INFORMATION TO USERS

The most advanced technology has been used to photograph and reproduce this manuscript from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer.

The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction.

In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion.

Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand corner and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book.

Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order.

University Microfilms International A Bell & Howell Information Com pany 300 North Z eeb Road. Ann Arbor, Ml 48106-1346 USA 313 761-4700 800 521-0600 Order Number 0031130

Cognitive processing ability: An examination of attention, coding and planning and their relationship to self-concept and locus-of-control in eight and nine-year-old children

Phillips-Carmichael, Irma Elodea, Ph.D.

The Ohio State Univeraity, 1990

Copyright ©1990 by Phillips-Carmichael, Irma Elodea. All rights reserved.

UMI 300 N. ZeebRd. Ann Arbor, MI 48106 COGNITIVE PROCESSING ABILITY:

AN EXAMINATION OF ATTENTION, CODING AND PLANNING

AND THEIR RELATIONSHIP TO SELF-CONCEPT AND LOCUS OF CONTROL

IN EIGHT AND NINE YEAR OLD CHILDREN

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree of Doctor of Philosophy in the Graduate

School of The Ohio State University

By

Irma Phillips-Carmichael, B.A., M.A.

*****

The Ohio State University

1990

Dissertation Committee: Approved by

James V. Wigtil

Henry Leland James V. Wigtily Jack Naglieri Advisor Educational Services Susan Sears and Research Copyright by Irma Phillips-Carmichael 1990 To my Mother Rosalind Fraser: Who Taught Me the Love of Learning and Ethel Johnson: Who Taught Me the Essentials of Accepting and Loving Others ACKNOWLEDGMENTS

With the completion of this degree, I am aware of myself as one who is grateful for the care and nurturance of others. Achieving this degree is indicative of my own efforts and the supportive energies of numerous others. I especially want to express my appreciation and gratitude to the following people.

Committee chair, James Wigtil, for assuming the leadership role in my doctoral program midstream and for being faithful in his guidance of me through this process.

I am especially indebted to Jack Naglieri for his critical guidance and direction in this process. I am also appreciative of his constant encouragement and willingness to give of his time.

Henry Leland for appreciating and encouraging scholarship in me.

Susan Sears for her support of my professional goals.

Donald Tosi who echoed that I had the ability to achieve at a time when I most thought I could not.

Tony Carmichael, my husband, whose love, faith and belief in me served as an anchor, and maintained me in times of trial and joy. He was also a good critic and "sounding board" for my ideas.

My children, Becky and Jamie, who helped me to maintain my ability to stay in touch with all of reality.

Joyce McCabe for many hours of emotional support which included direction in the design and interpretation of the research data.

Norm Lobdell for his critical contributions in the analysis and interpretation of the data.

My friends,* too numerous to mention, who provided continuous encouragement and support. However, special thanks are in order for Anita Jackson, who gave a tremendous amount of her time and energy in order for me to make deadlines in a timely manner. I am indebted also to Nancy Forman who helped me to calm my anxieties, and remain focused upon the task at hand.

~My employers, Ken Mitchell and Steve Leclair. Their support of me in the work environment facilitated the completion of this degree. I am very indebted to them for the time they gave to me. Special thanks to Steve Leclair for his critical comments and the many hours spent editing this document. Debra Wroe for stepping in as typist at the last minute and giving this project her very best effort.

Finally I am most grateful to Dr. Mary Claytor, Daria Clair, Betsy Dimond, Joanne Crabbe, Mary Talbert, Don Guss, Elmo Kallner, Dave Kindinger, Susan Van Atta, and Edgar Erlanger for their assistance in the gathering of the data.

iv VITA

April 25, 1949 ...... Born - St. Thomas, Virgin Islands

1971...... B.A., Psychology MacMurray College Jacksonville, Illinois

1973...... M.A., Rehabilitation Counseling University of Iowa Iowa City, Iowa

1974-1977...... Rehabilitation Counselor Curative Workshop Milwaukee, Wisconsin

1977-1981...... Rehabili tation Counselor/Manager Goodwill/Central Ohio Rehabilitation Center Columbus, Ohio

1981-1983...... Counselor/Program Manager Upward Bound The Ohio State University Columbus, Ohio

1986-198 7 ...... Therapist Comp Drug Corporation Columbus, Ohio

1987-198 9 ...... Counselor Marburn Academy Columbus, Ohio

1989-Present...... Program Manager International Center for Industry, Labor and Rehabilitation Columbus, Ohio

FIELDS OF STUDY

Major Field: Educational Services and Research, Studies in Counselor Education

Cognate Field: Studies in Counseling Psychology Studies in Developmental Psychology TABLE OF CONTENTS

DEDICATION...... ii

ACKNOWLEDGMENTS...... iii

VITA...... v

LIST OF TABLES...... ix

LIST OF FIGURES...... x

CHAPTER

I. INTRODUCTION...... 1

Nature of the Problem...... 1 Need for the Study...... 8 Significance of the Study...... 9 Purpose of the Study...... 10 Hypotheses...... 11 Definition of Terms...... 12 Limitations of the Study...... 15 Summary...... 16

II. REVIEW OF THE LITERATURE...... 17

Cognition...... 18 History and Development of Theories of Intellectual Functioning...... 18 Intelligence - Definitional Perspectives...... 20 Factorial Approaches to Intelligence...... 21 Non-Factorial Theoretical Approaches to Intelligence.. 22 The Planning, Attention Simultaneous and Successive Cognitive Processing Model...... 26 Emotion...... 32 Definitions of Emotion...... 32 Theories of Emotion...... 35 Experiential/Existentialist Theorists...... 36 Behavioral Theorists...... 37 Cognitive Theorists...... 38 Wholistic Theorists...... 40

vi The Interdependence of Emotion and Cognition: Empirical Findings...... 42 Attention...... 44 Perception...... 45 Memory...... 47 Learning...... 49 Academic Performance/Achievement...... 50 The Relationship Between Emotion and Personality...... 52 The Development of Self-Concept and Locus of Control in Children...... 54 Self-Concept Development...... 54 Locus of Control Development...... 59 The Relationship Between Intelligence and Self-Concept or Locus of Control...... 66 Self-Concept and Intelligence...... 68 Locus of Control and Intelligence...... 72 Summary...... 82

III. METHODOLOGY...... 85

Introduction...... 85 Research Setting...... 85 Population...... 86 Subjects...... 86 Instruments...... 87 Cognitive Assessment Battery...... 87 Piers-Harris Children's Self-Concept Scale...... 95 Nowicki-Strickland Locus of Control Scale for Children...... 98 Hollingshead Four Factor Index of Social Status...... 99 Data Collection...... 101 Statistical Analysis...... 103

IV. ANALYSIS OF THE DATA...... 109

Sample Demographics...... 109 Findings by Hypotheses...... Ill Canonical Analysis...... 121

V. SUMMARY, DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS 124 « Summary...... 124 Discussion...... 128 Conclusions...... 133 Recommendations...... 134

vii REFERENCES...... 137

APPENDICES

A. Parental consent form...... 159

B. The Hollingshead Four Factor Index of Social Status (HFFISS)...... 162

C. Letter of assent...... 164

D. Pearson Product Moment Correlation Coefficients between PASS Model and independent variables Piers-Harris Children Self-Concept Scale (PHCSCS).. 166

Pearson Product Moment Correlation Coefficients between PASS Model and independent variables Nowicki-Strickland Locus of Control Scale for Children...... 166

E. Intercorrelations among subscales of PASS Model 169 LIST OF TABLES

TABLE PAGE

1. Sample demographics by frequency and percent...... 110

2. Partial correlations of the PASS Model (age effects removed) and self-concept...... 117

3. Partial correlations of the PASS Model (age effects removed) and locus of control...... 118

4. Intercorrelation among subscales of the PASS Model (age effects removed)...... 120

5. Summary of canonical analysis for PASS Model PHCSCS and NSLCSC...... 123

6. Statistically significant Pearson Product correlations by null hypothesis (age effects removed)...... 128

ix LIST OF FIGURES

FIGURES PAGE

1. Diagram illustrating the relationship among the components of the Planning, Attention Simultaneous and Successive Model...... 30

x CHAPTER I

INTRODUCTION

Testing is a way of life in American schools. Ysseldyke and

Algozzine (1982) assert that an estimated 250 million tests are administered to 44 million school age children each year. The purpose of assessing human cognition, Sunberg (1977) maintains, is to gather information so that decisions about persons might be made. The ultimate goal of this decision-making process is to establish diagnoses and develop intervention plans (Osipow, Walsh, & Tosi, 1984; Sattler,

1982; Sunberg, 1977).

The primary method utilized for assessing individual differences, making decisions and developing intervention plans is tests of intelligence (Reschley, 1982; Ysseldyke & Algozzine, 1982). Originally designed to identify who might benefit from academic involvement

(Hothersall, 1984; Ysseldyke and Algozzine, 1982), the main purpose of tests of intelligence soon became and continues to be predicting school success. (Sunberg, 1977; Ysseldyke & Algozzine, 1982). Thus, measures of intelligence have emerged as the primary predictors of individual success and achievement in our culture.

Nature of the Problem

Numerous theorists and researchers maintain that achievement is not only the result of intellectual ability, but a combination and interaction of other cognitive factors such as motivation, self-concept and/or locus of control, each having the capacity to influence and

mediate individual success and achievement (Brookover, Thomas &

Paterson 1964; Coopersmith, 1967; Crandall, Katkovsky & Crandall, 1965;

Nowicki & Strickland 1973; and Shavelson, Huber & Stanton, 1976).

Farls (1967) found that a strong correlation between self-concept and

achievement existed. High achievers score high on self-concept scales

while low achievers score low on self-concept scales. In addition,

self-concept was found to be correlated to reading and mathematics

(Williams and Cole, 1968), with higher levels of functioning associated with high self-concept and vice versa.

Despite what appears to be an interdependence between these

personality factors and achievement, the impact of variables such as

self-concept and locus of control are infrequently incorporated into

intervention plans (Broadfoot, 1979). Instruments which assess

emotionality are typically used only when the child consistently

displays negative or acting out behavior in the school environment.

For the most part, only a portion of the possible assessment resources

are utilized in the planning and intervention process with school age

children.

The rationale for the lack of integration of emotionality into the

cognitive assessment and subsequent intervention process with school

children is unclear. One possible reason for this ambiguity is related

to the historical perception of emotion in psychology which have viewed

cognition and emotion as separate and discrete processes with little

relationship to one another (Meichenbaum, 1980; Zajonc, 1980).

Psychology has often viewed emotion as post cognitive, ancillary and/or a nominal process in human cognitive functioning (Campos & Barrett,

1984; Meichenbaum, 1980; Zajonc, 1980). More specifically, cognition is defined as the process of thinking, attending, reasoning, imagining, judging and remembering (Gerow, 1986; Houston, 1981), while Izard

(1978, 1984) defines emotion as having either (1) neurophysiological- biological, (2) behavioral-expressive or (3) subjective-experiential components. The subjective-experiential component refers to emotion as an inner or feeling state of the individual. Viewed from this narrow perspective, emotion is understood as a dynamic process which varies from moment to moment (e.g., a person is happy one moment and sad the next.) The broader subjective, which is the perspective on which this research proposal was based, views this construct as a subsystem of personality. As such, emotion is a sustained and purposeful behavior whose influence continues over time (Izard, Wehmer, Livsey & Jennings,

1965; Plutchik, 1980; Pribriam, 1980; Tompkins, 1963). Emotion can be viewed as a stabilizing, motivational, adaptive and organizing element in hyman development in general, and in the emergence of the self in particular.

Even though the relationship between cognition and emotion is enigmatic, the assessment of intellectual functioning through the use of tests of intelligence is not without other problems as well.

Controversy associated with labeling and its negative impact upon students, and cultural bias due to such things as racial and ethnic background, gender, and language differences are common and popular shortcomings levied against tests of intelligence (Kamin, 1974,

Sattler, 1982; Ysseldyke & Algozzine, 1982). However, a more salient weakness of intelligence testing is the focus on ability levels

rather than mental processes (Das, 1979, Hothersall, 1984; Sattler,

1982; Sternberg 1980, 1986; Ysseldyke & Algozzine, 1982).

A focus on ability is an emphasis on the outcome or the product of

intellectual assessment. The usual implication of such an emphasis has

been that those involved in diagnosis and planning with children have

been interested almost exclusively in what score the individual

achieves and what that score suggests the individual might be able to

do. Das (1979) and Sternberg (1980, 1986) assert that a major

limitation inherent in the ability orientation is the lack of focus on

how tasks are accomplished, or a process approach to the investigation of intellectual functioning. A process approach focuses on what

strategies a person has developed and how that individual might be

trained to perform a given task more efficiently. The theoretical perspective upon which much of the process approach to intellectual

functioning is based is referred to as the information processing model of intelligence (Das, Kirby and Jarman, 1975) and is an outgrowth of work by Luria (1966, 1973, 1980). Luria observed that there are three

functional units within the human brain responsible for its basic

functioning. The first functional unit is the diencephalan and medial

regions of the cortex, located in the brainstem and responsible for arousal and attention. This functional unit is characterized as a balance between excitation and inhibition and is associated with a high mobility of nervous processes which facilitate change from one activity

to another. An essential component of arousal is attention, which is

the ability to discriminate between stimuli. 5

The second functional unit of the brain is responsible for

reception, analysis and storage of information. This functional unit,

which encodes information in an all or nothing manner, is located in

the lateral regions of the neocortex on the convex surfaces of the

hemisphere. Luria (1966) postulated that this area was responsible for

the two ways used to encode information into the brain, simultaneous

and successive processing.

The third functional unit of the brain is responsible for

planning, organizing and programming. Located in the prefrontal lobe

of the human brain, this functional unit inspects and regulates.

Meichenbaum (1980) and Sternberg (1980) both have referred to this

function as metacognition. Luria (1966) maintained that the above

three units are interactive and function together as one. He believed

that no functional unit hierarchy existed because of their

interdependence.

These functional units are each subject to developmental changes.

Luria (1973) maintained that with the maturation of the prefrontal

regions of the cortex, a child becomes capable of planning at age three or four. Growth in this ability to plan peaks significantly again between the ages of seven or eight. The cells in the surface area of

the frontal cortex continue to increase slightly until late adolescence

and then plateau. Because of the above maturational considerations,

children between the ages of eight and nine will be the focus of this

study. Traditional tests of intellectual functioning have been designed

to examine only operation of functional unit two, which focuses on the

way in which information is encoded (Das, 1975; Naglieri, 1989;

Naglieri & Das, 1988). The current view of intellectual assessment is

therefore limited because it emphasizes coding alone. Other mental

processes such as arousal/attention and planning have yet to be

investigated as components of human intellectual functioning. To date,

no empirical investigations exist on the relationship between emotion

(as identified in personality variables, such as self-concept and locus

of control) and the concept of intelligence from a process perspective

including arousal/attention and planning.

Researchers investigating the relationship of self-concept and/or

locus of control and intelligence have utilized measures which focus only on ability or outcome. These studies have yielded conflicting and

contradictory results. While some researchers have found a positive

relationship between self-concept and intelligence (e.g. Lawrence,

1974; Phillips and Zigler, 1980), others have found no such

relationship (Gold, 1978; Kulkarni, 1982). Likewise, some research has yielded a non-existent or negative relationship between locus of

control and intelligence (Janoson, 1977; Martin and Coley, 1984), while others yielded a positive relationship (Brown, 1980; Crandall,

Katkovsky & Crandall, 1965).

Actual research examining self-concept or locus of control and its

relationship to intelligence among children is limited. Historically,

studies examining self-concept and/or locus of control among children

have usually focused on the relationship of these two personality variables to specific achievement areas. Nevertheless, the few studies examining these two personality variables with intelligence amnng children also yielded inconsistent findings. Positive relationships between intelligence and self-concept among children were found by

Phillips and Zigler (1980) and Schneider, et al. (1984). Negative relationships were found by Padwal (1984) and Yarborough (1980).

Likewise, studies designed to examine the relationship between locus of control and intelligence among children have resulted in the identification of both positive (Brown, 1980; Swanson, 1980; Walden &

Ramey, 1983) and negative relationships (Perna, Dunlap & Dillard,

1983).

In summary, studies which have examined the relationship between self-concept and/or locus of control and intellectual functioning have been based on models of intelligence which are ability or outcome oriented rather than focusing on a process perspective. The results of these studies have been mixed and inconclusive. Examination of intelligence from a process perspective may provide insight into the interdependence of cognition and emotion because of the inclusion and consideration of the metacognitive process of planning. Commonalities which describe and define intelligence (e.g., planning) and emotion

(e.g., self-concept, locus of control) may be the basis of the interdependence of these two systems of mental processing. Planning, self-concept and locus of control all refer to the processes of inspection, evaluation, regulation and/or goal-directed behavior (Das,

1984b; Das & Naglieri, 1989; Lefcourt, 1982; Luria, 1973; Newman &

Newman, 1986; Rotter, 1966). In some ways, these three constructs are all involved in gathering, selecting and/or evaluating available data in order to create new schemas. For example, the process of planning involves inspection and regulation, self-concept is the result of evaluation and interpretation of individual experience, and the categorization of the vast amounts of information gathered about self

(Newman & Newman, 1986). Locus of control may be viewed as a metacognitive process because it involves goal-directed behavior arising from a personal perception of control of the environment.

Lefcourt (1982) describes locus of control as the self-regulatory factor which assists individuals in the development of personal strategies. Thus, planning may be a regulatory link through which these two concepts of human mental processing may interrelate. The intent of this research was to examine the relationship between the personality variables of self-concept and locus of control and the information processing model of intelligence as operationalized by Das and Naglieri (1989), and Naglieri and Das (1988) among children who are eight and nine years of age.

Need for the Study

The relationship of an interdependence between cognition and emotion is typically not examined. The impact of the historical separation of cognition and emotion has been to impede the study of such variables as intelligence and its relationship to personality constructs such as self-concept and locus of control. In a manner consistent with tradition, most previous research designed to investigate the relationship between intellectual function and self-concept or locus of control focused on variables such as reading and math. Few studies have been developed to investigate the

relationship of intelligence and self-concept or locus of control.

Studies simply do not exist to assess the relationship between these personality variables and a process approach to intellectual assessment. In addition, research in the area of self-concept and

locus of control has often been conducted without careful attention to the impact of intervening variables such as socioeconomic status, gender, or ethnicity (Battle & Rotter, 1963; Bessey, 1982; Keith,

Pottebaum & Eberhart, 1985; Phillips & Zigler, 1980; Stipeck, 1980).

Finally, there is need to understand the way in which these two types of cognitive processes interact and function in order to enhance the effectiveness of intervention with clients. Leland (1982) asserts that positive change in individuals is demonstrated best when the intervention focuses on building personal strengths rather than lessening or eliminating weaknesses. If there is a consistent relationship among these variables, practitioners may be better able to develop intervention techniques which focus on personal strengths rather than weaknesses.

Significance of the Study

The significance of this investigation was based on its focus on a process approach to intellectual functioning and assessment (Das, 1979;

Naglieri & Das, 1987) rather than an ability approach (Weschler, 1974) to intellectual functioning. The primary utility of this approach is its potential use by counseling, psychological, and educational practitioners in understanding, developing and teaching more efficient strategies for learning and problem-solving (Brown, 1978; Das, 1979, 10

1984,a,b; Luria, 1973; Feuerstein, 1979); Naglieri 1989; Sternberg,

1986).

This research has the potential to broaden the knowledge base about one aspect of human functioning. More data about how a person thinks can help to make the diagnostic and predictive abilities of practitioners in the decision-making process more efficient (Sunberg,

1977). Specifically, Naglieri and Das (1987) note that a process approach to intellectual function which also focuses on arousal/attention may be of particular utility in identifying individuals who are learning disabled, hyperactive, mentally retarded or gifted. This study was designed to add to our understanding of the relationship between a process model of intelligence and personality variables such as self-concept and locus of control in children ages eight and nine. The impact of intervening variables such as gender, ethnicity, and socioeconomic status were also investigated.

Purpose of the Study

The purpose of this study was to determine if there is a relationship between the information processing model of intelligence theorized by Luria (1966, 1973, 1980) and operationalized by Das, Kirby

& Jarman, (1975, 1979), Naglieri and Das, (1988), and Das and Naglieri

(1989) and the personality variables of self-concept and locus of control among children who are eight and nine years of age. This information processing approach to intellectual functioning includes such factors as arousal (attention), coding and planning. 11

Hypotheses

Below are null hypotheses which were examined in this study.

Correlation coefficients for each of the following hypotheses were tested at the pc.Ol level of significance.

1. There is no relationship between intelligence measured by the

cognitive process of attention and self-concept among children

ages eight and nine.

2. There is no relationship between intelligence measured by the

cognitive process of coding and self-concept among children ages

eight and nine.

3. There is no relationship between intelligence measured by the

cognitive process of planning and self-concept among children ages

eight and nine.

4. There is no relationship between intelligence as measured by the

cognitive process of attention and locus of control among children

ages eight and nine.

5. There is no relationship between intelligence measured by the

cognitive process of coding and locus of control among children

ages eight and nine.

6. There is no relationship between intelligence measured by the

cognitive process of planning and locus of control among children

ages eight and nine.

7. There is no relationship between attention and coding among

children ages eight and nine. 12

8. There is no relationship between attention and planning among

children ages eight and nine.

9. There is no relationship between coding and planning among

children ages eight and nine.

10. There is no relationship between simultaneous and successive

processing among children ages eight and nine.

11. There is no relationship between intelligence and the personality

variables of self-concept and locus of control among children ages

eight and nine.

Definition of Terms

Below are definitions of terms used in this research. Each is stated below.

1. Affect/Emotion: A reaction involving the subjective inner feeling

states of individuals. These feelings often provide a sense of

continuity over a lifetime and may serve a stabilizing and

organizing function in development (Gerow, 1986; Izard, 1984).

2. Arousal: The component of intellectual processing responsible for

regulating and maintaining cortical tone and wakefulness. This is

the functional unit responsible for attention (Luria, 1973;

Naglieri & Das, 1987).

Selective Attention: A specific form of arousal which

involves the ability to discriminate between stimuli. This

may involve extended activity over a period of time and

specific signal detection (Naglieri, Prewett & Bardo, 1989;

Posner & Boies, 1971). For purposes of this study, attention

was measured by Selective Attention Receptive (SA-R) and Selective Attention Expressive (SA-E) of the Cognitive

Assessment System (CAS) (Naglieri & Das, 1988).

Children: Individuals who are between birth and 12 years old

(Eric, Psychology Abstracts). For purposes of this study, only eight and nine year olds were included as subjects.

Coding: The component of cognitive processing responsible for analysis, storage and rehearsal of information (Das & Heemsbergen,

1983).

Simultaneous Processing: Stimuli are arranged in a

simultaneous manner to make a decision. Processing is

integrated and usually in semi-spatial form; the synthesis of

separate elements into groups (Naglieri & Das, 1987; Sattler,

1982).

Successive Processing: Stimuli are arranged in sequence in

order to make a decision. Processing is sequence-dependent

and temporal based (Sattler, 1982). A system of cues

consecutively activates the component (Naglieri & Das,

1987). For purposes of this study, coding (simultaneous and

successive processing) was measured by the Figure Memory;

Matrices; Successive Word Recall; Sentence Repetitions and

Questions subtests of the Cognitive Assessment System (CAS).

(Das & Naglieri, 1989)

Cognition: The mental process of sensing, perceiving, knowing, judging, imagining and problem-solving; the differentiation and development of concepts involved in the processing of information about the world (Gerow, 1986; Houston, 1981; Sunberg, 1977). For purposes of this study, cognition was measured by the CAS (Das &

Naglieri, 1989). Subtests of the Cognitive Assessment System which were used in this study are: Selective Attention -

Receptive (SA-R); Selective Attention - Expressive (SA-E); Fi_gure

Memory; Matrices; Successive Word Recall; Sentence Repetitions and

Questions; Planned Connections and Visual Search.

Locus of Control: The perception of a casual relationship between behavior and a given reward contingent upon whether the individual views the reinforcement as the result of personal action or by chance. This process is generally believed to progress from external to internal locus of control over the lifespan of an individual (Damon & Hart, 1988). For purposes of this study, internal and external locus of control was measured by Nowicki

Strickland Locus of Control Scale for Children (NSLCSC). (Nowicki

& Strickland, 1973).

External Locus of Control: The interpretation of a

reinforcing event as the result of luck, chance, fate or under

the control of powerful others (Rotter, 1966).

Internal Locus of Control: The interpretation of a

reinforcing event as the result of personal behavior,

characteristics and action (Rotter, 1966).

Planning: The goal related component of intellectual processing responsible for executive functioning that acts to regulate or control the encoding of information; the transformation and manipulation of memory codes and production of a response.

Includes decision-making, judgment, evaluation of one's own activity and that of others; strategy-making (Das & Heemsbergen,

1983; Das 1984a,b). For the purposes of this study planning was

measured by the Planned Connections and the Visual Search subtests

of the Cognitive Assessment System. (Das & Naglieri; 1989).

8. Self-Concept: A group of integrated cognitions a person uses to

describe and explain self. A person's perception of him or

herself. Individual attribution of one's own behavior. These

perceptions are formed by personal experiences and interpretation

of one's environment. These perceptions are influenced by the

evaluations and reinforcement of others (Forisha-Kovach, 1983;

Newman & Newman, 1986; Shavelson & Marsh, 1986). These

perceptions are dynamic and persist throughout the life of the

individual. For purposes of this study self-concept was measured

by Piers-Harris Children's Self Concept Scale (PHCSCS) (Piers &

Harris, 1969; Piers, 1984).

9. Socioeconomic Status: The position individuals or nuclear

families occupy in the status structure of a given society as

determined by wealth, occupation or social class (Hollingshead,

1975; Sattler, 1982). For purposes of this study socioeconomic

status was measured by the Hollingshead Four Factor Index of

Social Status (HFFISS) (Hollingshead, 1975).

Limitations of the Study

The major limitation of this research may be that it was a correlational study. Correlations reflect relationships between variables which may vary with each circumstance. As such, no causal statements were made regarding any relationship found between variables

(Gay, 1981, 1987; Guilford & Fruchter, 1978). 16

A second limitation of this research was the use of self-report

instruments for self-concept and locus of control. The limitation of

self-report instruments is their susceptibility to conscious and

unconscious distortions (Nowicki & Strickland, 1973; Piers, 1984). In

addition, new evidence suggests that to some degree, self-report

measures assess individual differences in self-presentation (Archer,

Diaz-Loving, Gollwitzer, Davis, & Foushee, 1981; Batson, Bolen, Cross &

Neuringer-Benefiel 1986; Fultz, Batson, Fortenbach, McCarthy & Varney,

1986). Therefore, the results may describe how respondents want

themselves to be seen by others rather than presenting a clear picture

of that individual's perceptual and emotional reaction.

Last, even though the Piers-Harris Self Concept Scale for Children

has excellent validity and reliability, this measure of self-concept is

not firmly based on a single conceptual framework (Wylie (1961). Wylie

continues that this is true for most measures of self-concept. A lack

of a theoretical base or frame of reference creates difficulties when

discussing the implications of research findings (Gay, 1981, 1987).

Summary

This chapter introduced the problem, background information,

rationale for the study, purpose of the study, hypotheses to be tested,

definition of terms used, and limitations of the study. Chapter II

presents a review of literature pertaining to the correlation between measures of intelligence and the personality variables of self-concept

and locus of control. Chapter III presents a description of the

research methodology which includes descriptions of the research

setting, subjects, instruments, procedures for collecting the data,

research design and statistical procedures used to analyze the data. CHAPTER II

REVIEW OF THE LITERATURE

This study was designed to determine if a relationship exists between emotion (as operationalized in personality variables like self-concept and locus of control) and cognition (as operationalized in the process oriented models of intelligence espoused by Das, Kirby &

Jarman (1975, 1979), Das & Naglieri (1989), and Naglieri & Das (1988) in children ages eight and nine. This chapter includes a review of related and supporting background literature and is presented in three sections. The first section is a review of cognition as it is operationalized in the construct of intelligence. This discussion of intelligence includes a brief summary of definitions, theoretical conceptualizations of intelligence and a detailed description of the

Planning, Attention, Simultaneous and Successive (PASS) cognitive processing model of intelligence most recently operationalized by Das and Naglieri (1989), and Naglieri & Das (1988).

