Investigating the Relationship between Higher-order Personality Factors and
Leadership
Mark Huy Do
A thesis in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Management
UNSW Business School
March 2021
i
Abstract
In the trait theory of leadership, the Big Five and facets are the main personality traits studied to identify effective leaders. Recently, higher-order personality factors – traits that are
broader in content – have emerged as potentially meaningful constructs. As their nature
seems aligned to leadership, their effects may be as strong as, if not more so, the effects of
the Big Five and facets.
This thesis investigated four issues: the effects of higher-order factors – the General
Factor of Personality (GFP) and Big Two (Stability and Plasticity) – on leadership, how the
effects compared to the Big Five and facets, the conceptual basis of higher-order factors and
the way they relate to leadership, and practical issues related to different operationalisation
approaches. Study 1 used meta-analysis to investigate the relationship between higher-order
factors and leadership compared to the Big Five and whether any of the latter had unique
effects. The mediating role of transformational leadership behaviour between the GFP and
effective leadership was also examined as a partial test of the GFP’s substantive nature. Study
2 extended the comparison of effects to facets by using a dataset containing the California
Psychological Inventory. The possible existence of curvilinear and interactive effects was
also explored as well as practical matters related to extraction methods and the potential
predictive benefits of using fewer factors at smaller sample sizes. Lastly, Study 3 undertook a
quantitative review and used another dataset to investigate whether the higher-order factors
of the Myers-Briggs Type Indicator (MBTI) represent the GFP and Big Two. The findings
had implications for their conceptual basis as the MBTI uniquely considers personality in
terms of categorical types and preferences.
Overall, higher-order factors had up to strong effects on leadership. The effects were
higher than several individual narrower traits, most of which did not possess unique effects.
However, the set of facets did appear to account for meaningful variance beyond the GFP. ii
Support was also provided for the substantive nature of the GFP, particularly in terms of social characteristics. The higher-order factors corresponding to the GFP and Big Two did not emerge in the MBTI. Finally, different extraction methods produced similar results, and the use of fewer traits in the form of higher-order factors yielded better prediction than lower- order ones at smaller sample sizes. iii
Acknowledgements
First and foremost, I would like to thank my supervisor, Amirali Minbashian, who imparted so much knowledge and wisdom to me, gave me what seemed like an unlimited amount of his time, and was wholly flexible and supportive of my personal goals and circumstances. This thesis would not have been possible without you and I am honoured to have worked with you throughout all my postgraduate studies.
I also wish to acknowledge the support and helpful feedback that my co-supervisor,
Chris Jackson, provided. In addition, I would like to thank the Center for Creative Leadership for providing me with access to two datasets used in this thesis, and Elsevier for allowing me to include content from my publication in the journal Personality and Individual Differences.
Lastly, I am grateful for my family, friends and colleagues who have encouraged me in this journey. I wish to thank my mother for her endless love and understanding, my wife for her unconditional patience and optimism, and our soon-to-be-born son whose impending arrival was all the motivation I needed to complete this thesis.
iv
Publications and Conference Presentations
Publications and conference presentations arising from this research:
Do, M. H., & Minbashian, A. (2020). Higher-order personality factors and leadership
outcomes: A meta-analysis. Personality and Individual Differences, 163, 110058.
https://doi.org/10.1016/j.paid.2020.110058
Do, M. H. (2015, April 23–25). Higher-order personality factors and leadership: A
theoretical explication [Poster session]. 30th Annual Conference of the Society for
Industrial and Organizational Psychology, Philadelphia, PA, United States.
Do, M. H., & Minbashian, A. (2015, April 23–25). Determining which personality level best
predicts leadership effectiveness: Meta-analysis [Poster session]. 30th Annual
Conference of the Society for Industrial and Organizational Psychology, Philadelphia,
PA, United States.
v
Table of Contents
Abstract ...... i Acknowledgements ...... iii Publications and Conference Presentations ...... iv Table of Contents ...... v List of Tables ...... ix List of Figures ...... xi Chapter 1: Introduction ...... 1 1.1 Definitional Issues ...... 3 1.1.1 Personality Traits ...... 3 1.1.2 Leadership ...... 4 1.2 Contributions to the Literature ...... 5 1.3 The Importance of Personality for Effective Leadership ...... 7 1.3.1 The Trait Theory of Leadership ...... 7 1.3.2 Evidence for the Relationship between Personality and Leadership ...... 9 1.4 The Hierarchical Organisation of Personality Traits ...... 11 1.4.1 The Big Five Factors ...... 12 1.4.2 The Big Two Factors: Stability and Plasticity ...... 14 1.4.3 The Big One: The General Factor of Personality ...... 15 1.4.3.1 Measuring the GFP ...... 17 1.4.3.2 Alternative Explanations of the GFP ...... 19 1.5 Linking Higher-Order Factors with Leadership ...... 20 1.6 General Aims and Overview of Studies ...... 24 Chapter 2: A Meta-analytic Examination of the Relationship between Higher-order Personality Factors and Leadership Outcomes ...... 28 2.1 Introduction ...... 28 2.1.1 The Effects of Higher-order Factors and the Big Five on Leadership ...... 29 2.1.2 Substance or Artefact? The Nature of the GFP ...... 32 2.2 Method ...... 34 2.2.1 Literature Search ...... 35 2.2.1.1 Sources ...... 35 2.2.1.2 Criteria for Inclusion ...... 35 2.2.1.3 Coding of Studies ...... 36 vi
2.2.2 Meta-Analysis Procedures ...... 36 2.3 Results and Discussion ...... 38 2.3.1 Meta-Analysis ...... 38 2.3.2 Multiple Regression Analysis ...... 43 2.3.3 Mediation Analysis ...... 46 2.4 Conclusion ...... 48 Chapter 3: A Deeper Investigation: Comparison to Facets, Complex Relationships and Practical Considerations ...... 50 3.1 Introduction ...... 50 3.1.1 Facets and the Extraction of Higher-order Factors from this Level ...... 52 3.1.1.1 The Relationship between Facets and Leadership ...... 54 3.1.2 Complexity in Personality and Leadership Relationships ...... 56 3.1.2.1 The Main Types of Complex Relationships ...... 56 3.1.2.2 Evidence for Complex Relationships between Personality and Leadership .... 57 3.1.3 Practical Considerations ...... 61 3.1.3.1 Extraction Approaches of Higher-order Factors ...... 61 3.1.3.2 The Predictive Validity of Higher-order Factors at Smaller Sample Sizes ..... 63 3.2 Method ...... 65 3.2.1 Participants and Procedure ...... 65 3.2.2 Measures ...... 66 3.2.2.1 Personality ...... 66 3.2.2.2 Leadership ...... 67 3.2.3 Data Analysis Strategy ...... 68 3.3 Results and Discussion ...... 70 3.3.1 Extraction and Derivation of Personality and Leadership Factors ...... 70 3.3.1.1 GFP, Big Two and Big Five Derived via Unit-Weighted Facets ...... 70 3.3.1.2 GFP, Big Two and Big Five Derived via Factor Loadings on Facets ...... 75 3.3.1.3 GFP and Big Two Derived via Factor Loadings on Items ...... 76 3.3.1.4 Factor Analysis of Leadership ...... 77 3.3.2 Comparison of Effects of Higher-order Factors and Facets on Leadership ...... 77 3.3.3 Complex Relationships Between Higher-order Factors and Leadership ...... 82 3.3.4 Effects of Different Extraction Methods of Higher-order Factors ...... 85 3.3.5 The Predictive Validity of Higher-order Factors at Different Sample Sizes ...... 86 3.4 Conclusion ...... 93 vii
Chapter 4: An Exploration of the Higher-order Factors of the MBTI and their Relationship with Leadership ...... 95 4.1 Introduction ...... 95 4.1.1 Definition of Type in the MBTI ...... 98 4.1.2 Origins and Theory of the MBTI ...... 99 4.1.2.1 The Primary Criticisms of the MBTI ...... 100 4.1.3 The Potential Emergence and Nature of Higher-order Factors from the MBTI ... 102 4.1.3.1 Factor Analysis of the MBTI Items ...... 103 4.1.3.2 Relationships between MBTI Scales and the Big Five ...... 104 4.1.3.3 Conceptualisation and Nature of MBTI Type Preferences and Clarity ...... 106 4.1.4 The MBTI and Leadership ...... 107 4.2 Method ...... 110 4.2.1 Datasets and Procedure ...... 110 4.2.2 CCL Dataset Measures ...... 112 4.2.2.1 Personality ...... 112 4.2.2.2 Leadership Behaviour ...... 113 4.2.3 Data Analysis Strategy ...... 113 4.2.3.1 Extracting Higher-order Factors ...... 113 4.2.3.2 Relating Higher-order Factors of the MBTI to Leadership ...... 115 4.3 Results ...... 117 4.3.1 Extraction of Higher-order Factors from the MBTI ...... 117 4.3.1.1 Extraction via MBTI Scales from the CCL Dataset and Past Studies ...... 119 4.3.1.2 Extraction via MBTI Items from the CCL Dataset ...... 122 4.3.2 Preliminary Review of the Relationship Between Higher-order Factors Extracted from the MBTI and Leadership ...... 124 4.3.3 Comparison of Effects of Different Personality Models Extracted from the MBTI for Leadership ...... 128 4.3.3.1 Correlational and Multiple Regression Analyses ...... 128 4.3.3.2 Cross-validation Analyses ...... 131 4.4 Discussion ...... 134 4.4.1 Theoretical Implications ...... 137 4.4.1.1 The Higher-order Factors of the MBTI ...... 137 4.4.1.2 Relationships with Leadership ...... 140 4.4.2 Practical Implications ...... 141 viii
4.4.3 Conclusion ...... 142 Chapter 5: General Discussion ...... 143 5.1 Integration of Research Findings ...... 144 5.2 Theoretical and Empirical Contributions ...... 148 5.2.1 The Relationship between Personality and Leadership ...... 148 5.2.2 The Nature of Higher-order Factors ...... 152 5.3 Practical Contributions ...... 156 5.4 Limitations and Future Directions ...... 161 5.5 Conclusion ...... 165 References ...... 167 Appendices ...... 201 Appendix A: Study 1 ...... 201 Appendix B: Study 2 ...... 213 Appendix C: Study 3 ...... 227
ix
List of Tables
Table 2.1 ...... 39
Table 2.2 ...... 40
Table 2.3 ...... 42
Table 2.4 ...... 43
Table 2.5 ...... 44
Table 2.6 ...... 45
Table 3.1 ...... 67
Table 3.2 ...... 68
Table 3.3 ...... 73
Table 3.4 ...... 74
Table 3.5 ...... 75
Table 3.6 ...... 78
Table 3.7 ...... 80
Table 3.8 ...... 84
Table 3.9 ...... 84
Table 3.10 ...... 86
Table 3.11 ...... 90
Table 3.12 ...... 91
Table 4.1 ...... 111
Table 4.2 ...... 112
Table 4.3 ...... 113
Table 4.4 ...... 118
Table 4.5 ...... 119
Table 4.6 ...... 120 x
Table 4.7 ...... 121
Table 4.8 ...... 126
Table 4.9 ...... 127
Table 4.10 ...... 129
Table 4.11 ...... 130
Table 4.12 ...... 132
Table 4.13 ...... 133
xi
List of Figures
Figure 1.1 ...... 12
Figure 3.1 ...... 72
Figure 3.2 ...... 89
Figure 3.3 ...... 90
Figure 4.1 ...... 123
Figure 4.2 ...... 123
Figure 4.3 ...... 131
Figure 4.4 ...... 132
1
1 Chapter 1: Introduction0F
The relationship between personality and leadership continues to attract considerable
interest and research. The many studies on their association have led to several meta-analyses
in recent decades (e.g., Bono & Judge, 2004; Deinert, Homan, Boer, Voelpel, & Gutermann,
2015; Derue, Nahrgang, Wellman, & Humphrey, 2011; Do & Minbashian, 2014; Hoffman,
Woehr, Maldagen-Youngjohn, & Lyons, 2011; Judge, Bono, Ilies, & Gerhardt, 2002; Lord,
De Vader, & Alliger, 1986). From a theoretical standpoint, a deeper understanding of the link
between personality and leadership contributes to the trait theory of leadership (House &
Aditya, 1997; Zaccaro, Dubrow, & Kolze, 2018) by clarifying the differentiating traits that
are inherent and enduring of effective leaders. Questions on which traits uniquely relate to
leadership and the relative magnitude of the relationships are important (Bass & Stogdill,
1990). Beyond theory, greater clarification of the personality–leadership relationship enables
practitioners to more accurately identify and recruit leaders who are likely to succeed. In addition, given the billions of dollars spent annually on leadership development (Lynham,
2000; Moldoveanu & Narayandas, 2019), research can also inform the design of training programs to focus on developing the skills associated with the most effective leader traits
(Derue et al., 2011).
Most studies on the relationship between personality and leadership (including the above meta-analyses) have predominantly conceptualised personality in terms of the Big Five model as well as traits that are narrower in breadth. Alternatively, higher-order personality factors, that are broader in their level of abstraction and content than the Big Five, may have a stronger relationship with leadership based on results found in related organisational
1 This chapter is based on the article Do, M. H. & Minbashian, A. (2020). Higher-order personality factors and leadership outcomes: A meta-analysis. Personality and Individual Differences, 163, 110058 (Do & Minbashian, 2020). I therefore wish to acknowledge the help and guidance of my advisor and co-author, Dr Amirali Minbashian, in preparing this chapter. 2
domains such as job performance (e.g., Alessandri & Vecchione, 2012; van der Linden,
Bakker, & Serlie, 2011) and because of the stronger thematic links between higher-order
factors and leadership. This is particularly relevant in the case of performance as a leader, as
leadership is a complex and multifaceted construct, and it has been argued that such
constructs require similarly broad predictors to optimise prediction (Ones & Viswesvaran,
1996).
In general, higher-order factors have been proposed to offer many benefits: they
provide simpler theoretical explanations of phenomena and are more useful for theory
development; abstract and general phenomena such as leadership need to be matched with
similarly broad predictors to enhance prediction; broader factors generally have higher
reliabilities than narrower and specific factors; and broader factors have higher criterion-
related validity for job performance composites than narrower factors (Edwards, 2001; Ones
& Viswesvaran, 1996). On the other hand, critics have argued that higher-order factors are
too conceptually ambiguous, that lower-order factors are what contain meaningful variance
of a specific nature, and that while the criterion-related validity of a higher-order factor is
generally higher than most of their lower-order constituent factors, it is often lower than at
least one of their lower-order factors and cannot be higher than an optimally weighted linear
combination of the set of lower-order factors (Edwards, 2001).
Edwards (2001) advocated for an integrative approach where both higher-order and
lower-order factors are considered meaningful theoretical constructs that each have their own advantages. He asserted that theories incorporating both constructs can be leveraged to explain how each level may relate to relevant phenomena differentially. Thus, this thesis sought to establish and examine the relationship between higher-order personality factors and leadership, and to compare the effects to those of lower-order personality traits. Past research has predominantly compared personality levels for leadership at and below the Big Five level 3
(Bono & Judge, 2004; Do & Minbashian, 2014) but, to my knowledge, levels above the Big
Five have yet to be compared for leadership effectiveness with individuals who hold
leadership positions. To the best of my ability, I am aware of three published studies (i.e.,
2 Pelt, van der Linden, Dunkel, & Born, 20171F ; van der Linden, Oostrom, Born, Van der
Molen, & Serlie, 2014; Wu, Van der Linden, Dunkel, van Vugt, & Han, 2020) that have
studied the relationship between the GFP and leadership and are summarised below. This
thesis is distinct from these studies as they either did not include actual leaders and/or did not
compare the relationships to both the Big Five and facet levels of the personality hierarchy,
as defined next.
1.1 Definitional Issues
1.1.1 Personality Traits
Before proceeding, it is useful to define what a personality trait is or at least how the present thesis conceptualises this term based on past definitions. In general, a personality trait refers to an individual’s underlying cognitive or affective dispositions that pervasively influence one’s behavioural tendencies, and are enduring over time and stable across similar situations (Johnson, 1997; Mischel, 1973; Roberts, 2009; Soto, Kronauer, & Liang, 2015).
Conceptually, personality traits exist on a continuum of values (Johnson, 1997). In terms of measurement, they are typically assessed on continuous dimensions but have also been measured by dichotomous categories (Arnau, Green, Rosen, Gleaves, & Melancon, 2003;
Myers, McCaulley, Quenk, & Hammer, 2009).
It is important to note that the above definition of a personality trait does not make any assumptions about the breadth of a trait. Personality traits can be organised hierarchically
2 This study reanalysed a previous meta-analysis (Judge et al., 2002), however, this previous meta-analysis also included non-leaders and did not delineate between studies that used explicit and non-explicit Big Five measures (which is an important distinction for extracting higher-order factors that this thesis explores). 4
based on their breadth, which refers to the extent of their generality or abstractness, or the
number of behaviours a trait subsumes (John, Hampson, & Goldberg, 1991). Narrower traits
are located at lower orders and broader traits exist at higher orders of the hierarchy (Musek,
2017; Paunonen, 1998). A more comprehensive description of the personality hierarchy is
presented below. This thesis adopts the following convention: (1) facets refer to the lowest
3 order of the hierarchy consisting of related sets of narrow traits2F , (2) the Big Five factors of
Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness refer to the next level up from facets (or the mid-order) that includes domain-level groupings
4 of similar facets3F , and (3) higher-order factors are the highest and therefore broadest levels of
the hierarchy starting with the Big Two factors (Stability and Plasticity) and finally the
General Factor of Personality (GFP) at the apex. Separate to the personality hierarchy, the
term scale is used to refer to labels assigned to personality traits within a measure.
1.1.2 Leadership
Leadership is broadly conceptualised in the literature as the ability to influence
followers in such a way that increases their motivation to achieve collective goals and
ultimately has a positive impact on the organisation (De Vries, 2012; Hogan, Curphy, &
Hogan, 1994; Stogdill, 1950; Zaccaro, 2007). The construct of leadership is often measured
by appraisals of overall leadership effectiveness (e.g., leader performance evaluations; Derue
et al., 2011; Judge et al., 2002) or leadership behaviours that result in leadership
effectiveness (e.g., transformational, change-related and courageous behaviours; Bono &
Judge, 2004; Zaccaro, 2007). Leadership effectiveness, in particular, is distinct from other
types of workplace efficacy (such as generally high-performing employees) in that it is
3 The individual traits that make up the narrow set are so specific in breadth (e.g., discrete behaviours or habitual responses) that they are typically assessed via items (Digman, 1990). 4 Recently, researchers have also identified a level that exists between facets and the Big Five called Aspects (DeYoung, Quilty, & Peterson, 2007). However, examination of this level of the hierarchy is beyond the scope of this thesis. 5 inherently social in nature given the need for leaders to interpersonally interact with others in order to successfully influence them (Zaccaro, 2007).
1.2 Contributions to the Literature
This thesis makes four main contributions to the literature. The first is on trait theory in terms of the relationship between personality and leadership, and the second is comparing the effects of different personality levels on leadership. Previous theory and studies linking lower-order personality factors to leadership and other organisationally relevant behaviours have typically assumed that their effects occur independently of each other. For example, according to trait activation theory (Tett & Burnett, 2003), each of the Big Five is activated by a distinct set of trait-relevant cues that then results in that trait being expressed in work behaviour. The overall level of the behaviour is consequently determined by the multiple trait influences. An alternative possibility is that leadership is influenced by the common variance
(represented by higher-order factors) that underlies lower-order factors rather than each of their unique variances. Testing this can help clarify specifically what personality traits are most relevant for leadership and, to the extent that higher-order factors relate to leadership, whether narrower traits have any effect on leadership over and above broader ones.
The third major contribution relates to validating the conceptual basis of higher-order personality factors. Evidence for the existence and nature of higher-order factors can be obtained by factor analysing lower-order factors that are correlated to find areas of generalisability that form higher-order factors (Gorsuch, 1983). Subsequently, their criterion- related validity should be examined (Pelt et al., 2017). Past research has examined the validity of higher-order factors for predicting general job performance as a test of their substantive nature, with some evidence for the superiority of these factors over lower-order factors (Van der Linden, te Nijenhuis, & Bakker, 2010). However, job performance may not be an ideal criterion for this purpose as the content of higher-order factors has been 6
specifically linked to socially-oriented traits (DeYoung, 2015; Kowalski, Vernon, &
Schermer, 2016; Loehlin, 2012), which may only be partly relevant for job performance. Job
performance tends to focus on the completion of non-social tasks whereas leadership is an
inherently social construct (e.g., empowering people to achieve group goals etc.), making it a
more relevant test of the substantive nature of higher-order factors. Furthermore, personality
models can also differ in the way they are conceptualised (e.g., whether they measure traits as
defined above versus types or clarity of preferences) and operationalised (e.g., dimensional
versus dichotomised traits). The extent to which the GFP and Big Two are represented across
different personality conceptualisations also provides insight into their nature based on the
theoretical differences between certain personality models.
Fourth, this thesis examines specific practical issues in the operationalisation and use
of higher-order personality factors. There are different methods for extracting these factors
such as by factor analysing inventory items or scales at the facet and Big Five level.
Examining the relationship between higher-order factors extracted from these methods as
well as potential differences in their relationships with leadership can help clarify optimal
ways of operationalising them. Lastly, from a practical perspective, this thesis also compares
the predictive validity of higher-order factors to lower-order ones at different sample sizes to
determine whether having fewer factors (in the form of higher-order factors) yields predictive benefits at smaller sample sizes. This is relevant when personality constructs are used for
prediction in applied settings and sample sizes for deriving prediction equations are limited.
The remainder of this chapter outlines the trait theory of leadership and the
importance of personality for effective leadership, presents the different conceptualisations of
personality in the hierarchy of traits, discusses why the higher-order factors may be
particularly relevant for leadership, and summarises the three thesis studies and their aims. 7
1.3 The Importance of Personality for Effective Leadership
Advocates of personality explanations for leadership contend that leaders are born and
not made (Barling, Christie, & Hoption, 2011), and that leadership is at least partly
genetically influenced (House & Aditya, 1997). Similarly, it has been argued that situational
forces alone are not sufficient for predicting and explaining leadership (House, Shane, &
Herold, 1996). Instead, a leader’s genes can influence them to choose, or be chosen for,
certain leadership settings (Judge, Piccolo, & Kosalka, 2009). Judge et al. (2009) argued that
genetic predispositions to have psychological characteristics can make individuals want to be
a leader, to be chosen for these roles by others and to succeed once in these positions.
1.3.1 The Trait Theory of Leadership
The trait theory of leadership seeks to identify traits (particularly personality related)
that differentiate high versus low performing leaders (House & Aditya, 1997). Concepts
related to trait theory began to emerge in the mid-19th century. The great man theory (Carlyle,
1841) was one of the first to contend that certain individuals possess specific traits (e.g.,
courage, intellect) that give rise to heroes, leaders or highly influential figures who ultimately
leave a lasting impact on history. Shortly after, this idea was developed with the argument
that the unique traits that define effective leadership are heritable (Galton, 1869).
Seminal reviews of the trait–leadership literature in the mid-20th century (Mann,
1959; Stogdill, 1948) temporarily resulted in the abandonment of trait theory as a credible
explanation for effective leadership. These reviews argued against the existence of universal
traits for leadership effectiveness and, instead, that different situations faced by leaders would
require unique as opposed to consistent traits (House & Aditya, 1997; Zaccaro, 2007). After the publication of these reviews, it was a few decades before empirical studies were undertaken to challenge these notions (Kenny & Zaccaro, 1983; Lord et al., 1986) and to
revive and reinvigorate interest in trait theory (House & Aditya, 1997). Some of the findings 8 in both of the original reviews were also questioned given the results actually supported the validity of personality traits for leadership effectiveness (Zaccaro, 2007). For example, when results related to non-working adults were removed from Stogdill’s (1948) study, moderate to large effects were consistently found between leadership and traits such as dominance and self-confidence (House & Baetz, 1979), which is consistent with meta-analytic findings that have demonstrated similar effects for lower-order traits of this nature (Do & Minbashian,
2014). Stogdill subsequently conceded that his earlier research had undervalued the notion of universal traits for effective leaders (House & Aditya, 1997; Stogdill, 1974). Since then, trait theory has resurged with research in this area now prolific and deemed well and truly alive
(Germain, 2012).
Within trait theory of leadership, a number of theoretical perspectives on the importance of specific traits have emerged. The purpose of this section is not to exhaustively describe each of these perspectives, but instead to highlight the main traits for leadership effectiveness that have been identified across each viewpoint. These traits include (1) goal- orientation and a strong desire for accomplishment from Achievement Motivation Theory
(McClelland, 1961), (2) motivation for prosocial influence from Social Influence Motivation
(Winter, 1973), (3) the need to attain and altruistically use power and status to have an impact on others via social influence from Leader Motive Profile (McClelland, 1975), (4) self- confidence and perseverance in enacting change, a strong desire to selflessly acquire and exercise influence, and a focus on matters of morality from the Theory of Charismatic
Leadership (House, 1977), and (5) social sensitivity and flexibility from Leader Flexibility
(Kenny & Hallmark, 1992; Kenny & Zaccaro, 1983). Taken together, common themes emerge on the content of effective leader traits, that is, a relatively selfless drive to influence others to achieve goals with a level of adaptability and perceptiveness required for different social influence interactions. 9
1.3.2 Evidence for the Relationship between Personality and Leadership
Shortly after the revival of trait theory, reviews of the link between personality and
leadership found that various traits of seemingly dissimilar breadths were important. Across
the qualitative reviews, self-confidence consistently emerged as an important trait for
effective leadership but a number of others were also identified. These included (but are not
limited to) achievement motivation (House & Aditya, 1997), emotional maturity (Yukl,
1998), creativity (Daft, 1999), surgency (Hogan et al., 1994) and independence (Bass &
Stogdill, 1990). From a quantitative perspective, a meta-analysis revealed that traits related to
masculinity (r = .24), extroversion (r = .19), adjustment (r = .17) and conservatism (r = .16)
were also important (Lord et al., 1986). The diversity of these traits, and therefore the
apparent lack of universal leader traits found, was attributed to the different labels assigned to
similar traits (e.g., emotional maturity and adjustment) as well as the absence of a widely-
accepted taxonomic structure of personality during that period (Judge et al., 2002).
The discovery of the Big Five, whose development and location within the personality
hierarchy are detailed below, subsequently gained consensus as an organising structure of
personality for the myriad of traits that had been studied. The Big Five has since dominated
the trait literature for the past few decades (Colbert, Judge, Choi, & Wang, 2012; Judge &
Bono, 2000) and has spurred research to adopt its structure when investigating leadership.
Based on the technical manuals of widely-used Big Five measures (Costa & McCrae, 1985;
McCrae & Costa, 1992) and meta-analytic research linking them to leadership (Bono &
Judge, 2004; Judge et al., 2002), each of the Big Five are defined and proposed to relate to
5 leadership as follows. Neuroticism4F captures traits such as nervousness, worry, temper and
moodiness. Individuals who score high on this trait tend to lack self-esteem and self-
5 Neuroticism’s opposite pole, often referred to as Emotional Stability, measures tendencies related to being even-tempered, calm and relaxed. 10
confidence, which can drive them to avoid leadership positions and to not be perceived by others as leaders (Hogan et al., 1994). Extraversion refers to being sociable, talkative, energetic, assertive and confident. As these individuals are typically optimistic and ambitious, they tend to naturally emerge as and enjoy being leaders, create inspiring visions of the future and instil excitement in others in service of an established direction. Openness to
Experience involves seeking new and diverse experiences, being creative and imaginative, and possessing intellectual curiosity. The predispositions towards originality, thinking differently and being open are thought to aid leaders to generate a compelling vision that mobilises others (Yukl, 1998). Agreeableness includes traits such as friendliness, warmth, cooperativeness, and being trusting and trustworthy. The related tendencies of perceptiveness and concern for others motivate leaders to focus on the development needs of team members and to be available when required. Lastly, Conscientiousness refers to being hard-working, organised, disciplined and achievement-oriented. These individuals are persistent and tenacious, which drives them to achieve leadership-oriented goals and tasks, and to follow through on informal contracts that have been formed with followers (Kirkpatick & Locke,
1991).
Meta-analytic research has shown that the Big Five account for 28% and 15% of variance in leadership emergence and effectiveness, respectively (Judge et al., 2002). In another meta-analysis, the specific factors of Extraversion (r = .19) and Neuroticism (r =
−.15) were found to consistently relate to transformational leadership behaviours (Bono &
Judge, 2004). However, the effect sizes of these findings have generally ranged from small to
moderate based on guidelines specific to research on individual differences (Gignac &
6 Szodorai, 2016)5F . Moreover, there have been inconsistent findings between studies. For
6 Correlation sizes of .10, .20 and .30 are considered small, medium and large effects, respectively, based on normative guidelines. 11
example, in Bono and Judge’s (2004) meta-analysis, they concluded that the links between the Big Five factors and transformational and transactional leadership behaviours were weak
(i.e., r ≤ .19 for all Big Five factors), limiting their potential to meaningfully predict leadership to a large degree. In addition, the validity coefficients for the Big Five factors were similar in magnitude, raising the question of which factor, if any, uniquely predicts leadership over and above their common variance. Bono and Judge (2004) concluded that the relatively weak associations may be addressed by examining more theoretically relevant dispositional antecedents other than the Big Five factors. Unlike the Big Five, which are located at the mid-order of the personality hierarchy, higher-order traits have received relatively little attention in trait theory despite their potentially stronger thematic links with leadership.
1.4 The Hierarchical Organisation of Personality Traits
Since leadership is complex and multifaceted, contemporary views on the trait theory of leadership have argued for personality traits to be structured in a reasoned and coherent manner rather than as a long list of differentiating traits (Judge et al., 2002; Zaccaro, 2007).
The hierarchical organisation of personality traits by breadth is a worthy structure since it captures different levels of abstraction across traits, where some levels are more likely to be aligned to the multidimensional nature of leadership than others. Historically, the Big Five model was assumed to represent the highest level of breadth within a personality hierarchy where facets and habitual responses were located at lower orders (John et al., 1991).
However, John and colleagues did acknowledge the existence of even more abstract
constructs above this level but suggested that they were only evaluative (i.e., desirable or
undesirable) in nature (e.g., good/bad as evaluative traits at higher orders as opposed to
charitable/selfish as more descriptive traits at lower-orders).
Although the Big Five factors were initially thought to be orthogonal and therefore
the highest level of breadth at which personality can be represented, research over the past 12
few decades has shown that the factors are indeed correlated with each other. As such, traits
at two levels of personality that reside above the Big Five were found (Digman, 1997;
Musek, 2017; Rushton & Irwing, 2008). These are the Big Two comprising Stability and
Plasticity, and the GFP at the top of the hierarchy (see Figure 1.1). The following sections
summarise the origins of the Big Five, given their prominence in the literature, and the
higher-order factors.
Big One GFP
Big Two Stability Plasticity
Big Five N A C E O
Note: N = Neuroticism, A = Agreeableness, C = Conscientiousness, E = Extraversion, O = Openness to Experience; all relationships are positive, except for the relationship between Stability and Neuroticism, which is negative; the figure does not depict levels below the Big Five, including aspects and facets.
Figure 1.1
Three-level personality hierarchy from the Big Five factors up to the GFP.
1.4.1 The Big Five Factors
The origins of the Big Five model can be traced back to the lexical hypothesis, which
postulated that all meaningful personality traits become encoded in individual words in a
culture’s language (Goldberg, 1993). Goldberg (1993) asserted that Galton (1884) was one of
the first to review a dictionary to determine the extent of personality-related words in
existence and to recognise that many words shared similar meanings. Research continued to
focus on how personality was represented in words but was advanced by Thurstone (1934) who conducted an early form of factor analysis on a list of sixty adjectives to reduce them
down to a common set of factors. Five factors were found but were not entirely reflective of 13
how the Big Five are conceptualised today. For instance, a cluster of adjectives was
derogatory in nature (although some words in this set did appear to be related to Neuroticism
such as ‘quick-tempered’ and ‘cynical’). Instead, Goldberg (1993) suggested that the first
representations of the Big Five were discovered by Fiske (1949) with the factors in the study
labelled Emotional Control, Confident Self-Expression, Inquiring Intellect, Social
Adaptability and Conformity.
Over the next few decades, numerous studies were undertaken that continued to find
7 the same structure comprising five factors6F , albeit with different names, across different
samples (Goldberg, 1993). The Big Five emerged from the lexical approach of factor
analysing trait adjectives (Goldberg, 1981) but subsequently inspired the Five Factor Model of personality (Costa & McCrae, 1985; McCrae & Costa, 1992) with a questionnaire-based
method of assessing the five factors. In addition to explicit measures of the five factors such
as the one above, factor analyses of other inventories developed independently from the Big
Five have been shown to have a five-factor structure that corresponds to the constructs of the
Big Five. These include the California Psychological Inventory (McCrae, Costa, &
Piedmont, 1993), the Adjective Check List (Piedmont, McCrae, & Costa, 1991), the Sixteen
Personality Factors (Byravan & Ramanaiah, 1995) and the Personality Research Form
(Costa & McCrae, 1988).
In terms of the comparability of the Big Five and the Five Factor Model, they are essentially alike although the Five Factor Model includes an assessment of facets as a lower- order level and is arguably more prominent in both academia and practice (Block, 2010). In line with many researchers, including Block (2010), the thesis considers both models as one
7 Although there is some consensus, the precise number of traits that exist at this mid-order level of the hierarchy continues to be subject to debate (Strus, Cieciuch, & Rowiński, 2014). A relatively recent and popular view is that a sixth factor, Honesty-Humility, exists as part of the HEXACO model (Ashton, Lee, Perugini, Szarota, De Vries, Di Blas, Boies, & De Raad, 2004). 14
and the same. As such, the remainder of this thesis refers to this personality structure as the
Big Five.
1.4.2 The Big Two Factors: Stability and Plasticity
Directly above the Big Five are two stable higher-order traits known as the Big Two
(Digman, 1997): Stability, the shared variance between Agreeableness, Conscientiousness
and reverse-scored Neuroticism; and Plasticity, the shared variance between Openness to
Experience and Extraversion (DeYoung et al., 2002). The Big Two are defined by goal-
related functions that are broader in scope than the Big Five factors. Stability (versus
instability) relates to the protection of goals by preventing disruption from impulses, whereas
Plasticity (versus rigidity) involves the exploration of goals that can lead to the creation of
new goals (DeYoung, 2015). Stability reflects a socialisation process that involves the
tendency to avoid disrupting ongoing efforts towards attaining goals by being consistent
across emotional, motivational and social functioning. High Stability leaders are likely to
maintain existing efforts and be less distracted in striving to achieve a vision. Plasticity, on the other hand, relates to personal growth (DeYoung, 2010) because individuals who score
high on Plasticity tend to explore and engage with novel social and intellectual stimuli and
they are willing to face and interact with new opportunities in an environment. High
Plasticity leaders are likely to look beyond the organisation’s current vision for new
opportunities in the environment to ensure it remains competitive.
Theoretically, two broad motive patterns known as getting along and getting ahead
(Hogan, 1982) appear to conceptually align with Stability and Plasticity, respectively (Furtner
& Rauthmann, 2010). Although getting along and getting ahead have also been linked to
leadership (Hogan & Kaiser, 2005), the Big Two provide conceptual differences that may be more beneficial based on their goal-related functions. For example, while getting ahead tendencies relate to wanting to advance oneself in a group, Plasticity also helps an individual 15
to be open to a selection of different goals with some potentially more effective in enabling advancement over others. Similarly, while getting along relates to the desire to appear cooperative within a group, Stability focuses on goals that maintain efforts towards group
cohesion such as regulating impulses that may otherwise damage relationships. In addition,
although getting along and getting ahead are typically operationalised in terms of examining
individual Big Five factors separately, the Big Two assess the underlying common variance
between the relevant lower-order factors, which may be more strongly related to leadership
(Furtner & Rauthmann, 2010).
1.4.3 The Big One: The General Factor of Personality
Stability and Plasticity have also been found to be correlated (r = .24; DeYoung et al.,
2002) indicating that the Big Two, similar to the Big Five, are not orthogonal and that a
single general factor may exist at the apex of the personality hierarchy. Musek (2007) was the
first to formally propose and provide evidence for the GFP, though others (Irwing, 2013)
have argued that the concept of a general underlying personality factor was introduced much
earlier by Galton (1887). Similarly, more recent research (though still prior to Musek, 2007)
constructed a general factor from the Big Five scales using the Midlife Development in the
United States survey (Figueredo, Vasquez, Brumbach, & Schneider, 2004).
From a theoretical standpoint, proponents of the GFP argue that its roots lie within the
8 evolutionary-based Life History Theory7F (Rushton & Irwing, 2008; van der Linden, Dunkel,
Beaver, & Louwen, 2015), or at least co-evolved with a specific life history strategy via
directional selection (Figueredo & Rushton, 2009). A key focus of this theory is on genetic
inclinations towards certain reproductive strategies. Slow strategies (or K-reproductive
8 Conversely, recent research suggests that Life History Theory may not be able to fully account for GFP differences based on contradictory findings between ethnic groups that were expected to have certain reproductive strategies according to the theory (Andersen, 2020). However, a key limitation of the study was arguably its operationalisation of ethnicity. 16 strategies) involve lower investment in mating effort and therefore fewer offspring, allowing for greater parental care to help ensure their survival. Alternatively, fast strategies (or r- reproductive strategies) emphasise greater mating efforts that result in more offspring with less parental care. Individuals who adopt K-reproductive strategies are said to have higher levels of GFP because the environmental conditions of slow strategies favour social effectiveness, cohesion and prosocial behaviour (Figueredo & Rushton, 2009; van der Linden et al., 2015), which are at the core of the GFP as described below.
The GFP has increasingly been defined and conceptualised in terms of social competence (Kowalski et al., 2016; Loehlin, 2012; van der Linden, Dunkel, & Petrides,
2016). Theoretically, much research supports the link between the GFP and social competence. First, and most importantly, all personality traits have social implications since humans are highly social beings (DeYoung, 2015). Second, in the more established Big Two factors, there is a common social component, that is, maintaining adequate social behaviour
(i.e., Stability) and searching for social experiences (i.e., Plasticity). Third, advocates of the
GFP suggest that it likely arose via evolutionary selection, which is consistent with Darwin’s view that natural selection made people more cooperative and socially intelligent (Darwin,
1871; Figueredo & Rushton, 2009; Irwing, Booth, Nyborg, & Rushton, 2012; Rushton, Bons,
& Hur, 2008). Similarly, Block (2010) was one of the first researchers to link the GFP with a construct like social competence by suggesting that the GFP represented a type of ‘fitness’ for collective living. Others have also drawn on socioanalytic theory to argue that high GFP scorers evolved to develop greater social skills (to better collaborate with others in order to meet challenging goals that lead to success), ultimately giving them a competitive advantage in career contexts (Fisher & Robie, 2019).
From an empirical perspective, previous studies have also demonstrated that the GFP relates to social competence criteria (e.g., friendliness, communication, emotional 17
intelligence), that social competence characteristics (e.g., social ascendancy, empathy) load
on the GFP, and that high GFP scorers perform better on social judgement tests, all of which
can enable individuals to more successfully achieve social goals (Kowalski et al., 2016;
Loehlin, 2012; van der Linden et al., 2016). A recent literature review provided further
support for the link between the GFP and social competence (van der Linden et al., 2016),
and a meta-analysis also showed that the GFP correlated with trait and ability-based
emotional intelligence with the GFP likened to a social effectiveness factor (van der Linden,
Pekaar, Bakker, Schermer, Vernon, Dunkel, & Petrides, 2017). Taken together, individuals
who score high on the GFP are competent at knowing how to handle and respond to a variety of social situations and demands (van der Linden et al., 2016).
1.4.3.1 Measuring the GFP
For measurement, the predominant method for operationalising the GFP in the literature is to extract a single factor from existing personality inventories (van der Linden et al., 2016), many of which are listed below. In his review of multidimensional constructs,
Edwards (2001) summarised some of the common approaches for calculating higher-order factors, including those on personality, such as by considering them in terms of first-order or second-order factor models, or by summing relevant lower-order factors. Chapter 3 elaborates on and examines the differences between the various extraction approaches of higher-order personality factors.
It is worth noting that a past study did construct a 20-item inventory that purported to measure the GFP (Amigó, Caselles, & Micó, 2010). However, the inventory was scored along a continuum where the positive pole was extraversion (which the authors defined as seeking stimuli) and the negative pole was introversion (defined in terms of stress, fear and avoidance). Thus, this inventory’s conceptualisation of the GFP is too narrow, appears aligned to a factor at a lower order of the personality hierarchy (i.e., Extraversion from the 18
Big Five), and is inconsistent with how the GFP is defined in this thesis as well as the
majority of the literature on higher-order factors. To my knowledge, the 20-item measure has not been used to study the GFP aside from by the authors who constructed the inventory.
Thus, the studies in this thesis operationalised higher-order factors in line with most research in the area, that is, by extracting them from existing personality inventories.
Since its discovery, the GFP has been extracted from personality inventories that either directly measure or do not measure the Big Five: (1) explicit Big Five measures: the
NEO Personality Inventory Revised (Kowalski et al., 2016; Veselka, Schermer, Petrides, &
Vernon, 2009) and the Big Five Inventory (Erdle, Irwing, Rushton, & Park, 2010); (2) non- explicit Big Five measures: the California Psychological Inventory and the Guilford–
Zimmerman Temperament Survey (Rushton & Irwing, 2009b), the Personality Research
Form and the Jackson Personality Inventory (Rushton et al., 2008), the Multidimensional
Personality Questionnaire (Rushton & Irwing, 2009a), the Comrey Personality Scales and
9 the Multicultural Personality Questionnaire8F (Rushton & Irwing, 2009c), and the HEXACO
Personality Inventory (Veselka, Schermer, Petrides, Cherkas, Spector, & Vernon, 2009); and
(3) personality disorder measures: the Millon Clinical Multiaxial Inventory, the Dimensional
Assessment of Personality and the Personality Assessment Inventory (Rushton & Irwing,
2009d) and the Minnesota Multiphasic Personality Inventory-2 (Rushton & Irwing, 2009c).
Some of these studies also provided evidence for the genetic bases of the GFP (Veselka,
Schermer, Just, Hur, Rushton, Jeong, & Vernon, 2012; Veselka, Schermer, Petrides, &
Vernon, 2009; Veselka, Schermer, Petrides, Cherkas, et al., 2009), supporting the arguments
in the trait theory of leadership on the heritability of traits as described above.
9 The Multicultural Personality Questionnaire is also broadly based on the Big Five but is specifically designed to assess multicultural concepts (Van der Zee & Van Oudenhoven, 2001). 19
1.4.3.2 Alternative Explanations of the GFP
Although advocates of the GFP’s existence and nature argue that it is likely to reflect
social competence and to have originated from Life History Theory as described above, there
is also a body of literature that argues against the substantive nature of the GFP or what it
actually represents. Firstly, some researchers have suggested that the GFP reflects socially
desirable response tendencies (Bäckström, Björklund, & Larsson, 2009; Schermer &
MacDougall, 2013) or statistical artefacts (Ashton, Lee, Goldberg, & de Vries, 2009). That is,
the extraction of broader factors from lower-order traits solely resembles the extent to which
respondents are trying to put forward a positive impression of themselves on the measure.
However, opponents of this view have argued that the GFP cannot be explained by socially
desirable responding (Kowalski et al., 2016), and studies have provided evidence for a
substantive GFP construct beyond a response bias (Dunkel & Van der Linden, 2014; Rushton
& Erdle, 2010). For example, the effects of the GFP were found to remain even after socially
desirable responding was controlled for (Rushton & Erdle, 2010). The study in the next
chapter partly examines the question of whether or not the GFP represents a substantive
construct by investigating if the effect of the GFP on effective leadership is mediated via
displays of a meaningful and desirable leadership behaviour.
