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Exploring the Relationship of Meaning-Making Structure,
Emotional Intelligence, IQ and Managerial-Leadership
Effectiveness. A thesis presented
For the degree of Doctor of Philosophy The University of Western Australia
UWA Business School
Stacie Fae Chappell,
B. Comm, University of British Columbia
April 2011
1
ABSTRACT
This thesis explored the relationship between meaning-making structure (MMS),
emotional intelligence (EI), traditional intelligence (IQ) and managerial-
leadership effectiveness (MLE). Globalisation, technology, higher levels of
education and a growing crisis of meaning require managerial-leaders with
emotional, social and meaning-making abilities beyond traditional intelligence
(Pfeiffer 2001; Price 2003; Quatro, Waldman & Galvin 2007; Reams 2005;
Zohar & Marshall 2000). These meaning-making abilities include, but
transcend, those associated with emotional intelligence. For example,
developmental theory suggests differences in MLE are largely explained by an
individual’s internal MMS (Rooke & Torbert 2005).
Despite increased interest in the meaning-making abilities of managerial-
leaders, there is a gap in the literature exploring the relationship between MMS
and EI and their relative impact on MLE. The aim of this study was to explore
the relationship between MMS and EI and IQ in predicting MLE. The sample
consisted of 169 managerial-leaders who had completed a multi-source
feedback process as part of a leadership development program. Ability
measures were used to operationalise the three independent variables including
the Wonderlic Personal Test to measure IQ, the Mayer-Salovey-Caruso
Emotional Intelligence Test v2 to measure EI, and the Washington University
Sentence Completion Test to measure MMS. The dependent variable of MLE
was operationalized as other-average ratings on the multi-source feedback
instrument known as the Integral Leadership and Management Development
Profile (ILMDP).
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Based on the findings of this research, it is suggested that the independent
variables of MMS, EI and IQ do not uniformly predict ratings of MLE. Testing for
relationships at the full sample level revealed that MMS, EI and IQ are not
significantly related to MLE. Revisiting the trait and leadership literature
suggested one possible explanation of these results was the issue of
unobserved heterogeneity. Consequently, sample was tested for the existence
of subgroups which did in fact exist. Results of re-testing the hypotheses at the
subgroup level revealed differences in the existence and direction of the
relationships between IQ, EI and MMS with each other and in predicting MLE.
Testing for the mean differences in background characteristics and the
independent variables eliminated both as possible sources of variation between
the subgroups. However, significant differences existed between the groups in
terms of mean ratings of M LE. Implications for practice, theory and research
are discussed.
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ACKNOWLEDGEMENTS
Without a doubt, the first acknowledgement I wish to make on this thesis is to
the amazing Jeannette Chappell, who has at varying points along the way been
my research assistant, copy editor, sounding board, support system and best
friend. She also happens to be my mom. Thank you for being the beautiful way
that you are and for doing all that you did. This journey would not have been
possible without you.
Next of course are my supervisors Winthrop Professor Dr. David Plowman and
Winthrop Professor Dr. Geoffrey Soutar. Thank you for giving so generously of
your time, wisdom and patience. I have learned much from you both and it has
been an honour to work with you.
Thank you to Dr. Ron Cacioppe, former Managing Director of the AIM-UWA
Integral Leadership Center, for providing a rich field to learn about leadership
development, introducing me to Integral Theory, and providing access to the
UWA Integral Leadership and Management Development Profile clients and
data base.
Thank you to Donald Clarke who courageously took on the task of scoring the
MMS data with me. Your hours of work were a huge support and our
conversations were a powerful learning experience for me. Thank you to MHS
for providing access to research pricing for the MSCEITv2. Thank you to the
participants in the study for your interest in leadership development and support
of my research. Thank you to Mandie Colledge for help with data entry of
course, but equally if not more important for including me as part of your family.
Thank you to Roslyn Richards for support on technical matters of all sorts and
top ups of required wisdom.
I am grateful for the unconditional love and support I have received from my
family and circle of friends. Thank you to my sister Dr. Nicola Chappell for
nursing our father so lovingly and enabling me to feel safe to continue my
journey so far from home. Thank you to my dear friends Dr. Fiona Broadbent-
Bong and Dee Roche for the many conversations that helped me to stay the
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course. Thank you to Dr. George Trippe and Brendan McKeague for your
beautiful presence in our community of practice which fed my soul and reignited
my motivation to finish. Thank you to Melanie Pescud, Catherine Jordan, Dr.
Michele Roberts and Dr. Joanne Sneddon for sharing the journey and
encouraging me towards becoming an academic.
Last, but definitely not least, I want to acknowledge my gratitude to the late
Professor Peter Frost for providing an entry point into academia, introducing me
to leadership development and demonstrating the fundamental relationship
between meaning-making and leadership.
It might have appeared to go unnoticed,
but I’ve got it all here in my heart…. you are the wind beneath my wings.
Larry Henley and Jeff Sillbar, Wind Beneath My Wings
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STATEMENT OF CANDIDATE CONTRIBUTION
This thesis is my own composition, all sources have been acknowledged and
my contribution is clearly identified in the thesis. At the date of submission, no
material within this thesis has been published elsewhere.
Stacie F. Chappell
Geoffrey Soutar David Plowman
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LIST OF TABLES
Table 2.1 Loevinger’s Stages of Ego Development 61
Table 2.2 Implicit Values Within the CVF 75
Table 3.1 Relationship Between Total, Area, Function and Role ILMDP Scores 87
Table 3.2 Form 81 of the Washington University Sentence Completion Test 89
Table 3.3 Indicative Items for Faces and Pictures Tasks from MSCEITv2 Branch 1: Perceive and Appraise Emotion
96
Table 3.4 Indicative Items for Facilitation and Sensations Tasks from MSCEITv2 Branch 2: Facilitate Thought
97
Table 3.5 Indicative Items for Changes and Blends Tasks from MSCEITv2 Branch 3: Understanding Emotion
98
Table 3.6 Indicative Items for Emotion Management and Emotional Relations Tasks from MSCEITv2 Branch 4: Managing Emotions
99
Table 4.1 Mean IRR and IRA Statistics for the 32 ILMDP Items 129
Table 4.2 Central Tendency and Dispersion of Total # of ILMDP Items Meeting IRA Interpretive Standards
131
Table 4.3 Mean IRA and IRR Statistics for the ILMDP Scales 133
Table 4.4 Descriptive Statistics for The ILMDP Items 134
Table 4.5 CFA Summary Statistics for the Eight ILMDP Subscales 147
Table 4.6 AVE and Squared Correlations for the ILMDP Subscales 148
Table 4.7 Rotated Factor Loadings for the ILMDP Items 150
Table 4.8 Mean IRR Statistic for tems in Revised LE Scale 153
Table 4.9 Central Tendency and Dispersion of Total # of Revised LE Scale Items Meeting IRR Interpretive Standards
154
Table 4.10 Mean IRR Statistic for Items in Revised ME Scale 157
Table 4.11 Central Tendency and Dispersion of Total # of Revised ME Scale Items Meeting IRR Interpretive Standards
158
Table 4.12 MSCEITv2 Task Level Correlations 161
Table 4.13 MSCEITv2 Branch Level Correlations 161
Table 4.14 Comparison of the Reliabilities for the MSCEITv2 Total EI, Branch and Task Subscales across Three Studies
162
Table 5.1 Normality Statistics for Main Research Sample 168
Table 5.2 Background Characteristics of the Sample 170
Table 5.3 Industries Represented in the Sample 171
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Table 5.4 Frequency of Job Titles 172
Table 5.5 Descriptive Statistics for IQ, ED and MLE Variables 173
Table 5.6 Frequency of ED Level 174
Table 5.7 Descriptives for MSCEIT v2 Scales and Comparison to Norming Sample 175
Table 5.8 Correlations of the Independent Variables 176
Table 5.9 Latent Class Regression Fit Statistics: Leadership Effectiveness 182
Table 5.10 Latent Class Regression Fit Statistics: Management Effectiveness 183
Table 5.11 Size of the Latent Classes 183
Table 5.12 Descriptive Statistics on Variables for Leadership Effectiveness Groups 184
Table 5.13 Descriptive Statistics on Variables for Management Effectiveness Groups 185
Table 5.14 Correlations for Leadership Effectiveness Group One 186
Table 5.15 Regression for Leadership Effectiveness Group One 187
Table 5.16 Correlations for Leadership Effectiveness Group Two 188
Table 5.17 Regression for Leadership Effectiveness Group Two 188
Table 5.18 Correlations for Leadership Effectiveness Group Three 189
Table 5.19 Regression for Leadership Effectiveness Group Three 190
Table 5.20 Correlations for Management Effectiveness Group One 191
Table 5.21 Regression for Management Effectiveness Group One 191
Table 5.22 Correlations for Management Effectiveness Group Two 192
Table 5.23 Regression for Management Effectiveness Group Two 193
Table 5.24 Correlations for Management Effectiveness Group Three 194
Table 5.25 Regression for Management Effectiveness Group Three 194
Table 5.26 Nominally Scaled Background Variables 196
Table 5.27 Frequency (or Mean) of Background Variables for the LE Groups 197
Table 5.28 Structural Correlations After Varimax Rotation 198
Table 5.29 Group Centroid Values 199
Table 5.30 Frequency (or Mean) of Background Variables for the ME Groups 200
Table 6.1 Summary of Correlations for the Leadership Effectiveness Subgroups 209
Table 6.2 Summary of Correlations for the Management Effectiveness Subgroups 210
