due to some participants providing incomplete data sets. b = Cronbach’s alpha for internal consistency.

5.3.2 Factor Analysis of Measures

To confirm the factor structure of the measures used in this study principal axis factor analyses with a varimax rotation of extracted factors were performed on all measures with defined subscales. Factors were identified according to the number of eigenvalues over unity, which was then confirmed by examination of the scree plot. A maximum likelihood rotation factor analysis, which restricted the number of factors extracted to the number proposed, confirmed this factor 206

structure. The goodness of fit statistic and the rotated factor matrix was then examined to determine the adequacy of this structure.

5.3.2.1 Trait Meta Mood Scale

A three or four factor solution was found to be adequate for the TMMS.

However, a maximum likelihood extraction of the TMMS with three or four factors, along with visual inspection of the rotated factor matrix, determined that the four- factor solution [χ2(321) = 482.3; p < .001] (42.89% of explained variance) provided a slightly better ‘fit’ than the three-factor solution [χ2(348) = 632.5; p < .001] (39.08%), although this distinction was considered relatively minimal. Because the three- factor solution was more consistent with the developers of the measure (who identified three factors: Attention, Clarity and Repair of emotions) and because these factors were used in Study 1 it was decided to retain this solution for more useful comparisons.

5.3.2.2 Assessing Emotions Scale

Neither a one [χ2(495) = 1126.12; p < .001] (23.92%) or a four factor

[χ2(402) = 703.09; p < .001] (35.31%) solution for the AES was a particularly good fit, but a one factor solution was retained here to be consistent with the developers of this measure and the treatment of Study 1.

5.3.2.3 Mayer, Salovey and Caruso Emotional Intelligence Test

Assessment of the MSCEIT was via the internet with responses sent directly to the publisher for scoring. Responses from individual items were therefore not available for factor analysis.

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5.3.2.4 NEO Personality Inventory (Revised)

Confirming the factor structure identified by the developers of the NEO PI-

R (Neuroticism, Extraversion, Openness, Agreeableness and Conscientiousness), a five- factor solution [χ2(295) = 564.7; p < .001] (53.69%) was found for the NEO PI-R in this study.

5.3.2.5 Satisfaction With Life Scale

A distinct one-factor solution [χ2(5) = 18.8; p < .001] (61.38%), consistent with that identified by the developers of the SWLS, was found in this study.

5.3.2.6 The COPE

Inconsistent with the developers of the COPE, who identified 15 factors, a nine factor solution [χ2(1318) = 1804.9; p < .001] (49.43%) These nine factors appeared to represent: Emotion Focused Coping, Active Coping, Turning to Religion,

Humour, Alcohol/Drug Use, Focus on/Venting of Emotions, Acceptance, Planning and

Behavioural Disengagement.

5.3.2.7 Problem Solving Inventory

A three or four factor solution was found to be potentially adequate for the

PSI. Visual inspection of the rotated factor matrix after a maximum likelihood extraction of both three and four factors determined that the four-factor solution

[χ2(374) = 620.7; p < .001] (44.12%), was a slightly better ‘fit’ than the three-factor solution [χ2(403) = 754.4; p < .001] (40.38%), although there was not a substantial difference. As such, it was decided to retain the three-factor solution because this was consistent with the developers of the test who identified Problem Solving

Confidence, Approach Avoidance Style and Personal Control factors.

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5.3.2.8 Anxious Thoughts Inventory

A three-factor solution [χ2(168) = 367.7; p < 001] (50.85%) was found in relation to the ATI, which is consistent with the three factors (Social Worry, Health

Worry and Meta-worry) identified by the developer for this measure.

5.3.3 Factor Analysis of Emotional Intelligence variables

Based on findings in Study 1 (see Section 4.3.3), a principal axis factor analysis with varimax rotation attempted to determine whether the EI subscale scores in this study could be similarly reduced to two EI factors (“self-report EI” and “ability EI” factors) (39.08%). This was confirmed in this study, with the subscales of the two self-report EI measures (TMMS and AES) loading solely on the self-report EI factor and the ability EI measure (MSCEIT) loading solely on the ability EI factor.

5.3.4 Reduction of ‘Life Skills’ variables

Consistent with the treatment of variables in Study 1 (see Section 4.3.4), it was decided for ease of interpretation to reduce the number of life skills variables analysed by only using the total score to represent each measure. Furthermore, as with Study 1 attempts were also made to generate a three-factor solution of coping, representative of the common theoretical division within coping research (problem focused coping, emotion focused coping and avoidance coping). This was achieved using a maximum likelihood extraction method of three factors with a varimax rotation on the 15 developer generated subscales. A varimax rotation was chosen in

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this instance after a factor analyses using a direct oblimin indicated that the extracted factors were only minimally correlated.

Using this method, the three obtained coping factors were found to be representative of problem focused, emotion focused and avoidance coping. This solution was found to be similar to that obtained for Study 1 (Problem focused coping: Active, Planning, Suppression of Activities, Positive Reinterpretation and

Growth, Restraint Coping, Acceptance and Humour; Emotion focused coping:

Instrumental Social Support, Emotional Social Support and Focus on/Venting of

Emotions; Avoidance coping: Denial, Mental Disengagement, Behavioural

Disengagement and Alcohol/Drug Use), with only the Turning to Religion subscale not included in these factors.

This resulted in six life skills components (i.e., life satisfaction, problem focused coping, emotion focused coping, avoidance coping ability, perceived problem solving ability and anxiety). Results from analyses using subscales defined by test developers that were utilised in this study but not presented in the body of the thesis (i.e., the COPE and the PSI), are presented in the attached Appendices

(Appendix G).

5.3.5 Gender differences

5.3.5.1 Gender Differences in Emotional Intelligence

Although gender differences were not found in the first study (see

Section 4.3.5.1), it is possible that this was due to the relatively narrow age and intellectual range of the sample involved. It was thus expected that based on previous literature (see Section 3.6) gender differences favouring females would be found in relation to EI variables (Hypothesis 1).

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Independent sample t-tests examined the extent of gender differences in this sample, as shown in Table 20. Effect sizes for each evaluation were also calculated, where positive effect sizes indicate that females scored higher on this variable.

Table 20: Gender Differences in Emotional Intelligence (Study 2).

Measure Variable ♀ EI ♂ EI da Independent scores scores Samples t-test

Trait Meta Mood Scale Attention 49.7±7.5 48.4±7.3 + .18 t(209) = 1.17, p = .25

Clarity 42.5±6.5 42.6±5.8 + .01 t(209) = .09, p = .93

Repair 23.5±3.8 23.2±3.9 + .09 t(209) = .61, p = .54

Assessing Emotions Scale 127.8±12.2 124.1±13.7 + .29 t(208) = 1.89, p = .06

Mayer, Salovey and Caruso Perception 104.3±15.3 97.4±16.3 + .44 t(210) = 2.89, p<.001 Emotional Intelligence Test Utilisation 103.4±12.3 100.0±13.4 + .27 t(210) = 1.79, p = .08

Understanding 106.5±10.5 104.1±13.1 + .22 t(210) = 1.43, p = .16

Management 105.1±11.4 98.8±9.4 + .58 t(210) = 3.83, p<.001

a = Cohen’s effect size

The effect sizes obtained here in relation to gender differences in EI indicate that females tended to scored higher than males on all EI variables. However, only two significant gender differences in EI were found in this sample [Perception: t(210) = 2.89, p<.001, d = .44; Management: t(210) = 3.83, p<.001, d = .58].

Nevertheless, both of these differences were found to favour females, which fitted with theoretical expectations and provides some support for Hypothesis 1.

5.3.5.2 Gender Differences in Cognitive Abilities, Personality and Life Skills

The extent of gender differences in the other variables examined in this study were also examined by Independent Samples t-tests. These results are presented in Table 21.

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Table 21: The Gender Differences in Cognitive Abilities, Personality and ‘Life Skills’ (Study 2).

Construct Variable ♀ ♂ da Independent

scores scores Samples t-test

Cognitive Abilities RAPM (Matrices) 18.1±6.6 20.9±6.4 - .43 t(210) = 2.81, p = .01

PWAT (Phonetics) 42.4±7.3 42.8±7.4 - .14 t(208) = .37, p = .71

NEO Personality Neuroticism 79.9±25.6 79.2±27.5 + .03 t(209) = .18, p = .86

Inventory Extraversion 112.6±19.6 106.6±20.7 + .30 t(209) = 1.94, p = .05

Openness 130.0±19.2 128.0±18.2 + .10 t(209) = .68, p = .50

Agreeableness 130.4±15.3 123.8±17.2 + .42 t(209) = 2.73, p = .01

Conscientiousness 121.9±22.6 120.3±19.0 + .07 t(209) = .48, p = .63

Satisfaction With Life Scale 23.7±6.9 22.9±8.3 + .12 t(206) = .75, p = .45

The Cope Problem Focused -.04±0.95 .11±0.85 -.07 t(203) = 1.08, p = .28

Emotion Focused .18±0.94 -.45±0.95 +.28 t(203) = 4.26, p = .00

Avoidance -.01±0.80 .02±0.85 -.01 t(203) = .26, p = .80

Problem Solving Inventory Total 82.0±19.8 80.9±21.7 + .30 t(203) = .33, p = .74

Anxious Thoughts Inventory 43.5±11.7 43.1±12.8 + .03 t(202) = .20, p = .84

a = Cohen’s effect size

Significant gender differences amongst these variables in favour of females

were found only in relation to Extraversion, Agreeableness and the Emotion Focused

coping factor and for males only in relation to RAPM, with no other variables

exhibiting significant gender differences.

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5.3.6 Correlations between Emotional Intelligence, Cognitive Abilities,

Personality and Life Skills

The relationships between EI, cognitive abilities, personality and life skills were examined by correlations. These correlations (available data using pairwise correlations) are shown in Table 22 over the page. The reliability of these data were confirmed by comparing these results with that obtained from listwise correlations, which revealed markedly similar outcomes.

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Table 22: The Correlations Between All M easures and Subscales (Study 2) 2 2 3 1

.46** 1 .46**

2 21

-.30** .01 .51**

.21** .04 1 .27** 1 -.31** -.48** .01 .36** -.48** -.48** -.27** -.06 05 1

17 18 . y -.38** -.38** .05 .04 -.06 -.52** -.08 .02 -.45** -.09 -.21** -.12 -.35** -.08 .79** hts Inventor hts g .04 -.59** -.37** .04 -.59** 6 .08 .01 -.02 .06 .43** 1 1 1 1 .43** .52** .00 .06 1 -.03 1 -.02 .58** .50** .06 .01 1 .19** 1 .17* .20** .36** 1 1 .20** 2 2 .10 3 4 3 .08 .07 5 6 4 7 .06 8 9 5 16 15 14 13 11 12 10 .16* 20 19 6 .19** 7 15 .47** .26** .23** .45** -.01 -.09 .24** .26** .45** .07 .23** .16* -.06 .40** 1 1 .40** 1 -.06 -.03 1 1 .16* 1 1 -.06 .26** .04 .25** .38** 1 .23** .05 .41** .14* .14* .20** .07 .19** -.59** .06 -.01 .45** .45** .17* .02 .10 -.03 -.06 .81** .14* .22** .34** .26** -.01 .88** .81** .25** .02 -.06 .45** .24** .82** .05 .07 .15* .07 .19** -.09 .09 .38** .08 -.45** -.00 -.01 .16* 8 .23** -.04 .04 .76** 9 -.48** .45** .03 .10 .00 10 .05 .07 11 .23** 1 .21** -.64** .00 .19** 12 -.25** .26** .09 13 .03 .01 .13 .37** .03 .55** .04 .56** -.04 -.39** 14 .43** .10 .22** .02 1 .23** -.04 .37** .47** .08 .09 .38**.07 1 .23** 15.28** .16* .13 .32** .42** 16 -.51** .25** .06 .10 .04 .41** .13 17 .17*.01 .07 .04 .35** -.04 .36** .34** .07 .38** .43** 18 -.11 -.11 -.10 .27** .07 .33** -.02 .42** .12 -.12 -.59** -.43** .02 -.13 19 -.13 .17* -.14* 1 -.04 -.08 .45** .57** .02 -.02 .38** -.00 -.14 -.22** .40** 20 .02 .46** .20** -.15* .25** .01 -.02 -.01 -.06 -.10 . -.07 .48** .30** .06 -.35** -.40** 21 .19** .14 .04 -.09 .19**.09 .20** .16* .05 .17*.20** .14* -.17* -.05.19** .03 22 .21** .10 .25** -.03 -.42** .08 23 -.29** .05 -.03 -.39** -.04 -.06 -.31** -.05 -.08 -.518* -.11 -.22** .02 -.30** .54** Table 22: The Correlations Between All Measures and Subscales (Study 2). 2). (Study Subscales and Measures All Between Correlations The 22: Table 7=Understanding; 5=Perception; 6=Utilisation; Total; 4=AES 3=Repair; 2=Clarity; NB: 1=Attention; Progressive Advanced 11=Raven’s Total; EI 10=Ability Total; EI 9=Self-report 8=Management; 15=Openness; 14=Extraversion; 13=Neuroticism; Test; Association Word 12=Phonetic Matrices; focused 19=Problem Life Scale; With Satisfaction 18= 17=Conscientiousness; 16=Agreeableness; Coping;20=Emotion focused Coping; 21=Avoidance Coping; 22=ProblemSolving Inventory; Thou 23=Anxious 214

5.3.6.1 Correlations Between Emotional Intelligence Measures

Moderate correlations were found between the subscales of the TMMS

(average: r = .29) and the MSCEIT (average: r = .24) within each measure.

As expected, the self-report EI measures (TMMS and AES) were moderately-to-highly correlated with each other (Attention: r = .36; Clarity: r = .58;

Repair: r = .52), while both self-report measures had low correlations with the ability EI measure (MSCEIT) (TMMS: ranging between r = .01 and r = .25; AES: ranging between r = .00 and r = .14). These results are consistent with previous findings (see Section 2.3.4 and Section 4.3.6.1).

As in Study 1 (see Section 5.3.7) the dimensionality of EI, cognitive abilities and personality was measured. Using LISREL 8.54 (Jöreskog and Sörbom, 2003), subscales from TMMS and MSCEIT were fitted to a two correlated factors model where TMMS subscales defined a self-report EI factor and MSCEIT subscales defined an ability EI factor. This model provided a poor fit: chi-square(13) = 34.7, p < .001;

RMSEA = .089 (CI90 = .054,.130); CAIC = 178.0. However, as with Study 1, allowing the Understanding and Management subscales of MSCEIT to have secondary loadings on the self-report EI factor produced a good fitting model with all loadings statistically significant: chi-square(11) = 14.9, p = .19; RMSEA = .041 (CI90 = .000,.088);

CAIC = 122.9. A new model including AES loading on the self-report EI factor fit less well and the loading of MSCEIT Understanding on the self-report EI factor was not statistically different from zero. However, dropping this path from the model improved the fit: chi-square(18) = 35.6, p = .007; RMSEA = .068 (CI90 = .034, .100);

CAIC = 150.0. Of note is that the estimated correlation between the two EI factors was only r = .08.

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5.3.6.2 Correlations Between Life Skills

In the section that follows, correlations are reported between various measures of EI and several outcome measures. Although these correlations indicate the rela tionship between these variables, correlations are not able to identify causality.

Amongst the life skills, higher life satisfaction was also found to be low-to- moderately associated with lower anxiety (r = -.48), with higher perceived problem solving ability (r = -.31) and higher problem focused (r = .27) and emotion focused coping (r = .21), but less avoidance coping (r = -.27). As expected, problem focused coping was found to be moderately associated with better perceived problem solving ability (r = -.48) and with less anxiety (r = -.30). Conversely, avoidance coping was found to be moderately correlated with lower perceived problem solving ability (r = .36) and higher anxiety (r = .51). However, the correlations between emotion focused coping and both problem solving ability and anxiety were not statistically significant. Additionally, higher perceived problem solving ability was found to be moderately associated with lower anxiety (r = .46).