Secondly, although research has typically inferred the nature of the GFP via the factor
analysis of correlated personality factors, some studies have suggested that an analysis of
questionnaire items may give rise to a different construct (Anglim, Morse, De Vries,
MacCann, & Marty, 2017; Bäckström et al., 2009; Biderman, McAbee, Chen, & Hendy,
2018; Biderman, McAbee, Hendy, & Chen, 2019). Specifically, these studies contend that
10 item-level analyses can produce a general factor that is said to be different9F from higher-
10 However, Chen and colleagues noted that a general factor based on item-level analyses and a higher-order factor can be identical under certain conditions (Chen, Watson, Biderman, & Ghorbani, 2016). 20 order factors in that the former stems from the covariance of item responses (especially when items share highly similar content). Higher-order factors, on the other hand, reflect the shared variance of correlated factors. The general factor based on items is argued to reflect either a substantive characteristic of items or an artificial measurement characteristic (i.e., common method bias). However, assuming the content is substantive, advocates of the item-level analysis approach argue that there is not yet definitive agreement on what the nature of the general factor is, although some views, other than social competence, have been provided by the above authors including respondent affect, social desirability and the role of context.
On the nature and potential substance of the general factor, Biderman et al. (2019) suggested that these could be clarified by examining the relationship between the general factor and consequential outcomes. This thesis contributes to explicating the meaning of the
GFP by investigating its relationship with the workplace-based consequential outcome of leadership. Additionally, the study in Chapter 3 examines the potential differences in extraction approaches for higher-order factors via the factor analysis of both scales and items.
These analyses provide further indirect evidence for the nature of the GFP and also practical insights about different operationalisation methods.
1.5 Linking Higher-Order Factors with Leadership
There are several reasons why higher-order factors are expected to relate more strongly to leadership than any given lower-order factor, and also why the GFP may be even more relevant than the Big Two. First, as defined above, leadership involves a social influencing process of enhancing followers’ commitment to organisational goals and empowering them to achieve those goals (Yukl, 1998). This is similar to the social nature of each of the higher-order factors. That is, high GFP individuals could draw on their social competence to effectively solve social problems such as bringing about constructive change in others (e.g., empowering others). High Plasticity scorers explore diverse social 21
experiences, which may enable them to select from a range of past social knowledge and
approaches when influencing different people. High Stability scorers seek to maintain
cooperative social behaviour, which could help them to quickly resolve any conflicts that may arise when influencing others. Similarly, in summarising the extensive literature on the trait theory of leadership, House and Aditya (1997) alluded to the criticality of social intelligence as a differentiating trait since leadership is deeply entrenched in socially-oriented environments. This is supported by empirical research which has shown that measures of social problem solving and judgement predict leadership performance (Bartone, Eid, Johnsen,
Laberg, & Snook, 2009), and that high GFP scorers (as noted above) perform better on social
judgement tests (van der Linden, Oostrom, et al., 2014).
Social competence involves behaving strategically in order to affect one’s social world. In this regard, personality traits have been conceptualised in terms of the strategies that people use to bring about changes in others (Buss, 1992). That is, beyond the traditional notion that a high score on a particular personality trait reflects greater intrinsic motivation to engage in behaviour relevant to the trait (Tett & Burnett, 2003), a high score may also indicate the strategic use of those behaviours to affect one’s social world (Buss, 1992). For example, in terms of the Big Five, individuals may be sociable (Extraversion), kind
(Agreeableness), reliable (Conscientious), calm (Neuroticism) or adventurous (Openness to
Experience) either because they are intrinsically motivated to do so or for strategic purposes.
When used strategically, a leader can assess a social situation and then choose the most optimal trait to exhibit (such as calmness or reliability) in order to influence others. Viewed in this way, higher-order personality factors, and in particular the GFP, represent the breadth and cohesive use of strategies that are available to the individual to facilitate the process of change in others. Stated differently, each of the Big Five can be conceptualised as partly driven by strategic intent (i.e., the common variance captured by each factor) and partly 22
driven by intrinsic motivation to engage in behaviours related to that specific factor (i.e., the unique variance of the factor). By averaging across the unique variances associated with each lower-order factor, higher-order factors may provide a purer measure of the strategic aspect of personality that is especially relevant for leadership.
The strength of the relationship between higher-order factors and leadership over lower-order factors can also be framed in terms of bandwidth-matching theory. This theory proposes that optimal prediction occurs when the breadth of a predictor variable is equivalent to that of the criterion (Judge & Kammeyer-Mueller, 2012; O’Neill & Paunonen, 2013; Ones
& Viswesvaran, 1996). For example, Ones and Viswesvaran (1996) argued that job performance is best predicted with broad measures of personality because job performance itself is a broad and complex criterion. It is complex in the sense that the outcome or behaviour is multidimensional and multidetermined (Paunonen, Haddock, Forsterling, &
Keinonen, 2003). Similarly, leadership is a broad and complex criterion that may be better aligned with higher-order personality factors that are likely to be similarly broad.
Specifically, leadership is a multidimensional construct encompassing several dimensions that, at its highest level, reflects a leader’s engagement with others to enhance their motivation in achieving organisational goals (Burns, 1978). This higher-order construct of leadership is similar to the GFP in the sense that both higher-order factors exist at the top of their respective hierarchies, and both involve the social influence process, either in terms of the underlying competency (i.e., the GFP) or its manifestation in situations requiring effective leadership. Lower-order factors may be too narrow in scope to comprehensively capture the multidimensionality of leadership such that the relationship between personality and leadership will likely increase as one moves up each level of the personality hierarchy.
Beyond the theoretical rationale outlined above, results from past personality and leadership meta-analyses provide indirect evidence for the potential predictive validity of 23
higher-order factors compared to lower-order ones. Such studies have found similar effect
size magnitudes for the Big Five. For example, Judge and colleagues found that the estimated
11 corrected correlations10F for each of the Big Five for predicting leadership effectiveness
ranged from ρ = .16 to ρ = .24 (Judge et al., 2002). This finding is consistent with the
proposition above that the effect for each Big Five is largely attributable to the proportion of
variance that it shares with the other Big Five factors. As outlined above, if this is true then a
measure that aggregates across them will better predict leadership as it more reliably assesses
this shared variance than individual Big Five factors.
Given the relative novelty of studying the relationship between higher-order factors
and leadership, few empirical studies that directly assess their link can be drawn on. To my
12 knowledge, three relevant studies1F have been undertaken. Two studies demonstrated a
relationship between the GFP and leadership emergence (Wu et al., 2020) as well as
leadership experience and skills (van der Linden, Oostrom, et al., 2014). However, the first
study used a student sample based in China and the second included job candidates in a
recruitment context that was not specific to leadership positions. The third study examined
higher-order factors based on a re-analysis of previous meta-analytic data between the Big
Five and leadership (Pelt et al., 2017). However, the original meta-analyses did not
exclusively include studies with working adults (e.g., one contained teacher ratings of student
leadership) and there was no delineation between measures that directly and indirectly
assessed the Big Five. None of these three studies compared the relationship between higher-
order factors and leadership to both the Big Five and facet levels of personality. Taken
together, these three studies provide some empirical support for a relationship between
11 The uncorrected correlations were not provided for this set of results. 12 As noted above, another study (Furtner & Rauthmann, 2010) examined the Big Two and self-leadership, which was defined as the process for influencing oneself (an example item is “I establish specific goals for my own performance”). Therefore, this study was not included in the list of related past research as it was inconsistent with the definition of leadership in this thesis. 24
higher-order personality factors and leadership. This thesis builds on them and contributes to the literature by examining working adults and leaders, and also by comparing effects across all four levels of the personality hierarchy. In particular, the need to focus on working adults or leader-specific samples is critical given the conclusions from Stogdill’s (1948) review of traits for leadership changed when non-working adults were removed from the analyses as described above.
Finally, further indirect empirical support comes from studies in related (but distinct) domains to leadership that have demonstrated meaningful predictive validity for higher-order personality factors. One study found that the relationship between the Big Five factors and overall assessment ratings (in a selection procedure) was attenuated after the GFP was controlled for (van der Linden et al., 2011). The researchers also noted that the findings were conceptually similar to those in the field of cognitive ability, where the g-factor underlies a large proportion of the relationship between specific cognitive tasks and other criteria.
Moreover, research has shown that adding the Big Five beyond the GFP does not result in a significant increase in explained variance for job performance (Van der Linden et al., 2010).
1.6 General Aims and Overview of Studies
This thesis investigates the effects of higher-order personality factors on leadership using non-laboratory samples of leaders and working adults. The first aim is to examine if and to what extent the GFP and the Big Two are related to leadership effectiveness. The second aim is to compare the effects for these higher-order factors to those of the Big Five and facet levels of the personality hierarchy to determine which level is associated with optimal prediction, and whether the lower-order traits have unique effects. The third aim is to provide evidence for the conceptual basis of the GFP by examining the process through which it relates to leadership. Evidence is also provided on the nature of the GFP and the Big
Two by examining whether they emerge from a personality inventory that has less social 25 content and is more oriented towards assessing preferences as opposed to traits and tendencies. The fourth aim is to investigate specific practical issues related to operationalising and using higher-order factors to determine how their predictive validity for leadership may change under certain conditions. Three studies were undertaken to address these aims and are summarised below.
The first study, presented in Chapter 2, uses meta-analytic techniques to examine the effects of higher-order factors on leadership (including leadership effectiveness and transformational leadership behaviours) and to compare these effects to those of the Big Five.
The meta-analysis only includes studies that used personality inventories explicitly designed to assess the Big Five and that used samples of leaders and working adults. Following the extraction of higher-order factors, their effects are compared to those of the Big Five including whether any of the latter factors possessed unique effects. As part of the aim of validating the conceptual basis of higher-order factors, the study examines transformational leadership behaviour as a mediator of the relationship between the GFP and leadership effectiveness. This mediation process is consistent with models proposing that personality affects leadership and performance outcomes via causally antecedent behaviours (Derue et al., 2011; Tett & Burnett, 2003; Zaccaro, 2007). The extent to which the demonstration of actual and desirable behaviours (in the form of transformational leadership) mediates the link between higher-order factors and effective leadership provides partial evidence for the substantive nature of higher-order factors. Taken together, Study 1 in Chapter 2 addresses the first, second and third aims of this thesis.
Study 2, presented in Chapter 3, extends the first study by comparing the effects of higher-order factors on leadership to the effects at both the Big Five as well as the facet level of the personality hierarchy. Unlike Study 1 which included studies containing different measures that explicitly assess the Big Five, Study 2 uses a dataset that operationalised 26 personality using the California Psychological Inventory. Study 2 provides insight into how the strength of the relationship between personality and leadership differs across all four major levels of the personality hierarchy, and whether lower levels of the hierarchy account for meaningful variance in leadership that is otherwise lost at higher levels. Study 2 also investigates more deeply the relationship between higher-order factors and leadership by exploring the potential existence of complex relationships (i.e., curvilinear and configural effects) to better understand the nature of their relationship. From a practical perspective,
Study 2 examines whether different methods for operationalising higher-order factors influence the strength of the observed relationship with leadership. Using cross-validation techniques, the study compares the effects of higher-order factors to lower-order factors at different sample sizes to determine whether having fewer factors (in the form of higher-order factors) yielded predictive benefits at smaller sample sizes. Collectively, the research questions in Study 2 relate to all four aims of this thesis.
Lastly, Study 3, presented in Chapter 4, investigates the relationship between higher- order factors extracted from the Myers-Briggs Type Indicator (MBTI) and leadership.
Research has yet to directly endeavour to extract both the GFP and Big Two (in terms of
Stability and Plasticity) specifically from the MBTI, and to examine their effects on leadership. The MBTI is possibly the most used inventory in applied settings, but it does not measure personality traits in the conventional sense. The MBTI conceptualises personality in terms of types and clarity of preferences, and operationalises scales differently from most other inventories using categorical measures of types and continuous measures of preference clarity. As such, this study contributes to our understanding of the nature of higher-order factors by examining their composition when derived from a non-conventional conceptualisation and measure of personality, and if (and to what extent) the factors relate to leadership. If the GFP and Big Two can be extracted from the MBTI, the conceptual 27 implications would be that their nature may also subsume how people experience the world and how clearly they identify with their personality (as per the unique theoretical basis of the
MBTI). As such, Study 3 primarily addresses the third broader aim of this thesis, but also the first and second aims by examining the relationship between higher-order factors extracted from the MBTI and leadership and comparing them to their lower-order factors.
28
Chapter 2: A Meta-analytic Examination of the Relationship between Higher-order
13 Personality Factors and Leadership Outcomes12F
2.1 Introduction
This chapter has three aims: to meta-analytically examine the effects of the higher-
order personality factors on leadership, to compare these effects to the Big Five, and to
examine the behavioural process through which the GFP affects leadership effectiveness.
This study contributes to the broader aims of this thesis by examining if and to what extent
higher-order factors are related to leadership. The study also examines whether any
individual traits within the same personality level have unique effects beyond the others.
Lastly, examining the behavioural process builds a greater understanding of the nature of the
GFP and also provides indirect support for whether it may represent a substantive construct.
The present study uses meta-analysis (i.e., a quantitative synthesis of past studies) to
investigate the effects of higher-order factors for leadership and to compare them to the Big
Five. The purpose of meta-analysis as part of this first study is to determine whether higher-
order factors have non-trivial effects on leadership on a broad scale. Once meaningful effects
are established and are shown to generalise across studies, then related aims of this thesis are
explored in the next two chapters (including the nature of the relationship and specific
operational issues that are challenging to address via meta-analysis in which raw datasets are
not available). Also, by including two distinct conceptualisations of leadership in this study
(i.e., the behaviourally-oriented transformational leadership and the outcomes-focused
leadership effectiveness), the mediating role of behaviour can be examined. This mediating
process is consistent with theoretical models that propose that personality affects leadership
13 This chapter is based on the article Do, M. H. & Minbashian, A. (2020). Higher-order personality factors and leadership outcomes: A meta-analysis. Personality and Individual Differences, 163, 110058 (Do & Minbashian, 2020). I acknowledge the help and guidance of my advisor and co-author, Dr Amirali Minbashian, in preparing this chapter. 29 effectiveness (and also job performance) through the demonstration of behaviours (Derue et al., 2011; Tett & Burnett, 2003; Zaccaro, 2007). Examining this mediation helps build a greater understanding about a potential mechanism through which the GFP results in leadership effectiveness. Moreover, focusing on transformational leadership, a prominent and important leadership behaviour as described below, can provide evidence for the nature of the
GFP and whether it represents a substantive construct. Specifically, if the GFP is found to affect leadership effectiveness via transformational leadership, it would suggest that the GFP is at least related to the demonstration of meaningful behaviour. The following sections discuss the rationale for each of the present study’s aims and outline the hypotheses.
2.1.1 The Effects of Higher-order Factors and the Big Five on Leadership
Chapter 1 provided theoretical arguments and relevant empirical evidence on why higher-order factors may be particularly related to leadership. These included the thematic links between them (i.e., social influence from leadership with social competence, social cooperativeness and social exploration from the GFP, Stability and Plasticity, respectively), their similarity in breadth (especially the GFP which is at the apex of the hierarchy), and also that higher-order factors, especially the GFP, represent the cohesive use of behavioural strategies that help facilitate change in others. Based on these propositions, it is expected for its link with personality, leadership is most strongly aligned with and thematically related to the factor at the highest level of the personality hierarchy, the GFP. Furthermore, it is hypothesised that as one aggregates traits and moves up the personality hierarchy, the effects of these personality factors (as assessed by their correlation with leadership) will increase since one more closely taps into the highest-order factor.
Hypothesis 1: The validity of personality for predicting leadership increases as one
moves up the personality hierarchy. Specifically, the Big Two factors are better 30
predictors of leadership than their corresponding Big Five Factors (H1a), and the
GFP is a better predictor than the Big Two and Big Five factors (H1b).
This hypothesis compares the effects of personality traits on leadership in terms of individual relationships but not sets of traits at each level. Advocates of higher-order constructs contend that these factors individually possess higher criterion-related validity than their lower-order constituents (e.g., Ones & Viswesvaran, 1996). Others argue that the criterion-related validity cannot be higher than an optimally weighted linear set of lower- order factors (e.g., Paunonen, Rothstein, & Jackson, 1999) such as when they have been constructed from the coefficients of a multiple regression. The set of lower-order factors necessarily accounts for more (or as much) variance because the regression weights assigned to the lower-level factors optimise their predictive validity for a particular sample, especially at large sample sizes (Edwards, 2001). However, the more important question is whether the increase in explained variance from the set of constituent factors is worth the degrees of freedom that these factors use up (Edwards, 2001). For this study, if the increased variance explained by the lower-order factors (i.e., the Big Five) is trivial compared to a higher-order factor, then this would justify the loss of precision that narrow factors provide. More broadly, support would also be given to the parsimony of a single explanatory construct for leadership that would serve to develop a general theoretical understanding of work behaviour (Ones &
Viswesvaran, 1996).
An additional insight that can be gained from multiple regression analyses is the extent to which higher-order factors and the Big Five have independent effects. These factors are likely to strongly correlate with each other since they each, to some extent, assess the same underlying variance. Due to this lack of orthogonality, correlation coefficients do not indicate to what extent each factor uniquely predicts each leadership outcome. The regression analyses can also address this by calculating the incremental variance (i.e., unique effect) for 31
each factor. In this way, the independent effect of each Big Five factor can be determined.
Based on previous empirical findings in domains related to leadership, four of the Big Five
factors (with Conscientiousness being the exception) are not expected to incrementally
predict leadership over and above the GFP. Previous studies have shown that it is primarily the shared variance in personality measures (i.e., the GFP) that accounts for much of their criterion-related validity (Dunkel & Van der Linden, 2014). Consequently, the GFP is expected to account for the majority of variance for leadership with little, if any, unique effects relating to the Big Five factors.
One exception is the specific variance associated with Conscientiousness, which may incrementally predict leadership effectiveness since its effects are likely to transcend those related to social competence. As Conscientiousness generally comprises task-oriented traits
(e.g., organised, planful, reliable, efficient, detail-oriented) as opposed to more socially- oriented traits, it may account for leadership over and above the GFP. As conscientious individuals are achievement-striving and self-disciplined, these traits elicit motives related to getting ahead that result in effective leadership (Zaccaro, 2007), such as by being more tenacious and persistent in pursuing organisational objectives (Goldberg, 1990). In addition, a large body of research has demonstrated the predictive validity of Conscientiousness for general job performance (e.g., Barrick & Mount, 1991) and (to a slightly lesser extent) leadership (e.g., Bono & Judge, 2004), particularly when compared with other Big Five factors. Thus, given these findings and also that Conscientiousness is not primarily focused on interpersonal dynamics, it may have unique effects for leadership effectiveness over and above the GFP.
Hypothesis 2: Conscientiousness uniquely predicts leadership effectiveness. 32
2.1.2 Substance or Artefact? The Nature of the GFP
Chapter 1 discussed that one of the main arguments against the substantive nature of
the GFP is that it only reflects an artefact of socially desirable responding (e.g., Schermer &
MacDougall, 2013). However, contrary to this response bias view is that high GFP
individuals may demonstrate actual behaviour that is considered socially desirable in the
form of effective social skills (van der Linden, Dunkel, Figueredo, Gurven, von Rueden, &
Woodley of Menie, 2018). These authors noted that this is distinct from ‘faking’ on a test and
is instead a stable characteristic an individual would possess over time and contexts. This
study aims to present evidence that at least indirectly addresses whether the effect of the GFP
on leadership is due to the substantive nature of this construct. Specifically, this study differentiates the GFP from social desirability by demonstrating that the GFP may be directly related to the performance of transformational leadership behaviours (as judged by others), and that the performance of such behaviours is what mediates the effect of the GFP on leadership effectiveness outcomes. The conceptualisation of transformational leadership behaviour and the rationale for focusing on this behaviour in this study is discussed next.
As defined in Chapter 1, leadership effectiveness encapsulates the outcome or performance of a leader. It is typically assessed in terms of overall judgements of efficacy by
others. On the other hand, transformational leadership involves demonstrating specific
behaviours such as acting visionary and inspiring in order to influence and motivate followers
to look and act beyond self-interests (Antonakis & Day, 2012; Bass, 1985; Burns, 1978;
Fischer, Dietz, & Antonakis, 2017). Transformational leaders articulate high standards for
followers and instil confidence in them to build loyalty, encourage lateral thinking and
enhance belief in the direction of the organisation (Roush & Atwater, 1992). This study
focuses on transformational leadership behaviour over other types for several reasons. It is
regarded as one of the most prominent conceptualisations of leadership behaviour (Judge, 33
Woolf, Hurst, & Livingston, 2008). It has specifically been shown to mediate the relationship
between personality and leadership effectiveness (Deinert et al., 2015). Past meta-analytic research has found that transformational leadership behaviour has the highest overall validity for leadership effectiveness compared to other major leadership behaviours (ρ = .64; Judge &
Piccolo, 2004). Its conceptualisation is likely to be highly aligned to that of the GFP, as outlined below, such that a non-trivial relationship between them would help to reveal insights about the conceptual nature of the GFP.
Transformational leadership behaviour consists of four sub-factors (Bass, 1985): idealised influence (i.e., possessing a high standard of ethical conduct and building loyalty from others), inspirational motivation (i.e., articulating a clear and compelling, values-based vision that inspires followers), intellectual stimulation (i.e., ability to challenge established norms and encourage others to generate original ideas) and individualised consideration (i.e., recognising that each follower has different developmental needs and working with them to address these development gaps). Each sub-factor is likely to require a degree of social competence since an interpersonally effective, constructive and positive interaction with followers would be needed to instil meaningful change in them. For example, socially competent individuals are friendlier and more considerate, which can help them to earn trust and admiration from others (idealised influence); their orientation towards social ascendancy can help them display enthusiasm and optimism to others (inspirational motivation); their social judgement and communication skills can help them constructively challenge followers to think differently (intellectual stimulation) and; their empathy and perceptiveness can allow them to tailor development opportunities to the needs of each follower (individualised consideration). Research has shown that these sub-factors are highly correlated and ultimately assess a single, higher-order construct of transformational leadership (Carless, 34
1998). As such, this study focuses on the overall conceptualisation of transformational leadership behaviour.
In summary, transformational leadership behaviour is the main behaviour that is antecedent to leadership effectiveness (Seltzer & Bass, 1990), and it mediates the relationship between personality and leadership effectiveness (Deinert et al., 2015). The mediation effect stems from a leader’s ability to influence, transform, inspire, change and care for followers
(Cavazotte, Moreno, & Hickmann, 2012). To the extent that the relationship between the
GFP and leadership effectiveness is substantive, it should at least partially be mediated via the effect of the GFP on transformational leadership behaviour.
Hypothesis 3: The effect of the GFP on leadership effectiveness outcomes is at least
partially mediated through its effect on transformational leadership behaviour.
Specifically, higher GFP scores will lead to greater transformational behaviour that in
turn will lead to greater leadership effectiveness.
2.2 Method
Meta-analysis was used to estimate the effect sizes between the personality factors
(GFP, Big Two and Big Five) and both transformational leadership behaviours and leadership effectiveness. As the study sought to examine the predictive validity of higher-order factors with the Big Five factors as points of comparison, only studies that contained explicit measures of the Big Five were included. Although this reduced the number of potential studies included in the meta-analysis, this criteria is important since past meta-analytic research (Hurtz & Donovan, 2000) found differences in the criterion-related validity of personality factors between explicit and non-explicit measures of the Big Five (where the latter’s construct validity was questioned). 35
2.2.1 Literature Search
2.2.1.1 Sources
To identify relevant studies including both published and unpublished papers,
SciVerse Scopus and Google Scholar databases were searched for all available date ranges up
to August 2018. The following keywords and their variants were used as search terms:
personality, Big Five, leadership effectiveness, leadership performance, transformational
leadership, charismatic leadership. The names of various personality inventories (e.g.,
Hogan Personality Inventory, NEO PI-R) were also used. Of all citations found, at least the
titles and abstracts of each citation were reviewed for relevance.
2.2.1.2 Criteria for Inclusion
Studies that met each of the following criteria were included in the meta-analysis: (1)
results were quantitative and contained the necessary information to compute meta-analytic
correlations (i.e., effect size estimates and sample sizes), (2) participants were working adults
(i.e., laboratory studies and student samples were not included), (3) personality was self-
rated, (4) leadership was rated by others (e.g., direct reports, peers and/or managers), (5) data
for all Big Five factors was included in each study, and (6) personality measures explicitly
measured the Big Five. These inclusion criteria helped increase the generalisability of
findings to practical leadership settings and ensure that higher-order factors could be fairly
compared with the Big Five. A total of 34 studies were identified (transformational
leadership: k = 15, N = 2,669; leadership effectiveness: k =19, N = 3,032). The reference list
of studies included in the meta-analysis is in Appendix A. This total number of studies that
were available to use as part of the below multiple regression and mediation analyses was
relatively low. However, the number of studies included in the present meta-analysis is
comparable to a number of the personality-leadership meta-analytic studies cited in Chapter 1
(e.g., k = 7 to 8 between Extraversion facets and transformational leadership in Do & 36
Minbashian, 2014, k = 12 between the Big Five factors and transformational leadership in
Bono & Judge, 2004, and k = 17 to 23 between the Big Five and leadership effectiveness in
Judge et al., 2002).
2.2.1.3 Coding of Studies
Each study was coded for variable classification and effect size estimates. Coding of the Big Five was determined by examining the scale labels and scale definitions of each inventory. Criterion measures of leadership were coded as representing measures of leadership effectiveness (including leadership performance) or transformational leadership based on the conceptual definitions provided in the primary study.
For leadership effectiveness, the measures included ratings of the degree to which objective performance of the leader was achieved for the preceding year, ratings of outcomes associated with leadership behaviour (such as the leader’s ability to lead others to meet performance goals), ratings on the possession of requisite skills to perform their job as a leader and to achieve group goals, and overall leader performance ratings to meet job requirements.
2.2.2 Meta-Analysis Procedures
Using the methods detailed by Hunter and Schmidt (2004), meta-analysis was used to estimate population correlations between the variables of interest. If a study included more than one correlation for a particular sample (i.e., multiple correlations reported separately for different raters, or different measures of a construct were used such as for leadership), then a single composite correlation was calculated. This method of calculating a
single composite was also used to derive correlations for the higher-order factors of
Plasticity (Extraversion and Openness to Experience), Stability (Neuroticism, Agreeableness and Conscientiousness) and the GFP (all Big Five factors). These composite correlations are
equivalent to the correlation that would be obtained by aggregating the relevant variables of 37
a higher-order factor and correlating the aggregate with the criterion (see Hunter & Schmidt,
2004, p. 429-439). By calculating composite correlations, the Big Five factors are
aggregated with unit-weights to derive composites that correspond to higher-order factors.
Past research has shown that a GFP calculated via unit-weights and a GFP derived using
meta-analytically derived weights correlate strongly (r = .995; Dunkel, Stolarski, van der
Linden, & Fernandes, 2014). Unit-weights, however, have the benefit of being less prone to
sample-specific variability unlike factor loading weights which are dependent on the sample
from which they are derived (Dawes & Corrigan, 1974; Einhorn & Hogarth, 1975; Wainer,
1976).
For each relationship of interest across the samples, mean correlations were
calculated and were weighted by sample size. Corrections were made for sampling error,
unreliability and scale coarseness in the criterion measures. In line with Bono and Judge
(2004), Viswesvaran and colleagues’ estimate of the reliability of a single peer rating of
leadership for meta-analytic relationships involving leadership was used in this study
(Viswesvaran, Ones, & Schmidt, 1996). Additionally, to correct this reliability estimate
upward, the Spearman–Brown formula based on the average number of raters per leader for
each study was used. Reliability was also corrected for predictor measures (including higher-
order personality composites) in terms of both unreliability in the personality factor (using
Mosier’s [1943] formula for composites) as well as attenuations caused by scale coarseness
(using the formula of Aguinis, Pierce, & Culpepper, 2009).
14 For each personality–leadership relationship, the following were reported13F : the mean
correlation weighted by sample size (r); the estimated true (i.e., population) correlation (ρ);
14 Funnel plots were also calculated to test for publication bias and can be found in Appendix A, from Figure A 1 to Figure A 16. Based on the ‘funnel’ shapes obtained (Hunter & Schmidt, 2004), the pattern of findings did not indicate the presence of publication bias. 38
the standard deviation of the true correlation (SDρ); the percentage of variance due to
sampling error; and an 80% credibility interval and a 95% confidence interval for each meta- analytic estimate.
2.3 Results and Discussion
2.3.1 Meta-Analysis
Table 2.1 and Table 2.2 present the findings of the meta-analysis for the relationships between personality factors at different levels of the hierarchy and transformational leadership and leadership effectiveness, respectively. The results showed that, although all personality factors are positively related to both leadership outcomes, the GFP displayed the strongest effects across personality constructs for both transformational leadership (ρ = .38,
95% CI [.23, .53]) and leadership effectiveness: ρ = .30, 95% CI [.17, .43]). The effect sizes
for the Big Two were higher than four of the Big Five for both transformational leadership
(Plasticity: ρ = .34, 95% CI [.23, .45]; Stability: ρ = .32, 95% CI [.12, .51]) and leadership effectiveness (Plasticity: ρ = .25, 95% CI [.05, .45]; Stability: ρ = .27, 95% CI [.18, .36]).
Extraversion was the exception for transformational leadership (ρ = .35, 95% CI [.28, .41]) and Conscientiousness for leadership effectiveness (ρ = .28, 95% CI [.17, .39]).
Of note, the credibility intervals for higher-order factors were fairly wide across both transformational leadership (e.g., 80% CV [.21, .43] for Stability) and leadership effectiveness (e.g., 80% CV [.13, .37] for Plasticity). This suggests that there was some variability in the true effect of these factors across studies, although even at the lower limits the effects were non-trivially positive. Specifically for the GFP, the credibility intervals for both transformational leadership and leadership effectiveness showed that its effect ranged from moderate to strong.
39
Table 2.1
Meta-Analysis of the Relationship between Factors at Different Levels of the Personality
Hierarchy (GFP, Big Two and Big Five) and Transformational Leadership Behaviours
% variance Average 80% CV 95% CI Personality due to Factor sampling k N r ρ SD Lower Upper Lower Upper ρ error GFP 15 2,669 .26 .38 .12 43 .28 .47 .23 .53
Plasticity 15 2,669 .22 .34 .09 60 .24 .44 .23 .45
Stability 15 2,669 .19 .32 .15 33 .21 .43 .12 .51 Neuroticism 15 2,669 .19 .29 .19 26 .05 .53 .15 .42 (Reversed) Extraversion 15 2,669 .23 .35 .00 100 .35 .35 .28 .41 Openness to 15 2,669 .15 .24 .14 40 .06 .42 .12 .37 Experience Agreeableness 15 2,669 .15 .24 .17 33 .03 .45 .11 .38 Conscientious- 15 2,669 .11 .17 .17 32 -.04 .39 .06 .29 ness Note: k = number of correlations; N = combined sample size; r = average of sample weighted effect sizes; ρ = estimated population correlation; SDρ = standard deviation of estimated population correlation; CV = credibility interval; CI = confidence interval; GFP = General Factor of Personality.
40
Table 2.2
Meta-Analysis of the Relationship between Factors at Different Levels of the Personality
Hierarchy (GFP, Big Two and Big Five) and Leadership Effectiveness
% variance Average 80% CV 95% CI Personality due to Factor sampling k N r ρ SD Lower Upper Lower Upper ρ error GFP 19 3,032 .22 .30 .10 51 .22 .39 .17 .43
Plasticity 19 3,032 .18 .25 .16 32 .13 .37 .05 .45
Stability 19 3,032 .19 .27 .07 71 .20 .34 .18 .36 Neuroticism 19 3,032 .14 .20 .10 55 .07 .33 .11 .28 (Reversed) Extraversion 19 3,032 .17 .25 .13 43 .08 .41 .14 .35 Openness to 19 3,032 .12 .19 .17 31 -.03 .40 .07 .31 Experience Agreeableness 19 3,032 .10 .15 .04 89 .09 .20 .08 .21 Conscientious- 19 3,032 .19 .28 .14 37 .09 .46 .17 .39 ness Note: k = number of correlations; N = combined sample size; r = average of sample weighted effect sizes; ρ = estimated population correlation; SDρ = standard deviation of estimated population correlation; CV = credibility interval; CI = confidence interval; GFP = General Factor of Personality.
To obtain confidence intervals (and hence statistical significance tests) of the
differences between the effect sizes of (1) the GFP and both the Big Two and Big Five
factors, and (2) the Big Two factors and each of their corresponding Big Five constituents,
meta-analysis was conducted on the differences between each of the relevant effects. The
results are presented in Table 2.3 for transformational leadership and Table 2.4 for leadership
effectiveness. The magnitudes of the effect sizes of the reported differences in ρ are also
reported. When sample sizes are large, an effect may be statistically significant though trivial
in magnitude. The effect size measure used (calculated based on the ρ values in Table 2.1 and
Table 2.2) is Cohen’s q statistic (Cohen, 1988), which is the effect size index for differences
between correlations. Cohen suggested that q = .10 be considered a small effect size (which 41 was set as the minimum benchmark for practical significance in this study) and q = .30 is a medium effect size.
Table 2.3 and Table 2.4 show that, with a few exceptions, the GFP has effect sizes that are both significantly and non-trivially larger than for the Big Five. Extraversion is an exception for both transformational leadership and leadership effectiveness, whereas
Conscientiousness is an exception for leadership effectiveness. When comparing the Big
Two to their Big Five constituents for transformational leadership, significant and non-trivial differences were found between Plasticity and Openness to Experience and between
Stability and Conscientiousness. For leadership effectiveness, a significant and non-trivial difference was found between Stability and Agreeableness. Lastly for this analysis, the differences between the GFP and Big Two were non-significant and trivial. Taken together, partial support was found for Hypothesis 1 given that the predictive validity of personality for leadership generally appeared to increase as the level of the personality hierarchy increased, with some statistically significant differences between certain higher-order and lower-order factors.
42
Table 2.3
Confidence Intervals of the Effect Size Differences between Personality Factors for
Transformational Leadership
Personality Factor 95% CI Higher Order Big Two or Big Five Effect Size (q) Lower Upper Factor Factor GFP Plasticity -.02 .09 .05 GFP Stability -.01 .09 .07 GFP Neuroticism (Reversed) -.03 .16 .10 GFP Extraversion -.04 .11 .04 GFP Openness to Experience .04 .18 .16 GFP Agreeableness .04 .19 .16 GFP Conscientiousness .06 .19 .23 Plasticity Extraversion -.05 .05 -.01 Plasticity Openness to Experience .03 .12 .11 Stability Neuroticism (Reversed) -.05 .10 .03 15 Stability Agreeableness .0014F .13 .09 Stability Conscientiousness .02 .14 .16
15 Value is 0.0008 to four decimal places. 43
Table 2.4
Confidence Intervals of the Effect Size Differences between Personality Factors for
Leadership Effectiveness
Personality Factor 95% CI Higher Order Big Two or Big Five Effect Size (q) Lower Upper Factor Factor GFP Plasticity .00 .08 .05 GFP Stability .00 .06 .03 GFP Neuroticism (Reversed) .03 .14 .11 GFP Extraversion .01 .10 .05 GFP Openness to Experience .04 .16 .12 GFP Agreeableness .07 .18 .16 GFP Conscientiousness -.03 .09 .02 16 Plasticity Extraversion -.03 .05 .0015F Plasticity Openness to Experience .01 .10 .06 Stability Neuroticism (Reversed) .01 .10 .07 Stability Agreeableness .05 .15 .13 Stability Conscientiousness -.05 .05 -.01
2.3.2 Multiple Regression Analysis
To explore the independent effects of personality factors on leadership, and to test
Hypothesis 2, regression analyses were conducted to compare the effects of models that
included a higher-order personality factor with models containing all their corresponding
constituent factors in predicting leadership outcomes (Table 2.5 and Table 2.6). Correlations
that were uncorrected for unreliability in the predictors were used as corrected values were
inflated (i.e., r > 1 for most correlations between personality factors). For these results, the
statistical significance of relevant findings and the magnitude of incremental effects were
reported. Cohen’s (1988) minimum benchmark for a small effect size (incremental R2 = .02)
was used to distinguish between results that were trivial versus small but meaningful.
16 Value is -0.0004 to four decimal places. 44
Table 2.5
Regression Analysis Comparing Effects between Higher-Order Personality Factors and
Corresponding Constituent Factors in Predicting Transformational Leadership Behaviour
Model/Predictor(s) R2 Adjusted R2 β Incremental R2 Model 1 GFP .10** .10** .32** Model 2 Plasticity .21** .04 Stability .18** .03 .11** .10** Model 3 Neuroticism (Reversed) .13** .01 Extraversion .19** .03 Openness to Experience .07** .01 Agreeableness .08** .01 Conscientiousness .00 .00 .11** .11** Model 1 Plasticity .08** .08** .28** Model 2 Extraversion .24** .05 Openness to Experience .10** .01 .09** .09** Model 1 Stability .07** .07** .26** Model 2 Neuroticism (Reversed) .18** .03 Agreeableness .12** .01 Conscientiousness .04* .00 .07** .07** Note: GFP = General Factor of Personality; * p < .05; ** p < .001.
45
Table 2.6
Regression Analysis Comparing Effects between Higher-Order Personality Factors and
Corresponding Constituent Factors in Predicting Leadership Effectiveness
Model/Predictor(s) R2 Adjusted R2 β Incremental R2 Model 1 GFP .06** .06** .25** Model 2 Plasticity .15** .02 Stability .15** .02 .06** .06** Model 3 Neuroticism (Reversed) .05* .00 Extraversion .10** .01 Openness to Experience .07* .00 Agreeableness .01 .00 Conscientiousness .16** .02 .07** .07** Model 1 Plasticity .04** .04** .21** Model 2 Extraversion .17** .02 Openness to Experience .08** .01 .05** .05** Model 1 Stability .04** .04** .21** Model 2 Neuroticism (Reversed) .09** .01 Agreeableness .03 .00 Conscientiousness .18** .03 .06** .06** Note: GFP = General Factor of Personality; * p < .05; ** p < .001.
For transformational leadership behaviour (Table 2.5), the results showed that the
GFP by itself accounted for a substantial amount of variance (R2 = .10, p < .001). The Big
Two (R2 = .11, p < .001) and Big Five (R2 = .11, p < .001) models also accounted for similar
variances. For leadership effectiveness (Table 2.6), the results showed that the GFP by itself
accounted for significant variance (R2 = .06, p < .001), and again the Big Two (R2 = .06, p <
.05) and Big Five (R2 = .07, p < .001) models also accounted for similar variances. Taken
together, the results across each of these comparisons showed that models that include lower- 46 order factors as separate but simultaneously entered predictors add little to the total variance accounted for compared to a model that simply includes the GFP.
Table 2.5 and Table 2.6 also report the incremental effects of each Big Two and Big
Five factor. Although several individual Big Two and Big Five factors accounted for significant incremental variance over and above other factors for both leadership outcomes, the magnitude of most of these effects was trivial by Cohen’s standards (Cohen, 1988). Of the effects that were non-trivial, Plasticity (incremental R2 = .04 and .02) and Stability
(incremental R2 = .03 and .02) both accounted for non-trivial incremental variance over and above the effects of the other in predicting transformational leadership and leadership effectiveness, respectively.
In support of Hypothesis 2, Conscientiousness had the highest (and only non-trivial) incremental variance of all Big Five factors (incremental R2 = 0.02) for predicting leadership effectiveness. Thus, there still appear to be aspects of leadership effectiveness that require a focus on non-social traits. As Conscientiousness measures the direction and ability to persevere when attaining goals (McCrae & Costa, 1992), these traits are likely to facilitate a leader’s capacity to create and execute clear leadership goals. The GFP may subsequently help leaders to compel those around them to commit to the goals and then achieve them. In addition, although not hypothesised, Extraversion accounted for non-trivial incremental variance (incremental R2 = 0.03) above the other Big Five for predicting transformational leadership. Extraversion includes an agentic component related to achievement-orientation that may help leaders achieve non-social goals (Do & Minbashian, 2014), and it may be this aspect of Extraversion that accounts for its unique effects on leadership.
2.3.3 Mediation Analysis
Lastly, to test Hypothesis 3, a mediation analysis was conducted to examine the indirect effect of the GFP on leadership effectiveness via transformational leadership 47
behaviours. The Monte Carlo confidence interval method (Preacher & Selig, 2012) was used
to test the indirect effect, as this approach does not require access to raw data and because it
overcomes some of the distribution issues associated with other related tests such as the
Sobel test (MacKinnon, Lockwood, & Williams, 2004). Separate regression analyses were
first conducted on meta-analytically derived population correlations (corrected for
unreliability in predictors) to obtain relevant values for an online Monte Carlo confidence
interval tool (Selig & Preacher, 2008) including raw regression coefficients and asymptotic
sampling variance values (N = 2,669). Specifically, transformational leadership behaviour
was regressed on the GFP to obtain the effect coefficient for this relationship, and leadership effectiveness was regressed on the GFP and transformational leadership behaviours to obtain the effect coefficient of the transformational leadership on leadership effectiveness. The online Monte Carlo tool’s default setting of 20,000 simulation repetitions was used with a
95% confidence level.
The regression analyses showed that the GFP had a significant effect on transformational leadership behaviours (b = .38, SE = .02, p < .001), and that these behaviours in turn had a significant effect on leadership effectiveness when controlling for the GFP (b = .62, SE = .02, p < .001). In support of Hypothesis 3, the indirect effect (.23) was statistically significant based on the Monte Carlo derived confidence interval (95% CI [.21,
.26]). A significant positive direct effect between the GFP and leadership effectiveness remained after controlling for transformational leadership behaviours (b = .07, SE = .02, p <
.001). However, the effect size was relatively small in magnitude. Consequently, the results indicate that the effect of the GFP on leadership effectiveness is largely mediated by transformational leadership behaviours.
This finding can be interpreted as support for the substantive nature of the GFP. The results suggest that other people perceive tangible and positively valued behaviours from 48
leaders who score higher on the GFP that, in turn, result in effective leadership. Thus, the
GFP cannot be solely explained by socially desirable responding. Consequently, these findings support the view in the literature on the substantive nature of the GFP and studies which have shown that the GFP reflects socially competent behaviours (Kowalski et al.,
2016; van der Linden et al., 2016).
2.4 Conclusion
In summary, the findings presented in this chapter provide meta-analytic support for
the effect of higher-order personality factors on leadership outcomes. As hypothesised, the
effect of personality on leadership outcomes generally increased as the level of the
personality hierarchy moved from the Big Five, to the Big Two and finally to the GFP level.
In particular, the GFP effect was larger than each of the Big Five factors, and for the majority
of cases these differences were both statistically and practically significant. Secondly, there
was some, but limited, evidence to support the idea that the Big Five factors uniquely predict
leadership to a non-trivial extent. As hypothesised, Conscientiousness incrementally
predicted leadership effectiveness independently of the other Big Five factors. Although not hypothesised, Extraversion non-trivially and independently predicted transformational leadership. Finally, support was found for the hypothesis that the effect of the GFP on
leadership effectiveness would be mediated via transformational leadership, suggesting that
at least part of the GFP is substantive and occurs via the demonstration of behaviours that are important for leadership effectiveness.