Table 6.3 Summary of Standardised Regression Coefficients for All Subgroups 212
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LIST OF FIGURES
Figure 1.1 MMS is Positively Correlated With IQ and EI (H1) 16
Figure 1.2 MMS has Discriminant Validity Beyond IQ and EI in Predicting MLE (H2) 16
Figure 2.1 The Competing Values Framework 74
Figure 3.1 ILMDP Domain 2: Management and Leadership Functions 81
Figure 3.2 Internal Versus External Dimension of ILMDP Model 83
Figure 3.3 ILMDP’s 10-Point Scale 86
Figure 3.4 Structure of MSCEITv2: Total EI, Area, Branch and Task Levels 95
Figure 4.1 ILMDP Brokering Subcale Item Loadings and Fit Statistics 136
Figure 4.2 ILMDP Directing Subscale Item Loadings and Fit Statistics 138
Figure 4.3 ILMDP Monitoring Subscale Item Loadings and Fit Statistics 139
Figure 4.4 ILMDP Achieving Subscale Item Loadings and Fit Statistics 141
Figure 4.5 ILMDP Stewarding Subscale Item Loadings and Fit Statistics 142
Figure 4.6 ILMDP Coaching Subscale Item Loadings and Fit Statistics 144
Figure 4.7 ILMDP Facilitating Subscale Item Loadings and Fit Statistics 145
Figure 4.8 ILMDP Visioning Subscale Item Loadings and Fit Statistics 146
Figure 4.9 Scree Diagram of ILDMP Items 149
Figure 4.10 Revised Leadership Effectiveness Scale Item Loadings and Fit Statistics 155
Figure 4.11 Revised Management Effectiveness Scale Item Loadings and Fit Statistics 159
Figure 5.1 A Representation of Latent Class Regression 179
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TABLE OF CONTENTS
ABSTRACT 1
ACKNOWLEDGEMENTS 3
STATEMENT OF CANDIDATE CONTRIBUTION 5
LIST OF TABLES 6
LIST OF FIGURES 7
TABLE OF CONTENTS 8
13 CHAPTER 1: INTRODUCTION1.1 CHAPTER OVERVIEW 13 1.2 BACKGROUND TO THE RESEARCH 13 1.3 THE RESEARCH PROBLEM AND HYPOTHESES 15 1.4 JUSTIFICATION FOR THE RESEARCH 17 1.5 AN OVERVIEW OF THE STUDY’S METHODOLOGY 19 1.6 THE STRUCTURE OF THE THESIS 19 1.7 DEFINITION OF ACRONYMS 21 1.8 CHAPTER SUMMARY 21
23 CHAPTER 2: LITERATURE REVIEW2.1 CHAPTER OVERVIEW 23 2.2 BACKGROUND 24 2.3 THE EVOLUTION OF MLE: A RETURN TO TRAIT-‐BASED RESEARCH 25 2.4 INTELLIGENCE (IQ) 31 2.4.1 IQ AND MLE 34 2.5 EMOTIONAL INTELLIGENCE (EI) 36 2.5.1 BACKGROUND OF THE EI CONSTRUCT 37 2.5.2 ALTERNATE MODELS OF EI 39 2.5.2.1 The Expanded View: Mixed Models of EI 39 2.5.2.2 The Narrow View: Ability Models of EI 42 2.5.3 THEORETICAL CONTROVERSIES 46 2.5.4 ABILITY EI AND MLE 49 2.6 MEANING-‐MAKING STRUCTURE (MMS) 56 2.6.1 CONSTRUCTIVE-‐DEVELOPMENTAL THEORY 57 2.6.2 LOEVINGER’S EGO-‐DEVELOPMENT THEORY 59 2.6.3 MMS AND MLE 66 2.6.4 MMS AND EI 70 2.7 MANAGERIAL-‐LEADERSHIP EFFECTIVENESS (MLE) 72 2.8 CHAPTER SUMMARY AND HYPOTHESES 77
79 CHAPTER 3: MEASURES AND METHODS3.1 CHAPTER OVERVIEW 79
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3.2 DEFINITIONS AND MEASURES 79 3.2.1 THE INTEGRAL LEADERSHIP AND MANAGEMENT DEVELOPMENT PROFILE 80 3.2.1.1 ILMDP Management Roles 83 3.2.1.2 ILMDP Leadership Roles 84 3.2.2 THE WASHINGTON STATE SENTENCE COMPLETION TEST 88 3.2.3 THE MAYER, SALOVEY AND CARUSO EI TEST 94 3.2.3.1 Branch 1: Perceive and Appraise Emotion 95 3.2.3.2 Branch 2: Use Emotion to Facilitate Thought 97 3.2.3.3 Branch 3: Understand Emotion 97 3.2.3.4 Branch 4: Manage Emotion 98 3.2.3.5 MSCEITv2 Scoring and Measurement Properties 100 3.2.1 THE WONDERLIC PERSONALITY TEST 105 3.3 METHODS 106 3.3.1 SAMPLE SOURCES 106 3.3.1.1 The Separate ILMDP Sample 106 3.3.1.2 The Main Sample 106 3.3.2 DATA COLLECTION FOR THE MAIN SAMPLE 107 3.3.3 DATA SCORING AND ENTRY OF THE MAIN SAMPLE 109 3.3.4 DATA ANALYSIS 111 3.3.4.1 Data Preparation and Assumption Checking 112 3.3.4.2 Inter-‐Rater Similarity 113 3.3.4.3 Measurement Properties 119 3.3.4.4 Regression Analysis (H2) 122 3.4 CHAPTER SUMMARY 122
125 CHAPTER 4: PRELMINARY DATA ANALYSIS4.1 CHAPTER OVERVIEW 125 4.2 THE MEASUREMENT PROPERTIES OF THE ILMDP 126 4.2.1 THE SAMPLE AND DATA PREPARATION 127 4.2.2 INTER-‐RATER AGREEMENT AND RELIABILITY 128 4.2.2.1 IRA and IRR for the ILMDP Items 128 4.2.2.2 IRA and IRR for the ILMDP Scales 131 4.2.3 TESTING FOR OUTLIERS AND NORMALITY 133 4.2.4 MEASUREMENT PROPERTIES OF THE ILMDP SUBSCALES 135 4.2.4.1 The Brokering Subscale 135 4.2.4.2 The Directing Subscale 136 4.2.4.3 The Monitoring Subscale 138 4.2.4.4 The Achieving Subscale 139 4.2.4.5 The Stewarding Subscale 141 4.2.4.6 The Coaching Subscale 142 4.2.4.7 The Facilitating Subscale 144 4.2.4.8 The Visioning Subscale 145 4.2.4.9 Summary Statistics and Correlation Analysis with Original Scales 146 4.2.5 DISCRIMINANT VALIDITY OF THE ILMDP SUBSCALES 147 4.2.6 EXPLORING THE ILMDP’S STRUCTURE IN THE PRESENT STUDY 148 4.2.7 CONFIRMING THE REVISED MLE SCALES IN THE PRESENT STUDY 151 4.2.7.1 The Revised LE Scale 152 4.2.7.2 Revised ME Scale 155 4.3 THE RELIABILITY OF THE MSCEITV2 160 4.4 THE INTER-‐RATER SIMILARITY OF THE WUSCT SCORES 163 4.5 CHAPTER SUMMARY 164
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167 CHAPTER 5: RESULTS5.1 INTRODUCTION 167 5.2 THE INITIAL ANALYSIS OF THE MAIN SAMPLE 167 5.3 SAMPLE BACKGROUND CHARACTERISTICS 169 5.4 DESCRIPTIVE STATISTICS 172 5.5 TESTING HYPOTHESIS ONE 175 5.6 TESTING HYPOTHESIS TWO 176 5.7 TESTING FOR SUBGROUPS 177 5.7.1 LATENT CLASS REGRESSION: LE 182 5.7.2 LATENT CLASS REGRESSION: ME 182 5.7.3 MEAN DIFFERENCES WITHIN THE LE SUBGROUPS 184 5.7.4 MEAN DIFFERENCES WITHIN THE ME SUBGROUPS 184 5.8 TESTING THE HYPOTHESES FOR THE SUBGROUPS 185 5.8.1 H1 AND H2 FOR LEADERSHIP EFFECTIVENESS GROUP ONE 186 5.8.2 H1 AND H2 FOR LEADERSHIP EFFECTIVENESS GROUP TWO 187 5.8.3 H1 AND H2 FOR LEADERSHIP EFFECTIVENESS GROUP THREE 189 5.8.4 H1 AND H2 FOR MANAGEMENT EFFECTIVENESS GROUP ONE 190 5.8.5 H1 AND H2 FOR MANAGEMENT EFFECTIVENESS GROUP TWO 192 5.8.6 H1 AND H2 FOR MANAGEMENT EFFECTIVENESS GROUP THREE 193 5.9 SUBGROUP BACKGROUND DIFFERENCES 195 5.9.1 BACKGROUND DIFFERENCES IN THE LE GROUPS 196 5.9.2 BACKGROUND DIFFERENCES IN THE ME GROUPS 199 5.10 SUMMARY 200
203 Chapter 66.1 INTRODUCTION 203 6.2 A SUMMARY OF THE THESIS 203 6.3 DISCUSSION 208 6.3.1 THE RELATIONSHIPS BETWEEN IQ, EI AND MMS (H1) 208 6.3.2 INCREMENTAL PREDICTIVE VALIDITY OF MMS (H2) 211 6.3.3 THE THREE LEADERSHIP EFFECTIVENESS GROUPS 216 6.3.3.1 Manage the Emotional Landscape Leaders 217 6.3.3.2 Read the Emotional Landscape Leaders 219 6.3.3.3 Intelligent Ego Leaders 219 6.3.4 THE THREE MANAGEMENT EFFECTIVENESS GROUPS 220 6.3.4.1 Strategise the Emotional Environment Managers 221 6.3.4.2 Intelligent Perception Managers 221 6.3.4.3 I See It But I Don’t Understand It Managers 222 6.4 IMPLICATIONS FOR PRACTICE 223 6.5 IMPLICATIONS FOR THEORY AND FUTURE RESEARCH 225 6.6 LIMITATIONS TO THE PRESENT STUDY 229 6.7 CONCLUSIONS 231 REFERENCES 232 APPENDICES 259
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Chapter 1
INTRODUCTION
1.1 CHAPTER OVERVIEW
The purpose of this study was to contribute to our understanding of the
antecedents to Managerial-Leadership Effectiveness (MLE) by investigating the
relationships between this construct and meaning-making structure (MMS),
emotional intelligence (EI) and general mental ability (IQ). This chapter provides
an overview of the thesis, including the background to the study, the research
problem and related hypotheses, and the justification for the research. The
chapter also summarises the methodology used in the study, the thesis
structure and a list of the acronyms used throughout the thesis.
1.2 BACKGROUND TO THE RESEARCH
Organisational life is increasingly complex and the “hunger for compelling and
creative leadership” has never been greater (Burns 1978, pg 1). Corporate
scandals and the recent global financial crisis have illuminated the need for
managerial-leaders who have a broader set of skills and a view beyond the
bottom line. Globalisation, demands for corporate social responsibility, higher
levels of education and a growing crisis of meaning in society are some of the
reasons that traditional leadership abilities will not suffice (Hesselbein,
Goldsmith & Beckhard 1996; Tacey 2003; Wheatley 2006; Wheatley 2009).
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These and other challenges require managerial-leaders with emotional, social
and meaning-making abilities beyond traditional intelligence (IQ) (Pfeiffer 2001;
Price 2003; Reams 2005; Quatro, Waldman & Galvin 2007; Zohar & Marshall
2000).
Interacting with multiple stakeholders, the complexity of people management
and the stresses of organisational life are some of the reasons managerial-
leadership is an emotionally-laden process. Modern organisational life requires
managerial-leaders able to work within an emotional landscape, including
working with both their own emotions and the emotions of others. It is within
this context that the concept of Emotional Intelligence (EI) was introduced and
emerged as the sine qua non for effective leadership (Goleman 1998a).
The concept of EI struck a collective cord as an antidote to the perceived
monopoly of IQ and was quickly embraced by the corporate world. The EI
construct endorsed the importance of emotions and meaning-making to
effective managerial-leadership and had the added benefit of being an ability
that a person could develop. The result was the emergence of an industry
devoted to measuring and developing the EI of managerial-leaders. Alternate
models of EI have emerged, with varying degrees of support for their constructs
and their predictive and discriminant validity. However, there is general
agreement within the academic community that ability-based models of EI are
better than self-report and/or trait-based measures. The use of EI in applied
settings has also exceeded the boundaries of our research based
understanding of the construct (Mayer, Salovey & Caruso 2008). Despite
extravagant claims by some practitioners, it is unlikely EI is a panacea for
managerial-leadership effectiveness (Antonakis 2003; Antonakis 2004).
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Further, EI does not guarantee a moral high-ground. The often used ‘Hitler
example’ reminds us that people who are skilled at working with the emotional
landscape can operate within a fundamentally limited worldview (e.g. our tribe
versus their tribe). A more defensible hypothesis is that EI works in
combination with other intra-individual abilities to contribute to behaviour and,
ultimately, to managerial-leadership effectiveness.