5.3.6.3 Correlations Between Emotional Intelligence, Cognitive Abilities and

Personality

Consistent with the results from Study 1 (see Section 4.3.6.3) and supporting

Hypothesis 2, self-report EI measures were found to be moderately correlated with personality (Self-report EI factor: ranging between r = .37 and r = -.59), with the correlations with cognitive abilities being near-zero (Self-report EI factor: RAPM: r = .06; PWAT: r = .02). In contrast, ability EI had near-zero correlations with personality measures (Ability EI factor: ranging between r = -.02 and r = .13), but

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low correlations with cognitive abilities (Ability EI factor: RAPM: r = .14; PWAT: r = .19).

Although the correlations between EI and personality were higher for self- report EI measures than ability EI measures, the general trend from these results

(supporting Hypothesis 3 and previous research, see Sections 2.4 and 4.3.6.3) suggests that higher EI is associated with higher Extraversion, Openness,

Agreeableness and Conscientiousness, but with lower Neuroticism. Furthermore, although ability EI was more highly correlated with cognitive abilities, the correlations between EI and the cognitive abilities were found to be of a relatively similar magnitude on the basis of comparisons of the cognitive abilities for each EI subscale seperately. This is inconsistent with previous research (see Section 2.4), which has tended to suggest that EI assessments are more highly correlated with verbal abilities than they are with abstract reasoning abilities and thus does not support Hypothesis 4. These results, however, were consistent with those found in

Study 1 (see Section 4.3.6.3).

5.3.6.4 Correlations Between Emotional Intelligence, Cognitive Abilities, Personality and Life Skills

The correlations between EI and life skills were found to be higher with respect to the two self-report EI measures than they were for ability EI, with most correlations between the life skills and self-report EI being low-to-moderate, while most in relation to ability EI measures being near zero. Additionally, the correlations between the life skills were found to be higher in relation to personality than they were to cognitive abilities.

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Furthermore, supporting Hypothesis 5 – 8 (and confirming the results found in Study 1, see Section 4.3.6.4), self-report EI was found to be low-to-moderately correla ted with higher life satisfaction (Self-report EI factor: r = .41; Ability EI factor: r = .06), higher problem focused coping (Self-report EI factor: r = .48; Ability EI factor: r = -.07), emotion focused coping (Self-report EI factor: r = .17; Ability EI factor: r = .14), better perceived problem solving ability (Self-report EI factor: r = .31; Ability

EI factor: r = -.06) and with lower avoidance coping (Self-report EI factor: r = -.22;

Ability EI factor: r = -.04) and anxiety (Self-report EI factor: r = -.52; Ability EI factor: r = .02), although these same correlations in relation to ability EI were near-zero and are lower than those obtained for Study 1.

5.3.7 The Dimensionality of Emotional Intelligence, Cognitive Abilities and

Personality

A set of measurement models on the main constructs in this study were examined using LISREL 8.54. First, a proxy measure for IQ was calculated as the average of the z-scores for RAPM and PWAT. Two basic models: a trait model and a method model were then compared. For the trait model, all EI measures defined an EI factor and NEO PI-R measures and IQ defined a second factor (i.e., ‘Big Five plus one’). For the method model, all self-report measures (TMMS, AES, NEO PI-R) defined a self-report factor and all ability measures (MSCEIT and IQ) defined an ability factor. Neither of these models fit the data well but the method model was better fitting than the trait model (CAIC = 581.2 and 673.7, respectively). The next set of models tested defined four factors: EI self-report and EI ability, as above, a factor defined by Neuroticism, Agreeableness, and Conscientiousness (NAC), and a factor defined by Extraversion, Openness and IQ (EOIQ). This final factor is consistent with

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empiri cal finding on intelligence and personality constructs. By allowing secondary loadings of variables across factors for EI, as above, and setting the correlation between the two EI factors as zero and the correlation between EI self-report and

EOIQ as one, the model fit was improved over the basic model (CAIC = 556.5 and

517.9, respectively). The fact that none of these models provided a good fit to the data, together with the outcomes of the analyses, themselves suggests that there is substantial overlap between EI and personality measures. This outcome is tested further by regression analyses (see Section 4.3.7).

5.3.8 Prediction of Life Skills by Emotional Intelligence, Cognitive Abilities and Personality

In order to determine the predictive validity of EI for life skills, hierarchical regression analyses that investigated the amount of variance in each life skills measure attributable to EI were conducted. In order to investigate this issue, regression models that included each life skill as a dependent variable, with each EI measure entered in the first step, followed by personality and cognitive abilities in the second step as independent variables were undertaken. These results are shown in Table 23.

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Table 23: Hierarchical Regression of Life Skills on Emotional Intelligence (Step 1) and NEO Personality Inventory (Revised), Raven’s Advanced Progressive Matrices and Phonetic Word Association Task (Step 2) (Study 2).

TMMS AES MSCEIT Self Report Ability EI EI Factor Factor

Regression Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step 1 Step 2 Step SWLS .17* .14* .12* .17* .04 .27* .17* .13* .01 .29*

PFC .20* .15* .20* .14* .01 .33* .23* .11* .00 .32*

EFC .04* .08* .04* .08* .04 .09* .03* .09* .01 .11*

AC .19* .22* .01 .39* .03 .39* .05* .35* .00 .39*

PSI .17* .24* .17* .26* .03 .40* .19* .21* .00 .40*

ATI .38* .27* .15* .49* .05* .60* .28* .36* .00 .63*

NB: SWLS=Satisfaction With Life Scale; PFC=Problem Focused Coping; EFC=Emotion Focused Coping; AC=Avoidance Coping; PSI=Problem Solving Inventory; ATI=Anxious Thoughts Inventory; TMMS=Trait Meta Mood Scale; AES=Assessing Emotions Scale; MSCEIT=Mayer, Salovey and Caruso Emotional Intelligence Test; Self-report and Ability EI indicate the two EI factors derived from a factor analysis of all EI measures. *p < .001

From these results it can be seen that as in Study 1 (see 4.3.8), EI was found to reasonably predict the life skills when EI was entered into the regression equation first.

However, the incremental criterion validity of EI was of prime importance here. This was investigated by entering personality and cognitive abilities into each regression equation as the first step and the EI measure as the second step. For ease in interpreting the data, only a summary table of results from all EI measures and life skills are included here in Table 24 on the next page. This table indicates the contribution that is made to each life skill by personality and cognitive abilities

(Step 1) and the individual contribution to that life skill made by each EI measure,

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represented by R2 change (Step 2). The full details of all regression analyses are available in Appendix H.

Table 24: Hierarchical Regression of Life Skills on NEO Personality Inventory (Revised), Raven’s Advanced Progressive Matrices and Phonetic Word Association Task (Step 1) and the Additional R2 (Variance Explained) Contribution of Emotional Intelligence (Step 2) (Study 2).

Regression Step Step 1 Step 2

TMMS AES MSCEIT Self-report EI Ability EI Factor factor Satisfaction With Life Scale .29* .02 .00 .02 .00 .01

Problem Focused Coping .30* .03* .02* .02* .02* .00

Emotion Focused Coping .12* .01 .01 .01 .00 .01

Avoidance Coping .39* .02 .01 .03 .01 .00

Problem Solving Inventory .40* .01 .01 .02 .00 .00

Anxious Thoughts Inventory .62* .01 .00 .02 .00 .00

These results indicate that despite low-to-moderate correlations between EI and life skills and a reasonable amount of variance in life skills being attributable to personality, cognitive abilities and EI combined (up to 62%), the individual contribution of EI on the life skills was minimal. It was found that regardless of the

EI measure used, once the effects of personality and cognitive abilities were controlled, the contribution of each EI measure across each life skills was 3% or less. These results are consistent with the findings obtained in Study 1 (see

Section 4.3.8), and are in fact lower than the incremental predictive validity observed there (less than 7%). Thus EI does not appear to demonstrate significant

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incremental validity for the life skills over the contributions of personality and cognitive abilities which therefore does not provide support for Hypothesis 9.

5.3.9 Age-related Differences in Emotional Intelligence

Consistent with the notion that EI as an intelligence increases with age and experience and based on previous research (see Section 3.7) which has suggested that EI increases through middle adulthood, age related differences in EI were examined. Independent samples t-tests investigated whether there were any significant differences in the EI of individuals in this study (older individuals aged

40 – 68, mean age: 51.6 years) when compared to individuals in the first study

(younger individuals aged 16 – 39, mean age: 19.9 years). For comparative purposes changes in the cognitive abilities with age were also investigated. Effect sizes were also calculated, with positive effect sizes indicating that the older individuals scored higher. These results are presented in Table 25.

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Table 25: Age-related Differences in Emotional Intelligence and Cognitive Abilities Between the Two Studies.

Measure Variable Younger Older da Independent EI scores EI scores Samples t-test

Trait Meta Mood Scale Attention 21.6±4.3 23.4±3.8 +.44 t(455) = 4.85, p = .00

Clarity 49.7±6.5 49.4±7.4 -.04 t(455) = 0.45, p = .65

Repair 37.2±6.6 42.5±6.3 +.82 t(455) = 8.83, p = .00

Assessing Emotions Scale 123.8±12.5 126.7±12.7 +.23 t(447) = 2.43, p = .02

Mayer, Salovey and Caruso Perception 102.5±14.7 102.3±15.9 -.01 t(456) = 0.17, p = .87 Emotional Intelligence Test Utilisation 99.5±13.2 102.4±12.7 +.22 t(456) = 2.41, p = .02

Understanding 103.3±11.2 105.8±11.3 +.22 t(456) = 2.04, p = .04

Management 95.5±11.6 103.3±11.2 +.68 t(456) = 7.33, p = .00

Raven’s Advanced Progressive Matrices 23.5±7.0 18.9±6.7 -.79 t(456) = 7.23, p = .00

Phonetic Word Association Task 36.1±9.0 42.5±7.3 +.78 t(454) = 8.27, p = .00

a = Cohen’s effect size

These results indicate that the older individuals scored significantly higher than the younger individuals on almost all EI subscales (with the exception of

TMMS Clarity and MSCEIT Perception), with the resulting effect sizes all being in general quite large. This supports previous research (see Section 3.7) and

Hypothesis 10, suggesting that EI increases with age and experience.

Additionally, younger individuals were found to score significantly higher than older individuals on RAPM, but older individuals were found to score significantly higher than younger individuals on the PWAT.

5.4 Discussion

The results from this study (older community sample) were found to be remarkably similar to those found in Study 1 (younger university sample, see

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Chapter 4) in both magnitude and direction. As before, only a minimal number of significant gender differences in relation to EI (Perception and Management of emotions) were found. However, fitting with theoretical expectations both of these were found to favour females. Additionally, older individuals (40 – 68 year olds) were found to have significantly higher EI than younger individuals (primarily 18 –

20 year olds) on almost all subscales (with only the exception of Clarity and

Perception of emotions). The increases in EI with during middle age are in contrast to the corresponding decline in abstract reasoning ability that was also identified between the two samples, although older individuals were found to score significantly higher on verbal abilities. This suggests that EI and at least some cognitive abilities have different developmental trajectories, although this would need to be confirmed in more depth using longitudinal rather than cross-sectional studies.

The identified disparate relationships between different EI measures and personality and cognitive abilities were also upheld here, confirming the results from previous research and that of Study 1. The relationships between EI and the life skills were also found to be within theoretical expectations and were consistent with the findings obtained in Study 1. Consequently, EI was found to be low-to- moderately and positively correlated with life satisfaction, problem focused coping, emotion focused coping and perceived problem solving ability and negatively with avoidance coping and anxiety. More importantly, as in Study 1, the incremental validity of EI when the effects of cognitive abilities and personality were taken into account were similarly minimal (less than 3%), which was even lower than that observed in the younger sample (Study 1).

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That such findings were replicated on two such diverse populations provides some standing to these obtained results and perhaps raises some implications for the importance of the EI construct. A more comprehensive discussion of the results from these two studies and the future research directions generated by these findings is presented in Chapter 6.

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CHAPTER 6: CONCLUSIONS

This thesis has examined the relationship between various measures of EI, personality, cognitive abilities and a number of ‘life skills’ (academic achievement, life satisfaction, coping ability, problem solving perceptions and anxiety), as well as the relationship between EI and gender and age. In particular, attempts were made to determine whether EI exhibited significant predictive validity for these life skills over and above the contributions of personality and cognitive abilities. This was investigated in two different samples; a group of university students (N = 246, ages

16 – 39 years, Mean = 19.9±4.2) and a sample of older adults drawn from the community (N = 212, ages = 40 – 68 years, Mean = 51.6±7.35), which allowed for an evaluation of differences in EI with age.

The magnitude and direction of results across these two studies were found to be remarkably similar and, given the diverse populations sampled, this outcome suggests some degree of generalisability of the results.

A discussion of the results obtained from these two studies, together with an evaluation of the contribution that these results make to the rapidly growing knowledge about EI, follows. Limitations of the two studies and possible future directions that these results have generated will also be discussed.

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6.1 Discussion of Results from Study 1 (University Sample) and Study 2

(Older Community Sample)

6.1.1 The Relationship Between Emotional Intelligence Measures

In both studies, the two self-report EI measures (TMMS and AES) were found to be moderately correlated with each other, but the correlations between these self-report EI measures and ability EI (MSCEIT) were generally low. These findings are consistent with the results in relation to various self-report and ability

EI measures from a number of difference studies (e.g., Brackett & Mayer, 2003;

Lopes et al., 2003; O’Connor & Little, 2003; Schutte et al., 1998; Warwick &

Nettelbeck, 2004; see Section 2.3.4).

That self-report EI measures have consistently been found to be moderately- to-highly correlated with each other, suggests that these self-report EI measures assess similar constructs. But that self-report EI measures typically exhibit low and statistically non-significant correlations with ability EI measures, suggests that self- report and ability EI measures assess something different to ability EI measures.

Additionally, if we assume for the moment that ability EI measures like MSCEIT are tapping aspects of EI, then this finding further suggests that, as with other forms of abilities, individuals have unreliable insight (as expressed by self-report EI measures) into their actual EI ability (putatively assessed by ability EI measures).

Petrides and Furnham (2001) have argued that it is unsurprising that self- report and ability EI will be so minimally correlated given the differences in the way in which each model associated with these measures is defined. This is a reasonable proposition when comparing tests like the EQ-i (Bar-On, 1997) with, for example, the MSCEIT, which derive from very different conceptions of EI, but is

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less convincing for the tests included in this thesis (TMMS, AES, MSCEIT), all of which were based on Salovey and Mayer’s conceptions of EI as an ability (even though the TMMS and SES are self-report instruments).

Petrides and Furnham have nevertheless suggested that different measurement approaches, even if they are operationalised from the same model, will almost certainly produce different results because each form of measurement assesses a different construct (i.e., self-report EI assesses typical behavioural tendencies, whereas ability EI measures assess actual abilities). Petrides and

Furnham (2001) have consequently suggested that the EI concept should be regarded as two different EI constructs (ability and trait), to reflect such differences

(see Section 2.9), which is an important point and one for which there is abundant supporting evidence. Self-reported behavioural tendencies are not the same as actual behaviours and these different constructs have frequently been demonstrated to bear little relationships to one another (see Section 2.3.2).

However, ultimately, whether EI is usefully conceived and operationalised as a single construct or in different forms will depend on whether such forms have incremental validity for significant life outcomes, beyond what is predicted by measures of personality and of cognitive abilities. This concern has been the central core to this thesis.

6.1.2 Gender Differences in Emotional Intelligence

Based on emotions research (e.g., Barrett et al., 2000) and previous research in EI (e.g., Brackett, et al., 2004; Brackett et al., 2005; Charbonneau & Nicol, 2002;

Ciarrochi, et al., 2001a; Hunt & Evans, 2004; Mayer, et al., 1999; Schutte, et al., 1998, see Section 3.6), it was expected that females would score significantly higher than

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males on all measures of EI. However, contrary to these results, relatively few gender differences were apparent in both studies.

In the University sample, only one significant (albeit small) gender differe nce was found (TMMS: Attention), which, as expected, favoured females.

Given that few gender differences were also found in this sample in respect to the non-EI variables assessed, it is possible that the failure to find gender differences was due to uniformity in age and cognitive abilities within this sample. However, a similar result was found for the older community sample (with ages between 40 –

68 years), which displayed a reasonable range of cognitive abilities (see

Section 5.2.1). In this sample, only two gender differences (MSCEIT: Perception and

Management), both favouring females, were found.