One limitation of the present study was that effects were not compared to the facet level of the personality hierarchy. Unlike the Big Five, there is a lack of agreement on an organising framework for facets in terms of both content and quantity, which makes the use of meta-analysis somewhat more challenging for this purpose. However, it is still important to compare the effects of higher-order factors to facets since research has provided support 49 for the latter’s predictive validity for leadership and other work-based criteria (Ashton,
Paunonen, & Lee, 2014; Judge & Bono, 2000; Paunonen et al., 1999, 2003). The next chapter examines the effects of all four personality levels for leadership using a dataset that contained the California Psychological Inventory. This inventory’s scales measure personality at the facet level, but past studies have also extracted the Big Five, Big Two and the GFP from it, therefore allowing all four levels of the personality hierarchy to be investigated.
Another broad aim of the thesis is to build a greater understanding of the specific ways in which higher-order factors may relate to leadership. The study in this chapter assumed that their relationships were linear in nature. Without access to raw data, more complex relationships such as curvilinear and interactive effects cannot be examined via meta-analysis. As such, the study in Chapter 3 tests for potential complexity in the relationship between higher-order factors for leadership using the dataset.
Lastly, this study was also limited in that it did not investigate practical and operational issues that are pertinent in applied settings. Specifically, the predictive validity of higher-order personality factors can change under different conditions. For example, different methods for extracting higher-order factors as well as the sample size used to build prediction equations can impact their predictive validity for leadership. Thus, Chapter 3 also examines these issues to elucidate the conditions in which the predictive validity of higher-order factors would be highest and greater than those of lower-order factors.
50
Chapter 3: A Deeper Investigation: Comparison to Facets, Complex Relationships and
Practical Considerations
3.1 Introduction
Chapter 2 provided initial support for a relationship between higher-order personality factors and leadership with the Big Five used as a comparison. The study in this chapter builds on the last chapter by more deeply examining the relationship between higher-order factors and leadership in three ways: the link between higher-order factors and leadership is compared to facets, another major level of the personality hierarchy that is similarly worthy as a comparison given evidence of its predictive validity in multiple domains (Ashton et al.,
2014; Judge & Bono, 2000; Paunonen et al., 1999, 2003); the potential existence of complex relationships between higher-order factors and leadership is examined given past research has found complex links between certain traits and both leadership and job performance (Ames &
Flynn, 2007; Le, Oh, Robbins, Ilies, Holland, & Westrick, 2011; Vergauwe, Wille, Hofmans,
Kaiser, & De Fruyt, 2018); and various methods for operationalising higher-order factors from a practical perspective, including different extraction approaches and deriving prediction equations from different sample sizes, are tested to determine how the strength of the observed relationship with leadership might change. The examination of predictive validity at different sample sizes uses cross-validation methods as detailed below, which was not possible with the meta-analysis in the previous chapter.
Based on these examinations, this chapter aims to make the following contributions.
First, by examining facets, the relationship between personality and leadership can be
compared across all four major levels of the personality hierarchy. In line with
recommendations from proponents of trait theory to organise personality into a more
structured manner (Judge et al., 2002; Zaccaro, 2007), the hierarchy of traits can provide a
more holistic and meaningful view of how personality relates to leadership since each level is 51 organised by breadth. In doing so, bandwidth-matching theory (Ones & Viswesvaran, 1996) as well as concerns about whether mid- and higher-order factors are too broad to predict criteria (Block, 1995; Hough, 1992), can be investigated within the domain of leadership.
Second, the examination of complex relationships in the present study responds to calls in the literature to examine curvilinear and multiplicative effects of personality on leadership (Zaccaro, 2007). It has long been proposed that certain traits necessarily exhibit complex relationships with leadership (e.g., another individual difference trait, intelligence, is unlikely to have a large influence on leadership without social proficiency; Moss, 1931).
Thus, investigating whether complex relationships exist between higher-order factors and leadership may contribute to a more nuanced understanding of trait theory beyond simple linear relationships.
Third, the study contributes to practical concerns in the research and application of higher-order factors. Firstly, examining differences in extraction approaches for higher-order factors helps determine whether the calculation method has a material impact on the strength of the observed relationship with leadership. Secondly, comparing the predictive validity of higher-order factors to lower-order ones at different sample sizes (via cross-validation) can determine whether the higher-order factors have higher predictive validity at sample sizes that are often found in applied settings. This is because prediction equations derived using higher-order factors require the estimation of fewer parameters (given the smaller number of variables involved) compared to lower-order factors, which may make them more resilient for predicting leadership (and potentially other outcomes) at smaller sample sizes. Moreover, the use of cross-validation in this study extends the findings from Chapter 2 by examining the predictive validity of different personality levels against data that is not used to develop the prediction equations. 52
The final contribution of this chapter relates to whether the higher-order factors
extracted from a personality measure that does not explicitly measure the Big Five are also
meaningfully related to leadership. This is imperative since higher-order factors were
discovered and have predominantly been extracted from measures that directly assess the Big
Five, particularly when studying their relationship to various criteria. Thus, examining
whether higher-order factors extracted from non-Big Five measures also relate to leadership
helps to determine if their nature and effect generalises to inventories that are not explicitly
designed to assess the Big Five.
3.1.1 Facets and the Extraction of Higher-order Factors from this Level
The personality hierarchy presented in Chapter 1 depicted the GFP at the apex,
followed by the Big Two, the Big Five and then the facet level. Facets are groupings of
similar habitual response tendencies (Eysenck, 1947; Paunonen, 1998) and are considered
17 one of the narrowest levels of personality16F . Although there is relatively widespread
18 agreement about the number and type of factors at the Big Five level17F , this is not the case at
the facet level (Briggs, 1989; Costa, McCrae, & Dye, 1991). For example, there are 16 facets
in the Sixteen Personality Factors (Cattell, Eber, & Tatsuoka, 1970), 20 in the California
Psychological Inventory (CPI; Gough, 2002), the Myers-Briggs Type Indicator (Form Q;
(Myers et al., 2009) and the Personality Research Form (Jackson, 1984), 30 in the Revised
NEO Personality Inventory (McCrae & Costa, 1992) and 41 in the Hogan Personality
Inventory (Hogan & Hogan, 2007).
A unification of research on the precise number of personality facets is beyond the
scope of this thesis, however the study focused on the CPI in order to compare the effects of
17 Although the facet level is now relatively well-established, there is still a lack of consensus on the exact number of personality levels, particularly at the lower orders (Paunonen, 1998). However, this question is beyond the scope of the thesis. 18 As noted in Chapter 1, there is evidence that supports the existence of a sixth factor, Honesty-Humility, at this level as part of the HEXACO model (Ashton et al., 2004). 53
higher-order personality factors for leadership against a facet structure. Aside from the CPI’s
widespread use (Jay & John, 2004), its inclusion in this study allowed comparisons between
personality and leadership across all four levels of the personality hierarchy. Specifically, the
CPI is one of few measures that directly assesses facets and has had both the higher-order factors and the Big Five extracted from it, as outlined below. Using the CPI can also help determine whether the findings of Chapter 2 based on explicit measures of the Big Five generalise to measures whose theoretical underpinning and design are not based on the Big
Five. Some measures may more accurately capture the GFP than others depending on the extent to which their scales assess traits related to social effectiveness (Dunkel, van der
Linden, Beaver, & Woodley, 2014). The superior predictive validity of higher-order factors may generalise to instruments that contain these kinds of traits given the theorised links between the social nature of higher-order factors and the inherently social construct of leadership.
A general factor extracted from the CPI is likely to be particularly representative of the GFP’s nature since many of the facets (e.g., Capacity for Status, Social Presence,
Dominance etc.) are purported to measure social effectiveness and interpersonal characteristics (Dunkel, van der Linden, et al., 2014). In past empirical research, the GFP and
Big Two have been shown to emerge in a validation sample of the CPI (Rushton & Irwing,
2009b) where the extracted GFP explained 35% of the variance in two second-order factors
that appeared to resemble Plasticity (with loadings on first-order factors related to ascendance
and originality) and Stability (with loadings on first-order factors related to dependability and conventionality). In another study that factor analysed 77 scales from eight different
personality assessments, the CPI was found to contain scales (including Sociability and
Capacity for Status) with the highest loadings on the GFP (Loehlin, 2012). On the Big Five factors, meaningful relationships have been found between the CPI facets and four of the Big 54
Five (McCrae et al., 1993). Agreeableness was the exception, however it was still found to
strongly correlate with one of the facets (i.e., Masculinity/Femininity, which has since been
renamed to Sensitivity in later versions of the CPI; Gough & Bradley, 2005).
3.1.1.1 The Relationship between Facets and Leadership
To my knowledge, research has yet to compare the effects of higher-order personality factors (GFP, Big Two) versus facets on leadership. However, previous studies have examined the effects of facets on leadership, and have compared their effects to the Big Five factors (as described below). On the one hand, a facet contains specific variance that may be predictive of leadership and this variance might be lost when multiple facets are aggregated to form broader traits (Paunonen, 1998, 2003; Paunonen, Jackson, Trzebinski, & Forsterling,
1992). On the other hand, as argued in Chapter 1, leadership is a broad, multidimensional and complex criterion such that any given individual personality facet may be too narrow to have a strong effect. As such, the value of facets would diminish if broader personality traits are shown to have a stronger effect on leadership, especially since there would be substantially fewer traits to measure (Paunonen, 1998).
In general, past research has found mixed results for the relative predictive merits of facets versus the Big Five factors for predicting leadership. In one study that included 14 samples of leaders from more than 200 organisations (Judge & Bono, 2000), the regression- weighted multiple correlation of facets was generally higher than their respective Big Five counterparts though the differences were relatively small. However, the correlations between each of the Big Five with leadership were generally higher than the mean correlations between their corresponding facets. Although Judge and Bono (2000) concluded that facets were not as strongly related to leadership compared to the Big Five level, subsequent meta-
analytic studies have revealed alternative findings for certain facets (Do & Minbashian, 2014;
Judge et al., 2002). These meta-analyses demonstrated that specific facets within 55
19 Extraversion18F (particularly those related to agentic traits) were more strongly related to both
leadership effectiveness and transformational leadership behaviour. The results from Judge et
al.’s (2002) meta-analysis also appeared to support the predictive superiority of
Conscientiousness’ facets (i.e., Achievement and Dependability) compared to the overall Big
Five factor, although this was not discussed in their conclusions.
Of the CPI’s facets, Dominance, Capacity for Status, Sociability, Social Presence,
Self-acceptance, Independence and Empathy (all of which relate to Extraversion; McCrae et
al., 1993) have been argued as the most important for leadership (Gough, 1990). A limited
number of empirical studies have also found links between certain CPI facets and leadership
across non-leader specific samples. For example, Hogan (1978) found relationships between
general leadership ratings and Dominance (r = .62), Self-acceptance (r = .49), Responsibility
(r = .29) and Communality (r = .29) for undergraduate footballers. On the other hand, Grahek
and colleagues reported associations between various measures of leadership with
Responsibility (r = .16 to .27) and Self-control (r = .14 to .22) for predominantly retail-based
employees (Grahek, Thompson, & Toliver, 2010).
Although the criterion-related validity of an optimally weighted linear combination of
facets will necessarily be larger than higher-order factors, the main question (similar to
Chapter 2) is whether the increase in explained variance from the set of facets is worth the
degrees of freedom that these factors use up. Furthermore, given the relatively inconsistent
findings between specific facets and leadership in the literature, the unique effects of the CPI
facets are examined on an exploratory basis in this study. In terms of the Big Five, based on
the rationale for Hypothesis 2 in Chapter 2, a Conscientiousness factor extracted from the
19 Although Do and Minbashian (2014) broadly conceptualised the lower-order traits of Extraversion at the Aspect level, the studies used as part of their meta-analysis predominantly contained facets that were defined as in this thesis. 56
CPI is still expected to account for unique variance in leadership over and above other Big
Five factors. This is based on the argument that Conscientiousness has specific task-oriented
variance, which the more socially-oriented higher-order factors do not, that may
incrementally predict leadership.
3.1.2 Complexity in Personality and Leadership Relationships
Conceptually, most models of the relationship between personality and leadership theorise only linear or additive effects when more complex relationships such as curvilinear or multiplicative ones may exist instead (Zaccaro, 2007). In his review of trait-based perspectives on leadership, Zaccaro (2007) highlighted the need for researchers in this area to explore potential complex relationships to better understand the theoretical link between
personality and leadership. Complex relationships may clarify the mechanisms through which
personality affects outcomes (Le et al., 2011). These findings have important applied
implications as practitioners may favour very high personality scores when selecting and
promoting leaders based on statistical results that are seemingly positive and linear (Benson
& Campbell, 2007; Le et al., 2011) when more complex relationships may actually be
present. Thus, although Chapter 2 demonstrated practically meaningful relationships between
higher-orders factors and leadership that were assumed to be linear, there is a need to verify
whether or not their association is more complex in nature.
3.1.2.1 The Main Types of Complex Relationships
Curvilinear relationships and interactions are two predominant types of complex
relationships. Curvilinearity occurs when a criterion score changes direction (or inflects) at
some point of an increased predictor score. These curvilinear relationships can also be
affected by moderators (Le et al., 2011) that are represented by interaction effects. Within
personality research, curvilinear relationships are typically described as an inverted-U shaped
curve such that very high scores may result in tendencies that are maladaptive (Costa & 57
Widiger, 2002) or pathological (Moscoso & Salgado, 2004). The theory that underlies these
types of relationships, including for personality–leadership, is the too-much-of-a-good-thing
(TMGT) effect (Pierce & Aguinis, 2013). Pierce and Aguinis (2013) defined the TMGT
effect within the organisational domain as “ordinarily beneficial antecedents causing harm
when taken too far” (p. 314). They argued that the inflection point is context-specific, which
can come in the form of moderators as described next.
In contrast to curvilinearity, an interaction or moderation effect is another form of complex relationship that occurs when the direction and/or strength of the relationship between a personality trait and an outcome is contingent on another variable (Ganzach,
1997a, 1997b). The moderator may include other personality traits (Burke & Witt, 2002).
Investigation into the joint influence of multiple traits is warranted since trait interactions can lead to incremental variance for job performance over and above additive effects (Penney,
David, & Witt, 2011). Studies tend to limit trait interactions to the interplay between two traits for the purposes of parsimony (Penney et al., 2011).
3.1.2.2 Evidence for Complex Relationships between Personality and Leadership
There are somewhat mixed findings in the literature on complex relationships between personality and work-based outcomes. For job performance, curvilinear links have been found for Conscientiousness and Neuroticism (Le et al., 2011). Le et al. (2011) argued that very high Conscientiousness reflects inflexibility, compulsiveness and being overly detail-oriented such that its effect weakened and then dissipated for job performance at very high levels. However, contradictory evidence has also been found, including in Le et al.’s
(2011) study given they only found an effect in one of their two tests. In another study, curvilinear relationships were not found between Conscientiousness and job performance across five samples (Robie & Ryan, 1999). The authors did not elaborate on the reason for this unexpected finding but did suggest that the aggregation of traits to the broader 58
Conscientiousness factor may conceal non-linear relationships that may otherwise be present for its facets. If Conscientiousness does indeed relate to job performance in a curvilinear way, then given the need for leaders to be adaptable and to focus on strategic goals, it would be expected that traits related to rigidity (i.e., potentially very high Stability) may similarly attenuate the effects on leadership.
On the study of complex relationships between personality and leadership, past studies have provided support for certain personality traits such as charisma (Vergauwe et al.,
2018), assertiveness (Ames & Flynn, 2007), dominance and intimidation (Benson &
Campbell, 2007), and narcissism (Grijalva, Harms, Newman, Gaddis, & Fraley, 2015). In one of these studies (Vergauwe et al., 2018), curvilinearity and trait-based moderation effects were investigated between charismatic personality traits and leadership effectiveness. The charismatic traits comprised self-confidence, energy, limit-testing and creativity that when taken to extreme levels could manifest as intimidation, manipulation and overwhelming others, thus resulting in an inverted-U relationship with leadership effectiveness. Emotional
Stability (i.e., the reverse of Neuroticism) moderated this relationship such that the deleterious consequences of high levels of charisma were somewhat buffered. Consistent with a key principle from the TMGT effect, the researchers considered the Emotional
Stability trait as a plausible context-specific variable that could influence the inflection point.
In another study, an inverted-U relationship was found between an assertiveness trait and both overall leadership effectiveness and specific leader behaviours including influence, motivation and conflict (Ames & Flynn, 2007). Chronically low assertiveness was equated with being overly submissive whereas high assertiveness was deemed too competitive for effective leadership. Lastly, in further support of the potential existence of trait-based moderation effects, a proactive personality trait has been shown to moderate curvilinear links between transformational leadership and task performance such that it was found to delay the 59 inflection point in the relationship (Chen, Ning, Yang, Feng, & Yang, 2018). The authors attributed this moderation effect to the preference of proactive individuals for seeking out challenging environments.
Given that the above research has found curvilinear relationships between work outcomes and firstly two of the Big Five factors that make up Stability (i.e.,
Conscientiousness and the inverse of Neuroticism; Le et al., 2011) and secondly traits related to Plasticity (e.g., risk-taking and creativity; Vergauwe et al., 2018), there is a possibility of curvilinearity between each of the Big Two factors and leadership. That is, very high
Stability may lead to a strong focus on completing established goals but not proactively scanning the broader environment to find new strategic goals to work towards. On the other hand, extreme Plasticity may result in the constant creation of new goals without ever completing those previously set.
Past research on related traits also appears to provide some support for these propositions. Traits such as being reluctant to take risks or adopt innovations and being unusually creative and excitement-seeking, each of which appear conceptually aligned to what very high scores on Stability and Plasticity, respectively, could represent, have been found to negatively relate to transformational leadership dimensions (Khoo & Burch, 2008).
Similarly, the concepts of unmitigated communion and unmitigated agency (Helgeson &
Fritz, 1999) may also support the potentially negative consequences of very high scores on each of the Big Two for leadership. Unmitigated communion refers to concentrating too much on others without considering oneself, which may align to a very high Stability scorer’s focus on socialisation and the need to get along with others. On the other hand, unmitigated agency refers to focusing solely on the self to the point of completely disregarding others, which may relate to a very high Plasticity scorer’s focus on personal growth and the need to succeed over others. Leadership is likely to be impaired by both constructs since unmitigated 60
communion involves being excessively concerned about other people’s issues such that a
leader may not be able to empower followers to solve problems themselves, and unmitigated
agency includes being hostile, arrogant and greedy such that a leader may not be able to build
trust or inspire followers.
Stability and Plasticity may also moderate each other’s effects on leadership. For
example, the creation of new strategic goals (i.e., high Plasticity) may only result in effective
leadership when there is also a strong motivation to persist and achieve those goals (i.e., high
Stability). This is based on the argument that leaders need to both establish goals but also
track progress and influence others to accomplish them (Mumford, Zaccaro, Harding, Jacobs,
& Fleishman, 2000). Research on the interaction between certain constituent factors that
make up each of the Big Two also provides some indirect support. That is, individuals who
are both bold (i.e., high Extraversion) and diligent (i.e., high Conscientiousness) are likely to
set even more challenging goals and will be especially tenacious in attaining them (Penney et
al., 2011).
Hypothesis 4: The relationships between higher-order factors and leadership are
complex. Specifically, Stability and Plasticity each has a curvilinear (inverted-U)
relationship with leadership (H4a) and each interacts with one another such that the
combination of high scores on both factors produces a stronger effect for leadership
(H4b).
On the complex relationships between the GFP and leadership, this study did not hypothesise any specific curvilinear associations since it seems somewhat implausible that
being too socially competent would be detrimental for leadership. However, a contrary view
has been presented. Schneider and colleagues argued that higher social competence scores are
not necessarily desirable as extreme social influence may be perceived as domineering
(Schneider, Ackerman, & Kanfer, 1996). However, as conceptualised in Chapter 1, the GFP 61
is defined as possessing competence in knowing how to handle and respond to a variety of social situations and demands (van der Linden et al., 2016), which is unlikely to result in adverse domineering behaviours. As such, this study examines the possible curvilinear effects of the GFP (and also the facets and Big Five) on leadership on an exploratory basis.
3.1.3 Practical Considerations
3.1.3.1 Extraction Approaches of Higher-order Factors
In the higher-order personality literature, a number of approaches have been used to extract higher-order factors from personality measures. The main extraction approaches are to unit-weight (i.e., summing or averaging) personality scales that make up a higher-order factor (e.g., Dunkel, Stolarski, et al., 2014; Furtner & Rauthmann, 2010) or to aggregate relevant scales by applying factor loading weights following a factor analysis (e.g., Musek,
2007; Van der Linden et al., 2010). This factor analysis has been conducted on either the personality scales or the items. Different factor analysis techniques have been used but each
(including principal axis factoring, principal components analysis and maximum likelihood) have been found to produce similar, if not identical, GFPs (Musek, 2007; Van der Linden et al., 2010). As such, the following discussion on extraction approaches is limited to unit versus factor loading weights and the factor analysis of scales versus items.
The use of unit-weights is consistent with how most personality measures compute scales (Christiansen & Robie, 2011), including in leadership research (e.g., Judge & Bono,
2000). That is, items are averaged (or summed) to form facets or the Big Five, and facets are
averaged (or summed) to form the Big Five. Unit-weights have the advantage of being less
susceptible to sample-specific variability unlike factor loading weights which depend on the
sample they are calculated from (Dawes & Corrigan, 1974; Einhorn & Hogarth, 1975;
Wainer, 1976). As noted in Chapter 2, a GFP calculated via unit-weights and a GFP derived 62
using meta-analytically derived factor loading weights have been shown to be strongly related (r = .995; Dunkel, Stolarski, et al., 2014).
Many studies have also used factor loading weights to extract the Big Two and the
GFP such that the latter is typically considered the first unrotated factor from a factor
analysis. For example, Musek (2007) extracted the GFP from different Big Five measures,
the Big Five scales, facet scales and items, and two first-order factors resembling the Big
Two. All approaches were highly intercorrelated (r = .81 to r = 1.00, with a mean of r = .95).
Van der Linden et al. (2010) first extracted the Big Two (based on eigenvalues > 1) and then
extracted the GFP since the second eigenvalue was just 1 (i.e., 1.007), and there was a clear
bend in the scree plot after the first factor and the two factors were found to be highly
correlated (r = .47). Schermer and Goffin (2018) extracted the GFP from the first unrotated
factor from facets and also via six mid-order factors derived from a factor analysis of the
facets. Lastly, Anglim et al. (2020) extracted the GFP from items, facets and mid-order scales
(i.e., the six scales from the HEXACO) and found that all GFPs extracted were highly
correlated (r = .97 to .99).
Taken together, and consistent with the conclusion by Musek (2007), regardless of the
Big Five measure or factor analysis input (i.e., Big Five scales, facets or items) each GFP
appears to practically represent the same dimension. However, research has yet to examine
differences in extraction approaches from the CPI, a measure that does not explicitly measure
the Big Five nor resemble it as closely as other models including the HEXACO. In addition,
some studies have asserted that the question of whether to use unit or factor loading weights
to form higher-order constructs should be answered in the context of how their relationships
with consequences differ (Credé & Harms, 2015; Edwards, 2001). That is, not only should
the relationship between factors that are extracted via different approaches be examined (i.e., 63
intercorrelations), but also their relationships with meaningful criteria (i.e., criterion-related
validity).
This study compares the relationships between each higher-order factor that was extracted in one of three ways: unit-weighting relevant CPI facets, applying factor loadings to
CPI facets or applying factor loadings to CPI items. The effects of these differently extracted
higher-order factors on leadership were then examined. The three extraction approaches were
expected to produce highly similar results based on the empirical studies that have found very
large correlations between higher-order factors extracted with different methods. However,
examination is still needed to determine which method produces the strongest relationship
with leadership for practical purposes. In addition, as presented in Chapter 1, some have
argued that a general factor extracted via items may produce a general factor that is not
necessarily the GFP. By comparing the GFP extracted via items and facets, and also how
each relates to leadership, evidence can be provided for whether a general factor extracted
from the CPI may reflect the GFP construct, at least in terms of the social influence
characteristics that are inherent within leadership.
3.1.3.2 The Predictive Validity of Higher-order Factors at Smaller Sample Sizes
The second practical consideration is the predictive validity of higher-order factors for
leadership compared to lower-order factors at sample sizes that are more representative of
applied settings. In applied settings, personality inventories are extensively used to make
selection decisions and appear to be growing in popularity (Lundgren, Kroon, & Poell, 2017;
Tett & Christiansen, 2007). Analytical approaches (i.e., decision-making based on data) have
been advocated over intuitive ones since they increase the probability of making accurate
selection decisions (Highhouse, 2008). One analytical approach, which is also a professional
standard for using personality inventories in selection contexts, is to first undertake a
criterion-related validity study (Tett & Christiansen, 2007). This allows prediction equations 64
to be derived for criteria of interest, which are then used to make decisions about future job
candidates. One limitation of this approach is the number of parameters that have to be
estimated in the prediction equation relative to the sample size available to derive the
prediction equation, as discussed next.
At smaller sample sizes, higher-order factors may have higher predictive validity compared to lower-order ones because the higher-order factors require the estimation of fewer parameters (i.e., only one or two based on the GFP and Big Two, respectively, versus up to 20 based on the total number of CPI facets) and therefore provide less opportunity for capitalisation on chance. That is, when sample sizes are small, complex models are more likely to overfit the data thus producing poorer predictions for new cases than equations based on simpler models with fewer parameters. This is less likely to occur at large sample sizes as the large degrees of freedom available for estimating prediction equations accommodates the estimation of large numbers of parameters. To illustrate a practical example, a recruitment agency that is helping to select a new General Manager for an organisation may only have a limited number of past personality and performance data on previous General Manager candidates given there are fewer people in this position compared to other leadership positions in an organisation. To avoid overfitting the data, the agency may be better off using prediction models containing only the GFP rather than a multitude of lower-order factors since the GFP model will require the estimation of fewer parameters.
This study tests the above proposition using cross-validation since this method helps to demonstrate the extent to which predictive models generalise to independent datasets and how accurately they may perform in practice. Moreover, the process of cross-validation essentially simulates what happens (or at least should happen) in applied settings. That is, pre-existing data on past or current employees is used to derive prediction equations (or algorithms) that are then used to predict the performance of new job candidates. 65
In a cross-validation procedure, the available data is partitioned into two independent
sets of data: one set (i.e., the training set) is used to derive a prediction model, and the
remaining set (i.e., the test or validation set) is used to assess the prediction model’s
performance. This approach allows a true test of a model’s predictive potential since an
independent dataset is used rather than the dataset from which it is developed. Chapter 2
noted that the Big Five could not perform worse than higher-order factors since the dataset
used to derive the prediction equations was the same one used to evaluate their performance.
This ‘retrospective fit’ essentially uses the data twice, which can lead to an overly optimistic view (or upwards bias) because the prediction model maximises the predictive validity for that sample (Copas, 1983). Since the model capitalises on the idiosyncrasies of the sample
(e.g., biased sample selection, random sampling error, measurement error), the prediction model is unlikely to perform as well on the overall population from which the sample is derived nor a new sample from a different population (Ivanescu, Li, George, Brown, Keith,
Raju, & Allison, 2016). As such, by using cross-validation, the present study also builds on
Chapter 2 by assessing ‘prospective fit’ (or fit to new data) when comparing the effects of
higher-order and lower-order factors at different sample sizes.
Hypothesis 5: The predictive validity of higher-order personality factors is likely to
be higher than lower-order factors when the sample size of the training set in a cross-
validation is smaller.
3.2 Method
3.2.1 Participants and Procedure
A dataset was obtained from the Center for Creative Leadership, which comprised
3,427 managers (1,148 women, 2,265 men and 14 not reported; Mage = 43.71, SDage = 7.27, age range: 24–92). The participants were from 81 countries with the majority based in the
United States (n = 2,391) followed by Canada (n = 223), Australia (n = 169) and Singapore 66
(n = 73). Most were categorised as middle-level (n = 1,928) and top-level management (n =
1,123). Each manager completed a personality inventory, and leadership ratings were concurrently collected from each manager’s colleagues such as their superior, boss, peers, direct reports and other raters. The number of raters for each manager ranged from 1 to 45 raters (Mraters = 12.09, SDraters = 4.30). The dataset contained data collected between the years
2004 to 2011, and assessments were typically completed as part of leadership development activity such as coaching, feedback sessions and leadership training programs.
3.2.2 Measures
3.2.2.1 Personality
Personality was self-assessed using the California Personality Inventory 260 (CPI).
The CPI measures 20 facets (listed in Table 3.1), three structural scales and six special purpose scales across 260 true–false items (Gough & Bradley, 2005). In terms of internal consistency, the Cronbach alpha coefficients for the 20 facets have been reported to range from .54 to .86 (M = .72, SD = .08) such that the test publisher argues that the facets generally appear to measure clearly defined concepts (Gough, 2002). All 260 items are used to score the 20 facets, or stated differently, there are no unique items that are solely used for special scales such as impression management. As it was important to clarify this given factor analyses were also conducted at the item level, described below, the test publisher was contacted to confirm this as it was not clearly stated in the technical manual.
67
Table 3.1
The CPI’s 20 Facets
Facet CPI Facet Label Facet Abbreviation CPI Facet Label Abbreviation Dominance Do Good Impression Gi Capacity for Status Cs Communality Cm Sociability Sy Well-being Wb Social Presence Sp Tolerance To Self-acceptance Sa Achievement via Conformance Ac Independence In Achievement via Independence Ai Empathy Em Conceptual Fluency Cf Responsibility Re Insightfulness Is Social Conformity So Flexibility Fx Self-control Sc Sensitivity Sn
3.2.2.2 Leadership
Leadership was assessed via a 360 degree tool called the Benchmarks for Managers,
which assesses 16 leadership competencies (Lombardo & McCauley, 1994). The tool
contains 130 items and is scored on a 5-point scale that measures the extent to which a manager displays a leadership competency (1 = Not at all, 5 = To a very great extent). The 16
leadership competencies (listed in Table 3.2) represent the critical success factors that are
required for management and executive roles.
68
Table 3.2
The Benchmarks for Managers’ Scales and their Groupings
Instrument’s Grouping of Leadership Scales Leadership Scale Resourcefulness Doing Whatever It Takes Meeting Job Challenges Being A Quick Study Decisiveness Leading Employees Confronting Problem Employees Leading People Participative Management Change Management Building and Mending Relationships Compassion and Sensitivity Straightforwardness and Composure Balance Between Personal Life and Work Respecting Self and Others Self-Awareness Putting People At Ease Differences Matter Career Management
3.2.3 Data Analysis Strategy
A primary aim of this study is to compare the effects of higher-order factors on leadership with the effects at the facet level of personality. To derive factors that resembled the GFP, the Big Two and the Big Five factors, factor analysis was first used at the facet level of the CPI. Following the approach used by Dunkel and colleagues, unit-weighted GFP scores were computed by first standardising each of the 20 facets and then averaging them
(Dunkel, van der Linden, et al., 2014). The Big Two and Big Five were calculated by averaging the relevant standardised facets based on the Pattern Matrix output from the factor analyses of the two- and five-factor solutions described below.
To examine the relationship between higher-order personality factors and leadership, and to compare their effects with facets (as well as the Big Five), correlational and multiple regression analyses were conducted. In addition, quadratic and product terms were used in hierarchical polynomial regression analyses to test for the existence of curvilinear and 69
interaction effects, in line with statistical procedures from past research on complex
relationships (Benson & Campbell, 2007; Le et al., 2011; Robie & Ryan, 1999). Analyses
were also undertaken to examine the relationship between personality and leadership by rater
group (superior, boss, peers and direct reports). However, a similar pattern emerged across
the different rater groups. As such, only results on an aggregated view of leadership, with all raters combined, are presented below.
Another aim of this study is to compare the effects of higher-order personality factors on leadership when different methods were used to calculate the higher-order factors (in line with others who have investigated differences in extraction approaches for job performance;
Christiansen & Robie, 2011). Specifically, higher-order factors were calculated by unit- weighting the relevant facets (as described above), applying factor loadings to facets, and applying factor loadings to items. The second and third approaches were based on principal axis factoring with Direct Oblimin rotation, an oblique approach that allows for factors to be correlated. Missing item level data for the CPI was addressed via the SPSS function of replacing missing values with the series mean. Of the sample, 43.33% of cases had at least one missing item, and the maximum number of missing items for any one case was 18 out of the total 260 items.
Lastly, to examine how the cross-validated predictive validity of higher-order factors compares to lower-order ones at different (training set) sample sizes, k-fold cross-validation was used across the different sample sizes. Specifically, a 10-fold cross-validation procedure was used, which results in a less biased estimate of a model compared to other methods, such as the split-sample procedure (Molinaro, Simon, & Pfeiffer, 2005). The specific steps to perform this procedure are outlined below. 70
3.3 Results and Discussion
3.3.1 Extraction and Derivation of Personality and Leadership Factors
Before factor analysing the CPI and Benchmarks for Managers data, the suitability of each dataset for factor analysis was assessed. For the personality dataset, the correlation matrix based on the 20 facets (presented in Appendix B, Table B 1) revealed the presence of several correlations of .30 and above. The Kaiser–Meyer–Olkin value was .87, exceeding the recommended value of .60 (Kaiser, 1974) and Bartlett’s Test of Sphericity (Bartlett, 1954) reached statistical significance (p < .001), which support the factorability of the correlation matrix. For the 260 items, several correlations of .30 and above were found, the Kaiser–
Meyer–Olkin value was .88 and Bartlett’s Test of Sphericity was also statistically significant
(p < .001). For the leadership dataset, the correlation matrix based on the 16 leadership scales showed several correlations of .30 and above, the Kaiser–Meyer–Olkin value was .96 and
Bartlett’s Test of Sphericity was also statistically significant (p < .001). Therefore, both the personality and leadership datasets met the assumptions required for factor analysis.
3.3.1.1 GFP, Big Two and Big Five Derived via Unit-Weighted Facets
To derive a unit-weighted GFP from the CPI, each of the 20 facets was first standardised before being averaged. All of the CPI’s 20 facets were included because previous research has demonstrated that out of eight different personality measures the CPI contained scales with the highest loadings on the GFP (Loehlin, 2012), presumably reflecting the notion that the CPI facets were designed to measure interpersonal characteristics required for social effectiveness, as discussed above. In particular, Loehlin (2012) found that all of the facets had a loading of .22 or greater on the GFP with the only exception being Self-control, which had a loading of .06. All of the CPI loadings from Loehlin’s (2012) study are presented in Appendix B, Table B 2. However, in a separate study the Self-control scale had a loading of .46 on the GFP and was also positively correlated with 16 of the other 19 CPI 71
scales (Dunkel, van der Linden, et al., 2014), which is consistent with the idea that the GFP
exists within a manifold of trait intercorrelations that are positive (Irwing et al., 2012).
Furthermore, the Self-control scale has also been used to derive a GFP with a loading of .25
(Dunkel & Van der Linden, 2014). Given these previous findings and that Self-control is
related to traits that are expected to relate to social competence such as self-regulation, avoiding conflict and being modest (Schaubhut, Thompson, & Morris, 2011), the Self-control scale was included along with the other 19 CPI scales to derive a unit-weighted GFP.
Unit-weighted Big Two and Big Five factors were derived by first conducting principal axis factoring (with Direct Oblimin rotation) on the 20 facets with two and then five factors being specified for extraction, respectively. The scree plot is presented in Figure 3.1,
and the pattern matrix, structure matrix and communalities for the two-factor and five-factor
solutions are presented in Table 3.3 and Table 3.4, respectively. The highest loading for each
factor is bolded in both of these tables as well as for all subsequent tables containing factor
analysis results. The relevant standardised facets, based on loadings from the Pattern Matrix
output, were then averaged to derive each Big Two and Big Five factor. 72
Figure 3.1
Scree plot of eigenvalues associated with the principal axis factoring of the CPI facets.
In the two-factor solution, each factor explained 31.52% and 16.35% of the variance,
based on the extraction sums of squared loadings. On inspection of the pattern matrix (Table
3.3), Factors 1 and 2 appeared to represent Plasticity and Stability, respectively, and the
pattern of results was consistent with past findings (Rushton & Irwing, 2009b). Specifically,
the Plasticity factor had higher loading facets such as Capacity for Status, Social Presence,
Sociability and Dominance. These facets collectively describe the need for challenge, power,
social participation and recognition, which are consistent with the essence of Plasticity on
social and goal exploration. The Stability factor had facets such as Self-control, Good
Impression, Social Conformity and Responsibility. Together, these factors relate to self-
presentation and regulation, social conformance and dependability, which are in line with the social and goal maintenance aspects of Stability. 73
Table 3.3
Pattern and Structure Matrix for Principal Axis Factoring with Oblimin Rotation of Two-
Factor Solution of CPI Facets
Pattern Coefficients Structure Coefficients Communalities CPI Facets Factor 1 Factor 2 Factor 1 Factor 2 Initial Extraction Capacity for Status 0.85 0.03 0.85 0.18 0.72 0.73 (Cs) Social Presence (Sp) 0.85 -0.17 0.82 -0.03 0.73 0.69 Sociability (Sy) 0.81 -0.02 0.81 0.12 0.75 0.66 Self-acceptance (Sa) 0.81 -0.18 0.78 -0.04 0.70 0.64 Dominance (Do) 0.80 0.00 0.80 0.14 0.81 0.64 Independence (In) 0.69 0.09 0.70 0.21 0.65 0.50 Empathy (Em) 0.66 0.15 0.69 0.26 0.59 0.49 Conceptual Fluency 0.56 0.46 0.64 0.56 0.62 0.62 (Cf) Sensitivity (Sn) -0.35 0.04 -0.35 -0.03 0.35 0.12 Flexibility (Fx) 0.33 0.05 0.34 0.11 0.52 0.12 Self-control (Sc) -0.47 0.81 -0.33 0.73 0.77 0.74 Good Impression -0.18 0.75 -0.04 0.72 0.70 0.55 (Gi) Tolerance (To) 0.28 0.63 0.39 0.68 0.61 0.53 Well-being (Wb) 0.30 0.61 0.40 0.66 0.64 0.52 Social Conformity -0.05 0.57 0.05 0.56 0.39 0.32 (So) Responsibility (Re) 0.13 0.56 0.23 0.59 0.44 0.36 Achievement via 0.14 0.55 0.23 0.57 0.52 0.35 Conformance (Ac) Achievement via 0.42 0.49 0.50 0.56 0.70 0.48 Independence (Ai) Insightfulness (Is) 0.33 0.47 0.42 0.53 0.47 0.39 Communality (Cm) -0.14 0.34 -0.08 0.32 0.21 0.12
In the five-factor solution, these factors explained 32.19%, 16.97%, 7.70%, 3.62%
20 and 2.81%19F of the variance with a total of 63.29% based on the extraction sums of squared loadings. The pattern matrix (Table 3.4) showed that Factors 1, 2, 3, 4 and 5 appeared to resemble Extraversion, Neuroticism (reversed), Openness to Experience, Agreeableness
(reversed) and Conscientiousness (reversed), respectively. The higher loading facets for each
Big Five factor included Extraversion: Sociability, Dominance and Self-Acceptance;
Neuroticism (or Emotional Stability): Good Impression, Self-control and Well-being;
Openness to Experience: Achievement via Independence, Flexibility and Tolerance;
20 The initial eigenvalue for the fifth factor was just below 1, i.e., λ = 0.99. 74
Agreeableness: Sensitivity; and Conscientiousness: Achievement via Conformance,
Responsibility and Communality. These results are consistent with past research that has
examined the relationship between CPI facets and the Big Five factors, including the finding
that only one facet (i.e., Sensitivity) loaded strongly on the Agreeableness factor (McCrae et
21 al., 1993)20F .
Table 3.4
Pattern and Structure Matrix for Principal Axis Factoring with Oblimin Rotation of Five-
Factor Solution of CPI Facets
Pattern Coefficients Structure Coefficients Communalities CPI Facets Factor Factor Factor Factor Factor Factor Factor Factor Factor Factor Extrac Initial 1 2 3 4 5 1 2 3 4 5 tion Sociability (Sy) 0.93 0.10 -0.07 0.02 0.02 0.89 0.02 0.26 -0.29 -0.12 0.75 0.80 Dominance 0.81 -0.05 -0.17 -0.23 -0.21 0.85 -0.01 0.14 -0.52 -0.29 0.81 0.84 (Do) Self-acceptance 0.78 -0.15 -0.08 -0.15 -0.05 0.81 -0.18 0.17 -0.40 -0.08 0.70 0.70 (Sa) Capacity for 0.76 -0.07 0.25 0.05 -0.08 0.84 -0.03 0.50 -0.24 -0.16 0.72 0.76 Status (Cs) Empathy (Em) 0.64 0.15 0.33 0.14 0.07 0.69 0.12 0.55 -0.10 -0.09 0.59 0.62 Social Presence 0.63 -0.12 0.27 -0.18 0.20 0.77 -0.19 0.45 -0.37 0.11 0.73 0.72 (Sp) Independence 0.50 -0.01 0.08 -0.38 -0.07 0.66 0.04 0.29 -0.56 -0.21 0.65 0.58 (In) Good 0.08 0.86 -0.04 0.09 -0.03 -0.02 0.85 0.14 -0.02 -0.44 0.70 0.74 Impression (Gi) Self-control -0.24 0.80 -0.01 0.12 -0.10 -0.32 0.85 0.06 0.10 -0.44 0.77 0.81 (Sc) Well-being 0.08 0.57 0.24 -0.55 0.08 0.30 0.62 0.41 -0.63 -0.37 0.64 0.79 (Wb) Social Conformity 0.01 0.47 -0.03 -0.15 -0.17 0.04 0.57 0.10 -0.23 -0.43 0.39 0.37 (So) Achievement via 0.09 0.07 0.80 0.04 -0.18 0.36 0.30 0.87 -0.10 -0.34 0.70 0.80 Independence (Ai) Flexibility (Fx) 0.05 -0.10 0.76 0.15 0.20 0.24 -0.08 0.71 0.12 0.16 0.52 0.61 Tolerance (To) 0.02 0.34 0.59 -0.13 -0.09 0.25 0.50 0.68 -0.24 -0.38 0.61 0.64 Insightfulness -0.05 -0.04 0.53 -0.25 -0.35 0.26 0.27 0.59 -0.34 -0.46 0.47 0.54 (Is) Conceptual 0.30 0.03 0.42 -0.17 -0.34 0.53 0.28 0.59 -0.38 -0.49 0.62 0.65 Fluency (Cf) Sensitivity (Sn) -0.13 0.04 0.08 0.59 -0.05 -0.30 0.04 0.00 0.62 0.05 0.35 0.40 Achievement via 0.25 0.18 -0.13 0.07 -0.68 0.25 0.47 0.09 -0.16 -0.76 0.52 0.64 Conformance (Ac) Responsibility 0.11 0.09 0.19 0.15 -0.60 0.18 0.41 0.33 -0.04 -0.65 0.44 0.50 (Re) Communality -0.17 0.09 0.00 -0.07 -0.33 -0.11 0.27 0.02 -0.09 -0.37 0.21 0.17 (Cm)
21 McCrae and colleagues highlighted this as an unusual finding given that the CPI purports to measure all interpersonal aspects of life, which should encompass the traits of Agreeableness relating to cooperation, altruism and selflessness. 75
3.3.1.2 GFP, Big Two and Big Five Derived via Factor Loadings on Facets
The Big Two and Big Five factors were derived with factor loading weights based on the factor analysis of the two- and five-factor solutions, respectively, in the preceding section.
For the GFP, a factor analysis of the 20 facets (with one factor specified for extraction) found that the first factor explained 33.75% of the variance. The factor matrix coefficients and communalities are presented in Table 3.5.