The second section of this chapter contains a review of the literature on emotion. Included in this section are a review of (a) definitions and theories of emotion, (b) empirical data suggesting an interdependence between cognition and emotion and (c) the relationship between emotion and personality. This section also provides a review of the development of emotion in children with special attention paid to the emergence of personality variables such as self-concept and locus of control among children. The third section of this chapter

17 18 presents a review of empirical data which suggest a relationship between intelligence and self-concept or locus of control.

Cognition

History and Development of Theories of Intellectual Functioning

The investigation of intellectual variation has its origins in the study of individual differences whose history may be traced along a continuum including the contributions of several disciplines, the most prominent of which is physiology (Rattan & Rattan, 1987). Nineteenth century physiologist, H. L. Helmholtz was a pioneer in the study of individual reaction time through investigations which examined the speed of a nerve impulse from point of stimulation to muscle contraction in frogs. Sensory psychophysicists G. Fechner and E. Weber studied individual differences as well by examining the ability of humans to discern minute differences in thresholds of awareness to sensory and perceptual stimuli. F. C. Donders elevated the study of individual differences to the complex mental processing human beings

(Hothersall, 1984; Sattler, 1982).

Others outside the study of physiology have also provided impetus for the examination of individual differences. Charles Darwin's theory of natural selection which discussed the importance of individual adaptation and survival in confronting changes in nature helped to focus attention upon the importance of human variation (Hothersall,

1984). Statisticians Frances Galton and Karl Pearson developed mathematical methods to assess and represent these individual differences. Understanding and clarity regarding individual 19 differences relative to the ability of humans to do such things as

remember, perceive, and judge were enhanced by the investigatory work

of psychologists like Hugo Munsterberg and Hermann Ebbinghaus. In

fact, Ebbinghaus helped to devise an assessment method using analogies

to assess general intellectual ability (Hothersall, 1984). J. M.

Cattell demonstrated that mental ability could be studied experimentally (Sattler, 1982) and assisted in the development and dissemination of "mental tests" in the United States (Rattan & Rattan,

1987).

The initial impetus for developing measures of intelligence emerged from a desire to identify individuals who might benefit from

formal academic training and involvement (Hothersall, 1984; Sunberg,

1977; Ysseldyke & Algozzine, 1982). At the request of the French government, Alfred Binet and Theodore Simon developed the first

instrument to measure intelligence (Sunberg, 1977). The essential determination of who might be successful in formal academic settings was made according to the amount of subtests passed on this instrument.

The more subtests the child passed, the higher the total score. This early emphasis on product (or ability) and the prediction of school

success became and continues to be an important factor in the development of measures of intellectual functioning (Horn, 1979;

Hothersall, 1984; Sternberg, 1985).

Numerous measures of intelligence have emerged from this emphasis on product and outcome. Some measures of intelligence are derived from

factorial analysis while others are an outgrowth of specific

theoretical hypotheses. A brief review of the definitions of 20 intelligence and several factorial and non-factorial approaches to intellectual functioning is provided below.

Intelligence - Definitional Perspectives

There is little consensus regarding the definition of the construct known as intelligence. These definitions are as varied as their authors, representing many schools of thought. Examples of extremes of definitions of intelligence include (1) interaction of genes with the pre and post natal environment (Vernon, 1969) and (2) verbal, non-verbal and mechanical abilities obtained as results on intelligence tests (Hebb, 1966).

Individuals who have actually developed instruments to measure intellectual functioning have also varied in their definitions of intelligence. For instance, Binet and Simon viewed intelligence as a collection of faculties which included judgment, practical sense, initiative and the ability to adapt oneself to independent circumstances (Sattler, 1982). Weschler (1958), on the other hand, viewed intelligence as the aggregate or global capacity of an individual to act purposefully, think rationally, and deal effectively with the environment. R. B. Cattell (1963) referred to intelligence as a combination of culture free non-verbal skills as well as acquired knowledge reflecting exposure to the culture. Das (1973) referred to intelligence as the ability to plan and structure one's behavior with an end in view. In an attempt to summarize and integrate these varying definitions, Humphrey (1971) suggested that intelligence is the process of acquiring, storing in memory, retrieving, comparing and using in new contexts information and conceptual skills. Humphrey concluded that the definition of intelligence is at best an abstraction. 21

Factorial Approaches to Intelligence

Like the definition of intelligence, theoretical

conceptualizations of the construct of intelligence demonstrate

considerable variability. The factorial approach is one method of

conceptualizing the nature of intelligence. Factor analysis is a

mathematical process to help to determine the underlying structure of

constructs within a given test by the study of its patterns of

intercorrelation and variance (Sattler, 1982). Factorial modes range

from the two construct conceptualization of Spearman (1927) to the

multifactor construct proposed by Guilford (1967, 1982).

Spearman (1927), an early proponent of factorial analysis in

intelligence testing, proposed that intelligence involved a general

factor "g" and specific factors. The "g" factor involves more complex mental processes, is deductive in nature, and is universally common in

mental ability. The "s" factor, on the other hand, is specific to one

particular cognitive activity.

Building upon the work of Spearman, Thurstone (1938) proposed a more complex factorial approach to intellectual ability. Thurstone

believed that intelligence has seven multiple factors, he referred to

as the primary mental abilities. These seven factors, which became a

part of the Primary Mental Abilities Test, are verbal, perceptual

speed, inductive reasoning, number, rote memory, deductive reasoning, word fluency, and space. According to Thurstone, these primary

abilities are of equal weight so that no hierarchy is implied. 22

Cattell (1963) takes a slightly different perspective with this factorial approach. He conceptualized intelligence as both "culture fair" and acquired skills factors. Cattell theorized that two types of intelligences exist: fluid and crystallized. Fluid intelligence refers to general ability which is independent of experience, an individual's basic capacity to learn. Crystallized intelligence, on the other hand, emerges as a result of interaction with the culture and is reflected in acquired skills and knowledge which are usually influenced by educational exposure and opportunity.

Guilford (1967) presented a complex factorial approach which

Sternberg (1985) refers to as "cubic". According to Guilford, three basic dimensions of the intellect exist which have to do with the operation, content and product of a given cognitive activity.

Intelligence, then, is a result of the kind of mental operation performed, the type of content on which the operation is performed and the product of the above cognitive events. Guilford originally proposed five kinds of operations, four types of contents and six different products for a- total of 120 different factors. More recently, he has refined his model to include an additional level of content, thereby expanding the number of available factors of intelligence to 150 (Guilford, 1982).

Non-Factorial Theoretical Approaches to Intelligence

Non-factorial conceptualizations of intelligence have attempted to provide broader understandings of human intellectual functioning by focusing upon the ways in which human abilities interact. As a group, these theorist give consideration to the impact of human development 23 and the ways in which individuals integrate and manipulate information.

Several non-factorial theorists and their approaches to intelligence are reviewed below.

Piaget (1973) theorized that intelligent behavior is the result of the combination of biological development and interaction with the environment. Piaget maintained that individuals are constantly involved in organizing and adapting. Organization involves the tendency to combine information into a higher order integrated scheme and adaptation involves the processes of assimilation and accommodation. Assimilation refers to the interpretation of the environment while accommodation involves perceiving of the structural attributes of environmental data. Assimilation, therefore involves personal perception while accommodation is the reality check on the above perceptions (Sattler, 1982). Piaget theorized further that the above takes place throughout four different developmental stages, known as sensorimotor, preoperational, concrete and formal operations.

Jensen (1970, 1980) postulated that intelligence has associative and cognitive components. The associative component involves the ability to learn by rote memory. This level of intelligence utilizes short term memory. The cognitive component of intelligence refers to the ability to reason, problem solve and use concepts. The major distinction between associative and cognitive components is transformation of input, or the manipulation of information in order to obtain a desired goal. Contrary to the cognitive component, associative intelligence requires little transformation of incoming stimuli. 24

Gardner (1983) theorized that humans have multiple intelligences.

He views intelligence as an essential element in human problem solving.

Gardner's criteria for intelligence are as follows: First, intelligent behavior persists despite damage to the brain and is reflected in distinct ways in exceptional individuals (e.g., "prodigies and idiot savants"). Second, intelligent behavior has evolutionary antecedents whereby it is genetically activated through neural mechanisms by internal and external stimuli and consists of a distinct developmental history through which all persons journey in the course of ontogeny.

Third, intelligent behavior can sustain the test of experimental examination and psychometric scrutiny. Fourth, all intelligences are susceptible or migrate toward symbolic representations (e.g., language, mathematics). Gardner's theory of multiple intelligence include the use of linguistics, music, logic and mathematics, spatial ability, bodily-kinesthetic, and personal intelligences. Gardner maintains that all these intelligences are qualitatively different.

Sternberg (1985) presented a triarchic theory of intelligence based on the way in which individuals relate to their internal and external environment within the context of interaction between internal and external surroundings. For Sternberg, in order for a behavior to be intelligent it must be a) adaptive; b) in response to a novel situation and c) resulting from some type of metacognitive response.

As noted above, definitions as well as conceptualizations of the construct of intelligence vary. Ysseldyke and Algozzine (1982) point out that a major disadvantage of conceptual variance and confusion in terminology is conceptual confusion in measurement. As a result of 25 this confusion, the accuracy with which one may rely on any given instrument diminishes. While some theories of intelligence have progressed to be more inclusive of the totality of human intellectual functioning, measurement of the construct of intelligence has not kept pace with theoretical growth in this area. Metaphorically, theories and psychometric measurement of the construct of intelligence have developed as two separate branches on the same tree (Das et al., 1979;

Sternberg, 1985). Many instruments in popular use today come out of the psychometric-factorial tradition, which often developed instruments devoid of significant underlying theory (Das et al., 1979; Robinson &

Janos, 1987; Sternberg, 1985). For instance, building upon the knowledge obtained from the Army Alpha, Army Beta, and the Stanford

Binet, Weschler (1958) developed a test which assessed both verbal and non-verbal skills (Hothersall, 1984). Perhaps it is this separation between theory and instrument development which enabled the prevailing

Zeitgeist in intelligence testing (focusing on outcome, prediction and

selection to be maintained despite its limited utility to provide

information about how persons may be trained to perform given tasks more efficiently (Das et al., 1979, Sternberg, 1980, 1986).

Rather than focusing simply on ability or outcome the theory of

cognitive processing presented by Das et al. (1975; 1979) emphasizes

how individuals process information. The planning, attention,

simultaneous, and successive (PASS) cognitive processing model recently

operationalized by Das and Naglieri (1989), and Naglieri and Das (1987,

1988) and measured by as the Cognitive Assessment System, is based upon

the earlier work of Russian neuropsychologist, A. R. Luria (1966, 1973,

1980) who was influenced, in turn, by L. S. Vygotsky (1960, 1978). 26

The Planning, Attention Simultaneous and Successive Cognitive

Processing Model

The work of L. S. Vygotsky coupled with the predominant view among

Soviet psychologists after the Bolshevik revolution in the early 20th century helped to shape Luria's thinking regarding the development of higher mental processes in human beings (Das et al., 1979; Luria,

1971). Vygotsky believed that higher mental processing was social in origin, mediated in structure and voluntarily directed in functioning.

In the development of language, which both Vygotsky and Luria viewed as a higher mental process, it is the interaction between the child and the adult which helps to shape the contextual framework of the child's thinking. The resulting intellectual development of the child is contingent on mastering the social means of thought and language

(Vygotsky, 1978). Language itself has a mediated structure because there is capability for using tools or signs to organize a solution

(e.g., tying a knot on a finger to remember a future task) (Luria,

1980). Finally, language is a higher mental process because of the volition with which humans are able to use it (Luria, 1980).

Given the above, Luria conceptualized his clinical findings of the human brain in light of their functional significance. For Luria, the notion of function may be viewed in both specific and general terms, as the purpose of the particular tissue or as a complex entity with systematic properties. As such, more than one tissue may be responsible for output of a given behavior. For example, digestion may be seen as a specific function of a particular tissue (the stomach) and as a complex set of processes which result in absorption of nutrients 27 into the bloodstream. Likewise, functioning in the brain extends beyond specified or localized areas. "The brain is a highly differentiated system whose parts are responsible for different aspects of the unified whole." (Luria, 1980, p. 33).

The major implication of a social origin for higher mental processing in humans is its significance for intervention and subsequent behavioral change. The social origin of higher mental processes suggests greater potential for environmental influences and remedies (Das, 1983; Luria, 1971). Contrary to their cohorts in

America and Europe, Soviet psychology took a more multifaceted and flexible view of higher mental processing (Luria, 1971). Vygotsky's notion of the zone of proximal development is significant here because of its implications regarding therapeutic interventions. The zone of proximal is "the distance between the actual developmental age as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance or in collaboration with more capable peers." (Vygotsky, 1978, p. 86).

Vygotsky believed that training or intervention accelerates the developmental process. In turn, the maturation which occurs through intervention enables higher levels of material to be taught.

Essentially, Vygotsky believed that human potential lies between what an individual knows and what that individual may be taught.

Luria (1966, 1973, 1980) observed that all basic human functioning is controlled by three functional units of the brain. These units are at once independent, interrelated and non-hierarchial. 28

The first functional unit of the brain is made up of the brainstem, the structures of the reticular activating system, the diencephalon, and the medial regions of the cortex. This unit is responsible for adequate arousal and cortical tone and regulates these states in light of the changing demands of the organism. Luria (1973) maintains that this functional unit is essential for all other human mental processing and is characterized by a balance between excitation and inhibition. This balance, in turn, facilitates the ability with which humans change or attend to one activity rather than another.

Naglieri and Das (1988) assert that the difference between arousal and attention is that arousal may be defined as nonspecific alertness while attention is specific or object oriented. Sufficient arousal is necessary to insure adequate performance. Naglieri and Das maintain that too little or too much cortical tone interferes with proper processing. Too much arousal blocks learning because it interferes with the ability to distinguish relevant and irrelevant details while insufficient arousal or attention creates difficulty with coding and planning (Naglieri & Das, 1988).

The second functional unit of the brain is made up of the lateral regions of the neocortex on the convex surfaces of the hemisphere and is responsible for reception analysis or storage of information (Das et al., 1975). This functional unit encodes information in an integrated manner from the peripheral and sensory systems. Luria (1966) maintains that information is encoded in a simultaneous or successive manner.

Simultaneous processing involves the integration of information into spatial groups or wholes and the synthesis of separate elements into 29 groups (Naglieri & Das, 1987, 1988). Examples of tasks requiring simultaneous processing are arithmetic problem solving, figure copying and Progressive Matrices (Das et al., 1979). Successive processing involves the integration of information in a serial manner so that a system of cues consecutively activates the components. Serial recall of lists and digit span are examples of tasks requiring successive processing (Das, 1979). Coding, which includes simultaneous and successive processing is the functional unit most frequently assessed by ability testing (Das, 1975).

The third functional unit of the brain is located in the prefrontal lobe and is responsible for planning, organizing and programming behavior. This unit inspects, regulates and is involved in the development of strategies, decision making and the verification of mental activities (Das, 1984a, Naglieri, 1989). It is self-monitoring in that plans and strategies are adjusted as necessary depending upon the desired goal or direction.

Luria (1966) maintains that the above units are independent yet interactive and function together as one. As a result, no hierarchy exists between functional units. Arousal/attention is necessary for coding and coding is essential for planning. No planning occurs without adequate arousal/attention. All three functional units are influenced by the underlying knowledge base of the individual

(Naglieri & Das, 1987). "Without the base of coded information, planning becomes empty, and in the absence of a plan, information coding is blind." (Naglieri and Das, 1987, p. 11). Figure 1 is an

illustration of this model (Naglieri, 1989). 30

All Input

First Functional Unit: Attention & Arousal

Second Knowledge Functional Unit: Base Simultaneous & S u c c e ssiv e

Third Functional Unit:

Planning

Output

FIGURE 1 Schematic Diagram of the Planning. Attention, Simultaneous, and Successive Cognitive Processing Model "A cognitive processing theory for the measurement of intelligence." by J. A. Naglieri, 1989, Educational P sychologist, 24, p. 190. Copyright, 1989 by Lawrence Erlbaum Associates,' Inc. Reprinted by permission. 31

Naglieri and Das believe that the attention and planning

functional units have been overlooked in previous measures of

intellectual functioning such as the Weschler Intelligence Scale for

Children - Revised (Weschler, 1974) and the Stanford Binet (Thorndike,

Hagen & Sattler, 1986). The exclusion of the mental processes of

arousal/attention and planning by traditional measures of intellectual

ability contributed to the difficulty in making definitive statements

regarding the relationship between personality variables such as

self-concept, locus of control, and intelligence.

Given the fact that this investigation is designed to contribute

to a knowledge base which eventually may offer guidance for

intervention programs, the theory based process model recently operationalized by Das and Naglieri (1989) was chosen as a focus of

this study. This process oriented model has potential for intervention with individuals because of its emphasis on how tasks are performed, and more importantly, how individuals may be taught to perform tasks more efficiently. In addition, investigating attention and planning may provide new insights into the relationship between cognitive processing and the development of the personality variables of

self-concept and locus of control.

A discussion of the relationship between cognition and emotion

follows. The review of the literature on emotion is proceeded by a presentation of some methods used to define emotion. The definitions presented are not exhaustive, but represent a sample of the concepts

associated with the construct of emotion. 32

Emotion

Definitions of Emotion

Emotion is a complex construct which is difficult to define and

about whose conceptual nature there is little consensus. Used

interchangeably with words like affect, emotionality, feeling, or mood

(Izard, Wehmer, Livsey, & Jennings, 1965; Strongman, 1987), definitions of "emotions" vary depending upon the theorist defining the construct.

Conceptualizations about emotion have ranged from a negation of the construct to inclusive theoretical formulations including psychological, behavioral, or subjective factors. Duffy (1941) essentially denied the existence of emotion in stating: "emotion has no distinguishing characteristics. It merely represents an extreme manifestation of characteristics found in some degree in all

responses." (p. 242). Echoing what seems to be a similar position, behaviorist B. F. Skinner (1953) referred to emotion as "an excellent example of the fictional causes to which we commonly attribute behavior" (p. 160).

The existentialist, Sartre, provides the opposite perspective in his definition of emotion. Sartre (1948) maintained that emotion is

real and a mode of existence which enables individuals to experience themselves as "being in the world", thus emotion gives meaning and

"signifies something for psychic life" (p. 91). Cognitive theorist

Schachter (1964) referred to emotion as a "function of a state of physiological arousal and of a cognition appropriate to this state of arousal" (p. 51). He believed that cognitions are the steering

function which enables individuals to understand and label their feelings. Arnold (1960) believed that emotion was the "felt tendency to move toward something appraised as good [and liked] or away from something appraised as bad [and disliked]." (p. 82). Plutchik (1962) refers to emotion as a "patterned bodily reaction of either destruction, reproduction, incorporation, orientation, protection, deprivation, rejection, or exploration, or some combination of these, which is brought about by a stimulus." (p. 151.). Izard (1984) provided an inclusive and integrative definition of emotion which maintained that emotion may be defined along three separate dimensions:

(1) the neurophysiological-biological, (2) the behavorial-expressive and (3) the subjective-experiental.

The neurophysiological-biological components of emotion refer to brain structures, neural pathways, and neurotransmitters involved in the expression and experience of emotion. The limbic system, believed to be the origin and primary mediator of emotional expression (Netter,

1983; Rosenweig and Leiman, 1982) consists of the hyppocampus, amygdala, hypothalamus, and the septal area. The hyppocampus is associated with selective attention and the emotion of interest

(Schwartzbaum, 1960, Simonov, 1986). Both the amygdala and the hypothalamus are associated with heightened emotional responses such as anger, aggression, and fear (Worthman & Loftus, 1981). The hypothalamus has a role in the initiation and regulation of hormones which influence the pituitary gland and engineers the visceral responses of emotion, often evident in posture, gesture and facial expressions. The septal area appears to be most active in the suppression of emotional responses (Gerow, 1986). 34

Although these structures represent the physiological origins of emotion, the limbic system is poorly directed and cannot act alone.

These structures work in concert with higher levels of the brain found in the cerebral cortex (Netter, 1983; Rosenweig & Leiman, 1982;

Worthman & Loftus, 1981). The reticular formation is a complex network of crisscrossing neural fibers extending from the spinal cord up through the core of the brainstem to the thalamus (Worthman & Loftus,

1981). This is the structure of the brain responsible for relaying information from the lower to the higher levels. It is at the higher level of the brain, the frontal lobe of the cerebral cortex, that cognitive information is interpreted and memories are stored.

The behavorial-expressive component of emotion refers to sensory processing associated with emotion expression. This level of emotional function is manifested in facial expressions which are believed to be the essential motor patterns of emotional expression (Izard, 1971).

Sensorimotor information as expressed in facial expression, Izard

(1984) maintains, is preprogrammed and occurs without cognitive mediation. There is evidence which suggests that facial expressions may be universal manifestations of emotional states (Ekman, Friesen &

Ellsworth, 1972; Izard, 1971).

The subjective-experiential component of emotion refers to the feeling state of the individual. This feeling state is purposeful and stable over time. It is adaptive in nature and motivates as well as organizes human behavior. It influences individual perception of

internal and external events as well as provides a sense of personal continuity which contributes to the development of the self. As such 35

the feeling state operates as a subsystem of personality. The current

investigation was designed to examine emotion from the definitional perspective of the subjective-experiential approach described above.

Theories of Emotion

Like definitions of emotion, theoretical formulations of this construct also abound. Individuals from diverse and varied theoretical perspectives have conceptualized about the nature of emotion. The section below provides an overview of several perspectives on emotion.

One of the earliest theories of emotion to emerge in modern psychology was proposed independently by William James and Carl Lange in 1885. This theory proposed that emotion is essentially a process which involves mental and physiological properties. According to this theory bodily expression is an important manifestation of emotion. The cognitive perception of a mental fact and the subsequent bodily

response to it is emotion. Emotion then is the result of afferent

feedback to the brain from stimulated visceral and skeletal organs

(Hothersall, 1985; Strongman, 1987).

In contrast to the James-Lange theory outlined above, Cannon and

Bard (1927, 1931) presented a neurophysiological hypothesis regarding the nature of emotion. Cannon maintained that emotional expression was subcortical, based in the limbic system of the brain. The environment

stimulates receptors which relay impulses to the cortex. In turn, the

cortex stimulates the thalamus, which is responsible for the production of patterns corresponding to particular emotional responses expressed

in visceral and skeletal oroans. 36

Strongman (1987) contends that the basis for conceptualizing about

the nature of emotion was established by the theoretical perspectives

of individuals like James, Lange, Cannon, and Bard. Strongman

maintains that these early theorists suggested ideas which are still

common in current theories of emotion. A primary idea presented by

these theorists is the notion that emotion may be the result of the

interaction of the neurological, biological, and psychological

dimensions within each individual. According to Strongman, these

theorists also suggested that there is variation as well as similarity

in emotional responses. Therefore, despite apparent similarities,

emotions such as pensiveness, sadness, and grief are distinct.

Finally, these theorists suggested that an intricate link exists between emotion and cognition. The theories which follow, reflect in varying degrees the above concepts.

Experiential/Existentialist Theorists

Representing the traditional psychological view of a theory of

emotions are the phenomenological and experiential theorists.

Phenomenological and experiential theorists include the writings of psychoanalysts and existentialists who focus on the internal thoughts

and feelings relative to the emotional life of the individual. For

example, psychoanalytic theorist Rapaport (1942) maintained that

emotion involves the expression of unconscious processes. Emotion is

not only the investigation of physiology and consciousness, but the

representation of instinctual conflicts. The existentialist J. P. Sartre (1948), presenting a more

experiential perspective maintained that in order to be real, emotion must be experienced. According to Sartre, the subject and object of

emotion are inextricably bound together. Emotion is a way of

apprehending and perceiving the world in that it influences individual perception of reality resulting in a qualitative transformation of the world. This means that if pathways to particular goals are blocked,

the individual will attempt to change his/her environment in order to heighten the probability that a desired outcome, precipitated by emotion, will occur. A major weakness of the experiential perspectives of emotion is that they do not lend themselves well to empirical

investigation (Strongman, 1987). It is difficult to quantify the subjective unconscious or other apperceptions of personal experience.

Behavioral Theorists

Unlike the phenomenologists, the behavioral perspective of emotion

represented in the works of Watson (1929, 1930) and Millenson (1967) is more straight-forward and subject to empirical review. J. R. Millenson

(1967) maintained that emotional changes occur as the result of classical conditioning. Classical conditioning serves to either

inhibit or enhance nonemotional behavior. Like J. B. Watson, Millenson maintained that there are three dimensions of emotions which vary in

intensity. Dimension One includes terror, anxiety and apprehension;

Dimension Two consists of the emotions of pleasure; and Dimension Three

is anger. More complex emotions, Millenson believed, are a mixture or variation of the above three dimensions. All of these are responsive

to operant conditioning. 38

Reinforcing the behavioral perspective, Duffy (1941, 1962)

presented a theory of emotion based on motivation. According to Duffy,

emotion results from changing levels of energy, organization and

awareness of conscious states. Duffy believed emotion to be an extreme

of motivation. Essentially, emotion results in disorganized behavior

because it occurs at such high or low motivational levels. Finally,

Duffy maintained that emotion was the result of conscious awareness of

emotional situations.

Cognitive Theorists

Cognitive theoretical approaches, unlike the views described

above, emphasize the importance of individual mental processing in the experience of emotion. Lewis, Sullivan and Michalson (1984) maintain

that cognitive theories of emotion may be categorized according to whether they propose the role of cognition to be a consequence or antecedent of emotion. The perspectives which maintain emotion as a consequence of cognition include the theoretical writings of theorists

such as M. B. Arnold (1960, 1968); Lazarus (1966, 1970) and Schachter

(1964). The perspective which maintains that emotion is an antecedent of cognition include the writings of such theorists as Zajonc (1980,

1984) and Plutchik (1962, 1980). An example of each of the cognitive perspectives noted above is presented below.

Theorists who maintain emotion is a consequence of cognition generally assume that emotion results either from (1) appraisal of

stimuli or (2) discrepancies which are due to incongruities between

internal and external representations of schema. The cognitive appraisal theory of Arnold (1960, 1968) offers a framework for 39 theorists who believe that emotion is a consequence of cognition.

Arnold sought to illuminate understanding of emotion through the use of cognitive analysis, a technique that involves the appraisal of all events as either good or bad. The tendency, Arnold believed, is to avoid the bad and ignore the indifferent. Good appraisals create situations where future actions may be desired. One's memory continually impacts the judgment of events as both old and new experiences are evaluated in terms of past events. Imagination is the final step in the appraisal process, involving memory plus expectation.

Thus, emotional patterns are the result of positive, or negative appraisal of imagined objects. Similarly, feeling patterns are the result of appraisals which are deemed to be beneficial or harmful to human functioning.

Theorists who maintain that emotion is an antecedent of cognition typically assert that cognition is the consequence of some feeling state. Plutchik's (1962, 1980) theory of emotion is psychoevolutionary and multidimensional, suggesting that emotion is an adaptive process common to humans and animals alike, which developed in evolution in order to enable animals to survive. All emotions are multidimensional in that they are a mixture and combination of primary feelings which vary in intensity (apprehension and terror), similarity (fear and surprise), and polarity (ecstasy and grief). Viewing the role of cognition to be an artifact of the evolving brain, Plutchik maintains that cognition evolved in the service of emotion in that thinking enables an organism to predict and clarify the environment. 40

Although cognitive theorists have been successful in

re-integrating the role of cognition in emotion, Strongman (1987) maintains they add little to the understanding of the emotional

experience. The theories discussed below are based on the belief that

emotion, as a form of human functioning, has many dimensions. These

theorists focus on models of emotion designed to integrate the biological-physiological, cognitive-neuropsychological, and psycho­

logical subjective aspects of emotion and their relationship to each other. Unlike their predecessors, these theorists suggest that emotion has more than a passing role in the development of personality and the self.