Although these results were inconsistent with some research in the area, it should be noted that not all studies of EI have found significant gender differences for all EI components (e.g., Kafetsios, 2004; Reiff et al., 2001; Saklofske et al., 2003).

It is also possible that there are other variables at play here, such as the possibility that males are less motivated than females to do well on EI measures. This could be a factor in Study 1 as the motivation for participation in this sample was

Psychology 1 course credit. This is, however, less convincing for the participants of

Study 2 as participation was voluntary and completely self-motivated. This matter of whether females score higher than males on measures of EI should therefore be considered to be unresolved at this time.

6.1.3 Age Related Changes in Emotional Intelligence

Previous research (e.g., Carstensen et al., 2000; Turk-Charles et al., 2003;

Carstensen 1993, 1995) has suggested that emotional maturation is associated with

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increasing age in mid-to-late adulthood. A number of researchers (e.g., Bar-On,

2000; Derksen et al., 2002; Kafetsios, 2004; Mayer et al., 1999) have demonstrated that EI increases with age and experience from late adolescence to mid-adulthood and EI has thus been ostensibly related to some degree of ‘wisdom’ (see

Section 3.7). It was therefore, expected that there would be significant differences in

EI with age, favouring older individuals (i.e., individuals from the Study 2 - older community sample, as compared to individuals from the Study 1 - University sample).

Consistent with these expectations, the older individuals (aged 40 - 68, mean age = 51.6 years ) were found to score significantly higher than the younger individuals (aged 16 – 39, mean age = 19.9 years) on all EI subscales with the exception of TMMS Clarity and MSCEIT Perception in which no significant differences were found. This is in contrast with the corresponding decline in abstract reasoning ability that was also found in this population, which replicates the results of Derksen et al. (2002) who also found increases in EI, but lowered

GAMA scores with age.

Although increases in ability with age and experience are important criteria for defining EI as an intelligence, this finding is somewhat interesting considering emotional changes across adulthood are contrary to most other forms of cognitive performance. For instance, the trajectory of childhood cognitive development suggests little improvement beyond 18 – 19 years. In fact, it was also observed here that there was a significant corresponding decline in abstract reasoning ability, but a significant increase in verbal ability with age. This raises some questions that have not yet been answered by current research. Why then, might emotional development be different from other forms of abilities? Is such development

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consistent with the way in which intelligences have tended to be regarded? At what point does EI begin to increase and at what point, if any, does it begin to decline? Does this mean that emotional development and maturation is a ‘learnt’ behaviour? In which case does this mean that it may be possible for EI (or at least some components of EI) to be ‘taught’?

6.1.4 The Relationship Between Emotional Intelligence, Cognitive Abilities and Personality

In both studies, self-report EI measures were found to be low-to-moderately and positively correlated with Extraversion, Openness, Agreeableness and

Conscientiousness and negatively with Neuroticism (as assessed by the NEO

Personality Inventory). Although the correlations between ability EI and personality were generally low or not statistically significant, these were found to be in the same direction as that for self-report EI. This pattern of association with personality is consistent with that from previous studies (e.g., Brackett & Mayer,

2003; Saklofske et al., 2003; Schulte et al., 2004; Schutte et al., 1998; see Section 2.4).

Additionally, although self-report and ability EI measures were differentially associated with cognitive abilities with the correlations being higher for ability EI generally, it was observed that the correlations between EI and the two cognitive abilities (verbal ability and abstract reasoning ability) were of a similar magnitude when comparisons were made on the basis of each EI subscale individually. This is in contrast to the findings from other studies that have tended to find verbal abilities to be moderately correlated with EI (e.g., Lopes et al., 2003;

Mayer et al., 1999), whereas the correlations between EI and abstract reasoning abilities have been generally low and near zero (e.g., Chan, 2003; Ciarrochi et al.,

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2000a; Warwick & Nettelbeck, 2004; see Section 2.4). It should, however, be noted that in these studies, both types of cognitive abilities were not assessed on the same population and thus, previously, this identified pattern of differential associations with cognitive abilities has not been directly comparable. Moreover, it is acknowledged that the verbal abilities assessment used in the two studies outlined in this thesis (PWAT) was not particularly comprehensive, nor is it widely used, although this at least adds to the available literature on the subject.

Furthermore, the results from both studies support previous findings (see

Section 2.4) that have consistently found personality to be more closely related to self-report EI measures (generally moderate correlations) than ability EI measures

(generally low). In contrast, cognitive abilities measures were found to be more closely related to ability EI (low-to-moderate correlations) than to self-report EI measures (generally near zero).

This differential association is perhaps due to the nature of the theoretical constructs underlying each EI theory on which the different EI measurements have been based. For instance, ability EI has been conceptualised as an ‘intelligence’ and thus, on the presumption that intelligences are moderately correlated with each other (see Section 2.3.1), ability EI should theoretically be related to other forms of intelligence. Mixed EI models, from which self-report assessment of EI are derived, however, typically include personality, attitudes and dispositions as components and thus it could be expected that such measures will be more highly correlated with personality (see Section 2.3.2).

However, that the AES (a self-report EI instrument derived from Salovey and Mayer’s 1990 ability EI model) was found to be moderately correlated to personality, but only lowly correlated with cognitive abilities (as is typical of the

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pattern of association with self-report EI measures), is perhaps indicative of the influence of test construction of the various EI measures on these associations. Self- report EI measures, ask individuals to rate themselves on a frequency of occurrence basis, similar to the way in which personality is assessed. In contrast, ability EI measures are constructed in a similar fashion to traditional cognitive abilities measures in which there are defined ‘correct’ and ‘incorrect’ answers. Thus, it is likely that part of the association between self-report EI and personality measures

(and indeed ability EI and cognitive abilities) is due to the effects of method variance. Ability EI measures (e.g., the MEIS and the MSCEIT), however, have been widely promoted as being largely independent from other constructs such as personality (e.g., Mayer, 1999; Mayer et al. 2000d), which was also found to be the case in these studies. This phenomenon provides increasing support for Petrides and Furnham’s proposal (see Section 2.9) that the EI construct would be best served by being divided according to the type of measurement approach, rather than the model from which it is derived. It is, however, recognised that ultimately this is only useful if the EI measures themselves have been shown to be assessing what they are claimed to measure.

That there is such a high degree of overlap between self-report EI measures and personality raises questions about the redundancy of these types of measures given that personality has already been heavily researched within psychology. To reduce the personality effects on self-report EI measures, it seems likely that how the models of EI that derive these measures are conceptualised, as well as how EI is assessed on the basis of such models, should be reviewed (as discussed further in

Section 6.4). Alternatively, other forms of EI assessment (such as observer-rater measures) might be used in conjunction with self-report EI measures to assist in

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interpretation. At the very least, it would seem that the effects of personality should be considered when interpreting the predictive nature of (at least) self- report EI assessments.

6.1.5 Relationship Between EI and Life Skills

It was expected that higher EI would be associated with higher academic achievement, life satisfaction, more positive coping behaviours and higher self- appraised problem solving ability, but with lower negative coping behaviours and anxiety. Consistent with these theoretical predictions, in both studies, EI was found to be low-to-moderately and positively associated with life satisfaction, problem focused coping, emotion focused coping and perceived problem solving ability and negatively with avoidance coping and anxiety. However, correlations between EI and academic achievement (only applicable for the University sample) were found to be primarily non significant. Of note, the majority of correlations between the life skills were found to be much higher for the self-report EI measures (generally low- to-moderate) than they were for ability EI (generally near zero). Plausibly, this outcome could have been due to some degree of common method variance, arising from the reliance on self-report evaluations that characterised both the self-report

EI measures and the measures of the life skills.

In both studies, EI (principally self-report) was found to be low-to- moderately and positively correlated with higher life satisfaction (Study 1: Self- report EI factor: r = .51; Ability EI factor: r = .14; Study 2: Self-report EI factor: r = .41;

Ability EI factor: r = .06). These results are consistent with previous research (e.g.,

Brackett & Mayer, 2003; Ciarrochi et al., 2000a; Mayer et al., 1999; Saklofske et al.,

2003; Schutte et al., 2000), which has found low-to-high, positive correlations

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between both self-report and ability EI measures and various life satisfaction assessments (see Section 3.2.1).

The results obtained in both studies for a modified version of the COPE, in which three coping factors were created, indicated that higher EI (principally self- report) is low-to-moderately correlated to higher problem focused coping (Study 1:

Self-report EI factor: r = .40; Ability EI factor: r = -.04; Study 2: Self-report EI factor: r = .48; Ability EI factor: r = -.07) and emotion focused coping (Study 1: Self-report EI factor: r = .22; Ability EI factor: r = .04 ; Study 2: Self-report EI factor: r = .17; Ability EI factor: r = .14) and with less avoidance coping (Study 1: Self-report EI factor: r = -.27;

Ability EI factor: r = -.13; Study 2: Self-report EI factor: r = -.22; Ability EI factor: r = -

.04). This supports previous preliminary research in this area (e.g., Hunt & Evans,

2004; Salovey et al., 2002) that has suggested that higher EI is low-to-moderately correlated with more positive coping (essentially problem focused coping and emotion focused coping) and less passive coping (avoidance coping) (see

Section 3.3.1).

In both studies, higher self-report EI was also found to be low-to- moderately correlated with higher perceived problem solving ability (Study 1: Self- report EI factor: r = -.55; Ability EI factor: r = -.04; Study 2: Self-report EI factor: r = -.31;

Ability EI factor: r = -.06). These results are new because previous research in EI has not included this outcome variable, but these findings are consistent with theoretical predictions (see Section 3.4.1).

The findings obtained from both studies also suggest that EI is low-to- moderately correlated with lower anxiety (Study 1: Self-report EI factor: r = -.25;

Ability EI factor: r = -.24; Study 2: Self-report EI factor: r = -.52; Ability EI factor: r = .02). These findings were also consistent with theoretical predictions and they

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support previous preliminary research using a number of different EI and anxiety measures (e.g., Ciarrochi et al., 2000a; Salovey et al., 2002; Tsaousis & Nikolaou,

2005; see Section 3.5.1).

However, contrary to prediction, both self-report and ability EI were found to be only weakly correlated to self-reported TER scores (assessed in Study 1; Self- report EI factor: r = .07; Ability EI factor: r = .10), with all correlations with the exception of the Understanding (r = .33) and Management (r = .17) subscales of the

MSCEIT being near zero. Low-to-moderate, positive correlations between EI and

TER were expected based on previous research (e.g., Lopes et al., 2003; O’Connor &

Little, 2003; Parker et al., 2004; Schutte et al., 1998; see Section 3.1) that has found low-to-moderate correlations between EI measures and academic achievement

(typically GPA or SAT scores).

However, not all studies on EI and academic ability have reported significant correlations between EI and academic achievement (e.g., Brackett &

Mayer, 2003; Newsome et al., 2003). Furthermore, it should be noted that the results obtained by Parker et al. (2004) in relation to high school and college GPA were not consistent. High school GPA (equivalent to the high school TER analysed here), which was obtained from college students who had graduated from school two years previously showed entirely non-significant correlations with EI (EQ-i; rs ranging between -.01 and -.09). Some correlations for these same students, but using their college GPA were higher (rs ranged between .03 and .37).

Given that results testing a relation between EI and academic achievement have been mixed, the finding here for the University sample was not without precedent. This leaves an outcome where, to date, about half of the studies investigating this question have obtained promising results but half have not.

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Present results have, however, suggested that individuals with higher EI, who are better able to perceive and use their emotions to solve daily problems and then regulate their emotions, are better able to cope with life’s difficulties and manage their distress. This in turn, appears to result in reduced anxiety and higher life satisfaction. These findings have therefore been consistent with the prediction that higher EI will confer a significant life advantage. However, as emphasised throughout this thesis, if EI is to win widespread acceptance as a practical assessment tool, then its incremental predictive validity for important life outcomes, beyond the variance explained by personality variables and cognitive abilities, will need to be established. This important issue is discussed in the next section.

6.1.6 Incremental Predictive Validity of Emotional Intelligence

The view that EI offers a significant life advantage was further evaluated in terms of the extent to which EI predicts such outcomes, over and above the influence of other predictors, such as different aspects of personality and cognitive abilities. Hierarchical regression analyses demonstrated that the incremental predictive validity of EI for the life skills measured in these studies, after controlling for the effects of personality and cognitive abilities, was minimal, despite the low-to-moderate bivariate correlations between EI and these life skills and the reasonable predictive validity of EI without controlling for any variables.

For the University sample, the relative contribution of all measures of EI for all life skills was found to be 7% shared variance or less. Similar results were also obtained for the older community sample, with the evidence for the incremental validity of EI being about 3% at most. Of interest was the finding that the

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magnitude of these results was similar across each life skill with little observable difference between the three EI measures.

Some previous studies that have attempted to control for the influence of either personality or cognitive abilities (e.g., Brackett & Mayer, 2003; Gannon &

Ranzijn, 2005; Saklofske et al., 2003) have similarly demonstrated that the influence of EI is substantially reduced when cognitive abilities or personality (in particular) are taken into account, providing support for the results obtained here. For example, (as discussed in Section 2.7.7), using a modified version of the AES

Saklofske et al. (2003) have found otherwise low-to-moderate correlations between

EI and variables such as depression, loneliness and happiness to be considerably lower when personality (NEO Five Factor Inventory) was taken into account.

Additionally, Brackett and Mayer (2003) (as discussed in Section 2.7.6 and 3.1) found little incremental validity in criterion measures when cognitive abilities

(verbal SAT scores) and personality were controlled. Furthermore, similar to the results obtained in the studies outlined here, Gannon and Ranzijn (2005) have found the incremental validity of the SUEIT on SWLS to be only 1.3%, after controlling for demographic variables and personality (NEO Five Factor

Inventory), but not cognitive abilities because these did not significantly correlate with life satisfaction. An important difference between these studies and the current studies, however, is that the current studies have controlled for personality and two types of cognitive abilities (verbal ability and abstract reasoning ability), whereas Saklofske et al., 2003 and Gannon & Ranzijn, 2005 only controlled for personality assessments. Brackett and Mayer (2003) appear to be the only other major study that has sought to determine the predictive validity of EI when

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controlling for personality and some form of cognitive ability (in this case Verbal

SAT scores).

On the basis of current results it can be concluded that the current measures of EI used in these studies have limited predictive validity for the life skills, as assessed here. These results have confirmed that claims for the predictive validity of EI that are based on simple bivariate correlations with outcomes without taking account of personality and cognitive abilities may not be accurate. The usefulness of the EI construct is dependent on the extent to which it is able to predict something unique over and above already existing constructs (i.e., its incremental validity). It is clear from these findings that future studies of the predictive validity of EI should more comprehensively control for personality and for a wide coverage of cognitive abilities. However, until recently, surprisingly few studies have sought to control for the possible effects of personality or cognitive abilities, or even if one or the other has been included, typically both have not.

6.2 Contribution of Results to the Field of Emotional Intelligence

The research contained in this thesis is somewhat unique in the extensive battery of measures and variables that were analysed and the diverse samples

(Study 1: University sample and Study 2: Older community sample), range of ages

(16 – 68 year olds) and the sample sizes (Study 1: 246; Study 2: 212) that were assessed. In these two studies, three EI measures (two self-report and one ability measure), two different cognitive abilities (verbal ability and abstract reasoning ability), personality and five life skill variables (academic achievement, life satisfaction, coping, problem solving and anxiety) were assessed, only some of which have been extensively examined in relation to EI before. This has allowed a

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more comprehensive examination of the relationships between EI, personality, cognitive abilities and life skills and has added to the available research on the predictive nature of EI.

More particularly, this research has demonstrated that the incremental predictive validity of EI is minimal when the effects of personality and cognitive abilities are taken into account. Unlike other research, the two studies here have sought to control for the effects of both personality and a number of cognitive abilities (verbal abilities and abstract reasoning) when making such assessments.

Certainly this research is among the few to have controlled for both variables and one of the first to control for more than one cognitive ability in the same study. The obtained results from both studies, which has suggested that the predictive validity of EI without taking into account other factors is moderate, but that the overall incremental validity of EI is low, highlights the need for researchers to consider the effects of such variables before making claims about the relevance of the EI construct to real-life outcomes. This outcome does not necessarily mean that the EI measures evaluated here have little practical significance. However, current results do suggest that, from a theoretical perspective, these measures have limited incremental criterion-related validity for the measures of life skills and outcomes included here.