Organisational life consists of multiple worldviews that require managerial-
leaders to understand and incorporate different viewpoints, to transcend the
perspective of the system within which they operate and to identify
commonalities beyond differences. These meaning-making abilities include, but
go beyond EI abilities (Tischler, Biberman & McKeage 2002) . Developmental
psychologists argue differences in MLE are largely explained by an individual’s
internal meaning-making structure (MMS), also described as an individual’s
collection of action-logics (Rooke & Torbert 2005). Increasing levels of MMS
would provide managerial-leaders with increased complexity in their thinking
and meaning-making capacities. Despite significant research exploring the
relationship between EI and other individual differences (J. Ciarrochi, A.Y. Chan
& P. Caputi 2000; Day & Carroll 2004; Saklofske, Austin & Minski 2003; Schutte
et al. 1998), to date no empirical research has explored the relationship
between MMS and EI.
1.3 THE RESEARCH PROBLEM AND HYPOTHESES
The purpose of this study was to broaden our understanding of MLE through an
investigation of the relationships between MMS, EI and IQ and MLE. Despite
extensive research for each of the constructs individually, there is a clear gap in
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our knowledge of the relationships between them. Based on a review of the
literature, which is discussed in Chapter 2, three hypotheses were suggested.
MSS is a valid individual difference that is thought to contribute to individual
effectiveness. As such, the first hypothesis posited that MMS was positively
correlated with IQ and EI, as can be seen in Figure 1.1.
Figure 1.1 MMS is Positively Correlated with IQ and EI (H1)
The second hypothesis posited that MMS was a distinct construct from both IQ
and EI and as such would have validity in predicting MLE as can be seen in
Figure 1.2.
Figure 1.2 MMS IQ and EI Predict MLE (H2)
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1.4 JUSTIFICATION FOR THE RESEARCH
Understanding the antecedents of MLE is important for research and practice.
Significant amounts of time and money are invested in leadership development
programs that are focussed on developing the EI capacities of managerial-
leaders. However, the relationship between EI and MLE continues to be poorly
understood. As such, this study contributed to a better understanding of this
relationship.
More specifically, this study addressed an area that previous researchers have
neglected; namely the relationship between MMS and MLE. First and foremost,
this was an empirical study of the construct of MMS in the leadership arena.
While MMS has been explored extensively in psychology, there has been
limited application of the theory to increase our understanding of MLE in a
business context. Despite the existence of valid and reliable measures of MMS
(Cook-Greuter 2005; Loevinger 1998), there is limited empirical research to
support the theoretical claim that this construct is significantly related to MLE.
The potential application of this relationship is considerable as it lends credibility
to the life-long developmental journey required if people are to become and
remain effective managerial-leaders.
Finally, the study made a significant contribution through the specific methods
and measures used to examine the hypotheses. In his critique of the field,
Antonakis (2009) challenged EI researchers to design studies that:
1. Avoid self-reported measures of MLE.
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2. Obtain MLE measures and individual difference scores from different
sources.
3. Use measures specifically designed to tap into EI rather than personality.
4. Use practising managerial-leaders in real-world contexts.
5. Have acceptable sample sizes.
6. Control for hierarchical nestings if this is pertinent (i.e. levels of analysis)
(Antonakis, Ashkanasy & Dasborough 2009).
This study addressed many of these issues. The dependent variable was
operationalized as multi-source feedback ratings from others. The individual
difference scores were drawn from ability measures for MMS, EI and IQ.
Further, the sample, although moderate in size, was drawn from practising
managerial-leaders operating in real-world contexts. In summary, this study
explored an area of both theoretical and practical importance. The research
made at least four contributions to our understanding of MLE, in that it has:
1. Extended the application and understanding of the relationship between
EI and MLE,
2. Expanded the application and understanding of the relationship between
MMS and MLE,
3. Explored the relationship between EI and MMS, and
4. Used a methodologically strong approach within the study.
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1.5 AN OVERVIEW OF THE STUDY’S METHODOLOGY
This study used a hypothetical-deductive approach and a survey methodology.
The research population included people participating in customised corporate
leadership development programs delivered by the AIM-UWA Integral
Leadership Centre1, and/or a leadership effectiveness unit within the part-time
MBA program at The University of Western Australia Business School.
Respondents in the study included practising managers who had completed a
competency-based 360o feedback process as part of their individual
development program. The results of the 360o feedback process were used as
the dependent measure of MLE within the study. Managers were invited to
participate in the study after receiving the results of their 360o feedback. The
final sample included 169 respondents who completed a battery of tests,
including an ability measure of EI and MMS. Analysis of the data collected was
undertaken in two stages, starting with the preliminary data analysis, after which
the three hypotheses were examined.
1.6 THE STRUCTURE OF THE THESIS
The thesis has six chapters. As has already been described, Chapter 1
introduced the research and provided an overview of the thesis, while in
Chapter 2 the relevant literature on MLE, IQ, EI and MMS and their
relationships are reviewed. The development of our understanding of MLE is
discussed through a brief historical review that details the resurgence of interest
in individual differences as antecedents to MLE. The importance of traditional
IQ and personality, and the growing interest in identifying additional constructs,
1 The AIM-UWA Integral Leadership Center is a joint venture between the Australian Institute of Management (AIM) and the University of Western Australia.
20
are also examined. Of particular interest is the concept of EI, which has
received varying levels of attention for almost a century, but has had a
resurgence of interest in recent decades.
In the discussion, EI is delineated as an ability, rather than an aspect of
personality, and the important relationship between EI and MLE is outlined.
Building on this foundation, the discussion turns to the need for meaning-
making abilities beyond EI. The concept of MMS is introduced and the
relationships between MMS, EI and MLE are discussed. The chapter
concludes with a summary of the hypotheses posited.
In Chapter 3 the constructs within the research hypotheses are outlined and the
methods that were used in the study’s research design are described.
Operational definitions and measures for MLE, IQ, EI and MMS are provided,
including a discussion of the psychometric properties of each of the measures.
This discussion is followed by a description of the research methods that were
used, including data collection, entry, coding and analysis.
Chapter 4 presents the preliminary data analysis, including an analysis of the
measurement properties for the dependent variable measure, the reliability of
the EI measure, and inter-rater similarity and agreement for the relevant
measures. Building on this foundation, Chapter 5 presents results from the data
analysis including testing of the hypotheses. Chapter 6 provides a summary of
the study including a discussion of the results. Finally, the study’s conclusions,
limitations and implications for practice, theory and research are discussed.
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1.7 DEFINITION OF ACRONYMS
The following is a list of acronyms that are used throughout the thesis:
ED Ego-development
EI Emotional intelligence
GMA General mental ability, also referred to as traditional
intelligence within this study
IQ The empirical score on a test of traditional intelligence
ILMDP Integral Leadership and Management Development Profile
(measure employed in the study for behaviourally complex
managerial-leadership effectiveness)
LE Leadership effectiveness
ME Management-effectiveness
MLE Managerial-leadership effectiveness
MMS Meaning-making structure
MSCEITv2 Mayer, Salovey, Caruso Emotional Intelligence Test
(measure of EI employed in the study)
MSF Multi-source feedback
WPT Wonderlic Personnel Test (measure of IQ employed in the
study)
WUSCT Washington University Sentence Completion Test (measure
of MMS employed in the study)
1.8 CHAPTER SUMMARY
The present chapter presented an introduction and overview of the study. The
background to the study, including the re-emergence of the EI construct and the
importance of MMS to the phenomenon of MLE, was discussed with specific
reference to the lack of empirical research that has explored the relationship
22
between EI and MMS. This led to the specific research problem and research
hypotheses intended to explore the relationships between MMS, EI, IQ and
MLE. A brief justification for the research was provided followed by an overview
of the methodology of the study. The structure of the thesis and a list of key
terms used in the study were also presented. The next chapter provides a
review of past research that led to the present study.
23
Chapter 2
LITERATURE REVIEW
2.1 CHAPTER OVERVIEW
In the previous chapter the research question was introduced, which asked:
What are the relationships between meaning-making structure (MMS), emotional intelligence (EI), traditional intelligence (IQ), and managerial-leadership effectiveness (MLE)?
The purpose of this chapter is to establish the theoretical foundation for this
research question and the related hypotheses. To begin, a broad level
contextualisation of the research is provided in Section (Section 2.2) followed
by a review of the evolution in our understanding of MLE, specifically outlining a
return to the study of trait-based explanations (section 2.3). This section
concludes with an indication of three key trait-based antecedents to MLE that
are particularly salient given the current context of managerial-leadership: IQ, EI
and meaning-making structure (MMS).
Section 2.4 reviews the concept of IQ, its relationship with MLE and the
rationale for exploring other abilities as antecedents of MLE. Section 2.5
reviews the EI construct as it provides:
1. The background to the EI construct (section 2.5.1).
2. A discussion of alternate EI models: trait versus ability (section 2.5.2).
24
3. A summary of the key controversies in the field of study (section 2.5.3).
4. A summary of the research that has examined the relationship between
the ability model of EI and MLE (section 2.5.4).
Next, in section 2.6, the concept of meaning-making structure (MMS) is
reviewed. This section begins by articulating the difference between meaning,
meaning-making and MMS. Next, an overview of constructive developmental
theory, the theoretical domain within which MMS is placed (section 2.6.1), an
overview of Loevinger’s constructive developmental theory of ego-development
(ED) as a seminal operationalisation of MMS (section 2.6.2), a summary of the
current understanding of the relationship between MMS and MLE (section
2.6.3) and with EI (section 2.6.4).
Finally, the construct of MLE, the dependent variable in the research, is
delineated through the lens of behavioural complexity (section 2.7). The chapter
concludes with a summary of the chapter and the research hypotheses.
2.2 BACKGROUND
Globalisation and technology have fundamentally changed the dynamics of
organisational life, challenging organisations and managers to operate in an
increasingly dynamic and complex arena. Successful organisations have
realised that retaining a competitive edge in the new economy requires
harnessing their full potential through exceptional leadership. Organisational
innovation and creativity and/or learning are heralded as the critical ‘x’ factor
that will sustain organisations in the new information age, but management
gurus warn that traditional organisational structures and managerial
25
competencies and a unitary focus on profit and maximising shareholder wealth
alone will not suffice (Conger 1994; Duchon & Plowman 2005; Fairholm 2004;
Gehrke & Gehrke 2008). This is articulated succinctly by Pfeiffer (2001, p. 138):
Most agree that solutions to society’s most vexing problems will require citizens to possess not only well-developed intellectual abilities, but also equally impressive social and emotional skills.
Today, it is generally accepted that superior managerial leadership requires a
combination of abilities, although that has not always been the case (Quinn,
1996). In terms of how this applies to MLE definitions, the theory of behavioural
complexity is very useful in terms of capturing this perspective as detailed in
Section 2.7. In terms of antecedents to MLE, our understanding of relevant
abilities has similarly expanded. Until recently, our view of intelligence has
been narrowly defined and many theorists suggest significant variance in
managerial leadership effectiveness can be attributed to abilities beyond the
traditional IQ (Caruso & Salovey 2004; Rooke & Torbert 2005; Sternberg et al.