Table 3.5
Factor Matrix for Principal Axis Factoring of One-Factor Solution of CPI Facets
Factor Coefficients Communalities CPI Facets Component 1 Initial Extraction Capacity for Status (Cs) 0.80 0.72 0.64 Conceptual Fluency (Cf) 0.77 0.62 0.59 Dominance (Do) 0.74 0.81 0.55 Sociability (Sy) 0.74 0.75 0.54 Empathy (Em) 0.70 0.59 0.49 Independence (In) 0.69 0.65 0.47 Social Presence (Sp) 0.67 0.73 0.45 Achievement via 0.65 0.70 0.42 Independence (Ai) Self-acceptance (Sa) 0.64 0.70 0.41 Well-being (Wb) 0.58 0.64 0.34 Tolerance (To) 0.58 0.61 0.33 Insightfulness (Is) 0.56 0.47 0.31 Achievement via 0.41 0.52 0.17 Conformance (Ac) Responsibility (Re) 0.41 0.44 0.17 Flexibility (Fx) 0.34 0.52 0.11 Sensitivity (Sn) -0.31 0.35 0.10 Social Conformity (So) 0.25 0.39 0.06 Good Impression (Gi) 0.22 0.70 0.05 Communality (Cm) 0.05 0.21 0.00 Self-control (Sc) -0.01 0.77 0.00
The higher loading facets included Capacity for Status, Conceptual Fluency,
Dominance, Sociability and Empathy, which is consistent with past research (Loehlin, 2012).
Collectively, these facets represent social influence, presence and participation, as well as
comfort in and capacity to understand and respond to the needs of others. These
characteristics appear to be synonymous with the nature of the GFP in terms of social 76
competence, particularly with the presence of the Conceptual Fluency facet which, combined
with the more interpersonally-oriented facets, could help an individual draw on a wider range
of strategies to navigate more ambiguous or complex social scenarios.
The facets with the lowest loadings in the present study were largely consistent with past findings (Loehlin, 2012) including the negative loading of Sensitivity and the near-zero loading of Self-control. The only exception was Communality, which had a near-zero loading here but a small loading of .26 in Loehlin (2012) and a larger loading of .50 in Dunkel, van
der Linden, et al. (2014). Communality was still included in the unit-weighted GFP above
given its loadings on this factor in previous studies (Dunkel, van der Linden, et al., 2014;
Loehlin, 2012) and also because it involves behaving in a manner that is not too odd or
unconventional (Gough & Bradley, 2005), which presumably relates to the social competence
nature of the GFP.
3.3.1.3 GFP and Big Two Derived via Factor Loadings on Items
The GFP and Big Two were also derived by factor analysing the CPI’s items (see
Appendix B for the scree plot, pattern coefficients, structure coefficients and communalities
in Figure B 1, Table B 3 and Table B 4). The one-factor solution accounted for 5.05% of
variance, and the two-factor solution accounted for 5.07% and 2.93% of variance based on
the extraction sums of squared loadings. For the one-factor solution derived for the GFP, the
factor matrix results revealed a number of reverse-scored items that loaded strongly. The
highest loading items related to not speaking to others unless spoken to first, struggling to
think of what to say in a group of people and having trouble acting naturally around
strangers. Several non-reverse items about being an effective socialiser, lacking dread
entering a room of people who have already started conversing, and possessing a natural
ability to influence others also loaded moderately on this factor. In terms of the Big Two, the
first factor appeared to represent Stability, such that items with higher loadings were 77 negatively valenced and related to thinking that people do not like putting themselves out in order to help others, believing that most people lie to succeed and thinking that others pretend to be concerned about people more than they really are. The second factor appeared to represent Plasticity, with items on being proactive in entertaining others as well as negatively valenced items on having difficulty initiating conversations with others and being able to act naturally with strangers.
3.3.1.4 Factor Analysis of Leadership
The factor analysis of leadership was based on the 16 leadership scales. Appendix B presents the factor matrix coefficients and communalities for the one-factor leadership solution in Table B 5, and the scree plot is displayed in Figure B 2. Inspection of the scree plot supported the retention of an overall leadership factor since the trend appeared to ‘bend’ after the first factor. In addition, the first factor explained 72.24% of the initial variance and
22 there was limited support for a rotated two-factor solution21F which explained 71.13% and
8.20% of the variance with a total of 79.33% based on the extraction sums of squared loadings. Given these findings, and that this thesis is investigating overall leadership, the study focused on the relationships between different personality levels and this single overall leadership factor.
3.3.2 Comparison of Effects of Higher-order Factors and Facets on Leadership
The following set of analyses employed unit-weighted personality factors to be consistent with how they were operationalised in Study 1 in Chapter 2. Following this section, the results for different methods of deriving higher-order factors are compared further. To compare the effects of higher-order factors and facets on leadership, correlational analysis was first conducted between all personality traits (including the GFP, Big Two, Big
22 Similar patterns emerged when the 16 scales were factor analysed by each rater group. 78
Five and 20 facets) and the overall leadership factor using the Pearson product-moment correlation coefficient. Results are presented in Table 3.6. Correlation sizes of .10, .20 and
.30 were considered small, medium and large effects, respectively, based on normative guidelines for effect sizes specific to individual differences research (Gignac & Szodorai,
2016).
Table 3.6
Pearson Product-moment Correlations and Multiple Regression Analysis between the
Personality Factors at All Four Levels and Overall Leadership
Predictor(s) r R2 Adjusted R2 β Incremental R2 Big One GFP .15** .02** .02** .15** Big Two Plasticity .07** .03 .00 Stability .16** .15** .02 .03** .03** Big Five Neuroticism (Reversed) .13** .09** .01 Extraversion .07** .06* .00 Openness to Experience .12** .04 .00 Agreeableness -.03 -.05* .00 Conscientiousness .14** .05* .00 .03** .03** Facets Dominance .06** .08* .00 Capacity for Status .05* -.08* .00 Sociability .06** -.02 .00 Social Presence .03 .01 .00 Self-acceptance .06* .07* .00 Independence .01 -.13** .01 Empathy .14** .15** .01 Responsibility .12** .05* .00 Social Conformity .10** .00 .00 Self-control .11** .12** .00 Good Impression .10** -.09* .00 Communality .06** -.01 .00 Well-being .13** .11** .00 Tolerance .12** .00 .00 Achievement via Conformance .11** .05 .00 Achievement via Independence .11** .03 .00 Conceptual Fluency .09** -.02 .00 Insightfulness .08** -.01 .00 Flexibility .05* .01 .00 Sensitivity .03 .04 .00 .06** .05** Note: Pairwise deletion of missing data; n = 3,400. *p < .05; **p < .001, two-tailed. 79
In terms of higher-order factors, all three factors were positively and statistically
significantly related to overall leadership, with a small to medium effect found for the GFP (r
= .15) and Stability (r = .16). Of the Big Five factors, four factors were positively and
statistically significantly related to overall leadership with small effects for Neuroticism,
Openness to Experience and Conscientiousness whereas Agreeableness was unrelated to
overall leadership. The results for the facets and overall leadership revealed several
statistically significant findings with nine of the 20 facets exceeding the minimum value for a
small effect. The range of statistically significant correlations between the facets and overall
leadership ranged from r = .05 to r = .14. Although none of the individual Big Five factors
nor facet effects appeared to exceed those of the GFP and Stability, significance tests of the
differences between correlations were conducted as described next.
An online calculator was used to test for statistically significant differences between
the above correlations (see Lee & Preacher, 2013). Specifically, each of the correlations
between the higher-order factors (i.e., GFP, Plasticity and Stability) and leadership was
compared with each of the correlations between lower-order factors (i.e., Big Five factors and
facets) and leadership. Each test produces a z-score where values greater than the absolute
value of 1.96 are considered statistically significant (Lee & Preacher, 2013). The results
presented in Table 3.7 indicated that the effects of the GFP and Stability on leadership were
statistically significantly higher than most of the Big Five factors and facets. Of note, the effects of the GFP and Stability were not significantly different from the effect of
Conscientiousness. This finding appears to be consistent with the hypothesis and finding in
Chapter 2 that Conscientiousness is likely to possess some unique effects for leadership effectiveness. A more formal test of its independent contribution is examined below. In terms of Plasticity, its relationship with leadership was trivial (r = .07 as displayed in Table 3.6), 80 and its effects compared to those of lower-order factors were mixed. Plasticity’s effect was significantly higher than three but significantly lower than five of the facets for leadership.
Table 3.7
Tests of Correlational Differences between Higher-order and Lower-order Factors for
Leadership
23 z-score2F GFP Plasticity Stability Big Five Factors Neuroticism (Reversed) 1.21 -2.51* 3.13* Extraversion 6.52* 0.00 4.30* Openness To Experience 2.94* -3.37* 2.90* Agreeableness 8.11* 5.47* 8.19* Conscientiousness 0.69 -3.29* 1.82 Facets Dominance 6.15* 1.00 4.74* Capacity for Status 7.46* 2.13* 5.29* Sociability 6.23* 1.00 4.68* Social Presence 7.35* 3.79* 5.61* Self-acceptance 5.40* 0.92 4.30* Independence 9.19* 5.06* 7.25* Empathy 0.73 -5.70* 1.02 Responsibility 1.91 -2.36* 2.86* Social Conformity 2.76* -1.29 4.11* Self-control 1.93 -1.47 3.47* Good Impression 2.81* -1.25 4.64* Communality 4.21* 0.40 5.51* Well-being 1.47 -3.30* 1.35 Tolerance 2.28* -2.66* 3.21* Achievement via Conformance 2.51* -1.92 3.52* Achievement via Independence 3.21* -2.37* 3.43* Conceptual Fluency 5.32* -1.46 4.55* Insightfulness 4.79* -0.54 5.40* Flexibility 5.24* 1.06 4.70* Sensitivity 4.65* 1.38 5.23* Note: *p < .05, two-tailed.
23 This z-score represents a test of the equality of two correlations derived from the same sample and that share one common variable. Values greater than |1.96| are statistically significant based on a two-tailed test. 81
To further compare the effects of higher-order personality factors and facets on
leadership, multiple regression was used. In doing so, the variance in overall leadership that
is explained by each set of personality traits at all four levels was examined, as well as the
unique effects of individual traits within each set. Table 3.6 displays the R2 and Adjusted R2 for each personality level, and the β coefficient and incremental R2 values for each trait.
For higher-order factors, the GFP explained 2% of the variance in overall leadership
(p < .001) and the Big Two together explained 3% of the variance (p < .001); both R2 and
Adjusted R2 values were the same for each of these models. The standardised coefficient for the GFP was β = .15 (p < .001). For the Big Two factors, only Stability made a statistically
significant contribution to the prediction of overall leadership (β = .15, p < .001) and its
24 2 independent effect was non-trivial23F (incremental R = .02).
For the Big Five factors, this set explained 3% (R2 and Adjusted R2 were the same for
each model) of the variance in overall leadership (p < .001). Similar to the results in Chapter
2, the variance accounted for by the Big Five is fairly similar to that accounted for by the Big
Two and, to a slightly lesser extent, the GFP. Additionally, four of the Big Five factors individually made a statistically significant contribution. However, all of their incremental R2 values were trivial including for Conscientiousness, which is inconsistent with results in
Chapter 2 for this factor’s unique effects based on the meta-analytic results. Although a factor resembling Conscientiousness can be extracted from the CPI, it may not sufficiently capture the full extent of the construct’s nature that is argued to incrementally predict leadership, unlike in Chapter 2 which used explicit Big Five measures. The CPI facets that make up a
Conscientiousness factor include Achievement via Conformance (i.e., goal-oriented but only in highly structured environments), Responsibility (i.e., takes obligations seriously) and
24 As in Study 1, incremental R2 = 0.02 was the minimum benchmark used to distinguish between results that were trivial versus small but meaningful (Cohen, 1988). 82
Communality (i.e., behaves similarly to most people). However, the absence of other
conscientious characteristics from the CPI, such as achievement-orientation (including in unstructured settings), detail-orientation and being hard-working, may explain why
Conscientiousness had a unique effect for leadership in Chapter 2 but not in this study.
Lastly, at the facet level, this set explained 5% of the variance (Adjusted R2) in overall
leadership, which is more than twice that accounted for by the GFP in this study. Almost half
of the facets made a statistically significant independent contribution to overall leadership.
However, each one by itself accounted for at most a small amount of incremental variance in leadership, which is possibly not surprising given the large number of facets involved.
In summary, the GFP and Big Two appeared somewhat comparable to the Big Five in
their ability to explain the variance in overall leadership, with a unique and non-trivial effect
found for Stability over and above that of Plasticity. The set of facets appeared to explain
somewhat more variance in overall leadership than the GFP, Big Two and Big Five factors,
but none of the independent facet contributions reached a non-trivial size.
3.3.3 Complex Relationships Between Higher-order Factors and Leadership
Hierarchical multiple regression was conducted to examine complex relationships
between higher-order personality factors and leadership. Specifically, curvilinear effects for
each higher-order factor as well as the presence of an interaction effect between the Big Two
were tested. Curvilinear relationships and interactions can be represented by higher order
(e.g., quadratic) terms and product terms, respectively, in the regression equation (Aiken,
West, & Reno, 1991). Thus, the unit-weighted higher-order factors were first transformed by
squaring each higher-order factor (e.g., GFP GFP) and both Big Two factors were
multiplied by each other (i.e., Plasticity Stability).
Additionally, tests of non-linear or interaction effects must be hierarchical to control
for linear effects as the corresponding squared partial correlation represents the improvement 83
in the model due to the addition of the product term (McClelland & Judd, 1993). This is
necessary as a linear term and its quadratic are highly correlated, which means they should be
thought of in terms of a hierarchy. That is, the difference in variance accounted for between
the set of both linear and quadratic terms minus only the linear effect represents the
incremental variance by allowing for a quadratic curve (Cohen, 1968).
For the first hierarchical multiple regression in the study, the effects of the quadratic
GFP term were assessed for leadership, controlling for the influence of the single GFP
variable (i.e., its linear effect). The GFP variable was entered at Step 1 and the quadratic GFP
term was entered at Step 2. For the Big Two factors, the quadratic terms were introduced into
the regression prior to the product terms in line with recommendations from past studies
(Cortina, 1993; Ganzach, 1997b). That is, both Plasticity and Stability were entered at Step 1,
followed by both the quadratic Plasticity and the quadratic Stability variables at Step 2, and
finally the interaction variable (i.e., Plasticity Stability) at Step 3.
The results for the test of complex relationships are presented in Table 3.8 for the
GFP and in Table 3.9 for the Big Two. No statistically significant effects were found for any
of the complex relationships tested. As such, Hypothesis 4 was not supported since the results
did not reveal the existence of curvilinear relationships (H4a) nor interaction effects (H4b)
for the Big Two factors and overall leadership. However, as expected, the GFP did not have a
curvilinear (inverted-U) relationship with overall leadership, which suggests that having too
25 much GFP (as well as Stability and Plasticity) is unlikely to be detrimental for leadership24F .
25 Tests of curvilinear relationships between Big Five factors and facets with leadership were also conducted, however none of the relationships reached statistical significance. 84
Table 3.8
Hierarchical Multiple Regression Results to test for a Curvilinear Relationship between the
GFP and Leadership
2 2 R R R b SEb t Change Step 1 .15 .02** Constant 63.69 0.09 705.95 GFP 1.57 0.17 0.15 9.08** Step 2 .15 .02** .00 Constant 63.62 0.11 586.72 GFP 1.67 0.19 0.16 8.66** GFP GFP 0.27 0.22 0.02 1.24 Note: R2 values were the same as Adjusted R2 values to two decimal places; *p < .05; **p < .001.
Table 3.9
Hierarchical Multiple Regression Results to test for Complex Relationships between the Big
Two and Leadership
2 2 R R R b SEb t Change Step 1 .16 .03** Constant 63.69 0.09 706.86 Plasticity 0.20 0.13 0.03 1.50 Stability 1.27 0.15 0.15 8.58** Step 2 .17 .03** .00 Constant 63.51 0.12 516.06 Plasticity 0.28 0.14 0.04 2.00 Stability 1.39 0.16 0.17 8.43** Plasticity Plasticity 0.20 0.13 0.03 1.54 Stability Stability 0.19 0.14 0.03 1.36 Step 3 .17 .03** .00 Constant 63.50 0.13 500.83 Plasticity 0.28 0.14 0.04 1.98 Stability 1.39 0.16 0.17 8.44** Plasticity Plasticity 0.22 0.14 0.03 1.55 Stability Stability 0.22 0.16 0.03 1.39 Plasticity Stability -0.08 0.22 -0.01 -0.38 Note: R2 values were the same as Adjusted R2 values to two decimal places; *p < .05; **p < .001.
85
3.3.4 Effects of Different Extraction Methods of Higher-order Factors
As described above, the higher-order factors can be derived using different extraction methods. For the following analyses, the effects of higher-order factors for leadership are examined and compared via three approaches: unit-weighting relevant facets, applying factor loadings to facets, and applying factor loadings to items.
Correlations among the higher-order factor variants and overall leadership are presented in Table 3.10. First, the results showed that each higher-order variant was highly intercorrelated with its counterpart factors (GFP: r = .89 to .94; Plasticity: r = .87 to .98;
Stability: r = .89 to .97). Second, in terms of the relationships with overall leadership, the
GFP variants and Stability variants appeared somewhat consistently related. In particular, the unit-weighted GFP had the strongest correlation (r = .15), followed by the factor-derived
GFP at the item level (r = .13) and then the factor-derived GFP at the facet level (r = .12).
Unit-weighted Stability and factor-derived Stability at the facet level were similarly related to leadership (r = .16) followed by factor-derived Stability at the item level (r = .15). Unit- weighted Plasticity and factor-derived Plasticity at the facet level were also similarly related to leadership, however the effects did not appear practically meaningful (r = .07). Factor- derived Plasticity at the item level was unrelated to leadership (r = .02). Taken together, the findings suggest that the different methods of operationalising the higher-order factors generally yield a highly similar rank ordering of scores, and that the effect on leadership typically varies little (if at all) across the methods.
86
Table 3.10
Pearson Product-moment Correlations between the Different Higher-order Factor
Derivations and Overall Leadership
Higher-order Factor Variants 1 2 3 4 5 6 7 8 9
1. GFP: unit-weighted -
2. GFP: factor-derived (facet) .94* -
3. GFP: factor-derived (item) .92* .89* -
4. Plasticity: unit-weighted .78* .94* .78* - 5. Plasticity: factor-derived .77* .93* .74* .98* - (facet) 6. Plasticity: factor-derived .53* .73* .58* .88* .87* - (item) 7. Stability: unit-weighted .81* .60* .74* .31* .28* .03 - 8. Stability: factor-derived .75* .53* .70* .23* .19* -.05* .97* - (facet) 9. Stability: factor-derived .81* .64* .87* .41* .38* .10* .89* .89* - (item) 10. Overall Leadership .15* .12* .13* .07* .07* .02 .16* .16* .15* Note: Pairwise deletion of missing data; n = 3,400 to 3,425; values of n for the correlations vary due to more missing data on some variables than others. *p < .01, two-tailed.
3.3.5 The Predictive Validity of Higher-order Factors at Different Sample Sizes
Hypothesis 5 states that higher-order factors may possess greater predictive validity for leadership than their set of constituent lower-order factors at smaller sample sizes. To examine this hypothesis, the 10-fold cross-validation procedure detailed below was conducted for each of the four models (i.e., levels of the personality hierarchy) at four different sample sizes. The first sample size chosen was based on the maximum training set possible given the available dataset (i.e., n = 3,060) and this was categorised as a ‘very large’ sample size in the present study. The selection of the other three sample sizes (i.e., ‘small’ =
50, ‘medium’ = 150 and ‘large’ = 300) was based on past studies that examined different sample sizes commonly used in psychology (Liu, West, Levy, & Aiken, 2017) and in applied settings (Camstra & Boomsma, 1992; Mason & Perreault Jr, 1991). The present researcher also drew on his own consulting experience working with typical and limited sample sizes 87
when making selection decisions about leaders. Three colleagues in this field were also
conferred with on 5 June 2020 to ensure the identified sample sizes were representative of
applied settings. In particular, n = 50 was generally considered a minimum sample size in
past studies examining effects of phenomena at different sample sizes, and is also commonly
the minimum sample size used to conduct basic analyses in practical settings to inform
selection decisions; n = 150 was representative of more intermediate sample sizes in past
studies and is also typically observed when conducting criterion-related validity studies in
applied settings to build candidate suitability reports; and n = 300 was indicative of the
higher range of sample sizes in past studies as well as of larger sample sizes seen when
determining selection criteria for volume recruitment.
For the 10-fold cross-validation procedure, the first step involved randomly
partitioning the dataset into 10 equal groups or folds so that 10 iterations of training and
validation could be performed. Within each of the 10 iterations, nine folds (i.e., the training
set) were combined to be used to derive regression coefficients that defined the prediction
equations while the remaining one fold of the data was removed to be used for validation as
the test set. For example, for the very large sample, the overall sample of n = 3,400 was
randomly grouped into 340 participants per fold. The first to ninth folds were combined to
form the first training set (n = 3,060), and the tenth fold was used as the test set (n = 340).
The second training set comprised the first to eighth folds as well as the tenth fold, and the
ninth fold was used as the test set. This procedure was repeated for the remaining eight
iterations, and the entire procedure was also used for the remaining three smaller sample sizes
(i.e., n = 50, 150 and 300). For these smaller sample sizes, each was a random subsample
from the training sets of the very large sample such that the test sets that the equations were compared on were always the same. 88
Following this, the training sets were used to determine the prediction equations with
one prediction equation for each of the four models at each of the four sample sizes in each of
the ten iterations. Each equation was used to generate predicted leadership scores for its
corresponding test set which had not been used as part of the derivation of the equation being
tested. Cross-validity coefficients (CVR) and root mean square errors (RMSE) of residuals
were then calculated by comparing predicted scores on the test set with actual scores on the
test set. CVR and RMSE scores are indices typically used to evaluate the predictive accuracy
of prediction equations derived from training sets (Minbashian, Bright, & Bird, 2010). A
CVR score refers to the correlation between predicted scores and actual scores in the test set.
An RMSE score is derived by squaring the difference between each of the predicted and
actual scores, and then calculating the square root of the mean of these differences. Lower
RMSE values indicate that predicted scores are closer to the observed data and, thus, that the
prediction is better (Ivanescu et al., 2016). In sum, higher CVR and lower RMSE scores
indicate better performance of the derived prediction equation from the training set within each fold and therefore greater predictive validity of the set of variables that comprise the prediction equation.
One-way repeated measures ANOVA was used to determine whether the differences in CVR and RMSE scores between personality levels in each of the four sample sizes were statistically significant. Post hoc tests were conducted with Least Squared Differences. The confidence interval adjustment and relevant corrections were reported for each test when the assumption of sphericity was violated. The Greenhouse–Geisser correction was applied when
Epsilon values in Mauchly’s Test of Sphericity were less than .75. To allow for significance 89
testing of differences between correlations, the Fisher z-transformation was used to transform
26 the sampling distribution of correlation values to become normally distributed25F .
Figure 3.2 and Figure 3.3 display the mean RMSE and CVR scores across folds,
respectively (see Appendix B for scores by fold in Table B 6). Table 3.11 presents results
from the repeated measures ANOVA based on transformed CVR and RMSE scores, and
Table 3.12 displays the corresponding post hoc pairwise comparison tests between each
personality level by sample size.
8.00 GFP Big 2 Big 5 Facets
7.50 7.23
7.00
6.50 RMSE
6.00
5.56 5.54 5.43 5.50 5.34 5.37 5.39 5.29 5.29 5.27 5.26 5.3 5.26 5.25 5.25 5.2
5.00 n = 50 n = 150 n = 300 n = 3,060
Figure 3.2
Mean RMSE scores from the 10-fold cross-validation across different sample sizes and
personality levels.
26 The repeated measures ANOVA was also conducted on untransformed r values and the same pattern of findings emerged. 90
0.30 GFP Big 2 Big 5 Facets
0.25
0.21 0.20 0.17 0.16 0.16 0.15 0.150.15 0.15 0.15 0.15 0.14 0.14
CVR 0.12 0.11 0.11 0.10 0.10 0.09
0.05
0.00 n = 50 n = 150 n = 300 n = 3,060
Figure 3.3
Mean CVR scores from the 10-fold cross-validation across different sample sizes and personality levels.
Table 3.11
One-way Repeated Measures ANOVA of CVR and RMSE Scores by Sample Size
zCVR RMSE Sample Size F df df error F df df error n = 50 3.68* 3.00 27.00 36.07*** 1.07 9.62 n = 150 3.83 1.23 11.07 22.95*** 1.31 11.79 n = 300 2.34 3.00 27.00 10.36** 1.37 12.29 n = 3,060 23.00*** 3.00 27.00 13.98** 1.37 12.32 Note: *p < .05; **p < .01; ***p < .001.
91
Table 3.12
Post Hoc Pairwise Comparison Tests of CVR and RMSE Scores between Each Personality
Level by Sample Size
z RMSE Pair CVR Mean Difference Standard Error Mean Difference Standard Error n = 50 GFP – Big 2 0.04 0.02 -0.09* 0.04 GFP – Big 5 0.05* 0.02 -0.23** 0.05 GFP – Facets 0.06* 0.02 -1.89*** 0.29 Big 2 – Big 5 0.01 0.02 -0.13* 0.05 Big 2 – Facets 0.03 0.02 -1.80*** 0.30 Big 5 – Facets 0.02 0.02 -1.67*** 0.29
n = 150 GFP – Big 2 NA 0.00 0.00 GFP – Big 5 -0.08** 0.02 GFP – Facets -0.26*** 0.04 Big 2 – Big 5 -0.08** 0.02 Big 2 – Facets -0.25*** 0.04 Big 5 – Facets -0.17** 0.05
n = 300 GFP – Big 2 NA 0.01 0.01 GFP – Big 5 -0.03 0.02 GFP – Facets -0.12** 0.03 Big 2 – Big 5 -0.04* 0.01 Big 2 – Facets -0.10** 0.03 Big 5 – Facets -0.09* 0.04
n = 3,060 GFP – Big 2 -0.01 0.01 0.01 0.00 GFP – Big 5 -0.02** 0.00 0.01* 0.00 GFP – Facets -0.06*** 0.01 0.06** 0.01 Big 2 – Big 5 -0.01 0.01 0.00 0.01 Big 2 – Facets -0.05*** 0.01 0.05** 0.01 Big 5 – Facets -0.05** 0.01 0.05** 0.01 Note: *p < .05; **p < .01; ***p < .001.
For the very large sample size (n = 3,060), the facets had the highest CVR scores
(rFacets = .21 versus rGFP = .15) and the lowest RMSE scores (RMSEFacets = 5.20 versus
RMSEGFP = 5.26). The repeated measures ANOVA was statistically significant for both the
CVR and RMSE scores (F[3.00, 27.00] = 23.00, p < .001 and F[1.37, 12.32] = 13.98, p < .01, respectively). In particular, the pairwise comparisons showed that the facets had higher CVR 92
and lower RMSE scores than each of the other three personality levels. However, at the large
sample size (n = 300), CVR scores were similar across personality levels (r = .14 to r = .16)
such that the repeated measures ANOVA was not significant. RMSE scores, however, were
lower for higher-order factors than for the Big Five and facets (e.g., RMSEGFP = 5.27 versus
RMSEFacets = 5.39), indicating that the predictions were more accurate. In support of this
trend, the repeated measures ANOVA for RMSE scores was statistically significant F[1.37,
12.29] = 10.36, p < .01), and the pairwise comparisons showed statistically significant
differences between the GFP and facet level, and also between the Big Two and both the Big
Five and facet levels.
At the medium sample size (n = 150), the CVR values were higher and RMSE scores
were lower for higher-order personality factors than the Big Five and facets; the CVR and
RMSE scores were the same for the GFP and Big Two (i.e., r = .15 and RMSE = 5.29). This
pattern of results was supported by the pairwise comparisons for the RMSE scores (F[1.31,
11.79] = 22.95, p < .001), however, the repeated measures ANOVA for CVR scores was not
statistically significant for this sample size. A similar yet clearer pattern emerged at the
smallest sample size (n = 50) such that CVR values increased and RMSE scores decreased as
personality factors became broader (e.g., rGFP = .15 versus rFacets = .09). The repeated
measures ANOVA for both CVR and RMSE scores were significant (F[3.00, 27.00] = 3.68,
p < .05 and F[1.07, 9.62] = 36.07, p < .001, respectively), and the pairwise comparisons for
the RMSE scores supported this trend such that all tests between every pair of personality
levels were statistically significant; the pairwise comparisons were also statistically
significant for CVR scores between the GFP and both the Big Five and facet levels.
Taken together, and in support of Hypothesis 5, the results indicated that the higher- order factors may have greater validity for predicting leadership at smaller sample sizes compared to lower-order factors (including both facets and the Big Five). Specifically, at the 93
smallest sample size examined (i.e., n = 50), the predictive validity of the GFP was higher
than both the facets and the Big Five. Based on the RMSE results, there also appeared to be a
trend whereby the predictive validity decreased as one moved down each level of the
personality hierarchy (i.e., from the GFP to facets). Although slightly less clear, the two
intermediate sample sizes examined (i.e., n = 150 and 300) followed a similar trend (again,
especially based on the RMSE results). In particular, both the GFP and Big Two were found
to have higher predictive validity than the facets. However, at the largest sample size
examined (i.e., n = 3,060), the predictive validity of the facets was higher than each of the
three broader personality levels and the predictive validity of the Big Five was also higher
than the GFP at this sample size
3.4 Conclusion
The aims of this study were to compare the effects of higher-order factors and facets
on leadership, to examine the potential presence of complex relationships, and to investigate
practical considerations for different extraction approaches and the predictive validity of
higher-order versus lower-order factors at smaller sample sizes.
The study found that the higher-order factors, specifically the GFP and Stability, were
generally more strongly related to leadership compared to most individual Big Five factors
(which is consistent with the meta-analytic findings in Chapter 2) and facets. Additionally,
more than half of the facets were found to be unrelated to leadership or to have effect sizes
that were not practically meaningful. However, the set of facets did appear to account for
more variance in leadership than the higher-order factors suggesting that some of the variance
in narrower facets that is potentially meaningful for predicting leadership may be lost when
facets are aggregated to derive higher-order factors. Moreover, the significant facet effects were small in magnitude, and Hypothesis 2 from Chapter 2 was less clearly supported in 94 terms of the incremental prediction of Conscientiousness for leadership over and above the other four Big Five factors.
Contrary to Hypothesis 4, the results did not provide evidence for any curvilinear nor interaction effects between the Big Two and leadership. However, as expected, higher-order factors extracted with different approaches (i.e., by applying unit-weights, factor loading weights based on facets and factor loading weights based on items) were highly intercorrelated and were consistently related to leadership. Lastly, based on the cross- validation results at the smallest sample size (n = 50), higher-order factors were shown to be more predictive of leadership than lower-order factors in support of Hypothesis 5.
While the study in Chapter 2 examined higher-order factors extracted from the Big
Five and the present chapter examined a measure that does not explicitly assess the Big Five, they still both conceptualised personality traits in the same way. That is, traits across both measures were still aligned to the definition presented in Chapter 1. They are underlying cognitive or affective dispositions that pervasively influence behavioural tendencies, and are thought of in terms of a continuum. However, there are personality inventories that do not conceptualise nor even operationalise personality traits in the same way as the Big Five and the CPI. The next chapter investigates whether the Myers-Briggs Type Indicator, one of the most widely used personality inventories, contains the higher-order factors of the GFP and
Big Two factors, particularly as its theoretical basis is distinct from traits.
95
Chapter 4: An Exploration of the Higher-order Factors of the MBTI and their
Relationship with Leadership
4.1 Introduction
In the preceding two studies, the GFP and Big Two were extracted in line with past
studies from measures of the Big Five and the CPI. Aside from differences in breadth, both
the Big Five and CPI conceptualise personality in a similar manner and are consistent with
the definition of a trait presented in Chapter 1. Specifically, traits are dispositional tendencies that influence behaviour and exist on a continuum of values. Alternatively, another major model conceptualises personality in terms of types. The type approach theorises that individual differences can be thought of as discrete categories of personality characteristics that do not vary in quantity, and that these categories can combine to form interactive and complex personality groupings (Myers et al., 2009; Vincent, Ward, & Denson, 2013).
Possibly the most well-known type measure is the Myers-Briggs Type Indicator (MBTI;
Myers, McCaulley, & Most, 1985), which claims to be the most widely administered personality inventory in the world (Myers et al., 2009). Given the MBTI’s popularity, and in line with one of the overarching aims of this thesis to examine the effects of higher-order factors from non-explicit Big Five measures, this study examines whether the higher-order factors of the MBTI resemble the GFP and Big Two, and the extent to which these factors are related to effective leadership.
These research questions stem from the substantial criticisms the MBTI has received from both the academic literature (e.g., Gardner & Martinko, 1996; Moyle & Hackston,
2018) and the popular press (Grant, 2013; Paul, 2010), and yet it remains one of, if not, the most widely used personality measures in practical settings (Bayne, 2003; Furnham, 2008,
2017). For example, in one study, a survey of 255 practitioners who were responsible for personnel selection in their organisation including organisational psychologists, human 96 resource specialists and consultants found that the MBTI was the most frequently used test out of 21 personality and motivational measures commonly used in the workplace (Furnham,
2008). The study also found that the MBTI was ranked fourth in terms of perceived validity, nineteenth for utility for selection and first for utility for development. The MBTI has reportedly been used over two million times a year (Myers et al., 2009), three million times a year (Myers, McCaulley, Quenk, & Hammer, 2003) or up to five million times a year
(Furnham, 2017). Despite its popularity, there have been calls for a fundamental reconceptualisation of its factor structure (Barbuto, 1997; McCrae & Costa, 1989; Saggino &
Kline, 1996), and there have been mixed findings for its criterion-related validity, particularly for leadership (e.g., Berr, Church, & Waclawski, 2000; Brown & Reilly, 2009; Furnham &
Stringfield, 1993).
One possible avenue for reconceptualising the MBTI, and therefore potentially increasing its predictive validity for leadership, is in terms of higher-order factors.
Consequently, the first aim of this study is to determine if the higher-order factors from the
MBTI resemble the GFP, Stability and Plasticity. From a theoretical perspective, whether these higher-order factors are found within the MBTI has implications for the interpretation of the nature of the GFP and the Big Two, since personality types are conceptually different from traits. As argued below, the MBTI contains less social content and focuses more on preferences rather than the more functional elements of personality traits. Thus, if the interpretation of the GFP and Big Two as largely socially-oriented constructs that are functional in nature is true, then they should be less likely to be present in the MBTI.
However, if they do emerge strongly in the MBTI, then this could support an alternative interpretation for their nature (e.g., a statistical artefact of self-reports). In addition, since the constructs that the MBTI encompasses are assumed to be theoretically independent (Myers et al., 2009), the presence of higher-order factors would challenge the notion that the MBTI 97
constructs represent the highest level of breadth for personality types. A more general
contribution of this study is that, to my knowledge, it is the first to specifically attempt to
extract the GFP and Big Two from the MBTI scales based on a sample of leaders and
working adults.
The second aim of the present study is to determine whether the higher-order factors
from the MBTI are related to effective leadership, and to compare their effects with the conventional operationalisations of the MBTI. In terms of theoretical contributions, the first relates to providing indirect support for whether these broader factors might actually represent the GFP and Big Two constructs. This thesis argues that leadership is an inherently social construct and, therefore, is more directly contingent on socially-oriented personality characteristics. If the MBTI’s higher-order factors are shown to relate to leadership then this would be consistent with the notion that they resemble the GFP and Big Two. On the other hand, if they are unrelated, then this would be evidence against the idea that they resemble the GFP and Big Two. Second, the presence of such a relationship would also contribute to the argument that only a few broad personality factors are needed to account for leadership as opposed to the many personality types that the MBTI encapsulates. Similar to the studies in the last two chapters, this would support the notion that the commonality between personality constructs, even when conceptualised as types, may be what is needed for effective leadership rather than the unique effects of personality types. Third, the opposite poles of
each of the MBTI scales are theoretically assumed to be equally valuable such that the MTBI
authors warn users against adopting a ‘trait perspective’ where certain poles of scales are
usually deemed more beneficial than others for various criteria (Myers et al., 2009).
However, if the higher-order factors extracted from the MBTI are shown to relate to effective
leadership, this would indicate that at least parts of the type model are more valuable than
others. 98
Taken together, this chapter builds on the previous two by determining if the specific higher-order factors of the GFP and Big Two are also found in a type measure of personality
(since they have been predominantly extracted from trait measures) and to understand whether the extracted factors improve the predictive validity of the MBTI for leadership.
Additionally, in line with the proposition that personality categories can combine to form complex and interactive personality groupings, a secondary aim is to examine whether the
MBTI has additive or interaction effects for leadership on an exploratory basis as described below.
The following sections first clarify the definition of a type and how the term is used in the context of the MBTI. Second, the development and theory behind the MBTI and its main criticisms are summarised. Next, evidence on the potential existence of the higher-order factors of the GFP and the Big Two in the MBTI is presented, followed by a summary of the literature on how the MBTI relates to leadership.
4.1.1 Definition of Type in the MBTI
A type generally refers to the categorisation or assignment of an individual to a personality characteristic that does not vary in magnitude. Types can refer to either single- variable or multiple-variable typologies (Hicks, 1984; Michael, 2003; Myers et al., 2009).
The MBTI measures both. It has four type dichotomies that each contain a pair of opposing personality preferences (i.e., eight single preference types in total), and 16 multiple-variable type dynamics that are derived from all of the possible configurations of the preferences (i.e.,
2 2 2 2 preferences). In addition, the MBTI also includes a ‘Preference Clarity Index’, which is a continuous measure of how clearly an individual prefers a single-variable type within a dichotomy (as opposed to how much of that type a person has). The following naming conventions are used: type preference refers to the single-variable types, type clarity refers to the continuous ‘Preference Clarity Index’ for each variable, and type dynamic refers 99
to the 16 multiple-variable type configurations. The use of the term types on its own
encompasses both type preferences and type dynamics.
4.1.2 Origins and Theory of the MBTI
First developed in 1942, the MBTI purports to stem from assumptions within Jung’s
theory of psychological type preferences (Jung, 1971; Myers et al., 2009) that individuals
have distinct personality preferences which are dichotomised into two equally useful poles
(i.e., type preferences), and that preferences combine to form unique type dynamics. Jung’s
observations of people (based on anecdotes and personal experiences) originally led him to
conclude that two types of individuals exist, that is, extraverts and introverts (Jung, 1971).
Extraverts direct their energy externally to other people and situations whereas introverts
orient their energy internally towards inner thoughts and experiences. Jung subsequently
noted that people within both the extravert and introvert type preferences showed more
nuanced variability, leading him to establish an additional two dichotomies that further
subdivided the extravert and introvert groups into eight type preferences. Specifically, the
additional two dichotomies related to opposing mental functions: a perceiving function (i.e.,
how one becomes aware of things including ideas, people or events) comprising sensation versus intuition; and a judging function (i.e., how one draws conclusions from what is perceived) comprising thinking versus feeling.
Although Jung only conceptualised the above perceiving and judging functions as overarching classifications for sensing–intuition and thinking–feeling, respectively, the
MBTI incorporated the dichotomy of perceiving and judging as additional scales. These scales were included to help determine which functions are dominant or auxiliary, but also to
represent a fourth set of type preferences. Thus, the MBTI includes four dichotomies that
make up the type preferences of Extraversion–Introversion (EI), Sensing–Intuition (SN),
Thinking–Feeling (TF) and Judging–Perceiving (JP), each of which can also be measured 100
with the continuous type clarity scores. The right-hand letter in each scale represents the
positive pole, and this thesis follows this convention.
The MBTI test publisher (Myers et al., 2009) defines each of the type preferences as
follows, excluding Extraversion and Introversion as they are defined above. Perceiving
includes gathering information, seeking sensations and selecting stimuli to focus on whereas judging relates to evaluation, making decisions and choosing a response after a stimulus has
been observed. The perceiving categories of sensing and intuition are thought to be irrational
functions in the sense that there is a free-flowing experience of sensations. People with a
preference for sensing perceive experiences that are more proximal to their five senses (i.e.,
they live in the present moment and prefer being realistic) whereas people with a preference
for intuition perceive alternative future possibilities (i.e., they prefer abstract, imaginative,
theoretical or original thinking). Conversely, the judging categories of thinking and feeling
concern more rational functions in the sense that what is perceived is evaluated or appraised
either by reason or values-based beliefs. A preference for thinking primarily relates to being
logical, objective and impersonal when applying reason to decisions. Alternatively, feeling is
more subjective such that these individuals are more sensitive to their own as well as others’
feelings and values, and are predisposed to being warm, affiliative and harmonious.
4.1.2.1 The Primary Criticisms of the MBTI
Following its development, the MBTI has been critiqued in terms of the theory it is
based on, the operationalisation of that theory and its psychometric properties. First, a central
tenet from Jung’s theory concerns one’s unconscious (particularly for introverts given their
focus on the inner world), which is difficult to assess via self-report measures (McCrae &
Costa, 1989), Second, the claim that extraverts derive energy from socialising whereas
introverts derive energy from internal reflection is not supported by research (Fleeson,
Malanos, & Achille, 2002; Pavot, Diener, & Fujita, 1990). Third, the scale measuring judging 101
and perceiving attitudes was incorporated to determine the order of using preferences despite
Jung’s theory not incorporating this distinction (Michael, 2003). Fourth, both Jung’s theory
and the MBTI do not explicitly include emotionally stable (or conversely, neurotic) traits
(McCrae & Costa, 1989), which is a fundamental dimension of personality (Furnham, 1996).
Fifth, the argument that personality can be represented by dichotomised type preferences
lacks scientific evidence, described next.
If the MBTI actually contained mutually exclusive groups of people with opposite
preferences within a dichotomy then a bimodal distribution would emerge for each of the
MBTI scales. However, this view is not supported by statistical evidence (Arnau et al., 2003;
Girelli & Stake, 1993; Stricker & Ross, 1964). Instead, researchers have argued that not all
individuals are habitually predisposed to act in a singular way within a dichotomy (Garden,
1991; Michael, 2003). Furthermore, the categorical nature of the MBTI with respect to type
preferences (and type dynamics) has also resulted in poor test–retest reliabilities. However, it
should be noted that when the continuous type clarity scores are considered then their
reliabilities tend to meet acceptable standards (Moyle & Hackston, 2018).
The proposed uniqueness and interactive effects of the MBTI’s type dynamics have also been questioned. Since the conceptualisation of personality types also encompasses configurations of different type preferences, the type dynamics need to demonstrate interactive (and not just main) effects with other criteria (Hicks, 1984; Pittenger, 1993).
However, research has found limited support for the MBTI’s ability to classify individuals into 16 qualitatively different type dynamics based on the absence of interaction effects that are needed to support this claim (e.g., Hicks, 1984; McCrae & Costa Jr, 1989; Stricker &
Ross, 1964). Moreover, in a sample of leadership program participants, no MBTI interaction effects were found for the criterion examined (i.e., ego development, defined as the propensity to create meaning across the lifespan; Vincent et al., 2013). Thus, as a secondary 102 aim of this study, the potential existence of additive and interaction effects for MBTI type dynamics with leadership is examined on an exploratory basis.
Lastly, the construct, convergent and discriminant validity of the MBTI has also been questioned. Saggino and Kline (1996) undertook a factor analysis with the MBTI scales along with scales from the Sixteen Personality Factors (Cattell et al., 1970) and Eysenck
Personality Questionnaire (Eysenck, 1975). Although the EI scale appeared to more clearly reflect a construct related to extraversion, the SN and JP scales overlapped with one another
(seemingly measuring the same construct) and the TF scale appeared to load on multiple factors related to extraversion and anxiety (and therefore the scale was deemed conceptually impure). Saggino and Kline (1996) argued that the MBTI is an invalid measure since it did not measure Jung’s types and instead its scales appeared to more closely resemble Eysenck’s three-factor model (Eysenck, 1991).