6.3 Limitations of studies

Nevertheless, a number of factors may have affected the obtained results. It is recognised that all the life skills that were measured here were based on self- report assessments. As discussed in Section 3.2.1 on self-report EI measures, there are known problems with self-report measures (and not just for self-report EI

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measures). For instance, self-reports are subjected to intentional (self-presentation bias) or accidental (forgotten or inaccurate information) misreporting. Moreover, self-reports of abilities do not necessarily correlate highly with actual abilities in those domains and therefore it is not certain that the assessment of the life skills that was conducted here captured actual ability per se. Moreover, to the extent that one relies on self-report measures, there is always the possibility of conceptual overlap when responding.

The one life skill aspect that readily lends itself to self-report assessments is life satisfaction, which, as discussed in Section 3.2, is best left to the individual to evaluate based on self perceptions of life experience, rather than on the basis of externally imposed researcher criteria. Self-reports of the remaining life skills, however, were chosen in these studies primarily from a practical perspective in terms of time constraints or ease of administration to the large number of participants involved. Additionally, there were other considerations, such as privacy issues or an inability to access certain information (academic achievement) or, because in some instances (e.g., coping ability, problem solving ability and anxiety), more direct laboratory-based exercises contravene ethical considerations or are simply not readily available, which limited the use of other forms of assessments.

In contrast to many previous studies that have utilised official student records, the TER assessments analysed here were obtained through voluntary self- reporting. Ideally, official records would have been accessed here too. However, given that these results were held by another institution (the South Australian

Tertiary Admissions Centre) this proved difficult because of privacy and permission constraints. It is recognised that some students with useable TERs may

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have not felt comfortable disclosing these results for a number of reasons and so may have left this field intentionally blank (it should be noted that TERs were provided by only 185 of the 246 University participants in the first study).

Alternatively, it is certainly possible that incorrect results may have been supplied, either deliberately or inadvertently. Both of these occurrences may have had an unknown effect on these results.

Additionally, it is recognised that in these two studies, an individual’s perception of problem solving and coping ability was assessed, rather than the actual ability for these tasks. It is unclear how self-appraised coping and problem solving behaviour may relate to actual abilities. It is therefore possible that different results would have been obtained with actual, objective assessments of these skills.

However, the majority of coping research since the 1970s has made use of self- report measures for assessment (Ptacek, Smith & Dodge, 1994; Endler & Parker,

1994). Assessing coping in any other way, it is argued, is difficult, given that much of coping behaviour is typically covert and is therefore not easily observed

(Lazarus & Folkman, 1987; Ptacek et al., 1994). Similarly, laboratory based problem solving exercises or ‘puzzles’ designed to elucidate problem solving behaviours are difficult to assess and may not necessarily equate to the way in which individuals deal with everyday problems (which was of relevance here) (Heppner et al., 1982).

Additionally, assessing anxiety in non self-reported ways may pose ethical issues.

Furthermore, apart from the discussed methodological issues with self- report, as opposed to objective assessments of these actual skills, it appears that some of the results may have been affected by some degree of method variance.

That is, the fact that self-report EI measures (and personality) were more highly correlated with all life skills measures than were the ability EI measure (and

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cognitive abilities), could be due to the reliance on self assessment utilised by EI measures, personality measures and the measurement of life skills. This should be a consideration when interpreting the somewhat surprising low correlations that were found between the MSCEIT (widely considered to be the ‘superior’ EI measure) and the life skills that were assessed here.

Moreover, the concept of EI is still relatively new and there are known proble ms with the measurement of EI (see Section 2.3). It is therefore possible that the current EI measures do not assess EI adequately, which would influence the conclusions that can be drawn based on such assessments.

The methodology used in these two studies also limits the conclusions that can be drawn about changes in EI with age. The current samples assessed here offered a wide age range (16 – 68 years), with no overlap between the two studies, from which it was determined there were significant age-related differences, which is consistent with previous research (see Section 3.7). However, these comparisons were conducted on a cross-sectional rather than longitudinal evaluation and thus a true assessment of the developmental progression of EI cannot be made on the basis of this sample. A cross-sectional design was chosen for the current research due to associated time constraints.

It should also be noted that both samples were slightly biased towards being highly educated. This is obviously the case for the ‘University sample’, which consisted predominantly of current or recently graduated university students.

However, in the older community sample, although significant attempts were made to reach individuals from a broad range of backgrounds (and to some extent this was achieved), the majority of recruited participants had University degrees, with many having postgraduate qualifications. No doubt this was an artefact of

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attempting to recruit individuals in and around a University as requests for participants were circulated in a University publication that went out to individuals at the University and anyone else interested in University issues.

Additionally, as is the case with most studies dependent on voluntary participation, it is possible that individuals who were more interested in the concept of EI (perhaps because they considered themselves to have a relatively high EI) were more willing to participate and thus there may have been a self selection bias. Thus, particularly if EI is a threshold variable relevant to individuals with lower IQ (as suggested for instance by Petrides et al. 2004), it is possible that the sig nificance of the effect of EI on the measured life skills was not adequately assessed in the current samples.

6.4 Summary and Future Directions

The results obtained from these two studies suggest that despite previous strong claims for the relative importance of EI as a means for improving prediction for real world achievements, the incremental predictive validity of EI is actually not particularly high, at least for the life skills that were assessed here. This is consistent with other findings (e.g., Van Rooy & Viwesvaran, 2004) who have determ ined that although EI may add some predictive validity over that of cognitive abilities it does not exceed it and as such EI alone may not be a particularly strong predictor of performance or at least not for all situations. It is, however, appreciated that these results should be interpreted on the basis of the quality of the life skill measures that were utilised here, which were all self-report, a form of assessment with known methodological problems. However, although the importance of EI is without a doubt reliant on its independence from other

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constructs, these findings should not be taken to mean that the concept of EI is without potential. Rather it would seem that the currently available EI measures are not adequate to elucidate the EI construct effectively, or at least are unable to reliably do so, independent of personality and cognitive abilities. It is, nevertheless, appreciated that the field of EI is still in its infancy and it is thus not surprising, nor should it necessarily be expected that measurement attempts will be able to meet such demands at this stage.

Of the current measures, it would appear that ability EI measures (the

MSCEIT) are the best available in this regard. As assessed here, unlike self-report

EI measures, the MSCEIT was at least distinct from personality and cognitive ability measures, even if its incremental validity was still minimal. It will, however, be necessary to prove that the MSCEIT – or some other assessment of EI – can adequately demonstrate incremental validity for relevant criterion of a non-trivial magnit ude. Additionally, substantial difficulties with the scoring methods for the

MSCEIT (as discussed throughout this thesis) have been observed, which either need to be more adequately addressed or alternative scoring methods derived.

Furthermore, although it has been suggested that self-report measures may be viewed to be assessing ‘typical’ performance and ability EI measures ‘maximal’ performance, it cannot be adequately determined whether the EI being assessed is actually how people think and act in the course of their daily lives or only when they are actively thinking deeply about emotions. Matthews et al. (in press) have also argued that it is unclear whether measures such as the MSCEIT are assessing actual emotional abilities, declarative knowledge of emotion or conformity with cultural values and beliefs and further work will need to be conducted in this regard.

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Although currently the MSCEIT appears to be the most psychometrically sound measure of EI and appears to be the measure with the most potential for future development there are some limitations to this. Impacting necessary further development of this measure is that the scoring key is closely held by the MSCEIT’s publishers (Multi Health Systems International), which prevents independent analysis of the underlying factor structure. Obviously, the authors and publishers of this measure are well within their right to protect the commercial applications of their research. However, doing so limits a thorough investigation of the associated constructs. Furthermore, in making test assessment such a commercial venture, in which all test items must be returned to the publishers for scoring at a cost per unit, the associated costs involved with researching are greatly increased. For instance, the free use of the MSCEIT here was only available because of a private negotiated arrangement with Multi Health Systems International which was contingent on collecting and returning responses to a recently developed measure that they required participant responses to for norming purposes. If this assistance had not been generously provided, it would not have been possible to use the MSCEIT in this research. Such a situation obviously places limits on the potentially valuable research that might otherwise be conducted.

At this stage, the field of EI would perhaps be best served by attempts to further test development, such as a move away from biased self-report measures and subjective consensual or expert-based measurement to perhaps more reliable behavioural measures. At the moment, ability measures seek to evaluate knowledge of emotions, rather than assessing the ability to perform the tasks that are related to this knowledge. An alternate spin on the current MSCEIT tasks could be that instead of asking scenario based questions based on the actions of other

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(fictional) characters, questions could be phrased in such a way as to ask the test taker what they, themselves, would think or do in that specific situation. It is likely that individuals may be aware of the most ‘intelligent’ or ‘useful’ or ‘socially correct’ response to a particular problem, but this action may in fact be different from what they would perform in a particular situation.

Roberts et al. (2005) have suggested that the use of alternate measures of EI, particularly those that do not rely on consensus or expert scoring or even paper and pencil tests, would be the most useful for the development of the field. Their latest work has focused on understanding emotional processing. Assessments that they are currently collecting data on include asking participants to indicate the extent to which they agree or disagree with statements about their emotions in specific contexts and to rate scenarios for emotional relevance and salience. They have hypothesised that emotional self-confidence is likely to vary according to whether evaluations relate to the self or to others and that the salience of emotions may vary depending on whether the emotion is positive or negative. They have also attempted to develop measures that assess the ability to recognise emotions in various stimuli, such as the ability to circle ‘sad’ faces amongst an array of faces with different emotions and making assessments on the basis of accuracy and speed. Alternatively, they have attempted to assess the ability to label emotions and recognise the relationships between them by having individuals assign emotions to complete selected quotes on human emotion from famous figures. It is, however, unclear at this stage whether these methods will generate measures that are independent from existing constructs.

It is also possible that a true assessment of EI is only possible for a select number of abilities, such as face recognition. The MSCEIT currently assesses the

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ability to distinguish the emotions displayed by photographs. O’Sullivan and

Ekman (2004), however, have argued that such an evaluation is inadequate, because it only seeks to quantify generally the ability to distinguish facial expression across a range of emotional categories, rather than trying to quantify which emotions individuals are skilled at detecting and which they are not. They have argued that it is likely that not only are some individuals better or worse at recognising emotions, but that there is also likely to be variation in which particular emotions individuals are better or worse at detecting. They have furthermore pointed out that the MSCEIT does not provide adequate justification for why particular emotions have been included, which raises concerns about the content validity of the task.

O’Sullivan and Ekman (2004) have suggested that a better approach to this type of assessment would be to sample a range of different emotions using multiple examples of varying grades of difficulty. They have also suggested that greater variation in the task could be achieved by presenting facial images for shorter and shorter durations in time before asking individuals to make a decision on the emotion that was being expressed. This type of assessment not only increases the difficulty of the task, but also simulates observable micro expressions of emotions that are often experienced when individuals are trying to get their emotions under control and represents subtleties in emotional detection.

Complexity could also be added by the use of emotional blends, where two universal emotions are subtly combined to create a unique emotion.

Alternate mediums could also be useful in attempting such tasks, such as dynamically assessing emotions via the use of film, rather than by still photographs. The use of film could also lend itself to other tasks, such as

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displaying different types of human interactions and having test takers determine the relationships between the individuals on the film. Identifying such relationships would be on the basis of emotional and non-verbal cues and therefore such a task has noted face validity. Also, given that the relationships between the participants are known, the empirical nature of the scoring is enhanced as the subjective aspect of scoring emotional related problems is eliminated.

Future assessment of EI would also benefit from a more comprehensive analysis of the relationships between EI and different cognitive abilities. Given that

EI has been asserted to be an intelligence, it would be useful to define where exactly within Carroll’s (1993) three stratum model of cognitive intelligence the field of EI is positioned. In this thesis, it is appreciated that since only two cognitive abilities were assessed here this is insufficient to adequately define a model of intelligence from which to demonstrate EI’s independence. Therefore, administering a large battery of EI and cognitive abilities in order to establish EI’s position within a broad cognitive abilities framework would be a worthwhile future endeavour.

The other main finding generated from this research has been the observation that EI (or at least most aspects of EI) increases with age and experience. These findings, however, were based on a cross-sectional study, which examined two age cohorts. It can therefore not be adequately determined at what point in the age range such changes in EI occurred. Ideally, future research will seek to conduct long-term longitudinal studies of this kind by examining individuals at regular intervals across the age span, as has been done for cognitive abilities. Such an evaluation would be of particular interest, given that it is more than probable (as preliminary evidence by Derksen et al. 2002 has suggested) that

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some aspects of EI will increase with age, while other aspects will simultaneously decrease.

Of further interest, would be an investigation of the development of EI through childhood. In 2003, MultiHealth Systems International indicated that they were in the process of developing a youth version of the MSCEIT, for administration to 10 – 18 year olds. Currently, however, this version is still not available for research, even for the purpose of norming and validation. A youth version of the Bar-On EQ-i aimed at 7 – 18 year olds has also been developed and is currently available. However, to the best of knowledge, no data using this instrument have yet been published.

6.5 Conclusion

In summary, the findings from these two studies indicate that although EI, assessed by various measures, is low-to-moderately correlated with a number of life skills (life satisfaction, coping, problem solving and anxiety), the incremental validity of EI once personality and cognitive abilities have been taken into account is minimal. These findings are strengthened by their almost exact replication in two diverse samples, which suggests some degree of generalisability of the results.

Thus, it would appear that previous studies and opinions that have asserted the importance of EI have done so largely on the basis of identified relationships that have not taken into account the predictive validity of personality and cognitive abilities for important life outcomes. As demonstrated here, this is an important shortcoming which drastically inflates the relevance of EI. It is therefore recommended that future research should seek to evaluate the incremental predictive validity of EI after such variables have been taken into account.

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276

APPENDIX A

Demographic Information:

Sex: MALE FEMALE

Age: years

Marital Status: Single Married Defacto Divorced/Separated Widowed

Education: Primary School Secondary School Some University Completed Undergraduate Degree Completed Masters Degree or Higher

Year graduated from school:

Tertiary Entrance Rank (if graduated in the last year)

TAFE/University [Name]: …………………………………

Faculty/Course: …………………………………

Occupation: …………………………………

Email Address (if you wish to receive feedback about your results):

….…………………………………………………………

277

APPENDIX B

The Functional Importance of Emotional Intelligence

Investigator: Veneta Bastian (PhD student)

Research: This study aims to determine the predictive validity, and thus importance, of the construct ‘Emotional Intelligence’. Emotional Intelligence has been purported to be a factor responsible for success in a variety of life endeavours, yet insufficient research has been conducted to adequately confirm the predictive nature of Emotional Intelligence. The focus of this study will be to determine the extent to which Emotional Intelligence contributes to ‘life success’ variables (Problem solving ability, coping ability, anxiety and life satisfaction) over and above the contributions made by Intelligence and Personality. Attempts will also be made to determine whether Emotional Intelligence increases with age.

Required Participants: Males and females who are either recent school graduates or aged over 40, who are fluent in English.

Study Requirements: Participants in this study will complete ‘paper and pencil’ tests of Emotional Intelligence, general intelligence, personality and ‘life success’. Total testing time will be around 2 – 2 ½ hours, which may be completed in stages. For your convenience, it is possible for the majority of the tests to be taken home and completed in your own time. However, there are two intelligence tests that are timed and therefore must be conducted under my supervision, at a mutually convenient time and location. It is essential that ALL tests be completed, as it limits the effectiveness of the conclusions that can be made if some tests are missing.

Participant’s rights: This research has been approved by The University of Adelaide’s Human Ethics Committee. This study will not result in any adverse effects on those who participate. Participation in this study is entirely voluntary and you are free to withdraw at any time. Information obtained from this study will be analysed on a group basis only. Individuals will not be identified by their data in any resulting report, however, for ethical reasons, a consent form will need to be completed. If they so desire, participants may receive individual feedback of their results if they supply their email address.

If you have any questions regarding this study, please contact me on (08) 8303 3855 or by email: [email protected] or my supervisor, Dr. Ted Nettelbeck, Department of Psychology (08) 8303 5738 or by email: [email protected]. The convener of the Psychology Department’s Human Ethics Subcommittee, Dr. Peter Delin, may also be contacted on 8303 5007.