1995; Zohar & Marshall 2000). However, having argued the importance of
multiple abilities for MLE, much of the work has focussed on measuring and
developing EI, rather than understanding how it relates to other fundamental
abilities, particularly as antecedents to MLE.
The next section describes the origins of trait-based research of MLE, including
the journey away from and ultimate return to this line of enquiry.
2.3 THE EVOLUTION OF MLE: A RETURN TO TRAIT-BASED RESEARCH
Managerial-leadership is a broad and multi-faceted phenomenon that can be
conceptualised at an individual, dyadic, group or organizational level (Lussier &
26
Achua 2009). Theories vary based on their focus (leader or follower centred),
their intention (descriptive or prescriptive), and/or their applicability (universal or
contingent) (Yukl 2006). Despite more than a century of study, managerial-
leadership researchers have failed to reach an agreement as to a unitary theory
or definition of the construct (Bass 1990). Instead, a spectrum of theories and
models of managerial-leadership have emerged and exhaustive reviews
continue to be written (see Bass 1990; Daft & Pirola-Merlo 2009; Yukl 2006).
During the Nineteenth Century and into the Twentieth Century, MLE was
thought to be explained solely by individual differences. Great man theories
and trait theories suggested superior managerial-leaders were born with key
personality characteristics (i.e. energy, intelligence, self-confidence, honesty
etc.). This path of inquiry, which was spearheaded by Carlyle (1840/1993) and
pursued for more than 100 years, produced mixed results in establishing a short
list of personality characteristics that would consistently predict superior MLE.
Stogdill (1948) identified some traits that appeared to differentiate leaders from
non-leaders (intelligence, alertness to needs of others, task awareness,
initiative, persistence, and self-confidence) but, otherwise, concluded trait
research was not consistent in predicting MLE (Kirkpatrick & Locke 1991).
The emphasis then shifted to identifying an appropriate balance of task versus
relationship focused behaviours. Examples of these distinctions include
initiating structure versus consideration, as described in the so-called Ohio
State University studies (Kerr et al. 1974), and the employee oriented versus
production oriented behaviour, as described in the so-called University of
Michigan studies (Kahn et al. 1960). These initial models did not incorporate
situational factors and had varied success in identifying behaviours that could
27
be applied across a range of circumstances. The work of managerial-leaders
was increasingly seen as dynamic and unpredictable, rather than linear, logical
and analytical (Mintzberg 1973). As such, behaviourist models, known as
contingency theories, attempted to isolate the critical situational factors that
shaped leadership and management effectiveness. Effectiveness was thought
to result from the appropriate interaction of a managerial-leader style with the
context in which the leader operated and the followers who were involved.
Examples include Fiedler’s (1967) Contingency Model, Hersey-Blanchard’s
(1969) Life Cycle Theory of Leadership (later termed Situational Leadership
Theory), and House’s (1971) Path-Goal Theory.
The next evolution of thinking in managerial-leadership theory involved a step
change from focusing on the managerial-leader’s personality, behaviours and
contingency skills to include the interactions between the leader and their
followers (Yukl 2006). Relationship theories that emphasised the processes
and structures of influence in the relationship between the managerial-leader
and followers became important in understanding MLE. Examples include
servant (Greenleaf 2007), transformational (Bass 1998) and charismatic
(Conger & Kanungo 1988) leadership theories. Such theories focused on
followers’ values, motivations and the interpersonal dynamics between leaders
and followers. This was a critical shift in the study of leadership, which was now
seen to be a shared meaning-making process and an emotionally laden
phenomenon (Hunt 1999).
In recent decades, trait theories have re-emerged through an increasing interest
in intra-personal capacities (Day & Zaccaro 2007 ; Lord, De Vader & Alliger
1986; Zaccaro 2007). Such capacities are thought to differentiate between
28
authentic and pseudo transformational leaders (Bass & Steidlmeier 1999; Price
2003; Zaccaro 2007). The emotional, ethical and spiritual capacities of
managerial-leaders are increasingly considered to be central to MLE (Crosby &
Kiedrowski 2008). However, there is an important distinction in the thinking now
when compared with that of a century ago. Managerial-leadership, and some of
the associated attributes, are no longer considered to be something with which
you are born but, rather, as capacities that can be developed and learned
(Bennis 2003; Thomas 2008; Zaccaro 2007). A further distinction can be seen
in the increasing theoretical foundation for current trait-based research in
contrast to research that was undertaken in the early Twentieth Century, which
was largely atheoretical (House & Aditya 1997).
In a review of trait-based perspectives of leadership, Zaccaro (2007, p. 6)
argued for the inclusion of multiple leader attributes that were organised “in a
coherent and meaningful conceptual construction” and that the interaction
effects of attributes should be examined. For example, the model of leader
attributes and leader performance includes distal traits, such as cognitive
abilities, proximal attributes, such as social appraisal skills, and the managerial-
leader’s environment (Zaccaro, Kemp & Bader 2004). This is just one of many
taxonomies suggested for organising individual differences related to MLE
(Bass 1990; Locke et al. 1991; Mumford et al. 2000; Yukl 2006). The current
challenge for trait-based research is to explore multivariate predictions (Lord,
De Vader & Alliger 1986; Zaccaro 2007) and to test “not only on multiple
personal attributes but also on how these attributes work together to influence
performance” (Zaccaro, Kemp & Bader 2004, p. 8). Towards this end, Tischler,
Biberman and McKeage (2002) argue the case for exploring the relationship
29
between performance, EI and complimentary constructs that might stand as
proxies for spiritual intelligence.
The approach to understanding MLE has returned to the beginning with the re-
emergence of interest in individual differences. The obvious question then, is
what are the critical trait-based antecedents? The prevalence of traditional
intelligence (IQ) as an important antecedent to MLE has withstood the test of
time, as detailed in Section 2.4 below. Leaders require IQ in order to analyse
and to solve problems using logic. IQ is critical to the process of strategic and
operational planning. Setting objectives/goals and working through strategies
towards their achievement requires processing and integrating information from
a variety of sources. In fact, higher levels of IQ would benefit the analysis
phase of any type of managerial problem (i.e. staffing, production, development
or marketing etc.). However, the literature suggests that traditional intelligence
(IQ) has a positive but small relationship with leadership (Stogdill, 1969). IQ
appears to be threshold skill; a minimum level is required for effective
leadership after which, other competencies, such as emotional intelligence,
contribute more to predicting effective leadership (George 2000; Leban & Zulauf
2004; Rosete & Ciarrochi 2005).
Emotional intelligence (EI) is the ability to recognise, understand, use and
manage emotions in oneself and others (Mayer, Caruso & Salovey 1999b). EI
underpins a number of leadership competencies: accurate self-assessment,
emotional self-control, empathy, the ability to read, attune, empathise with and
influence other’s emotional states. Leaders with accurate assessment of their
own abilities are able to contribute from their strengths and to leverage others’
strengths to compensate for their weaknesses. Leaders without emotional self-
30
control are at the mercy of their emotional reactions and run the risk of seriously
damaging their credibility and even their career by simply being in the wrong
place at the wrong time. Finally, the ability to empathise and influence others is
central to creating effective relationships, managing constructive conflict and
achieving through other people. The case for EI as an important antecedent to
MLE is made in Section 2.5 below.
When leaders possess both IQ and EI abilities, they bring a broader range of
skills to their role, but to what end? Logic, reason and emotional savvy are no
longer enough. Drath and Palus (1994, p. 13) ‘refer to leadership as a social
meaning-making process’. The premise is effective managers will require very
specific meaning-making abilities in addition to intellectual and emotional
abilities (King & Nicol 1999; Tischler, Biberman & McKeage 2002). Drawing on
the work of Ken Wilber, Young (2002a, p. 35), describes meaning-making
capacity as different levels of consciousness:
..as individuals progress or move to higher levels of consciousness, their perspectives enlarge, their identities broaden and both become more comprehensive… they engage in Wilber’s definition of transformative spirituality ….each stage (has) new dimensions of existence, modes of learning, desires, fears apperceptions, motivations, moral sensibilities (Wilber 2000).
A higher, or more complex, level of consciousness leads to the capacity for
more complex meaning-making and is critical to the way that individuals
perceive the world (Loevinger 1976; Cook-Greuter 2004). It follows that a more
complex meaning-making structure would enable a leader to recognise,
facilitate meaning and be of service at increasing levels of complexity (Rooke &
Torbert 2005). As leaders move to increasing levels of consciousness, they
gain a higher awareness of their mind/body connections; conduct more
31
complete environmental and stakeholders analysis; understand the
perspectives of all stakeholders better; utilise a more encompassing and
therefore enhanced morality; leverage enhanced states of intuition and
synchronicity; and ultimately effect transformation in others (Young 2002a). As
Rooke and Torbert (2005, p. 67) noted:
Most developmental psychologists agree that what differentiates leaders is not so much their philosophy of leadership, their personality, or their style of management. Rather, it’s their internal ‘action logic’ – how they interpret their surroundings and react when their power or safety is challenged.
Consequently, Section 2.6 below presents the argument that MMS is an
individual difference that has predictive validity over and above IQ and EI.
In summary, the above discussion has presented a brief review of the history of
MLE starting with and returning to an interest in trait-based constructs. The
current challenge for researchers of trait-based antecedents to MLE is to
explore multivariate attributes and also interaction effects. Towards this end the
case was made for the study of a multivariate antecedent to MLE that includes
an intellectual, emotional and meaning-making dimension. What follows next is
a detailed discussion of each of the aspects of the proposed multivariate
antecedent to MLE: IQ, EI and the capacity for meaning-making.
2.4 INTELLIGENCE (IQ)
There have been different explanations for the basis of human intelligence
since the beginning of recorded history, including its location in the heart, liver
and head (Gardner 1983). A single definition for the construct is elusive, as is
reflected in a statement by a group of prominent psychologists:
32
Individuals differ from one another in their ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought…Concepts of ‘intelligence’ are attempts to clarify and organize this complex set of phenomena. Although considerable clarity has been achieved in some areas, no such conceptualization has yet answered all the important questions, and none commands universal assent. Indeed, when two dozen prominent theorists were recently asked to define intelligence, they gave two dozen, somewhat different, definitions (Neisser et al. 1996, p. 77).
Modern intelligence theories are varied, but they generally fall within two
categories that suggest intelligence is a unitary quality of mind, (which is often
referred to as ‘g’) or that intelligence is a set of independent factors (Sternberg
1990). Charles Spearman (1904) pioneered the former argument and
suggested intelligence consists of two factors, which he termed general
intelligence and specific intelligence. The first factor reflected a person’s
general pool of mental energy, which is consistent with the concept of ‘g’, while
the second factor, often referred to as ‘s’, represents a person’s level of mental
energy that is specific to the particular domain of knowledge being tested
(Spearman 1927). Today ‘g’ is commonly referred to as cognitive ability,
general mental ability (GMA) or the g-factor (Viswesvaran & Ones 2002). It has
been defined as “the aggregate or global capacity of the individual to act
purposefully, to think rationally, and to deal effectively with his environment”
(Wechsler 1944, p. 3).