The finding that the SN and JP scales overlap is further supported by their typically strong correlation coefficient (r = .47 based on a normative dataset of N = 3,036; Myers et al.,
2009). Others have argued that both scales may actually be a measure of impulsivity (e.g., making quick decisions and giving up when faced with a challenge) rather than simply becoming aware of one’s environment as per the definition of JP (Sipps & DiCaudo, 1988).
4.1.3 The Potential Emergence and Nature of Higher-order Factors from the MBTI
Some of the above criticisms have implications for the nature of higher-order factors extracted from the MBTI. For example, the absence of an explicit personality construct related to emotional stability in the MBTI may impact the ability to extract higher-order factors since Neuroticism has been found to be the highest loading Big Five factor for the
GFP (e.g., Veselka, Schermer, Petrides, & Vernon, 2009). Conversely, the addition of the JP scale to the MBTI may actually contribute to finding higher-order factors within the inventory since those scales are defined in relation to appraising situations and choosing a 103 response. This is somewhat similar to the evaluative nature of the GFP where individuals are proposed to know what to do and how to competently respond to various social situations.
Similarly, higher-order factors may also be extracted from the MBTI given the strong overlap between factors (i.e., SN and JP) as well as the lack of evidence supporting the unique differences between the type dynamics. These findings suggest that at least some elements of the MBTI type preferences are not as independent as they are theoretically assumed to be such that there may be common elements that give rise to broader constructs.
In addition to these criticisms, there are a number of other considerations for whether or not higher-order factors can be extracted from the MBTI including past factor analytic results of the MBTI’s items, research examining the relationship between the MBTI scales and the Big Five factors, and differences in the conceptualisation and nature of the MBTI’s type preferences as distinct from traits. These considerations are discussed below.
4.1.3.1 Factor Analysis of the MBTI Items
Only two previous studies (to my knowledge) have sought to extract higher-order factors from the MBTI (Johnson, Johnson, Murphy, Weiss, & Zimmerman, 1998; Johnson,
Mauzey, Johnson, Murphy, & Zimmerman, 2001). Both studies examined the higher-order factor structure by first factor analysing items (retaining only components that had eigenvalues of greater than one and supported by scree plot patterns) and then factor analysing subsequent intercomponent correlation matrices until extracted factors no longer correlated. The first study (Johnson et al., 1998) of 486 university students found three higher-order factors consisting of “a generalised factor represented essentially equally in number by the four MBTI scales” (p. 828), a factor related to organisation (i.e., planning, scheduling and structure) and a factor related to attention (i.e., sensation and imagination).
The second study (Johnson et al., 2001) was intended as a follow up and included a larger sample of 926 undergraduate students. However, this study found only two higher-order 104
factors comprising a factor that related to preferring adaptability, flexibility, versatility and
analytical thinking, and to sociability and activity (including initiating, creating and
organising). On the difference in the number of higher-order factors found, the authors of the more recent study (Johnson et al., 2001) asserted that the first extracted factors from both studies were equivalent (and represented a generalised personality factor), and that the second factor was a combination of the second and third factors from the initial study since the themes were conceptually similar.
Based on the above findings alone, it is not entirely clear whether the GFP and Big
Two factors actually emerge from the MBTI, particularly when extracted from items.
Although the GFP has gained more attention in the last decade, the Big Two factors were first identified (Digman, 1997) prior to the publication of both studies above (Johnston et al.,
1998, 2001), neither of which described the extracted higher-order factors as being reflective of Plasticity nor Stability (or at least factors conceptually related to these). The highest-order factor extracted from both studies may at first appear to reflect the GFP. However, Johnson and colleagues’ interpretation of this general factor as flexible and thinking of others is not entirely aligned to the full essence of the GFP of social competence. Moreover, the higher- order factor that Johnson and colleagues labelled organisation initially seemed to relate to
Stability given the focus on being structured and planful. However, as noted in the follow up study (Johnson et al., 2001), the factor also included themes related to initiating and creating that are more consistent with Plasticity.
4.1.3.2 Relationships between MBTI Scales and the Big Five
Past research linking the MBTI and the Big Five also has implications for whether the
GFP and Big Two are likely to emerge in the MBTI. One prominent recommendation for reconceptualising the MBTI and abandoning Jung’s theory has been to reinterpret the personality preferences in terms of the Big Five (McCrae & Costa, 1989). McCrae and Costa 105 found that each of the MBTI scales had statistically significant and non-trivial correlations with at least one of the Big Five factors. Across self-reported and peer-rated Big Five factor correlations with MBTI scales (with results separated by women and men), there were four strong links: between Extraversion and EI from the MBTI (r = -.34 to -.74; the correlations were negative given the right-hand letter of MBTI scales represents the positive pole, i.e.,
Introversion for EI); between Openness to Experience and SN (r = .41 to .72); between
Agreeableness and TF (r = .25 to .46); and between Conscientiousness and JP (r = -.34 to -
.49). This pattern of relationships was found based on a community sample, and has subsequently been replicated with samples of students (MacDonald, Anderson, Tsagarakis, &
Holland, 1994) and working adults (Furnham, 1996).
Based on these relationships, it is possible that the GFP and Big Two may also emerge from the MBTI scales given the ability to extract these higher-order factors from the
Big Five, in line with procedures outlined in the preceding chapters. Specifically, EI (reverse- scored) and SN are expected to load on a first-order factor resembling Plasticity, since this higher-order factor comprises Extraversion and Openness to Experience from the Big Five;
TF and JP (reverse-scored) are expected to load on a first-order factor resembling Stability, since this higher-order factor comprises Agreeableness and Conscientiousness from the Big
Five; and all four MBTI scales are expected to load on a one-factor solution resembling the
GFP. However, given the strong overlap between the SN and JP scales (Saggino & Kline,
1996), as described above, it is possible that a separate higher-order factor may emerge instead representing impulsivity (Sipps & DiCaudo, 1988). Moreover, McCrae and Costa
(1989) found that Neuroticism was not highly correlated with any of the MBTI scales, which is an important construct for extracting higher-order factors. 106
4.1.3.3 Conceptualisation and Nature of MBTI Type Preferences and Clarity
Conceptual differences between traits and both type preferences and type clarity may
also affect whether the GFP and Big Two can be extracted from the MBTI. The nature of
type preferences seems somewhat less functional, strategic and motivational compared to the
major trait models investigated in Chapter 2 and Chapter 3. As described above, the theory
underpinning the MBTI pertains to how people prefer to perceive and judge what is seen.
That is, it was designed to measure a preference for how one experiences the world or
becomes aware of information, which appears somewhat innocuous, to lack strategic intent
and to not contain explicitly social motivations. However, as argued in Chapter 2 traits as
assessed via the Big Five are more strategic in nature, helping people to navigate through the social world and to influence those in their social environment (Buss, 1992). Consequently, the GFP and Big Two may not emerge from the MBTI since these higher-order factors are more likely to be strategic in nature. Likewise, the continuous type clarity scores in the MBTI
were designed only to represent how clearly an individual aligns to a preference as opposed
to the amount, strength or ability of that preference (Myers et al., 2009). As such, higher- order factors extracted from the MBTI type clarity scores may merely represent a general
construct of how comfortable and confident someone is in rating their personality instead of
something more functional.
In addition, while some trait-based measures are rooted in theories related to social
dynamics and motivations, the majority of the MBTI type preferences do not appear to
contain social content, which is fundamental to the nature of the GFP and Big Two. For example, the trait-based California Psychological Inventory used in Chapter 3 and Hogan
Personality Inventory are argued to measure social effectiveness and interpersonal characteristics (Dunkel, van der Linden, et al., 2014) and propensities to get along with
people in order to succeed in life and work (Hogan & Holland, 2003), respectively. On the 107
other hand, only two of the MBTI type preferences appear to contain some social content
such as orienting energy towards people (i.e., Extraversion) and being attuned to the
emotions of others (i.e., Feeling). However, even the MBTI manual specifically asserts that
the EI scale is different from personality constructs like sociability in that the former is
focused more on whether energy is outwardly or inwardly directed (Myers et al., 2009). The
remaining six type preferences do not seem related to social characteristics: Introversion
relates to inner experiences and reflection, Sensing focuses on realistic and objective
thoughts, Intuition involves contemplating the future, Thinking pertains to logic and
analysing thoughts, Judging concerns planning operations and organising activities, and
Perceiving involves being open to new ideas. Thus, while the MBTI appears to include some
social content, there may not be a sufficient amount (given its general focus on how people
prefer to experience and attend to information) to give rise to broader socially-oriented
constructs such as the GFP and Big Two.
In summary, past factor analyses of the MBTI items suggest that higher-order factors
can be extracted but may not be entirely representative of the specific nature of the GFP and
Big Two. Studies demonstrating the non-trivial links between the MBTI scales and the Big
Five support the possible extraction of the GFP and Big Two. Lastly, the conceptualisation of what the MBTI broadly measures, including the nature of each of its type preferences, provides limited support. Therefore, this study aims to determine whether higher-order factors extracted from the MBTI’s items and scales resemble the GFP, Stability and Plasticity on an exploratory basis.
4.1.4 The MBTI and Leadership
As argued above, examining the relationship between the higher-order factors of the
MBTI and leadership can help provide indirect evidence for whether or not these factors represent the GFP and Big Two (given the social connection between these constructs and 108
leadership) and explore whether the predictive validity of the MBTI can be improved for
leadership beyond how it traditionally operationalises personality. Given the widespread use
of the MBTI among leaders, a number of studies have been undertaken to examine its
usefulness for leadership. It is important to note that the test publisher does state that it is
inappropriate to use the MBTI for job selection or advancement decisions (Myers et al.,
2009). However, studies have still been undertaken to examine its ability to predict important
workplace phenomena and, arguably, it should still be subject to this scrutiny given its wide-
scale administration amongst leaders as well as the general working population. Not only has
research continued to examine its efficacy for selection (e.g., Furnham, 2008), but there are
also suggestions that it is used to inform promotion decisions and job assignment allocations
(Pittenger, 1993). In addition, it has also been recommended for use in career counselling
settings to help people uncover the kinds of careers they may be suited to (Myers et al.,
2009). Thus, it is important that the MBTI can actually predict real-world, organisational
outcomes if it is to be used for such purposes.
Much research on the MBTI’s criterion-related validity for general workplace
outcomes has typically found sparse to no relationships. For example, there were no
statistically significant correlations with team effectiveness criteria (Varvel, Adams, Pridie, &
Ruiz Ulloa, 2004), it did not predict assessment centre skill ratings for prospective principals
(Wendel, Kilgore, & Spurzem, 1991), and no direct or moderator effects were found for job satisfaction (Thomas, Buboltz, & Winkelspecht, 2004). In terms of expected relationships with phenomena for specific scales, SN did not relate to managerial cognitive styles as hypothesised (Schweiger, 1985) but did modestly relate to overall emotional intelligence, although the TF scale did not (Higgs, 2001).
In the domain of leadership behaviour and effectiveness, the findings have been somewhat less trivial but still mixed. One comprehensive review of the MBTI literature 109
inspected studies that included managers as subjects or that studied issues related to
management (Gardner & Martinko, 1996). On leadership effectiveness, it concluded that
managers (particularly executives) were likely to prefer thinking and judgement over feeling
and perceiving compared to the general population, and that extraverted managers were
assertive in general settings as well as in resolving conflicts. A number of other studies were
also included in the review. However, because of the broad inclusion criteria many of the
dependent variables (e.g., betting decisions, project adoption, information usage) were not
specifically related to leadership or management outcomes. Moreover, Gardner and Martinko
(1996) argued that much of the reviewed literature, especially the studies designated as non-
experimental in nature, was of poor quality and had likely undermined the MBTI’s reputation
and generated much of the cynicism surrounding it.
In studies that have examined the statistical relationship between the continuous type
clarity scores and other ratings of leadership, some found no relationships (e.g., Berr et al.,
2000; Brown & Reilly, 2009) while others inconsistently found small to moderate effects for
specific scales such as EI (e.g., r = -.29; Fransen, Haslam, Steffens, & Boen, 2020), SN (e.g.,
r = .21; Church & Waclawski, 1998) and TF (e.g., r = .19; Bergner, Davda, Culpin, &
Rybnicek, 2016). Although the significant finding for EI is consistent with past research that has shown that extraverted personality characteristics are predictive of leadership (e.g., Bono
& Judge, 2004; Do & Minbashian, 2014), the finding for TF contradicts the conclusion drawn from Gardner and Martinko’s (1996) review of the managerial literature as described above.
Thus, given the mixed findings in the literature, this study also undertook a preliminary quantitative review of studies that have examined the relationship between all four MBTI type clarity scales with leadership criteria. Based on the data collected from this quantitative review as well as from a separate dataset that was obtained, the predictive validity of the MBTI’s higher-order factors for leadership was also investigated and 110
compared to the conventional operationalisations of the MBTI on type clarity, type
preferences and type dynamics. If the higher-order factors do in fact represent the GFP and
Big Two, then, based on the findings from the previous two studies in this thesis, the
predictive validity of the information captured by the MBTI may be higher than suggested by
past studies that have examined its criterion validity and relied on the conventional operationalisations.
4.2 Method
4.2.1 Datasets and Procedure
This study examined whether the higher-order factors of the MBTI resemble the GFP
and Big Two, and the extent to which these factors are related to effective leadership. Two
different sources of data were used to address these research questions. The first was collated
by undertaking a quantitative review of the literature for studies that reported correlations
between the MBTI scales and leadership. Google Scholar’s database was searched for all date
ranges up to August 2020. For search terms, the inventory name and all scales (i.e., MBTI,
Extraversion, Introversion, Sensing, Intuition, Thinking, Feeling, Judging and Perceiving)
were simultaneously entered in combination with either the term Leadership, Leader,
Management or Manager, resulting in approximately 2,430, 1,460, 3,200 and 1,410 search
results respectively. Studies were retained based on the following criteria: (1) all correlations
between each of the four MBTI scales and the leadership criterion were available, (2)
participants were working adults, not a student or laboratory-specific sample, (3) personality
was self-rated, and (4) leadership was rated by others. Eight studies met the inclusion criteria
and are summarised in Table 4.1, with the journal in which they were published, the sample
size and type of participants, the MBTI form and the leadership measure used in the study.
Unpublished studies were also examined for suitability but none met the inclusion criteria
above. 111
Table 4.1
MBTI Studies Investigating Effects on Leadership
Authors Journal Sample Form Leadership Measure Bergner, Davda, Journal of Leadership & 196 senior leaders N.S. Leadership Effectiveness Culpin, & Rybnicek Organizational Studies Ratings (2016) Berr, Church, & Human Resource 343 senior level N.S. Management Practices Waclawski (2000) Development Quarterly managers Questionnaire (Customised) Brown & Reilly Journal of Management 148 managers, K Multifactor Leadership (2009) Development supervisors, engineers Questionnaire and professional staff Carless & Allwood Australian Psychologist 875 employees identified G Assessment Centre (1997) as having middle- Ratings of Managerial management potential Competencies Church & Journal of Occupational 253 executives, vice N.S. Leadership Assessment Waclawski (1998) and Organizational presidents and senior Inventory Psychology level managers Conway (2000) Human Performance 1,567 managers of F Leadership and varying levels Development Ratings Fransen, Haslam, Scandinavian Journal of 384 athletes N.S. Motivational Leader Steffens, & Boen Medicine & Science in Effectiveness Ratings (2020) Sports Zaccaro, Mumford, The Leadership Quarterly 894 army officers N.S. Leadership Social Connelly, Marks, & Judgement Ratings Gilbert (2000) Note: N.S. = Not specified.
Only three of the eight studies (Bergner et al., 2016; Berr et al., 2000; Church &
Waclawski, 1998) contained intercorrelations between the four MBTI personality scales. As correlations between these scales are required to conduct regression analyses and derive the higher-order factors, the intercorrelation matrix from the MBTI user manual (Myers et al.,
2009; N = 3,036) was used to estimate the intercorrelations between the four MBTI personality scales for the five studies that did not report this data.
Second, to conduct item-level analyses and cross-validation analyses (which require access to the raw data), a dataset containing MBTI and leadership scores was obtained from the Center for Creative Leadership (CCL; n = 400; 81 women and 319 men; Mage = 48.41,
SDage = 7.12; age range: 29–69). Most participants were executive level leaders (n = 194) followed by top (n = 126) and upper-middle level leaders (n = 38). Participants were from 37 countries with the majority based in the United States (n = 245) followed by Sri Lanka (n =
31), Australia (n = 8) and Singapore (n = 8). In addition to self-rated MBTI scores, leadership 112
ratings were also collected from each manager’s colleagues (i.e., their boss, peers, direct
reports, other raters and/or board members given the seniority of the participants) using the
Benchmarks for Executives 360-degree tool. The number of raters for each manager ranged
from 1 to 37 raters (Mraters = 11.16, SDraters = 4.64). The dataset contained data collected
between the years 2004 to 2012, and assessments were typically completed as part of
executive coaching, executive team workshops or as part of exercises to help prompt
discussions for intact teams.
4.2.2 CCL Dataset Measures
4.2.2.1 Personality
Participants self-assessed their personality using the MBTI questionnaire (Form Q).
The measure contains 144 items with two unique response options that are specific to the
question being asked. Table 4.2 presents the four type preference scales for this measure, each of which is defined above. For each dichotomy, individuals are assigned one type preference, based on the higher raw continuous score in the pair. that combine to form a type dynamic (i.e., a four letter code). In total, 16 type dynamics are possible based on the different combinations of type preferences. In addition to being categorised with a type dynamic, individuals can also receive a continuous score for each scale that represents an individual’s clarity for that preference (i.e., type clarity). The Cronbach alpha coefficients for each of the scales range from .91 to .92 (Myers et al., 2009).
Table 4.2
The Four Scales of the MBTI
Scale Negative Pole Positive Pole EI Extraversion Introversion SN Sensing Intuition TF Thinking Feeling JP Judging Perceiving
113
4.2.2.2 Leadership Behaviour
The Benchmarks for Executives is a 360-degree tool containing 92 items that assesses leadership behaviours needed for effectiveness at higher levels of an organisation. The assessment measures how individuals lead the business, lead others and lead by personal example across 16 scales. Table 4.3 presents each scale. The measure uses a 5-point rating scale based on levels of effectiveness (1 = Deficient, 2 = Marginally Effective, 3 = Effective,
4 = Highly Effective, 5 = Exceptional).
Table 4.3
The Benchmarks for Executives’ 16 Leadership Behaviour Scales and their Descriptions
Instrument’s Grouping of Leadership Scales Leadership Scale Sound Judgement Strategic Planning Leading Change Leading the Business Results Orientation Global Awareness Business Perspective Inspiring Commitment Forging Synergy Developing and Empowering Leading Others Leveraging Differences Communicating Effectively Interpersonal Savvy Courage Executive Image Leading by Personal Example Learning from Experience Credibility
4.2.3 Data Analysis Strategy
4.2.3.1 Extracting Higher-order Factors
To determine whether higher-order factors extracted from the MBTI resemble the
GFP and Big Two, both sources of data (including the intercorrelations from the MBTI user manual) were analysed. Specifically, the MBTI intercorrelation matrices from three previous studies (Bergner et al., 2016; Berr et al., 2000; Church & Waclawski, 1998), the intercorrelation matrix from the MBTI user manual (Myers et al., 2009) and scores on the four scales from the CCL dataset (i.e., EI, SN, TF and JP) were individually factor analysed 114
using principal axis factoring with Direct Oblimin rotation to extract two-factor and one-
factor solutions.
This study also sought to derive higher-order factors based on items since their
content may reveal the presence of the GFP and Big Two that may not be as apparent when
extracted from the four scales. In line with previous research (Johnson et al., 2001), the 144
27 items from the MBTI were first subject to principal axis factoring with Promax rotation26F .
The factor analysis was repeated on extracted factors, based on scree plot and eigenvalue results, until only one factor remained. Since the accuracy of interpreting higher-order factors in a factor analysis decreases as the number of orders increases (Gorsuch, 1983), each higher- order level produced needs to be related back to the original items on which the factor analysis is based. This can be achieved by postmultiplying pattern matrices across the different levels. For example, if a factor analysis produced three orders, the first-order pattern matrix (which would contain loadings by items) would be postmultiplied by the second-order pattern matrix, and the resultant pattern matrix would be postmultiplied by the third-order pattern matrix (Gorsuch, 1983; Johnson et al., 2001). In doing so, item loadings for the third- order factors would be derived (as would item loadings for the orders in between) to facilitate interpretation of the higher-order factors.
For missing item level data for the MBTI, the SPSS function of replacing missing values with the series mean was used. Of the sample 29.25% of cases contained at least one missing item, and the maximum number of missing items for any one case was five out of
144 items.
27 Promax rotation was chosen for this analysis to be consistent with the higher-order factor analyses of items by Johnson et al. (2001). Both Promax and Direct Oblimin are oblique rotations (correlated factors) that aim to simplify the structure, however, Promax is more efficient with larger datasets (Yong & Pearce, 2013). 115
4.2.3.2 Relating Higher-order Factors of the MBTI to Leadership
To examine how well the MBTI predicts leadership based on its traditional forms
(i.e., four type clarity, four type preference and 16 type dynamic scales) and to compare this
with the predictive validity of higher-order factors extracted from the MBTI, the following
analyses were undertaken. First, the correlations between the MBTI’s type clarity scales and
the leadership criterion were reported for the eight previous studies and the CCL dataset, and
a sample-weighted mean for each scale was calculated across the studies. Higher-order factor
composite correlations were then calculated for each study. This was achieved by applying
the factor weights derived for the two-factor and one-factor solutions (based on the factor
analyses noted above and detailed below) to the cumulation formula for deriving weighted-
composite factors (Hunter & Schmidt, 2004, p. 436). These composites aggregate multiple
related independent variables with a dependent variable. As highlighted above, the
intercorrelation matrix from the MBTI user manual was used to calculate composites for the
five past studies that did not report their own intercorrelation matrix. Again, sample-weighted
means were calculated for the correlations between higher-order personality factors and
leadership. Multiple regression analyses were also conducted based on the correlation
28 matrices between personality factors and leadership27F .
Next, using the CCL dataset, five operationalisations of personality models were
prepared: a one-factor solution, a two-factor solution, four continuous scores based on the
type clarity scales, four categorical scores based on the type preference scales, and a model
based on the 16 personality type dynamics. The four categorical scales were derived by effect
coding each of the type preference dichotomies. As noted above, individuals are assigned a
preference (e.g., Extraversion or Introversion) within each MBTI dichotomy (e.g., EI) based
28 For each study, the correlation between the two first-order factors was based on the factor correlation matrix output from each corresponding factor analysis to derive two first-order factors. 116
on the preference with the higher raw type clarity score in the pair. To capture the effect of
type dynamics, the four effect coded scales and the 11 possible product combinations of these
four effect coded scales (e.g., EI TF, JP EI TF, SN JP EI TF etc.) were entered
simultaneously. These 15 variables together capture whether there are interaction effects that
occur due to specific combinations of the dichotomous type preferences. Based on these five
29 models, correlational, multiple regression and cross-validation analyses28F were conducted to compare their validities for leadership.
Similar to Study 2, a 10-fold cross-validation procedure was used for the CCL dataset.
The sample of 400 participants was partitioned into 10 equal folds to allow for 10 iterations of training and validation analyses across three sample sizes (i.e., ‘small’ = 50, ‘medium’ =
150 and ‘large’ = 360). That is, the 10 possible combinations of nine folds were individually
pooled together (i.e., training sets) so that regression coefficients could be calculated to
construct prediction equations. See section 3.3.5 in Chapter 3 for a detailed description of the
cross-validation procedure. Each prediction equation was then validated with the fold that
was not used as part of the equation’s derivation (i.e., the test set). Cross-validity coefficients
(CVR) and root mean square error (RMSE) scores were derived for each fold and across both the five personality models and three sample sizes. One-way repeated measures ANOVAs were also performed on the CVR and RMSE scores to determine whether any differences between personality models were statistically significant within each sample size.
29 For the cross-validation analyses involving the type dynamics model, certain coefficients could not be assessed due to multicollinearity (particularly categorical product terms). As such, these coefficients were set to zero. 117
4.3 Results
4.3.1 Extraction of Higher-order Factors from the MBTI
Prior to factor analysing each dataset, the personality intercorrelation matrices from the CCL dataset, the three past studies and the user manual (presented in Table 4.4) were inspected. First, the consistency of all correlations in terms of both magnitude and direction was reviewed. Second, whether specific MBTI scales correlated with one another in a direction that was expected based on how they align to each of the Big Two factors and the
GFP was inspected, with these expected directions based on past research that has linked each of the MBTI scales with specific Big Five factors (McCrae & Costa, 1989). The direction and magnitude of correlations between the four MBTI scales were fairly consistent.
For example, across the four matrices, the largest correlation was consistently between SN and JP (and positive in direction), EI was consistently negatively correlated with SN and TF, and the effects of EI were consistently small in magnitude.
118
Table 4.4
Correlation Matrices between the Four MBTI Scales across the CCL Dataset, the Three Past
Studies and the MBTI Normative Dataset
Study EI SN TF JP CCL dataset EI 1.00 SN -.18 1.00 TF -.13 .30 1.00 JP -.02 .37 .24 1.00 Bergner et al. (2016) EI 1.00 SN -.13 1.00 TF -.15 .08 1.00 JP -.01 .29 .15 1.00 Berr et al. (2000) EI 1.00 SN -.09 1.00 TF -.07 .19 1.00 JP .08 .44 .28 1.00 Church & Waclawski (1998) EI 1.00 SN -.10 1.00 TF -.08 .19 1.00 JP .01 .46 .14 1.00 Myers et al. (2009) EI 1.00 SN -.18 1.00 TF -.12 .12 1.00 JP -.09 .47 .18 1.00 Note: Positive pole represented by the right-hand initial.
In terms of how certain MBTI scales correlated based on their alignment to the Big
Two, EI (which has been linked to reverse-Extraversion from the Big Five) and SN (which has been linked to Openness to Experience from the Big Five) correlated in the expected direction (i.e., negatively) for Plasticity. However, the correlations between TF (linked to
Agreeableness from the Big Five) and JP (linked to reverse-Conscientiousness from the Big
Five) did not correlate in the expected direction for Stability in that they were positive across the matrices when they were expected to be negative. Consequently, the intercorrelations between all four scales were also inconsistent with what would be expected for a single higher-order factor representing the GFP. In addition, the intercorrelations among the four 119
variables were generally small, which is inconsistent with the idea of a strong underlying
factor.
The following factor analyses first individually extracted higher-order factors from
30 the intercorrelation matrix of the CCL dataset29F , the three past studies (Bergner et al., 2016;
Berr et al., 2000; Church & Waclawski, 1998) and the MBTI user manual (Myers et al.,
2009). Before factor analysing each dataset, their suitability for factor analysis was assessed.
Table 4.5 contains the Kaiser–Meyer–Olkin values and significance results for Bartlett’s Test
of Sphericity for each dataset. For all five datasets, Bartlett’s Test of Sphericity reached
31 statistical significance (p < .001) and the Kaiser–Meyer–Olkin values were at least .5030F ,
supporting the factorability of each dataset.
Table 4.5
Factor Analysis Suitability Statistics Across the Five Datasets
Church & Bergner et al. Berr et al. Myers et al. Suitability Statistic CCL Dataset Waclawski (2016) (2000) (2009) (1998) Kaiser–Meyer– .61 .52 .54 .54 .55 Olkin Measure Bartlett’s Test of < .001 < .001 < .001 < .001 < .001 Sphericity (p)
4.3.1.1 Extraction via MBTI Scales from the CCL Dataset and Past Studies
To help facilitate the interpretation of loadings from the factor analysis below, the
expected directions of loadings between each of the MBTI scales with the GFP and Big Two
factors are displayed in Table 4.6. These are based on the associations identified between the
MBTI scales with each Big Five factor based on past research (McCrae & Costa, 1989).
30 The 20 MBTI facets were also factor analysed, and a similar pattern of results emerged for the one-factor and two-factor solutions. See Appendix C for the scree plot and loadings for the one-factor and two-factor solutions (Figure C 1, Table C 1 and Table C 2, respectively). 31 A minimum Kaiser–Meyer–Olkin value of .60 is typically recommended, however values of at least .50 are considered the minimum required for factor analysis (Hair, Black, Babin, Anderson, & Tatham, 2006). 120
Table 4.6
Expected Loadings between each MBTI Scale with the GFP and Big Two Factors
Loading on Loading on MBTI Scale Related Big Five Factor the Big Two the GFP EI Extraversion (Reverse) Plasticity (-) (-) SN Openness to Experience Plasticity (+) (+) TF Agreeableness Stability (+) (+) JP Conscientiousness (Reverse) Stability (-) (-) Note: Symbol in parentheses indicates expected direction of loading.
Table 4.7 presents the loadings for the two-factor and one-factor solutions based on
32 the factor analysis of the four MBTI scales from the CCL dataset31F , the three past studies and
the MBTI manual. Across the five datasets, the loadings for the one-factor and two-factor
solutions were consistent and did not appear to align with the nature of the GFP and/or Big
Two factors. Specifically, the one-factor solution included a positive loading from the JP scale, where a negative loading was expected (see Table 4.6). In addition, EI and TF had the lowest loadings out of the four MBTI scales on the one-factor solution. This was counterintuitive since the EI and TF scales are presumably the most socially-oriented factors out of the MBTI and the GFP is meant to reflect social competence. Taken together, the loadings for the one-factor solution, of higher positive loadings from JP and SN, a smaller positive loading from TF and a somewhat trivial loading for EI, suggest that it could resemble a preference for versatility including adaptability, openness and accommodating as opposed to the conceptualisation of the GFP in terms of social competence.
32 See Appendix C for the one-factor solution communalities (Table C 3) and the two-factor solution communalities and structure matrix coefficients (Table C 4) based on the CCL dataset. 121
Table 4.7
Factor Loadings for the Two-Factor and One-Factor Solutions across the Five Datasets
Pattern Coefficients Factor Coefficients (Two-factor Solution) (One-factor Solution) Study Factor 1 Factor 2 Factor 1
CCL dataset EI -.02 .45 -.20 SN .59 -.19 .73 TF .38 -.16 .45 JP .68 .18 .50 Bergner et al. (2016) EI .06 .65 -.19 SN .38 -.11 .54 TF .16 -.19 .25 JP .77 .15 .51 Berr et al. (2000) EI -.02 .55 -.01 SN .54 -.08 .56 TF .34 -.07 .35 JP .86 .26 .78 Church & Waclawski
(1998) EI .02 .39 -.09 SN .68 -.12 .81 TF .20 -.17 .24 JP .72 .17 .56 Myers et al. (2009) EI .01 .57 -.22 SN .57 -.09 .70 TF .17 -.13 .24 JP .85 .15 .66 Note: Positive pole represented by the right-hand initial.
Similarly, the two-factor solution did not appear to clearly represent the Stability and
Plasticity factors from the Big Two. The first factor had a similar pattern of loadings as the one-factor solution. Specifically, there were higher positive loadings from JP and SN and a smaller loading from TF while the loading for EI was similarly trivial. The second factor primarily contained a single loading from the EI scale. Thus, while the second factor essentially represented a factor related to extraversion, the first factor appeared to resemble a preference for versatility similar to the one-factor solution. 122
Therefore, these results did not appear to support the notion that higher-order factors extracted from the four MBTI scales reflect the GFP and Big Two factors. This finding also raises the question of whether the factors can indeed be reinterpreted from the Big Five factors as argued in previous research (McCrae & Costa, 1989).
4.3.1.2 Extraction via MBTI Items from the CCL Dataset
Since past research has extracted higher-order factors from MBTI items (Johnson et al., 2001), the study also examined whether the Big Two and GFP would emerge from a factor analysis at the item level. The 144 items from the MBTI were first subject to principal axis factoring with Promax rotation. An inspection of the scree plot (Figure 4.1) suggested that four factors could be retained based on the ‘bend’ of the graph, which accounted for a total of 25.03% of the variance based on the extraction sums of squared loadings. The four extracted factors were again factor analysed and the scree plot (Figure 4.2) and initial eigenvalues (λ1 = 1.62 and λ2 = 0.96) suggested the possible retention of two factors accounting for 30.12% of the variance based on the extraction sums of squared loadings. In line with Johnson et al. (2001) who factor analysed components until they no longer correlated, these two factors were subject to a final factor analysis where the single factor retained accounted for 90.79% of the variance based on initial eigenvalues (λ1 = 1.82). 123
Figure 4.1
Scree plot of eigenvalues associated with the principal axis factoring of MBTI items.
Figure 4.2
Scree plot of eigenvalues associated with the principal axis factoring of the four extracted factors derived via MBTI items.
124
Next, pattern matrices were postmultiplied using the procedures outlined in the data analysis strategy above (see Appendix C, Table C 5 for the postmultiplied loadings by items for each order). The four factors from the first-order solution appeared to resemble the four
MBTI scales such that items generally described a preference for pragmatism versus imagination (i.e., SN), gregariousness versus reserved (i.e., EI), organisation and structure versus flexibility (i.e., JP) and critical and firm-minded versus compassionate and caring (i.e.,
TF). In terms of the two factors from the second-order solution, the items from the first factor generally related to being planful, systematic and grounded in reality, whereas the second factor’s items related more to being private and preferring one’s own company. This pattern of findings was consistent with the two-factor solution extracted from the four MBTI scales above (i.e., the reverse of both the versatility and extraversion factors, respectively). The highest loading items for the single third-order factor appeared to relate to preferring factual, methodical and concrete thinking, and preferring reality over more abstract thoughts. Taken together, again these results based on items did not appear to support the idea that the higher- order factors extracted from the MBTI’s items represent the Big Two and GFP. Additionally, the first factor of the two-factor solution and the one-factor solution again appeared to be highly similar (i.e., a preference for more structured and realistic thinking or the opposite characteristics of versatility and openness).
4.3.2 Preliminary Review of the Relationship Between Higher-order Factors Extracted from the MBTI and Leadership
The CCL dataset and the eight studies identified from the literature were analysed to examine the relationship between the higher-order factors extracted from the MBTI and leadership. For the CCL dataset, the leadership variables were first factor analysed to determine if a single overarching leadership factor could be derived. Appendix C contains the factor matrix coefficients and communalities for the one-factor leadership solution in Table C 125
6, and the scree plot in Figure C 2, similar to Study 2 in Chapter 3. The leadership data met
the assumptions required for factor analysis since the correlation matrix based on the 16
leadership scales showed several correlations of .30 and above (specifically, the minimum
correlation between the scales was .68), the Kaiser–Meyer–Olkin value was .96 and Bartlett’s
Test of Sphericity was also statistically significant (p < .001). The scree plot also supported the retention of an overall leadership factor such that the trend could be seen to ‘bend’ after the first factor. Based on the initial eigenvalues (λ1 = 13.42), the results provided strong
support for the retention of one overriding leadership factor accounting for 83.84% of the initial variance. Given these findings, the present study focused on the single overall leadership factor when analysing the CCL dataset.
Table 4.8 contains the correlations between the four MBTI scales and various leadership measures obtained from the eight past studies and from the CCL dataset. Sample- weighted means for each personality factor across the eight studies were also calculated and reported. Correlations between the factors from the two-factor and one-factor solutions with each leadership criterion are also shown in Table 4.8. Unlike for Study 2 where the 20 personality scales from the CPI were unit-weighted to form higher-order factors, factor
33 loading weights32F (based on factor score coefficient outputs) were used in this study given the
composition of loadings for the two-factor and one-factor solutions. For example, for the one-
factor solution, SN and JP appeared to have the highest positive loadings followed by TF and
lastly a smaller negative loading (trivial in some cases) from EI. This pattern of results
contradicted the propositions that the highest-order of personality comprises all factors that
are similarly weighted, that factors related to an extraverted personality trait are non-trivially
represented, and that traits about being conscientious load positively. Therefore, to better
33 These analyses were also undertaken with unit-weights and a similar pattern of results was found. 126
reflect the MBTI factor loadings that were found for higher-order factors, factor loading
weights were used to derive higher-order scores for these as well as subsequent analyses.
Table 4.8
Correlations between MBTI Scales and Leadership, including the Relationships between
Extracted Higher-order Factors and Leadership
TFS TFS Study EI SN TF JP OFS (1 of 2) (2 of 2) Bergner et al. (2016) -.20 .09 .19 -.01 .06 -.24 .12
Berr et al. (2000) .00 .04 .04 .02 .03 -.02 .04
Brown & Reilly (2009)* -.08 -.07 -.02 -.02 -.03 -.04 -.05
Carless & Allwood (1997)* -.25 .15 -.10 -.01 .06 -.24 .10
Church & Waclawski (1998) -.13 .21 -.01 .16 .21 -.16 .22
Conway (2000)* -.06 .11 .10 .04 .08 -.10 .10
Fransen et al. (2020)* -.29 .07 .06 .01 .07 -.28 .08
Zaccaro et al. (2000)* .02 -.03 -.05 -.14 -.12 .04 -.10
CCL dataset -.03 .18 .05 .00 .11 -.13 .14
Sample-weighted mean: -.10 .09 .02 .00 .04 -.12 .07 Note: TFS = Two-factor Solution. OFS = One-factor Solution. *Study did not contain intercorrelations between MBTI scales and, therefore, factor weights were applied based on factor analytic results from an MBTI correlation matrix in the user manual (Myers et al., 2009).
The results from the past studies showed that of the four MBTI scales only EI had a non-trivial (but small) effect (r = -.10) on leadership. Similarly, only the second factor from the two-factor solution had a small effect (r = -.12) on leadership, which was unsurprising
given that EI was by far the strongest loading scale on this factor. The first factor of the two-
factor solution did not appear to be related to leadership, and this was similar to that of the
one-factor solution (both of which seemed to be assessing highly similar constructs, as described above).
Table 4.9 presents the results of the multiple regression analyses across the eight past
studies and the CCL dataset. The regression analysis was undertaken based on the correlation 127 matrices for models containing the four MBTI scales, the two-factor solution and the one- factor solution. An overall multiple regression analysis was also conducted across all studies based on the combined correlation matrices (i.e., one each for the model containing the four scales, the two-factor solution and the one-factor solution). To derive the correlation coefficients for each matrix, sample-weighted means were calculated between the personality variables of interest. The sample-weighted means between each personality factor and leadership were obtained from Table 4.8 above. As shown in Table 4.9, the aggregated
Adjusted R2 values indicate that all three personality models appeared to account for a similar amount of variance in leadership of 1% based on the one-factor and two-factor solutions and
2% based on the MBTI scales.
Table 4.9
Multiple Regression Analysis across the Eight Reviewed Studies and CCL Dataset for the
Four MBTI Scales, Two-Factor Solution and One-Factor Solution
Study Model Aggregat 1 2 3 4 5 6 7 8 9 ed R2 MBTI Scales .07* .00 .01 .10** .07* .02** .09** .02* .04* .02** Two-Factor Solution .06* .01 .00 .06** .06** .01** .08** .01* .02* .01** One-Factor Solution .02 .00 .00 .01* .04** .01** .01 .01* .02* .01** Adjusted R2 MBTI Scales .05* -.01 -.01 .10** .05* .02** .08** .02* .03* .02** Two-Factor Solution .05* -.01 -.01 .06** .05** .01** .08** .01* .01* .01** One-Factor Solution .02 .00 -.01 .01* .04** .01** .01 .01* .02* .01** β MBTI Scales EI -.17* .01 -.10 -.24** -.12 -.03 -.28** .01 .01 -.09** SN .07 .04 -.09 .16** .17* .11** .03 .05 .21** .10** TF .17* .04 -.02 -.13** -.06 .09** .03 -.03 .01 .01 JP -.06 -.01 .02 -.08* .09 -.03 -.04 -.16** -.08 -.05** Two-Factor Solution Factor 1 -.02 .03 -.05 -.04 .18* .05 -.05 -.12** .06 -.01 Factor 2 -.25* -.02 -.06 -.26** -.11 -.08* -.30** -.01 -.09 -.12** One-Factor Solution Factor 1 .14 .04 -.04 .10* .21** .11** .10 -.10* .14* .07** Note: 1 = Bergner et al. (2016); 2 = Berr et al. (2000); 3 = Brown & Reilly (2009), 4 = Carless & Allwood (1997); 5 = Church & Waclawski (1998); 6 = Conway (2000); 7 = Fransen et al. (2020); 8 = Zaccaro et al. (2000); 9 = CCL dataset. *p < .05; **p < .001. 128
4.3.3 Comparison of Effects of Different Personality Models Extracted from the MBTI
for Leadership
4.3.3.1 Correlational and Multiple Regression Analyses
The CCL dataset was used to compare the effects of different personality models extracted from the MBTI for leadership including higher-order versus lower-order factors,
and continuous versus categorical models. Correlational and multiple regression analyses were first conducted based on factors from four of the different personality models (i.e., one-
factor solution, two-factor solution, four continuous scores based on the type clarity scales
and four categorical scores based on the type preference scales). A model based on
interaction terms for the MBTI categorical type dynamics was also included as part of the
regression analyses.
The Pearson product-moment correlation coefficients are reported in Table 4.10. The
higher-order factors were statistically significantly correlated with leadership and the effects
for the one-factor solution (r = .14) and two-factor solution (r = .11 and r = -.13) were small,
based on guidelines from Gignac and Szodorai (2016). As noted above, the one-factor
solution and the first factor of the two-factor solution are likely to represent the same
construct, which was not found to relate to leadership based on the sample-weighted
aggregation of results across past studies, as shown in Table 4.8. For both the continuous and
categorical models containing MBTI scales, only SN was statistically significantly correlated
with leadership (r = .18 for both continuous and categorical operationalisations). These
effects were small to moderate and were also higher than those of any of the higher-order
factors.
129
Table 4.10
Pearson Product-moment Correlations between Personality and Overall Leadership
One- Two- MBTI Scales MBTI Scales factor r factor r r r (Continuous) (Categorical) Solution Solution Factor 1 .14* Factor 1 .11* EI -.03 EI .01 Factor 2 -.13* SN .18** SN .18** TF .05 TF .09 JP .00 JP .01 Note: Negative values for MBTI scores indicate a preference for the left-most letter in the pair. EI = Extraversion–Introversion; SN = Sensing–Intuition; TF = Thinking–Feeling; JP = Judging– Perceiving. n = 396. *p < .05; **p < .01, two-tailed.
Table 4.11 displays the results from the multiple regression analyses for the above four personality models as well as the model based on type dynamics. The Adjusted R2 values for all five models were statistically significant, and the models based on the MBTI scales (continuous and categorical) and type dynamics appeared to account for marginally higher variance (i.e., 3% for both the continuous and categorical MBTI models and 4% for the model based on type dynamics) compared to the higher-order models (i.e., 2% for the one-factor solution and 1% for the two-factor solution). Although the model based on type dynamics appeared to have the highest Adjusted R2, most individual regression coefficients were non-significant.
130
Table 4.11
Multiple Regression Analysis for Personality and Overall Leadership
Overall Leadership Predictor(s) R2 Adjusted R2 β One-factor Solution Factor 1 .02* .02* .14* Two-factor Solution Factor 1 .06 Factor 2 -.09 .02* .01* MBTI Type Clarity Scales (Continuous) EI .01 SN .21** TF .01 JP -.08 .04* .03* MBTI Type Preference Scales (Categorical) EI .03 SN .17** TF .06 JP -.02 .04* .03* MBTI Type Dynamics (Categorical) EI -.03 SN .25* TF -.01 JP -.11 SN JP .02 SN EI -.03 SN TF .15 JP EI -.12 JP TF -.12 EI TF -.05 SN JP EI .11 SN JP TF .12 SN EI TF -.11 JP EI TF -.06 SN JP EI TF .06 .08* .04* Note: *p < .05; **p < .001.