278

APPENDIX C

THE UNIVERSITY OF ADELAIDE HUMAN RESEARCH ETHICS

COMMITTEE

Standard Consent Form for People who are Participants in a Research Project

1. I, ……………………………………………………………….(please print name)

consent to take part in the research project entitled:

The Functional Importance of Emotional Intelligence

2. I acknowledge that I have read the attached Information sheet entitled:

The Functional Importance of Emotional Intelligence

3. I have had the project, so far as it affects me, fully explained to my satisfaction

by the research worker. My consent is given freely.

4. It has been explained to me that my involvement in this project may not be of

any benefit to me.

5. I have been given the opportunity to have a member of my family or a friend

present while the project was explained to me.

6. I have been informed that, while information gained during the study may be

published, I will not be identified and my personal results will not be divulged.

7. I understand that I am free to withdraw from the project at any time with no

repercussions.

8. I am aware that I should retain a copy of this Consent Form, when completed,

and the attached Information Sheet.

…………………………………………………………………………………………… (signature) (date)

279

APPENDIX D

Table D1: The Correlations Between the 15 Cope Scales (Study 1)

1 14

.1 *

* 1

1 1 13 .1 .3

* * *

* * 2

5* 5 0 1 1 4 . .2 .3

* * *

* * 9 1

8* 7 1 0 1 .0 4 2 4 . .

* * * * 3

9 0 3* 3* 8 1 . 10 2 2 - .1 . .

* *

* * 1 5 0

8* 8 0 1 0 0 1 . .0 . .

.3 * * * *

* 2 3

7* 4* 8 4* 3* 1 8 9 0 1 1 . . 2 3 2 . . .2 . .

* * * * *

* 0

7 11 09 2 8* 2* 3* 1 . . 3 5 2 3 - - .1 . .

. .

* * * * 9 8

7 5 0

5 0 0 8* 4* 1 5* 0 0 . . . .1 . 2 2 2 - - .1 .

* * * * * * * . * 6 7

* * * 3 )

7* 5 9* 6* 9 4 3* 0* 9 1 5 1 1 .1 1 2 4 3 . . 1 . .1 . .2 . . .3 y d u

* * * * * * *

* * * * 0 4 7 S

5* 3 1* 4 0* 8* 3 1 2 0 1 .1 . .0 6 3 2 . .2 .3 es ( l a c

. .2 . . .4

* * * * * * * * * s 4

* * * * * 8 2 7

b 9* 2 0* 3 2* 6* 1 8 9 7 1 . .1 u 1 5 4 3 . .2 . .3 .7 .3 S E . . .4

* * * * * * * * 9

* * * * * 3 6

6* 10 0 1* 9* 1 5 6 1 1 8 2 3 18 . . 1 .1 .0 . 4 4 - . . . .6 .3 .3 .4 .1 CO 5 1

- e -

* * * * * * * * * * *

* * * * * * * 5 7 h 8 04 0* 8* 6* 9 6 0 2 4 9 8 1 1 0 . . .0 t 5 5 2 6 - .1 . .4 .4 .4 .6 . . . .1 .2 een w t e

(13) ) )

7 B 0 ) ( nt s 1

2 e

) n n 1 ( ) ( o o m

3 i i (5

t e ( ns ) t

t ) ) s g n o t a

4 e 4 i e r a ) i (6 ta 1 ( el t g 8 ( e n t m i ot

r n

( o e rr e v ) e i p

m or i s o 1 r s g

t ng ( ag i

e E

c li ) U t Suppo g e 01 9

e C g l n D ng A n ng ) opi i ) ( u h a

i g 5 Supp e R <. r se ) al e l T n i 1 C o 1 p (2 nt

nt opi D t a R : ( ur * 1 e e g / t e n n D 1 o anc ( n g i * C V i v o m i ; essi / e a i al r our u al n in s pt ti r v r i p t e i n n u ohol e D ot 05 nt t m ni s t s c r hav p c c a e s e u c e c l bl l u m u <. n a A P I E S T Po R A Fo D M Be A H p T * 280

Table D2: The Correlations Between the 15 COPE Subscales, Cognitive Abilities and Personality (Study 1).

t n

* me * * * e 0 0 3 0 3 3 5 6 7 04 02 03 3 g 0 14* 17* . .0 .0 .0 .1 .1 .1 . . a -. -. -. .1 .1 - - .2 n Ma

st s g e u in T r

* n

* ce * C a 2 7 3 8 6 4 2 8 5 5

t 13 n 03 10 10 1 s . . . . 27 d .0 .0 .0 .0 .0 .0 .1 .0 .0 . e - - - - r - -. e n ig d ll y a Un te ve n o I l l

na . S ion

)

r, 0 4 2 at 3 5 0 6 7 6 6 03 06 1 01 05 0 0 09 tio 1 0 0 0 0 0 0 0 . . . is ...... ye -. -. -. -. - - -. - l o y i a d u M Em t S Ut ce (

on i *

t * en 4 5 2 4 5 2 3 4 3 0 p 4 03 13 0 0 05 0 0 0 0 07 g e 0 ...... 2 i .0 .0 . .0 c -. -. -. ------. l r -. l e t Pe n I

l a

ns n

ing * e

* * * * * *

o o * * * * * * * * i 6 0 8 3 1 4 4 ss al 14 8 0 3 7 9 6 0 0 0 . 2 . . .0 .1 . ot 22* - .1 . .3 .4 .3 .3 .4 .5 -. Sc sse m Emoti A d E

* * ir

* *

* * * * * * * * * 1 8 9 3 4 6 es an 4 2 5 6 0 pa 1 0 15 18 28* 22* . .0 .0 .0 .0 . . -. -. .3 .3 .3 .3

- - Re d scal o b o u

y S

t *

* * * * * * e i 5 2 * * * * * E 2 . 0 3 4 6 r ta 12 03 06 0 1 8 1 0 3 6 al 0 1 1 . . . . . a P c S O C it Me 5 Cl a r n -. .1 .2 .2 .3 .2 .1 - T . * - -. - .2 io - * * * * * * * e 1 t 7 0 3 8 * * * * * * * 9 - 5 h 5* 0 1 0 0 5 1 4 3 8 7 2 0 . . . . 1 20* . .0 t - - - - . ten .3 .4 .2 .2 .5 .3 .2 -. t A een w t e

t B

n s

e n n

o o m

e t

ti n ons at es i e rt ag i

t n t m ot rel

re ng

o e r e vi e m i g s rp s gi t por n

ag i i E

c i l U te Suppo p e e Co g ng D n A ng ng i

h < .01 u al i Sup R r se al e t p T ng i

nt * n i opi D t Co Re :

e e / s * e n D 2 our s ; anc ng i C V v onal m e / e i al our u al s ni pt ti v r i e tra i u e D ohol ot nt t m ni s t c s rning to havi c an e s e u < .05 c c l e bl o m

n a A Pl I E Suppr Tu P R A Foc D M Be A H p T *

281

Table D3: The Correlations Between the 15 COPE Subscales, Cognitive Abilities and Personality (Study 1).

s snes

* * * o * * * i * * 2 3 8 7* 6 6 11 9** 2 3 9* 1 0 1 1 23** 34* 21** . . 3 5 1 -. . . .1 .1 . . ent -.17* . . .3 . -. - nsci o C s

enes *

* * * * 8 8 * * 2 bl 4 09 08 0 0 9* 9* 1 1 05 00 05 04 . . 1 . . .0 . . 1 1 -. -. - - . . . .2 .3 -.18* eea r g A

y t i -R) I

al s P n

* o ** 5 * O . 3 4 7* 4* 5 6 03 20 1 13 2 1 10 06 13 01 E . 1 1 2 ...... 2 ennes 0 -. -. - 1) -. . . .1 . . p - Pers (N O dy u t S n o i y (

* *

* * * * * * * ers 8 0 5 1 0 2 lit 4 4* 12 4 4 2 4 v 0 1 1 0 0 a 1 1 . . .0 . . . 3 2 31** 27** a -...... 3 .4 . . r t on x s r e E d P m s

* * *

c * *

* * * * ** i 5 s an 1 3 t 8 6 0 10 11 0 . ie 27 35 21 3 3 .0 . 27* 25** 20* 26** 27** - -. -...... -. -. -. - -. uro e N Abilit

ive

sk it rd a o

n T

* . W * 1 1 0 o c 1 4 7 5 i 7 i 04 08 05 0 03 0 10 1 t 0 0 1 . 18** 22* Co a . et -. i n c o h sso ales,

P c A s ve es b i t u i iti

l i S

es gn b E c o ed i r A P C t nc a a -. - v -

-. * . .1 .0 -. -. -. CO * M 4 5 7 0 2 4 7 -. 3 . e 0 05 13 -. 10 -. 04 06 1 0 0 0 0 17 v . . 24* . . . .0 .0 . . . -. - -. -. - -. s Ad - si 15 -. e n’ e h res v g t a o r n R e P e w t e

B t

n s

s on ion

em t n t n o es e rt ag i

tati t o m oti re ng p

on e

rrela e vi e p ort m g s rp s gi ti u n i i

c i .01 l pp U te Co S g E g e e g ngag D n A n n l i u h < al i g Su e r a se t p l p T n i r ce

* nti i D t Cop o R : u e n s * n na e D l/ 3 o s a

g to R ng l Ve o v ur m t o / 5; n e C i D vi re o al s ni p h t ti i trai iv u o nta tru ni s rni s ce ha c pp an e < .0 e ble e l u m

a Act P Ins E Su T Po R Ac Fo De M B Alco Hum p T *

282

Table D4: The Correlations Between the 15 COPE Subscales and ‘Life Skills’ (Study 1).

s y s t r

o *

t * * o gh 2 9 1 9 5 0 i 1 2 06 n 05 17 17 07 1 1 0 1 24* 2 2 33* . .0 . .0 . . -. -. -. -. -. . . ou -. ve Anx Th In

y g )

* * * * n

* *

* e * t al 8 i * * * l 3 t 6 0 n 07 12 13 07 4 3 4 v 0 b 1 o 49* 38* 42* 19* .11 . l 3 4 -. -. -. -. -. . .2 . . -. -. -. -. ve (T So n Pro I

l al

o * * * *

* * * n * * * * 9 3 3 o 10 03 03 13 6 7 8 8 0 0 15* ntr 32* 35* 27* . .0 . . 2 2 2 -. -. -. -. - . .2 . . -. -. -. Co Pers

e h c c

* * * a e

* * * l * * o 8 1 3 1

5 . da r 4 0 0 0 0 15* 16* 46* 29* 35* i . . . .0 2 3 -.12 -.08 -.09 .1 -. -. . . -. -. -. 1) Sty

App Avo dy u t S

* * * *

* * * ills’ ( ing * * * lem 9 4 k 9 9 8 9** 0 iden 18* b 28* 40* 35* 43* . .0 lv 2 4 1 -.01 -.07 -.10 -.11 o -. .1 . . . -. -. -. -. nf So ife S Pr Co d ‘L

n fe

tio

* e

* * * * * * ales an * ** ** c l 2 7 L 4 1 c 9 8 5 a 1 06 04 0 1 s h 1 1 2 . . . t sf b Sca ti u a Wi S

S E P

e -. - - - c ry .24* . * .0 * * * -. * a * * * 8 2 15 i an 9* 7 8 8 0 01 .19* 0 01 .20* 06 -. 09 e r . 1 21 2 2 2 .08 .22* .01 .25* .10 .29* .01 . -. -. - -. -. -. t . . . . h - - - -. Rank t Tert En n e e w t e

B t

n s

s on ion

em t n t n o es e rt ag i

tati t o m oti re ng p

on e

rrela e vi e p ort m g s rp s gi ti u n i i

c i .01 l pp U te Co S g E g e e g ngag D n A n n l i u h < al i g Su e r a se t p l p T n i r ce

* nti i D t Cop o R : u e n s * n na e D l/ 4 o s a

g to R ng l Ve o v ur m t o / 5; n e C i D vi re o al s ni p h t ti i trai iv u o nta tru ni s rni s ce ha c pp an e < .0 e ble e l u m

a Act P Ins E Su T Po R Ac Fo De M B Alco Hum p T *

283

Table D5: The Correlations Between the Problem Solving Inventory Subscales (Study 1).

Problem Approach Personal Solving Avoidance Control Confidence Style Problem Solving Confidence 1 Approach Avoidance Style .55** 1 Personal Control .48** .49** 1 *p<.05; **p<.01

284

Table D6: The Correlations Between the Problem Solving Inventory Subscales and Emotional Intelligence (Study 1).

nt e

* * m * e 8 08 g 1 26 . a -. - -. n a M

o st s g e . n u r a 1) ndi ce T C a

t n 17* 15* d . e -.13 -. - n ig ders n ll y a (Study U te e ve c n o n I l l e a n o na S i

, t llig o r 3 1 a i 06 t s te 0 0 i . . ye o l n i a I t M Em ional U t n o o i -. t m 2 p 08 02 0 . . -. - erce P and E s

le g a

ns c in * e s io b ss al t 45** 60** 25* u o . . - - Sc sse y S Em A r nto

-. r * i ** ** a 0 7 p 3 4 23* Inve e .

-. -. - R d

lving

y o * Moo t e ** ** i a r 4 7 t al a 2 3 l . . m S Sc le Me t ob ai r r n C P o T

i ** - ** - 2 -.33* 4 8 0 2 1 . . . tent the - - n At e twe e

B c e en ns yl t S fid

atio n e l o e nc r

r 1 g C o da i .0 C in trol o

e v lv < h o p A T Con h **

c ; al m S oa le D6 on r b e p l rs o < .05 p b r e

a P A P p T *

285

Table D7: The Correlations Between the Problem Solving Inventory Subscales, Cognitive Abilities and Personality (Study 1). ss e n s

u * * * o i 47* 47* 56* ent -. -. -. nsci o . ) 1 C

tudy * * eness ) bl 18* 18* 27* -R -. I -. eea r P g A O nality (S o

s (NE r

-. e * * y t i 13 nness l 44* 43* d P e -. a -. p n n o a s s O Per n o ilitie

* * rsi e Ab v 14* 40* 24* a -. r -. -. t x E gnitive o

m C s , i

s c * * * i t 3* 5* 1* o ale 5 6 3 . . . c ur s e N Sub

d y r r n o o o

t i

* t k W n a ic 05 18* ci t ve o e Tas -.22* ies n t I on i Ass il Ph b A

lving d e e v c n

iti ve s -. m So e

-. va n * d ssi g ic le r re 17* 17* 31* ob g s A Co -. -. -. ’ r o r Mat P P ven Ra n the e twe

e Be c e l ns en y d t i f atio l n e ce S o r n

r C l a o 1 g d i C

in o e ntro v lv h < .0 o p T A

* Co l * ch a ; m S a n 5 e o l D7: o 0 r b e p l rs o < . b r

a P Ap Pe p T *

286

Table D8: The Correlations Between the Problem Solving Inventory Subscales and ‘Life Skills’ (Study 1).

s y s t r

u o * * t o gh n 11 . 38* 38* ou . . ve

. n Anxi ) Th I 1 y d

n u e t

tio * * * e c Lif (S al ’ h 24* 22* 47* s sfa l -. -. -. Sc l ti i k Wit Sa e S f i

L e y ‘ c r * d n n a 01 08 38* . rtia -. -. e Rank es a T Entr l ca s b e

u S * * * ng y danc r pi i 37* 37* 46* o . . . o o v C nt e A v n I

n g d

e n i s tio 12 v u 06 00 ping o l o Co Foc Em S m e l b

r d . . -. e P s lem e u b ping 34** 42** 32** h o -. -. -. t Co Foc Pr een w t e

ce s e n l n e y o d i i t St

a nf e o

C rrel

l o o danc r ng oi e C vi v < .01 h ont p A T *

C h Sol : 8 m e oac l onal e D s r ob < .05; * ppr bl

a Pr A Pe p T *

287

APPENDIX E

Table E1: Hierarchical Regression of Tertiary Entrance Rank on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 1).