Wholistic Theorists

In contrast to the prevailing thought among his contemporaries,

Leeper (1948) maintained that emotion influenced behavior by organizing and motivating it. According to Leeper, emotions sustain behavior and give it goal directedness. The stronger the emotional arousal process, the greater the probability that the behavior will be governed in a way that is consistent with the emotional reaction. Leeper believed that emotions are an essential element and catalyst in human decision making. He believed that emotions are not only cognitive, but the manifestation and integration of physiological and psychological motives.

Like Leeper, Izard's (1971, 1978, 1984) contextual frame of reference regarding emotion appears to be more global and inclusive of the totality of human functioning. Izard presents a broad based differential theory of emotion that deals with neural activity, 41 glandular, visceral, psychophysiological responses, subjective experience, expressive behaviors, and instrumental responses. Izard viewed emotion as a motivational and personality system which gives meaning to human existence. Izard believed emotion is but one of five subsystems of personality which is made up of nine fundamental innate emotions. These emotions are interest, enjoyment, surprise, distress, disgust, anger, shame, fear, and contempt. The nine basic emotions are inborn syndromes of behavior which are manifested in feedback from facial and bodily activity. According to Izard, expression of emotion is made up of neurophysiological, facial-postural activity, and subjective experience. This involves the reticular arousal system which assists in the amplification and attentuation of emotion, the visceral system which prepares the basis for emotion and sustains it and subjective experience which gives the entire process its meaning and power. Effective behavior is the result of the interaction and interfacing of these subsystems which are at times interdependent as well as independent.

As illustrated in the paragraphs above, theorists have essentially suggested that emotion is an integration of the functioning of several systems within an individual. As is true with all theories, derived hypotheses and assumptions do not always withstand empirical scrutiny.

Below is a review of research which generally suggest an interdependence of emotion and cognition. 42

The Interdependence of Emotion and Cognition: Empirical Findings

There is empirical evidence which suggests an interdependence between emotion and cognition. This interdependence may be observed in studies which present findings from clinical experience and experimental data.

The notion that there is an interdependence between emotion and cognition has enjoyed greater acceptance in the clinical arena. For instance, the diagnostic manual of psychiatric disorders, the

DSM-III-R, actually designates categories of disorders of feeling and/or mood. There is general agreement in the clinical arena that depression impacts cognition by influencing an individual's mood, physiological functioning, and ability to think, evaluate and relate interpersonally (Beck, 1967; Becker, 1974; Grinker, Miller, Sabshin,

Nunn & Nunnally, 1961; Mendels, 1970). Selected evidence in research studies of depression and anxiety are provided below.

Using the Incomplete Sentence Blank as a means to examine ideational content of depression, Weintraub, Segal and Beck (1974) found that in comparison to nondepressed persons, depressed individuals tended to give responses that reflected negative content. The fact that negatively oriented cognitions or thoughts were congruent with negative affect was also verified in studies by Averill (1969), Beck

(1967), Hale & Strickland (1976), Moore, Underwood & Rosenhan (1973) and Natale (1977).

In another study, Henry, Weingartner, and Murphy (1973) explored the impact of emotion on cognition by observing individuals with bipolar manic-depression over a period of months. Subjects were asked 43 to generate 20 free associations to each of two novel stimuli. Four days later patients were asked to reproduce the same 40 words they had previously generated. The researchers found that the greater the change in the patient's affective states - from mania to depression or vice versa - the greater the frequency of forgetting target associations generated earlier.

A study by Covington (1983) reinforced the earlier hypothesis of

Yerkes & Dodson (1908) that anxiety has a curvilinear relationship to cognition and performance. Covington found that highly anxious students performed more poorly than less anxious students on tests which measured their problem-solving ability. The more difficult the assigned task, the lower the performance of highly anxious students.

In comparison to highly anxious students, less anxious students spent less time on each assigned task. Anxiety was found to be positively asociated with number of ideas (cognitions) generated. For instance, less demanding ideational input with anxious persons produced slightly more ideas than it did with less anxious individuals. When task complexity increased, the direction of the relationship between anxiety and output was reversed in that highly anxious individuals were at an increasing disadvantage in terms of their ideational input.

Aside from the clinical orientation noted above, there are studies which focus on the impact of emotion on traditional cognitive processes such as attention, perception, memory, learning, and academic achievement. A review of some of the empirical studies which suggest a relationship between emotion and the preceding traditional cognitions follow. 44

Attention

Initiated in the limbic structures of the brain, attention

involves cortical tone, arousal and awareness (Netter, 1983; Rosenweig

& Leiman, 1982). Some theorists have maintained there is a

multidimensional tuning which results in response reactivity,

individual preparedness and attention (Adrian, 1954; Derryberry &

Rothbart, 1984; Luria, 1973; Panksepp, 1981). This reactivity may also

bias the flow of information so that emotions like fear, anger,

depression influence not only what is perceived, but how the

environment is interpreted (Derryberry & Rothbart, 1984)..

Easterbrook (1959) hypothesized that emotion impacts attention by narrowing the field of concentration so that relevant stimuli becomes more salient. Verification of Easterbrook's hypothesis was verified by

Bursill (1958), Cornsweet (1969) and McNamara & Fisch (1964), among others.

Bursill (1958) investigated the ability of subjects to respond to a peripheral and central task concurrently under conditions of thermal stress. Subjects were required to maintain vigilance of both a centrally located object and a peripheral stimulus presented while being exposed to room temperatures ranging from 60-105 F. Bursill

found that with increasing temperatures the attention of subjects were

funneled toward the center alone.

McNamara and Fisch (1964) demonstrated that attention is related

to vigilant activity directed toward stimuli which are relevant to the

task. Negative emotion was found to impact generalized attention by decreasing the efficiency with which peripheral information is learned. 45

Taking the work of the above researchers one step further, Cornsweet

(1969) demonstrated the impact of emotion on attention by examining the influence of task relevant peripheral information on primary tasks.

Cornsweet found that compared to subjects who could not avoid a shock, individuals who feared being shocked for less than average response time were able to utilize tasks relevant to peripheral cues to a greater degree.

In a more recent study of adult temperament, Derryberry and

Rothbart (1984) investigated the relationship of emotion, attention and self-regulatory processes. A 300 item questionnaire consisting of measures of arousal and self-regulation was given to 231 undergraduates. Emotionality was divided into positive and negative components. The research question focusing on emotionality and attention addressed the way in which arousal, affective - motivational, and self-regulatory systems are related to one another. Items were selected to refer to sensations, feelings and response in reaction to particular stimuli. The researchers found that some individuals capable of flexible shifting of their attention, demonstrated the ability to weaken negative arid strenghten positive aspects of exciting situations. They indicated that their findings suggest positive affect facilitates the process of attending while negative affect disrupts the flexible utilization of attention.

Perception

Emotional states have been demonstrated to have physiological and psychological effects on perception (Dimberg, 1982; Dixon, 1981;

Easterbrook, 1959). Easterbrook (1959) demonstrated that emotion 46 influences perception by constricting the visual field toward the center of vision. Dixon (1981) found an increased awareness threshold for a faint light stimulus presented to the eye when negative words were subliminally projected to the other eye. Dimberg (1982) found evidence which suggested that subjects tended to subtly mimic the responses of facial expressions perceived in a facial expression identification task when positive and negative visual stimuli were presented.

A psychological perspective of the relationship between emotion and perception was examined in a study by Izard, Wehmer, Livsey and

Jennings (1965). These researchers studied the effects of induced joy or anger on selective attention. Joy was promoted by warm friendly interactions and compliments while anger was induced by cold hostile interactions and insults. The researchers presented two treatment groups with photographs of 26 people each portraying joy in one pose and anger in another. These photographs were simultaneously mounted and presented on a steroscope. The steroscope exposed these contrasting stimuli to different visual fields and subjects were asked to resolve the binocular rivalry by perceiving either the right or left photograph or creating a fused third alternative. The results of this study indicated that the joy induced subjects resolved the binocular rivalry more frequently in favor of the joy expression and friendly interpersonal interaction while subjects in the anger induced condition perceived significantly more angry expressions and hostile scenes in the stereoscope. 47

In a second study of the psychological effects of emotion on

perception, Roth and Rehm (1980) found when clinically depressed patients rated video tapes of themselves for socially positive and negative behaviors, patients observed twice as many negative as positive behaviors. The above was true even though neutral judges in

the study scored equivalent numbers of these behaviors.

Finally, Forgas, Bower, and Krantz (1984) hypnotically induced

feelings of social well being, failure, and rejection in their

subjects. While hypnotized in their respective moods, subjects were asked to view a videotape of an interview they participated in earlier

in the experiment. The experimenters asked subjects to evaluate positively or negatively those behaviors they observed in themselves or their partners on the videotape. Subjects in the pleasant mood observed far more socially positive than negative behaviors for themselves and others and subjects feeling rejected observed mostly negative behaviors in themselves, but roughly equal numbers of positive and negative behaviors in their partners. Control subjects rated the two groups roughly the same proportionally in terms of observed negative and positive behaviors in themselves and others.

Memory

Emotion has been found to influence both memory and recall.

Several studies have yielded results which suggest positive or negative ways in which mood impacts recall. Most of these findings indicate that: (1) individuals who are in a positive mood recall more information than their cohorts who are in a negative mood, and/or (2) the efficiency with which information is recalled is dependent upon the 48

congruency with which it is learned and retrieved (Bartlett & Santrock,

1979; Dutta & Kanungo, 1975; Gilligan & Bower, 1980; Henry, Weingartner

& Murphy, 1973; Isen, Shalker, Clark & Karp, 1978; Laird, Wagener,

Halal, Szegda, 1982; Lloyd & Lishman, 1975; Teasdale & Fogarty, 1979).

A series of experiments by Bower (1981) and his associates demonstrated and linked the interactive nature of emotion, memory, and

thinking. Bower examined state-dependency memory by hypnotizing

subjects and then asking them to recall word lists and personal and

childhood experiences which were recorded in a daily diary. Bower

found during the recall period that subjects remembered a greater percentage of experiences which were congruent with the mood they currently were feeling. In another experiment Bower demonstrated that when the feeling tone of a narrator agrees with the reader's emotion, the memorability of the events in the narrative are increased.

Leight and Ellis (1981) obtained results which suggest that emotion impacts recall as well as the ability to strategize in the learning process. They devised a task where subjects were required to use chunking in order to optimize performance. Chunking involves the patterning of information in a meaningful way in order to facilitate memory (Gerow, 1986). They found that depressed mood decreased the use of a facilitative strategy, at that time as well as on a task 24 hours later. Leight and Ellis suggest that sadness produced cognitive rigidity which interfered with task performance.

In another study examining the impact of emotion on memory,

Mischel, Ebbesen, and Zeiss (1976) induced success or failure in their subjects in order to explore the relationship between affect and memory 49

for personality information. Their subjects were exposed to equal amounts of positive and negative information about themselves, purportedly obtained from previous personality testing. Subjects were

tested for recall of this information. Mischel et al. found that subjects who experienced success were better able to recall personality strengths and liabilities than those whose experience had not been so positive.

Learning

In a manner that is similar to other cognitive processes, learning is influenced by emotional state. Generally, positive emption tends to facilitate learning while negative emotion interferes with the learning process. Barden, Garber, Duncan, and Masters (1981) found that positive mood enhances both the speed and accuracy of learning on a discrimination task. Similar findings were obtained by Lewis, Sullivan and Michalson (1984). Gilligan and Bower (1984) found learning was enhanced with the increasing intensity of mood related materials but not irrelevant materials. For example, intensifying a happy state improved the learning of happy vingettes. When material to be learned was irrelevant to the happy mood, intensifying the mood had little impact on learning (Bursill, 1958; Calloway & Stone, 1960; Cornsweet,

1969; McNamara & Fisch, 1964). Negative emotion was found to increase the amount of time young children need to learn a discrimination task

(Master, Barden & Ford, 1979). Response latencies, and the amount of errors made were also found to be influenced by sadness (Gouaux &

Gouaux, 1971; Moore, Underwood, Doyle, Heberiein f. Litcie, 1979). In general, extreme sadness was found to produce low motivation for learning or handling demanding tasks (Gilligan & Bower, 1984). 50

Bower, Monterio, and Gilligan (1978) demonstrated that emotion not

only impacts learning, but influences the way in which individuals

selectively attend to materials they are learning. Bower et al.,

(1978) asked subjects to read a text in which one character described

many unrelated happy and sad incidents. Participants were hypnotically

made to feel happy or sad while recalling this text. The researchers

found that mood during the reading segment caused selective learning of

mood congruent incidents, while mood during recall had little effect.

In a similar experiment, Bower, Gilligan, and Monterio (1981) found

that readers not only identified with the mood congruent character, but

selectively learned material congruent with their emotional state

across characters in the story.

Academic Performance/Achievement

As is true with other areas of cognitive functioning, there is

some evidence to suggest that emotion influences academic achievement

and performance. In a meta-analysis of early studies in this area,

Lavin (1965) maintained that emotional stability was positively related

to academic performance. Those students who were more stable

emotionally demonstrated better overall academic performance.

Anxiety, as discussed earlier, has the ability to strongly

influence academic performance both positively and negatively.

Richardson and Shinn (1973) found that accelerated mass and standard

systematic desensitization were successful in reducing math anxiety and

increasing math performance. Bennett and Wark (1980) found that

reading achievement was impaired as a result of increasing levels of

anxiety. 51

Several authors maintain that academic performance and achievement

are influenced by perceptions and feelings of self-image, confidence,

and competence. In particular, Nicholls, Jagociniski, and Hiller

(1986) make a distinction between task and ego motivated performance in

academic or achievement situations. The main goal of task oriented

performance situations is mastery. In these situations, learning is

perceived as a goal in itself. On the other hand, ego involved

academic performance situations contain behaviors which stress

superiority over others. Learning and performance in ego involved

situations are more rigid and serve a time-limited purpose. Ryan

(1982) found evidence which suggested that individuals who were task

involved tended to be more interested and intrinsically satisfied after

a positive individual performance than after successful competition with others.

Nicholls, Jagociniski and Miller's interpretation of the above is

that sustained achievement seems to be fostered more by task

involvement than with ego oriented situations where competition is a

factor. An end result of task oriented performance is an increase in

effort and productive activity.

The research results presented in the preceding pages suggest that

cognition is a process which may be influenced by various emotional

factors. Given the evidence presented, it is reasonable to hypothesize

that there is a relationship between the cognitive process of

intelligence and emotion, conceptualized in the personality constructs

of self-concept and locus of control. 52

A review of the literature on the relationship between self-concept and/or locus of control and intelligence follows. A discussion of the relationship between emotion and personality will be followed by a review of the development of self-concept and locus of control in children.

The Relationship Between Emotion and Personality

Theorists have not traditionally considered the impact of emotion upon personality. According to Strongman (1987), except for Freud and

Jung, only two other theorists have attempted to integrate or link emotion and personality development. These theorists are Mandler

(1984) and Izard (1971, 1978, 1984). Izard's theoretical position on emotion and its relationship to personality is discussed above. A synopsis of Mandler's view and that of Freud and Jung on the connectedness of emotion and personality follow. The views of Freud and Jung are found in a review by Keen (1977). “

Mandler (1984) maintained that individual differences are at the basis of personality and emotional experience. For Mandler, the personality and the emotional system of individuals are "idiosyncratic"

(Mandler, 1984, p. 286). He believes that a general unwillingness exists to link emotion and personality because psychology has not developed personality scales which are able to characterize emotional reaction. This gap is in part a result of the high degree of variability inherent in biological characteristics, early experiences, and cultural differences as is evident in such things as child rearing practices. Mandler suggested that personality scales may be devised which help to characterize individual emotional reaction. He also 53 called for a greater understanding of specific cognitive systems within a culture which allow for individual prediction of emotional responses.

According to Mandler such a cognitive system is found in the psychoanalytic tradition initiated by Freud.

According to Keen (1977) both Freud and Jung believed emotion to be (1) qualitatively different from thought, (2) motivating in human life, (3) referential of unconscious thought, and (4) expressive of individual nature not readily apparent to the conscious mind. All behavior is referential with varying meaning. Personality influences emotion by facilitating the interpretation of emotional events. Keen maintains that for Freud and Jung, emotion is an instinctual process which is not always filtered by the ego. Directionality of the instinctual process may be relational, reflective,or anxious.

Relational emotions are directed outside self, reflective emotions are directed toward the self, and anxiety refers to feelings of danger with identifiable directionality. Emotion is therefore a part of an individual's relationship to the world, oneself, and an indication of psychological reaction to danger. In this respect, emotion is understood as adaptive in human existence. The result of this process is cognition as filtered through personality theory to provide the context for interpreting particular emotional events. Personality theory acts as a tool whereby the individual and others gain insight into personal behavior. A discussion of the development of personality, understood in the constructs of self-concept and locus of control in children, follows. 54

The Development of Self-Concept and Locus of Control in Children

Self-Concept Development

Pioneers in the new "Zeitgeist” in psychology, which seeks to reexamine the impact of emotion in daily life, assert that emotion is the organizer that regulates the flow of information and the selection of response outputs of the organism. Emotion refers to how past, present and future events are related to individual striving (Campos &

Barrett, 1984; Rosemann, 1979). Lewis and Brooks (1978) maintain that cognition and emotion are irrevocably bound. These authors assert that emotion cannot be experienced in the absence of cognitive appraisal and/or evaluation. An understanding of self, then, is essential to an understanding of emotion. Lewis and Brooks assert that emotional states cannot be experienced without some knowledge of self.

Many theorists agree that the construct of self-concept is generally defined as an individual's perception of him/herself.

Self-concept is alsc referred to as the objective self or the self defined as "me." This perception of self is a combination of personal attitudes, feelings, and knowledge about one's own abilities, skills and behaviors. The perception is formed by personal experience, interpretation of one's environment, and the evaluations and reinforcement of significant others (Cooley, 1902; Damon & Hart, 1988;

Erikson, 1950; Martin & Coley, 1984; Newman & Newman, 1986, 1987;

Piers, 1984, Shavelson & Marsh, 1986; Wylie, 1979; Yawkey, 1980). Self is understood as the organizer and conceptual framework for personal identity (Damon & Hart, 1988). 55

The interaction with the environment which helps to produce a

sense of selfhood is a dynamic process beginning with the distinction of self from the environment and moving through the development of a

set of attributions and perceptions over time. A variety of positions have been taken to describe the above process.

Mahler, Pine, and Bergman (1975) present a psychoanalytic perspective of the development of self-concept. They argue that

self-development occurs in distinct stages. The first phase, known also as the differentiation stage, occurs between birth and two months of age. In this stage the infant's awareness is dominated by physiological needs and internal sensations. The dominant developmental task of the differentiation stage is to distinguish internal from external determinants of personal need satisfaction. The second phase, known as the symbiotic stage, occurs between two and five months and involves "delusional" fusion with the mother or caretaker and eventual attunement to perceptions of the surrounding environment.

The third and final phase, known as separation and individuation, occurs at six months and older. It is during the separation and individuation stage that the infant becomes more alert, attentive, and goal directed. With the advent of walking, the child is able to negotiate the environment, and becomes more autonomous, resulting in greater differentiation and separation of self from the environment and the caretaker. Mahler and her associates maintain that the caretaker, usually the mother, has the primary role in this process of encouraging

independence and self-reliance. 56

Kagan (1981) presents a biological model of the development of

self. He believes that the development of the self and

self-understanding are inevitably the result of neurological maturation. Kagan proposes that once biological maturation takes

place, the self develops rapidly, independent of social interaction.

Socio-cognitive theorists have presented evidence that several

factors exists which are essential in the development of self.

Socio-cognitive theorists, Broughton (1978) and Selman (1980) propose

two major stages in the development of self, the physical and the psychological. In the physical stage (birth through age eight) the

child focuses on distinguishing parts of the body. There is general

confusion between what is inner and outer experience as well as what is body, self and mind. An essential step in the development of self during the physical stage is the actual recognition of self. In a

series of studies of self-recognition designed to deal with contingency

issues confounding research when mirrors are used with infants, Lewis and Brooks-Gunn (1979) presented children ages nine to 36 months with

images of themselves in mediums such as mirrors, photographs, and videotapes. Lewis and Brooks-Gunn found that children as young as nine months old were able to visually recognize themselves. In fact, their

research lead them to hypothesize that infants go through several steps

in the process toward self-recognition including: (1) an unlearned

attraction to the images of humans; (2) the recognition of self through

contingency cues; (3) object permanency which has enduring qualities

and (4) defining self through categorical features alone, independent

of any contingency knowledge of self. 57

The primary focus of the second stage, emerging at age eight, is

to distinguish the body from the mind. This is the stage when the

psychological self emerges and it is during this stage that the

subjective self is understood as the child realizes that individual

thoughts and feelings are variable from person to person.

In a study designed to validate the development of the

psychological self, Johnson and Wellman (1982) asked children to make

judgments regarding the part of the body needed for mental and behavioral functioning. They found that children as young as five years old were able to distinguish psychological and behavioral

realities. In another study, Guardo and Bohan (1971) found evidence which suggested that children ages six to nine are aware of their

special status as humans, gender identity, individual uniqueness, and

continuity (the belief that one is connected to a past and future

self). The conceptualization process which enabled a child to move

from a physical to a more psychological developmental stage was found

to change with age. For instance, six and seven year-old explanations of self were more physically oriented than that of eight and nine year-old. Eight and nine year old explanations, on the other hand, were more psychologically oriented than those of their six and seven year-old cohorts.

Some investigators have also identified activity level and social

characteristics as important variables in the development of self which

occur prior to the emergence of the psychological self (Aboud & Skerry,

1983; Keller, Ford & Meacham, 1978; Livesly & Bromley, 1973; Mohr,

1978; Secord & Peevers, 1974). Keller, Ford and Meacham (1978) 58 utilized an incomplete sentence blank format to ask children ages three to five to describe themselves. Keller, Ford, and Meacham found that children of this age use the activities in which they are involved to describe themselves (e.g. "I play baseball" or "I walk to grandma").

Mohr (1978) asked children in the first, third, and sixth grades the following: (1) what has changed about them since they were babies; (2) what would they change about themselves to become like their best friend and (3) what would they change about themselves when they grow up. Mohr found that children in the first grade provided more responses related to their physical or external characteristics (name, age, personal possessions); those in third grade provided more responses related to behavioral characteristics (a trait or some other regularity of behavior) and those in the sixth grade responded most with internal or psychological characteristics (feelings, thoughts and knowledge).

In terms of social characteristics, Secord and Peevers (1974) found that third graders were not only able to describe their active qualities, but did so relative to the competencies of others (e.g., "I can ride a bike better than Jane"). Secord and Peevers indicated that the shift to a comparative and evaluative sense of self is essential in the differentiation process which occurs in the development of self.

Confirming the work of Secord and Peevers, McGuire and

Padawer-Singer (1976) found that the most common characteristics 10 and

11 year old children used to describe themselves are those features which were uncommon in relation to other children. For instance, if the most distinguishing feature of a child was ethnic origin, the child was more likely to describe self according to this attribute. 59

Ruble (1983) investigated a child's comparative sense of self by giving children a difficult task and then providing feedback regarding their own performance and that of other children involved in the study.

Ruble found that children under seven made no references to the performances of other children while those who were older than seven, based their evaluations upon comparisons with others. Livesly and

Bromley (1973) found that social characteristics were found to triple at approximately age seven in children's self-descriptions.

Damon and Hart (1988) suggested that the development of the self is a multifaceted process which involves the emergence and integration of physical, active, social, and psychological factors. It appears that essential characteristics in the development of self are the ability to: (1) separate self from the environment; (2) distinguish parts of the body; (3) distinguish the body from the mind; (4) attribute activities to self; (5) compare and evaluate those attributes which distinguish self from others; and (6) understand the uniqueness of thoughts and feelings from person to person. The above process appears to involve evaluation, planning, and organization in that there is a gathering and prioritization of data about the self.

Locus of Control Development

Personal agency is known as that aspect of the self which refers to volitional control over individual thoughts and actions (Damon &

Hart, 1988). Agency refers to an individual's ability to choose a particular direction, despite one's physical, active, social, and 60

psychological self. This is the aspect of self most directly

associated with self-determination and is often referred to as the

subjective self or the self defined as "I." Research on the

development of a sense of agency has occurred most frequently in

studies examining the personality construct known as locus of control

(Damon & Hart, 1988). Locus of control is defined as the degree to

which an individual perceives a reward to be dependent or independent

of their own behavior. Locus of control is the subjective perception

that one either has or lacks control over his/her environment (Martin &

Coley, 1984; Rotter, 1966). Rotter maintained that this perception is

a variable on a continuum from internal to external locus of control.

Internal locus of control occurs when an individual perceives the

reward as contingent upon personal behavior or characteristics and is

believed to involve skill and individual adaptation. Internal locus of

control involves the attempt of individuals to adapt to their life

circumstances. Conversely, external locus of control occurs when an

individual attributes rewards to be the result of influence of powerful others, fate and/or luck. Life is understood as out of the control of

the individual and as unpredictable. External locus of control is generally viewed as maladaptive, therefore in Western culture, internal

locus of control is the socially desired dimension.

From a reading of prior research, Lefcourt (1982) suggested that

the familial origins of the development of internal locus of control are based on warmth, supportiveness and parental encouragement.

Conversely, external locus of control is developed due to a familial

environment of rejection, domination and uncertainty. 61

Claiming a general lack of developmental perspective by locus of

control theorists, Damon and Hart (1988) present the following model of

the development of agency, and more specifically, locus of control in

children. A longitudinal and cross sectional study of children between

the ages of four and 18, asking their subjects, "How did you get to be

the kind of person you are now?" and "How do you change in the present

and future?" was performed. The authors found that children (grades 1

and 2) initially experience self-development as nonvolitional. For

these children the self is influenced by numerous biological, social,

and supernatural forces. In mid-childhood (grades 3 and 4) the belief

in external shaping of the self is replaced by the understanding that

the power of individual desires are sufficient to control the evolution

of self. This particular finding of Damon and Hart reinforce earlier

studies done by Crandall, Katkovsky and Crandall (1965) and Nowicki and

Strickland (1973). In late childhood (grades 5 and 6) and early

adolescence (grades 7 and 8), children reflect a sense of agency which

is based in an understanding that the self develops as a consequence of

relating to others. Finally, in mid-adolescence (grades 9 and 10) volitional control of the self emerges from deeply held personal values

or philosophies.

Damon and Hart, in agreement with several locus of control

theorists, conclude that studies examining the development of locus of

control indicate that older children are more internally controlled

than younger children. That is, with increasing age children tend to

believe the world is more responsive to their individual actions

(Bailer, 1961; Damon & Hart, 1988; Lefcourt, 1976; Nowicki &

Strickland, 1973; Sherman, 1984; Skinner & Chapman, 1987). 62

The notion that internal locus of control increases with age is not universally believed or supported by the data (Skinner & Chapman,

1987; Weisz & Stipek, 1982). These authors reference the Piagetian position which asserts that perceptions of control decreases with age.

According to Piaget, young children believe they can influence the function of objects external to them because the "world and self are one" (Piaget, 1930, p. 244). For example, the young child perceives that the "sun and moon follow us and if we walk, it's enough to make them move along" (Piaget, 1930, p. 244-245). It is only with age that children become aware of the number of events that are externally controlled. The result is an externalizing of perceptions of control

(Piaget, 1930; Piaget & Inhelder, 1975).

In an attempt to lend clarity to the differing perspectives regarding the relative strength of the relationship between locus of control and age, Skinner and Chapman (1987) examined two independent samples of children between the ages of seven and twelve. The purpose of Skinner and Chapman's study was to gain insight into children's belief in the efficacy of their efforts and attributions. Skinner and

Chapman found that perceived control increases, decreases, or remains the same across childhood based upon the means-end beliefs upon which the child is focused. More specifically, children's reasoning about desired outcomes impacts the degree to which their sense of personal control is internal or externally oriented.