131
4.3.3.2 Cross-validation Analyses
34 Figure 4.3 and Figure 4.4 display the mean RMSE and CVR3F scores, respectively,
across the five personality models for each of the three sample sizes examined (i.e., n = 50, n
= 150 and n = 360). See Appendix C for scores by fold and means in Table C 7. In addition,
to test for statistically significant differences in CVR and RMSE scores between personality models within each sample size, one-way repeated measures ANOVAs were conducted.
Results based on transformed scores are displayed in Table 4.12.
1.30 OFS TFS Continuous Categorical Types 1.25
1.20 1.17 1.15
1.10 1.06 1.06 1.03 1.05 1.02
RMSE 1.00 0.99 1.00 0.99 0.990.99 0.98 0.99 0.99 0.98 0.98
0.95
0.90
0.85
0.80 n = 50 n = 150 n = 360
Note: OFS = One-factor solution; TFS = Two-factor solution; Continuous = Four continuous type clarity scales; Categorical = Four categorical type preference scales; Types = 16 categorical type dynamics.
Figure 4.3
Mean RMSE scores from the 10-fold cross-validation across different sample sizes and personality models.
34 The same pattern of findings emerged for analyses conducted on the Fisher z-transformed values for the cross-validity coefficients. 132
0.25 OFS TFS Continuous Categorical Types
0.20 0.17 0.16 0.150.15 0.15 0.15 0.15 0.15 0.12 0.12 ) r 0.10 0.09
CVR ( 0.06 0.05 0.05 0.03 0.01 0.00 -0.01
-0.05 n = 50 n = 150 n = 360
Note: OFS = One-factor solution; TFS = Two-factor solution; Continuous = Four continuous type clarity scales; Categorical = Four categorical type preference scales; Types = 16 categorical type dynamics.
Figure 4.4
Mean CVR (r) scores from the 10-fold cross-validation across different sample sizes and personality models.
Table 4.12
One-way Repeated Measures ANOVA of CVR and RMSE Scores by Sample Size
Sample zCVR RMSE Size F df df error F df df error n = 50 0.59 4.00 36.00 25.95* 3.49 31.40 n = 150 0.56 1.73 15.57 2.45 1.09 9.80 n = 360 0.27 4.00 36.00 0.27 2.21 19.85 Note: *p < .001.
At the largest sample size (n = 360), the RMSE scores were highly consistent across all five personality models (ranging from .98 to .99) as were the CVRs, with the exception of the two-factor model which appeared somewhat lower. However, any apparent differences were not statistically significant as per the one-way repeated measures ANOVA results. For the medium sample size (n = 150), the type dynamics model demonstrated some degradation 133 in performance in RMSE and CVR scores whereas the performance of the other models remained largely unchanged. However, again differences between models did not reach statistical significance.
Lastly, at the lowest sample size (n = 50), all models showed some degradation in performance when considering both RMSE and CVR scores. The CVR for the type dynamics and two-factor models appeared lower than the others, although the differences were not statistically significant. The RMSE scores showed that the performance of the type dynamics model was the lowest followed by type preference, type clarity, two-factor and lastly the one- factor model. The one-way repeated measures ANOVA revealed there were statistically significant differences. The corresponding post hoc pairwise comparison tests for this
ANOVA are shown in Table 4.13. The results indicate that the performance of the type dynamics model was statistically significantly lower than all of the other four models, and that the performance of the type preference model was significantly lower than the one-factor solution.
Table 4.13
Post Hoc Pairwise Comparison Tests of RMSE Scores between Personality Models for the
Smallest Sample Size (n = 50)
Mean Standard Pair Difference Error One-factor Solution – Two-factor Solution -0.01 0.01 One-factor Solution – MBTI Type Clarity Scales (Continuous) -0.03 0.01 One-factor Solution – MBTI Type Preference Scales (Categorical) -0.06* 0.02 One-factor Solution – MBTI Type Dynamics -0.17* 0.02 Two-factor Solution – MBTI Type Clarity Scales (Continuous) -0.02 0.01 Two-factor Solution – MBTI Type Preference Scales (Categorical) -0.04 0.02 Two-factor Solution – MBTI Type Dynamics -0.15* 0.02 MBTI Type Clarity Scales (Continuous) – MBTI Type Preference Scales (Categorical) -0.03 0.01 MBTI Type Clarity Scales (Continuous) – MBTI Type Dynamics -0.13* 0.02 MBTI Type Preference Scales (Categorical) – MBTI Type Dynamics -0.11* 0.02 Note: *p < .05.
134
4.4 Discussion
The primary aims of the present study were to determine if the MBTI comprises
higher-order factors that resemble the GFP and Big Two, and whether these broader factors
relate to leadership, compared to the conventional operationalisations of the MBTI. In
general, the results did not support the notion that higher-order factors of the MBTI represent
the GFP and Big Two constructs. Additionally, evidence was not found for the predictive
validity of the higher-order factors extracted from the MBTI for leadership. The secondary
aims of this study were to determine whether any of the MBTI scales related to leadership
based on a preliminary quantitative review of past studies (given the mixed findings in the
literature), and to explore whether the MBTI types had additive and/or interaction effects on
leadership. The quantitative review found a small and negative effect for the EI scale on leadership, and the results based on an analysis of the CCL dataset did not support the
presence of any interaction effects of personality type configurations on leadership.
The findings indicate that the higher-order factors extracted from the MBTI do not clearly resemble the GFP and Big Two factors. The factors from the two-factor solution
appeared to represent a preference for versatility and openness (or its inverse, organisation and pragmatism) based on higher loadings of JP and SN and a smaller loading of TF, and a preference for extraversion (or its inverse, introversion) based on the higher EI loading.
Similar to this first factor of the two-factor solution, the single factor from the one-factor solution contained generally higher loadings from SN and JP, followed by TF, and lastly a small (negative) loading of EI, which was trivial in some instances. The general factor extracted from items reflected a preference for methodical and factual thinking. These characteristics are consistent with the left-hand poles of SN and JP, respectively, and thus are
broadly in line with the general factor extracted from the four scales as described above.
Given the similarity between the first factor from the two-factor solution and the factor from 135
the one-factor solution, and that the second factor from the two-factor solution largely
reflects the EI scale, the findings only provide support for a single distinct higher-order
factor. Furthermore, this single higher-order factor appears to be different from both the GFP and Big Two.
These findings are generally consistent with past research that extracted two higher- order factors from the MBTI items (i.e., Johnson et al., 2001). Specifically, in both this study and the past study, a higher-order factor related to a preference for versatility and openness
(or its opposite, related to preferring organisation and pragmatism) was found, particularly given the higher loadings from JP and SN items across both studies. The second factor in this study essentially represented another operationalisation of extraversion, which also aligned to the one found in the past study since the highest loading items were from EI and the factor was described in terms of sociability and activity.
On relationships with leadership, the general factor related to a preference for versatility (including the first factor from the two-factor solution and the one-factor solution) did not relate to leadership. However, the second factor from the two-factor solution, representing a preference for extraversion, had a small relationship with leadership. This effect was similar in magnitude and direction for the effect of the individual EI scale. In comparing the effects of the higher-order factors extracted from the MBTI with its conventional operationalisations (i.e., the type preference, type clarity and type dynamics models), the cross-validation results revealed that all models appeared to be similarly predictive of leadership at medium and large sample sizes. Specifically, the CVR values indicated that the effects were generally small in magnitude. At the smallest sample size, although the RMSE values showed that the models based on type dynamics and type preferences were particularly detrimental for leadership, the cross-validation coefficients indicated that all five personality models did a poor job of predicting leadership (with all 136
CVRs less than or equal to .06). Thus, the predictive advantage of using models with fewer parameters at smaller sample sizes found in Chapter 3 was not apparent in this chapter’s study when using the higher-order factors of the MBTI.
A secondary aim of the present study was to undertake a preliminary quantitative
review of studies that have examined the relationship between the MBTI and leadership. To
my knowledge, this is the first study to quantitatively examine the effects of the MBTI on
leadership across a number of past studies, partly by using meta-analytic procedures of
calculating sample weighted means. Although a previous review of the MBTI had been
undertaken with a focus on managers (Gardner & Martinko, 1996), that review did not
exclusively focus on leadership criteria (i.e., dependent variables) for these managers nor
were all studies quantitative or empirical in nature. The present review of previous studies
showed that out of the four scales, only EI had a small and negative relationship with
leadership. This finding challenges research that argues that other MBTI scales, such as the
negative poles of TF and JP, are more relevant for leadership (Gardner & Martinko, 1996).
Lastly, the other secondary aim related to examining potential additive and interaction
effects of types for leadership. The additive effects of the four type preferences were similar
to the effects of the type clarity measures, in that only SN had a significant effect.
Furthermore, the multiple regression analyses did not reveal any significant interactive
effects that would support the idea that some type combinations make more effective leaders
than others. This is consistent with previous research that has provided limited evidence for
the idea that different type configurations are unique or differently relate to various criteria
(Hicks, 1984; McCrae & Costa, 1989; Stricker & Ross, 1964; Vincent et al., 2013). 137
4.4.1 Theoretical Implications
4.4.1.1 The Higher-order Factors of the MBTI
The nature of the higher-order factors extracted from the MBTI in this study raises a number of theoretical implications. The first relates to whether the GFP and Big Two exist in all major models of personality regardless of the model’s conceptual basis. Chapter 1 identified over a dozen studies that had extracted the GFP and Big Two factors from existing personality measures including four that measured personality disorders. However, this thesis study showed that the GFP and Big Two are unlikely to reside within the prominent type- based MBTI model. The absence of the GFP and Big Two from the MBTI suggests that the
MBTI model may not sufficiently capture nor relate to the social nature of personality, especially given that the GFP, Stability and Plasticity are argued to reflect social competence, socialisation and social exploration, respectively. This finding is consistent with the premise that the MBTI mainly focuses on measuring individual differences in terms of how people broadly experience the world, rather than more socially-motivated differences. In support of this view, past research has found that only the JP scale from the MBTI relates to social judgement (r = -.14; Zaccaro et al., 2000), which is somewhat counterintuitive since EI and
TF appear to be the most socially-oriented traits of the MBTI as described above. In addition, another study found that participants who completed the MBTI and a related training program focused on understanding the scales, interpreting their own results and recognising others’ scores performed worse than two other groups in understanding and responding to the social styles of others (Kraiger & Kirkpatrick, 2010). As such, the present findings challenge the idea that all major models of personality contain the GFP or Big Two.
Secondly, although the MBTI did not appear to contain the GFP and Big Two, the fact that at least one other higher-order factor could be extracted challenges the MBTI test publisher’s assertion that the four scales are theoretically independent (Myers et al., 2009). 138
The possibility of extracting a higher-order factor in the present study is consistent with
research that has found that the SN and JP scales are highly correlated and that the TF scale is
conceptually ambiguous in that it contains multiple characteristics (Saggino & Kline, 1996;
Sipps & DiCaudo, 1988). Thus, the four scales that the MBTI measures may not actually represent unique nor the broadest level of type preferences.
An alternative explanation for the nature of the MBTI’s highest-order factor could be
that it is a general measure of how clear and confident an individual is in choosing a
personality preference. The test publisher states that the continuous scale of the MBTI solely
reflects type clarity as opposed to how much of a type preference a person has. As such, the
common variance of the extracted higher-order factor may not reflect a broader and
substantive personality construct (i.e., versatility and openness as described above) but perhaps only the general extent to which an individual is comfortable choosing a personality
preference.
The MBTI’s focus on preferences is different from how traits conceptualise
personality in terms of behavioural tendencies. As argued in Chapter 1, high scores on
individual traits may represent an intrinsic motivation to engage in that trait’s associated
behaviour or a more strategic use of the trait-associated behaviour to influence one’s social
world. It was argued that the common variance of traits may represent the breadth and
cohesive use of strategies that an individual can use to bring about change in others.
Similarly, some have argued that individuals who score high on the GFP may possess the
requisite motivation, skills and knowledge to achieve social goals (van der Linden et al.,
2016). In contrast, the MBTI’s focus on preferences may not sufficiently (if at all) capture any information that is relevant to the strategic or social competency aspect of the GFP.
However, it is worth noting that EI did not load on the MBTI’s single higher-order factor.
One possible explanation for this may be that an individual’s preference for extraversion or 139
introversion is more visible or apparent to others. This is dissimilar from the potential
interpretations of the MBTI’s single higher-order factor as simply preferring versatility or
being clear in choosing a personality preference, neither of which may be as observable. The
visibility of extraversion and introversion is consistent with how Jung initially observed and
categorised people solely based on these characteristics and also that extraversion refers to
directing energy outwardly to other people and situations as described above.
The third theoretical contribution relates to calls for the MBTI to be reconceptualised
in terms of the Big Five model given the strong relationships between them. The present
study’s inability to find the GFP and Big Two was somewhat surprising given that the GFP
and Big Two have been consistently extracted from the Big Five, and that each of the four
MBTI scales was shown to relate to at least one of the Big Five in McCrae and Costa (1989).
Nevertheless, the MBTI scales are not related to each other in the same manner as their
corresponding Big Five factors. For example, Agreeableness and Conscientiousness are
generally positively correlated (such that their shared variance, along with Emotional
Stability, form Stability), and yet the MBTI counterparts are negatively related (i.e., rTF-JP(r) =
-.21; Myers et al., 2009). Similarly, Openness to Experience and Conscientiousness are often
positively correlated, however the MBTI equivalents are again negatively related (i.e., rSN-JP(r)
= -.38; Myers et al., 2009).
One possible explanation for these negative relationships is that the MBTI’s JP scale may only measure facets of Conscientiousness that are negatively correlated with Openness and Agreeableness, but not those that are primarily responsible for its positive correlation with Openness or Agreeableness. For example, Conscientiousness includes a rule-orientation sub-factor comprising self-control (i.e., deliberation, impulse control and cautiousness) and traditionalism (i.e., compliance with norms and not challenging authority figures; Roberts,
Chernyshenko, Stark, & Goldberg, 2005). Research has shown that these kinds of traits are 140
negatively related to Agreeableness (specifically, deliberate and rigid) and Openness to
Experience (specifically, traditional and conventional; Hofstee, De Raad, & Goldberg, 1992).
Thus, the JP scale may only be capturing the rule-orientation part of Conscientiousness, as
opposed to the full construct of Conscientiousness that also includes traits such as
achievement-orientation and integrity that positively relate to Openness to Experience and
Agreeableness (e.g., helpful and perceptive, respectively; Hofstee et al., 1992).
Since the GFP and Big Two have been found in several personality inventories that directly assess the Big Five and ones that do not (Musek, 2007; Rushton & Irwing, 2009b,
2009c; Veselka et al., 2012), the lack of these factors in the MBTI raises questions about how
meaningful its relationship with the Big Five really is and what it actually assesses in the
domain of personality. Thus, from a theoretical perspective, it may not be appropriate to
reconceptualise the MBTI model in terms of the Big Five model until additional research is undertaken to better understand the relationship between the models.
4.4.1.2 Relationships with Leadership
The relationships (or lack thereof) between the two-factor and one-factor solutions
extracted from the MBTI and leadership in the present study have implications for the trait
theory of leadership and for personality theory more generally. Firstly, the findings from the
quantitative review of past studies including the CCL dataset showed that the extracted factor
related to extraversion had a small effect on leadership. This was not surprising given that
extraversion was shown to similarly relate to leadership effectiveness in Chapter 2 (r = .17)
as well as in past meta-analytic studies (e.g., r = .13; Do & Minbashian, 2014). In terms of
35 the trait34F theory of leadership, extraversion appears to consistently relate to leadership
regardless of whether it is derived from trait or type models of personality. Secondly, the
35 In this instance, the term ‘trait’ is used to refer to any personality construct that may differentiate effective leaders from ineffective leaders. 141
single higher-order factor that was extracted (i.e., a preference for versatility) did not relate to
leadership. This finding was evident in both the quantitative review of past studies and the
cross-validation analyses, particularly at more practical sample sizes.
Taken together, the present findings suggest that reconceptualising the MBTI in terms
of higher-order factors does not appear to increase its ability to predict leadership compared to traditional operationalisations of the MBTI. On the one hand, this can be further evidence
that the MBTI’s higher-order factors do not correspond to the GFP and/or Big Two given that
the GFP and Big Two would be expected to relate to leadership effectiveness, as observed in the previous chapters. On the other hand, the findings provide indirect evidence that it is not
simply the breadth of higher-order factors such as the GFP and the Big Two that accounts for
their effects on overall leadership ratings, but that the effect is at least partly attributable to
the specific content of the GFP and Big Two factors. This point is discussed further in the
next chapter.
4.4.2 Practical Implications
Given the limited evidence for the quantitative effects of the MBTI scales on
leadership in this study, another practical implication is the extent to which the MBTI should
be used across leadership settings. From the perspective of selecting and recruiting leaders,
the non-trivial (but small) effect of EI suggests that this particular type clarity scale may still
be useful in the absence of other relevant selection information such as from trait-based measures. Moreover, the cross-validation results, especially the CVR values, indicate that the
MBTI scales are able to distinguish between effective and ineffective leaders to some extent given at least moderate sample sizes (i.e., n = 150). However, extracting higher-order factors
from the MBTI is unlikely to be additionally useful for predicting leadership, compared to
using its traditional operationalisation. 142
While the findings are especially pertinent for the identification of effective leaders, it
should be restated that the MTBI test publisher acknowledges that the inventory should not
be used for leadership selection but instead advocates for its efficacy in leadership
development (Myers et al., 2009). However, a primary focus for using personality scores in
leadership development and coaching is to help leaders refine or change skills and behaviours
to be more consistent with an ideal personality profile (McCormick & Burch, 2008). The
findings suggest that practitioners should still apply some caution when using the MBTI for
leadership development, particularly scales other than EI. This warning has similarly been
advised in the past (Michael, 2003).
4.4.3 Conclusion
Despite being found in a number of personality models and inventories, the specific
higher-order factors of the GFP and Big Two do not necessarily emerge in all major models
of personality. The social and trait-based nature of the GFP and Big Two may preclude their existence in certain inventories that do not sufficiently conceptualise personality in this way.
This includes the MBTI since it does not appear to contain much information on social
characteristics and also because it predominantly measures personality in terms of
preferences rather than behavioural tendencies. Additionally, although some form of a
higher-order factor could be extracted from the MBTI, it did not appear to relate to
leadership. This finding suggests that it is not just any higher-order factor that relates to
leadership but factors that are matched in both breadth and content that are more likely to be
relevant.
143
Chapter 5: General Discussion
This thesis investigated the relationship between higher-order personality factors and
leadership for leaders and working adults. The need to examine this link stemmed from the
recent emergence of these broader personality constructs as potentially meaningful predictors
of organisational phenomena (e.g., Alessandri & Vecchione, 2012; van der Linden, Bakker,
& Serlie, 2011), and also because their socially-oriented nature (DeYoung, 2015; Kowalski et
al., 2016; Loehlin, 2012) appears to be particularly relevant for leadership.
The present research had four overarching aims. The first aim was to examine the
relationship between higher-order personality factors and leadership, and the second was to
compare the effects to mid-order Big Five factors and lower-order facets. These were
undertaken to determine the extent to which traits of varying breadths relate to effective
leadership and to help clarify the extent to which it is the common variance versus unique
variance of personality traits that accounts for leadership. The third aim was to provide evidence on the conceptual basis of higher-order factors to gain a better understanding of their nature and to provide evidence for whether the GFP is reflective of a substantive construct. Lastly, the fourth aim was to investigate specific operational issues when using higher-order factors in order to provide practical insights to improve the validity of personality instruments for predicting effective leadership in applied settings.
This final chapter summarises and integrates the findings across the three studies
undertaken on the overarching aims of the thesis. Based on the synthesis of findings, the main
theoretical and practical contributions of this research are discussed. Finally, some of the
limitations of this research are outlined and suggestions provided for possible areas of future
investigations between higher-order personality factors and leadership. 144
5.1 Integration of Research Findings
The following integration of research findings is discussed in relation to the four
overarching aims of this thesis. Support or not for each of the five hypotheses across the
studies is also summarised.
Firstly, this thesis found that the higher-order factors of the GFP and Big Two had a
non-trivial effect on leadership. The meta-analysis in Chapter 2 found that the magnitude of
effects for higher-order factors was medium in size with slightly stronger effects for the GFP
based on effect size guidelines from Gignac and Szodorai (2016). The analysis of the CPI
dataset in Chapter 3 similarly found that the effects of the GFP and Stability on leadership
were small whereas the effect of Plasticity, although statistically significant, was trivial in
magnitude. The population correlations from the meta-analysis, corrected for sampling error,
unreliability and scale coarseness, showed that all three higher-order factors had a large effect
on transformational leadership, and that the GFP had a large effect on leadership
effectiveness. These findings are generally consistent with past studies that used student and
non-leader specific samples to examine the relationship between the GFP and leadership
effectiveness (rcorrected = .40; Pelt et al., 2017), transformational leadership (rcorrected = .32; Pelt
et al., 2017), leadership emergence (runcorrected = .70; Wu et al., 2020) and general leadership competencies (runcorrected = .22; van der Linden et al., 2014). Furthermore, the finding in
Chapter 4 that the higher-order factor from the MBTI did not have an effect on leadership
suggests that it is not necessarily any higher-order factor extracted from a personality
instrument that relates to leadership, but specifically the GFP and Big Two which are social
in nature and derived from trait-based lower-order factors.
Secondly, this thesis revealed differences in effects between each level of the
personality hierarchy on leadership. Specifically, and in partial support of Hypothesis 1, the
results from Chapter 2 not only showed that the effect of the GFP tended to be stronger and 145
to occur more consistently than the Big Two, but also that the effect of the GFP was higher
than all of the Big Five and facets. The latter finding was not the case at the Big Two level.
However, the meta-analysis in Chapter 2 did show that each of the Big Two had a stronger effect than at least one of their Big Five constituent factors. Moreover, Chapter 3 found that the effect of Stability was stronger than most of the individual facets. In terms of the amount of leadership variance that was explained by sets of personality traits at different levels, the
GFP, Big Two and Big Five were found to be largely similar. However, Chapter 3 showed that the set of facets appeared to account for around twice as much variance in leadership than the GFP. This suggests that facets may capture some information that is useful for leadership that would otherwise be lost when aggregating them to broader factors.
The effects for each of the individual facets and Big Five factors were also examined.
Chapter 3 found that less than half of the individual facets uniquely contributed to the explanation of leadership, and none accounted for meaningful incremental variance over and above other facets. The facets that possessed the largest non-trivial effects are generally consistent with research that has shown and argued that certain CPI facets are important for leadership. These include Empathy, Self-Control and reverse-scored Independence (Gough,
1990; Grahek et al., 2010; Greif & Hogan, 1973). Across these past studies, Empathy and
Self-control were important for leading with integrity whereas the reverse of Independence
(i.e., a tendency to seek support from others) was related to being more open as a leader, such as candidly sharing information with team members. For the Big Five factors, the meta- analysis in Chapter 2 showed that there were non-trivial unique effects between
Conscientiousness and leadership effectiveness (in support of Hypothesis 2) and between
Extraversion and transformational leadership behaviour. Chapter 3 also showed that the unique effect of Conscientiousness was trivially small for overall leadership. This finding was not necessarily surprising given the large credibility intervals found for the effect of 146
Conscientiousness on both leadership effectiveness and transformational leadership in
Chapter 2 which suggests that the effect systematically varies across study contexts. As highlighted in Chapter 3, one of the potential reasons for this inconsistency could be that the
Conscientiousness factor extracted from the CPI does not fully capture some of the other
important characteristics, such as work ethic and detail orientation, that are typically assessed
by explicit measures of this Big Five construct.
Taken together, the above two overall findings of the effects of higher-order factors
on leadership and comparisons to lower-order factors indicate that higher-order factors,
particularly the GFP, account for much of the overall effect of personality on leadership but
that lower-order factors may still be useful in some instances. The first instance relates to the
potential incremental effects of Conscientiousness and Extraversion, and the second relates to
using a set of facets for predicting leadership.
Thirdly, the findings from all three studies helped elucidate the conceptual basis of
the GFP and Big Two factors. The social nature of the factors, especially the GFP, was
supported by the results in Chapters 2 and 3 given they were relatively strongly related to
leadership, a construct that is highly socially-oriented and that operates within predominantly
social environments (Zaccaro, Gilbert, Thor, & Mumford, 1991). In support of Hypothesis 3,
the mediation effect in Chapter 2 suggests that the GFP may be a substantive construct as it
resulted in the demonstration of meaningful leadership behaviour that improved leadership
effectiveness. In Chapter 3, complex relationships between higher-order factors and
leadership were not found, and therefore did not support Hypothesis 4. The findings indicate
that the very high poles of higher-order factors are unlikely to be counterproductive (at least
within the domain of leadership), and that the positive effect of each of the Big Two factors
does not necessarily depend on the other. Their nature may be mostly functional in that
increasingly high scores on each factor appear to independently produce a desirable effect on 147
an important real-world outcome, and that having too much of any one of these factors does
not seem to be disadvantageous. In Chapter 4, the results from the factor analysis of the
MBTI suggest that the GFP and Big Two may be less prominent in models of personality that
measure preferences and whose theoretical bases are less oriented towards social and
interpersonal matters.
Lastly, although all of the findings have practical implications for using higher-order
factors to predict leadership as discussed below, Chapter 3 addressed specific issues in the operationalisation and use of these factors in applied settings. The first issue was related to different approaches used to calculate higher-order factors such as unit-weights, factor
loading weights based on facets and factor loading weights based on items. The study in
Chapter 3 found very strong relationships between scores derived using each of the extraction
methods and also that each method resulted in similar effects on leadership. As such, the
approach used to extract higher-order factors did not appear to be of practical importance.
The findings are consistent with past research that has found that a higher-order factor
calculated via unit-weights and one calculated using meta-analytically derived factor loading
weights are strongly related (r = .995; Dunkel, Stolarski, et al., 2014).
The second specific practical issue related to whether having fewer factors in the form
of higher-order factors yielded predictive benefits at smaller sample sizes. Chapter 3 also
found that the cross-validated predictive validity of the GFP and Big Two for leadership was
higher than lower-order factors at smaller sample sizes, which supports Hypothesis 5. This
finding has important practical implications for deciding on which level to operationalise
personality when predicting leadership in applied settings where only a small sample of
previous data is available. Moreover, in Chapter 4, the higher-order factors of the MBTI were
not predictive of leadership, and did not have any predictive advantage over the conventional
operationalisations of the MBTI, at smaller sample sizes. Thus, simply using any higher- 148
order personality factors that are not specifically representative of the nature of the GFP and
Big Two will unlikely result in predictive advantages at smaller sample sizes even if there are
fewer parameters to estimate.
5.2 Theoretical and Empirical Contributions
5.2.1 The Relationship between Personality and Leadership
On the implications for the trait theory of leadership, the first theoretical contribution of this thesis relates to the finding of the higher-order factors (GFP, Stability and Plasticity)
as personality traits that meaningfully account for effective leadership. As summarised in
Chapter 1, various theoretical perspectives, and therefore different traits, have been presented within the trait theory that describe relatively similar characteristics as antecedents of an effective leader including a somewhat selfless drive to influence others to achieve goals, and being adaptable and perceptive across diverse interactions requiring social influence. The essence of the GFP, particularly based on its conceptualisation as social competence (van der
Linden et al., 2016), appears to reflect this common theme across the different theoretical perspectives. Specifically, that being competent at managing and responding to different social challenges presumably underlies an individual’s ability to influence others in social settings. Similarly, the Big Two factors also appear to align to these past trait-based
leadership perspectives. That is, Stability reflects a socialisation process (Digman, 1997) such that it may help leaders to gain acceptance or consensus (or to minimise dissent) when influencing others. Plasticity, on the other hand, refers to the exploration of social stimuli
(Digman, 1997) such that being open to social experiences and taking risks within these experiences may help a leader to navigate ambiguous social scenarios that require influence.
These higher-order factors, especially the GFP, may also serve to explain or even unify some of the seemingly different viewpoints on differentiating traits that have been put forward. For example, effective leadership is variously argued to stem from a desire to (1) 149 have prosocial influence (Social Influence Motivation theory; Winter, 1973), (2) have an impact on others via social influence behaviours (Leader Motive Profile; McClelland, 1975), and (3) attain and exercise influence to facilitate change (Charismatic Leadership Theory;
House, 1977). As described above, these theoretical perspectives generally reflect a motivation to influence others. The GFP may account for this common theme of being motivated to influence, particularly since it has been defined as (and correlates with measures of) having knowledge, skill and motivation to be socially effective (Dunkel & Van der
Linden, 2014; van der Linden et al., 2016; van der Linden, Oostrom, et al., 2014). Future research could examine how and the extent to which the GFP relates to each of the above trait-based perspectives of leadership, and whether it can indeed help integrate and explain them.
The findings have implications for theory on the nature of the relationship between personality and leadership. A theoretical assumption underlying research between personality and leadership is that each factor independently affects leadership, and that leadership outcomes are the sum of individual effects. For example, trait activation theory posits that each of the Big Five is activated by distinct cues, resulting in behaviour that is determined by multiple trait influences (Tett & Burnett, 2003). Additionally, previous studies have typically theorised about and studied the effects of lower and mid-order traits on leadership independently and additively (e.g., Bono & Judge, 2004; Judge et al., 2002; Judge & Bono,
2000). On the one hand, the thesis findings provide some evidence for the independent effects of a subset of mid-order and lower-order traits. On the other hand, the findings make it clear that much of the effect of personality on leadership can be accounted for by a single broad personality factor, the GFP (and to a slightly lesser extent, the Big Two). Although there were a small number of instances in which a facet or Big Five factor independently predicted leadership to a non-trivial extent, the findings suggest that it is what these traits 150
have in common rather than what makes them distinct that drives leadership. This thesis
posits that the GFP’s conceptualisation as social competency (van der Linden et al., 2016)
reflects this commonality, and enables leaders to strategically respond to various social scenarios and to solve social problems. Social competence would allow a leader to draw on a
range of strategies that can address the social problems they encounter. In contrast, the
unique effects of the facets and Big Five capture the intrinsic motivation to enact the specific
behaviours associated with the trait, and these may or may not be effective for the social
problem at hand. Thus, the findings of this thesis challenge the viewpoint that the effect of
personality on leadership is primarily due to the unique effects of multiple lower-order and
mid-order personality traits.
Related to the above findings, the thesis also adds to the empirical literature on the
merits of broad versus narrow personality variables in predicting workplace criteria. It should
be noted that the majority of this literature has focused on the Big Five as the broad factors,
which are then compared to the lower level facets (e.g., Ones & Viswesvaran, 1996;
Paunonen et al., 1999). Only a few studies have extended this comparison to higher-order
factors as defined in this thesis, particularly for workplace criteria such as leadership and job
performance (e.g., Pelt et al., 2017; Van der Linden et al., 2010; Wu et al., 2020). Within the
literature, broad measures are argued to predict broad criteria moderately, whereas narrow
measures are argued to produce maximum validity for specific criteria (Cronbach & Gleser,
1957; Salgado, 2017). There is a quandary (known as the bandwidth–fidelity dilemma) as to
which approach to adopt given a limited number of survey questions (Salgado, 2017). Three
different approaches or recommendations have been proposed for this dilemma: broad
measures are the most optimal predictor of broad and narrow criteria (Ones & Viswesvaran,
1996); narrow measures are more predictive of narrow criteria, and also account for
additional variance in broad criteria beyond broad measures (Ashton et al., 2014; Tett, Steele, 151
& Beauregard, 2003); and the breadth of a measure should match the breadth of the criterion used (Hogan & Roberts, 1996; Schneider, Hough, & Dunnette, 1996).
As discussed above, the thesis findings showed that the higher-order factors performed better than most individual Big Five factors and facets for leadership but when considered as a set, the facets may capture some meaningful information that is important for leadership beyond that of the GFP, Big Two and Big Five. Thus, based on these findings, this thesis contributes to the bandwidth–fidelity debate in two ways. First, it extends the comparisons between the higher-order factors and mid-order Big Five to also include the lower-order facets. The previous studies that (to my knowledge) have compared the effects of higher-order factors to narrower ones (Pelt et al., 2017; Wu et al., 2020) did not also compare the effects to facets. For a more complete understanding of the bandwidth–fidelity issue, the effects across all major levels of the personality hierarchy need to be investigated and compared, especially since this thesis found that facets may still capture some meaningful information for leadership beyond broader factors.
Second, the thesis findings provide support for the argument that very broad measures
(i.e., the GFP and Big Two) are likely to be optimal predictors of a broad leadership criterion for working adults and leaders unlike past studies that did not exclusively examine these types of individuals. As such, there is also merit to the argument that the predictor and criterion breadths should align. Moreover, narrower measures (specifically facets) may still account for some additional variance in a broad leadership criterion too. This is consistent with certain past studies that have shown that facets can account for additional variance in leadership compared to their broader Big Five factors (Do & Minbashian, 2014; Judge et al.,
2002). However, some caution should be taken when interpreting the thesis finding given that it was based on a single study in Chapter 3 and that the CPI facet model investigated is only one of many facet structures in the literature. Future studies should compare the effects of 152 higher-order factors to different facet models, and potentially also via meta-analytic techniques to enhance the generalisability of findings.
5.2.2 The Nature of Higher-order Factors
The last theoretical contribution relates to the nature of the GFP. Compared to the Big
Two whose nature has been linked to biological functions and genetic factors (DeYoung et al., 2001; Jang, Livesley, Ando, Yamagata, Suzuki et al. 2006), the GFP’s nature is comparatively less clear and not as widely agreed. As outlined in Chapter 1, various propositions have been presented on the nature and existence of the GFP since it was first discovered. The main arguments are whether it reflects a substantive construct or a statistical response artefact (Ashton et al., 2009; Bäckström et al., 2009; Dunkel & Van der Linden,
2014; Irwing, 2013; Kowalski et al., 2016; Schermer & MacDougall, 2013), what it actually represents if it is in fact substantive (Biderman et al., 2019; van der Linden et al., 2016), and that an analysis of survey items rather than scales may result in a general factor that is not necessarily the GFP nor a factor that resides at a higher order (Anglim et al., 2017;
Bäckström et al., 2009; Biderman et al., 2018, 2019).
Firstly, the present thesis provided evidence in support of the substantive nature of the
GFP. Chapter 2 found that the effect of the GFP on leadership effectiveness is largely mediated by transformational leadership behaviours. As such, the GFP is likely to have some substance in that others perceive tangible and constructive behaviours from high GFP-scoring leaders that in turn result in effective leadership. Thus, the GFP cannot be solely explained by socially desirable responding or as only a statistical artefact. This finding is consistent with research that has shown that the GFP reflects socially competent behaviours (Kowalski et al.,
2016; van der Linden et al., 2016) and more generally that it is associated with behavioural outcomes that influence the judgements of other people (van der Linden, te Nijenhuis,
Cremers, van de Ven, & van der Heijden-Lek, 2014). However, it is acknowledged that the 153
mediation results in this thesis provide only an indirect examination of the nature of the GFP
and whether it represents a substantive construct, and additional research is needed to fully
understand what the GFP actually represents.
Assuming that the GFP is a substantive construct, a number of different
conceptualisations have been suggested. Van der Linden et al. (2016) asserted that the leading interpretation of the GFP is in terms of social competence (or social effectiveness).
However, research has also shown that the GFP correlates with other meaningful phenomena such as affect (Biderman et al., 2018), emotional intelligence (van der Linden et al., 2017) and integrity (van der Linden, te Nijenhuis, et al., 2014). The thesis provides support for the social competence interpretation for two reasons. Firstly, the meta-analysis in Chapter 2 showed that the GFP correlates strongly with leadership, a construct that is contingent on one’s ability to understand, analyse and appropriately respond to various social scenarios
(Zaccaro et al., 1991). Secondly, given that the content of the higher-order factor extracted
from the MBTI in Chapter 4 did not resemble the GFP or either of the Big Two, nor did the extracted factor relate to leadership, there are indirect implications for the nature of the GFP
and Big Two. Unlike the Big Five and the CPI, the theoretical underpinning of the MBTI
does not appear to be strongly rooted in social motivations. As such, the absence of the GFP
and Big Two from the MBTI (but their presence within the Big Five and CPI) lends indirect
support for their essence being social in nature. Furthermore, the MBTI measures
preferences, which may not be as functional nor capture the strategic intent that may underlie
a trait. Thus, the absence of the GFP within the MBTI again provides indirect support to the
argument that the GFP is both social and functional in nature.
As described above, the third issue on the nature of the GFP stems from differences in
analyses at the item as opposed to scale level. The thesis findings did not support past
research that has argued that item-level analyses produce a general factor that is different 154
from the GFP (e.g., Anglim et al., 2017). Specifically, the factor analysis of survey items in
Chapter 3 resulted in factors that were, in general, not materially different from the factor analysis of scales (i.e., they were highly intercorrelated and also correlated similarly with leadership). As such, their nature is likely to be consistent regardless of the extraction approach employed. Instead, the findings support those who have argued that a general factor extracted from item-level approaches can be identical to higher-order factors derived from scales (Chen, Watson, Biderman, & Ghorbani, 2016).
Lastly, the absence of curvilinear relationships between all higher-order factors and leadership was relatively unexpected and has implications for the nature of the Big Two and also the GFP. Past studies have found mixed support for complex relationships between personality traits and job performance (Le et al., 2011; Robie & Ryan, 1999) and some support for specific traits such as charisma, assertiveness and dominance with leadership
(Ames & Flynn, 2007; Benson & Campbell, 2007; Vergauwe et al., 2018). However, none
(to my knowledge) have examined complex relationships for higher-order personality factors.
The inability of the thesis research to find complex relationships between higher-order factors and leadership could be due to methodological constraints. For example, effects may not have been detected due to low statistical power as a result of insufficient sample size (e.g., Jones,
Carley, & Harrison, 2003). Similarly, there may not have been a large enough range of higher-order factor scores at the extreme ends, potentially because not as many extreme- scoring Stability and Plasticity individuals become leaders. The study in Chapter 3 included
3,427 participants so it is unlikely that the absence of effects was due to low statistical power based on an insufficient sample size. However, it is possible that there was not an adequate range of extreme scores since. For example, the number of participants who scored higher than two standard deviations above the mean across each higher-order factor was fairly low 155
at 21 or 0.61% of participants for Plasticity, five or 0.15% for Stability and 13 or 0.38% for the GFP.
If there truly is a lack of complex effects between higher-order personality factors and leadership and methodological limitations are not masking them, then it is possible to
conclude that very high levels of the Big Two and the GFP may not be counterproductive, at
least for leadership. The first implication of this finding is that the too-much-of-a-good-thing
(TMGT) effect (Pierce & Aguinis, 2013) may not apply to higher-order factors and
leadership. Pierce and Aguinis (2013) suggested that the TMGT effect may exist within the
domain of leadership but provided limited evidence for traits that have this effect. They only
argued for characteristics related to initiating structure and consideration. There are two potential theoretical reasons for why the TMGT effect may not have been found in the thesis.
First, Pierce and Aguinis (2013) argued that the inflection point for traits that have the TMGT effect is context-specific. There may be situational cues that cause the effect of a trait to
inflect (or become detrimental) in one setting but not another. Future studies could examine
the kinds of situational cues that may have an impact. For example, the extent to which an
organisation is agile or possesses a growth mindset culture may delay or completely remove
the inflection point for Plasticity. This is because of Plasticity’s conceptualisation in terms of
exploration and flexibility, which could be viewed as beneficial in agile or growth types of
environments. Second, the absence of the TMGT effect for higher-order factors on leadership
may also be due to (and also provides further indirect support for) the functional nature of
higher-order factors. Narrower traits may possess a curvilinear relationship with leadership,
such as those listed above including assertiveness and dominance, because the intrinsic
motivation of performing too much of a specific behaviour that is not as functional or
strategic may become problematic. Since the Big Two are linked to broader goal-oriented
motivations (i.e., goal exploration for Plasticity and goal maintenance for Stability) and the 156
GFP is related to (social) competence, these more functional aspects may safeguard the
factors from being disadvantageous for leadership at very high levels. Researchers have argued that constructs that have a curvilinear relationship with outcomes may be psychopathological or may even represent completely different constructs at extreme levels
(Le et al., 2011; Pierce & Aguinis, 2013). The thesis findings suggest this is unlikely to be the case for the nature of the Big Two and the GFP.
5.3 Practical Contributions
The practical implications of this thesis relate to both the selection and development of leaders. First, HR professionals and management practitioners may need to consider
revising or updating existing competency models that are often used in organisations to
define success for leaders and that help guide selection and development initiatives.
Competency models typically comprise observable behaviours that arise from an individual’s
underlying dispositions and intent (Boyatzis, 2008). Personality can be used to assess
individuals against competencies since the essence of a competency largely aligns to, and
often includes (Asumeng, 2014), personality traits which are underlying cognitive or
affective dispositions that pervasively influence behavioural tendencies, as defined in Chapter
1. In one review of competencies for leaders, managers and advanced professionals, the
broad competency categories that distinguished high performers included social, emotional
and cognitive intelligences (Boyatzis, 2008). The social intelligence category included being
socially aware and empathetic, managing relationships and leveraging social perceptions to
enhance performance. The thesis findings support the inclusion and emphasis of these
behaviours in leadership competency models, particularly those behaviours directly related to
the GFP such as social competence, drawing on various strategies to manage social
situations. 157
For selection decisions, the thesis findings suggest that leaders can be selected based
on the GFP and this factor may even provide predictive advantages over the widely-used Big
Five factors in certain situations. For example, in situations where the sample of past
candidates or incumbent leaders used to inform a selection decision is small (e.g., senior or
executive leaders), the GFP (and to a slightly lesser extent, the Big Two) could be used instead of the Big Five and facets. However, when the sample used to calculate prediction equations is much larger such as frontline leaders and managers and when shorter test-taking
time is not as imperative as discussed below, facets may be more advantageous for selecting
leaders.
One potential practical benefit of focusing on higher-order factors is that fewer items
and less time may be required to assess one or two very broad factors compared to the
hundreds of items that are often used to assess narrower traits (e.g., McCrae & Costa, 1992).
It is worth highlighting that the higher-order factors operationalised in the thesis studies used the same number of items and would have had the same test-taking time as other personality levels. However, past research suggests that higher-order factors can retain their reliability and validity when assessed with substantially fewer items. For example, one study found that
GFPs extracted based on 10, 20, 50 and 100 items possessed similar and generally acceptable
36 reliabilities (Cronbach’s = .6835F to .80), were highly intercorrelated (r = .73 to .79) and
correlated similarly with various criteria (Burns, Morris, Periard, LaHuis, Flannery et al.,
2017). These criteria included counterproductive work behaviours (r = -.34 to -.29), organisational citizenship behaviours (r = .23 to .30) and burnout (r = -.41 to -.33). In another study, a GFP was also extracted from a 10-item personality measure and was found to strongly correlate with an overall measure of emotional intelligence (r = .47; Kawamoto,
36 This value was based on the 10-item measure; the remaining three measures based on 20, 50 and 100 items had values greater than .70. 158
Kubota, Sakakibara, Muto, Tonegawa et al., 2020). Therefore, test-taking time can be
reduced via shorter measures of the GFP, which do not necessarily have compromised
reliabilities and validities. However, future research should still investigate whether their
predictive validities are also maintained for leadership-specific criteria.