.04 a p = t

, EI 7

3 Be , y 0

.0 lit i = 0. ) b 1 le tor

A c e d i 17 ab ,

e i l d 1 c ur a ( e t s F n r a Var To e va M e 61

c . s Ad

’ 05 n -. EI n ta p = t e e

, r B 7

o , llige 2 . 0

Rav ep t = .0

n R , - tor 71) es lf c l I i 1 e able

, i l d S 1 a na ( e t scal r F b Var To u otio S m

)

E d 7 d P

13 01 .0 14 13 h

s . . ta -. -. i e est y an p = B

, Eac 2 T ve

20 (Rev , o . n l 3

2 ep E g

a t

y .0 n o i = so S nt

d d

, or e u 3) n r n t n r 7 o a tor on m e i i t a c t 1 t y i , ge a able C a i ers ven ep d is (4 na 1) a d e il n F a M t n r erc p I Var P U U M y t

s 7 al n (Ste .3 ) n P

io 01 o t -. ta p = o

e ,

ers 2 B m e

8 , P l WAT . 0

0 E a

P g .0 (

O = Sc

in le E tor k s 71) b c ss i a

1 i l , N d se r a 1 e t n ( r F As n Ta Va To o k

atio

1 i d c .8 o o P

01 07 05 s o . -. -. ta p = ce Ran

e ,

M 3 B e

an 3 As , l . r ta 0

d a t 0 r .0 =

En le n to t Me Wo

74) b c

io i y t

r a t 1 ai i i , ry n i d r a r r ic 3 a e p ( a t T i tte r e F Va A Cl Re rt n e o h

P of s 13 13 02 16* .00

d e ta 29* 38* 16* . . -. . . . i -. e n t nd i on a B p =

il si . ) S 2, y a b

1 t 1

es s 1) . , i

M s r 5

e ep 13 e A g al

.3 t sn s

iv S s = AP

e on

r ou m on tudy it 7) i s n

i Re s o t e i 7 s s r l n le S t l s r e (

a (R

ic g 1 c e en

, t i i s c ab T P o ab ) o 7 c e i av d ( r s nne C r e ree u e F tr n ic p g o x p 2 e tr Va Ne E O A C RAPM PWA archi a r e i (St M

e H lly v : ge a i 1 n s u s a TER E 1 d e

i r e = l g Ch V 2 b o <.00 a D Pr R p T Pr Indiv *

288

Table E2: Table 33: Hierarchical Regression of Satisfaction With Life Scale on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 1). 4

a 1

. I p =

, E 9 Bet

0 y , it l .01

r = 4. e ) l to

7 b c e Abi d i a r

21 i l d , ce r u a e 1 t s ( r F an Va To

0 Mea Adv

s c I P

’ 33* p = n E . en ta

e t e g r 7, i B 9

o . , ll 5 e t

16 Rav .0

r = n Rep

e s, o ) l t 7 lf- b l I 1 e a

a i l cale S , 2 r a n edic t (1 r bs io F Va To u ot S m

4 ed) P E 2 6 6 3

nd s .4 a h i .0 .0 .0 .0 t c st v a y a e p = Be

R Te 4 E 2

( , ove

l 1 p EI y g 0.

.0 o on = t in

or s Sa ) n d

d r t

, e u 4 n n r e o r l t on m io i ta a b t 21 t an , ven ge a a rs C ) aye 4 a s i e ep i n ( r n l d edic i F M r erc y I p 1 Va P Ut Un Ma t e t

S ali 0 s

n .0 ( on ) P

io a rs 26* t p = .

e o

0, Bet m 7 e P

. , WAT 3 E al O P

11 g .0

r E = Sc (

e o in ) l k t 7 s N b ss 1 a a

i l se , 2 r a edic t s on T (1 r e A F n Va To al o i c

0 S i

.0 f

0 i P

soc a ood 19* 13* .1 . . p = L

h M 4 Bet t e

0 As a , t 5 d al e .0 Wi = 6.

Sc ) e M l n to 5 t o b on c

i i i i y Wor r a t 21 a i i , c i nt d r a r i r e 3 e t ( a T r t et F sfact Va A Cl Rep i t on a h

* * P 0 P s 9 6 04

04 07 08 33* . .0 d e ta . . .

- of S .2 .1 i d -. e n it B p = on

an il i , . Ste s 9 ) y a b

t 1

s .6 , i es 1 M) s p 6 5 e r y e A al 1

.3 g sn s

Ste iv s AP =

) e on t

r ou m on Re i 5 R i

i e o t

en is 2 s s rs n l s r (Stud (

ic g bl en

t , 2 i s ict ) Pe o o c ea M ave ical r (7 d nne s e 2 AT r C e

r F e h n t ice p g o x p r t Variab Neu E O A C RAP PW arc a er

i (Ste M S y

e l H e L l

g : iv 2 s s an 1 dua SW e 0 r vi Ch 2 ble E og <.0 a r DV = Pr R p T P Indi *

289

Table E3: Hierarchical Regression of Problem Focused Coping on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 1).

8 .8

a 01

. I p =

, 2 E Bet

0 , y 0

.0 lit

r i = 0. e d o ) le b r e 5 b c A ict a

n 19 i l , asu r a a ed 1 t ( r v F d Va To Me

A 0 ce s ’

.0 n

e n I P

e a 36* ig p = E .

t ll 7, r e Bet 9

Rav o t . ,

p n e

16 .06 s,

r I e R = l e l o l t a a 5) lf- b 9 n e a

o i l 1 S , i bsc r a edic t 3 u ( r ot F Va To S m

ed)

s P 1 1

33 ch nd 06 04 a a .1 .1 t -. -. evi st y a p = . R Be E

Te 6 n ( 2

, ove y

l 2 p EI g 1.

.0 o d o or = t in

t s Sa ) n

d r

, e u 2 n n r e o r l t on m an io i ta a ven b t ) 19 t , ge a a rs C n 1 aye 4 a s i e ep i ( r n

l d edic i F M p r erc y I e Va P Ut Un Ma t it S

0 s ( ) n .0

on 1

P io rs a t .3 p = e

o ,

7 WAT Bet m 2 P e

. , P 3 5 E O al

1 g .0 ( E

r = Sc

k e o in l s t b a N ss 95) a

T i l 1 se , r a on

edic t 1 s

( r n A F g Va To o i n i at

0 c

.0 o

Cop 4 P

s 07

a ood 31* .1 -. . p = d

M 3 se Bet e

7 a , u t 7 al c rd As e .0 o o = 6.

Sc ) e M l n to 3 F t o b c

i i W i y r a c t 19 a i i , i nt d i r a r r e 3 e t ( a T et r t F oblem Va A Cl Rep r on h P

* * P of 0 s 06 d P

05 03 26 15 .0 . e ta 20* 26* . .

- . . i d -. -. e n it B p = an

il sion , . Ste 9 ) y a b

t es 1

s .1 , i M) 1 s p 1 9 e gr e A al 1

.2 e sn s

AP Ste iv s =

) e on t

r ou m on R i 3 i R

i e o t l en is 0 s s rs n l s r ( a (Study

ic g bl en

t , 2 s c i ict Pe o ) o c ea e M ave r (7 d nne s e 2 AT c hi r C e r F e n t i c p g o x r p r t Neu E O A C RAP PW Variab a a r e i (Ste M

e ly e H l : v C g i 3 s s an 1 PF dua e E vi l Ch i 2 b ogre <.00 a r DV = Pr R p T P Ind *

290

Table E4: Hierarchical Regression of Emotion Focused Coping on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 1).

1 .5

05 a .

- I p =

, 3 E Bet

4 , y 0

.0 lit

r i = 0.

o ) le b e 5 d b r e A ict a

c 19 i l , r a ed 1 t asu ( r F van Va To Me

1 c

s .0

’

I P

n en a 25* e E . g p =

i t , r 5 ll Bet

o 9 e , t p Rav e

n , .03

r I = 6. s R ) e o e

l t 5 l lf- b a al e a

19 i l , S sc r a 1 edic t b ion ( r F u Va To ot S m E

ed) 0 P 4 0 4 s

nd 12 .7 a ch .0 .0 .0 t -. a st evi y a p = Be

, R E

Te 5 2

, ove

l 1 p EI g 0.

.0 o = t in ory ( d on

s Sa ) n

d r

, e u 1 n n r e o r l t on m io i ta a an b t 19 t ven , ge a a rs C aye 1 a s i e ep n i ( r n l d edic i F M r I erc p 1) y Va Ut Un Ma P e t it S

( al s 1

n ) .0 on

P s T io a t 24* r . p = e o

3 Bet m e WA

4 P , 3 P E al O (

g .0

r = 6. E Sc ) e o in l 5 t sk b ss a N a

19 i l , se r a 1 edic t s T ( r on F A Va To g ion n i

ciat

o 1 P

02 Cop a ood 28* .0 -. . p = d

M 1 Bet se e

Ass 6 a , u d t 5 al c r e .0 o = 3. o

Sc ) e F M l n to 3

t o b c

i i i y n r a t 19 a i i , o i nt d r ic W a r i r e 3 e t ( a T r t et F ot Va A Cl Rep m on h E P

0 s 03

d 00 . of e ta 26* 29* 15* 15* 16* . .0 - . . . . . i d e n it B p = an il

. sion , Ste ) y a b s 3

t 1

e s 2 , i M) 1 s p r 6 5. e

e A g al

.1 sn s

= AP Ste iv s )

e Re on t

r 3 ou m on R i i

i e o t en is s s rs n l s r 20 ( (Study al

ic g bl , en

t i ict c 7 Pe o ) o c ea M es ( ave r d nne s e 2 AT r C F e r e n t ic chi p g o x p r Neu E O A C RAP PW Variab ar r e i (Ste Mat

H e ly e

l v : C g i 4 F s s an 1 E dua e E vi l Ch i 2 b ogre <.00 r a DV = Pr R p T P Ind *

291

Table E5: Hierarchical Regression of Avoidance Coping on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 1).

0 .2

09 a .

- I p =

, E Bet

, y .6 1 1

.0 lit =

i ) o le b 5 b 9 e A p ict a

v i l , 1 i r a 1 ed t ( r ss (Ste F e

Va To r lly

og a 4 r

u .4

7 I d P P

0 i a d -. E v p = e

i t , c r d 1 Bet

o 6 , n a p I

.00

r = 0. R ) e o l t 5 ure lf- b s s Adv e a

a 19 ’ i l , S e r a 1 edic t ( r F Va To M e Raven

nc ,

s 3 ge P e

nd 02 01 02 06 .8 a t al -. -. -. -. lli st y a sc p = Be

, b

Te 7 Inte 2 u

, ove l

l S 1 p EI

g 0.

.0 ) o = t na in

s d Sa ) n

d r

, e e u 2 io n n r e o r s t l t on m i io i ta a o b t 19 t v , ge a a rs C e aye 4 a s i m e ep i ( r n l d edic R i F E M r (

erc

Va P Ut Un Ma h y c or

t Ea 3

n .6 n e ons P

i 04 v a o -. p = n d ot

I n 3 Bet

m e

2 y , a t 0 E al )

g .0

r ali = 0. n Sc ) e o i l p 1 5 t s on b s te s a

e 19 i r l , s r a e 1 edic t s ( (S r P F A

Va To T) O A

E 3

W N

1 P P

07 15* a ood .0 . ( -. p = -

on k ,

s g M Bet

91 a a

, n . t 2 al e 1 pi .0

r = o n T

Sc e M o l n to i t o b c

93) i t i e C i y r a a t a i 1 c i i nt d i , r a r r e 3 c e t a T ( r t o F s Va A Cl Rep dan s A

d * * P 0 r Avoi s 01 31 06

o 10 19 18 .0 . . . e ta 25* .

of - - - . i d -. -. e

n it B W p =

il c , i Ste 0 y a b t sion

t 1

s .3 e , i s p 1 es 9 e n e A r al 1

.2 sn s

g Ste iv s =

) e on t

r ou m on i 3 i

i Re e Pho o t en is 0 s s rs n l s r d

ic g bl en t , 2 i ict n Pe o o c ea M cal ave r (7 d nne s e AT r C a e r F e n t ) p g o x Variab Neu E O A C RAP PW rchi M a P r e . i A )

1 e

H g : (R C s 5 e an 1 A c i tudy e E l Ch tr 2 b a <.00 (S a ) DV = Pr R p T M 2 *

292

Table E6: Hierarchical Regression of Problem Solving Inventory (Total) on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 1).

3 .2

a 06

. I p =

, 3 E Bet

4 , y 0

.0 lit

r i = 1.

o ) le b e s 5 ’ b c A ict a

19 i l , en r a ed 1 t g ( r i F l Va To l Raven e

t ,

s n 2

I e .0

I l P al

15* a a . E p = sc - n

t , b r o 0 i Bet

u o 7 , p S ot e

.01

r m = 5. R ) e o l t 5 ed) lf- b s e a

19 i l , S ch E r a 1 edic evi t a ( r F R E Va To

( y

or 3 d P t 5 4

nd 01 06 .6 a .0 .0 t -. -. st an y a ven p = Be

, n

Te 1) 5 I 2

, ove

l 1 p p EI y g 0. e

t .0 o = t t i in

s Sa ) n

d r

S , e u al 2 n n r e o r l t on m ( io i ta a b t ) 19 on t , ge a a rs C aye 4 a s i e ep i rs ( r n l d edic e i F M r erc Va Ut Un Ma P WAT P P O

(

0 E

n k .0 s

P io a N 18* a t . p = - o

T 6 Bet m e on

3 n , ) o 2 E al i al

g .0

r = 8. Sc ) ot e o in l 5 t ciat T b ss ( o a

19 s i l , se r a 1 edic t s ( r As F A ory Va To t n e

6 v

.6 n

7 I P

03 03 Word a ood .0 c -. -. p = i g

, t

M n 4 e Bet e i

5 a , v t 0 al l on e .0 o = 0. h

Sc ) e P M l n to 3 S t o b c

i i i y d r a t 19 a i i , i nt d r a r r e 3 lem e t ( a T r t F an ob Va A Cl Rep r

. ) M) P

* * P 0 AP s 01 06 07

of 06 38 47 .0 . . . e ta 33* .

- - - R . i d -. -. e ( n tudy on it B

p = i

il , s (S es Ste 5 s y a

c t 1

s .9 i , i s p 2) 7 r 3 e gre e A al 4

.6 p e sn s

Ste iv s =

te ) e on t

r ou m on i 3 i R

i S e o t en is 0 s s rs n l s r ( e Mat al

ic g bl en t , 2 c i ict v Pe o o i c ly ea M i ave r (7 d nne s l e AT s r h C e r F e n t s p g o x e Neu E O A C RAP PW Variab r arc er og i r H

: I g Individua 6 ed P e r c an 1 PS u e E s l Ch a van 2 b e <.00 a DV = Pr R p T Ad M *

293

Table E7: Hierarchical Regression of Anxious Thoughts Inventory on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 1).

0 .0

a 22*

I -. p =

, E Bet 4

, y 3 . 4

9 .0 lit

r i

= o le b ) e d 7 b r A ict a

ce i , 9 l r a n ed t (1 asu a r F Va To Me

Adv e 3 s c

’

9 .3 I P

n 0 en a e -. E g p = i t , r ll Bet 5

o e , 9 t . p Rav e

n 0 .00 s,

r I R e = e o

) l t l 7 lf- b a 9 cal e a

, i l S r a bs (1 edic t ion r F u Va To ot S

m E

ed)

* s 0 P 7 7

nd 17 10 a 1 ch .0 .0 t -. -. evi a -. st y a R Be p =

, Te 7 2

, ove 0

l 6 p EI g ory (

.0 o t t in d on

s Sa = 4. n

d r

, n ) e u n n r e e o r l t on m io v i ta a 94 an b t , t ge a n a rs 4 C aye a s i ( e ep i r I n l d F edic i M y r erc p 1) Va P Ut Un Ma it e t al S

(

s on

n ) .25 rs P

T io 11 e a t -. o p = P

, Bet m 7 e WA

, O 3 . 1 P E al E (

1 g .0

r = Sc

e o in ) N l t sk 7 b ss a a

, 9 i l on se r a (1 edic t s y T r F A Va To or t ion

ven ciat

.59 n o 6 0 P

10 a ood I .0 .0 -. s p = t

, M Bet 5 e

Ass a , 6 gh . d t 1 al 0 r e

.0 o

r ou Sc = e h ) M l n to t 5 o b c

i i i y T r a t , 9 a i i s i nt d r ic W 3 a r r e ( e t a T r t F et ou Va A Cl Rep xi on h

* An P

0 s 07

d 04 04 01 15 .0 . e ta 79* 19* . . .