Sue (1981) reported similar findings in adults from diverse cultural backgrounds. Sue maintained that adults from various cultural backgrounds tended to appear to have a more external locus of control 63 depending upon the cultural context of their world view. For instance, persons of Chinese decent, whose world view focuses on the group rather than the individual were found to be highly external. In addition,

African-Americans were also found to be more external in their sense of personal control as a response to racism and discrimination.

The literature indicates that factors such as age, sex, ethnic background, socioeconomic status, and achievement influence the development of internal locus of control in children (Bachrach,

Huesmann & Peterson, 1977; Cooper, Burger & Good, 1981; Gruen, Korte &

Baum, 1974; Nowicki & Strickland, 1973; Rabinowitz, 1978; Stipek &

Weisz, 1981). Young and Shorr (1986) designed a study to verify if in

fact the above variables influence the development of internal locus of

control. Locus of control questionnaires and achievement tests were administered by Young and Shorr to an ethnically mixed population of

1,962 children in the fourth, fifth and seventh grades. Their findings

confirmed most of the previous work done in this area. They found that

internality (1) increased with age, (2) was more common among females

across grade levels sampled, (3) is associated with higher academic

achievement and (4) is linked to higher socioeconomic status. When

socioeconomic status was controlled, ethnic background by itself was

not associated with greater internal locus of control.

Maccoby (1980) maintains that childrearing practices significantly

influence the development of a child's sense of personal control.

Maccoby concludes from her analysis of previous research that parental

responsiveness which directs by supervision is essential to the

development of internal locus of control (Bee, 1967; Loeb, 1975). 64

Parental interactions which indicate to children that they are persons of worth capable of solving their own problems help to create a feeling of personal competence. In addition, parents who are reciprocal in terms of their interactions with their children are more likely to assist their children with their ability to make independent decisions and develop feelings of personal control.

In an investigation to identify factors which influence the development of internal locus of control, Dubois (1988) examined the acquisition of internal locus of control in 400 children between the ages of eight and sixteen. Dubois also hoped to determine if gain approval or disapproval and orientation toward teachers or parents effected the acquisition of internal locus of control. Subjects were divided according to grade level, the kind of locus of control or attribution scale administered, the type of instruction (gain approval or disapproval), and the orientation toward teachers or parents.

Dubois found that gain approval instructions increased internal scores on both locus of control and attribution questionnaires. Similarly, gain disapproval decreased the internal scores on both questionnaires.

Compared to interactions with parents, Dubois also found a massive effect of the kind of instruction on internal scores. Dubois indicated that these findings suggest that children are aware, at quite a young age (seven and eight), of how to provide a good or poor image of self.

Dubois continued that one of the ways in which internal locus of control is established is through formal evaluation situations.

Children were able to respond to gain approval instructions oriented toward teachers with more internal responses than to those gain 65 approval instructions oriented toward parents. Dubois concluded that the development of internal locus of control is actually the acquisition of a social norm. The results obtained by Dubois are consistent with those of a group of studies by Dweck, Davidson, Nelson, and Enna (1978) which indicated that differences in self-attribution was due to direct training by teachers.

The development process associated with the personality construct of locus of control appears to be a complex progression which generally proceeds from external locus of control to internal locus of control.

Several authors agree that internal control is demonstrated in children as young as seven or eight (Crandall, Katkovsky & Crandall, 1965; Damon

& Hart, 1988; Nowicki & Strickland, 1973). The development of locus of control appears to be influenced by parental responsiveness, age, sex, socioeconomic level, the reasoning a child may have about a desired outcome, or the gain approval of teachers (Dubois, 1988; Gruen, Korte &

Baum, 1974; Nowicki & Strickland, 1973; Maccoby, 1980; Young & Shorr,

1986).

The results of the above, particularly the work of Dubois, Sue,

Skinner & Chapman, suggest what Lefcourt (1982) argued earlier, that locus of control is a metacognitive process. This may be a reasonable hypothesis because locus of control appears to involve planning which, in turn, includes some gathering of data, evaluating, and organizing a perspective regarding personal control. 66

The Relationship Between Intelligence and Self-Concept or Locus of Control

In the past, investigations of the relatedness of intelligence and self-concept or locus of control have served as an adjunct to studies investigating the relationship between these personality variables and achievement. Generally, these studies have yielded contrary and inconsistent results. Some studies maintain that there is no relationship between achievement and self-concept (Brown, 1980; Neufeld and Cozac, 1980; Swanson, 1980; and Tremans-Ziremba, Michayluk &

Taylor, 1980) or locus of control (Brown, 1980; Samuels, 1980; Swanson,

1980) while others have identified a relationship between achievement and self-concept (Abbott, 1981; Brookover, LePere, Hamachek, Thomas &

Erickson, 1965; Bledsoe, 1964; Cattell, Sealy & Sweeney, 1966; Chapman,

Silver & Williams, 1984; Coopersmith, 1959; Farls, 1967; Fink, 1962;

Finnegan, 1986; Piers and Harris, 1964; Wescott, 1985) or internal locus of control (Buck & Austrin, 1971; Crandall, Katkovsky & Crandall,

1965; Findley & Cooper, 1983; Houtz, 1980; Kifer, 1975; Maqsud, 1983;

Messer, 1975; Pawdal, 1984; Perna, Dunlap & Dillard, 1983; Reid &

Croucher, 1980; Remanis, 1973; Rushton, 1966; Shaw & Alves, 1963; Shaw,

Edson & Bell, 1960; Stipek, 1980; Schultz & Pomerantz, 1976; Walden &

Ramey, 1983 and Vogel, 1976).

The data regarding the relatedness of intelligence and achievement appears to be more consistent. Numerous researchers have found evidence to suggest a strong relationship between intelligence and achievement (Abbott, 1981; Beck & Spruill, 1987; Drudge et al., 1981;

Houtz, 1980; Keith, Pottebaum : Eberhart, 1985; Lamar, 1985; McDermott,

1984; Poteat et al., 1988; Reilly et al., 1985; Silverstein et al., 67

1987; Swanum & Bringle, 1982; Tremans-Ziremba, Michayluk & Taylor,

1980; Walden & Rainey, 1983; Weise et al., 1988; Weithorn & Marcus,

1985). Despite the apparent relatedness of self-concept and internal locus of control to achievement as well as the strong relationship that has been identified between intelligence and achievement, there seems to be no overwhelming body of evidence suggesting an interrelatedness of intelligence and either of the personality constructs of self-concept or locus of control.

The inconsistency found in the above studies is clarified by

Wylie's (1979) analysis of variables which serve to confound findings in the area of self-concept research. She suggests that a significant source of error stems from the use of idiosyncratic measures of self-concept, achievement, and/or intelligence by investigators.

Idiosyncratic measures are instruments which are not well standardized and for which adequate psychometric information is not available. For instance, some authors created their own measures of self-concept or used grade point average (GPA) as a measure of achievement (Abbott,

1981; Bledsoe, 1964; Boshier, 1973; Fink, 1962; Gill & D'Oyley, 1970;

Maqsud, 1983; Piers & Harris, 1964; Remanis, 1973; Rosenberg & Simmons,

1973; Shaw, Edson & Bell, 1960). The use of GPA's is particularly problematic because of the lack of consistency in criteria across school districts and regions. Instruments which lack standardization create a situation where equivalent interpretation of results among studies is difficult because there is no basis from which comparative statements can be made (Wylie, 1979). 68

Wylie maintains that a second methodological flaw confounding the

data that support a relationship between self-concept and intelligence

is the lack of control for one when measuring the other. Wylie

contends that control of these variables is necessary because of the

strong evidence which suggests a relationship between academic

achievement and intelligence. If control for achievement is not

considered when measuring intelligence in combination with other

variables, such as self-concept and locus of control, it becomes

difficult if not impossible to define the relationship between these

variables.

Self-Concept and Intelligence

Few investigations exist which directly examine the relationship

between self-concept and intelligence among children. The studies that

do exist yield results which are mixed and generally inconclusive. The

studies reviewed below have all supported a positive relationship

between self-concept and intelligence among children. However, several

of the flaws outlined above by Wylie are in operation in these

investigations.

Bledsoe (1964) examined self-concept and its relationship to

intelligence while attempting to control for achievement. He also examined the relationship of self-concept to interests and manifest anxiety among fourth and sixth grade elementary students. A randomized

sample of 271 subjects from four different schools were administered a

self-concept scale modified from Lipsett's adaptation of Bill's Index of Adjustment, the Californio Test of r^nK-vl Maturity, Californio

Achievement Test, and the Taylor Manifest Anxiety Scale. Bledsoe found 69 a small to moderate correlation between self-concept and intelligence, but only among boys. A major weakness of this study is seen in the use of a non-standardized subjective measure of self-concept. No information regarding the reliability and validity of this measure of self-concept was provided.

Anastasiow (1967) investigated the relationship between self-concept and intelligence in children of high and low abilities.

Anastasiow obtained a sample of 510 fourth and six graders from team teaching and self-contained classrooms. Each subject was administered a self-concept scale developed by Sears (1963) along with measures of ability and achievement. The high ability subjects were designated as those individuals scoring in the top 25%ile on an achievement test identified as SCAT. Low ability subjects were designated as those individuals scoring in the bottom 26%ile on the SCAT. Anastasiow found that those who scored low on the SCAT also demonstrated lower self-concept scores.

This study has several limitations. First, the investigators did not use a well standardized measure of self-concept. In addition, neither the measure of ability or achievement were well defined.

Finally, the authors do not state whether the reverse of their findings, that high scorers on the SCAT demonstrate high self-concept scales also occurred.

Ringness (1961) investigated the general self-concept of children of low, average and high intellectual functioning. This two year study was designed to assess the emotional reactions of mentally retarded children to learning. The author hypothesized that differenrps in 70 intellectual functioning among children would parallel variation in self-perceptions. The sample consisted of 120 fourth graders who were divided into intellectual ability groups of low, medium and high as identified by the Weschler Intelligence Scale for Children. During the first year of the project, subjects were given individualized training in arithmetic and were also asked to rate themselves on eight scales dealing with self-estimates of achievement. The above self-estimates were compared to an external criterion of achievement, the California

Achievement Test, during the second year of the project. The investigator found that mentally retarded students have, as a rule, an overestimated, yet less realistic self-concept than average and bright students. Bright students tended to rate themselves more highly than average or retarded students. The major limitation of this study is the author's use of a non-standardized measure of self-concept. No information about reliability and validity was provided on the measure of self-concept employed in this study.

Piers and Harris (1964) developed a standardized measure of self-concept to be used with children. In order to' validate their instrument, the authors investigated the correlates of self-concept in children. One of the correlates examined was the relationship between self-concept and intelligence. Piers and Harris administered their 140 item self-concept scale to 360 subjects in grades four, six and ten.

Subjects were also administered a measure of achievement. Responses to the self-concept scale were judged as adequate (high) or inadequate by three judges. Piers and Harris did not have access to intelligence scores for all of their subjects. Piers and Harris found a moderate correlation between self-concept and intelligence for sixth graders. The strength of this study lies in its attempt to assess self-concept on a scale designed specifically for use with children.

Weaknesses of this study include the fact that the authors did not (1) specify the instrument used to measure achievement or (2) mention the inter-rater reliability of the judges used in the research project.

Most importantly, however, is the fact that the authors did not have intelligence scores for all subjects in their study. The above weaknesses serve to confound the results obtained in the study, thus diminishing the accuracy and generalizability of the conclusions.

Similar to the studies by Anastasiow and Ringness cited above,

Karnes and Wherry (1981) designed a study to examine the relationship of self-concept and intelligence among gifted students using a well standardized self-concept scale, the Piers-Harris Children's

Self-Concept Scale (PHCSCS). Karnes and Wherry's sample consisted of

120 children in grades four through seven with intelligence scores of

120 or better as identified by the Weschler Intelligence Scale for

Children - Revised (WISC-R) or the Stanford-Binet, Form LM. Along with measures of intelligence, subjects were given the PHCSCS. The results of this study were compared to findings obtained earlier as part of the standardization process for the PHCSCS. The investigators found that gifted children scored significantly higher than the standardized group. This finding suggests that the self esteem of gifted children was much higher than that of their average intellectual ability cohorts. A particular weakness of the Karnes and Wherry study is the lack of control for any variation in achi^vompnt. O'Such, Havertape and Pierce (1979) investigated the relationship of age, academic performance, and school placement to self-concept among educable mentally retarded (EMR ), educationally handicapped (EH), normal, and gifted school children. The researchers reported that the educationally handicapped group consisted of learning disabled and emotionally disturbed children. The sample consisted of 128 children who were assigned to each of these groups based upon measure of

intellectual ability and adaptive behavior. All children in the study were between the ages of eight and nine or eleven and twelve. The

PHCSCS was verbally administered to all participants. The investigators found that self-concept varied with school placement, that is, the EMR group scored lowest in terms of self-concept, followed by the EH group. Normal and gifted students demonstrated higher self-concepts than the other two groups in the study. The gifted group scored highest with their self-concept scores being significantly higher than those of the students in the normal group. Despite the use of a well standardized measure of self-concept and the differentiation of ability groups based upon measures of intellectual ability and class placement, the generalizability of this study is diminished because there was no control for achievement.

Locus of Control and Intelligence

As is true with the self-concept studies noted above, few studies exist which investigate the relationship between locus of control and intelligence among children. Below is a review of literature which suggests a positive relationship between intelligence and locus of control among children. 73

An early study by Bailer (1961) examined the development of locus of control among mentally retarded and normal children. Bailer's study

involved a total of 89 children between the ages of six and fourteen.

He administered the Bailer Locus of Control Scale, the Peabody Picture

Vocabulary Test and a measure of repetitive choice and gratification pattern. Bailer found a significant relationship between mental age

(intelligence) and locus of control when the effect of chronological age was partialed out. He concluded that both normal and mentally

retarded children develop internal locus of control, however the rate of development of internality is slower for children in the retarded than in the normal range of intellectual functioning. Bailer did not differentiate or discuss his two research groups further, even though subjects in the study represented a wide range of intellectual ability.

He interpreted his findings in terms of mental and chronological age.

Sattler (1982) cites other authors who assert that the use of mental age as a designation of cognitive function is limited because mental growth increments are not consistently measuring the same difference from one age to another. Abilities ascribed to mental age may not be the same over a given population due to the fact that the same mental age at different chronological ages may have varying meaning and/or the same mental age at the same chronological age may have different meaning. These limitations diminish the degree of certainty with which statements may be made about the relationship between intelligence identified by mental age and locus of control. In addition, Nowicki and Strickland (1973) note that Bailer's measure of

locus of control "suffers from reliability and format shortcomings" (p. 74

149). According to these authors the format of the Bailer scale has approximately half of the items consecutively keyed in one direction which facilitates response style bias.

In order to determine the impact of social and ethnic background upon locus of control, Battle and Rotter (1963) studied a sample of eighty (80) sixth and eighth grade students from five metropolitan schools. The relationship of intelligence to these variables was also examined. Students were selected according to sex, social class, and ethnic group membership. Each child was administered a line matching task developed by James (1957) which allowed the experimenter to control success and failure without the subject's knowledge. Subjects were given a six-item cartoon projective test, The Children's Picture

Test of Internal-External Control (designed to ascertain attribution of responsibility), the California Mental Maturity Scale (used as a measure of intellectual functioning) and the Bailer Locus of Control

Scale. Battle and Rotter found a significant relationship between intelligence and external locus of control among individuals belonging to the African-American lower class. This finding indicated at this age level there was a positive correlation between intelligence and externality for lower class African-Americans.

This study presents several limitations. The first methodological flaw is the use of idiosyncratic measures such as the Children's

Picture Test of Internal-External Control. Information regarding validity and reliability is not available nor is it provided by the authors. Second, the sample size used in this study is much too small for generalizability. Last, these researchers neglected to control for 75 the effects of achievement which Wylie indicates is imperative because of the bounty of empirical evidence suggesting a strong relationship between intelligence and achievement.

Crandall, Katkovsky and Crandall (1965) examined the relationship between a child's locus of control and intelligence as part of a normative study of the Intellectual Achievement Responsibility (IAR) questionnaire. The IAR is an instrument specifically designed to assess locus of control in a school environment. The questions on this measure focus upon internal-external locus of control as it pertains to a child's academic performance.

The Crandall, Katkovsky and Crandall study consisted of 923 students in grades three through twelve representing five different school districts. Each student was given the IAR and described along such demographic variables as age, sex, socioeconomic level and intelligence. The measure of intelligence used in this sample was the

California Test of Mental Maturity or the Lorge-Thorndike which was administered by the host school. These authors found a moderate relationship between intelligence and internal locus of control and interpreted this correlation to be the result of their large sample size and indicative of the motivating propensity of the construct of internal and external locus of control. This study did not control for achievement which Wylie suggests is an important corollary for any study examining intelligence and its relationship to other variables.

Mulgram and Mulgram (1976) investigated the relationship between personal-social adjustment variables among gifted and non-gifted

Israeli students in the fourth through eighth grades. They 76 administered an Israeli adaptation of the Tennessee Self-Concept Scale, a locus of control scale developed by the authors and the Wallach and

Kagan version of the Sarason scales of general test anxiety translated into Hebrew. The above measures were given to 140 gifted and 310 non-gifted students in grades four through eight. As determined by class placement, the range of intelligence for gifted students was between 135 and 146. The range of intelligence for the non-gifted group, not initially available, was later determined by the Raven

Standard Progressive Matrices to be generally at the 507 of the Raven norms. Generally, Mulgram and Mulgram found a positive relationship between intellectual giftedness and personal adjustment. In particular, a significant relationship was found between gifted children and internal locus of control. Gifted children assumed greater responsibility for past events and expressed greater confidence to effect future events than non-gifted children. The authors also found a significant relationship between intelligence and self-concept.

Mulgram and Mulgram weaken the power of their findings for several reasons. First, the authors did not define their non-gifted subjects with the same degree of specificity with which they clarified their gifted subjects. Second, the authors provided no information regarding the reliability and validity of the locus of control measure, nor is there any mention of the reliability or validity of the version of the

Sarason scales translated into Hebrew. These authors also did not control for the variable of achievement. 77

Walden and Ramey (1983) examined the relationship of locus of control, achievement and environmental conditions in a five (5) year longitudinal study. These researchers were particularly interested in determining if locus of control was related to school achievement, independent of intelligence. They were also interested in whether locus of control could be modified by participation in intervention programs focusing on the efficiency with which tasks are accomplished.

The sample consisted of 65 kindergarten and first grade children.

These children were randomly assigned to one of three groups - a high risk intervention group that received preschool compensatory education

(the intervention group), a control (the non-intervention group) and a comparison sample representative of the general population referred to as the general population sample group. Members of the high risk group were designated according to low socioeconomic status, parental education and maternal intelligence. Beginning at three months of age, all three groups were exposed to a preschool program designed to support optimal intellectual and social development. The intervention group was also involved in an individualized educational program which emphasized mastery of social and intellectual skills through a series of attainable steps. All children were given feedback regarding their performance and rewarded for desired outcomes. A child's ability to control his or her outcomes were emphasized.

Each child in the Walden and Ramey study was administered the

Peabody Individual Achievement Test (PIAT), the Intellectual

Achievement Responsibility (IAR) questionnaire, and the Stanford

Preschool Internal-External Scale. Teachers of students in the high 78

risk group were asked to fill out the Classroom Behavior Inventory

(CBI) for each participant. Intellectual functioning was assessed by

the Weschler Preschool and Primary Scale of Intelligence (WPPSI) for

five year-olds and under or the Weschler Intelligence Scale for

Children-Revised (WISC-R) for those six years and older. They found

that intelligence was a significant predictor of achievement and was

found to be "somewhat related" (Walden & Ramey, p. 351) to locus of control. In the general population sample group both intelligence and

locus of control were related to achievement. Intervention was found

to significantly effect the perceptions of control over academic

success. The at-risk control group tended to have lower perceptions of control of their academic success than those children in the high risk

intervention group. The sole predictor of achievement for the at risk group was intelligence while the sole predictor of school achievement

for the intervention group was locus of control. Finally, children who had strong beliefs in their ability to control academic outcomes with effort were scored by teachers as more internally motivated, task-oriented and less distractible in the classroom.

A limitation of Walden and Ramey's study is the small sample size.

Gay (1981) maintains that insufficient sample size limits the generalization of the findings of a study. In addition, 65 subjects is much too small for the scope of the study attempted by Walden and

Ramey.

Using an older population of children and a generalized measure of locus of control specifically designed for children in grades 3 through

12 (the Nowicki-Strickland Locus of Control Scale for Children, 79

(NSLCSC), Brown (1980) investigated the relationship between locus of control, intelligence and academic achievement among adolescents.

Brown's sample consisted of 58 subjects with a mean age of 15. The group included 25 girls and 33 boys, all were Caucasian and represented socioeconomic Classes II, III and IV of the Hollingshead Index.

Subjects were administered the Peabody Picture Vocabulary Test (PPVT), the NSLCSC and the Wide Range Achievement Test in reading, arithmetic and spelling. Brown found that achievement was not significantly related to locus of control for either boys or girls. Rather, a significant relationship was found between intelligence and internal locus of control. Limitations of the study include the small sample size and the use of a measure not designed primarily to measure intelligence. Sattler (1982) indicates that the PPVT is a non-verbal measure of receptive ability designed to estimate hearing ability. In addition, as noted above, Gay (1976, 1981, 1987) indicates that insufficient sample size serves to minimize the generalizability of results.

Hallahan, Gajar, Cohen and Tarver (1978) take an unusual approach to the assessment of locus of control and its relationship to intelligence. Holding intelligence constant, they compared normal and learning disabled students in order to determine differences in selective attention and the relationship between selective attention and locus of control. All participants in the study were of average intellectual ability as determined by the Weschler Intelligence Scale for Children. The sample consisted of 28 junior high school students identified as learning disabled and achieving two years below grade 80 level for their chronological age. The control group consisted of 28 normal subjects matched for sex, age, and grade.

Subjects were administered the Short Test of Education

Achievement, the Intellectual Achievement Responsibility questionnaire, the Nowicki-Strickland Children's Locus of Control Scale and Hagan's

(1967) central incidental learning task which measures selective attention. Consistent with previous research, the researchers found that learning disabled subjects were less efficient than normal children in selective attention. Unlike their normal peers, locus of control for learning disabled children tended to be externally oriented. Internal locus of control and selective attention, however, were not related for non-learning disabled children.

The strength of this study lies in its examination of a process rather than of an ability oriented intellectual variable. The locus of control orientation of a child and how he or she attends offers information both about how the child processes information, and what intervention strategies might be used to best meet the child's needs.

A shortcoming of this study is the use of Hagan's selective attention task without documentation of its reliability and validity. Another limitation of this particular study is the small sample size.

A review of research which examine the relationship between intelligence and self-concept or locus of control reveal studies which are replete with the methodological flaws. Investigators have not used standardized instruments and have often neglected adequate consideration of the interactive effects of achievement upon intelligence. Another problem with some of these studies is their use 81 of inadequate sample sizes. Despite the limitations of past research in this area, Wylie notes, there appears to be some small relationship between intelligence and self-concept or locus of control. Wylie suggests that further investigation and exploration of this relationship is warranted.

Methodological flaws aside, none of these studies have examined the relationship between intelligence and self-concept or locus of control from a process orientation to cognitive functioning. The focus on intelligence in most of these studies relies upon an ability approach to intellectual assessment. This study investigated the relationship between intelligence and self-concept or locus of control utilizing a process model of intellectual functioning. This approach to intelligence also included examination of the cognitive process of planning and attention which, historically, have not been examined as intellectual processes on measures of cognitive processing (Thurstone,

Hagan & Sattler, 1986).

Empirical examination of the Planning, Attention, Simultaneous and

Successive cognitive processing model of intelligence suggests that it no longer may be appropriate to conceptualize academic areas in terms of a single factor (Garofalo, 1986). Garofalo found that arithmetic consists of successive, simultaneous and planning processes.

Computation was found to be moderately correlated to planning as well as simultaneous processing. There is also evidence to suggest that the process of reading involves the cognitive functions of successive, simultaneous and planning processes. Some studies have found that reading decoding is related to simultaneous and successive processing 82

(Ashman and Das, 1980; Das and Heemsbergen, 1983), while others have

found that this academic skill is related to planning in some

elementary school children (Das, 1984a; Leong, Cheng & Das, 1985).

Moreover, achievement was found to be less related to intellectual

functioning at younger age levels (Naglieri & Das, 1988). These authors maintain that achievement becomes more correlated to

intelligence with increasing age and grade level. As a result,

achievement does not appear to be a confounding variable with

intelligence in the present study (Naglieri, 1989).

Summary

This chapter contained an examination of the literature which

suggests a relationship between emotion (operationalized in a personality variable like self-concept or locus of control) and

cognition (operationalized in the construct of intelligence). The

specific theoretical perspective utilized in this study to investigate

intelligence is the process oriented approach of A. R. Luria (1966,

1973, 1980), most recently operationalized by Das and Naglieri (1989).

Few measures of intellectual functioning are theory derived.

Historically, factorial analysis has been the primary method employed

to identify the structure of constructs in the development of measures of intelligence. As a result, both in test development and in the way

in which measures of intelligence have been used, there has been an emphasis on ability or product of intellectual assessment. Such a

focus emphasizes how well things are done which has limited the ability 83 approach to assessing intellectual functioning, particularly if a desire for therapeutic intervention has existed. The Planning,

Attention, Simultaneous and Successive (PASS) cognitive processing model recently operationalized by Das and Naglieri relies upon a process orientation to the assessment of intellectual functioning.

Process oriented approaches to the measure of intellectual functioning focus on how tasks are performed. As a general rule, this approach has greater utility for intervention because of its focus on the development of strategies and how a person might be trained to perform prescribed tasks more efficiently.

The literature suggests that definitions and theories of emotion vary widely. However, many theorists agree that the relationship between emotion and cognition is represented in an integration of neurological, biological and psychological dimensions. Theorists like

Freud and Jung believed that emotion has significant influence in the development and maintenance of personality. They believed this relationship between emotion and personality is involved in the facilitation and interpretation of emotional events. Emotion, as reflected in personality, is an indication of an individual's relationship to self and environment. Self-concept and locus of control are understood as personal perceptions which are adaptive, interpretative and facilitative of individual organization.

A review of the literature suggests that a series of significant changes occur in children between the ages of seven and ten. First,

Luria suggests that a child becomes capable of planning at the age of three or four with the maturation of the prefrontal regions of the 84

cortex. This ability to plan peaks between the ages of seven or eight.

Second, self-concept theorists suggest that it is not until children are seven or eight that the psychological sense of self emerges. This

is the awareness by the child that individual thoughts and feelings are variable from person to person. It is at this age that children are

first able to compare and evaluate those attributes which distinguish

self from others. Last, locus of control theorists maintain that

children between the ages of seven and ten are capable of understanding

that the power of individual desires is sufficient to control the evolution of self.

Studies investigating the relationship between self-concept and/or

locus of control and intelligence have yielded results that are

inconsistent and inconclusive. Some studies have found a relationship among these variables, while others have not. A prominent weakness of many of these studies has been the focus upon ability rather than on a process model of intelligence. The present study examined the

relationship between the personality variables of self-concept and

locus of control and the information processing model of intelligence

operationalized by Das and Naglieri (1989) in children ages eight and

nine. CHAPTER III

METHODOLOGY

Introduction

This chapter contains a description of the research setting, population from which the sample was chosen, the instruments utilized in the study, and the procedures employed for collecting the data.

These sections are followed by a description of the statistics used in the data analysis.

Research Setting

The settings for this research were three school systems in the metropolitan area of Columbus, Ohio (representing approximately

1 million people). The Columbus, Upper Arlington, and Hamilton Local

School districts were the educational systems involved in this study.