Although the thesis findings have important implications for selection, applied
settings do not typically use personality measures in isolation. Personality measures are often
used in combination with other selection methods such as structured interviews and cognitive
ability tests. Each method typically aims to measure a limited number of constructs or
competencies that may overlap across certain methods. For instance, both a personality
measure and interview may seek to assess a candidate’s emotional stability or stress
tolerance. Consequently, the incremental validity of measures of the GFP and Big Two may
not be as high if other measures capture some of this information. For example, past research
has shown that interview ratings are correlated with multisource scores of personal
effectiveness and flexibility (r = .46), and interpersonal effectiveness (r = .28; Darr &
Catano, 2008). To the extent that these competencies align with Plasticity (in terms of
personal growth and adaptability) and the GFP (in terms of social competence), respectively,
measures of Plasticity and/or GFP will have lower incremental validity. Future research could
investigate whether measures of the GFP and Big Two retain their incremental validities for
leadership when used with other selection methods that capture similar information.
Another implication for selection relates to the relatively wide credibility intervals
found in the meta-analysis in Chapter 2. On the one hand, a wide credibility interval could
suggest the need to conduct a personality-based job analysis to identify narrow traits that are
relevant for success in specific contexts (Tett & Christiansen, 2008). A composite of those
traits can then be calculated for predicting performance. On the other hand, recent research has shown that in high stakes selection circumstances where response distortion (or faking) is 159
likely, the loadings on a common factor are substantially larger such that the structure and
dimensionality of narrow traits becomes less apparent (Christiansen, Robie, Burns, Loy,
Speer, & Jacobs, 2021). Thus, higher-order factors such as the GFP could be used in these
higher stakes scenarios, which would remove the need to conduct a job analysis in such
circumstances.
One final consideration for using the GFP and Big Two factors for selection purposes
is the need to undertake adverse impact studies, which has also been recommended for the
Big Five (Judge & Bono, 2000). In addition to criterion-related validity and construct
validity, employers need to ensure that the selection tools they use do not unfairly exclude
certain candidates, which can be represented as a substantial and reliable mean difference in
test scores between demographic groups (Sackett & Ellingson, 1997). Adverse impact across selection tools needs to be examined and minimised for candidates of various demographics including gender, ethnicity and age given the important legal and ethical implications
(Hough, Oswald, & Ployhart, 2001; Ones & Anderson, 2002). Past research has found mean differences between ethnic groups at the facet level, gender differences at both the Big Five and facet level, and minimal differences based on age (Hough et al., 2001). To my knowledge, no specific adverse impact studies have been undertaken on higher-order factors.
However, it has been argued that there are likely to be gender differences in the GFP because
different genders are argued to fulfil unique social roles (Just, 2011). Additionally, past
studies have found gender differences in how higher-order models fit data (Rushton &
Irwing, 2009a) and also in the way the GFP loads on various traits and emotional intelligence
components (McIntyre, 2010). These findings suggest that some adverse impact may exist for
higher-order factors. Further investigation is needed to understand and minimise differences
between different demographic groups, particularly if a GFP-specific questionnaire is
designed in the future, as described below. 160
For development, and for the GFP specifically, leadership development programs
involving personality assessments could include a module or content on enhancing social competence. These personality-oriented programs typically focus on providing developmental recommendations for low and high scores across each lower-order factor. The findings of the thesis suggest that, although some of the lower-order factor development
recommendations may still be useful, training should also incorporate development
interventions related to the GFP to increase a leader’s social competence. Development
programs could emphasise the skills associated with social competence, such as coaching,
negotiation, transferring verbal and non-verbal signals, given the potential for these skills to
change and develop through training interventions (Schneider, Ackerman, et al., 1996). For
the Big Two, the training could also focus on enhancing one’s capacity to strategically cooperate with others and maintain effective relationships (i.e., Stability), and challenging oneself to explore new interpersonal connections and social networks (i.e., Plasticity). These training interventions could include guided role-plays, simulations or videos that teach leaders how to deal with challenging or ambiguous social scenarios or problems as a leader.
These development implications on state-based skill or behaviour changes relate to higher-order factors. Nevertheless, meta-analytic research does support pervasive changes in the Big Five personality factors over time as a result of interventions (Roberts, Luo, Briley,
Chow, Su, & Hill, 2017). As such, it may also be possible for a leader’s GFP or Big Two factors to change. Future research could examine the extent to which leadership development programs focused on the GFP and the Big Two lead to pervasive changes in a leader’s personality on these factors, discussed further below.
Lastly, it is important to highlight that the thesis findings do not necessarily advocate for practitioners to solely focus on developing the characteristics associated with higher-order factors. Both the higher-order factors as well as the component scores that are used to derive 161
them could be of practical use, especially the factors that were found to have unique and
incremental effects for leadership. These lower-order factors could still highlight specific
traits or behaviours that may require development beyond only social competence
characteristics associated with the GFP. For example, leaders who score high on the GFP but
low on Conscientiousness may not need as much training in social competence. Instead, they
may need to focus their development on being more organised and to set more structured
goals in order to lead effectively and ensure that team members follow through on
commitments.
5.4 Limitations and Future Directions
One of the limitations of the thesis research was that only transformational leadership
behaviour was examined as a mediator in the relationship between the GFP and leadership
effectiveness. In Chapter 2, a significant positive direct effect (although small in magnitude)
remained between the GFP and leadership effectiveness after controlling for transformational
leadership behaviour. As such, future research should explore other behaviours or reasons for why the GFP is related to effective leadership. For example, the ability to accurately perceive the requirements of a social situation based on existing and diverse knowledge (i.e., social perceptiveness) and to choose and demonstrate an appropriate behavioural response from a range of options (i.e., behavioural flexibility) are two socially-oriented behaviours that have been argued to underpin successful leadership (Zaccaro et al., 1991). These qualities appear highly aligned to the social competence nature of the GFP and may be more proximal behaviours that could explain the link between the GFP and effective leadership. Given the thesis research also did not examine mediators between each of the Big Two factors and leadership, the mediating effect of social perceptiveness and behavioural flexibility could also be investigated in future studies on the Big Two. In particular, behavioural flexibility 162
may be an especially relevant mediator for Plasticity since this factor relates to being socially
adaptable.
Given the relatively wide credibility intervals for higher-order factors in the meta-
analysis in Chapter 2, future research could explore situational moderators that influence the
relationship between these factors and leadership, such as organisational culture. Although
leader traits and dispositional tendencies are generally stable across situations, trait theorists
still acknowledge that there may be variability in the way traits are expressed and appraised
across different contexts (Zaccaro, 2007). For example, Stability may be less useful for
leadership in agile and flexible work environments since leaders who score high on Stability
are focused on goal maintenance and consistency. In contrast, Plasticity may be detrimental
in environments that are process and compliance-focused, with minimal appetite for risk-
taking tendencies such as in highly regulated organisations. Since the GFP is primarily
related to social competence and influencing strategies, this factor may not have as much
relevance in environments where team sizes are quite small or where there are fewer
stakeholder interactions.
Future research could also develop and validate a personality inventory that
specifically measures the GFP and Big Two factors. The thesis studies operationalised the
higher-order factors in line with previous research, that is, by extracting them from existing
personality inventories. However, to take advantage of the proposed practical benefit of
reduced test-taking time, a new measure could be constructed that only includes items that
load most strongly on the GFP and Big Two. As highlighted in Chapter 1, only one past
37 study36F has explicitly developed a 20-item questionnaire that purportedly measured the GFP
37 The study cited above (i.e., Burns, Morris, Periard, LaHuis, Flannery et al., 2017) that calculated a GFP based on 10, 20, 50 and 100 items did not explicitly create new questionnaires. Rather, these sets of items were based on 2, 4, 10 and 20 item measures (respectively) of each Big Five factor from which a GFP was then extracted via factor analysis. 163
(Amigó et al., 2010). However, since the poles of that inventory were labelled and defined in
terms of extraversion and introversion, the inventory’s conceptualisation of the GFP may be
too narrow and is also inconsistent with how the GFP is defined across the personality
literature. The construction and validation of an explicit GFP and Big Two measure may also
provide greater consistency for ongoing research into their nature and relationships with various criteria, rather than extracting them from different personality measures which may vary in the extent to which they contain higher-order factors.
From a statistical analysis perspective, future studies could examine the use of different or non-traditional analyses in three ways, and whether the same conclusions as
found in this thesis can be drawn. Firstly, future meta-analytic investigations between higher- order personality factors and leadership could also incorporate more sophisticated analyses such as a meta-regression. A meta-regression seeks to model the unique relationship between predictors and outcomes whilst other variables in the model are controlled for (Murphy,
Fisher, & Robie, 2021). For example, since the meta-analysis in Chapter 2 included only studies that explicitly measured the Big Five and the study in Chapter 3 examined a non- explicit Big Five measure via a single dataset, future research could employ meta-regression to investigate whether or not the effects of higher-order factors for leadership generalise across explicit and non-explicit Big Five measures. Moreover, the thesis included both leaders and working adults across the studies, but future research could investigate whether the higher-order effects differ between working adults that hold a leadership position and actually lead others as opposed to those who do not. This question is particularly relevant based on research suggesting that one’s work role may affect one’s personality (as described below). Issues such as low statistical power, regression weights that cannot be interpreted, and unreliability and measurement bias can be alleviated when undertaking meta-regression.
Secondly, the non-linear relationships in Chapter 3 were investigated through the use of 164 power polynomials (i.e., squared terms to test for a quadratic effect) but can also be examined via segmented regression. Unlike quadratic regression models which serve to detect a gradual directional change in the regression line, segmented regression tests for a more pronounced or sudden change in direction (Robie, Christiansen, Bourdage, Powell, & Roulin, 2020). If a non-linear relationship does exist between higher-order personality factors and leadership but the point of inflection is abrupt, then a segmented regression would be a more powerful test of such an effect. Thirdly, the elastic net technique, another form of regression, could also be employed in future research comparing the effects of higher-order factors to the lowest levels of the personality hierarchy. This statistical technique automatically selects predictors for a regression but is unique in that it also selects groups of strongly correlated variables and is useful in situations where there are a much greater number of predictors than observations
(Zou & Hastie, 2005). Elastic net regression analyses would be particularly pertinent for future studies that seek to compare the effects of higher-order personality factors to items or the relatively newer concept of nuances. As noted in Chapter 1, there are levels of the personality hierarchy that were beyond the scope of this thesis (i.e., Aspects). Nuances represent another lower-level of the hierarchy that can refer to individual test items or a grouping of highly similar items (Mõttus, Kandler, Bleidorn, Riemann, & McCrae, 2017).
The elastic net technique could accommodate the potentially very large number of predictors in the form of nuances and has also been shown to lead to greater predictive accuracy compared to other comparable regression techniques (i.e., lasso regression; Zou & Hastie,
2005).
Finally, in line with prominent trait-based models of job performance and leadership effectiveness (Derue et al., 2011; Tett & Burnett, 2003; Zaccaro, 2007), this thesis assumed that personality traits are antecedent and causally related to leadership. However, recent research suggests that work roles and environments may instead shape one’s personality. For 165
example, chronic job insecurity has been shown to increase Neuroticism and decrease
Agreeableness (Wu, Wang, Parker, & Griffin, 2020), and job autonomy has been found to
increase locus of control (Wu, Griffin, & Parker, 2015). From a leadership perspective,
moving from an individual contributor role to a leadership position has also been found to
increase job role demands that in turn increase Conscientiousness (Li, Li, Feng, Wang,
Zhang, Frese, & Wu, 2020). Future research could investigate the extent to which leadership
tenure and progression across more senior leadership roles may change one’s personality across the higher-order factors. Given the frequency and complexity of social problems that leaders face and their need to be socially perceptive and behaviourally flexible (Zaccaro et al., 1991), social competency, social exploration and social cooperation would presumably develop over time, which may manifest in terms of higher scores on the GFP, Plasticity and
Stability, respectively. This would also have implications for leadership development as greater exposure and experience to solving social problems through long-term training and executive coaching may result in lasting changes in these personality traits.
5.5 Conclusion
The thesis found that higher-order personality factors have meaningful effects on leadership in samples of leaders and working adults, and that most narrower traits do not individually possess unique effects. Taken together, the results suggest that the GFP accounts for much of what lower levels account for, including both facets and the Big Five, and has the practical benefit of having fewer factors to assess. In addition to contributing to the trait theory of leadership and to knowledge about the nature of higher-order factors, the findings have important practical implications for the selection of leaders, especially when the size of samples used to construct prediction equations are small, as well as the development of leaders when personality assessments are involved. 166
In conclusion, the social challenges, complexity and ambiguity facing leaders in organisations are likely to continue to increase. Socially competent leaders are better equipped to navigate through the multitude of social scenarios and problems that need quick and effective resolution (Zaccaro et al., 1991). Based on this need, this thesis advocates for the use of higher-order personality factors, particularly the GFP, as differentiating traits that determine successful leadership.
167
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205
Meta-analysis Funnel Plots
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 1
Funnel Plot for the relationship between transformational leadership behaviour and
Neuroticism.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 2
Funnel Plot for the relationship between transformational leadership behaviour and
Extraversion. 206
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 3
Funnel Plot for the relationship between transformational leadership behaviour and
Openness to Experience.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 4
Funnel Plot for the relationship between transformational leadership behaviour and
Agreeableness.
207
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 5
Funnel Plot for the relationship between transformational leadership behaviour and
Conscientiousness.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 6
Funnel Plot for the relationship between transformational leadership behaviour and
Stability.
208
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 7
Funnel Plot for the relationship between transformational leadership behaviour and
Plasticity.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 8
Funnel Plot for the relationship between transformational leadership behaviour and the
GFP.
209
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard ErrorStandard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 9
Funnel Plot for the relationship between leadership effectiveness and Neuroticism.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 10
Funnel Plot for the relationship between leadership effectiveness and Extraversion.
210
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 11
Funnel Plot for the relationship between leadership effectiveness and Openness to
Experience.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 12
Funnel Plot for the relationship between leadership effectiveness and Agreeableness.
211
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 13
Funnel Plot for the relationship between leadership effectiveness and Conscientiousness.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 14
Funnel Plot for the relationship between leadership effectiveness and Stability.
212
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 15
Funnel Plot for the relationship between leadership effectiveness and Plasticity.
Funnel Plot of Standard Error by Fisher's Z 0.0
0.1
0.2 Standard Error Standard 0.3
0.4
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Fisher's Z
Figure A 16
Funnel Plot for the relationship between leadership effectiveness and the GFP.
213
Appendix B: Study 2
Table B 1
Pearson Product-moment Correlations Between the CPI Facets
Facets Do Cs Sy Sp Sa In Em Re So Sc Gi Cm Wb To Ac Ai Cf Is Fx
Do –
Cs .66* –
Sy .75* .73* –
Sp .56* .69* .70* –
Sa .77* .65* .73* .62* –
In .73* .56* .53* .52* .59* –
Em .51* .66* .60* .59* .47* .38* –
Re .24* .24* .17* .02 .11* .19* .21* –
So .11* .02 .09* -.03 -.03 .10* .08* .29* – ------Sc .34* .44* – .23* .26* .25* .46* .35* .12* .09* - - Gi .03 .00 .06* .04* .13* .35* .43* .77* – .17* .16* - - - - - Cm .00 -.03 .25* .23* .23* .19* – .12* .06* .11* .10* .09* Wb .33* .28* .29* .30* .23* .43* .30* .29* .49* .36* .48* .22* –
To .17* .36* .24* .25* .13* .26* .39* .45* .34* .32* .40* .14* .60* –
Ac .33* .25* .27* .02 .14* .23* .21* .51* .41* .33* .44* .28* .35* .27* –
Ai .22* .48* .30* .38* .20* .30* .52* .41* .17* .16* .27* .09* .42* .65* .26* –
Cf .48* .55* .46* .45* .40* .51* .47* .43* .24* .12* .24* .08* .49* .54* .42* .64* –
Is .26* .36* .21* .26* .18* .35* .31* .36* .22* .17* .22* .17* .44* .51* .35* .58* .56* – - - - - - Fx .06* .33* .16* .34* .11* .17* .43* .09* .11* .36* .56* .27* .32* – .12* .12* .08* .13* .16* ------Sn .05* .19* .03 -.01 -.02 .12* .41* .20* .26* .28* .31* .39* .12* .06* .32* .08* .05* .21* .19* Note: *p < .01. Dominance = Do, Capacity for Status = Cs, Sociability = Sy, Social Presence = Sp, Self-acceptance = Sa, Independence = In, Empathy = Em, Responsibility = Re, Social Conformity = So, Self-control = Sc, Good Impression = Gi, Communality = Cm, Well-being = Wb, Tolerance = To, Achievement via Conformance = Ac, Achievement via Independence = Ai, Conceptual Fluency = Cf, Insightfulness = Is, Flexibility = Fx, Sensitivity = Sn.
214
Table B 2
CPI Facet Loadings on the GFP from Loehlin’s (2012) Study
CPI Facet Loadings Dominance .76 Capacity for Status .78 Sociability .81 Social Presence .76 Self-acceptance .76 Independence .68 Empathy .76 Responsibility .40 Social Conformity .22 Self-control .06 Good Impression .31 Communality .26 Well-being .58 Tolerance .46 Achievement via Conformance .51 Achievement via Independence .62 Conceptual Fluency .71 Insightfulness .58 Flexibility .33 Sensitivity -.24
215
Figure B 1
Scree plot of eigenvalues associated with the principal axis factoring of the CPI items.
216
Table B 3
Factor Matrix for Principal Axis Factoring of One Factor Solution of CPI Items
Factor Coefficients Communalities CPI Items Component 1 Initial Extraction Itm_192 0.53 0.51 0.28 Itm_078 0.50 0.54 0.25 Itm_100 0.49 0.43 0.24 Itm_134 0.49 0.52 0.24 Itm_058 0.49 0.45 0.24 Itm_052 0.48 0.66 0.23 Itm_082 0.47 0.47 0.22 Itm_139 0.47 0.51 0.22 Itm_221 0.46 0.41 0.21 Itm_021 0.44 0.61 0.19 Itm_230 0.44 0.36 0.19 Itm_253 0.42 0.45 0.17 Itm_046 0.42 0.35 0.17 Itm_233 0.41 0.47 0.17 Itm_256 0.40 0.35 0.16 Itm_195 0.39 0.30 0.15 Itm_149 0.39 0.32 0.15 Itm_176 0.39 0.31 0.15 Itm_124 0.38 0.37 0.14 Itm_163 0.38 0.62 0.14 Itm_177 0.38 0.32 0.14 Itm_113 0.36 0.31 0.13 Itm_110 0.36 0.37 0.13 Itm_218 0.36 0.34 0.13 Itm_035 -0.36 0.48 0.13 Itm_215 0.36 0.52 0.13 Itm_178 0.35 0.32 0.12 Itm_152 0.35 0.33 0.12 Itm_184 0.34 0.33 0.12 Itm_105 0.34 0.34 0.12 Itm_010 0.34 0.35 0.11 Itm_065 0.34 0.37 0.11 Itm_155 0.34 0.32 0.11 Itm_199 0.33 0.35 0.11 Itm_092 0.33 0.31 0.11 Itm_188 0.33 0.47 0.11 Itm_231 0.33 0.34 0.11 Itm_048 -0.33 0.37 0.11 Itm_203 0.33 0.27 0.11 Itm_194 -0.32 0.40 0.11 Itm_036 0.32 0.34 0.11 Itm_081 0.32 0.41 0.10 Itm_077 -0.32 0.45 0.10 Itm_145 0.32 0.29 0.10 Itm_129 0.32 0.45 0.10 Itm_179 0.32 0.43 0.10 Itm_146 0.32 0.30 0.10 Itm_018 0.32 0.33 0.10 Itm_138 0.32 0.35 0.10 Itm_219 0.32 0.36 0.10 Itm_212 0.31 0.27 0.10 Itm_207 0.31 0.33 0.10 Itm_258 0.31 0.27 0.10 Itm_154 0.31 0.35 0.10 Itm_161 0.31 0.36 0.10 Itm_119 0.31 0.43 0.10 Itm_104 -0.31 0.59 0.09 Itm_047 0.31 0.31 0.09 Itm_069 0.30 0.30 0.09 Itm_057 0.30 0.30 0.09 Itm_075 -0.30 0.29 0.09 Itm_103 0.29 0.29 0.09 Itm_193 0.29 0.29 0.09 Itm_083 0.29 0.27 0.08 Itm_074 0.29 0.38 0.08 Itm_059 0.29 0.41 0.08 Itm_086 0.29 0.32 0.08 217
Itm_205 0.29 0.25 0.08 Itm_186 0.28 0.33 0.08 Itm_173 0.28 0.36 0.08 Itm_076 0.27 0.29 0.08 Itm_167 0.27 0.34 0.08 Itm_247 0.27 0.32 0.07 Itm_222 0.27 0.29 0.07 Itm_168 0.27 0.27 0.07 Itm_099 -0.27 0.38 0.07 Itm_170 0.27 0.47 0.07 Itm_172 0.26 0.26 0.07 Itm_024 0.26 0.41 0.07 Itm_190 0.26 0.30 0.07 Itm_249 0.25 0.34 0.06 Itm_114 0.25 0.26 0.06 Itm_017 0.25 0.32 0.06 Itm_259 0.25 0.39 0.06 Itm_025 0.25 0.27 0.06 Itm_106 0.25 0.27 0.06 Itm_107 -0.24 0.31 0.06 Itm_239 0.24 0.31 0.06 Itm_034 0.24 0.31 0.06 Itm_014 0.24 0.28 0.06 Itm_142 0.23 0.33 0.05 Itm_175 0.23 0.30 0.05 Itm_033 0.23 0.26 0.05 Itm_020 0.23 0.25 0.05 Itm_088 0.22 0.27 0.05 Itm_248 0.22 0.27 0.05 Itm_044 0.22 0.34 0.05 Itm_180 -0.22 0.39 0.05 Itm_116 0.22 0.26 0.05 Itm_196 0.22 0.30 0.05 Itm_097 0.22 0.27 0.05 Itm_140 -0.22 0.37 0.05 Itm_160 0.21 0.27 0.05 Itm_125 0.21 0.33 0.05 Itm_208 0.21 0.32 0.05 Itm_062 -0.21 0.39 0.05 Itm_260 0.21 0.30 0.04 Itm_111 0.21 0.29 0.04 Itm_027 -0.21 0.26 0.04 Itm_250 0.21 0.32 0.04 Itm_066 0.20 0.31 0.04 Itm_127 -0.20 0.60 0.04 Itm_143 0.20 0.25 0.04 Itm_007 0.20 0.33 0.04 Itm_234 -0.20 0.55 0.04 Itm_040 0.20 0.27 0.04 Itm_229 0.20 0.22 0.04 Itm_093 0.19 0.26 0.04 Itm_201 -0.19 0.20 0.04 Itm_162 -0.19 0.53 0.03 Itm_101 0.18 0.30 0.03 Itm_118 0.18 0.23 0.03 Itm_204 -0.18 0.32 0.03 Itm_026 0.18 0.22 0.03 Itm_156 0.18 0.23 0.03 Itm_112 0.18 0.54 0.03 Itm_064 0.17 0.26 0.03 Itm_084 0.17 0.24 0.03 Itm_009 0.17 0.28 0.03 Itm_224 0.16 0.30 0.03 Itm_067 0.16 0.31 0.03 Itm_132 0.16 0.25 0.03 Itm_136 -0.16 0.35 0.03 Itm_236 0.16 0.32 0.02 Itm_041 0.16 0.28 0.02 Itm_094 0.15 0.28 0.02 Itm_251 0.15 0.27 0.02 Itm_226 0.15 0.31 0.02 Itm_130 0.15 0.21 0.02 Itm_181 0.15 0.24 0.02 Itm_164 0.15 0.35 0.02 Itm_240 0.15 0.30 0.02 218
Itm_096 -0.14 0.32 0.02 Itm_159 0.14 0.28 0.02 Itm_232 0.14 0.20 0.02 Itm_102 0.14 0.30 0.02 Itm_016 0.14 0.26 0.02 Itm_006 0.14 0.28 0.02 Itm_079 0.14 0.21 0.02 Itm_005 0.13 0.18 0.02 Itm_133 0.13 0.23 0.02 Itm_063 -0.13 0.33 0.02 Itm_038 -0.13 0.34 0.02 Itm_030 0.13 0.30 0.02 Itm_054 0.13 0.32 0.02 Itm_117 0.13 0.22 0.02 Itm_171 -0.13 0.45 0.02 Itm_165 -0.13 0.32 0.02 Itm_045 -0.13 0.26 0.02 Itm_011 0.13 0.25 0.02 Itm_031 0.12 0.20 0.02 Itm_209 0.12 0.21 0.02 Itm_068 0.12 0.23 0.02 Itm_185 0.12 0.26 0.02 Itm_157 0.12 0.19 0.01 Itm_022 0.12 0.31 0.01 Itm_122 0.11 0.33 0.01 Itm_198 -0.11 0.32 0.01 Itm_039 0.11 0.15 0.01 Itm_049 0.11 0.26 0.01 Itm_148 -0.11 0.35 0.01 Itm_128 0.11 0.22 0.01 Itm_238 -0.10 0.57 0.01 Itm_137 -0.10 0.21 0.01 Itm_246 -0.10 0.21 0.01 Itm_131 -0.10 0.40 0.01 Itm_220 -0.10 0.20 0.01 Itm_153 -0.10 0.23 0.01 Itm_072 -0.10 0.31 0.01 Itm_120 -0.10 0.33 0.01 Itm_191 -0.10 0.31 0.01 Itm_254 -0.10 0.23 0.01 Itm_135 0.09 0.39 0.01 Itm_028 0.09 0.34 0.01 Itm_123 0.09 0.21 0.01 Itm_169 -0.09 0.21 0.01 Itm_197 0.09 0.35 0.01 Itm_141 -0.09 0.19 0.01 Itm_073 -0.09 0.22 0.01 Itm_189 0.08 0.26 0.01 Itm_019 0.08 0.21 0.01 Itm_206 -0.08 0.32 0.01 Itm_237 -0.08 0.26 0.01 Itm_098 -0.08 0.58 0.01 Itm_228 0.08 0.18 0.01 Itm_223 -0.08 0.17 0.01 Itm_089 -0.08 0.53 0.01 Itm_183 -0.07 0.18 0.01 Itm_115 -0.07 0.41 0.01 Itm_244 -0.07 0.18 0.01 Itm_050 -0.07 0.32 0.01 Itm_056 -0.07 0.36 0.01 Itm_147 0.07 0.23 0.01 Itm_055 -0.07 0.61 0.00 Itm_255 0.07 0.31 0.00 Itm_144 0.06 0.24 0.00 Itm_003 -0.06 0.30 0.00 Itm_213 0.06 0.37 0.00 Itm_051 0.06 0.30 0.00 Itm_126 0.06 0.18 0.00 Itm_008 0.06 0.21 0.00 Itm_214 0.06 0.24 0.00 Itm_071 0.06 0.32 0.00 Itm_241 0.06 0.26 0.00 Itm_182 0.06 0.44 0.00 Itm_001 -0.05 0.39 0.00 Itm_087 -0.05 0.22 0.00 219
Itm_202 -0.05 0.29 0.00 Itm_225 -0.05 0.22 0.00 Itm_095 0.05 0.30 0.00 Itm_080 0.05 0.48 0.00 Itm_166 -0.05 0.40 0.00 Itm_210 0.05 0.35 0.00 Itm_108 -0.05 0.25 0.00 Itm_217 0.05 0.46 0.00 Itm_150 -0.05 0.31 0.00 Itm_109 0.05 0.25 0.00 Itm_037 -0.04 0.31 0.00 Itm_029 0.04 0.25 0.00 Itm_042 -0.04 0.47 0.00 Itm_151 -0.04 0.17 0.00 Itm_070 -0.04 0.42 0.00 Itm_053 0.04 0.17 0.00 Itm_013 0.04 0.40 0.00 Itm_043 0.03 0.36 0.00 Itm_243 0.03 0.28 0.00 Itm_187 0.03 0.25 0.00 Itm_002 -0.03 0.29 0.00 Itm_252 -0.03 0.33 0.00 Itm_242 -0.03 0.28 0.00 Itm_235 -0.03 0.19 0.00 Itm_090 -0.02 0.43 0.00 Itm_091 -0.02 0.32 0.00 Itm_257 0.02 0.23 0.00 Itm_061 -0.02 0.34 0.00 Itm_015 -0.02 0.36 0.00 Itm_227 0.02 0.26 0.00 Itm_060 -0.02 0.30 0.00 Itm_012 0.02 0.31 0.00 Itm_004 0.02 0.26 0.00 Itm_245 0.01 0.27 0.00 Itm_121 0.01 0.32 0.00 Itm_158 0.01 0.30 0.00 Itm_216 0.01 0.16 0.00 Itm_085 0.01 0.30 0.00 Itm_174 -0.01 0.32 0.00 Itm_200 0.00 0.47 0.00 Itm_023 0.00 0.23 0.00 Itm_211 0.00 0.21 0.00 Itm_032 0.00 0.18 0.00
220
Table B 4
Pattern and Structure Matrix for Principal Axis Factoring with Oblimin Rotation of Two
Factor Solution of CPI Items
Pattern Coefficients Structure Coefficients Communalities CPI Items Factor 1 Factor 2 Factor 1 Factor 2 Initial Extraction Itm_124 0.45 0.00 0.45 -0.04 0.37 0.20 Itm_215 0.45 0.03 0.45 0.00 0.52 0.20 Itm_230 0.44 -0.11 0.45 -0.14 0.36 0.21 Itm_152 0.42 0.01 0.42 -0.02 0.33 0.18 Itm_188 0.42 0.03 0.42 0.00 0.47 0.17 Itm_036 0.40 0.03 0.40 -0.01 0.34 0.16 Itm_199 0.40 0.01 0.40 -0.02 0.35 0.16 Itm_142 0.40 0.17 0.39 0.14 0.33 0.18 Itm_207 0.39 0.03 0.39 0.00 0.33 0.15 Itm_146 0.39 0.02 0.39 -0.01 0.30 0.15 Itm_086 0.39 0.07 0.38 0.04 0.32 0.15 Itm_177 0.39 -0.09 0.39 -0.12 0.32 0.16 Itm_145 0.38 0.00 0.38 -0.03 0.29 0.14 Itm_138 0.37 -0.01 0.37 -0.04 0.35 0.14 Itm_161 0.37 0.00 0.37 -0.03 0.36 0.13 Itm_253 0.36 -0.19 0.38 -0.21 0.45 0.18 Itm_059 0.36 0.03 0.36 0.00 0.41 0.13 Itm_259 0.36 0.09 0.35 0.06 0.39 0.13 Itm_219 0.35 -0.03 0.36 -0.06 0.36 0.13 Itm_176 0.35 -0.15 0.36 -0.17 0.31 0.15 Itm_218 0.35 -0.10 0.36 -0.13 0.34 0.14 Itm_155 0.35 -0.07 0.35 -0.09 0.32 0.13 Itm_092 0.35 -0.07 0.35 -0.09 0.31 0.13 Itm_065 0.35 -0.07 0.35 -0.10 0.37 0.13 Itm_154 0.34 -0.04 0.35 -0.06 0.35 0.12 Itm_186 0.34 0.02 0.34 0.00 0.33 0.12 Itm_046 0.34 -0.21 0.36 -0.24 0.35 0.17 Itm_231 0.34 -0.07 0.34 -0.10 0.34 0.12 Itm_222 0.33 0.02 0.33 -0.01 0.29 0.11 Itm_260 0.33 0.12 0.32 0.09 0.30 0.12 Itm_017 0.33 0.05 0.33 0.02 0.32 0.11 Itm_193 0.33 -0.02 0.33 -0.05 0.29 0.11 Itm_167 0.33 0.00 0.33 -0.02 0.34 0.11 Itm_179 0.33 -0.08 0.33 -0.10 0.43 0.12 Itm_034 0.32 0.05 0.32 0.03 0.31 0.10 Itm_212 0.32 -0.07 0.33 -0.10 0.27 0.11 Itm_129 0.32 -0.09 0.32 -0.12 0.45 0.11 Itm_135 0.31 0.28 0.29 0.26 0.39 0.16 Itm_007 0.31 0.10 0.30 0.07 0.33 0.10 Itm_256 0.31 -0.24 0.32 -0.26 0.35 0.16 Itm_239 0.30 0.02 0.30 0.00 0.31 0.09 Itm_076 0.30 -0.03 0.30 -0.06 0.29 0.09 Itm_149 0.30 -0.23 0.31 -0.25 0.32 0.15 Itm_057 0.30 -0.08 0.30 -0.11 0.30 0.10 Itm_105 0.30 -0.15 0.31 -0.17 0.34 0.12 Itm_164 0.28 0.15 0.27 0.13 0.35 0.10 Itm_203 0.28 -0.15 0.29 -0.17 0.27 0.11 Itm_205 0.28 -0.08 0.28 -0.11 0.25 0.09 Itm_175 0.28 0.00 0.28 -0.02 0.30 0.08 Itm_069 0.28 -0.11 0.29 -0.13 0.30 0.09 Itm_249 0.28 -0.03 0.28 -0.05 0.34 0.08 Itm_066 0.27 0.05 0.27 0.03 0.31 0.08 Itm_195 0.27 -0.27 0.29 -0.29 0.30 0.16 Itm_010 0.27 -0.18 0.29 -0.20 0.35 0.11 Itm_119 0.27 -0.13 0.28 -0.15 0.43 0.10 Itm_025 0.27 -0.03 0.27 -0.05 0.27 0.08 Itm_258 0.27 -0.14 0.28 -0.16 0.27 0.10 Itm_041 0.26 0.11 0.26 0.09 0.28 0.08 Itm_194 -0.26 0.17 -0.28 0.19 0.40 0.11 Itm_116 0.26 0.00 0.26 -0.02 0.26 0.07 Itm_248 0.26 -0.01 0.26 -0.03 0.27 0.07 Itm_170 0.26 -0.08 0.27 -0.10 0.47 0.08 Itm_172 0.26 -0.07 0.26 -0.09 0.26 0.08 Itm_168 0.26 -0.09 0.27 -0.11 0.27 0.08 221
Itm_160 0.26 0.01 0.26 -0.01 0.27 0.07 Itm_236 0.26 0.10 0.25 0.08 0.32 0.07 Itm_173 0.26 -0.10 0.26 -0.12 0.36 0.08 Itm_102 0.26 0.13 0.25 0.11 0.30 0.08 Itm_111 0.26 0.01 0.25 -0.01 0.29 0.07 Itm_247 0.25 -0.10 0.26 -0.12 0.32 0.08 Itm_101 0.25 0.06 0.25 0.04 0.30 0.07 Itm_006 0.25 0.12 0.24 0.10 0.28 0.07 Itm_229 0.25 0.02 0.25 0.00 0.22 0.06 Itm_251 0.24 0.08 0.23 0.06 0.27 0.06 Itm_097 0.24 -0.03 0.24 -0.05 0.27 0.06 Itm_125 0.23 -0.03 0.24 -0.04 0.33 0.06 Itm_240 0.23 0.08 0.22 0.06 0.30 0.06 Itm_159 0.23 0.08 0.22 0.07 0.28 0.06 Itm_040 0.23 -0.01 0.23 -0.03 0.27 0.05 Itm_143 0.22 -0.02 0.23 -0.04 0.25 0.05 Itm_014 0.22 -0.08 0.23 -0.10 0.28 0.06 Itm_214 0.22 0.21 0.20 0.19 0.24 0.09 Itm_074 0.22 -0.17 0.23 -0.19 0.38 0.08 Itm_009 0.22 0.03 0.22 0.01 0.28 0.05 Itm_185 0.22 0.10 0.21 0.08 0.26 0.05 Itm_027 -0.21 0.04 -0.22 0.06 0.26 0.05 Itm_075 -0.21 0.19 -0.23 0.21 0.29 0.09 Itm_156 0.21 0.00 0.21 -0.01 0.23 0.05 Itm_088 0.21 -0.07 0.22 -0.09 0.27 0.05 Itm_049 0.21 0.11 0.20 0.09 0.26 0.05 Itm_132 0.21 0.03 0.21 0.01 0.25 0.04 Itm_197 0.21 0.15 0.20 0.13 0.35 0.06 Itm_095 0.21 0.21 0.19 0.19 0.30 0.08 Itm_112 0.21 0.00 0.21 -0.02 0.54 0.04 Itm_024 0.21 -0.14 0.22 -0.15 0.41 0.07 Itm_123 0.20 0.14 0.19 0.12 0.21 0.06 Itm_182 0.20 0.19 0.18 0.17 0.44 0.07 Itm_044 0.20 -0.10 0.20 -0.11 0.34 0.05 Itm_026 0.20 -0.02 0.20 -0.04 0.22 0.04 Itm_080 0.19 0.19 0.18 0.17 0.48 0.07 Itm_130 0.19 0.02 0.19 0.00 0.21 0.04 Itm_011 0.19 0.06 0.19 0.05 0.25 0.04 Itm_093 0.19 -0.06 0.19 -0.07 0.26 0.04 Itm_033 0.19 -0.12 0.20 -0.14 0.26 0.05 Itm_020 0.19 -0.12 0.20 -0.14 0.25 0.05 Itm_133 0.18 0.04 0.18 0.03 0.23 0.03 Itm_054 0.18 0.05 0.18 0.03 0.32 0.03 Itm_094 0.18 0.00 0.18 -0.01 0.28 0.03 Itm_118 0.18 -0.05 0.18 -0.06 0.23 0.04 Itm_107 -0.18 0.15 -0.19 0.16 0.31 0.06 Itm_114 0.18 -0.17 0.19 -0.18 0.26 0.06 Itm_162 -0.18 0.06 -0.18 0.07 0.53 0.04 Itm_016 0.18 0.02 0.17 0.00 0.26 0.03 Itm_031 0.17 0.04 0.17 0.03 0.20 0.03 Itm_189 0.17 0.10 0.16 0.09 0.26 0.04 Itm_084 0.16 -0.05 0.17 -0.06 0.24 0.03 Itm_022 0.16 0.03 0.16 0.02 0.31 0.03 Itm_067 0.16 -0.04 0.16 -0.05 0.31 0.03 Itm_153 -0.16 -0.06 -0.16 -0.05 0.23 0.03 Itm_232 0.16 -0.01 0.16 -0.03 0.20 0.03 Itm_117 0.16 0.01 0.16 0.00 0.22 0.03 Itm_210 0.16 0.14 0.15 0.13 0.35 0.04 Itm_147 0.14 0.09 0.14 0.08 0.23 0.03 Itm_128 0.14 0.03 0.14 0.01 0.22 0.02 Itm_071 0.14 0.10 0.13 0.09 0.32 0.03 Itm_079 0.14 -0.04 0.14 -0.05 0.21 0.02 Itm_003 -0.13 -0.08 -0.13 -0.07 0.30 0.02 Itm_064 0.13 -0.10 0.14 -0.11 0.26 0.03 Itm_181 0.13 -0.06 0.14 -0.07 0.24 0.02 Itm_228 0.12 0.05 0.12 0.04 0.18 0.02 Itm_068 0.12 -0.03 0.12 -0.04 0.23 0.02 Itm_005 0.12 -0.06 0.12 -0.06 0.18 0.02 Itm_122 0.12 -0.02 0.12 -0.03 0.33 0.02 Itm_157 0.12 -0.03 0.12 -0.04 0.19 0.02 Itm_137 -0.12 0.01 -0.12 0.02 0.21 0.01 Itm_144 0.11 0.05 0.11 0.05 0.24 0.02 Itm_019 0.11 0.01 0.11 0.00 0.21 0.01 Itm_063 -0.11 0.07 -0.11 0.08 0.33 0.02 Itm_246 -0.10 0.03 -0.10 0.04 0.21 0.01 222
Itm_126 0.10 0.04 0.10 0.03 0.18 0.01 Itm_213 0.10 0.03 0.10 0.03 0.37 0.01 Itm_072 -0.10 0.02 -0.10 0.03 0.31 0.01 Itm_171 -0.10 0.08 -0.10 0.09 0.45 0.02 Itm_198 -0.10 0.06 -0.10 0.06 0.32 0.01 Itm_056 -0.09 -0.02 -0.09 -0.01 0.36 0.01 Itm_013 0.09 0.07 0.09 0.07 0.40 0.01 Itm_238 -0.09 0.04 -0.10 0.05 0.57 0.01 Itm_244 -0.09 -0.01 -0.09 0.00 0.18 0.01 Itm_039 0.08 -0.07 0.09 -0.08 0.15 0.01 Itm_254 -0.08 0.05 -0.08 0.05 0.23 0.01 Itm_141 -0.08 0.04 -0.08 0.04 0.19 0.01 Itm_098 -0.08 0.02 -0.08 0.03 0.58 0.01 Itm_073 -0.07 0.04 -0.08 0.04 0.22 0.01 Itm_008 0.07 0.00 0.07 -0.01 0.21 0.01 Itm_120 -0.07 0.06 -0.08 0.07 0.33 0.01 Itm_089 -0.07 0.03 -0.07 0.04 0.53 0.01 Itm_053 0.06 0.03 0.06 0.02 0.17 0.01 Itm_029 0.06 0.01 0.06 0.01 0.25 0.00 Itm_217 0.06 0.00 0.06 0.00 0.46 0.00 Itm_257 0.05 0.03 0.04 0.03 0.23 0.00 Itm_243 0.04 0.01 0.04 0.01 0.28 0.00 Itm_150 -0.04 0.02 -0.04 0.03 0.31 0.00 Itm_109 0.04 -0.02 0.04 -0.03 0.25 0.00 Itm_001 -0.04 0.04 -0.04 0.04 0.39 0.00 Itm_090 -0.03 0.00 -0.03 0.00 0.43 0.00 Itm_060 -0.01 0.01 -0.01 0.01 0.30 0.00 Itm_052 0.16 -0.58 0.21 -0.59 0.66 0.38 Itm_134 0.19 -0.55 0.23 -0.57 0.52 0.36 Itm_078 0.20 -0.55 0.25 -0.57 0.54 0.36 Itm_139 0.18 -0.55 0.22 -0.56 0.51 0.34 Itm_035 -0.05 0.54 -0.09 0.54 0.48 0.29 Itm_163 0.09 -0.52 0.13 -0.52 0.62 0.28 Itm_021 0.17 -0.51 0.20 -0.52 0.61 0.30 Itm_082 0.21 -0.50 0.25 -0.52 0.47 0.31 Itm_104 -0.02 0.49 -0.05 0.49 0.59 0.25 Itm_192 0.29 -0.48 0.33 -0.50 0.51 0.34 Itm_180 0.07 0.48 0.04 0.47 0.39 0.23 Itm_100 0.27 -0.45 0.30 -0.47 0.43 0.29 Itm_058 0.26 -0.45 0.30 -0.47 0.45 0.29 Itm_077 -0.07 0.44 -0.11 0.45 0.45 0.21 Itm_048 -0.08 0.44 -0.11 0.44 0.37 0.20 Itm_233 0.18 -0.43 0.21 -0.45 0.47 0.23 Itm_099 -0.02 0.43 -0.05 0.43 0.38 0.18 Itm_136 0.11 0.41 0.07 0.41 0.35 0.18 Itm_018 0.09 -0.41 0.13 -0.41 0.33 0.18 Itm_115 0.19 0.40 0.16 0.38 0.41 0.18 Itm_127 0.03 0.39 0.00 0.38 0.60 0.15 Itm_221 0.27 -0.38 0.30 -0.41 0.41 0.24 Itm_165 0.11 0.37 0.08 0.36 0.32 0.14 Itm_140 0.00 0.36 -0.03 0.36 0.37 0.13 Itm_110 0.17 -0.36 0.20 -0.37 0.37 0.17 Itm_047 0.11 -0.36 0.14 -0.37 0.31 0.15 Itm_234 0.02 0.35 -0.01 0.35 0.55 0.13 Itm_148 0.12 0.35 0.10 0.34 0.35 0.13 Itm_190 0.06 -0.35 0.08 -0.35 0.30 0.13 Itm_178 0.18 -0.33 0.20 -0.35 0.32 0.15 Itm_081 0.15 -0.33 0.18 -0.34 0.41 0.14 Itm_062 -0.02 0.32 -0.05 0.33 0.39 0.11 Itm_196 0.03 -0.32 0.06 -0.32 0.30 0.10 Itm_113 0.21 -0.30 0.24 -0.32 0.31 0.15 Itm_252 0.17 0.28 0.15 0.27 0.33 0.10 Itm_037 0.15 0.28 0.12 0.27 0.31 0.09 Itm_096 0.03 0.28 0.01 0.28 0.32 0.08 Itm_184 0.21 -0.27 0.23 -0.29 0.33 0.13 Itm_106 0.10 -0.27 0.12 -0.28 0.27 0.09 Itm_045 0.04 0.27 0.02 0.26 0.26 0.07 Itm_204 -0.03 0.26 -0.05 0.26 0.32 0.07 Itm_206 0.09 0.26 0.07 0.25 0.32 0.07 Itm_158 0.18 0.24 0.16 0.23 0.30 0.08 Itm_103 0.18 -0.23 0.20 -0.24 0.29 0.09 Itm_208 0.10 -0.21 0.12 -0.22 0.32 0.06 Itm_083 0.19 -0.21 0.21 -0.22 0.27 0.09 Itm_201 -0.07 0.21 -0.09 0.21 0.20 0.05 Itm_070 0.10 0.20 0.08 0.19 0.42 0.05 223
Itm_187 0.17 0.19 0.15 0.18 0.25 0.06 Itm_200 0.14 0.19 0.13 0.18 0.47 0.05 Itm_250 0.11 -0.19 0.12 -0.20 0.32 0.05 Itm_224 0.06 -0.19 0.07 -0.19 0.30 0.04 Itm_051 -0.06 -0.19 -0.04 -0.18 0.30 0.04 Itm_174 0.13 0.19 0.11 0.18 0.32 0.05 Itm_220 0.01 0.18 -0.01 0.18 0.20 0.03 Itm_061 0.10 0.18 0.09 0.17 0.34 0.04 Itm_245 0.14 0.17 0.12 0.16 0.27 0.04 Itm_085 0.13 0.16 0.11 0.15 0.30 0.04 Itm_226 0.07 -0.16 0.08 -0.17 0.31 0.03 Itm_043 -0.08 -0.16 -0.06 -0.16 0.36 0.03 Itm_108 0.05 0.15 0.04 0.15 0.25 0.02 Itm_038 -0.05 0.15 -0.06 0.15 0.34 0.03 Itm_131 -0.02 0.15 -0.03 0.15 0.40 0.02 Itm_169 -0.01 0.13 -0.02 0.13 0.21 0.02 Itm_227 0.11 0.13 0.10 0.12 0.26 0.03 Itm_209 0.05 -0.13 0.06 -0.13 0.21 0.02 Itm_012 -0.07 -0.13 -0.06 -0.12 0.31 0.02 Itm_191 -0.03 0.11 -0.04 0.11 0.31 0.01 Itm_050 -0.01 0.11 -0.02 0.11 0.32 0.01 Itm_028 0.03 -0.11 0.04 -0.11 0.34 0.01 Itm_241 -0.01 -0.10 0.00 -0.10 0.26 0.01 Itm_030 0.08 -0.10 0.09 -0.11 0.30 0.02 Itm_151 0.02 0.09 0.01 0.09 0.17 0.01 Itm_255 0.01 -0.09 0.02 -0.09 0.31 0.01 Itm_087 0.00 0.09 -0.01 0.09 0.22 0.01 Itm_121 0.08 0.09 0.07 0.08 0.32 0.01 Itm_237 -0.04 0.08 -0.04 0.09 0.26 0.01 Itm_002 0.03 0.08 0.02 0.08 0.29 0.01 Itm_004 0.08 0.08 0.07 0.08 0.26 0.01 Itm_202 -0.01 0.07 -0.02 0.07 0.29 0.01 Itm_183 -0.04 0.07 -0.04 0.07 0.18 0.01 Itm_166 -0.01 0.07 -0.02 0.07 0.40 0.01 Itm_223 -0.05 0.06 -0.05 0.06 0.17 0.01 Itm_042 -0.01 0.06 -0.01 0.06 0.47 0.00 Itm_216 0.05 0.06 0.05 0.06 0.16 0.01 Itm_091 0.01 0.06 0.01 0.06 0.32 0.00 Itm_015 0.02 0.06 0.01 0.05 0.36 0.00 Itm_225 -0.03 0.05 -0.03 0.05 0.22 0.00 Itm_055 -0.05 0.05 -0.05 0.05 0.61 0.00 Itm_242 0.00 0.04 0.00 0.04 0.28 0.00 Itm_032 -0.03 -0.03 -0.02 -0.03 0.18 0.00 Itm_023 -0.02 -0.03 -0.02 -0.03 0.23 0.00 Itm_235 -0.01 0.02 -0.02 0.02 0.19 0.00 Itm_211 0.01 0.02 0.01 0.01 0.21 0.00
224
Table B 5
Factor Matrix for Principal Axis Factoring of One Factor Solution of the Benchmarks for
Managers Leadership Scales
Factor Coefficients Communalities Benchmarks Component 1 Initial Extraction Change Management 0.97 0.94 0.94 Leading Employees 0.95 0.92 0.91 Participative Management 0.94 0.93 0.89 Career Management 0.93 0.87 0.86 Building and Mending 0.93 0.93 0.86 Relationships Self-Awareness 0.90 0.85 0.82 Resourcefulness 0.90 0.89 0.81 Compassion and Sensitivity 0.86 0.85 0.74 Straightforwardness and 0.84 0.79 0.71 Composure Differences Matter 0.84 0.77 0.71 Doing Whatever It Takes 0.82 0.89 0.68 Confronting Problem 0.77 0.78 0.59 Employees Putting People At Ease 0.72 0.79 0.52 Decisiveness 0.70 0.78 0.49 Being A Quick Study 0.68 0.67 0.46 Balance Between Personal 0.57 0.48 0.32 Life and Work
225
Figure B 2
Scree plot of eigenvalues associated with the principal axis factoring of the Benchmarks for
Managers leadership scales.