- . . of i d -. e n it B p =

an il , . Ste 5 sion ) y a b

t 1

s .2 , i M) 1 es s p 8 7 e e A al 1

.5 sn s

AP Ste iv s =

) e on t

r ou m on R i 5 i

i e o t en is 0 s s rs n l s r ( (Study

ic g bl en

al Regr t , 1 i ict Pe o ) o c c ea M es ave r (7 d nne s e 2 AT r C e r F e n t ic p g o x p r chi Variab Neu E O A C RAP PW ar r e (Ste i Mat

e ly H e l

I v g : i T 7 s s an 1 A dua e E vi l Ch i 2 b ogre <.00 r a DV = Pr R p T P Ind *

294

APPENDIX F

INSTRUCTIONS FOR COMPLETING THESE QUESTIONNAIRES:

Thank you for agreeing to participate in this research. Your participation in this research is greatly appreciated. Please contact me if there are any questions or concerns.

The measures contained in this package may be completed in your own time and in any order you wish.

Before commencing, please read the information sheet and sign the consent form. It is a legal requirement when conducting research with human participants, that a consent form be signed before commencing the research. All sheets containing your responses are coded by a number. This coding is necessary to keep the responses for each person together. This is particularly important if you are completing the tests in a number of different testing sessions. Once the completed measures are returned, the consent form is removed and stored separately. Matching responses to the consent form will ONLY be done if you have requested personal feedback about your results. Please be assured that the anonymity of your responses will be upheld at all times.

The measures enclosed in this package are: A demographic information sheet. Satisfaction With Life Scale Anxious Thoughts Inventory Trait Meta Mood Scale Assessing Emotions Scale Problem Solving Inventory The COPE NEO Personality Inventory

These measures require you to read each question and indicate your level of agreement with each statement. On these measures, I am interested in your thoughts and perceptions. There are no right or wrong answers. Please note that for most pages there are questions on both sides of the pages. PLEASE circle a response for EACH question. Questions for the NEO Personality Inventory are in a booklet. Please DO NOT write on this booklet. Answers may be scored on the sheet labelled “NEO PI-R”, which is attached to the question booklet.

After completing all measures in this package, please contact me so that we can arrange a session time to complete the timed tasks. There are three measures that must be completed under my supervision. This should take approximately 1 ¼ hours. I can be contacted on 8303 3855 (work) or 8165 0875 (home) or via email at [email protected]. Completed questionnaires (please return ALL sheets) can be brought to the arranged testing session or could be posted to: Veneta Bastian C/O The University of Adelaide (Psychology Department) North Terrace Adelaide, SA 5005

295

APPENDIX G

Table G1: The Correlations Between the 15 COPE Subscales (Study 2)

1 15

08 14 . 1

1 04 13 19* .

* *

6 9 1 15* 3 2 . .

1 * *

1 11 13 . 11 . . 34* 23* . .

* *

1 10 02 10 16* . . 27* 23* . . .

* * 6 8

0 0 1 09 05 . 20* 24* -. . .

* **

06 03 . 9 1 11 02 -. 11 . . . . 2 20* - - . .

* ** **

11 . 01 08 7 8 9 4 7 1 05 10 . . . . . 3 3 36* - - - . . .

* * 2 1

08 01 07 11 1 7 07 0 0 02 ...... 1 - - - - .

* * *

* * * 0 7 8 8

05 05 1 5 0 1 1 0 0 0 16* . 4 3 3 . . . . .

* * 2). 5 6 ** ** **

0 . 0 . 2 -. 1 -.

4 6 0 1 1 3* 7* 1 0 1 0 15* 18* . . . . 2 2 2 2 3 ...... tudy S

* * * s (

** ** ** **

1 7 6 e

02 02 l 3 2 8 7 0 1 6* 2* 9* 1 0 0 . . . a 8 2 2 2 2 2 2 -. -...... bsc u

* S * *

** ** ** ** **

05 01 E 2 0 5 1 7 7 1 09 04 17* . . 23* . . 4 2 5 3 2 52* 18* - . P -...... - CO

- * * * * 1 5 ** ** ** ** **

* 1 1 0 3 9 9 0 6 1 1 1 e 01 10 06 18 . . . 3 1 4 3 3 7 43* 22* -. -...... -. th

n ee 13) tw

( e ) )

t 7 0 ) ( n

2 e )

n 1 (1 ) ns B s o ( 3 o (5

t em n ) i ( ) ) s t g n o t e 4 4 i a e r a ) i (6 tati t 1 t el e ( n m i o

ng po (8 o e e v ) rr e i

ort ( m g i g s 1 rp s 01 g t n a

( E c i Co ) pp U te Sup g p 9

l n ng

Di e A ng ) < . o i ) e ( ug a

in h g 5 Su e Reli p 2 r s ) e t pi l i 1 C ral * o 1 ( nt n t o D a * R : T 1 e e / t

n D ; l ou 1 ssin anc ( ng g in l C V o ve ur ( m 5 e e a i i a vi r o u al ni in s/ pt r t t G v r i p e i n e oho o .0 nt t ni l s t st c r ha p cu c an e s < c c l b o u m u o n A Pl I E S T P Re A F De M Be A Hum p Ta *

296

Table G2: The Correlations Between the 15 COPE Subscales and Emotional Intelligence (Study 2).

nt e

m * e 5 4 3 4 9 0 4 5 5 10 03 02 01 g 0 0 1 0 0 0 0 0 23* 23* ...... a -. -. -. -. .1 -. -. n a M

st s e ng i ru T d

* n *

ce C * * a 3 9 8

05 n 2 9 00 04 02 03 12 05 03 16* d 19* . . . . .0 .0 .0 . . . e -. rst . -. .2 .1 - e n ig d a n ll y U te ve n o I l l a

a n o n i

t o r, S 0 2 a i 6 4 1 2). 10 1 1 12 05 t s 10 01 00 06 01 05 00 . . i . . . .0 . .0 .0 . . . ye o -. - - -. -. l i t m Ma E (Study e U c n o n

* t 4 6 5 7 7 0 0 1 5 11 06 14 1 02 02 ep 0 0 1 0 1 14* . . .0 .0 .0 . . . . -. -. -. - -. -. llig - -. rc e te P In

ing

e * * * * * * * * * * * * * * * * * 0 0 2 4 ss tio 07 1 2 7 9 1 6 2 1 6* otional 0 0 0 0 17* . . . . o 4 2 3 2 -. -. . . .2 . .3 .2 .4 .2 . m Scal sse Em A

and E

* * i *

* * * s * * * * * * * a 1 8 0 5 4* 5* 12 7 1 3 0 p 0 15 15 1 1 21* 39* .1 .0 . e ale 3 4 -. .1 . . -. -. . .3 .2 .

-. -. c R s d o

Sub

* t * * i 7 4 e * * * E 0 3 0 1 5 3 r 5 8 0 0 02 8 9 3 ta Mo 0 0 1 1 a P 1 3 l O Scal ait Me n C r 15 C . .1 .1 .0 .1 -.

o .1 * i . T * * * * * -. * . * -. * * . * . * * * * * * * * 5 -.19* 5 8 7 0 08 . 9 2 7 5 4* 2 9 6 0 0 1 1 the . . 2 2 3 2

-. .2 tent -. -. . . .1 . .4 . .2 .2 -.28* n e At e tw e

t B

e n

s o m

t e n ti s g n o ations e i e l a i

e t g ort n t m i ot

r n

o e r e v e pp or m g i 1 o rpre s gi t n

ag i i

E c .0 l pp te C Su g g pi e e n ng D A n n i l < h al i i g Su e R se a t p l p T i r ce

n nt t Co Drug Us R

** n e g /

n na r D l ou ;

a g to n i l Co Ve o ve u t o essin / n e i a vi r o al i s ni h t ti G2: v i p tra i u e o o nt trum m l ni s t cep s ha c p c an e s e < .05 c e l b o m u

a Ac Pl In E S Turn P R A Fo D M Be A Hu p T *

297

Table G3: The Correlations Between the 15 COPE Subscales, Cognitive Abilities and Personality (Study 2). ss sne

* *

* * * o * * * * i * * * 1 8 0 0 7 8 3 10 8* 7 9 1* 8 1 15 1 2 2 34** . .0 .0 .1 . 2 2 2 -. . ent - -. . .4 .4 . . - -. -. nsci Co

eness * * * * * * * * 9 * * * * 3 9 bl 5 5 06 0 09 05 1* 6 0 9 6 9* 1 0 . 18** . . 2 2 1 -. - -. -. .1 .1 . . .2 .2 .2 . . - eea r g

A ) R ity -

al s

* * * * * * * * * * o * * * * * O 0 7 s 07 4 6 1 8 1 0* 3* 5* 9* 0 17* . .02 . ennes 3 2 4 2 1 - .1 -. .2 .2 .2 .1 . . . . . p Per (NE dy 2) O u t S

n o y (

* * * * * * *

lit * * * * * * * er 2 0 .13 0 0 a 04 02 04 1 9 8 1 1 1 3 v 0 1 0 0 24** . . . 3 3 a -. -. -. . .3 .2 .2 .3 .2 . . r - on t s x r e P E d m s .

an * * * * *

c * * * * * * * ** i 0 6 6 * * * t 2 8 4 2 3 06 1 0 0 2** 5** ies o . . 2 24* 3 4 2 3 5 2 -. - - -...... 30 .31 .43 . . -. - - - - -. ur e N Abilit

ive sk it rd a o

n T

W 0 1 3 o c 0 8 6 4 6 4 i i 06 01 07 1 0 1 t 0 0 0 . . . 19** 22** 21* . Cogn a . . et i n s, c o le h sso a

P c A s ve es i b t u i iti

l S i

es gn b c E o ed i c r A C t n a -. va . - - - - .0 .0 .0

-. - . -. M * 4 4 4 4 2 2 3 3 2 5 e 02 -. 09 12 0 1 0 0 0 16* v . . .0 .0 .0 .0 . . .1 . . . -. - - .14 s Ad -. si ’ e 15 COP res h ven g t o r n Ra e P e w t e

B t

n s

s on ion

em t n n o es e rt ag i

tati t o m oti re ng p

on e

e vi rrelat e p ort m g s rp s gi ti u n i i

c i .01 l pp U te Co S g E g e g e ngag D n A n n l i u < al i h g Su e r a se t p l p n i T r ce

n * nti i

D t Cop o R u e n s * n na e D l/ o s a

g to R ng l Ve o v ur m t o / 5; n e C i G3: vi re o al s ni p h t ti i trai iv u o nta tru ni s rni s ce ha c pp an e < .0 e ble e l u m

a Act P Ins E Su T Po R Ac Fo De M B Alco Hum p T *

298

Table G4: The Correlations Between the 15 COPE Subscales and ‘Life Skills’ (Study 2).

s y s t

or * * * * t ** ** ** o gh ** 4 9 5 n 13 04 11 3 06 11 16* 14* xi 25* 4 4 3 25* . . 19* 23* 3 -. -. -. . . . ou . . . - - - -. -. ve n An Th I

l) or * * * * * * t ** ** ** a ing ** lem t 8 0 4 13 06 -. 09 9 03 03 b o 3 4 3 25* 19* . . lv 22* 35* 19* 24* 3 -. -. -. . . . o . . . . . - - - -. -. ven (T So n Pr I

. l ) al

* * * n 2 ** ** ** ** o 5 3 7 y 09 13 09 3 05 08 09 ntr s 17* 15* 23* 3 4 2 30* 27* . d 2 -. -. -...... - - - -. -. -. tu Co Per S ( ’ s l

l e i h k c

a e ** ** ** l ** o 0 1 1 e S 06 07 07 10 r danc 7 06 05 . 00 10 08 . 15* f 14* 4 4 3 i . . . 2 i -. -. -. . . . . -. . o - - - Sty L v ‘

App A d n a

s e

ce l a .

c * * * * ** ** ** ing s ** lem 0 4 5 05 01 05 -. 06 12 4 v b iden 18* 16* b 3 3 2 l 27* 4 36* 31* 27* -. . . . u o . . nf S So

Pr E Co P O

n C e 5

tio -. -. -. * . * e * * c ** - ** - ** - 9 a al e 1 13 . 07 . 6 9 1 06 -. 04 -. 15* 16* 16* 15* 18* 2 22* . . 2 2 3 24* h -. -...... sf -. . . . . t - -. Sc

ti n With Lif Sa ee w t e

t B

n s

e n

n o o

t em ns t

ti s n o a e

i e ag i

t el ort n t m i ot

re ng r

o e e rr v e pp m g i s o rp s gi t n ag i i

c i C l ppo U te

Su g g E p e e l n ng D A n n i l

< .01 ug a h i i g Su e R r se a e t t p p l i T r c

* n n

o D t Co R

: u e g / e na n r D l o 4

an g to n i C Ve v o u m l essin / n e i al G vi r o u a i s ni pt ho ti ra

t v i r i p t e i u e o ot n t m n s l rn s c ha c p c an e s < .05; * e c l b l e u m u

n a Act P I E S T Po R A Fo De M B A Hu p T *

299

Table G5: The Correlations Between the Problem Solving Inventory Subscales (Study 2)

Table G5: The Correlations Between the Problem Solving Inventory Subscales (Study 2).

Problem Approach Personal Solving Avoidance Control Confidence Style Problem Solving Confidence 1

Approach Avoidance Style .56** 1

Personal Control .54** .56** 1

*p<.05; **p<.01

300

Table G6: The correlations Between the Problem Solving Inventory Subscales and Emotional Intelligence (Study 2).

t n

me 6 4 2 e 0 0 0 g . . . a - - - n a M

o st

s e g u in T . d )

n ce Car a 2 n t 03 07 02 s e -. -. -. r dy e ig d and ll tu y e te Un (S v

n e I c l

n e na Salo , g ion

r at 1 e tio lli 04 06 0 o . -. -. lis te i n I May Em

l na Ut o i on t i t 5 7 0 p e 0 0 1 . . . c r Emo d Pe n a

s g e l

ns a in * * * e * * * c s ss tio al 39 32 36 b -. Sc u sse Emo A y S r

-. -. nto * * * ir * * * a ve p 29 27 21 e

. . .

- - - R In d o o ing

y lv M e ** ** ** it o 9 7 3 ta al 4 3 2 e . . . Sc m S le it M b a Clar o r T - ion * Pr t 3 - 1 -

4 n 0 0 1 e . . . t t the A n e e w t

e Be

c s e n n e yl d i tio f St n e la e r

Co r

l o g o danc r C .01 in

voi lv < o p The *

Cont h A : * 6 ; al m S G oac le

on r b e s p l r o < .05 p b r

P A Pe p Ta *

301

Table G7: The Correlations Between the Problem Solving Inventory Subscales, Cognitive Abilities and Personality (Study 2). s s sne

u * * * o i 57* 40* 39* ent -. -. -.

nsci o 2). C y d

tu S y ( eness l b it 09 04 13 -. -. -. al eea r on

g s A r ity e -R) al PI n d P ss

o O s ne 10 an n 18* 24* e -. -. -. p es Per (NE i t O i l i b n A o i

e * * v ers i 10 t v i 31* 24* a -. r -. -. n t x Cog E es, m l s a i

c c * * * i t 5* 7* 5* bs o 4 3 6 u . . . ur S e

N ory

t rd n n o e o v i

t W k n a 2 1 2 c i I 0 0 0 ci et g Tas so n o in As

h v e l s P o e i tiv t i

S i l n ed m g e nc

Abi ve a s Co e v obl r ssi ic r P re -.10 . -.07 . -.01 . g s Ad e o h r n’ Mat t e P

v a R een w t e

e B

c s e n en yl o t i S fid

at n e el o nc

1 g C da i .0 in trol o e Corr v lv < h o p A T

Con h : **

c 7 ; al m S oa le G on r b e p rs o < .05 p bl r e

a P A P p T *

302

Table G8: The Correlations Between the Problem Solving Inventory Subscales and ‘Life Skills’ (Study 2).