Measures of general mental ability are commonly referred to as a person’s
intelligence quotient (IQ). The first measure of IQ, the Metrical Scale of
Intelligence (Binet & Simon 1905) was devised to identify children who were at
risk of failing within the standard school system. In the past century alternative
tests have been developed to measure IQ, more specific cognitive abilities and
33
also related constructs, such as scholastic aptitude (Neisser et al. 1996).
However, measures of IQ are thought to be better predictors of performance
than are measures of specific abilities, such as mathematical reasoning, verbal
aptitude, memory or perceptual speed (Ceci & Williams 2007). Despite
differences in content, various IQ tests correlate positively with each other,
which supports the ‘g’ concept of intelligence (Neisser et al. 1996).
Despite differences of opinion regarding the structure and processes involved in
cognitive ability, experts in the field tend to agree on the fundamentals (Eysenck
2000). For example, Cattell’s (1963; 1987) distinction between fluid and
crystallised intelligence is widely accepted. Fluid intelligence represents the
power of reasoning and ability to process information. Fluid intelligence peaks
within a person’s first 20 years of life and remains relatively constant after that,
with the exception of a decline in old age. Crystallised intelligence is consistent
with the concept of ‘s’ that was proposed by Spearman and represents
specialised knowledge that is context specific (i.e. an ability to write an
academic paper). This form of intelligence can continue to expand with use
throughout life. However there are at least three controversies that continue to
plague IQ researchers, namely race differences (Gottfredson 2005; Hunt &
Carlson 2007; Rushton & Jensen 2005; Rushton & Jensen 2006; Sternberg
2005), gender differences (Irwing & Lynn 2006; Jackson & Rushton 2006; Lynn
& Irwing 2004) and a suggestion of an increasing level of intelligence in the
general population, often dubbed the Flynn effect (Flynn 1984; Sundet, Barlaug
& Torjussen 2004 ). These issues represent a continued debate about the
relative importance of heritability and the environment in predicting peoples’ IQ
scores.
34
IQ tests were the subject of much criticism in the 1960s and 1970s, with claims
of bias and limited validity (McClelland 1973). Advances in meta-analytic
procedures facilitated the improvement of counter arguments and provided
statistical support for the predictive relationship between IQ and work outcomes
(Bertua, Anderson & Salgado 2005; Hülsheger, Maier & Stumpp 2007; Hunter,
Schmidt & Judiesch 1990; Hunter 1986; Hunter & Hunter 1984; Schmidt &
Hunter 1998; Schmidt & Hunter 2004). Currently, measures of general
cognitive ability are thought to predict job performance across a spectrum of
occupations and to be one of best predictors of success in applied settings
(Ones, Viswesvaran & Dilchert 2005; Salgado et al. 2003). Further, the
strength of the relationship between IQ and performance seems to increase
with job complexity (Schmidt & Hunter 1998).
2.4.1 IQ and MLE
There is strong support for a relationship between IQ and managerial-
leadership (Bass 1990; Fiedler & Garcia 1987; Lord, De Vader & Alliger 1986;
Mann 1959; Stogdill 1948). The need to deal with complexity is one of the
theoretical underpinnings for the relationship between IQ and MLE (Fiedler &
Garcia 1987; Locke et al. 1991). The role of creativity in effective problem
solving and working with and through others also supports this relationship (Kim
2008; Simonton 2008). At a strategic level, managerial-leaders have to
engineer sustainable competitive advantage, align purposes and values and
harness the human potential of their organisation. Operationally, managers
must engage a broader, more educated and diverse set of stakeholders
including employees, customers, shareholders and various related interest
groups. In their re-examination of Mann’s (1959) meta-analysis using modern
35
statistical methods, Lord et al. (1986) found IQ was correlated (r= .50) with
perceptions of managerial-leadership effectiveness. Further, cognitive ability
seemed to predict managerial-leadership success 20 years down the track
(Howard & Bray 1988).
It is important to remember the phenomenon of intelligence is not the same as
IQ (Nettelbeck & Wilson 2005). A ubiquitous belief in the primacy of intelligence
underpins an implicit leadership theory within followers which has been found to
contribute to the relationship between IQ and MLE beyond the tangible
advantages that have already been discussed (Judge, Colbert & Ilies 2004;
Rubin, Bartels & Bommer 2002). Rubin et al. (2002) found that the perceived-
intelligence of an managerial-leader was not equivalent to their IQ and that both
IQ and self-monitoring skills assisted a managerial-leader. Judge et al. (2004)
found a positive relationship between IQ, perceived-intelligence and
perceptions of MLE.
The terms ‘book-smart’ and ‘street-smart’ are familiar in lay vocabulary and
reflect the intuitive notion that IQ alone does not predict real world success.
Indeed, IQ alone may have a curvilinear relationship with performance unless
moderated by social and/or emotional abilities (Zaccaro 2007). Neisser (1976)
explored the difference between practical intelligence (PQ) and academic
intelligence (IQ) and suggested there were significant differences in the nature
of the tasks that were best addressed by each intelligence. However, IQ
continues to be one of the most significant individual differences predictors of
MLE (Van Rooy & Viswesvaran 2004). At the same time it is clear IQ is not
sufficient if we are to fully understand the phenomenon of MLE. A number of
theorists have suggested the need for a broader conceptualisation and see
36
intelligence as a multi-faceted construct with abilities that are relatively
independent of one another.
An early proponent of multiple intelligence theory was Thorndike (1920) who will
be discussed in more detail in Section 2.4.1. Thurstone (1938) claimed that
intelligence was made up of seven primary mental abilities, which he termed
verbal comprehension, verbal fluency, numbers, spatial visualisation, memory,
reasoning, perceptual speed. Guilford (1967) suggested there were 120
different intelligences. His view was based on a three dimensional view of
basic mental abilities, which he termed operations (the act of thinking), contents
(the terms in which we think such as words or symbols) and products (the ideas
we come up with). In a much simpler model, Gardner (1983) suggested there
were seven types of intelligence, which he termed verbal, mathematical-logical,
spatial, kinaesthetic, musical, interpersonal and intrapersonal. Following this
line of thinking, many theorists have suggested expanded views of intelligence
(Das, Naglieri & Kirby 1994; Sternberg 1988).
In summary, IQ is an important individual difference construct that contributes to
individual job performance and to MLE. IQ has consistently predicted variations
in MLE and, consequently, studies that explore the relationship between
individual differences and MLE need to include a measure of IQ in order to test
the incremental predictive validity of any additional constructs.
2.5 EMOTIONAL INTELLIGENCE (EI)
Awareness of, and interest in, the relationship between emotional intelligence
(EI) and MLE has grown exponentially since the 1990s. However, the construct
37
itself has a long and somewhat controversial history. The background to the EI
concept is presented in the next section, followed by a discussion of alternate
models of EI, a review of the key controversies in the field and a summary of
current understanding regarding the relationship between the ability model of EI
and MLE.
2.5.1 Background of the EI Construct
In order to understand the concept of emotional intelligence (EI) it is important
to appreciate its historical roots, starting with Thorndike’s (1920, p. 228) notion
of social intelligence, which he saw as “the ability to understand and manage
men and women… to act wisely in human relations”. Social intelligence was
seen as part of intelligence in its multi-faceted form (Thorndike 1920; Thurstone
1938). It has been argued that the psychological “ability to judge correctly the
feelings, moods, motivations of individuals” (Wedeck 1947, p. 133) is distinct
from personality. However, after some initial attempts to define and measure
social intelligence (Moss & Hunt 1927; Vernon 1933), the concept was declared
untenable (Cronbach 1960). An important shift in focus from ‘social’ to
‘emotional’ intelligence began in the 1960s when it was suggested that
emotions “do not at all deserve being put into opposition with
‘intelligence’…they are, it seems, themselves a high order of intelligence”
(Mowrer 1960, p. 307 ).
Research on social intelligence waned until theories suggesting a broader
conceptualization of intelligence, such as the structure of intellect theory
(Guilford 1967) and multiple intelligences (Gardner 1983), gained prominence.
At least two separate fields of study emerged, namely the specific study of
38
interactions between emotions and cognition and the expansion of the concept
of intelligence (Jordan et al. 2002). Gardner (1983, p. 43) suggested two
distinct emotionally laden intelligences as part of a spectrum of multiple
intelligences, with interpersonal intelligence being defined as “a person’s
capacity to understand the intentions, motivations and desires of other people
and consequently to work effectively with others” and intrapersonal intelligence
being defined as “the capacity to understand oneself – including one’s own
desires, fears, and capacities – and to use such information effectively in
regulating one’s own life”.
The concept of emotional intelligence was formally introduced into the
academic literature when Salovey and Mayer (1990, p. 189) presented their
ability-based model of emotional intelligence (EI), defining it initially as:
A subset of social intelligence that involves the ability to monitor one’s own and other’s feelings and emotions, to discriminate among them, and to use this information to guide one’s thinking and action.
The new label of EI focused attention on “the relatedness of several aspects of
emotion processing that together contribute to social psychological functioning”
(Jordan et al. 2006, p. 200). Salovey and Mayer continued to develop the
ability model of EI that was explicitly placed at the intersection of the two fields
of emotion and intelligence (Mayer & Salovey 1993; Mayer & Geher 1996;
Mayer, Caruso & Salovey 1998; Mayer, Salovey & Caruso 2000; Salovey et al.
1995) and presented an evidenced-based argument that EI met the standards
of a form of intelligence (Mayer, Caruso & Salovey 1999a; Mayer et al. 2001).
However, many researchers were reluctant to embrace the construct.
Notwithstanding, and actually predating this reluctance, Goleman (1995)
39
popularised the term ‘EI’ in his best-selling book that laid the foundation for
alternative EI models.
2.5.2 Alternate Models of EI
The existence of alternate models of EI is a central controversy in the literature:
…the construct of EI has become fractured in the struggle between the scientist trying to develop a valid psychological construct on the one hand, and marketers attempting to develop a commercially viable psychological framework on the other. (Jordan et al. 2006, pg 198)
While the field of psychology has been studying the intersection of affect and
cognition for more than 30 years (Forgas 2001), the exploration of EI is
relatively new, particularly when compared to other constructs, such as IQ, or
personality variables, such as the Big Five (Van Rooy & Viswesvaran 2004).
Some see the continuing search for a clear, universally accepted definition and
measure of EI as elusive (Matthews, Roberts & Zeidner 2004), while others
believe this search is consistent with the natural evolution of scientific inquiry
(Antonakis, Ashkanasy & Dasborough 2009; Ashkanasy & Daus 2005). At
present, the literature separates the EI field into two distinct paradigms. In one,
EI is seen to be a mental ability, as conceptualised by Mayer and Salovey
(1990) while, in the other, EI is seen as a mixture of traits, competencies and
skills (Bar-On 1997; Sala 2002; Schutte et al. 1998).
2.5.2.1 The Expanded View: Mixed Models of EI
Central to the EI literature over the past two decades has been the emergence
and commercial dominance of mixed models of EI, as described by Mayer
(2008, p. 504) who noted:
40
Subsequent interpreters of our work, however were instrumental to (what we regard as) unmooring the concept from its key terms. These interpreters appear to have confused what we thought of as expressions of EI with the ability itself.
The mixed model paradigm conceptualises EI in a variety of ways, including as:
• A collection of non-cognitive capabilities, competencies and skills (Bar-
On 1997); traits and competencies (Cooper & Sawaf 1997; Goleman
1998b).