The Columbus Public School system is located in a metropolitan area and consists of a mixture of socioeconomic levels, language, ethnic, and racial differences. Many children in this school district are members of families where both parents are employed. The average yearly income for families for this school district is $20,000 (Bureau of Census, 1983).

The Upper Arlington school district is a suburb of the city of

Columbus, northwest of the metropolitan center. This school district consists of individuals with a socioeconomic level above other districts in the state. The average yearly income for families in this

85 86 school district is $42,000. Many children in this school district are from families of professional persons where only one parent is employed

(Bureau of Census, 1983).

Located 10 miles south of the city of Columbus, the Hamilton Local

School district is part of a rural community of 133,000 persons. The average yearly income of families in this school district is $21,000

(Bureau of Census, 1983).

Tests were administered in private classrooms in elementary schools in the Central Ohio area in the school where each student

regularly attends. All rooms were well lit, and seating arrangements were designed for the comfort of eight and nine year olds; therefore

small tables and chairs were utilized in the testing situation.

Population

The population for this study consisted of children, ages eight

and nine, all of whom resided in the Columbus metropolitan area. This

sample included a cross section of individuals from different

socioeconomic, educational, ethnic and racial groups.

Subjects

A sample of 132 children was included in the study. All children

ages eight and nine in the consenting school systems had the

opportunity to be part of this study. Parental approval via a consent

form was obtained for each student before inclusion in the study (see

Appendix A). Once each child was assessed, parents were encouraged to

complete the Hollingshead Four Factor Index of Social Status in order

to determine socioeconomic status. 87

Instruments

Four instruments were employed in this investigation. They are:

(1) The Cognitive Assessment System (CAS), (2) Piers-Harris Children's

Self Concept Scale (PHCSCS), (3) Nowicki-Strickland Locus of Control

Scale for Children (NSLCSC), and (4) The Hollingshead Four Factor Index

of Social Status (HFFISS). Each instrument is described below:

The Cognitive Assessment System.

This process oriented measure, developed by Das and Naglieri

(1989), operationalizes the Planning, Attention, Simultaneous and

Successive cognitive processing model (PASS) and focuses on how

intellectual functioning works. The Cognitive Assessment System

operationalizes the theoretical work of Luria (1966, 1973, 1980). The

tasks included elaborate on an earlier model of cognitive processing

presented by Das, Kirby & Jarman (1975). Two tasks measure each of the

following kinds of cognitive processing: attention, coding

(simultaneous processing, successive processing) and planning.

Subtests employed to measure attention were (1) Selective

Attention-Receptive and (2) Selective Attention-Expressive. These

subtests focus on the examination of some stimuli, comparing it to an

instructed target and responding if the target meets the desired

specifications (Naglieri, Prewett & Bardos, 1989).

Subtests used to measure simultaneous and successive processing

(coding) were Figure Memory, Matrices, Successive Word Recall, and

Sentence Repetitions and Questions. These subtests focus on tasks which generally require integration of parts into a whole (Naglieri,

Prewett & Bardos, 1989). 88

The subtests employed to measure planning were Planned Connections and Visual Search. These subtests generally require respondents to determine an efficient way to solve items (Naglieri, Prewett & Bardos,

1989). Each of the eight subtests are described below.

Planning

Visual Search. This subtest requires the subject to point to the object, number or letter in the response field that matches the target located in the center box. Each item consists of two searches presented on a single page. For each item (page) the subject's score is the time it takes in seconds to point to the objects in the response field that match the targets in the center boxes. Each target has only one match in their respective response field (Ashman & Das, 1980; Das,

1984a; Das & Naglieri, 1989).

There is a 90 second time limit for this subtest. Timing begins when the page is exposed to the child and stops when the subject points to the matching object in the second response field. Subjects are told of incorrect responses and asked to continue looking. The final score is the total time taken to point to the objects in the response fields that match targets in the center boxes. Visual search has been found to be significantly related to the planning factor of the PASS model (Ashman, 1982; Ashman & Das, 1980; Das, 1980; Kirby &

Ashman, 1984; Naglieri & Das, 1987; Snart, O'Grady & Das, 1982,

Stutzman, 1986). Test-retest reliability for tasks measuring planning was found to be .76 (Petree, 1988). 89

Planned Connections. This subtest requires the individual to connect, using a No. 2 lead pencil, a series of boxes in the correct sequence as quickly as possible. There are two types of items in this subtest, one requiring the respondent to connect boxes with numbers [1,

2, 3] and the other requiring a connection between boxes with simultaneous numbers and letters (i.e. 1-A-2-B-3-C) (Ashman & Das,

1980; Das & Naglieri, 1989). If a mistake is made, the person is asked to return to the previous response and locate the correct response.

The score on this task is the total time it takes to complete each item. Three minutes is the time limit for each item on this subtest.

This subtest has been found to be significantly related to the planning factor of the PASS model (Ashman & Das, 1980; Naglieri & Das, 1988).

Attention

Selective Attention-Receptive. This subtest is based on the work of Naglieri (1989) and Das and Naglieri (in press). The subtest consists of two forms with two conditions to each form. Form A is administered to children between the ages of five and nine. Form A requires the child to underline specific numbers in a stimulus grid containing 180 integers. Condition 1 of Form A is printed in boldface type and requires the child to find the numbers one, two, and three in the stimulus grid. Condition 2 of Form A requires the child to find the numbers four, five, and six. Form B is administered to children age eight and older. Form B requires the child to find and underline specific numbers within a stimulus grid of 180 integers. Condition 1 of Form B requires the child to find the numbers one, two, and three printed in openface type within the stimulus grid. Condition 2 of 90

Form B requires the child to find the numbers one, two, and three in

standard type as well as the numbers four, five and six in openface

type within the stimulus grid. The four conditions within Form A and

Form B are presented separately with examples. The child is asked to

complete the task row by row from left to right and top to bottom.

There is a maximum time limit of one minute and ninety seconds for each

condition. Scoring of the selective attention-receptive subtest is achieved by recording the number correct as well as the time it takes each student in minutes and seconds to complete each condition.

Selective Attention-Expressive. This subtest was developed by Das & Naglieri (1989) and is based on the earlier work of Stroop

(1935). The subtest consists of six stimulus cards; three are demonstrations and three are scored tests items. Sample A consists of

the words red, green, yellow and blue printed in black ink. Sample B consists of a series of rectangles printed in the colors red, green, yellow and blue. Sample C consists of the same words printed on Sample

A in the colors printed on Sample B. In no case is the word and the color it is printed on the same. Cards A, B, and C follow in parallel

form and are scored.

After the samples are demonstrated, the child is instructed to

read the words on Card 1, name the colors on Card 2 and name the color of ink the words are printed in on Card 3. This subtest is not administered if the child is below grade 3 or color blind. Color blindness is determined by the administration of the color demonstration exercise included in this test. There is a three minute

time limit per card. Self corrections are not considered errors. 91

Scoring of the Selective Attention Expressive subtest is achieved by recording the time needed to complete each card. There is a three minute maximum per card. The child is given a check (v^-) for each correct, and an (x) for each incorrect response.

Naglieri, Prewett, and Bardos (1989) performed an exploratory study examining the validity of Selective Attention-Receptive and

Selective Attention-Expressive in relationship to the Planning,

Attention Simultaneous and Successive (PASS) model which found that these subtests produced results consistent with this approach. That is, in a factor analysis, these subtests were significantly related to the attention factor of the (PASS) model. The test-retest reliability of attention tasks was found to be .63 (Petree, 1988).

Simultaneous Processing

Figure Memory. Similarly to the Memory for Design Test

(Graham & Kendall, 1960) and the Embedded Figure's Test (Witkin, 1950), this subtest requires the child to locate and outline a geometric figure that is embedded within a more complex design. Each figure is printed on a separate stimulus card and is exposed to the child's sight for five seconds. The child is then instructed to outline the stimulus within the more complex design presented on the response page. This subtest is discontinued after four consecutive failures. The final score is the total number of figures traced correctly (Das & Naglieri,

1989). Factor analyses of the validity of this subtest indicates that this factor is significantly related to the simultaneous factor of the

(PASS) model (Naglieri & Das, 1987; Naglieri, Prewett & Bardos, 1989;

Stutzman, 1986). 92

Matrices. The Matrices Analogies Test Short-Form (MAT-SF) is

a 34 item test of non-verbal ability for children ages 5-17. There are

four types of items on MAT-SF: pattern completion, reasoning by

analogy, serial reasoning and spatial visualization (Naglieri, 1985).

Pattern completion items require the respondent to choose from one of

six options which best completes the figural matrix. Reasoning by

Analogy items require the individual to identify how the change(s) in

one figure is analogous to change(s) in another. Serial reasoning

items require the person to discover the order in which items appear in

the matrix. Spatial visualization items require the individual to

imagine how a figure might look when two components are combined.

Scoring of the MAT-SF is accomplished by computing the age of the

student, determining the total number of correct responses, and

obtaining the derived scored from the relevant conversion table in the manual (Naglieri, 1985).

Internal reliability for this instrument was established by

calculating Cornbach's Alpha for each standardization age group using

raw scores. The age range of the students in the standardization

sample was 5-17. Internal reliability coefficients ranged from .63

(age 5.6 - 5.11) to .89 (ages 8.0 - 8.5 and 9.6 - 9.11), with a median

coefficient being .83. (Naglieri, 1985). The manual noted that all

test-retest reliability coefficients for the MAT-SF were obtained on a

sample tested four weeks after the initial testing. Correlation

coefficients computed by student grade in order to minimize ability

related inflation were found to range between .51 and .91. The median

test-retest coefficient across all grades was .75. The standard error 93 of measurement ranged from 1.8 -2.4 depending upon the age of the

individual.

Developmental changes in mean scores were identified and item factor analyses were performed for each grade level of the standardization sample in order to determine the construct validity of the MAT-SF. A clear increase in raw score means with age, which demonstrates age differentiation for the MAT-SF was found. Results of the factor analysis revealed that most of the items on the MAT-SF (over

78%) at grades 2, 5, and 9 were significantly related above .40.

Factor analyses also demonstrate that this subtest is significantly related to the simultaneous factor of the PASS model of intellectual functioning (Naglieri & Das, 1987, 1988; Price, 1987; Stutzman, 1986).

Concurrent validity was established by correlating the MAT-SF and the Multilevel Academic Survey Test (MAST). The MAST is an achievement test designed to help educators make classification decisions in the areas of reading and mathematics. At the p<.01 level the MAT-SF and the MAST are correlated for academic achievement in reading and math for grades 4-12. Correlations between the MAT-SF and the MAST range from .32 in reading and .43 in math for kindergarten students to .66 in reading and .60 in math for students in grade 12. in a second study of concurrent validity, hearing impaired students were administered the

MAT-SF which was then correlated to previously obtained scores on the performance scale of the Weschler intelligence Scale for Children

Revised (WISC-R). Results revealed that the MAT-SF was highly related to the performance scale of the WISC-R (.68) and, as such, a good measure of non-verbal ability (Naglieri, 1985). 94

Successive Processing

Successive Word Recall. Based on the earlier work of Das,

Kirby & Jarman (1975) this task requires the individual to repeat a series of words in the same order in which the examiner says them.

Words are presented at the rate of one per second in a uniform pitch.

The subtest is discontinued after the subject fails four consecutive items. The total score is the number of words successfully repeated

(Das & Naglieri, 1989). Factor analyses indicate that this subtest is significantly related to the successive factor of the PASS model (Das et al, 1979; Price, 1987)

Sentence Repetitions and Questions. Developed by Das and

Naglieri (1989) this subtest consists Qf two parts. Part 1 requests the individual to repeat a sentence, while Part 2 asks the individual to answer a question about each sentence presented in Part 1. The sentence contains color names in place of content words. For example, the subject may be asked to repeat the sentence, "The blue is yellowing" then asked the question, "Who is yellowing." The correct response is "the blue." The words are presented at approximately one per second. Directions for sentence questions may be repeated only once, but the sentences themselves are not repeated. This subtest is discontinued after four consecutive failures. The individual is given a score of one for each sentence repeated perfectly and again when the question in Part 2 is answered correctly. The total score equals the number of correct responses in Parts 1 and 2. 95

This subtest was found to be significantly related to the

successive factor of the PASS model (Stutzman, 1986). Hurt (1988) also

found a significant correlation of .56 between the successive marker

tasks of Successive Word Recall and Sentence Repetitions and Questions

in a study examining the PASS Model among delinquent and non-delinquent males. Test-Retest reliability for these successive tasks was found to be .76 (Petree, 1988).

Piers-Harris Children's Self-Concept Scale (PHCSCS)

Developed by Ellen Piers and Dale Harris (1969), this self-concept

scale is an 80 item self-report instrument designed to measure how children between the ages of 8 and 18 feel about themselves.

Self-concept is defined by the authors of this instrument as a stable set of self attitudes which are descriptive and evaluative of personal behavior. The items are answered in a dichotomous "yes" or "no" manner. This instrument may be administered individually or in a group and it can be administered and scored in less than 30 minutes.

This instrument has six cluster scales: (1) behavior; (2)

intellectual and school status; (3) physical appearance and attributes;

(4) anxiety; (5) popularity, and (6) happiness and satisfaction. The

(PHCSCS) yields a raw percentile score and an overall stanine score which can be converted to T scores. Cluster scales are scored in the direction of positive self-concept. A high score indicates a high level of assessed self-concept in that specific dimension. A low score

reflects a low level of assessed self-concept in the assessed domain.

Results are interpreted on the basis of obtained individual cluster or summary scores. Each of the six cluster scales are described below. 96

Behavior - This scale measures the extent to which the child admits or denies problematic behavior in school or at home. An example of an item on this scale is "I get into a lot of fights."

Intellectual and School Status - This scale measures the child's self-assessment of his or her abilities with respect to intellectual and academic tasks. This scale also measures general satisfaction with school and future expectations. An example of an item on this scale is

"I am smart" or "I am slow in finishing my school work."

Physical Appearance and Attributes - This scale assesses the child's attitudes regarding his or her physical characteristics. The scale also measures attributes associated with this dimension of self-concept such as leadership and the ability to express ideas. An example of an item on this scale is "I am good-looking."

Anxiety - This scale measures general emotional disturbance and dysphoric mood. The scale taps emotions such as worry, nervousness, shyness, sadness, fear, and a general feeling of being left out of things. An example of an item on this scale is "I worry a lot."

Popularity - This scale measures a child's evaluation of his or her popularity with classmates, being chosen for games or the ability to make friends. Low scores reflect a lack of interpersonal skills or personality traits which tend to isolate the child from others. An example of an item on this scale is "I am among the last to be chosen for games." 97

Happiness and Satisfaction - This scale measures a general feeling of happiness and satisfaction with life. This scale also measures the ease with which the child gets along with others. An example of an item on this scale is "I have a pleasant face."

In order to verify the validity of obtained responses, the

PHCSCS has a response bias and inconsistency index. The response bias index measures the degree to which a child responded independently to individual items or was swayed by a need to agree or disagree with items as written. The inconsistency index measures the extent to which the child's responses are internally consistent across individual items

(Piers, 1984).

Piers (1984) states that the test-retest reliability coefficients range from .42 - .96 for intervals as long as 8 months; and .96 for intervals of 3 - 4 months. Based on the KR-20 formula developed by Kuder-Richardson, internal consistency among items ranged from .88 - .93 for various subgroups. The standard error of measurement was calculated to be 4 (Piers, 1984).

Content validity was achieved by utilizing the Jersild (1952) items emphasizing "just like me, myself," personality, character, inner resources and emotional tendencies. Significant but moderate criterion validity coefficients were found between the Piers-Harris Children

Self-Concept scales and ratings of self-concept completed by teachers

(.43) and peers (.49). There was also a significant correlation of .64

(criterion validity) between this measure of self-concept and items relating to school attitude (Mettes, 1974; Piers, 1984; Querry, 1970). 98

Significant correlations coefficients were found between the

PHCSCS and self-concept scales such as the Children's Personality

Questionnaire (.73); the Tennessee Self-Concept Scale (.51); and the

Coppersmith Self Concept Scale (.85), (Karnes & Wherry, 1982; Johnson,

Redfield, Miller & Simpson, 1983; Piers, 1984). A study of bi-racial children ages 10-17 also provided factor validity on seven of the

scales on the Piers-Harris Children's Self-Concept Scale (Wolf, Sklov,

Hunter, Webber & Berenson, 1982b).

Nowicki-Strickland Locus of Control Scales for Children (NSLCSC)

This scale is a measure of generalized locus of control based on the theoretical work of Rotter (1966) and designed to be used with children, grades 3-12. The NSLCSC is a pencil and paper measure consisting of 40 questions which may be answered dichotomously with a yes or no. Scoring is keyed so that the higher the total score, the more external the locus of control orientation (Nowicki & Duke, 1974;

Nowicki & Strickland, 1973). This instrument may be administered individually or in a group.

Nowicki and Strickland (1973) report test-retest reliability coefficients for a six-week period are .67 for 8-11 year olds and .75 for 12-15 year olds. Determined by the split half method, the internal consistency of this measure is .63 for grades 3, 4, and 5; .68 for grades 6, 7, and 8; .74 for grades 9, 10, and 11 and .81 for grade 12

(Nowicki & Strickland, 1973).

Factor analysis of the NSLCSC yielded that the content of this instrument reflects factors related to personal control and helplessness; achievement, and friendship. This instrument also 99

reflects factors related to social control; self control, and luck

(Walters & Klein, 1980; Wolf, Sklov, Hunter & Berenson, 1982a).

The NSLCSC was found to have significant relationships with other

measures of locus of control. For example, correlations with the

Bailer-Cromwell and the Rotter Internal/External Locus of Control was

.41 and .76 respectively (Nowicki & Strickland, 1973). Evidence for

construct validity was also found in a study comparing locus of control and magical beliefs (Belter & Brinkman, 1981). Magical beliefs (the

crediting of events to superstitions or astrology) were found to be

strong among individuals whose locus of control orientation was external. Individuals with external locus of control tend to look outside themselves for attribution or resolution of personal problems.

The Hollingshead Four Factor Index of Social Status (HFFISS)

August Hollingshead (1975) has developed a method to assess the socioeconomic status of nuclear families. This is accomplished by determining the level of education, choice of occupation, sex, and marital status of the individual and spouse in each respondent family.

Respondents are given a questionnaire which asks personal income level, occupational status and level of educational achievement. (See

Appendix B. Sex is self-evident. Hollingshead indicated that educational and occupational levels are generally assumed to be equal determinants of socioeconomic status. After the above data are obtained, it is analyzed in the following manner. Educational and occupational levels are given scale scores which are designated in the manual. Educational level is given a scale score from 1-7.

Occupational level is given a scale ranging from 1-9. These scale 100 scores are then used in the calculation of the status score. Status scores are calculated by multiplying obtained scale scores by weights of 5 or 3 for occupational status and education respectively. The scale times the weighted scores are added to find socioeconomic status.

For couples where both members are employed, the status score is calculated by separately multiplying the scale score obtained by both husband and wife for occupation and education by the designated weight, adding the sums of both and dividing by two. Status scores are then compared to a table which designates social strata. Computed status score range from 66 to 8. The higher the computer status score, the higher the socioeconomic status of the members of the designated nuclear family.

Hollingshead (1975) validated the above method by analyzing educational and occupational data gained from the 1970 census. He found a definite gradient for males and females between number of years of school completed and scores assigned to similar groups of occupation. The correlation between median years of school completed and occupational group score was .83 for males and .84 for females.

Again using 1970 census data, Hollingshead found that the correlation between assigned occupational code groups and income was .78 for males and .67 for females. Construct validity for this method was obtained by comparing it to the prestige data gathered by the National Opinion

Research Center (NORC) in their general social survey. The correlation between Hollingshead's index and the NORC data was .92. Data Collection

Permission to conduct this research was obtained from the

Superintendent/Research Chairperson of the designated school system.

Testing took place in small quiet rooms in the elementary school where each student attends. The CAS was administered first. The CAS consists of eight subtests which measure the cognitive processes of arousal/attention; coding (simultaneous and successive processing); and planning. Seven of the eight subtests of the CAS were individually administered to each child in one sitting. The length of time required to administer and complete these seven subtests was 45 minutes to one hour. The eighth subtest of the CAS, the MAT-SF, was administered later in a group setting.

After all students had completed the measures of cognitive processing, the PHCSCS, NSLCSC and the MAT-SF were administered in a group setting. The order of administration was the MAT-SF first and then the PHCSCS and NSLCSC.

Before the beginning of testing, each child with a signed parental consent form met individually with an examiner to learn about the purpose of the assessment. They were encouraged to ask questions about the project and their role in it. Children were also encouraged to sign a letter of assent (Appendix C). If the child preferred not to participate in the study, the process stopped and the subject was returned to class. 102

The PHCSCS and NSLCSC were read aloud to students as they were asked to follow the written questions in the test booklet. The examiner answered definition questions as they arose. The small group administration sessions ranged from three to six subjects. Each of the above instruments was administered at one time. The time required to administer the above tests measuring self-concept, locus of control and the MAT-SF was 45-50 minutes.

The testing outlined above was administered by one of five examiners. All examiners were female and ranged in age from 40-52.

There were three Caucasian and two African-American examiners. All examiners had at least one graduate level course in test administration and had between eight and 17 years of experience in the counseling profession.

The investigator trained four additional examiners in how to administer the instruments. Examiners were observed administering the test prior to actual involvement with student subjects. Within the

Columbus Public School system, each examiner gathered data in one school only. The investigator called the examiners weekly to determine if problems existed or if assistance was needed. There were no problems experienced by any examiner in the gathering of data. The four additional examiners completed a total of 44 subjects, each averagine 11 students in their respective schools. The investigator completed a total of 88 subjects in the Upper Arlington and Hamilton

Local School districts. All data was collected in five months. 103

Each student was administered the assessment measures by one of the five examiners upon receipt of the permission slips from parents.

The data are described based upon the impact of socioeconomic level, gender and racial background. The Hollingshead Four Factor Index of

Social Status was used to make determinations about the socioeconomic status of the parents and children involved in this study.

Statistical Analysis

The group of subjects were described by use of descriptive statistics such as frequency and percent. Hypotheses 1 through 10 were tested via a univariate statistical analytic technique, Pearson

Product Moment Correlation Coefficient (Gay, 1981), in order to determine relationships. The multivariate data analytic strategy, canonical correlation, was used in order to avoid alpha error build up with multiple dependent variables (Pedhazur, 1982). Hypothesis 11 was tested via a canonical correlation in order to identify any relationships that existed between intelligence and selected personality variables such as self-concept and locus of control

(Pedhazur, 1982; Thompson, 1988). Hypotheses were tested at the pc.Ol level of significance. This level of significance was established in order to increase the probability that a relationship, in fact, exists.

Each of the eleven null hypotheses are separately listed below. 104

1. No relationship exists between intelligence as measured by the

cognitive process of attention and self-concept among children

ages eight and nine.

1-A. No relationship exists between intelligence as measured by

attention and self-concept as measured by behavior among

children ages eight and nine.

1-B. No relationship exists between intelligence measured by

attention and self-concept measured by intellectual and

school status among children ages eight and nine.

1-C. No relationship exists between intelligence, measured by

attention and self-concept measured by physical appearance

and attributes among children ages eight and nine.

1-D. No relationship exists between intelligence measured by

attention and self-concept measured by anxiety among

children ages eight and nine.

1-E. No relationship exists between intelligence measured by

attention and self-concept measured by popularity among

children ages eight and nine.

1-F. No relationship exists between intelligence measured by

attention and self-concept measured by happiness and

satisfaction among children ages eight and nine.

2. No relationship exists between intelligence as measured by the

cognitive process of coding and self-concept among children

ages eight and nine.

2—A. No relationship exists between intellicence measured hy

simultaneous processing and self-concept measured by

behavior among children ages eight and nine. 105

2-B. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

intellectual and school status among children ages eight and

nine.

2-C. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

physical appearance and attributes among children ages eight

and nine.

2-D. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by anxiety

among children ages eight and nine.

2-E. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

popularity among children ages eight and nine.

2-F. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

happiness and satisfaction among children ages eight and

nine.

2-G. No relationship exists between intelligence measured by

successive processing and self-concept measured by behavior

among children ages eight and nine.

2-H. No relationship exists between intelligence measured by

successive processing and self-concept measured by

intellectual and school status among children ages eight and

nine. 106

2-1. No relationship exists between intelligence measured by

successive processing and self-concept measured by physical

appearance and attributes among children ages eight and

nine.

2-J. No relationship exists between intelligence measured by

successive processing and self-concept measured by anxiety

among children ages eight and nine.

2-K. No relationship exists between intelligence measured by

successive processing and self-concept measured by

popularity among children ages eight and nine.

2-L. No relationship exists between intelligence measured by

successive processing and self-concept measured by happiness

and satisfaction among children ages eight and nine.

3. No relationship exists between intelligence as measured by the

cognitive process of planning and self-concept among children

ages eight and nine.

- 3-A. No relationship exists between intelligence measured by

planning and self-concept measured by behavior among

children ages eight and nine.

3-B. No relationship exists between intelligence measured by

planning and self-concept measured by intellectual and

school status among children ages eight and nine.

3-C. No relationship exists between intelligence measured by

planning and self-concept measured by physical appearance

and attributes among children ages eight and nine. 107

3-D. No relationship exists between intelligence measured by

planning and self-concept measured by anxiety among children

ages eight and nine.

3-E. No relationship exists between intelligence measured by

planning and self-concept measured by popularity among

children ages eight and nine.

3-F. No relationship exists between intelligence measured by

planning and self-concept measured by happiness and

satisfaction among children ages eight and nine.

4. No relationship exists between intelligence as measured by the

cognitive process of attention and locus of control among

children ages eight and nine.

5. No relationship exists between intelligence as measured by the

cognitive process of coding and locus of control among children

ages eight and nine.

5-A. No relationship exists between intelligence measured by

simultaneous processing and locus of control among children

ages eight and nine. *

5-B. No relationship exists between intelligence measured by

successive processing and locus of control among children

ages eight and nine.

6. No relationship exists between intelligence as measured by the

cognitive process of planning and locus of control among

children ages eight and nine.

7. No relationship exists between attention and coding among

children ages eight and nine. 108

7-A. No relationship exists between attention and simultaneous

processing among children ages eight and nine.

7-B. No relationship exists between attention and successive

processing among children ages eight and nine.

8. No relationship exists between attention and planning among

children ages eight and nine.

9. No relationship exists between coding and planning among

children ages eight and nine.

9-A. No relationship exists between simultaneous processing and

planning among children ages eight and nine.

9-B. No relationship exists between successive processing and

planning among children ages eight and nine.

10. No relationship exists between simultaneous and successive

processing among children ages eight and nine.

11. No relationship exists between intelligence and the personality

variables of self-concept and locus of control among children

ages eight and nine. CHAPTER IV

FINDINGS

The purpose of this study was to determine if a relationship exists between the information processing model of intelligence theorized by Luria (1966, 1973, 1980) and operationalized by Das and

Naglieri (1989) and the personality variables of self-concept and locus of control among children ages eight and nine. This chapter contains the findings of the study. The results of the statistical analyses are presented by null hypotheses. All hypotheses were tested at the p<.01 level of significance.

Sample Demographics

The sample consisted of 132 subjects in grades 3 (59.2%) and 4

(40.8%) ranging in age from 97-125 months. There were 78 males (60%) and 54 females (40%), and the racial composition of this sample included two Asians (1.5%), 34 African-Americans (26%), and 96

Caucasians (72.5%). The socioeconomic status of the participants' families varied in that 51 were professional (48%), 40 semi-skilled

(37%), and 16 unskilled (15%). Frequencies and percentages for the demographic variables are presented in Table 1. Bureau of Census population statistics (1989) indicate that within the United States, semi-skilled and unskilled workers represent 31% and 13% of the total labor force while professional workers are 25% of this group.