2). y

s y s t tud

or t ou gh ** ** 1* n 0 4 4 xi 3 5 ’ (S ou ve h n An ills T I Sk

n e o f . i ti * e * . * . c * d ‘Life 7 7 a al n 1 3 31 c . . sf - - a -. ith L S s ti W le Sa a c s

b e

u c n ** a ** y S 7 d 3 r 0 ping 40 . 4 . -. voi Co nto A

n g Inve d

e n ng io ** i s t 2 05 8** v u pi o 4 l 2 , o Co Foc Em S m e l

o d m r e -. ng ** - s le 5 9 7** . u 0 b pi 3 e P . 2 . o . - h Co t Foc Pr een w t e

ce s e n n e yl o d i i t St a e onf

rrel C l o danc r ng e Co vi voi < .01 h ont p T

C h A Sol : ** 8 m e oac l onal e G s l r ob < .05; ppr b e r

a P A P p T *

303

APPENDIX H

Table H1: Hierarchical Regression of Satisfaction With Life Scale on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 2).

9 .1

08 a

. I p =

, 7 E Bet

7 , y

1 e d

r .0 lit

r i = 1. ce o ) le b 7 b asu A an ict a

19 i v l , r a ed 1 t ( r F Ad Me To Va e

s c ’

n 8 en e g .3

i I P

l 08 l a . E e p =

t t , Rav r 8 , n Bet

o 7 s , p e I e l

.00 al

r a = 0. R ) e o n l sc t 7 o lf- b b i e a

19 u i l , S ot r a 1 edic t ( r S m F To Va E ed) s

0 a evi P 7 1 0

nd 01 .4 a R .0 .0 .1 t -. st E y a p = Be

y ( ,

Te r 2 2

on 0 o

, ove

t l 2 d p EI g 1. n

.0 o = t e in

s Sa ) n

d r

, v e u 4 an n n r e o r n ) l t on m io I i ta a b t 19 t

, ge a rs a C aye 4 a s i e y ep i ( r n p 1 l d edic i F it e M r erc t al Ut Un Ma P Va S on

(

) s 0 rs n

e .3 9

P io a t .0 P p = o

WAT ,

O 8 Bet m e P

0 E , 0 E al ( N

g .0 k

= 1. Sc s ) e o in l 7 t a b ss on a

19 i e l , T se r a 1 edic t s ( n al r F o c A To Va i S at i e

f c

5 i

.1 L s

3 P

12 a ood 16* .0 h -. . p = t

As M 7 Bet e

7 d a , t 2 al Wi e .0 = 1.

Sc ) e on Wor M l n i to 5 t o

b c

i i i y c r a t 19 a i i i , i nt d r a r r e 3 e t et ( a sfact T r t i F t A Cl Rep Va on a h S

* P P 0 s d of

10 08 01 13 08 01 38 .0 e ta ......

i d -. e n on it B i p = an

s il , . s Ste 2 ) y a b

e t 1

s .5 M) , i r 2 s p 1 9 e g e A al 1

.2 sn s

AP Ste iv s =

) e on t

r R ou m on Re i 4 i

i e o t en is 9 s s rs n l al s r ( (Study

ic g bl en

c s t , 1 i ict Pe o ) o e c ea M ave r (7 d nne s e 2 AT c r C P e r F e i n t p g A o r x p Neu E O A C R PW Variab archi er i

(Ste Mat S

e ly e L l : H v g i 1 s s an 1 SW dua e H vi l Ch i 2 b ogre <.00 a r DV = Pr R p T P Ind *

304

Table H2: Hierarchical Regression of Problem Focused Coping on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 2).

7 .4

05 a .

- I p =

, 4 E Bet

5 , y

0 e d

r .0 lit e

r i = 0. c o ) le b 4 b asu A ict a

19 i van l , r a ed 1 t ( r F Me Va To e c s Ad ’

2 en

g .0

i I P

l l a 22* E . e p =

t t , r Raven 0 n Bet ,

o 6 , p I e l

.02

r a = 5. R ) e o cales n l t 4 o lf- b bs i e a

19 i l u , S ot r a 1 edic t ( r m F S Va To E ed) s

9 a P 1 1

evi nd 05 10 .2 a .0 .1 t -. -. R st E y a p = Be

Te 6 2

on 2

, ove

ory ( l 2 d p EI t g 1.

.0 o = t n in

s Sa ) e n

d r

, e u 4 an n n r e v o r ) l t on m io n i ta a b t 19 t , ge a a rs C aye 4 a s i I e ep i ( r n p 1 l d y edic i F e M r erc t it Va Ut Un Ma P S al

(

on ) s 2

n .0 rs e P

io a t 20* P . p = o

WAT ,

4 Bet m e P

O 0 , 2 E E al (

g .0 k

r = 6. Sc s ) e o in l N 4 t a b ss a

19 i l , T se r a on 1 edic t s ( n g r F o A Va To n i i at i

.0 s

Cop 5 P

12 d a ood 19* .0 -. . e p =

s ,

As M 7 u Bet e

0 d a , c t 3 o al e .0 = 3.

Sc ) e F Wor M l n to 2 t o

b c

i i i y c r a t 19 a i i i , i nt d r a r r e 3 e t et ( a T r t oblem F r Va A Cl Rep on h

P P 0 of P s d 12 12

05 06 08 .0 . . e ta 26* 38* . . .

- - . . i d e on n i it B p = s an

il , s . Ste 0 ) y a b e

t 1 r

s .1 M) , i 2 s p 3 2 e e A al 1

.3 sn s

AP Ste iv s =

) e on t

r R ou m on i 2 i

i e o t en is 0 s s rs n l s r ( (Study

ic g bl en

s t , 2 ical Reg i ict Pe o ) o e c ea M ave r (7 d nne s e 2 AT c r C e ch r F e i n t p g o r x p ar Neu E O A C RAP PW Variab r e i (Ste Mat

e ly H e : l v C g 2 i s s an 1 PF dua e H vi l Ch i 2 b ogre <.00 a r DV = Pr R p T P Ind *

305

Table H3: Hierarchical Regression of Emotion Focused Coping on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 2).

7 .2

a 08

. I p =

, 4 E Bet

2 , y

1 e d

r .0 lit e

r i = 1. c o ) le b 4 b asu A ict a

19 i van l , r a ed 1 t ( r F Me Va To e c s Ad ’

8 en g .6

i I P

l 05 l a . E e p =

t t , r Raven 8 n Bet ,

o 1 , p I e l

.00

r a = 0. R ) e o cales n l t 4 o lf- b bs i e a

19 i l u , S ot r a 1 edic t ( r m F S Va To E ed) s

8 a P 5 1 4

evi nd 02 .5 a .0 .1 .0 t -. R st E y a p = Be

Te 2 2

on 7

, ove

ory ( l 1 d p EI t g 0.

.0 o = t n in

s Sa e ) n

d r

, e u 4 an n v n r e o r ) l t on m io n i ta a b t 19 t , ge a a rs C aye 4 a s i e ep i ( r n p 1 l d y I edic i F e M r erc t it Va Ut Un Ma P S al

(

on ) s 9 n

.2 rs 0 e P

io a t .1 P p = o

WAT ,

4 Bet m e P

O 1 , 1 E E al (

g .0 k

r = 1. Sc s ) e o in N l 4 t a b ss a

19 i l , T se on r a 1 edic t s ( n g r F o A n Va To i i at i

1 o

.7 s

1 8 P

08 d Cop a ood .0 .0 -. e p =

s ,

As M 6 Bet e

4 d a , cu t 1 o al e .0 F = 0.

Sc )

e Wor M l n to 2 t o

b c

i i i y c r a ion t 19 a i i i , i nt d t r a r r e 3 e o t et ( a T r t F m Va A Cl Rep on h

P of E 0 s d 09 07

04 07 11 . . e ta 24* 18* . . . .0 - - . . i d e on n i it B an s p = il

s . , Ste ) y a b 3

t 1

s 7 M) , i 2 s p 2 3. e

e A al .1 sn s

AP = Ste iv )

on es t

r R 2 ou m on i i n

i e o t e is s s rs n l al Regre l s r ( 20 (Study

ic g c b , en

s t i ict a 7 Pe o ) o e c e M ( ave r d nne s 2 AT c r C F e chi re e i n t p g o r x p ar Variab Neu E O A C RAP PW r e i (Ste H Mat

e ly

e : l v C g 3 i F s s an 1 E dua e H vi l Ch i 2 b ogre <.00 a r DV = Pr R p T P Ind *

306

Table H4: Hierarchical Regression of Avoidance Coping on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 2).

7 .2

06 a .

- I p =

, 0 E Bet

0 , y

0 e

.0 lit v

r i i = 0. o ) le b p 2) 4 ss b A e ict te a

19 i l , r a ed 1 t ( (S r ogr F r Va To ly l P

5 ed dua c .1

I P

vi 13 i a . E p =

t van , r 0 Bet

o 1 , Ind p e Ad e

.01 s

r = 2. ’ R ur ) e o l s t 4 lf- b a e e a

19 i l , S r a 1 edic t ( Raven r M F

e Va To , c n e ales

* c ig 7 P 9 s 4

nd 11 02 ll .0 a 1 b .0 e t -. -. -. st t u y a p = n Be

, S

Te I 3 ) 2 l

, ove d

l 3 p EI g 2. na

.0 o = ise t in

s io Sa ) n

d r

, t e u 4 e n n r e o r l t on m io i ta R a b t 19 t , ge a rs a C ( aye 4 a s i e ep i ( r n l y d Emo edic i F M r erc h or c Ut Un Ma P Va t Ea

s ven 7

n n n .0 1 I

P 4 io a t d o .1 y p = o

n ,

it 4 Bet m e

3 a , al ) 1 E al 1

g .0

r = 3. Sc ) e p o in l rs 4 t e b ss a

19 i l , (Ste P se r a 1

edic t s ( ) r O F A Va To T E A

0 W

on 4 6 P

a ood .1 .0 g -. p = k

, s n

i M 7 a Bet e

5 a , t 2 al e .0 = 1. Cop

r n T

Sc ) e M l n io e to 2 t o t c b c

i i i y a r a t 19 a i i i , i nt d r a r c r e 3 e t ( a T o dan r t F s Va A Cl Rep s A

Avoi d

* r P 0 s o 02 11

03 05 19 .0 . . of e ta 52* 18* . .

- - . . i d -. e n it B on W p = i

c il , s i Ste 1 y a b t s

t 1

e e s .8 , i s p r 7 9 n e e A g al 1

.3 sn s

Ste iv s =

) e on t Re

r ou Pho m on i 2 i

e o t en is 0 s s rs n l d s r

ic g bl en t , 2 i ict n Pe o o ical c ea M ave r (7 d nne s e AT r C h a e r F e n t ) p g o x Neu E O A C RAP PW Variab arc M P er i A

: g (R 4 C s 2). e an 1 A c i e H l Ch tr 2 b a <.00 a DV = Pr R p T M (Study *

307

Table H5: Hierarchical Regression of Problem Solving Inventory (Total) on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 2).

.7 2

-. a p = t

, EI 2

1 Be , y 0

e .0 lit d i r = 0. e ) b c u 4 le tor s n A c a i 19 ab e ,

va i l d 1 a ( e t M F r

Var To e s Ad c ’ n

n 9 e .8

02 llige -. EI ta e p = Rav

t t e , r s, B

o 12 e , . l 0 In 0 a ep l .0 =

R - tor bsc lf 94) c u iona i e able

1 t i l , d S S a 1 e t ( ) r F d Var To e Emo s i

v h e

c d 7 P R

08 10 .2 07 05 Ea

. . ta -. -.

e est y an p = n B

, ory ( 2 T ve t

o 30 , o . n l 2 1 ep E e g

a t

.0 v n i = so S n nt

, e u 4) and n r n I r 9 o a tor on m e i i t y a c t 1 t y t i , ge a able C i a i ers ep d is (4 p 1) na d al e il F a e M t n r erc t Var P U U M on s r

e s 8 n P .1 P

io 10 O t -. ta p = E o

WAT) e ,

0 B m e P

8 N , l . 1 1 E a

( g .0 k

on = Sc s ) ) in le l tor 7 a a 9 b c ss i a

i l ot , 1 d se r a e t T (1 n T r F As ( io Va To t y r a i o

c t

9 o d .4 s o P

01 09 01 ven o . . . ta p = n

e , I

As M 1 B e

d 8 , g l r ta 1 a n i .0 = 0.

v Sc

) l Wo le n 5 to o t Me

b c

io i y t r a t 19 ai ic S i i , n i d r a t r r 3 e p e ( m a T tte r e F n l Va A Cl Re ob r Pho

d 08 00 08 05 26* 33* .00 es ta 33* . . . n -. . i of -. -. e t a nd i B p = on

il i . S 5, y a s b

5 t 1

s 2) . , i

s es 0 e ep 18 e A al

.4 t sn s

APM) iv S s =

e on

r R ou m on tudy it 2) i s n

i s o t Regr e i 0 s s r n le S t l s r ( e (

ic g 2 c e en

s , t i i ab T P o ab ) cal o e 7 c i av i d ( r nne s c C r e ree u e F i h tr n p g o r x p 2 t e Va Ne E O A C RAPM PWA erarc i (St Ma

e H lly I v : ge a i S n s u s P a 1

H5 d e

i r e = l g Ch 2 b o <.00 a DV Pr R p T Pr Indiv *

308

Table H6: Hierarchical Regression of Anxious Thoughts Inventory on NEO Personality Inventory (Revised) Subscales, Raven’s Advanced Progressive Matrices (RAPM) and Phonetic Word Association Task (PWAT) (Step 1) and on Each Emotional Intelligence Measure Individually (Step 2) (Study 2). 1 .6

02 a .

p = - I

, E Bet 61

, y

0. 0 e

r = .0 lit

i ) ce o le b 1 3 b asu A ict 9 a

i van l 1 r a , ed t 1 r ( Ad Me Va F To e s c ’

n 3 en e

g .4

5 i I P

l 0 l a -. E e p = Rav

t t ,

r 2 , n Bet

o 6 s , p I e l

.00 ale

r a = 0. R c ) e o n s l t 3 o b lf- b i e a

u 19 i l , S ot r a 1 edic t ( S r m F Va To E ed) s

* 6 evi a P 1 4 4

nd 03 .0 a 1 R .0 .0 t -. -. st E y a p = Be

y ( ,

r Te 7 2 o

on 2

, ove t

l 2 d p EI g 2. n

.0 o e = t in

s Sa ) n v

d r

, e u 4 an n n r e o n r ) l t on m io i ta a b t 19 t , ge a a rs C y I aye 4 a s i e ep i ( t r n p 1 l d i edic i F e M r erc t al Va Ut Un Ma P S on

(

) s rs 9 n e

.9 0

P P io

a t .0 p = o

WAT , O

0 Bet m e P

E 0 , 0 E al N (

g .0

r = 0. Sc s ) e o in l 7 t a b ss a

19 i l , T se r a 1 edic t s ( n ory on r t F o A Va To i n e at i v

0 n

.1 s

I 8 1 P

s 15* a ood .0 .0 t . p = -

h ,

As M 2 g Bet e

1 d a , t 1 al e .0 = 2.

Sc ) e Wor M l n to 5 Thou t o

b c

i i s i y c r a t 19 a i i i , i nt d r a r r e 3 ou e t et ( a T r t F xi Va A Cl Rep on h

P P 0 s d 01 07 00 02 03

of An 01 .0 . . . . . e ta 79* .

- - - - - . i d e n on it B p = i an

il , s . Ste 9 s ) y a b

t 1 e

s .8 M) , i 2 s p 7 2 e e A al 4

.6 sn s

AP Ste iv s =

) e on t

r R ou m on i 1 i

i e o t en is 0 s s rs n l s r al Regr ( (Study

ic g bl en

s c t , 2 i ict Pe o ) o e c ea M ave r (7 d nne s e 2 AT c r C e r F e i n t chi p g o r x p Neu E O A C RAP PW Variab ar r e i (Ste Mat

e ly H e l I : v g i 6 T

s s an 1 H A dua e vi l Ch i 2 b ogre <.00 r a DV = Pr R p T P Ind *

309