226
Table B 6
CVR and RMSE Scores from the 10-fold Cross-Validation Analyses Across Different Sample
Sizes and Personality Levels (CPI Dataset)
CVR RMSE Fold/Test Set GFP Big 2 Big 5 Facets GFP Big 2 Big 5 Facets n = 3,060 Fold 1 .24** .27** .26** .29** 5.46 5.44 5.44 5.38 Fold 2 .13* .13* .17** .22** 5.13 5.14 5.11 5.05 Fold 3 .16** .14** .17** .22** 5.23 5.24 5.22 5.17 Fold 4 .18** .18** .18** .27** 5.07 5.07 5.07 4.96 Fold 5 .07 .11 .09 .17** 5.19 5.16 5.18 5.13 Fold 6 .10 .10 .10 .17** 5.17 5.18 5.19 5.12 Fold 7 .19** .19** .20** .26** 5.50 5.49 5.48 5.41 Fold 8 .15** .18** .18** .17** 5.22 5.20 5.20 5.23 Fold 9 .17** .18** .19** .20** 5.59 5.58 5.57 5.55 Fold 10 .13* .13* .14** .16** 5.02 5.03 5.01 5.02 Mean .15 .16 .17 .21 5.26 5.25 5.25 5.20 n = 300 Fold 1 .24** .27** .20** .22** 5.50 5.47 5.54 5.55 Fold 2 .13* .13* .15** .12* 5.14 5.14 5.12 5.29 Fold 3 .16** .14** .09 .12* 5.24 5.26 5.32 5.34 Fold 4 .18** .17** .12* .16** 5.06 5.07 5.21 5.19 Fold 5 .07 .12* .08 .13* 5.18 5.15 5.18 5.18 Fold 6 .10 .10 .09 .11* 5.21 5.20 5.24 5.38 Fold 7 .19** .19** .19** .21** 5.50 5.49 5.50 5.47 Fold 8 .15** .18** .17** .12* 5.23 5.20 5.21 5.50 Fold 9 .17** .17** .16** .17** 5.58 5.58 5.60 5.66 Fold 10 .13* .12* .12* .08 5.03 5.03 5.09 5.32 Mean .15 .16 .14 .14 5.27 5.26 5.30 5.39 n = 150 Fold 1 .24** .26** .21** .13* 5.50 5.48 5.52 5.73 Fold 2 .13* .11* .11 .14* 5.14 5.14 5.15 5.23 Fold 3 .16** .13* .13* .08 5.23 5.25 5.28 5.61 Fold 4 .18** .17** .09 .20** 5.06 5.07 5.23 5.18 Fold 5 .07 .10 .02 .01 5.18 5.17 5.31 5.63 Fold 6 .10 .10 .04 .05 5.19 5.20 5.36 5.46 Fold 7 .19** .16** .02 .26** 5.53 5.55 5.70 5.58 Fold 8 .15** .16** .13* .08 5.28 5.28 5.33 5.55 Fold 9 .17** .18** .15** .10 5.61 5.61 5.65 5.94 Fold 10 .13* .11* .11 .02 5.14 5.16 5.16 5.50 Mean .15 .15 .10 .11 5.29 5.29 5.37 5.54 n = 50 Fold 1 .24** .24** .26** .23** 5.51 5.75 5.60 6.74 Fold 2 .13* -.02 .00 -.05 5.29 5.55 5.85 8.37 Fold 3 .16** .13* .17** .14* 5.23 5.25 5.33 7.92 Fold 4 .18** .18** .15** .17** 5.12 5.16 5.28 7.00 Fold 5 .07 .12* .05 .03 5.24 5.20 5.62 6.82 Fold 6 .10 .08 .10 .02 5.29 5.40 5.42 6.47 Fold 7 .19** .17** .03 .04 5.71 5.71 5.88 7.46 Fold 8 .15** .00 .06 .14** 5.23 5.50 5.69 6.55 Fold 9 .17** .14* .13* .13* 5.68 5.73 5.83 6.33 Fold 10 .13* .12* .11* .04 5.06 5.07 5.16 8.62 Mean .15 .12 .11 .09 5.34 5.43 5.56 7.23 Note: *p < .05; **p < .01.
227
Appendix C: Study 3
Figure C 1
Scree plot of eigenvalues associated with the principal axis factoring of MBTI facets.
228
Table C 1
Factor Matrix for Principal Axis Factoring of One Factor Solution of MBTI Facets
Factor Coefficients Communalities MBTI Facets Component 1 Initial Extraction SN: Concrete–Abstract 0.77 0.68 0.60 SN: Realistic–Imaginative 0.71 0.59 0.50 SN: Experiential–Theoretical 0.66 0.58 0.44 SN: Traditional–Original 0.66 0.53 0.44 JP: Systematic–Casual 0.56 0.49 0.32 JP: Scheduled–Spontaneous 0.52 0.61 0.28 SN: Practical–Conceptual 0.52 0.45 0.27 JP: Planful–Open-Ended 0.43 0.57 0.19 JP: Early Starting–Pressure-Prompted 0.42 0.36 0.18 TF: Logical–Empathetic 0.38 0.37 0.14 EI: Enthusiastic–Quiet -0.35 0.64 0.12 JP: Methodical–Emergent 0.35 0.39 0.12 TF: Reasonable–Compassionate 0.33 0.49 0.11 TF: Tough–Tender 0.32 0.45 0.10 EI: Expressive–Contained -0.28 0.43 0.08 EI: Active–Reflective -0.27 0.55 0.07 EI: Initiating–Receiving -0.26 0.56 0.07 TF: Critical–Accepting 0.25 0.32 0.06 EI: Gregarious–Intimate -0.22 0.58 0.05 TF: Questioning–Accommodating -0.13 0.22 0.02 Note: Positive pole represented by the right-hand initial/facet.
229
Table C 2
Pattern and Structure Matrix for Principal Axis Factoring with Oblimin Rotation of Two
Factor Solution of MBTI Facets
Structure Pattern Coefficients Communalities Coefficients MBTI Facets Extracti Factor 1 Factor 2 Factor 1 Factor 2 Initial on SN: Concrete–Abstract 0.76 -0.07 0.77 -0.17 0.68 0.60 SN: Realistic–Imaginative 0.69 -0.08 0.70 -0.17 0.59 0.50 SN: Experiential–Theoretical 0.66 -0.05 0.67 -0.13 0.58 0.45 JP: Scheduled–Spontaneous 0.63 0.17 0.61 0.09 0.61 0.40 SN: Traditional–Original 0.61 -0.17 0.63 -0.25 0.53 0.43 JP: Systematic–Casual 0.60 0.02 0.59 -0.06 0.49 0.35 SN: Practical–Conceptual 0.53 -0.01 0.53 -0.08 0.45 0.28 JP: Planful–Open-Ended 0.52 0.16 0.50 0.09 0.57 0.28 JP: Early Starting–Pressure-Prompted 0.47 0.08 0.46 0.02 0.36 0.22 JP: Methodical–Emergent 0.44 0.17 0.41 0.11 0.39 0.20 TF: Logical–Empathetic 0.35 -0.10 0.36 -0.15 0.37 0.14 TF: Tough–Tender 0.33 0.00 0.33 -0.05 0.45 0.11 TF: Reasonable–Compassionate 0.32 -0.05 0.32 -0.09 0.49 0.11 TF: Critical–Accepting 0.24 -0.03 0.25 -0.07 0.32 0.06 TF: Questioning–Accommodating -0.12 0.04 -0.12 0.06 0.22 0.02 EI: Enthusiastic–Quiet -0.09 0.80 -0.19 0.81 0.64 0.67 EI: Gregarious–Intimate 0.05 0.78 -0.05 0.77 0.58 0.60 EI: Initiating–Receiving 0.01 0.78 -0.09 0.78 0.56 0.60 EI: Active–Reflective -0.01 0.75 -0.11 0.76 0.55 0.57 EI: Expressive–Contained -0.06 0.65 -0.14 0.66 0.43 0.44 Note: Positive pole represented by the right-hand initial/facet.
230
Table C 3
Communalities for Principal Axis Factoring of One Factor Solution of MBTI Scales
Communalities MBTI Scales Initial Extraction SN 0.21 0.53 JP 0.16 0.25 TF 0.12 0.20 EI 0.04 0.04 Note: Positive pole represented by the right-hand initial.
Table C 4
Structure Matrix and Communalities for Principal Axis Factoring with Oblimin Rotation of
Two Factor Solution of MBTI Scales
Structure Pattern Coefficients Communalities Coefficients MBTI Scales Extracti Factor 1 Factor 2 Factor 1 Factor 2 Initial on JP 0.68 0.18 0.62 -0.02 0.16 0.42 SN 0.59 -0.19 0.65 -0.37 0.21 0.45 TF 0.38 -0.16 0.43 -0.27 0.12 0.21 EI -0.02 0.45 -0.16 0.46 0.04 0.21 Note: Positive pole represented by the right-hand initial.
231
Table C 5
Postmultiplication Loadings for the First-order, Second-order and Third-order Factors
Third-order First-order Second-order (One-factor MBTI Items (Four-factor Solution) (Two-factor Solution) Solution) Factor 1 Factor 2 Factor 3 Factor 4 Factor 1 Factor 2 Factor 1 Itm_001 -0.09 0.05 -0.65 0.01 -0.40 0.17 -0.20 Itm_002 0.62 0.10 0.03 0.01 -0.33 -0.11 -0.40 Itm_003 -0.04 0.71 -0.04 -0.11 -0.10 0.36 0.24 Itm_004 -0.02 0.07 -0.57 0.02 -0.38 0.15 -0.21 Itm_005 -0.54 -0.06 0.11 0.08 0.40 0.09 0.44 Itm_006 -0.05 0.08 0.12 0.36 0.20 0.05 0.23 Itm_007 0.05 0.56 -0.01 0.00 -0.08 0.26 0.16 Itm_008 -0.02 0.04 0.68 0.00 0.48 -0.10 0.34 Itm_009 0.57 0.05 -0.04 0.05 -0.34 -0.11 -0.40 Itm_010 0.00 0.39 -0.06 0.10 -0.04 0.21 0.15 Itm_011 -0.40 0.05 -0.02 -0.05 0.19 0.12 0.28 Itm_012 0.06 -0.04 -0.62 0.08 -0.44 0.09 -0.32 Itm_013 -0.05 -0.48 0.07 -0.11 0.08 -0.24 -0.15 Itm_014 -0.01 0.00 -0.46 0.03 -0.31 0.09 -0.19 Itm_015 -0.13 0.22 -0.05 0.35 0.12 0.17 0.27 Itm_016 -0.09 -0.62 -0.05 0.03 0.07 -0.27 -0.18 Itm_017 -0.03 0.04 0.46 -0.08 0.31 -0.07 0.22 Itm_018 -0.44 0.12 -0.01 -0.05 0.21 0.17 0.34 Itm_019 -0.04 0.54 -0.02 -0.03 -0.05 0.28 0.21 Itm_020 -0.07 0.10 -0.57 0.04 -0.36 0.18 -0.16 Itm_021 0.02 0.05 0.14 0.38 0.19 0.02 0.19 Itm_022 -0.53 -0.06 -0.06 0.06 0.27 0.12 0.35 Itm_023 0.00 0.46 -0.01 -0.05 -0.06 0.22 0.14 Itm_024 -0.01 0.01 0.56 -0.04 0.38 -0.10 0.25 Itm_025 0.49 -0.03 -0.08 0.16 -0.28 -0.11 -0.35 Itm_026 -0.05 -0.62 0.01 -0.06 0.07 -0.29 -0.20 Itm_027 -0.54 -0.08 0.06 0.06 0.36 0.09 0.41 Itm_028 0.05 0.04 -0.67 -0.01 -0.50 0.13 -0.33 Itm_029 -0.06 -0.06 -0.01 0.52 0.18 0.02 0.18 Itm_030 0.63 -0.03 0.02 -0.12 -0.37 -0.19 -0.50 Itm_031 0.00 -0.12 -0.06 -0.44 -0.15 -0.08 -0.21 Itm_032 -0.16 0.50 0.09 -0.18 0.06 0.26 0.29 Itm_033 0.00 -0.01 -0.02 -0.39 -0.13 -0.03 -0.14 Itm_034 0.55 -0.02 -0.06 -0.06 -0.36 -0.14 -0.45 Itm_035 -0.09 -0.03 -0.09 -0.48 -0.15 -0.01 -0.14 Itm_036 0.06 -0.10 -0.46 -0.23 -0.41 0.01 -0.36 Itm_037 0.04 0.08 -0.01 0.43 0.09 0.06 0.13 Itm_038 0.05 -0.59 -0.05 0.09 0.01 -0.29 -0.25 Itm_039 0.50 0.04 0.05 0.00 -0.25 -0.12 -0.33 Itm_040 0.11 0.03 0.07 0.41 0.10 0.00 0.09 Itm_041 -0.10 -0.03 -0.41 -0.24 -0.30 0.07 -0.21 Itm_042 0.09 -0.61 0.04 0.09 0.06 -0.32 -0.24 Itm_043 0.02 -0.09 0.04 -0.37 -0.08 -0.08 -0.15 Itm_044 -0.63 -0.06 0.11 -0.06 0.41 0.10 0.46 Itm_045 0.01 0.02 0.08 -0.35 -0.05 -0.03 -0.07 Itm_046 -0.49 0.02 0.04 0.11 0.33 0.14 0.42 Itm_047 -0.01 0.13 0.11 -0.47 -0.06 0.01 -0.05 Itm_048 -0.56 -0.03 -0.06 0.10 0.30 0.14 0.40 Itm_049 0.15 -0.18 -0.07 -0.25 -0.19 -0.13 -0.29 Itm_050 0.52 -0.04 0.14 -0.07 -0.21 -0.18 -0.35 Itm_051 0.00 0.07 0.05 0.47 0.17 0.06 0.20 Itm_052 0.46 -0.14 -0.03 0.01 -0.26 -0.18 -0.40 Itm_053 -0.09 0.04 -0.02 0.49 0.17 0.08 0.22 Itm_054 0.55 0.06 0.10 -0.04 -0.26 -0.13 -0.35 Itm_055 0.00 0.02 0.33 0.17 0.28 -0.04 0.22 Itm_056 0.07 -0.03 0.12 -0.43 -0.07 -0.08 -0.14 Itm_057 0.12 -0.57 -0.04 0.14 -0.01 -0.29 -0.27 Itm_058 -0.19 -0.01 -0.19 -0.34 -0.12 0.06 -0.06 Itm_059 -0.26 0.00 0.52 0.03 0.51 -0.03 0.43 Itm_060 0.56 0.03 -0.15 -0.05 -0.43 -0.10 -0.47 Itm_061 0.09 0.04 -0.02 -0.26 -0.14 -0.02 -0.14 Itm_062 -0.05 0.50 0.03 0.09 0.03 0.26 0.26 Itm_063 0.63 -0.01 0.07 0.08 -0.28 -0.17 -0.40 Itm_064 0.02 -0.05 -0.37 -0.17 -0.31 0.03 -0.25 Itm_065 -0.61 -0.04 0.01 0.13 0.38 0.14 0.48 232
Itm_066 0.04 -0.01 0.04 -0.42 -0.11 -0.05 -0.15 Itm_067 -0.67 -0.09 0.07 0.02 0.43 0.11 0.49 Itm_068 -0.04 -0.56 0.02 -0.10 0.06 -0.27 -0.20 Itm_069 -0.37 0.09 0.06 0.15 0.28 0.14 0.38 Itm_070 -0.12 0.03 -0.04 0.50 0.18 0.09 0.24 Itm_071 -0.51 0.11 0.03 0.04 0.31 0.18 0.44 Itm_072 -0.06 -0.16 0.07 0.35 0.19 -0.05 0.13 Itm_073 0.66 0.01 0.06 -0.02 -0.33 -0.17 -0.45 Itm_074 0.01 0.66 -0.04 -0.03 -0.10 0.32 0.20 Itm_075 -0.07 0.02 -0.03 -0.40 -0.10 0.01 -0.08 Itm_076 -0.13 -0.04 -0.70 0.03 -0.40 0.15 -0.23 Itm_077 -0.04 -0.53 0.01 -0.02 0.07 -0.25 -0.17 Itm_078 0.04 -0.05 0.71 0.00 0.47 -0.17 0.27 Itm_079 0.02 0.73 -0.04 0.06 -0.08 0.36 0.26 Itm_080 0.08 0.00 0.70 0.00 0.44 -0.15 0.26 Itm_081 0.08 0.52 -0.07 0.15 -0.09 0.26 0.15 Itm_082 -0.59 -0.01 -0.04 0.05 0.31 0.16 0.42 Itm_083 -0.03 0.55 -0.02 -0.01 -0.05 0.28 0.21 Itm_084 -0.01 0.05 0.72 0.00 0.50 -0.11 0.36 Itm_085 -0.06 -0.52 0.02 -0.05 0.07 -0.25 -0.16 Itm_086 -0.03 0.03 -0.42 -0.05 -0.29 0.10 -0.18 Itm_087 0.07 -0.04 0.01 -0.45 -0.15 -0.07 -0.20 Itm_088 -0.05 -0.03 0.59 0.01 0.44 -0.12 0.29 Itm_089 -0.01 0.01 0.03 0.33 0.12 0.02 0.13 Itm_090 0.13 -0.04 0.53 0.08 0.32 -0.15 0.15 Itm_091 0.01 -0.60 0.05 -0.04 0.07 -0.31 -0.22 Itm_092 0.23 -0.14 0.02 0.24 -0.03 -0.12 -0.13 Itm_093 -0.07 -0.08 0.55 -0.02 0.42 -0.13 0.26 Itm_094 0.04 0.05 0.09 0.40 0.16 0.03 0.16 Itm_095 0.06 -0.01 0.51 0.04 0.34 -0.12 0.20 Itm_096 0.05 0.43 0.00 0.14 -0.03 0.21 0.16 Itm_097 -0.18 0.05 0.40 -0.21 0.31 -0.02 0.27 Itm_098 -0.09 0.54 -0.04 -0.02 -0.03 0.30 0.24 Itm_099 0.33 -0.13 0.02 -0.02 -0.16 -0.15 -0.28 Itm_100 -0.04 -0.38 -0.08 -0.01 -0.01 -0.16 -0.15 Itm_101 -0.21 0.08 0.36 -0.12 0.33 0.02 0.31 Itm_102 0.02 0.06 -0.40 0.11 -0.26 0.11 -0.14 Itm_103 -0.17 -0.43 -0.01 -0.03 0.12 -0.17 -0.05 Itm_104 -0.05 0.34 -0.03 -0.03 -0.03 0.18 0.14 Itm_105 -0.41 -0.09 -0.09 0.04 0.19 0.08 0.24 Itm_106 0.08 -0.02 0.04 -0.56 -0.18 -0.08 -0.23 Itm_107 0.10 0.30 0.00 -0.22 -0.14 0.11 -0.03 Itm_108 0.32 0.23 0.13 0.09 -0.08 0.01 -0.06 Itm_109 0.09 -0.04 0.11 0.29 0.11 -0.04 0.06 Itm_110 0.42 0.10 0.04 -0.13 -0.25 -0.07 -0.29 Itm_111 -0.01 -0.05 0.01 0.56 0.17 0.01 0.17 Itm_112 0.10 0.36 0.06 0.05 -0.03 0.14 0.10 Itm_113 -0.13 -0.02 -0.10 0.31 0.09 0.06 0.14 Itm_114 -0.03 0.05 -0.25 -0.20 -0.22 0.06 -0.14 Itm_115 0.12 0.04 -0.03 -0.45 -0.22 -0.03 -0.23 Itm_116 0.14 -0.47 -0.05 0.09 -0.05 -0.25 -0.26 Itm_117 -0.04 -0.09 0.06 -0.30 -0.01 -0.07 -0.07 Itm_118 -0.08 -0.02 -0.04 0.29 0.10 0.04 0.12 Itm_119 0.28 -0.04 -0.06 0.22 -0.13 -0.06 -0.18 Itm_120 -0.02 0.37 -0.09 0.14 -0.04 0.21 0.16 Itm_121 -0.06 -0.03 0.00 0.45 0.16 0.03 0.18 Itm_122 0.27 -0.15 -0.12 0.29 -0.14 -0.10 -0.22 Itm_123 0.36 -0.12 0.16 0.20 -0.02 -0.17 -0.17 Itm_124 0.38 0.20 0.10 -0.07 -0.18 -0.02 -0.18 Itm_125 0.54 0.02 -0.09 0.05 -0.35 -0.10 -0.41 Itm_126 0.16 0.05 0.02 0.00 -0.08 -0.02 -0.09 Itm_127 -0.47 -0.01 0.12 0.07 0.36 0.10 0.42 Itm_128 0.12 0.01 0.14 -0.21 -0.03 -0.07 -0.08 Itm_129 0.14 0.39 0.12 -0.05 -0.04 0.13 0.08 Itm_130 -0.13 -0.19 0.04 -0.33 0.02 -0.09 -0.06 Itm_131 0.03 0.36 -0.05 -0.02 -0.09 0.17 0.08 Itm_132 -0.39 0.07 -0.11 -0.01 0.13 0.15 0.26 Itm_133 0.05 0.02 -0.41 0.00 -0.31 0.08 -0.21 Itm_134 0.10 -0.19 0.01 0.24 0.04 -0.10 -0.06 Itm_135 -0.02 0.02 -0.52 0.01 -0.34 0.11 -0.21 Itm_136 0.01 0.07 -0.12 0.16 -0.05 0.07 0.02 Itm_137 0.06 0.32 -0.02 0.12 -0.04 0.15 0.10 Itm_138 -0.36 0.04 0.08 -0.12 0.22 0.09 0.28 Itm_139 0.05 -0.34 0.02 -0.14 -0.02 -0.19 -0.20 Itm_140 -0.06 -0.53 -0.01 -0.04 0.06 -0.25 -0.17 233
Itm_141 -0.01 0.00 -0.24 -0.04 -0.18 0.05 -0.12 Itm_142 -0.01 -0.12 0.19 0.06 0.17 -0.09 0.07 Itm_143 0.03 -0.08 -0.37 0.04 -0.25 0.03 -0.20 Itm_144 -0.04 0.00 -0.44 0.00 -0.28 0.09 -0.17 Itm_001 -0.09 0.05 -0.65 0.01 -0.40 0.17 -0.20 Itm_002 0.62 0.10 0.03 0.01 -0.33 -0.11 -0.40 Itm_003 -0.04 0.71 -0.04 -0.11 -0.10 0.36 0.24 Itm_004 -0.02 0.07 -0.57 0.02 -0.38 0.15 -0.21 Itm_005 -0.54 -0.06 0.11 0.08 0.40 0.09 0.44 Itm_006 -0.05 0.08 0.12 0.36 0.20 0.05 0.23 Itm_007 0.05 0.56 -0.01 0.00 -0.08 0.26 0.16 Itm_008 -0.02 0.04 0.68 0.00 0.48 -0.10 0.34 Itm_009 0.57 0.05 -0.04 0.05 -0.34 -0.11 -0.40 Itm_010 0.00 0.39 -0.06 0.10 -0.04 0.21 0.15 Itm_011 -0.40 0.05 -0.02 -0.05 0.19 0.12 0.28 Itm_012 0.06 -0.04 -0.62 0.08 -0.44 0.09 -0.32 Itm_013 -0.05 -0.48 0.07 -0.11 0.08 -0.24 -0.15 Itm_014 -0.01 0.00 -0.46 0.03 -0.31 0.09 -0.19 Itm_015 -0.13 0.22 -0.05 0.35 0.12 0.17 0.27 Itm_016 -0.09 -0.62 -0.05 0.03 0.07 -0.27 -0.18 Itm_017 -0.03 0.04 0.46 -0.08 0.31 -0.07 0.22 Itm_018 -0.44 0.12 -0.01 -0.05 0.21 0.17 0.34 Itm_019 -0.04 0.54 -0.02 -0.03 -0.05 0.28 0.21 Itm_020 -0.07 0.10 -0.57 0.04 -0.36 0.18 -0.16 Itm_021 0.02 0.05 0.14 0.38 0.19 0.02 0.19 Itm_022 -0.53 -0.06 -0.06 0.06 0.27 0.12 0.35 Itm_023 0.00 0.46 -0.01 -0.05 -0.06 0.22 0.14 Itm_024 -0.01 0.01 0.56 -0.04 0.38 -0.10 0.25 Itm_025 0.49 -0.03 -0.08 0.16 -0.28 -0.11 -0.35 Itm_026 -0.05 -0.62 0.01 -0.06 0.07 -0.29 -0.20 Itm_027 -0.54 -0.08 0.06 0.06 0.36 0.09 0.41 Itm_028 0.05 0.04 -0.67 -0.01 -0.50 0.13 -0.33 Itm_029 -0.06 -0.06 -0.01 0.52 0.18 0.02 0.18 Itm_030 0.63 -0.03 0.02 -0.12 -0.37 -0.19 -0.50 Itm_031 0.00 -0.12 -0.06 -0.44 -0.15 -0.08 -0.21 Itm_032 -0.16 0.50 0.09 -0.18 0.06 0.26 0.29 Itm_033 0.00 -0.01 -0.02 -0.39 -0.13 -0.03 -0.14 Itm_034 0.55 -0.02 -0.06 -0.06 -0.36 -0.14 -0.45 Itm_035 -0.09 -0.03 -0.09 -0.48 -0.15 -0.01 -0.14 Itm_036 0.06 -0.10 -0.46 -0.23 -0.41 0.01 -0.36 Itm_037 0.04 0.08 -0.01 0.43 0.09 0.06 0.13 Itm_038 0.05 -0.59 -0.05 0.09 0.01 -0.29 -0.25 Itm_039 0.50 0.04 0.05 0.00 -0.25 -0.12 -0.33 Itm_040 0.11 0.03 0.07 0.41 0.10 0.00 0.09 Itm_041 -0.10 -0.03 -0.41 -0.24 -0.30 0.07 -0.21 Itm_042 0.09 -0.61 0.04 0.09 0.06 -0.32 -0.24 Itm_043 0.02 -0.09 0.04 -0.37 -0.08 -0.08 -0.15 Itm_044 -0.63 -0.06 0.11 -0.06 0.41 0.10 0.46 Itm_045 0.01 0.02 0.08 -0.35 -0.05 -0.03 -0.07 Itm_046 -0.49 0.02 0.04 0.11 0.33 0.14 0.42 Itm_047 -0.01 0.13 0.11 -0.47 -0.06 0.01 -0.05 Itm_048 -0.56 -0.03 -0.06 0.10 0.30 0.14 0.40 Itm_049 0.15 -0.18 -0.07 -0.25 -0.19 -0.13 -0.29 Itm_050 0.52 -0.04 0.14 -0.07 -0.21 -0.18 -0.35 Itm_051 0.00 0.07 0.05 0.47 0.17 0.06 0.20 Itm_052 0.46 -0.14 -0.03 0.01 -0.26 -0.18 -0.40 Itm_053 -0.09 0.04 -0.02 0.49 0.17 0.08 0.22 Itm_054 0.55 0.06 0.10 -0.04 -0.26 -0.13 -0.35 Itm_055 0.00 0.02 0.33 0.17 0.28 -0.04 0.22 Itm_056 0.07 -0.03 0.12 -0.43 -0.07 -0.08 -0.14 Itm_057 0.12 -0.57 -0.04 0.14 -0.01 -0.29 -0.27 Itm_058 -0.19 -0.01 -0.19 -0.34 -0.12 0.06 -0.06 Itm_059 -0.26 0.00 0.52 0.03 0.51 -0.03 0.43 Itm_060 0.56 0.03 -0.15 -0.05 -0.43 -0.10 -0.47 Itm_061 0.09 0.04 -0.02 -0.26 -0.14 -0.02 -0.14 Itm_062 -0.05 0.50 0.03 0.09 0.03 0.26 0.26 Itm_063 0.63 -0.01 0.07 0.08 -0.28 -0.17 -0.40 Itm_064 0.02 -0.05 -0.37 -0.17 -0.31 0.03 -0.25 Itm_065 -0.61 -0.04 0.01 0.13 0.38 0.14 0.48 Itm_066 0.04 -0.01 0.04 -0.42 -0.11 -0.05 -0.15 Itm_067 -0.67 -0.09 0.07 0.02 0.43 0.11 0.49 Itm_068 -0.04 -0.56 0.02 -0.10 0.06 -0.27 -0.20 Itm_069 -0.37 0.09 0.06 0.15 0.28 0.14 0.38 Itm_070 -0.12 0.03 -0.04 0.50 0.18 0.09 0.24 Itm_071 -0.51 0.11 0.03 0.04 0.31 0.18 0.44 234
Itm_072 -0.06 -0.16 0.07 0.35 0.19 -0.05 0.13 Itm_073 0.66 0.01 0.06 -0.02 -0.33 -0.17 -0.45 Itm_074 0.01 0.66 -0.04 -0.03 -0.10 0.32 0.20 Itm_075 -0.07 0.02 -0.03 -0.40 -0.10 0.01 -0.08 Itm_076 -0.13 -0.04 -0.70 0.03 -0.40 0.15 -0.23 Itm_077 -0.04 -0.53 0.01 -0.02 0.07 -0.25 -0.17 Itm_078 0.04 -0.05 0.71 0.00 0.47 -0.17 0.27 Itm_079 0.02 0.73 -0.04 0.06 -0.08 0.36 0.26 Itm_080 0.08 0.00 0.70 0.00 0.44 -0.15 0.26 Itm_081 0.08 0.52 -0.07 0.15 -0.09 0.26 0.15 Itm_082 -0.59 -0.01 -0.04 0.05 0.31 0.16 0.42 Itm_083 -0.03 0.55 -0.02 -0.01 -0.05 0.28 0.21 Itm_084 -0.01 0.05 0.72 0.00 0.50 -0.11 0.36 Itm_085 -0.06 -0.52 0.02 -0.05 0.07 -0.25 -0.16 Itm_086 -0.03 0.03 -0.42 -0.05 -0.29 0.10 -0.18 Itm_087 0.07 -0.04 0.01 -0.45 -0.15 -0.07 -0.20 Itm_088 -0.05 -0.03 0.59 0.01 0.44 -0.12 0.29 Itm_089 -0.01 0.01 0.03 0.33 0.12 0.02 0.13 Itm_090 0.13 -0.04 0.53 0.08 0.32 -0.15 0.15 Itm_091 0.01 -0.60 0.05 -0.04 0.07 -0.31 -0.22 Itm_092 0.23 -0.14 0.02 0.24 -0.03 -0.12 -0.13 Itm_093 -0.07 -0.08 0.55 -0.02 0.42 -0.13 0.26 Itm_094 0.04 0.05 0.09 0.40 0.16 0.03 0.16 Itm_095 0.06 -0.01 0.51 0.04 0.34 -0.12 0.20 Itm_096 0.05 0.43 0.00 0.14 -0.03 0.21 0.16 Itm_097 -0.18 0.05 0.40 -0.21 0.31 -0.02 0.27 Itm_098 -0.09 0.54 -0.04 -0.02 -0.03 0.30 0.24 Itm_099 0.33 -0.13 0.02 -0.02 -0.16 -0.15 -0.28 Itm_100 -0.04 -0.38 -0.08 -0.01 -0.01 -0.16 -0.15 Itm_101 -0.21 0.08 0.36 -0.12 0.33 0.02 0.31 Itm_102 0.02 0.06 -0.40 0.11 -0.26 0.11 -0.14 Itm_103 -0.17 -0.43 -0.01 -0.03 0.12 -0.17 -0.05 Itm_104 -0.05 0.34 -0.03 -0.03 -0.03 0.18 0.14 Itm_105 -0.41 -0.09 -0.09 0.04 0.19 0.08 0.24 Itm_106 0.08 -0.02 0.04 -0.56 -0.18 -0.08 -0.23 Itm_107 0.10 0.30 0.00 -0.22 -0.14 0.11 -0.03 Itm_108 0.32 0.23 0.13 0.09 -0.08 0.01 -0.06 Itm_109 0.09 -0.04 0.11 0.29 0.11 -0.04 0.06 Itm_110 0.42 0.10 0.04 -0.13 -0.25 -0.07 -0.29 Itm_111 -0.01 -0.05 0.01 0.56 0.17 0.01 0.17 Itm_112 0.10 0.36 0.06 0.05 -0.03 0.14 0.10 Itm_113 -0.13 -0.02 -0.10 0.31 0.09 0.06 0.14 Itm_114 -0.03 0.05 -0.25 -0.20 -0.22 0.06 -0.14 Itm_115 0.12 0.04 -0.03 -0.45 -0.22 -0.03 -0.23 Itm_116 0.14 -0.47 -0.05 0.09 -0.05 -0.25 -0.26
235
Table C 6
Factor Matrix for Principal Axis Factoring of One Factor Solution of the Benchmarks for
Executives Leadership Scales
Factor Coefficients Communalities Benchmarks Component 1 Initial Extraction Executive Image 0.95 0.92 0.91 Sound Judgement 0.94 0.94 0.89 Inspiring Commitment 0.94 0.92 0.89 Strategic Planning 0.94 0.94 0.88 Learning from Experience 0.94 0.91 0.88 Communicating 0.94 0.89 0.88 Effectively Results Orientation 0.93 0.92 0.86 Interpersonal Savvy 0.93 0.95 0.86 Developing and 0.92 0.90 0.85 Empowering Forging Synergy 0.91 0.94 0.83 Leading Change 0.90 0.86 0.80 Courage 0.89 0.86 0.78 Leveraging Differences 0.88 0.88 0.78 Business Perspective 0.87 0.81 0.75 Credibility 0.86 0.84 0.75 Global Awareness 0.82 0.74 0.67
236
Figure C 2
Scree plot of eigenvalues associated with the principal axis factoring of the Benchmarks for
Executives leadership scales.
237
Table C 7
CVR and RMSE Scores from the 10-fold Cross-Validation Analyses Across Different Sample
Sizes and Personality Levels (MBTI Dataset)
Fold/Test CVR RMSE Set OFS TFS Cont. Categ. Types OFS TFS Cont. Categ. Types n = 360 Fold 1 .28 .28 .35* .09 .04 0.98 0.98 0.97 1.01 1.03 Fold 2 -.13 -.04 -.23 .00 .06 1.16 1.14 1.22 1.13 1.13 Fold 3 .20 .21 .36* .14 .14 1.01 1.01 0.98 1.02 1.04 Fold 4 .11 .04 .30 .18 .10 1.00 1.00 0.96 0.98 1.01 Fold 5 -.13 -.14 -.06 .00 -.07 0.97 0.96 0.97 0.95 1.02 Fold 6 -.03 .02 .04 .06 .08 1.07 1.06 1.06 1.05 1.06 Fold 7 .33* .31* .12 .19 .12 0.77 0.78 0.80 0.79 0.81 Fold 8 .29 .03 .33* .40** .29 0.99 1.01 0.97 0.96 0.97 Fold 9 .24 .15 .12 .14 .15 1.02 1.03 1.04 1.04 1.05 Fold 10 .37* .35* .37* .29 .55** 0.89 0.89 0.88 0.89 0.81 Mean .10 .07 .05 .12 .10 0.99 0.99 0.98 0.98 0.99 n = 150 Fold 1 .28 .12 .24 -.01 -.09 1.01 1.01 0.99 1.02 1.07 Fold 2 -.13 .04 -.13 .13 .02 1.16 1.13 1.18 1.11 1.15 Fold 3 .20 .12 .34* .10 -.38* 1.00 1.01 0.98 1.01 1.43 Fold 4 .11 .05 .23 .23 .20 0.99 1.00 0.97 0.98 0.98 Fold 5 -.13 -.14 -.06 -.02 -.01 0.97 0.96 0.99 0.94 1.15 Fold 6 -.03 -.05 -.02 -.06 -.09 1.07 1.08 1.08 1.08 1.11 Fold 7 .33* .35* .18 .21 .19 0.77 0.78 0.79 0.78 0.81 Fold 8 .29 .12 .19 .33* .38* 0.99 1.01 1.00 0.98 0.95 Fold 9 .24 .24 .18 .18 .13 1.01 1.01 1.02 1.03 1.10 Fold 10 .37* .35* .43** .38* .50** 0.88 0.88 0.86 0.87 0.81 Mean .10 .06 .10 .10 .05 0.99 0.99 0.99 0.98 1.06 n = 50 Fold 1 .28 .03 .12 .06 .03 0.98 1.00 1.06 1.10 1.19 Fold 2 -.13 -.13 -.28 -.13 -.31 1.13 1.13 1.22 1.27 1.42 Fold 3 .20 .22 .07 .07 .18 1.04 1.03 1.10 1.16 1.09 Fold 4 .11 .05 .33* .09 .09 1.00 1.01 0.96 1.06 1.16 Fold 5 -.13 -.12 -.09 -.04 -.08 1.00 1.00 1.02 1.01 1.10 Fold 6 -.03 .06 .12 -.13 -.11 1.09 1.08 1.07 1.13 1.30 Fold 7 .33* .23 .17 .20 .05 0.77 0.79 0.81 0.79 0.96 Fold 8 .29 -.03 .33* .38* .18 1.00 1.03 0.98 0.96 1.07 Fold 9 -.24 -.11 -.04 .02 -.08 1.10 1.14 1.12 1.15 1.30 Fold 10 -.37 -.27 -.13 -.02 .11 0.95 1.00 1.01 0.98 1.14 Mean .00 -.01 -.01 .01 .01 1.00 1.02 1.03 1.06 1.17 Note: OFS = One-factor solution; TFS = Two-factor solution; Cont. = Four continuous type clarity scales; Categ. = Four categorical type preference scales; Types = 16 categorical type dynamics. *p < .05; **p < .01.