• Personal characteristics and behaviours (Dulewicz & Higgs 1998;
Dulewicz & Higgs 2000; Dulewicz, Higgs & Slaski 2003).
• Behavioural tendencies (Pérez, Petrides & Furnham 2005; Petrides &
Furnham 2001).
With such broad conceptualisations, mixed models failed to attain content
validity beyond established measures of personality, such as IQ or the Big Five
(Brackett & Mayer 2003; Dawda & Hart 2000; Mayer, Roberts & Barsade 2008;
Petrides & Furnham 2001; Rosete & Ciarrochi 2005). The use of mixed models
in commercial settings, along with extravagant and unsubstantiated claims of
EI’s predictive validity (Goleman 1995; Goleman 1998a) has led to reduced
credibility for the EI construct (Landy 2005).
A key criticism of mixed models is the use of self-judgement scores to assess
EI. The use of the term ‘intelligence’ implies that what is being measured is a
mental ability. However, self-judgement and ability measures are conceptually
and empirically different (Brackett & Mayer 2003; Brackett et al. 2006; Mayer &
41
Salovey 1997; Mayer, Caruso & Salovey 1999a; Mayer, Caruso & Salovey
2000; Mayer, Salovey & Caruso 2000).
Low correlations between self-report and ability measure of EI suggested they
are different constructs (Ashkanasy & Dasborough 2003; Bastian, Burns &
Nettelbeck 2005; Brackett & Mayer 2003; Brackett et al. 2006; Brannick et al.
2009; Karim & Weisz 2010; Lopes, Salovey & Straus 2003; Matthews, Zeidner
& Roberts 2002; Rosete & Ciarrochi 2005; Van Rooy & Viswesvaran 2004;
Warwick & Nettelbeck 2004). Another concern is the issue of common method
variance when researchers use self-judgement measures for both antecedent
and dependent variables (Barbuto Jr & Burbach 2006; Gardner & Stough 2002;
Newcombe & Ashkanasy 2002; Schutte et al. 2001; Sy, Tram & O’Hara 2006;
Wong & Law 2002). Both of these criticisms extend to ‘Stream 2’ models and
measures of EI (Daus & Ashkanasy 2005) that, although based on the Mayer
and Salovey (1997) ability model, use self and/or peer judgment methodologies.
Examples include the Trait-Meta-Mood-Scale (Salovey et al. 1995), the
Workgroup Emotional Intelligence Profile (Jordan et al. 2002), Wong & Law’s
self-report measures of EI (Wong & Law 2002) or the Assessing Emotions
Scale (AES) (Schutte et al. 1998).
The growing academic view is that mixed models do not qualify as measures of
intelligence (Mayer, Roberts & Barsade 2008; Van Rooy & Viswesvaran 2004;
Zeidner, Matthews & Roberts 2004); that they lack discriminant and incremental
validity from existing valid and reliable constructs such as the Big Five
(Cartwright, Pappas & West 2008; Davies, Stankov & Roberts 1998; MacCann
et al. 2003; Matthews, Zeidner & Roberts 2002; Warwick & Nettelbeck 2004);
and that they are more susceptible to faking on the part of respondents (J.
42
Ciarrochi, A.Y. Chan & P. Caputi 2000; Day & Carroll 2008; J.V. Ciarrochi, A.Y.
Chan & P. Caputi 2000). Increasingly there have been calls for delimiting the
label of EI exclusively for ability models (Ashkanasy & Daus 2005; Brackett &
Mayer 2003; Mayer, Roberts & Barsade 2008).
2.5.2.2 The Narrow View: Ability Models of EI
The ability paradigm assumes specific mental abilities exist that are associated
with processing and integrating emotional information that can be measured
(Mayer, Roberts & Barsade 2008). Ability-based measures use performance
based tasks that can be assessed as being completed correctly or not (Mayer &
Salovey 1997; Mayer, Caruso & Salovey 1999a; Mayer, Caruso & Salovey
2000; Mayer, Salovey & Caruso 2000). This seems straight forward in theory
but, in practice, “there is considerable difficulty in determining objectively correct
responses to stimuli involving emotional content and in applying truly veridical
criteria in scoring tasks of emotional ability” (Roberts, Zeidner & Matthews 2001,
p. 201-202). Mayer et al (2000) argued that there are evolutionary and cultural
foundations for establishing correct responses to tests of emotional ability.
Three common methods are target criteria (asking the target individual
displaying the emotional stimuli what they are feeling), expert criteria (using a
panel of experts in emotion), and consensus criteria (the most common
response of a large sample of people).
Emotional abilities can be studied individually or as an integrated whole (Mayer,
Roberts & Barsade 2008). Measures of individual emotional abilities include
the Diagnostic Analysis of Non-Verbal Accuracy (DANVA-2), which measures
people’s ability to discriminate emotional cues (Nowicki & Duke 1994); the
43
Profile of Non-Verbal Sensitivity (PONS), which measures people’s ability to
accurately decode non-verbal emotional cues (Rosenthal 1979); the Japanese
and Caucasian Brief Affect Recognition Test (JACBART), which measures
people’s ability to discriminate emotional cues (Matsumoto et al. 2000); and the
Levels of Emotional Awareness Scale, which measures people’s ability to
accurately detect and discriminate emotional signals in oneself and others
(Lane et al. 1990)
In contrast to these scales, there are two integrated EI models (Mayer, Roberts
& Barsade 2008). The first model is based on a theory of Emotional Adaptation
(EA), which is related to “the functioning of the emotion systems and their
distinct motivational properties”, as opposed to an intelligence (Izard et al. 2001,
p. 252). EA is operationalised in this case as Emotional Knowledge (EK) and
measured though the Emotional Knowledge Test (EKT). This provides a
performance score on two abilities, namely, emotional perception and labelling
(Izard et al. 2001).
The second model expands the focus beyond the knowledge of emotions.
Salovey and Mayer’s (1990) model is now known as the four-branch ability
model that suggests EI is made up of an ability to:
1. Accurately perceive emotions in self and others.
2. Use emotions to facilitate thought.
3. Understand emotion.
44
4. Regulate and manage emotions in self and others (Mayer & Salovey
1997).
The Salovey and Mayer (1990) EI model was originally measured using the
Multifactor Emotional Intelligence Scale (MEIS), which included 402-items
across four subscales (Mayer, Caruso & Salovey 1999a). The MEIS was
criticised because of differences between the expert and consensus scoring
methods and because of the low reliability of some of the subscales (Conte
2005; Roberts, Zeidner & Matthews 2001; Wong & Law 2002).
In response to the latter concern, the shorter and more reliable Mayer-Salovey-
Caruso-Emotional-Intelligence-Test version 2 (Mayer, Caruso & Salovey 2000)
was introduced to researchers in 2000 and published for more general use in
2002 as the MSCEITv2 (Mayer, Salovey & Caruso 2002). This version of ability
EI seemed to have acceptable discriminant validity from traditional measures of
intelligence (J. Ciarrochi, A.Y. Chan & P. Caputi 2000; Farrelly & Austin 2007;
Lopes, Salovey & Straus 2003; Mayer, Salovey & Caruso 2004; O'Connor &
Little 2003; Schulte, Ree & Carretta 2004; Roberts et al. 2006; Rosete &
Ciarrochi 2005; J.V. Ciarrochi, A.Y. Chan & P. Caputi 2000) and personality
(Farrelly & Austin 2007; Lopes, Salovey & Straus 2003; Lopes et al. 2004;
Mayer, Salovey & Caruso 2004; Rode et al. 2008; Rosete & Ciarrochi 2005).
The lack of relationship between the MSCEITv2 and Raven’s Progressive
Matrices may be evidence that ability-EI is more like crystalised than fluid
intelligence (Farrelly & Austin 2007).
Ability EI has been found to be associated with better social relations for
children and adults, positive perceptions by others, better family and intimate
45
relationships, academic achievement, better social relations at work and in
negotiations and better psychological well-being (Mayer, Roberts & Barsade
2008). In addition, evidence is amassing that ability EI has incremental
predictive validity. After controlling for cognitive ability and personality, EI, as
measured by the MSCEITv2, was significant in predicting lower alcohol use
(Brackett, Mayer & Warner 2004; Rossen & Kranzler 2009); a lower incidence
of deviant behaviour (Brackett & Mayer 2003); positive relationships with others
(Rossen & Kranzler 2009); lower anxiety (Bastian, Burns & Nettelbeck 2005)
and fewer psychiatric symptoms of distress (David 2005). EI, however, was not
a significant incremental predictor of academic grade-point-average (GPA) or
life satisfaction and has been found to be more correlated with social desirability
than were IQ or long term affect (Rode et al. 2008).
Nevertheless, ability EI is empirically distinct from measures of social
desirability (Rode et al. 2008) and appears to increase with age (Kafetsios &
Zampetakis 2008; Mayer, Caruso & Salovey 1999a; Palmer et al. 2005).
However, the “the underlying process remains somewhat vague” (Lindebaum
2009, p. 226). It appears there are gender differences with regard to ability EI
with some evidence that women score higher than men on some branch scores
(Bay & McKeage 2006; Day & Carroll 2004; Kafetsios & Zampetakis 2008;
Lyons & Schneider 2005). There are also significant positive relationships
emerging for men only on some outcome measures (Brackett, Mayer & Warner
2004; Brackett et al. 2006).
In summary, the Mayer and Salovey (1997) ability model has emerged as the
dominant paradigm for scientific inquiry into EI:
46
Thus far, the measurement evidence tends to favour the ability-based EI approach described here over other research alternatives (such as dismissing EI or using mixed models). (Mayer, Roberts & Barsade 2008, p. 510)
The MSCEITV2 v2.0, appears to have greater convergent, discriminant and
incremental validity than any of the mixed models of EI (Brackett & Mayer 2003;
Daus & Ashkanasy 2005) and is the most accurate test currently available for
measuring the four-branch EI ability model (Rossen & Kranzler 2009).
However, as with any emerging area of scientific inquiry, there are clear
differences of opinion regarding the ability EI model and measure. These are
summarised in the next section.
2.5.3 Theoretical Controversies
The construct of EI is not without its critics (Antonakis 2004; Antonakis,
Ashkanasy & Dasborough 2009; Fiori & Antonakis 2011; Gignac 2005; Palmer
et al. 2005; Roberts et al. 2006; Rossen & Kranzler 2008). First, the very
existence of the construct is still being debated. Critics suggest there is a lack of
convincing evidence to support the claim that EI meets the criteria of an
intelligence and that emotion and cognition cannot be studied as separate
constructs (Antonakis, Ashkanasy & Dasborough 2009; Conte 2005; Landy
2005; Matthews, Roberts & Zeidner 2004). In response, supporters of ability EI
have suggested the case has been argued sufficiently in the research effort to
date (Antonakis, Ashkanasy & Dasborough 2009; Ashkanasy & Daus 2005;
Cote & Miners 2006; Gohm 2004; Mayer 2001; Mayer, Salovey & Caruso 2004;
Mayer, Roberts & Barsade 2008; Mayer, Salovey & Caruso 2008) and that a
failure to accept the available evidence is symptomatic of a tenacious belief in
47
‘g’ and an unwillingness to admit the debate has moved onto establishing
reliable and valid EI measures.