109 110

Table 1

Sample Demographics by Frequency and Percent *

0 f 9 Cum. %

Age (in months) 97-118 79 88.5 88.5 109-125 53 11.5 100.0 Total 132 100.0

SES Unskilled - 8-19 16 15.0 15.0 Clerical - 20-39 40 37.0 56.1 Professional - 40-66 51 48.0 100.0 Total 107 100.0

Gender Male 78 60.0 60.0 Female 54 40.0 100.0 Total 132 100.0

Grade 3 78 59.2 59.2 4 54 40.8 100.0 Total 132 100.0

Ethnicity Asians 2 1.5 1.5 African- Americans 34 26.0 27.5 Caucasians 96 72.5 Total 132 100.0

* All variables were examiners identified except SES which was based on parent self-report. Ill

Findings by Hypotheses

Since the PASS model suggests that there is a significant relationship between planning and chronological age, partial correlations were computed controlling for age for the null hypotheses stated below.

1. No relationship exists between intelligence as measured by the

cognitive process of attention and self-concept among children

ages eight and nine.

The null hypothesis was rejected (r12.3=22, p=.006).

1-A. No relationship exists between intelligence measured by

attention and self-concept as measured by behavior among

children ages eight and nine.

The null hypothesis was not rejected ( 12.3=.17, p=.023).

1-B. No relationship exists between intelligence measured by

attention and self-concept measured by intellectual and

school status among children ages eight and nine.

The null hypothesis was not rejected ( 12.3=.19, p=.015).

1—C. No relationship exists between intelligence measured by

attention and self-concept measured by physical appearance

and attributes among children ages eight and nine.

The null hypothesis was not rejected (r12.3=.14, p=.058).

1-D. No relationship exists between intelligence measured by

attention and self-concept measured by anxiety among

children ages eight and nine. £ The null hypothesis was not rejected ( 12.3=.14, p=.052). 1-E. No relationship exists between intelligence measured by

attention and self-concept measured by popularity among

children ages eight and nine. £ The null hypothesis was not rejected ( 12.3=.14, p=.061).

1-F. No relationship exists between intelligence measured by

attention and self-concept measured by happiness and

satisfaction among children ages eight and nine.

The null hypothesis was not rejected (r12.3=.15, p=.050).

No relationship exists between intelligence as measured by the

cognitive process of coding and self-concept among children ages

eight and nine. £ The null hypothesis was not rejected. ( 12.3=.13, p=.075).

2-A. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

behavior among children ages eight and nine.

The null hypothesis was rejected (r12.3=.26, p=.002).

2-B. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

intellectual and school status among children ages eight and

nine.

The null hypothesis was not rejected (r12.3=.14, p=.060).

2-C. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

physical appearance and attributes among children ages eight

and nine.

The null hypothesis was not rejected (L12.3=.08, p=.224). 113

2-D. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by anxiety

among children ages eight and nine.

The null hypothesis was not rejected (l12.3=.08, p=.193).

2-E. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

popularity among children ages eight and nine.

The null hypothesis was not rejected (r12.3=.08, p=.177).

2-F. No relationship exists between intelligence measured by

simultaneous processing and self-concept measured by

happiness and satisfaction among children ages eight and

nine. £ The null hypothesis was not rejected ( 12.3=.03, p=.381).

2-G. No relationship exists between intelligence measured by

successive processing and self-concept measured by behavior

among children ages eight and nine.

The null hypothesis was rejected (r12.3=.32, p=.000).

2-H. No relationship exists between intelligence measured by

successive processing and self-concept measured by

intellectual and school status among children ages eight and

nine.

The null hypothesis was not rejected ( 12.3=.09, p=.144).

2-1. No relationship exists between intelligence measured by

successive processing and self-concept measured by physical

appearance and attributes among children ages eight and

nine. £ The null hypothesis was not rejected ( 12.3=.13, p=.441). 2-J. No relationship exists between intelligence measured by

successive processing and self-concept measured by anxiety

among children ages eight and nine.

The null hypothesis was not rejected (r12.3=-.001, p=.493).

2-K. No relationship exists between intelligence measured by

successive processing and self-concept measured by

popularity among children ages eight and nine.

The null hypothesis was not rejected (r12.3=.12, p=.098).

2-L. No relationship exists between intelligence measured by

successive processing and self-concept measured by happiness

and satisfaction among children ages eight and nine.

The null hypothesis was not rejected ( 12.3=.14, p=.055).

No relationship exists between intelligence as measured by the

cognitive process of planning and self-concept among children

ages eight and nine.

The null hypothesis was rejected (r12.3=.26, p=.002).

3-A. No relationship exists between intelligence measured by

planning and self-concept measured by behavior among

children ages eight and nine.

The null hypothesis was not rejected (r12.3=.12, p=.089).

3-B. No relationship exists between intelligence measured by

planning and self-concept measured by intellectual and

school status among children ages eight and nine.

The null hypothesis was rejected (r12.3=.24, p=.004).

3-C. No relationship exists between intelligence measured by

planning and self-concept measured by physical appearance

and attributes among children ages eight and nine.

The null hypothesis was not rejected (r12.3=.12, p=.084). 115

3-D. No relationship exists between intelligence measured by

planning and self-concept measured by anxiety among children

ages eight and nine.

The null hypothesis was rejected (r12.3=.27, p=.001).

3-E. No relationship exists between intelligence measured by

planning and self-concept measured by popularity among

children ages eight and nine.

The null hypothesis was rejected (r12.3=.22, p=.006).

3-F. Mo relationship exists between intelligence measured by

planning and self-concept measured by happiness and

satisfaction among children ages eight and nine.

The null hypothesis was not rejected (r12.3~.16, p=.032).

4. No relationship exists between intelligence as measured by the

cognitive process of attention and locus of control among

children ages eight and nine.

The null hypothesis was rejected (r12.3=-.35, p=.000).

5. No relationship exists between intelligence measured by the

cognitive process of coding and locus of control among children

ages eight and nine.

The null hypothesis was rejected (r12.3=-.21, p=.008).

5-A. No relationship exists between intelligence measured by

simultaneous processing and locus of control among children

ages eight and nine.

The null hypothesis was not rejected (r12.3=-.20, p=.013). 116

5-B. No relationship exists between intelligence measured by

successive processing and locus of control among children

ages eight and nine. £ The null hypothesis was not rejected ( 12.3=-.17, p=.025).

6. No relationship exists between intelligence as measured by the

cognitive process of planning and locus of control among children

ages eight and nine. £ The null hypothesis was rejected ( 12.3=-.33, p=000). Partial

correlations of the PASS model with age effects removed and the

independent variables are presented in Table 2 and Table 3.

Pearson Product Moment Correlation Coefficients among the PASS

variables and independent variables without effects removed are

presented in Appendix D.

7. No relationship exists between attention and coding among

children ages eight and nine.

The null hypothesis was rejected (r12.3=.35, p=.000).

7-A. No relationship exists between attention and simultaneous

processing among children ages eight and nine.

The null hypothesis was rejected (r12.3=*.41, p=.000).

7-B. No relationship exists between attention and successive

processing among children ages eight and nine.

The null hypothesis was rejected (r12.3=.23, p=.004.).

8. No relationship exists between attention and planning among

children ages eight and nine. £ The null hypothesis was rejected ( 12.3=.50, p=.000). 117

Table 2

Partial Correlations of Pass Model with Independent Variables (Age Effects Removed)

Piers-Harris Children Self-Concept Scale (PHCSCS)

PASS MODEL Total Beh Int-SS Phy-AA Anx Pop H-S

Planning .26* .12 .24* .12 .27** .22* .16

Visual Search .24* .10 .19 .15 .21* .20 .16

Planned Connections .18 .10 .20 .05 .24* .17 .11

Attention .22* .18 .19 .14 .14 .14 .15

Attention-Expressive .12 .09 .07 .08 .09 .07 .08

Attention-Receptive .22* .18 .21* .14 .14 .14 .14

Coding .13 .34** .13 - .02 .04 .12 .09 O CO ro o i r Simultaneous .11 .26* .14 - .07 .08 •

Figure Memory .10 .21* .14 - .04 .08 -.09 .01

Matrices .08 .22* .09 - .07 .05 -.05 -.05

Successive .11 .32** .09 .01 -.002 -.12 .14

Word Recall .09 .30** .10 .01 .00 -.13 .11

Sentence Repetitions h H o r~ and Questions .10 .28* .01 .01 -.09 .14 n ii ii II it

s s s s s s s s :

* p<.01 ** p<.001

Beh = Behavior Int-SS = Intellectual and School Status Phy-AA = Physical Appearance and Attributes Anx = Anxiety Pop = Popularity H-S * Happiness and Satisfaction 118

Table 3

Partial Correlations of Pass Model with Independent Variables (Age Effects Removed)

Nowicki Strickland Locus of Control Scale for Children (NSLCSC)

PASS MODEL NSLCSC

Planning -.33**

Visual Search -.24*

Planned Connections -.30**

Attention -.35**

Attention-Expressive -.14

Attention-Receptive -.38**

Coding -.21*

Simultaneous -.20

Figure Memory -.15

Matrices -.18

Successive -.17

Word Recall -.15

Sentence Repetitions and Questions -.16

* p<.01 ** pc.001

NOTE: The NSLCSC is scored in the external direction, therefore the lower the score the more internal the locus of control. This results in a negative relationship with the PASS Model variables. No relationship exists between coding and planning among

children ages eight and nine.

The null hypothesis was rejected (r12.3=.35, p=.000).

-A. No relationship exists between simultaneous processing and

planning among children ages eight and nine. £ The null hypothesis was rejected ( 12.3=.37, p=.000).

-B. No relationship exists between successive processing and

planning among children ages eight and nine.

The null hypothesis was rejected (r12.3=.25, p=.002).

No relationship exists between simultaneous processing and

successive processing among children ages eight and nine.

The null hypothesis was rejected (r12.3=.45, p=000).

Intercorrelations among the subscales of the PASS model with age

effects removed are presented in Table 4. Intercorrelations

among the subscales of the PASS model without effects removed

are presented in Appendix E.

No relationship exists between intelligence and the personality

variables of self-concept and locus of control among children

ages eight and nine.

The results are explained in the canonical analysis which

follows. Table 4

Intercom relations Among Subscales of PASS Model

Age Effects Removed

r vs rc AIT AE ARC 81 FM HA SU UR SRq

r — .S3** .81** .30** .17** .41“ .33“ .37“ • 23* .37** .23* .17 .26*

vs — .33** .3A»* .14 .37** .11* .23* .13 .24* .13 .08 .17

rc — .48** .31** .43“ .38** .37“ .23* .26** .11* . 2 A*

ATI — .73** .83“ .33“ .41“ .27** .16 .74*

A£ — .13* .33“ .23“ • 13* .23“ .22* .14 .74*

AR — .27“ .33“ .22* .11 .13

C — .78** • 63** .87“ .31** .7*** .88**

SI — .83** .4 3* * .33“ .43“

th — .42** • 36** .23* .33“

HA — .33** .38“

SU — .83“ .33“

VR — .62**

SRq —

* p<.01 ** pC.OOl

r - M a n n i n g VS - Vleual Search fC ” Planned Connectlone AIT - Attention AE - Attentlon-Eapreeelve AA - Attention-Receptive C - Coding SI " Slaoltancoue IH - Tlgute Meaorp HA - Metrical SU - Succeaalve VI - Word Recall SRq - Sentence Repetltlona end Queatlona 121

Canonical Analysis

A canonical analysis was utilized to investigate the relationship

between two sets of variables. In this instance, canonical analysis

attempts to examine the relative importance of each variable in

explaining the variance of the cognitive processing model (PASS) and

its relationship to selected personality variables such as self-concept

and locus of control. The dependent variables examined were planning,

attention, simultaneous and successive processing (PASS model). The

second set of variables which were independent are self-concept and

locus of control represented by the Piers-Harris Children Self-Concept

Scale (PHCSCS) and the Nowicki-Strickland Locus of Control Scale for

Children (NSLCSC).

The squared canonical correlation for both pairs of canonical variates were statistically significant (0.17 and 0.10 respectively, p<.01). Only the first of the canonical variates was dealt with as meaningful since only squared canonical correlations greater than 0.10 may be treated as significant enough for further examination (Pedhazar,

1982). The squared canonical correlation of 0.17 implied that 17% of

the variance between the two sets of variables was shared by the first pair of canonical variates.

Ihe standardized canonical weight was interpreted similarly to

standardized regression coefficients (BETA'S). Standardized canonical weights are sample specific and desirable when comparing the relative effects of different variables within a given population. Care should be taken when interpreting these weights since they are unstable 122

due to their multicollinearity. Multicollinearity refers to the

interrelatedness among variables in that there is a lack of

orthogonality.

Of the dependent variables, planning (B=-0.57), was most important

followed by attention (B=-.0.51), simultaneous, and successive. Of the

independent variables the NSLCSC (B=-0.85) was more important than the

PHCSCS.

Given the problems associated with standardized coefficients,

structure coefficients are preferred for interpretative purposes

(Pedhazur, 1982). Examining the structure coefficients, planning

(s— 0.88) was most important followed by attention (s=-0.85)

simultaneous (s=-0.56) and successive (s=-0.46) respectively.

Structure coefficients for the independent variables revealed the

NSLCSC (s-0.97) was more important than the PHCSCS (s-0.68). All dependent variables are negatively related to the NSLCSC in that the higher the PASS scores, the lower the scores obtained on the NSLCSC.

The first canonical variate accounted for 50.61% of the total variance of the dependent variable (PV=0.5061) and 70.98% of the total variance in the independent variable (PV=0.7098). The average squared coefficient multiplied by the squared canonical correlation yields the

redundancy index. The redundancy index indicated that 8.65% of the

total variance in the dependent variable was explained by the first canonical variate or a linear combination of the independent variables.

Similarly, 12% of the total variance in the independent variables was explained by the first canonical variate or the linear combination of

the dependent variables. It could be interpreted that nine percent of 123

the variability in intelligence as measured by the PASS model was due

to the NSLCSC and the PHCSCS. Twelve percent of the variance in the

NSLCSC and the PHCSCS could be attributed to the PASS model of

cognitive processing. A summary of the canonical analysis for the PASS

model, PHCSCS and NSLCSC are presented in Table 5.

Table 5

Summary of Canonical Analysis for PASS Model, PHCSCS, and NSLCSC

Root 1 Root 2 Variables BS B S

Simultaneous -.04 -.55 .22 .20

Successive -.12 -.45 -.09 -.02

Planning -.57 -.88 -1.03 -.44

Attention -.50 -.85 .97 .50

Average Squared Structured Coefficient 50.61% 12.33%

Redundancy Index 8.64% .23%

PHCSCS -.24 -.68 -1.14 -.73

NSLOC-SC .85 .97 -.79 -.21

Average Squared Structured Coefficient 70.98% 29.01%

Redundancy Index 12.11% .54%

Squared Canonical Correlation 0.17* 0.10

* p<.01

B = Standardized Weight S = Structure Coefficient CHAPTER V

SUMMARY, DISCUSSION, CONCLUSIONS, AND RECOMMENDATIONS

This chapter contains a summary of findings of the study, and

related conclusions. A discussion of these conclusions is presented

along with recommendations for future practice and research.

Summary

Every year millions of American children are administered measures of intelligence as a means of predicting school success. Emotional variables which may also mediate school success are often neglected in the assessment process because of the lack of clearly identified empirical relationships between emotion and cognition in the field of psychology (Coopersmith, 1967; Crandall, Katkovsky & Crandall, 1965;

Meichenbaum, 1980; Shavelson, Huber & Stanton, 1976; William & Cole,

1968; Zajonc, 1980). Emotionally based variables are often perceived as ancillary or nominal in human cognitive functioning (Campos &

Barrett, 1984; Meichenbaum, 1980; Zajonc, 1980). There is little understanding of emotion as a subsystem of personality which sustains, gives purpose to and organizes behavior and its impact upon intelligence. More specifically, little is known about personality variables such as self concept and locus of control and their

relationship to intellectual ability.

124 125

The use of tests of intelligence to ascertain school success is further complicated by the current focus on measuring ability levels rather than mental processes (Das, 1979; Hothersall, 1984; Sattler,

1982; Sternberg, 1980, 1986; Ysseldyke & Algozzine, 1982). This focus on ability emphasizes outcome or the product of intellectual assessment. Das (1979) and Sternberg (1979, 1986) maintain that an ability orientation to intellectual functioning focuses on how well tasks are performed while, in contrast, a process approach to the investigation of intellectual functioning focuses on the mechanics of how a task is performed. A process approach to intellectual functioning also focuses on the development of strategies or how a person might be trained to perform a given task more efficiently.

In the present investigation, the theoretical perspective used to explain the concept of intelligence is based upon the information processing model developed by Das et al., (1975, 1979) and most recently operationalized by Das and Naglieri (1989). It is referred to as the Planning, Attention, Simultaneous and Successive (PASS) cognitive processing model and is grounded in the theoretical and clinical work of A. R. Luria (1966, 1973, 1980). Luria observed that three functional units exist within the human brain that are responsible for its basic functioning. These functional units are responsible for the mental processes of: (a) arousal and attention;

(b) reception, analysis and storage of information; and (c) planning, organizing and programming. These units are interactive and function as one. To date, no measure of intellectual functioning has been designed to assess the arousal attention or planning functional units. 126

The predominant view of intellectual assessment is limited because

its emphasis is on the reception, analysis and storage of information

alone. No previous empirical investigations have studied the

relationship between emotion as identified in the personality variables

of self-concept and locus of control and intelligence from a process

perspective including attention and planning. The purpose of this

study was to determine if a relationship exists between the Planning,

Attention, Simultaneous and Successive (PASS) cognitive processing

model and self-concept and/or locus of control among eight and nine

year old children.

In this study, there were 132 subjects between the ages of eight

and nine from three school districts in Central Ohio, representing a

cross section of individuals from different socioeconomic, ethnic and

racial groups. Each participant was administered three instruments:

The Cognitive Assessment System (CAS), Piers-Harris Children's

Self-Concept Scale (PHCSCS), and the Nowicki-Strickland Locus of

Control Scale for Children (NSLCSC). In addition, parents of each

participant were asked to complete the Hollingshead Four Factor Index

of Social Status (HFFISS).

Multivariate and univariate statistical analyses were used to test

the hypotheses. The multivariate technique, canonical correlational

analysis, was employed to determine how the independent variables

(self-concept and locus of control) were related to the dependent

variables (planning, attention simultaneous and successive processing)

(Pedhazur, 1982; Thompson, 1988). In order to determine the

relationship between individual variables, the univariate statistical 127 technique employed in this study was a partial correlation coefficient partialling out the effects of age.

The 132 subjects in this study represented variation in gender, ethnicity, and socioeconomic (SES) level. Subject age, gender, grade and ethnicity were determined by the examiner, while SES is based on parent self-report. Sixty percent of the subjects were male and 40% were female. Almost three-fourths (72.5%) of the sample were

Caucasian, over one-fourth (26%) were African-Americans, and less than two percent (1.5%) were Asian Americans. Almost one-half (48%) of the sample was represented by children from professional families. Over two-thirds (37%) of the children were from clerical families, and 15% of the children were from families where one or both parents were unskilled laborers.

The results of this study indicated that there is a significant relationship (p<.01) between the planning and attention components of the PASS model and self-concept and locus of control among eight and nine year old children. The relationship among these variables are generally low to moderate.

A summary of the significant findings by null hypotheses 1 - 10 are presented in Table 6. The hypothesis, correlation and level of significance are designated. The findings are further discussed in the next section. All null hypotheses are specified in Chapters I, III and

IV. 128

Table 6

Statistically Significant Pearson Product Moment Corrrelations by Null Hypotheses (Age Effects Removed)

Hypothesis r p<.01

1 .22 .006 2A .26 .002 2B .32 .000 3 .26 .002 3B .24 .004 3D .27 .002 3E .22 .006 4 -.35 .000 5 -.21 .008 6 -.33 .000 7 -.35 .000 7A .41 .000 7B .23 .004 8 .50 .000 9 .35 .000 9A .37 .000 9B .25 .002 10 .45 .000

Bsssasesass

Discussion

The multivariate approach, canonical correlation was employed to determine how the dependent variables (planning, attention, simultaneous and successive processing - PASS) are related to the

independent variables (self-concept and locus of control) among children ages eight and nine. The canonical analysis indicated that generally the relationship among the dependent and independent variables was low. This finding is consistent with results obtained by 129 measures of cognitive processing or intelligence which assess coding alone'(Anastasiow, 1967; Bailer, 1961; Battle & Rotter, 1963; Bledsoe,

1964; Crandall, Katkovsky & Crandall, 1965; Karnes & Wherry, 1981;

Mulgram & Mulgram, 1976; Wylie, 1979).

The non-hierarchial and integrative nature of the PASS model

increases the probability of such a finding because of the intercorrelations among components of the model. As such, definitive statements regarding the strength or power of the relationship among the dependent and independent variables, given the linear approach of the statistical model, are difficult.

The univariate statistical technique, Pearson product moment and partial correlational coefficients, were employed in order to determine individual relationships among the dependent and independent variables among children ages eight and nine. Findings and patterns of

relationships among the dependent (PASS model) and independent variables (self-concept and locus of control) are discussed.

Overall findings indicate that the relationship between the PASS model and self-concept or locus of control among children ages eight and nine were generally low to moderate. More specifically, these

findings suggest that there is a significant relationship between the planning and attention components of the PASS model and self-concept and locus of control among children ages eight and nine. The canonical analysis supports this finding as well, in that planning and attention appear to be the two most important of the dependent variables in explaining the variance of the respective model. Although these

findings are not strong, these relationships imply that the PASS model, 130

self-concept, and locus of control share a degree of relationship which

is statistically significant.

Patterns of significant relationships among the self-concept

and/or locus of control and the PASS model suggested the following sets

of interrelationships. Total self-concept was significantly related to

the combined planning, visual search, combined attention and the

attention-receptive subscales of the PASS model. No significant

relationship was found between self-concept and either planned

connections or the attention-expressive subscales of the PASS model.

The significant relationships suggest that more efficient scores on the planning and attention subscales are associated with higher overall

self-concept scores or vice versa. It appears, however, that the

association between total self-concept, planning and attention may be

influenced if the complexity of the planning task increases as in the

case of planned connections or the subjects have to suppress competing

stimuli in order to respond as in the case of selective

attention-expressive (Naglieri, 1989).

In contrast to other findings, self-concept as assessed by the behavior scale was significantly related to all aspects of coding which

include the simultaneous and successive components of the PASS model.

Piers (1984) indicated that the behavior scale refers to the degree to which children perceive, deny or accept responsibility for their own

problematic behaviors. Low scores reflect a willingness to acknowledge

behavioral difficulties while high scores reflect either denial or lack

of behavorial problems. A significant relationship between

self-concept and coding (simultaneous, successive processing) may be 131

reflective of the degree to which the cognitive processes of coding are linked to academic achievement (Abbott, 1981; Brookover, LePere,

Hamachek, Thomas, & Erickson, 1965; Bledsoe, 1964; Cattell et al, 1966;

Finnegan, 1986; Piers and Harris, 1964; Wescott, 1985) and the extent to which academic achievement and appropriate classroom behavior are

interconnected (Denscombe, 1985; Fink, 1962; Jones, 1987; Parrish &

Reimers, 1988; Wells & Forehand, 1985).

Self-concept as understood in the intellectual and school status subscale achieves significance on the total planning and the attention-receptive subscales. This result suggests that a child's self-assessment of his or her own academic abilities is associated with the ability to plan and voluntarily discriminate among stimuli

(Naglieri, 1989; Piers, 1984).

The anxiety and popularity subscales of the self-concept measure illustrated a significant relationship only with the planning component of the PASS model. These subscales measure internalized self-evaluation which are more subjective and unique to the individual

(Piers, 1984) and as a result, much less in need of awareness of environmental stimuli. In the instance of anxiety, a descriptor of anxiety is self preoccupation or focus on future events (Sarason,

1986). Anxiety is capable of narrowing the cues available in a given situation, thus interferring with attention to the range of situational cues or alternatives (Derryberry & Rothbart, 1984; Sarason, 1986).

Consistently with the findings obtained above, the relationship between locus of control and the PASS model was generally low to moderate. However, unlike self-concept, a significant relationship 132

occurred between locus of control and all the major components of the

PASS model. The relationship between the PASS model and locus of

control was generally stronger than that reflected between the model

and self-concept. This finding was supported by the canonical analysis whereby locus of control was more important in explaining variability

among the independent variables. The strongest relationship was

between receptive attention and ", r 'us of control while the weakest

relationship resulted between locus of control and coding. This

finding suggests that more internal locus of control is associated with more efficient use of planning among eight and nine year olds in the

sample.

Intercorrelation of the PASS Model

A Pearson Product Moment Correlation was employed in order to determine the integrative and non-hierarchial nature of the components

of the PASS model. Significant correlations were found between all

components of the PASS model. The strongest relationship occurred

between planning and attention. A relationship of similar strength was

obtained between simultaneous and successive processing. The weakest

relationship between components of the PASS model was between attention

and successive processing. These findings are consistent with the

conceptual framework of the PASS model (Das & Naglieri, 1989; Naglieri

& Das, 1987, 1988), and other research examining this construct (Ashman

& Das, 1980; Hurt, 1988; Naglieri & Das, 1987, 1988; Naglieri, Prewett

& Bardos, 1989; Stutzman, 1986). 133

Conclusions

The findings of this study among eight and nine year olds suggest

that only a low to moderate relationship exists between self-concept and/or locus of control and the PASS model. The degree of correlation among self-concept and/or locus of control and the PASS variables

though statistically significant are not strong enough to make definitive statements regarding their relatedness. However, the processes of self-concept and locus of control are most related to the planning and attention components of the PASS model. More specifically, in comparison to self-concept, locus of control most closely approximates the processes being assessed by the planning and attention components of the PASS model.

As indicated earlier, locus of control is a subjective perspective of self understanding because it involves volitional control over thoughts and actions as well as self-determination (Damon & Hart,

1988). In contrast to the objective notions of self identified and measured by the PHCSCS, locus of control appears to be more closely

related to variables measured in the PASS model. Thus, even though there is some level of evaluatory process involved in both self-concept and locus of control, volitional control or the "I" as subject may be a more pertinent issue in terms of planning or metacognition than understanding of self as the object or "me."

Interpretation of the moderate relationship found between self-concept and/or locus of control and the PASS model is made more difficult by what Damon and Hart (1988) refer to as the inadequate developmental orientation used to assess self-concept and locus of 134

control. Damon and Hart maintain these measures of self-concept do not

evaluate self-concept from a perspective which considers the

developmental stages of the growing child. They feel that assumptions

are made that the same questions are of equal value across the

developmental spectrum. For example, the eight and nine year olds in

this study are not at the same developmental level as toddlers

concerned with their physical selves, or adolescents who have a more

firmly established sense of their psychological self-concept. Damon and Hart conclude that adequate assessment of self-concept cannot be measured apart from a child's cognitive understanding of self, and this may only be understood through the process of development.

Second, there was no significant relationship found between

selective attention-expressive and self-concept or locus of control among eight and nine year olds. Interpretation of this result is difficult because it is contrary to a pattern of significant

relationships established among the PASS model total self-concept measure subtests, and the locus of control scale. In this case, it may be interesting to determine the extent to which the integrative and non-hierarchial approach of the PASS model influences this result.

Recommendations

Several recommendations emerge from this study which may promote a better understanding of the variables under examination. They are discussed below.

First, given the limitations of self-concept measures cited by

Damon and Hart (1988) and Wylie (1979) it is recommended that different

instruments be used to assess self-concept and locus of control. New 135

instruments will need to assess self-concept from a developmental conceptual framework which integrates as a part of the norm, affective concepts as part of an overall cognitive understanding of self. Such a developmental-cognitive approach may provide more accurate and specific information about self-concept and locus of control in terms of how they relate to the PASS model. Such an approach may also be sensitive to any interaction among locus of control, self-concept and the PASS model in the developing child. Damon and Hart recommend the partial use of a qualitative method such as the interview. This approach may also help to neutralize concerns that, to some degree, the results of self-report instruments are reflective of individual differences in self presentation (Archer et al., Batson, Bolen, Cross & Neuringer-

Benefiel, 1986; Fultz et al., 1986).

Finally, there may be some utility for exploring the implications of the above study in clinical practice. Since the focus of the PASS model is on processing and how tasks may be achieved more efficiently, what, may be of importance for clinical practice in counseling is the fact that there is a relationship among these variables, rather than the degree of that relationship. As a result, exploring alternative

intervention methods with children and/or adults for enhancing the development of efficient planning skills or internal locus of control may be of value. Research evidence in planning or metacognition suggests that individuals are better able to generalize or transfer learning when they are provided with strategies as well as a contextual

frame of reference for utilizing the strategies they are taught

(Bransford, Sherwood, Vye & Reiser, 1986; Brown, Ferrara & Campione, 136

1983). Vygotsky's (1978) notion of "zone of proximal development" which maintains that an individual's ability to independently solve problems is facilitated through guidance from others is relevant in

this case because, as in learning, counseling and psychotherapy are

educative processes. The educative orientation of counseling and psychotherapy enables individuals to reframe thinking through

influencing and changing their contextual frames of reference. Thus,

it may be true that teaching the use of efficient plans within the

therapeutic setting may affect the degree to which a person attributes meaning and control of behavior to internal processes. REFERENCES

Abbott, A. (1981). Factors related to third grade achievement: Self-perception, classroom composition, sex and race. Contemporary Educational Psychology, 6, 167-179.

Aboud, F., & Skerry, S. (1983). Self and ethnic concepts. Canadian Journal of Behavioral Science, 15, 3-34.

Adrian, E. D. (1954). General discussion: The physiological basis of perception. In E. Adrian, J. Bremer, H. H. Jasper (Eds.), Brain mechanisms and consciousness. (pp. 237-242). Springfield, Illinois: Thomas

Anastasiow, N. J. (1967). Sex differences in self-concept scores of high and low ability elementary students. Gifted Child Quarterly, Summer, 112-116.

Archer, R. L., Diaz-Loving, R., Gollwitzer, P. M., Davis, M. H., & Foushee, H. C. (1981). The role of dispositional empathy and social evaluation in the empathic mediation of helping. Journal of Personality and Social Psychology, 40, 786-796.

Arnold, M. B. (1960). Emotion and personality, Vol. II Neurological and physiological aspects. New York: Columbia University Press.

Arnold, M. B. (1968). Neural mediation of the emotional components of action. In M. B. Arnold (Ed.), The nature of emotion (pp. 318-336). Baltimore, Maryland: Penguin.

Ashman, A. F. (1982). Cognitive processes and perceived language performance of retarded persons. Journal of Mental Deficiency Research, 3, 131-141.

Ashman, A. F., & Das, J. P. (1980). Relation between planning and simultaneous-successive processing. Perceptual and Motor Skills, 51, 371-382.

Averill, J. R. (1969). Autonomic response patterns during sadness and mirth. Psychophysiology, 5, 399-314

Bachrach, R., Huesmann, L. R. & Peterson, R. A. (1977). Relation between locus of control and development of moral judgment. Child Development, 48, 1340-1352.

Bailer, I. (1961). Conceptualization of success and failure in mentally retarded and normal children. Journal Of Personality, 29, 303-320.

137 138

Bardon, R. C., Garber, J., Duncan, S. W., & Masters, J. C. (1981). Cumulative effects of induced affect in children: Remediation, inoculation and accentuation. Journal of Personality and Social Psychology, 40, 750-760.

Bartlett, J. C., & Santrock, J. W. (1979), Affect dependant episodic memory in young children. Child Development, 50, 513-518.

Batson, C. D., Bolen, M. H., Cross, J. A., Neuringer-Benefiel, H. E. (1986). Where is the altruism in altruistic personality? Journal of Personality and Social Psychology, 50, 212-220.

Battle, E. S., Rotter, B. (1963). Children's feeling of personal control as related to social class and ethnic group. Journal of Personality, 31, 482-490.

Beck, A. T. (1967). Depression: Clinical, experimental and theoretical aspects. New York: Harper and Row.

Beck, B. & Spruill, J. (1987). External validation of the cognitive triad of the personality inventory for children. Journal of Consulting and Clinical Psychology, 55, 441-443.

Becker, J. (1974). Depression: Theory and research. New York: Wiley.

Bee, H. L. (1967). Parent-child interaction and distractability in nine year old children. Merrill Palmer Quarterly, 13, 175-190.

Belter, R. W . , & Brinkmann, E. H., (1981). Construct validity of the Nowicki-Strickland locus of control scale for children. Psychological Reports, 48, 427-432.

Bennett, J. M . , & Wark, D. M. (1980). Test anxiety of college students: Test of a model. In M. L.; Kamil & A. J. Moe (Eds.), Perspectives on reading research and instruction, (pp. 255-258). Twenty-ninth yearbook of the National Reading Conference. Washington, D.C.: The National Reading Conference.

Bessey, C. D. (1982). Developmental trends and differences in locus of control among learning disabled children with and without special educational _ help and normal learning ability children? Unpublished doctoral dissertation. fEi Ohio State University, Columbus, Ohio.

Bledsoe, J. C. (1964). Self-concepts of children and their intelligence achievement, interests, and anxiety. Journal of Individual Psychology, 20, 55-58.

Boshier, R. (1973). Educational participation and dropout: A theoretical model. Adult Education, 23, 255-282.

Bower, G. (1981). Mood and memory. American Psychologist, 36, 129-148. 139

Bower, G. H., Gilligan & S. G. Monteiro (1981). Selectivity of learning caused by affective states. Journal of Experimental Psychology: General, 4, 451-473.

Bower,' G. H., Monteiro, K. P., & Gilligan, S. G. (1978). Emotional mood as a context for learning and recall. Journal of Verbal Learning and Verbal Behavior, 17, 573-587.

Bransford, J., Sherwood, R., Vye, N . , & Reiser, J. (1986). Teaching thinking and problem solving. American Psychologist, 41, 1078-1089.

Broadfoot, P. (1979). Assessment, schools and society. New York: Methuen.

Brookover, W. B., Thomas, S., & Paterson, A., (1964). Self-concept of ability and school achievement. Sociology of Education, 37, 271-279.

Brookover, W. B., LePere, J. M., Hamachek, D. E., Thomas, S. & Erickson, E. L. (1965). Self-concept of ability and school achievement: Improving academic achievement through students' self-concept enhancement. (Cooperative Research Project No. 1636). East Lansing, Michigan: Office of Educational Research Services, Michigan State University.

Broughton, J. M. (1978). Development of concepts of self, mind, reality and knowledge, in W. Damon (Ed), Social cognition. San Francisco: Jossey-Bass.

Brown, A. (1978). Knowing when, where, and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in instructional psychology, (pp.77-165). Hillsdale, New Jersey: Lawrence Erlbaum.

Brown, A. L., Bransford, J. D., Ferrara, R. A., Campione, J. C. (1983). Learning, remembering and understanding. In J. H. Flavell, E. M. Markman (Eds.), Handbook of child psychology (4th ed.) (pp. 71-166). New York: Wiley.

Brown, R. T. (1980). Locus of control and its relationship to intelligence and achievement. Psychological Reports, 46, 1249-1250.

Buck, M. R. & Austrin, H. R. (1971). Factors related to school achievement in an economically disadvantaged group. Child Development, 42, 1813-1826.

Bureau of the Census (1983). Summary tape file 3F. Census of population and housing. U. S. Department of Commerce: Author.

Bureau of the Census (1989). National databook and guide to sources. Statistical abstract of the United States. (109th ed.) U. S. Department of Commerce: Author. 140

Bursill, A. E. (1958). The restriction of peripheral vision during exposure to hot and cold conditions. Quarterly Journal of Experimental Psychology, _10, 113-129.

Calloway, E., & Stone, G. (1960). Re-evaluating the focus of attention. In L. Uhr & J. G. Miller (Eds.), Drugs and behavior. New York: Wiley.

Campos, J. J., & Barrett, K. C. (1984). Toward a new understanding of emotions and their development. In C. Izard; J. Kagan; & R. Zajonc (Eds.). Emotions, cognition and behavior. (pp.229-263). Cambridge: Cambridge University Press.

Cannon, W. B. (1927). The James-Lange theory of emotion: A critical examination and an alternative theory. American Journal of Psychology, 39, 106-124.

Cannon, W. B. (1931). Again the James-Lange and the thalamic theories of emotions. Psychological Review, 38, 281-95.

Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54, 1- 22 .

Cattell, R. B., Sealy, A. D. & Sweeney, A. P. (1966). What can personality and motivation source trait measurement add to the prediction of school achievement? British Journal Educational Psychology, 36, 280-295.

Chapman, J. W . , Silver, P. A., & Williams, S. M. (1984). Academic self-concept: Some developmental and emotional correlates in nine-year old children. British Journal of Educational Psychology, 54, 284-292.

Cooley, C. H. (1902). Human nature and the social order. New York: Scribner.

Cooper, H. J., Burger, J. M., & Good, T. L. (1981). Gender differences in the academic locus of control beliefs of young children. Journal of Personality and Social Psychology, 40, 562-572.

Coopersmith, S. (1959). A method of determining types of self-esteem. Journal of Abnormal and Social Psychology, 59, 87-94.

Coopersmith, S. (1967). The antecedents of self-esteem. San Francisco: W. H. Freeman.

Cornsweet, D. M. (1969). Use of cues in the visual periphery under conditions of arousal. Journal of Experimental Psychology, 80, 14-18.

Covington, M. V. (1983). Anxiety, task difficulty and childhood problem-solving: A self worth interpretation. In H. Ploeg; R. Schwarzer, & C. Spielberger (Eds.), Advances in test anxiety research, (pp. 101-107). Hillsdale, N.J: Lawrence Erlbaum. 141

Crandall, V. C., Katkovsky, W., & Crandall, V. J. (1965). Children's beliefs in their own control of reinforcements in intellectual-academic situations. Child Development, 36, 91-109.

Damon, W. & Hart, D. (1988). Self understanding in childhood and adolescence. New York: Cambridge University Press.

Das, J. P. (1973). Cultural deprivation and cognitive competence. In N. R. Ellis (Ed.), Internal Review of Research in Mental Retardation, (Vol. 6). New York: Academic Press.

Das, J. P. (1980). Planning: Theoretical considerations and empirical evidence. Psychological Research, 41, 141-151.

Das, J. P. (1983). Process training and remediation of reading disability: Examples of some Soviet tasks. The Mental Retardation and Learning Disability Bulletin, 11, 32-41.

Das, J. P. (1984a). Aspects of planning. In J. R. Kirby (Ed.). Cognitive strategies and educational performance, (pp.35-51). Orlando: Academic Press.

Das, J. P. (1984b). Intelligence and information integration. In J. R. Kirby (Ed.). Cognitive strategies and educational performance, (pp. 13-31). Orlando: Academic Press.

Das, J. P., Kirby, J., & Jarman, R. F. (1975). Simultaneous and successive synthesis: An alternative model for cognitive abilities. Psychological Bulletin, 82, 87-103.

Das, J. P., Kirby, J., & Jarman R. F. (1979). Simultaneous and successive cognitive processes. New York: Academic Press.

Das, J. P., & Heemsbergen, D. B. (1983). Planning as a factor in the assessment of cognitive processes. Journal of Psychoeducational Assessment, 1, 1-16.

Das, J. P., & Naglieri, J. A. (1988). Assessment of attention, simultaneous-successive coding and planning. Manuscript submitted for publication.

Das, J. P., & Naglieri, J. A. (1989). Das-Naglieri: Cognitive assessment system. Unpublished manuscript.

Denscombe, M. (1985). Classroom control: A sociological perspective. London: George Allen and Unwin.

Derryberry, D., & Rothbart, M. K. (1984). Emotion, attention and temperment. In C. E. Izard; J. Kagan; & R. B. Zajonc (Eds.), Emotions, cognition and behavior, (pp. 132-166). Cambridge: Cambridge University Press. 142

Dimberg, U. (1982). Facial reactions to facial expressions. Psychophysiology, 19, 643-647.

Dixon, N. F. (1981). Preconscious processing. New York: Wiley.

Drudge, 0. & et al. (1981). Comparison of the WISC-R, McCarthy Scales, Woodcock-Johnson and academic achievement: Concurrent and redictive validity. (ERIC Document Reproduction Service No. ED f44 996). Dubois, N. (1988). The norm of internality: Social valorization of internal explanations of behavior and reinforcements in young people. Journal of Social Psychology, 128, 431-439.

Duffy, E. (1941). An explanation of "emotional" phenomena without the use of the concept of emotion. Journal of General Psychology, 25, 283-293.

Duffy, E. (1962). Activation and behavior. New York: John Wiley & Sons.

Dutta, S., & Kanungo, R. N. (1975). Affect and memory: A reformation. Oxford: Pergamon.

Dweck, C., Davidson, W., Nelson, S., Enna, B. (1978). Sex differences in learned helplessness. II. The contingencies of evaluative feedback in the classroom. III. An experimental analysis. Developmental Psychology, 14, 268-276.

Easterbrook, J. A. (1959). The effects of emotion on cue utilization and the organization of behavior. Psychological Review, 66, 183-201.

Ekman, P., Friesen, W. V. & Ellsworth, P. (1972). Emotion in the human face. New York: Pergamon.

Erikson, E. (1950). Childhood and society. New York: Norton.

Farls, R. J. (1967). High and low achievement of intellectually average intermediate grade students related to the self-concept and social approval. Dissertation Abstracts International, 28, 1205A.

Feuerstein, R. (1979). The dynamic assessment of retarded performers: The learning potential assessment device, theory, instruments and techniques. Baltimore, Maryland: University Park.

Findley, M. J. & Cooper, H. M. (1983). Locus of control and academic achievement: A literature review. Journal of Personality and Social Psychology, 44, 419-427.

Fink, M. B. (1962). Self-concept as it relates to academic underachievement. California Journal cf Educational Research, 13, 57-62. 143

Finnegan, W. F. (1986). The relationship of intelligence and self-concept to school achievements among gifted and talented elementary school children. Unpublished doctoral dissertation. University of San Francisco, San Francisco, California.

Forgas, J. P., Bower, G. H., & Krantz, S. E. (1984). The influence of mood and perceptions of social interactions. Journal of Experimental Social Psychology, 20, 497-513.

Forisha-Kovach, B. (1983). The experience of adolescence: Development in context. Glenview, Illinois: Scott, Foresman.

Fultz, J., Batson, C. D., Fortenbach, V. A., McCarthy, P. M. & Varney, L. L. (1986). Social evaluation and the empathy altruism hypothesis. Journal of Personality and Social Psychology, 35, 691-707.

Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books.

Garfalo, J. (1986). Simultaneous synthesis, regulation and arithmetic performance. Journal of Psychoeducational Assessment, 4, 229-238.

Gay, L. R. (1976). Educational research. Columbus: Charles E. Merrill.

Gay, L. R. (1981). Educational research: Competencies for analysis and application. (2nd. ed.). Columbus: Charles E. Merrill.

Gay, L. R. (1987). Educational research: Competencies for analysis and application. (3rd. ed.). Columbus: Charles E. Merrill.

Gerow, J. R. (1986). Psychology: An introduction. Glenview, Illinois: Scott Foresman.

Gill, M. P., & D'Oyley, V. R. (1970). The construction of an objective measure of self-concept. Interchange, 1, 110-113.

Gilligan, S. G. & Bower, G. H. (1984). Cognitive consequences of emotional arousal. In C. E. Izard, J. Kagan, & R. Zajonc (Eds.), Emotions, cognition and behavior, (pp. 547-586), Cambridge: Cambridge University Press.

Gold, R. S. (1978). Self-concept related to discrepancy between intellectual ability ar.d academic achievement. Dissertation Abstracts International, 39, 3468A.

Gouaux, C., & Gouaux, S. (1971). The influence of induced affective states on the effectiveness of social and non-social reinforcement in instrumental learning task. Psychonomic Science, 22, 341-343.

Graham, F. K., & Kendell, B. S. (1960). Memory for designs test: Revised general manual. Perceptual and Motor Skills, 43, 1051-1058. 144

Grinker, R. R., Miller, J., Sabshin, M., Nunn, & Nunnally, J. C. (1961). The phenomena of depression. New York: Hoeber.

Gruen, G. E., Korte, J. R., & Baum, J. F. (1974). Group measure of locus of control. Developmental Psychology, 10, 683-686.

Guardo, C. & Bohan, J. (1971). Development of a sense of self-identity in children. Child Development, 42, 1909-1921.

Guilford, J. P. (1967). The nature of human intelligence. New York: McGraw Hill.

Guilford, J. P. (1982). Cognitive psychology's ambiguities: Some suggested remedies. Psychological Review, 89, 48-59.

Guilford, J. P. & Fruchter, B. (1978). Fundamental statistics in psychology and education. (6th. ed.). New York: McGraw-Hill.

Hagan, J. W. (1967). The effects of distraction on selective attention. Child Development, 38, 685-694.

Hale, W. D., & Strickland, B. R. (1976). The induction of mood states and their effect on cognitive and social behaviors. Journal of Consulting and Clinical Psychology, 44, 155.

Hallahan, D. P., Gajar, A. H., Cohen, S. B., & Tarver, S. G. (1978). Selective attention and locus of control in learning disabled and normal children. Journal of Learning Disabilities, 4, 47-52.

Hebb, D. 0. (1966). A textbook of psychology (2nd. ed.) Philadephia: Saunders.

Henry, G . , Weingartner, H., & Murphy, D. (1973). Influence of affective states and psychoactive drugs on learning and memory. American Journal of Psychiatry, 130, 966-971.

Hollingshead, A. B. (1975). Four factor index of social status. New Haven, Connecticut: Yale University.

Horn, J. L. (1979). Trends in the measurement of intelligence. Intelligence, 3, 229-240.

Hothersall, D. (1984). History of psychology. New York: Random House.

Hothersall, D. (1985). Psychology. Columbus: Charles E. Merrill.

Houston, J. P. (1981). Fundamentals of learning and memory. (2nd. ed.). New York: Academic Press.

Houtz, J. C. (1980). Problem solving and personality characteristics related to differing levels of intelligence and ideational fluency. Contemporary Educational Psychology, 5, 118-123. 145

Humphreys, L. G. (1971). Theory of intelligence. In R. Cancro (Ed.), Intelligence: Genetic and environmental influences, (pp. 31-42). New York: Grune & Stratton.

Hurt, F. D. (1988). Male Delinquent and Non-delinquent Performance on Planning, Attention, Simultaneous and Successive Processing Tasks. Unpublished doctoral dissertation. The Ohio State University, Columbus, Ohio.

Isen, A. M., Shalker, T. E., Clark, M., Karp, L. (1978). Affect, accessibility of material in memory and behavior. A cognitive loop? Journal of Personality and Social Psychology, 36, 1-12.

Izard, C. E. (1971). The face of emotion. New York: Appleton-Century-Crofts.

Izard, C. E. (1978). On the ontogenesis of emotions and emotions-cognition relationships in infancy. In M. Lewis & L. Rosenblum (Eds.). The development of affect (pp.389-413). New York: Plenum.

Izard, C. E. (1984). Emotion-cognition relationships in human development. In C.E. Izard., J. Kagan., & R. B. Zajonc (Eds.), Emotions, cognition and behavior (pp. 17-37). Cambridge: Cambridge University Press.

Izard, C. E., Wehmer, G. M., Livsey, W. J., & Jennings, J. R. (1965). Affect, awareness and performance. In S. Tompkins, C. Izard (Eds.), Affect cognition personality: Empirical studies. (pp.2-41). New York: Springer.

James, W. H. (1957). Internal vs. external control of reinforcement as a basic variable in learning theory. Unpublished doctoral dissertation. The Ohio State University.

Janoson, M. A. (1977). The effect of prior history of success or failure, locus of control and intelligence on the imitative behavior of elementary school children. Dissertation Abstracts International, 5814B.

Jensen, A. R. (1970). A theory of primary and secondary familial mental retardation. In N. R. Ellis (Ed.)., International review of research in mental retardation. (Vol. 4)^ (pp. 33-105). New York: Academic Press.

Jensen, A. R. (1980). Bias in mental testing. New York: The Free Press.

Jersild, A. T. (1952). In search of self. New York: Teachers College Bureau.

Johnson, B. W . , Redfield, D. L., Miller, R. L., & Simpson, R. E. (1983). The Coopersmith self-esteem inventory: A construct validation study. Educational and Psychological Measurement, 43, 907-913. 146

Johnson, C. N. & Wellman, H. (1982). Children's developing conceptions of the mind and brain. Child Development, 53, 222-234.

Jones, F. H. (1987). Positive classroom discipline. New York: McGraw-Hill.

Kagan, J. (1981). The second year of life. Cambridge: Harvard University Press.

Kamin, L. J. (1974). The science and politics of IQ. Hillsdale, New Jersey: Lawrence Erlbaum.

Karnes, F. A., & Wherry, J. N. (1981). Self-concepts of gifted students as measured by the Piers-Harris children's self-concept scale. Psychological Reports, 49, 903-906.

Karnes, F. A . , & Wherry, J. N. (1982). Concurrent validity of the children's personality questionnaire 0 factor as suggested by the Piers-Harris children's self-concept scale. Psychological Reports, 50, 574.

Keen, E. (1977). Emotion in personality theory. In D. K. Candland, J. P. Fell, E. Keen, A. I. Lashner, R. M. Tarpy and R. Plutchik. (Eds.), Emotion, (pp. 213-251). Monterey, California: Brooks/Cole.

Keith, T. Z., Pottebaum, S. M., & Eberhart, S. (1985). Effects of self-concept and locus of control on achievement. Annual Meeting of the National Association of School Psychologists. (Eric Document Reproduction Service No. ED 260 327).

Keller, A., Ford, L. & Meacham, J. (1978). Dimensions of self-concept in preschool children. Developmental Psychology, 14, 483-489.

Kennedy, J. J. (1978). An introduction to design and analysis of experiments in education and psychology. Lanham, Maryland: University Press of America.

Kifer, E. (1975). Relationships among academic achievement and personality characteristics: A quasi-longitudinal study. American Educational Research Journal, 12, 191-210.

Kirby, J. R., & Ashman, A. F. (1984). Planning skills and mathematical achievement: Implications regarding learning disability. Journal of Psychoeducational Assessment, 2, 9-22.

Kulkarni. A. V. (1982). Relationship between intelligence and self perceived intelligence. Perceptual and Motor Skills, 55, 654.

Laird, J. D., Wagener, J. J., Halal, M., Szegda, M. (1982). Remembering what you feel: The effects of emotion on memory. Journal of Personality and Social Psychology, 42, 646-657. 147

Lamar, M. G. (1985). The Raven of I-E locus as measures of high ability (Raven standard progressive matrices, gifted assessment Rotter I-E Scale locus or control, nonverbal tests, accelerated. Unpublished doctoral dissertation. University of Denver. Denver, Colorado.

Lavin, D. E. (1965). The prediction of academic performance: A theoretical analysis~and review of research. New York: Russell Sage Foundation.

Lawrence, W. A. (1974). The relationship of intelligence, self-concept, socioeconomic status and sex to level of career maturity of twelfth-grade students. Dissertation Abstracts International, 35, 3426A-3427A.

Lazarus, R. S. & Opton, E. M., Jr. (1966). The study of psychological stress: A summary of theoretical formulations and experimental findings. In C. D. Spielberger (Ed.), Anxiety and behavior (pp. 225-262). New York: Academic Press.

Lazarus, R. S., Averill, J. R. & Opton, E. M., Jr. (1970). Toward a cognitive theory of emotion. In M. B. Arnold (Ed.), Feelings and emotions: The Loyola Symposium, (pp. 207-32). New York: Academic Press.

Leeper, R. W. (1948). A motivational theory of emotion to replace emotion as disorganized response. Psychological Review, 55, 5-21.

Lefcourt, H. M. (1976). Locus of control: Current trends in theory and research. New York! Wiley.

Lefcourt, H. M. (1982). Locus of control: Current trends in theory and research. (2nd.ed.) Hillsdale, New Jersey: Lawrence Erlbaum.

Leight, K. E., & Ellis, H. C. (1981). Emotional mood states, strategies and state dependency in memory. Journal of Verbal Learning and Verbal Behavior, 20, 251-266.

Leland, H. (1982). Assessment of adaptive behavior. In K. Paget & B. Bracken (Eds.), The psychoeducational assessment of preschool children (pp. 191-205).: Grune and Stratton.

Leong, C. K., Cheng, S. C. & Das, J. P. Simultaneous-successive synthesis and planning in Chinese readers. International Journal of Psychology, 20, 19-31.

Lewis, M. & Brooks, J. (1978). Self-knowledge and emotional development. In M. Lewis & L. Rosenblum (Eds.), The development of affect, (pp. 205-226). New York: Plenum.

Lewis, M. & Brooks-Gunn, J. (1979). Social cognition and the acquisition of self. Nev.- York: Plenum. 148

Lewis, M., Sullivan, M. W. & Michalson, L. (1984). The cognitive-emotional fugue. In C. Izard, J. Kagan & R. Zajonc (Eds.), Emotions, cognition and behavior. (pp. 264-287). Cambridgel Cambridge University Press.

Livesly, W. J. & Bromley, D. B. (1973). Person perception in childhood and adolescence. New York: John Wiley.

Lloyd, G. G., & Lishman, W. A. (1975). Effects of depression on the speed of recall of pleasant and unpleasant experiences. Psychological Medicine, 5, 173-180.

Loeb, R. (1975). Content-concomitants of boys' locus of control examined in parent-child interactions. Developmental Psychology, 11, 353-359.

Luria, A. R. (1966). Human brain and psychological processes. New York: Harper Row.

Luria, A. K. (1971). Towards the problem of the historical nature of psychological processes. International Journal of Psychology, 6, 259-272.

Luria, A. R. (1973). The working brain. (B. Haigh, Trans.). New York: Basic Books.

Luria, A. R. (1980). Higher cortical functions in man. (2nd ed.). New York: Basic Books.

Maccoby, E. (1980). Social development: Psychological growth and Lhe parent-child relationship! New York: Harcourt Brace.

Mahler, M., Pine, F., & Bergman, A. (1975). The psychological birth of the human infant. New York: Basic Books.

Mandler, G. W. (1975). Mind and emotion. New York: John Wiley.

Mandler, G- M. (1984). Mind and body. New York: W. W. Norton.

Maqsud, M. (1983). Relationships of locus of control to self esteem, academic achievement and prediction of performance among Nigerian secondary school pupils. British Journal of Educational Psychology, 53, 215-221.

Martin, J. D. & Coley, L. A. (1984). Intercorrelations of some measures of self-concept. Educational and Psychological Measurement, 44, 517-522.

Masters, J. C., Barden, R. C., & Ford, M. E. (1979). Affective states, expressive behavior and learning in children. Journal of Personality and Social Psychology, 37, 380-390. 149

McDermott, P. (1984). Comparative functions of preschool learning styles and IQ in predicting academic performance. Contemporary Educational Psychology, 9, 38-47.

McGuire, W . , Padawer-Singer, A. (1976). Train salience in the spontaneous self-concept. Journal of Personality and Social Psychology. 33, 743-754.

McNamara, H. J. & Fisch, R. I. (1964). Effects of high and low motivation on two aspects of attention. Perceptual and Motor Skills, 19, 571-578.

Mendels, J. (1970). Concepts of depression. New York: Wiley.

Messer, S. B. (1972). The relation of internal-external control to academic performance. Child Development, 43, 1456-1462.

Mettes, D. (1974). Self report versus teacher observation. Unpublished manuscript, Purdue University, Lafayette, Indiana.

Meichenbaum, D. (1980). A cognitive behavioral perspective on intelligence. Intelligence, 4, 271-283.

Millenson, J. R. (1967). Principles of behavioral analysis. New York: Macmillan.

Mischel, W . , Ebbeson, E. E., & Zeiss, A. (1976). Determinants of selective memory about self. Journal of Consulting and Clinical Psychology, 44, 92-103.