An enduring and erroneous concern often raised is the supposed lack of a
singular definition and measure of EI (Conte 2005; Landy 2005; Locke 2005).
This is based on the failure to discriminate between the mixed and ability
models, as was discussed earlier in this chapter (Ashkanasy & Daus 2005;
Locke 2005).
A second key criticism involves operationalisation. Researchers who have
explored the EI construct agree existing measures can and should be improved.
A specific limitation of current ability measures is that they do not put
respondents in situations in which they experience real emotions (Ashkanasy &
Daus 2005; Ashton-James 2003). For example, there may be a method effect
related to the scales used to measure the fourth branch of the ability EI model
(i.e. manage emotions) because the test-taker provides their considered
response to a hypothetical situation rather than performing a specific task
(Rode et al. 2008). Additionally there is some confusion as to whether ability EI
is best modelled and measured as a global score or as distinct abilities (Lopes
et al. 2004; Maul 2011; Palmer et al. 2005). In addition, the measurement
design of the MSCEITv2 has drawn criticism with regard to its generalisability
(Follesdal & Hagtvet 2009). The measurement properties of the MSCEITv2 are
discussed in more detail in the measures section of Chapter 3.
The scoring methodology used by the MSCEITV2 is a limitation that is
recognised by both EI critics and advocates. Despite its inherent limitations,
proponents of the MSCEITv2 have argued that consensus/expert scoring can
48
provide reliable indicators of individual differences (Ashkanasy & Daus 2005;
Mayer, Salovey & Caruso 2004). Critics have argued that consensus scoring is
“in direct contrast to traditional measures of intelligence where an objective
measure of truth is considered” (Matthews, Zeidner & Roberts 2002, p. 186).
Further, they have suggested that, because consensus scores are based on
modal responses, they may not provide accurate scores for individuals with
above average EI (Conte 2005; Matthews, Zeidner & Roberts 2002; Matthews,
Roberts & Zeidner 2004). Finally, critics have suggested that this type of
measurement produces what is known as difference scores, which can “suffer
from unreliability, ambiguous interpretation, confounded effects and untested
constraints, thus resulting in potentially flawed findings” (Antonakis, Ashkanasy
& Dasborough 2009, p. 250). Antonakis (2009) recommends an analysis
method to see whether the difference between a respondent’s score and that of
the consensus/expert score is predicted by their IQ. However, this approach is
not commonly employed as yet.
There are a number of suggestions within the literature for moving forward.
First, the need to use more representative samples by moving beyond
populations of children, adolescents or college students is well recognised
(Landy 2005). It is also important for researchers to include control variables of
IQ and/or personality in order to facilitate the assessment of incremental
predictive validity beyond these antecedents (Landy 2005). Similarly, there is
interest in undertaking research that explores the relationship between EI and
actual performance, rather than self-reported behaviour (Rossen & Kranzler
2009). Finally, many studies have used cross-sectional designs that cannot
unequivocally demonstrate EI leads to dependent variables (Landy 2005). To
49
do so would require randomised control trials and, given the claim that a
person’s EI can be developed, longitudinal data.
Daus and Ashkanasy (2005, p. 457) reviewed the stages involved in developing
and validating a new construct and have concluded that:
Mayer, Caruso and Salovey are to be applauded in that it has only been a decade and a half since the construct/term was first introduced and they have developed a solid and comprehensive measure, in addition to amassing considerable evidence/data regarding the psychometric and predictive properties.
Such differences of opinion are examples of a healthy debate on a robust
construct, with the general opinion that ability measures “exhibit test validity as
a group… [and that]...EI is a predictor of significant outcomes across diverse
samples in a number of real-world domains” (Mayer, Roberts & Barsade 2008,
p. 527). Researchers on both sides of the EI debate agree it is important to
explore the relationship between this construct and important outcome
variables, such as MLE (Antonakis, Ashkanasy & Dasborough 2009). As such,
the next section summarises our current understanding of this relationship.
2.5.4 Ability EI and MLE
Leadership is intrinsically an emotional process, whereby leaders recognize followers emotional states, attempt to evoke emotions in followers, and then seek to manage followers’ emotional states accordingly. (Kerr et al. 2006, pg 268)
Over recent decades a clearly articulated argument has emerged suggesting
managerial-leadership is an emotion-laden process and that EI is a key factor in
MLE (Ashkanasy & Tse 2000; Boal & Hooijberg 2000; Dasborough &
Ashkanasy 2002; George 2000; House & Aditya 1997; Humphrey 2002;
Megerian & Sosik 1996). At a foundational level, EI underpins individual
50
effectiveness (George 2000; Mayer & Salovey 1993; Mayer & Salovey 1997;
Salovey & Mayer 1990; Salovey et al. 1999; Salovey, Mayer & Caruso 2002).
For example, EI contributes to an individual’s ability to perceive and use
emotional information which can have a positive impact on cognitive decision-
making (Day & Carroll 2004) and individuals with greater EI are better equipped
to cope with emotionally challenging situations (Salovey et al. 1999).
A recent meta-analysis provides empirical support for the relationship between
EI and job performance (O'Boyle Jr et al. 2010). In a study of clerical and
administrative staff, evidence suggests that after controlling for age, education,
verbal ability and the Big Five personality traits, EI is associated with greater
stress tolerance, merit increases, higher company rank and better peer and/or
supervisor ratings of interpersonal facilitation (Lopes et al. 2006). Ability EI also
explains a significant amount of the variance in public speaking effectiveness, a
task that requires the ability to calm one’s nerves in a stressful situations and an
ability to include appropriate levels of emotion in order to influence listeners
(Rode et al. 2007). EI also has social utility in terms of the negotiation process
in that the ability to understand emotions “positively predicts one’s counterpart’s
outcome satisfaction” (Mueller & Curhan 2006, p. 122) and emotion recognition
has predictive validity in negotiation performance outcomes (Elfenbein et al.
2007).
EI abilities also seem to be integral to developing relationships (Ashkanasy &
Tse 2000; Pescosolido 2002). Studies exploring the ripple effect of emotion
and its influence on group behaviour have provided evidence that positive
emotional contagion at a group level exists, and can lead to “improved
cooperation, decreased conflict, and increased perception of task performance”
51
(Barsade 2002, p. 3). Emotional regulation is also positively associated with the
quality of social interactions (Lopes et al. 2005; Rossen & Kranzler 2009).
Beyond indices of individual effectiveness, EI is also thought to contribute to
MLE. Transformational and charismatic styles of leadership, which engage at
an emotional level, can create strong affiliations from followers (Conger 1990;
Megerian & Sosik 1996). George (2000) identified five areas in which ability EI
enhances MLE, namely:
1. Developing a collective sense of goals and how to go about achieving
them.
2. Instilling knowledge and appreciation of the importance of work activities
and behaviours in others.
3. Generating and maintaining excitement, enthusiasm, confidence and
optimism in an organization as well as trust and cooperation.
4. Encouraging flexibility in decision making and change.
5. Establishing and maintaining a meaningful identify for an organization.
Prati et al. (2003, p. 27) suggested EI enables managerial-leaders to “induce
collective motivation in team members”, which can impact on the performance
of their team. The extent to which a leader experiences positive moods has
also been found to be correlated with pro-social behaviour and better quality
customer service (George & Bettenhausen 1990; George 1995). Similarly,
Jordan et al. (2006, p. 8) found “teams with members who were able to regulate
52
their experience and expression of emotions achieved a higher performance
than those teams where members were not able to control their emotions”.
While the theoretical relationship between EI and MLE has been clearly
articulated, empirical evidence to support these claims is still mixed (Walter,
Cole & Humphrey 2011). Much of the exploration of the relationship between
EI and MLE has used trait-based or self/other-reported measures of EI which,
as was discussed earlier in this chapter, are not sufficiently dissimilar to
established personality measures (see Barbuto Jr & Burbach 2006; Barling,
Slater & Kelloway 2000; Dulewicz & Higgs 1999; Gardner & Stough 2002;
Mandell & Pherwani 2003; Palmer et al. 2001; Sears & Holmvall 2009; Skinner
& Spurgeon 2005; Sosik & Megerian 1999; Wong & Law 2002). Other valid
concerns include the lack of samples that “avoid problems associated with
common-source/methods variance…[and]…use practising leaders in real world
contexts” (Antonakis, Ashkanasy & Dasborough 2009, p. 249). For example, in
a student sample, EI predicted leader emergence and was related to
transformational leadership (Daus & Harris 2003).
However, studies examining the relationship between ability EI and measures
of MLE with practising managerial-leaders are limited and have produced mixed
results including those that support the hypothesised relationship (Byron 2007;
Cote et al. 2010; Clarke 2010; Kerr et al. 2006; Leban & Zulauf 2004; Rosete &
Ciarrochi 2005; Rubin, Munz & Bommer 2005), and those that do not (Byron
2007; Collins 2001; Weinberger 2009). A summary table of these studies can
be found in Appendix A.
53
Collins (2001) did not find support for the hypothesised relationship between
ability EI and three measures of leadership success (multi-source feedback,
position and salary). Similarly, using a sample of managers from a multinational
organisation, Weinberger (2009) tested the relationship between ability EI and
transformational, transactional and laissez-fair leadership styles but did not find
any significant relationships.
In contrast, Leban and Zulauf (Leban & Zulauf 2004) found significant
relationships between facets of ability EI and subscale scores of
transformational leadership. Similarly, Rubin (2005) found the ability to
recognise emotions positively predicted subordinates’ ratings of
transformational leadership behaviours and that this main effect was moderated
by the personality trait of extraversion. Other researchers have found ability EI
significantly predicted supervisors’ ratings of employees’ job performance
(Janovics & Christiansen 2001). Rosete and Ciarrochi (2005) also explored
the relationship between ability EI and performance ratings. Significant
correlations were found between the direct manager’s ratings and Total EI, the
Strategic and Experiential EI areas and the Understand Emotion branch of EI.
Kerr et al. (2006) found evidence of significant relationships between MLE
ratings and Total EI, Experiential EI, the Perceive Emotion branch of EI and the
Facilitate Thought branch of EI. Most of this variation was accounted for by the
combined effects of two branch scores (Perceiving Emotion and Facilitate
Thought), which was labelled as Experiential EI. Surprisingly, the managing
emotions branch was not correlated with average other-ratings on 360o
feedback (Kerr et al. 2006). Most recently, aspects of ability-EI were associated
54
with leadership emergence (Cote et al. 2010) and multi-source feedback
measures of MLE (Clarke 2010) after controlling for IQ and personality.
Variation across the studies may suggest the existence of interaction effects
with regard to ability EI (Van Rooy & Viswesvaran 2004). While the evidence
supports a direct relationship between EI and MLE, it also suggests an indirect
relationship. Individuals low in one EI ability may compensate for that deficit by
being better at other aspects (Carroll 1993). Indeed, Prati et al. (2003, p. 30)
argued EI is likely to “moderate the effect of specific personality traits on leader
and team member interactions”. Cote & Miners (2006, p. 2) presented the
foundation for interactive models and suggested:
Compensatory effects may explain why emotional intelligence predicted job performance in some past studies but not in others. If compensatory effects exist, emotional intelligence should predict job performance only some of the time, depending on the other abilities that individuals possess.