Mohr, D. (1978). Development of attributes of personal identity. Developmental Psychology, 4, 427-428.

Moore, B. S., Underwood, B., Rosenhan, D. L. (1973). Affect and altruism. Developmental Psychology, 8, 99-104.

Moore, B. S., Underwood, B., Doyle, L., Heberlein, P., & Litzie, K. (1979). Generalization of feedback about performance. Cognitive Therapy and Research, 3, 371-380.

Mulgram, R. M. & Mulgram, N. A. (1976). Personality characteristics of qifted Israeli children. Journal of Genetic Psychology, 129, 185-194.

Naglieri, J. A. (1985). Matrix Analogies Test-Short Form. San Antonio: Texas: Psychological Corporation.

Naglieri, J. A. (1989). A cognitive processing theory for the measurement of intelligence. Educational Psychologist, 24, 185-206.

Naglieri, J. A. & Das,. J. P. (1987). Construct and criterion related validity of planning, simultaneous and successive cognitive processing tasks. Journal of Psychoeducational Assessment, 4, 352-363. 150

Naglieri, J. A. & Das, J. P. (1988). Planning-arousal-simultaneous- successive (PASS): A model for assessment. Journal of School Psychology, 26, 35-48.

Naglieri, J. A., Prewett, P. N . , & Bardos, A. N. (1989). An exploratory study of planning, attention, simultaneous and successive cognitive processes. Journal of School Psychology, 27, 347-364. ~

Natale, M. (1977). Effects of induced elation-depression on speech in the initial interview. Journal of Consulting and Clinical Psychology, 45, 45-52.

Netter, F. H. (1983). Nervous system. In A. Brass & R. Dingle (Eds.), Vol 1: Pt. 1: Anatomy and physiology (pp. 214-217). Caldwell, New Jersey: The CIBA Collection of Medical Illustrations.

Neufeld, J. S. & Cozac, E. (1980). A study of the self-concept of intellectually superior children. Alberta Journal of Educational Research, 26, 149-158.

Newman, B. M. & Newman, P. R. (1986). Adolescent development. Columbus, Ohio: Merrill.

Newman, B. M. & Newman, P. R. (1987). Development through life: A psychosocial approach. (4th ed.). Chicago: Dorsey.

Nicholls, J. G., Jagacinski, C. M., & Miller, A. T. (1986). Conceptions of ability in children and adults. In R. Schwarzer (Ed.), Self-related cognitions in anxiety and motivation. (pp. 265-284"n Hillsdale, N. J.: Lawrence Erlbaum.

Nowicki, S. & Strickland, B. R. (1973). A locus of control scale for children. Journal of Consulting and Clinical Psychology, 40, . 148-154.

Nowicki, S. & Duke, M. P. (1974). A preschool and primary internal external control scale. Developmental Psychology, 10, 874-880.

O'Such, T. G., Havertape, J. H., & Pierce, K. A. (1979). Group differences in self-concept among handicapped, normal and gifted learners. The Humanist Educator, 18, 15-22.

Osipow, S. H., Walsh, W. B. & Tosi, D. J. (1984). A survey of counseling methods, (rev. ed.). Homewood, Illinois: Dorsey Press.

Padwal, R. S. (1984). The relationship of self-concept to intelligence, anxiety and academic achievemeritT Saskatchewan, Canada: Saskatchewan School. Trustees Association (Eric Document Reproduction Service No: ED 260 513).

Panskepp, J. (1981). Hypothal'"T'ic integration of behavior: Rewards, punishments and related psychological processes. In P. J. Morgane & J. Panksepp (Eds.), Handbook of the hypothalamus Vol. 3, Pt. B: Behavorial studies of the hypothalamus. New York: Dekker. 151

Parrish, J. M. & Reimers, T. M. (1988). Behavioral approaches to the assessment and treatment of handicapped children and adolescents. In J. Gerring, L. McCarthy (Eds.), The psychiatry of handicapped children and adolescents. Boston: College-Hill.

Pedhazur, E. A. (1982). Multiple regression in behavioral research. New York: Holt, Rinehart & Winston.

Perna, S. J., Dunlap, W. R., & Dillard, J. W. (1983). The relationship of internal locus of control, academic achievement and IQ in emotionally disturbed boys. Behavioral Disorders, 9, 36-42.

Petree, D. (1988). Test retest reliability of planning, attention, simultaneous and successive processing task in strategy assessment with referred elementary students. Unpublished master's thesis. The Ohio State University, Columbus, Ohio.

Phillips, D. A. & Zigler, E. (1980). Children's self-image disparity: Effects of age, socioeconomic status, ethnicity and gender. Journal of Personality and Social Psychology, 39, 689-700.

Piaget, J. (1930). The child's conception of physical causality (Trans. M. Gabain). London: Routledge & Kegan Paul.

Piaget, J. (1973). The child and reality (Trans. A. Rosin). New York: Grossman.

Piaget, J. & Inhelder, B. (1975). The origin of the idea of chance in children. (L. Leake, Jr., P. Burrell & H. Fishbein, Trans.). New York: W. W. Norton.

Piers, E. V. & Harris, D. B. (1964). Age and other correlates of self-concept in children. Journal of Educational Psychology, 55, 91-95.

Piers, E. V. & Harris, D. B. (1969). The Piers-Harris children's self-concept scale. Nashville, Tennessee: Counselor Recordings and Tests.

Piers, E. V. (1984). Piers-Harris children's self-concept scale: revised manual. Los Angeles, California: Western Psychological Services.

Plutchik, R. (1962). The emotions: Facts, theories and a new model. New York: Random House.

Plutchik, R. (1980). A general psychoevolutionary theory of emotion. In R. Plutnick & H. Kellerman (Eds.). Emotion: Theory, research and experience. Vol. 1: Theories of emotion, (pp. 1-34). New York: Academic. 152

Posner, M. I. & Boies, S. J. (1971). Components of attention. Psychological Review, 78, 391-408.

Poteat, G . , et al., (1988). An investigation of differential prediction with the WISC-R. Journal of School Psychology, 26, 59-69. ~~

Pribram, K. H. (1980). The biology of emotions and other feelings. In R. Plutchik & H. Kellerman (Eds.). Emotion: Theory, research and experience. Vol. 1: Theories of emotion, (pp. 245-271). New York: Academic.

Price, P. S. (1987). Validity of planning, attention, simultaneous and successive cognitive processing tasks. Unpubli shed doctoral dissertation. The Ohio State University, Columbus, Ohio.

Querry, P. H. (1970). A study of self-concept of children with functional articulation disorders and normal children. Unpublished master's thesis. University of Pittsburgh, Pennsylvania.

Rabinowitz, R. G. (1978). Internal-external control expectancies in Black children of differing socioeconomic status. Psychological Reports, 42, 1339-1345.

Rapaport, D. (1942). Emotions and memory. Baltimore: Williams & Wilkins.

Rattan, A. I., & Rattan, G. (1987). Historical perspective on the nature of intelligence. In R. Dean (Ed.). Introduction to assessing human intelligence: Issues and procedures, (pp. 5-20). Springfield, ill: Charles C. Thomas.

Reid, I., & Croucher, A. (1980). The Crandall intellectual achievement responsibility questionnaire: A British validation study. Educational and Psychological Measurement, 40, 255-258.

Reilly, T., et al. (1985). Concurrent and predictive validity of the WISC-R, McCarthy Scales, Woodcock-Johnson and academic achievement. Psychology in the Schools, 22, 380-382.

Reimanis, G. (1973). School performance, intelligence and locus of reinforcement control scales. Psychology in the Schools, 10, 207-211.

Reschley, D. J. (1982). Psychological interventions: The foundations of school psychology. In J. Grimes (Ed.), Psychological approaches to problems of children and adolescents, (pp. 3-14). Des Moines, Iowa: Iowa State Department of Public Instruction. 153

Richardson, F. C. & Shinn, R. M. (1973). A comparison of traditional systematic desensitization, accelerated mass desensitization and anxiety management training in the treatment of mathematic anxiety. Behavior Therapy, 4, 212-218.

Ringness, T. A. (1961). Self-concept of children of low, average and high intelligence. American Journal of Mental Deficiency, 65, 453-461. —

Robinson, N. M. & Janos, P. M. The contribution of intelligence tests to the understanding of special children. In J. Day, J. Borowski (Eds.), Intelligence and exceptionality: New directions for theory, assessment, and instructional practices. Norwood, New Jersey: Albex.

Roseman, (1979, September). Cognitive aspects of emotion and emotional behavior. Paper presented at the meeting of the American Psychological Association, New York City: New York.

Rosenberg, M. & Simmons, R. G. (1973). Black and white self-esteem: The urban school child. Washington, D.C.: American Sociological Association.

Rosenweig, M. R. & Leiman, A. L. (1982). Physiological psychology. Lexington, Mass.: D. C. Heath.

Roth, D., & Rehm, L. P. (1980). Relationships among self monitoring processes, memory and depression. Cognitive Therapy and Research, 2, 149-157.

Rotter, J. B. (1966). Generalized expectancies for internal versus external locus of control of reinforcement. Psychological Monographs: General and Applied, 80, 1-28.

Ruble, D. (1983). The development of social comparison processes and their role in achievement-related self-socialization. In E. Higgins, D. Ruble & W. Hartup (Eds.), Social cognitive development: A social-cultural perspective. (pp. 134-157). Cambridge: Cambridge University Press.

Rushton, J. (1966). The relationship between personality characteristics and scholastic success in eleven-year-old children. British Journal of Educational Psychology, 36, 178-184.

Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450-461.

Samuels, W. (1980). Personality correlates of IQ by race and sex of subject. Journal of Personality and Social Psychology, 38, 993-1004. 154

Sarason, I. G. (1986). Test anxiety, worry and cognitive interference. In R. Schwarzer (Ed.), Self-related cognitions in anxiety and motivation (pp. 19-33). Hillsdale, New Jersey: Lawrence Erlbaum.

Sartre, J. P. (1948). The emotions. New York: Philosophical Library.

Sattler, J. M. (1982). Assessment of children's intelligence and special abilities. (2nd. ed.). Boston: Allyn and Bacon.

Schachter, S. (1964). The interaction of cognitive and physiological determinants of emotional state. In L. Berkowitz (Ed.), Advances in experimental social psychology, (pp. 49-80). New York: Academic.

Schneider, W. et al. (1984). The effects of intelligence, self-concept and attributional style on metamemory and memory behavior: A developmental study. Annual Meeting of the American Educational Research Association. (Eric Document Reproduction Service No. ED 243 940).

Schultz, C. B., & Pomerantz, M. (1976). Achievement motivation, locus of control, and academic behavior. Journal of Personality, 44, 38-51.

Schwartzbaum, J. S. (1960). Changes in reinforcing properties of stimuli following ablation of the amygdaloid complex in monkeys. Journal of Comparative and Physiological Psychology, 53, 388-395.

Sears, P. S. (1963). The effect of classroom conditions on the strength of achievement motive and work output on elementary school children. (Cooperative Research Project No. OE 873). Stanford California: Stanford University Press.

Secord, P. & Peevers, B. (1974). The development of person concepts. In T. Mischel. (Ed.), Understanding other persons. Oxford: Blackwell.

Selman, R. (1980). The growth of interpersonal understanding. New York: Academic Press.

Shavelson, R. J., Huber, J. J. & Stanton, J. C. (1976). Self-concept: Validation of construct interpretations. Review of Educational Research, 46, 407-441.

Shavelson, R. J. & Marsh,. W. (1986). On the structure of self-concept. In R. Schwarzer (Ed.), Self-related cognitions in anxiety and motivation, (pp.305-330). Hillsdale, New Jersey: Lawrence Erlbaum.

Shaw, M. C. & Alves, G. J. (1963). The self-concept of bright academic underachievers: Continued. Personnel and Guidance Journal, 42, 401-403. 155

Shaw, M. C., Edson, K. & Bell, H. M. (1960). The self-concept of bright underachieving high school students as revealed by an adjective checklist. Personnel and Guidance Journal, 39, 193-196.

Sherman, L. W. (1984, August). Perceptions of control: Cross-sectional and longitudinal analyses^ Paper presented at the Annual Convention of the American Psychological Association. Ontario, Canada.

Sherman, L., & Hoffmann, R. (1986, September). Age dependent and age independent measures of locus of control. Paper presented at the European Conference on Personality and Measurement. Gdansk, Poland.

Silverstein, A. B. et al. (1987). Correlates of the concept assessment kit - conservation for educable mentally retarded children. Psychology in the Schools, 24, 325-328.

Simonov, P. V. (1986). The emotional brain. (M. Hall, Trans.). New York: Plenum.

Skinner, B. F. (1953). Science and behavior. New York: Macmillan.

Skinner, E., & Chapman, M. (1987). Resolution of a developmental paradox: How can perceived internality increase, decrease and remain the same across middle childhood? Developmental Psychology, 23, 44-48.

Snart, F., O'Grady, M. & Das, J. P. (1982). Cognitive processing by subgroups of moderately retarded children. American Journal of Mental Deficiency, 86, 465-472.

Spearman, C. E. (1927). The abilities of man. New York: Macmillan.

Sternberg, R. J. (1980). "A review of six authors in search of a character." A play about intelligence tests in the year 2000. In R. Sternberg & D. Detterman (Eds.), Human intelligence: Perspectives on its theory and measurement^ (pp. 257-268). Norwood, New Jersey: Albex.

Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. New York: Cambridge University Press.

Sternberg, R. J. (1986). Identifying the gifted through IQ: Why a little bit of knowledge is a dangerous thing. Roeper Review, 8, 143-147.

Stipek, D. (1980). A casual analysis of the relationship between locus of control and academic achievement in the first grade. Contemporary Educational Psychology, 5, 90-99. 156

Stipek, D. J., & Weisz, J. R. (1981). Perceived personal control and academic achievement. Review of Educational Research, 51, 101-137.

Strongman, K. T. (1987). The psychology of emotion. (3rd ed.). New York: Wiley.

Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643-661.

Stutzman, R. L. (1986). A cross validation study of Das' simultaneous- successive - planning model. Unpublished doctoral dissertation. The Ohio State University, Columbus, Ohio.

Sue, D. W. (1981). Counseling the culturally different: Theory and practice. New York: John Wiley & Sons.

Sunberg, N. D. (1977). Assessment of persons. Englewood Cliffs, New Jersey: Prentice HalT^

Svanum, S., & Bringle, R. (1982). Race, social class and predictive bias: An evaluation using the WISC, WRAT, and teacher ratings. Intelligence, 6, 275-286.

Swanson, L. (1980). Cognitive style, locus of control and school achievement in learning disabled females. Journal of Clinical Psychology, 36, 964-967.

Teasdale, J. D., Fogarty, S. J. (1979). Differential effects on induced moods on retrieval and unpleasant events from episodic memory. Journal of Abnormal Behavior, 88, 248-257.

Thompson, B. (1988). Canonical correlation analysis: An explanation with comments on correct practice. Annual Meeting of American Educational Research Association. (Eric Document Representation Service No. 295 957).

Thorndike, R. L., Hagen, E. P., Sattler, J. M. (1986). The Stanford-Binet Intelligence Scale (4th. ed.) Chicago: Riversldi Publishing Company.

Thurstone, L. L. (1938). Primary mental abilities. Psychometric Monographs No. 1.

Tompkins, S. S. (1963). Affect, imagery, consciousness, Vol 2: The negative affects. New York: Springer-Verlag.

Tremans-Ziremba, M., Michayluk, J., & Taylor, L. (1980). An examination of some predictors of reading achievement in grade four children. Reading Improvement, 17, 260-266.

Vernon, P. E. (1969). Intelligence and cultural environment. London: Metheven. 157

Vogel, N. R. (1976). An analysis of the relationship between perceived locus of control and the academic achievement of fifth and sixth grade students. Unpublished doctoral dissertation. University of Washington.

Vygotsky, L. S. (1960). Thought and language. Cambridge: Massachusetts Institute of Technology.

Vygotsky, L. S. (1978). Mind in society. Cambridge, Massachusetts: Harvard University Press.

Walden, T. A. & Ramey, C. T. (1983). Locus of control and academic achievement: Results from a preschool intervention program. Journal of Educational Psychology, 75, 347-358.

Walters, L. H. & Klein, A. E. (1980). A cross validation factor analysis of the Nowicki-Strickland locus of control scale for children. Educational and Psychological Measurement, 40, 1059-1064.

Watson, J. B. (1929). Psychology: From the standpoint of a behavorist (3rd. ed.). London: J. B. Lippmcott.

Watson, J. B. (1930). Behaviorism (rev. ed.). Chicago: University of Chicago Press.

Weintraub, M . , Segal, R. M . , & Beck, A. T. (1974). An investigation of cognition and affect in the depressive experiences of normal men. Journal of Consulting and Clinical Psychology, 42, 911.

Weise, M. J., et al. (1988). WISC-R factor scores and student self ratings of behavior as predictors of academic achievement. Psychology in the Schools, 25, 35-40.

Weisz, J. R., & Stipek, D. J. (1982). Competence, contingency, and the development of perceived control. Human Development, 25, 250-281.

Weithorn, C., & Marcus, M. (1985). High active children and achievement tests: A two year follow-up. Psychology in the Schools, 22,

Wells, K., & Forehand, R. (1985). Conduct disorders. In P. Bornstein & A. Kazdin (Eds.), Handbook of behavior therapy with children. Homewood, Illinois: Dorsey.

Weschler, D. (1958). The measurement and appraisal of adult intelligence (4th. ed.). Baltimore: Williams & Wilkins.

Weschler, D. (1974). Weschler intelligence scale for children - revised manual. San Antonio, Texas, The Psychological Corporation.

Westcott, G. L. (1985). Differentiation in cognitive abilities and self structure in gifted children. Unpublished doctoral dissertation. University of Washington. 158

Williams, R. L. & Cole, S. (1968). Self-concept and school adjustment. Personnel and Guidance Journal, 46, 478-81.

Witkin, H. A. (1950). Individual differences in ease of perception of embedded figures. Journal of Personality, 19, 1-15.

Wolf, T. M., Sklov, M. C., Hunter & Berenson, S. M . , G. S. (1982a). Factor analytic study of the children's Nowicki-Strickland locus of control scale. Educational and Psychological Measurement, 42, 333-337.

Wolf, T. M., Sklov, M. C., Hunter & Berenson, S. M., G. S. (1982b). Factor analytic study of the Piers-Harris children's self-concept scale. Journal of Personality Assessment, 46, 511-513.

Worthman, C. B. & Loftus, E. F. (1981). Psychology. New York: Alfred A. Knopf.

Wylie, R. C. (1961). The self-concept: A critical survey of pertinent research literature. Lincoln, Nebraska: University of Nebraska Press.

Wylie, R. C. (1979). The self-concept: Theory and research on selected topics. (rev. ed.). (Vol. 2). Lincoln: University ol Nebraska Press.

Yarborough, B. H. (1980). A six year study of sex differences in intellectual functioning, reading, language arts achievement and affective development. Journal of Psychology, 106, 55-61.

Yawkey, T. D. (1980). Through the looking glass self: An introduction. In T. Yawkey (Ed.), The self-concept of the young child, (pp. 1-6). Provo, Utah: Brigham Young University Press.

Yerkes, R. M., & Dodson, J. D. (1908). The relation of stimulus to rapidity of habit formation. Journal of Comparative Neurology and Psychology, 18, 459-482.

Young, T. W., & Schorr, D. N. (1986). Factors affecting locus of control in school children. Genetic, Social and General Psychology Monographs, 112, 405-417.

Ysseldyke, J. E. & Algozzine, B. (1982). Critical issues in special and remedial education. Boston: Houghton Mifflin.

Zajonc, R. B. (1980). Feeling and thinking: Preferences need no inferences. American Psychologist, 35, 151-175. APPENDIX A

PARENTAL CONSENT FORM

159 160

February 26, 1990

Dear Parent

My name is Irma Phillips-Carmichael. I am a candidate for the Ph.D. at The Ohio State University in the Department of Educational Services and Research. I am writing to request permission for your child to participate in a research project sponsored by The Ohio State University, supported by the Hamilton Local School District and supervised by Dr. James V. Wigtil.

The purpose of this research project will be to determine if there is a relationship between intelligence and personality variables such as self-concept and locus of control in eight and nine year old children. Generally, locus of control refers to the idea that a reward for a given behavior is the result of either personal action or chance. Examples of questions children will be asked on the instrument which measures locus of control are:

Do you believe that most problems will solve themselves if you just don't fool with them? Or Do you believe that you can stop yourself from catching a cold?

Examples of the type of questions children will be asked on the instrument which measures self concept are:

I am a happy person. Or I am shy.

If such a relationship exists, the information gathered may help to broaden the knowledge base from which educators (e.g. teachers, counselors) are able to facilitate the academic and social development of students in the educational setting.

All necessary testing will take place during the school day. Your child will be given a measure of intellectual ability, a self-concept and locus of control tests. I will be administering all the necessary test instruments. The measure of intellectual ability has eight subtests, seven of which will be administered individually to each child. Administration time for the intelligence measure is approximately one hour. One subtest of the measure of intellectual ability, the self-concept and locus of control scales will be administered in small group sessions. Anticipated administration time for the tests administered in small groups is one hour and a half. The Page 2 February 26, 1990

total time needed for each student to participate in this study is approximately 2 1/2 hours.

Upon receipt of your written consent, each student will be met with individually to discuss the purpose of the research project and outline what will take place. Students will be encouraged to ask questions and their willingness to participate will be requested.

If you would like additional information about this research project, please do not hesitate to call me at 258-7118 (Home) or 785-1664 (Work). You may also feel free to contact my advisor, Dr. James Wigtil, 292-8936 for additional information.

All information obtained from these tests will be kept confidential. This testing will not become a part of your child's permanent record. , It will in no way impact your child's academic involvement at school. If you desire to personally discuss the results of these tests, please call me at the number listed above. I will be happy to schedule a time to discuss your child's results from the testing with you.

We have enclosed a consent form and a brief survey for your signature. The information requested on this one page survey will assist us in analyzing the responses given by your child. After signing the consent form and brief survey, please return them to the building principal by February 28, 1990 in the enclosed envelope. This research project is entirely voluntary and we would appreciate the participation of your child. Please understand, however, that you may withdraw your child from this study at anytime should you change your mind about his/her participation. Your child may also withdraw from this study at anytime. Those children not participating in this research project will not be meeting with this researcher and will not be taken out of their respective classes. Also, if a child does not want to participate after parental consent has been given, he/she will be returned by the researcher to their class immediately.

Thank you for your attention to this matter. Please do not hesitate to contact me if you have any questions. I am looking forward to hearing from you soon.

Sincerely,

James V. Wigtil, Ed.D Irma Phillips-Carmichael Professor and Coordinator of Counseling APPENDIX B

THE HOLLINGSHEAD FOUR FACTOR INDEX OF SOCIAL STATUS (HFFISS)

162 163 THE HOLLINGSHEAD FCXJR FACTOR INDEX OF SOCIAL STATUS (HFFISS)

Instructions: Please respond to the following items by circling the most appropriate number or by filling in the blanks. A. Gender: 1. Male 2. Female

B. Marital Status: 1. Single 2. Married 3. Separated 4. Divorced

d Income:

1. Under $10,000 2. $10,001 - $20,000 3. $20,001 - $30,000 4. $30,001 - $40,000 5. $40,001 - $50,000 Over $50,000

The Personal Gross Annual Income of Spouse:

1. Under $10,000 2. $10,001 - $20,000 3. $20,001 - $30,000 4. $30,001 - $40,000 5. $40,001 - $50,000 Over $50,000

What is your present occupation? ______What is the present occupation of your spouse?

Highest level of formal education achieved: 1. Completed grade school 2. High School diploma 3. Some college but no degree received 4. Associate's degree 5. Bachelor's degree 6. Master's degree 7. Doctorate degree 8. Other (please specify) ______Highest level of formal education achieved by spouse: 1. Completed grade school 2. High School diploma 3. Some college but no degree received 4. Associate's degree 5. Bachelor’s degree 6. Master's degree 7. Doctorate degree 8. Other (please specify)______APPENDIX C

LETTER OF ASSENT

164 165

WRITTEN ASSENT

I ______agree to be in this study. (Child's Name) APPENDIX D

PEARSON PRODUCT MOMENT CORRELATION COEFFICIENTS BETWEEN PASS MODEL AND INDEPENDENT VARIABLES PIERS-HARRIS CHILDREN SELF-CONCEPT SCALE (PHCSCS)

PEARSON PRODUCT MOMENT CORRELATION COEFFICIENTS BETWEEN PASS MODEL AND INDEPENDENT VARIABLES NCWICKI-STRICKLAND LOCUS OF CONTROL SCALE FOR CHILDREN

166 167

Pearson Product Moment Correlation Coefficients Between PASS Model and Independent Variables

Piers-Harris Children Self-Concept Scale (PHCSCS) ii n zsscazsas =====---=as:s=_aa n H n X = = = = = _ = S x=====

PASS MODEL Total Beh Int-SS Phy-AA Anx Pop H-S

Planning .29** .14 .24* .14 .29** . 26** .21*

Visual Search .26** .19 .19* .17 .22* .22* .19*

Planned Connections .23* .13 .22* .07 .25* .22* .17

Attention .19* .16 .15 .14 .11 .16 .13

Attention-Expressive .08 .07 .03 .08 .04 .07 .04

Attention-Receptive .21* .17 .19* .15 .13 .18 .15

Coding .15 . 35** .13 -.001 .06 -.06 .11

Simultaneous .13 .25* .15 -.05 .10 -.02 .03 o Figure Memory .12 .21* .15 -.03 .10 l .04 o in Matrices .11 .21* .10 1 .06 .005 .003 o 00 Successive .13 .33* .09 .03 .02 r .15

Word Recall .12 .32** .09 .04 .02 -.09 .11

Sentence Repetitions and Questions .12 .29** .08 .02 .01 -.06 .15

* p<.01 ** pC.OOl

Beh - Behavior Int-SS « Intellectual and School Status Phy-AA * Physical Appearance and Atributes Anx «= Anxiety Pop « Popularity H-S = Happiness and Satisfaction

Note: The NSLCSC is scored in the external direction, therefore the lower the score the more internal the locus of control. This results in a negative relationship with the PASS model variables. Pearson Product Moment Correlation Coefficients Between PASS Model and Independent Variables

Nowicki-Strickland Locus of Control Scale for Children

PASS MODEL (NSLCSC)

Planning -.34**

Visual Search -.24*

Planned Connections -.33**

Attention -.36**

At tent i on-Exp re s s i ve -.14

Attent i on-Re cept i ve -.40**

Coding -.23*

Simultaneous -.23*

Figure Memory -.18*

Matrices -.21*

Successive -.18*

Word Recall -.15

Sentence Repetitions and Questions -.17 APPENDIX E

INTERCORRELATIONS AMONG SUBSCALES OF PASS MODEL

169 lntercorrelatlona Anong Subaeales of PASS Model

P VS PC ACC AE AR C SI PM MA SO ffR StQ

p — .83**. .83** .33** •3C** .33** .40** .41** .28** .43** .30** . » * * .30**

vs — .38** .36** .13 .40** .26** .27** .17 .28** .19* .13 .20*

PC — .32** .3*** .48** .41** .42** .29** .43** .32** .27** .30**

ATI — • 7A** .83** .41** .46** .30** .48** .28** .23** J8*»

AC — .28** .32* .31** .21* .32** .25* .18 .26**

At — .34** .41** .26** .44** .21* .19* .20*

C — .79** .66** .69** .91** .73** .88**

SI — .85** .85** .47** .36** .47**

TH — .44** .38** .23* .40**

KA — .42** .36** .40**

SU— .84** .64**

Vt — • S3**

SB} —

* pC.OI •• pC.001

? - Pluming VS - VI um l Search PC • Planned Connection! U T - ACtenclon AE ■ Attencion-Expreaclve At • Attention-Recepclve C - Coding SI « Slaulcaneoua IM - PI go re Memory MA - Hacrlcea SD - Sticcecalve VJt - Word Recall StQ - Sentence Repetition! and Queetiooa