Thus, the relationship between EI in predicting MLE may be moderated by other
variables (Cote & Miners 2006). Evidence of this effect was found when the
association between EI and task performance and organisational citizenship
behaviour (directed at the organisation, but not at individuals) became more
important as cognitive intelligence decreased (Cote & Miners 2006). Similarly,
EI was found to enhance the relationship between the personality construct of
agreeableness and both task and contextual performance (Shaffer 2005).
Interaction effects between ability EI and extraversion significantly and
positively predicted ratings of transformational leadership behaviours (Rubin,
Munz & Bommer 2005). However, high extraversion combined with low EI did
not lead to the same results. Further, Rode et al. (2007) found
conscientiousness moderated the relationship between EI and both
55
interpersonal effectiveness and public speaking effectiveness. There is
mounting evidence of an interaction effect between EI and gender (Brackett et
al. 2006; Byron 2007; Day & Carroll 2004; Joseph & Newman 2010; Lyons &
Schneider 2005) and the relevance of subgroups (Joseph & Newman 2010;
Karim & Weisz 2010). For example, Joseph and Newman (2010) found
evidence that the relationship between emotional regulation and job
performance was stronger for high emotional labour jobs than for low emotional
labour jobs.
Finally, another area of interest that has received limited attention in the study
of EI’s relationship with MLE relates to MSF self-other agreement (SOA). There
is support for a relationship between SOA and MLE (Sosik & Megerian 1999).
Managers who are highly self-aware can incorporate information from their
context about themselves and more accurately gauge their skill level (Atwater &
Yammarino 1992; Atwater & Yammarino 1997; Yammarino & Atwater 1997).
Self-awareness has been conceptualised as the degree of SOA and has been
shown to correspond to measures of managerial skill and unit effectiveness
(Shipper et al. 2003). Self-awareness, as indicated by SOA, has been found to
be correlated with personality measures of EI and performance ratings (Atwater
& Yammarino 1997; Megerian & Sosik 1996; Sosik & Megerian 1999).
However, results have been mixed and are not easily comparable due to
differences in the ways in which SOA has been conceptualised. Most
significantly, no studies have explored the relationship between ability EI and
SOA.
In summary, the construct of EI is an important individual difference that
contributes to individual effectiveness and to MLE. Further, ability based
56
measures of EI have better construct validity. The next section explores the
concept of meaning-making structure (MMS) that may provide complementary
but distinct information about individual differences as they relate to MLE.
2.6 MEANING-MAKING STRUCTURE (MMS)
The concept of meaning-making, originally grounded in the field of philosophy
and then explored extensively within psychology, has gained increasing
prominence as an important aspect of understanding MLE. Although related, it
is important to understand the differences between meaning, meaning-making
and meaning-making structure (MMS).
Meaning can be defined as ‘mental representation of possible relationships
among things, events, and relationships’ (Baumeister 1991, p. 15). Consistent
with this definition, much of the initial research focused on the content and
sources of what gives people meaning (Debats 1999; O'Connor & Chamberlain
1996; Schnell 2009) and also the intensity and depth of this sense of meaning
(Ebersole 1998; Reker 2005).
Meaning-making is the process of constructing these mental representations
and can be understood differently depending on the epistemology chosen (Park
2010). One view is meaning-making as a process or outcome of dealing with
trauma (Park & Folkman 1997; Taylor 1983; Thompson & Janigian 1988).
Competing explanations within the cognitive sciences define meaning-making
as the result of brain activity (Barsalou 2008). A third perspective is that
meaning-making is a process of constructing our reality and happens at both
the individual and collective levels (Kegan 1980; Loevinger 1976; Piaget 1954).
57
It is through the latter perspective, referred to as constructive developmental
theory, that the concept of meaning-making structure (MMS) emerges. MMS’s
are the ‘set of assumptions in your head that allows you to interpret sensory
information, anticipate future events, and plan accordingly’ (Drath 1994, p. 3).
The remainder of this section addresses the concept of internal action logics, or
the complexity of people’s MMS. First, the theoretical domain of constructive
developmental theory is introduced which is followed by an explanation of one
particularly influential theory of MMS: Loevinger’s (1976) theory of ED. Finally,
a summary of the application of MMS to the study of MLE is presented.
2.6.1 Constructive-Developmental Theory
Being is a process, and human being is the process of organizing meaning, making meaning. (Drath 1990, pg 485)
Constructive-developmental (CD) theory focuses on the evolution of meaning-
making processes throughout the human experience. Originating from Piaget’s
(1954) seminal work on child development, CD (or neo-Piagetian) theory
suggests there are stages of psychosocial growth that extend into adulthood
and for the duration of a person’s life (Kegan 1980). The emphasis is on the
inherent structure within these stages and the antecedents and processes
involved in their development over time (McCauley et al. 2006).
Complimentary ‘stage’ models within the CD theoretical domain include:
1. Loevinger’s (1976) theory of ego-development, which was subsequently
developed by Cook-Greuter (2004), and Torbert (2009).
2. Kohlberg’s (1969) theory of moral development.
58
3. Kegan’s (1980) orders of consciousness theory.
While differences exist among the theorists, there are important commonalities
that include the idea of articulated stages of meaning-making that can be
placed along a continuum. At each successive stage, the structure for
meaning-making expands to become more complex. “CD theory posits that the
capacity to make meaning of oneself and one’s environment is an unfolding
process moving towards deeper understanding, wisdom and effectiveness in
the world” (Cook-Greuter 2004, p. 277).
Stages at one end of the continuum reflect guiding principles that are ego-
centric in contrast to stages at the other end of the continuum, which are world-
centric (Cook-Greuter 2004). The number of, and labels for, these stages vary
depending on the theory that is used. As such, the convention of dividing the
developmental spectrum into three tiers, or categories of stages, facilitates
comparison and discussion across the theories, despite differences in stage
labels (e.g. pre-conventional, conventional and post-conventional). Pre-
conventional stages are the most ego-centric and represent the simplest
meaning-making lens. These stages are developmentally normal in children,
but may be suboptimal for adults (Pfaffenberger 2005). Conventional stages
have more complexity and represent the dominant paradigm, or most
commonly held set of meaning-making structures in the adult population
(approximately 75% to 80% of adults) (Cook-Greuter 2004). Finally, post-
conventional stages reflect the most complex meaning-making structures and
are experienced by very few individuals, possibly 10% to 20 % of adults (Cook-
Greuter 2004).
59
Loevinger’s (1966) theory of ego development (ED) is considered a seminal
contribution to CD theory, not only because it was the first to develop a rigorous
measurement methodology, but also because it attempted to describe and
measure personality as a whole. Consequently, the next section describes
Loevinger’s (1966) ED theory.
2.6.2 Loevinger’s Ego-Development Theory
Meaning-making structure can be conceptualised as the stage of an individual’s
ego-development (ED) (Loevinger 1976). Loevinger defines ego as a person’s
fundamental frame of reference to the world in which they operate, as a unitary
domain, or as ‘master trait’ in contrast to theorists who conceptualised it as a
multifaceted construct (Snarey, Kohlberg & Noam 1983). This frame of
reference acts as a lens through which the world is experienced and
interpreted. The capacity for this lens to change, to expand and to
accommodate increasing levels of complexity is described as ego-development
(ED) and represents the course of an individual’s character development. ED is
less about cognitive or reasoning growth than about “impulses and methods for
controlling impulses, personal preoccupations and ambitions, interpersonal
attitudes and social values – what psychologists normally call personality” (Blasi
1998, p. 15). Throughout the remainder of the thesis the terms MMS and ED
will be used interchangeably.
Originally conceptualised as a four stage theory and drawing on the concepts of
interpersonal integration (Sullivan, Grant & Grant 1957), Loevinger’s (1976)
theory and measure of ED evolved through an iterative process of testing and
revising scoring instructions with successive data sets (Loevinger 1993). The
60
result was a stage model of ED with detailed qualitative differences in the four
domains of impulse control, interpersonal modes, conscious preoccupation and
cognitive style. Each of these domains progressively builds more complexity
into how people define themselves and operate in their world (Loevinger 1976;
Loevinger 1997).
Consistent with constructive developmental theory, Loevinger’s theory
articulates a continuum of ED stages, each with key guiding principles also
known as action-logics (Rooke & Torbert 2005). Ego development “represents
a structural stage change in a hierarchical, invariantly sequential manner, with
an inner logic to the stages and to their progression” (Loevinger 1997, p. 543).
Loevinger (1966) was loathe to provide a more specific definition of ED and
preferred instead to point to the broad manifestations of ED at different stages
as is detailed in Table 2.1.
There are at least nine stages of ED, which move from the single action logic of
survival through to increasing stages of complexity and ambiguity (Hy &
Loevinger 1996). The first three stages of E1: Presocial and Symbolic, E2:
Impulsive and Self-Protective, and E4: represent the pre-conventional stages of
ED. The next three stages, E4: Conformist, E5: Self-Aware, and E6:
Conscientious, represent the conventional stages of ED. Finally, the post-
conventional stages of ED are E7: Individualistic, E8: Autonomous, and E9:
Integrated. Loevinger’s ED theory describes the evolution of an individual’s
theories-in-use (Argyris & Schön 1974) or mental models (Senge 1990).
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Table 2.1 Loevinger’s Stages of Ego Development (Manners 2001, pg 544)
EI
Presocial and Symbiotic
Exclusive focus on gratification of immediate needs; strong attachment to mother, and differentiating her from the rest of the environment, but not her/himself from mother; preverbal, hence inaccessible to assessment via the sentence completion method.
E2
Impulsive and Self-Protective
Demanding; impulsive; conceptually confused; concerned with bodily feelings, especially sexual and aggressive; no sense of psychological causation; dependent; good and bad seen in terms of how it affects the self; dichotomous good/bad, nice/mean.
E3
Self-Protective
Wary; complaining; exploitive; hedonistic; preoccupied with staying out of trouble, not getting caught; learning about rules and self-control; externalizing blame.
E4
Conformist
Conventional; moralistic; sentimental; rule-bound; stereotyped; need for belonging; superficial niceness; behaviour of self and others seen in terms of externals; feelings only understood at banal level; conceptually simple, ‘black and white’ thinking.
E5
Self-Aware
Increased, although still limited, self-awareness and appreciation of multiple possibilities in a situation; self-critical; emerging rudimentary awareness of inner feelings of self and others; banal level reflections on life issues: God, death, relationships, health.
E6
Conscientious
Self-evaluated standards; reflective; responsible; empathic; long term goals and ideals; true conceptual complexity displayed and perceived; can see the broader perspective and can discern patterns; principled morality; rich and differentiated inner life; mutuality in relationship; self-critical; values achievement.
E7
Individualistic
Heightened sense of individuality; concern about emotional dependence; tolerant of self and others; incipient awareness of inner conflicts and personal paradoxes, without a sense of resolution or integration; values relationship over achievement; vivid and unique way of expressing self.
E8
Autonomous
Capacity to face and cope with inner conflicts; high tolerance for ambiguity and can see conflict as an expression of the multifaceted nature of people and life in general; respectful of the autonomy of the self and of others; relationship seen as interdependent rather than dependent/independent; concerned with self-actualization; recognizes the systemic nature of relationships; cherishes individuality and uniqueness; vivid expression of feelings.
E9
Integrated
Wise; broadly empathic; full sense of identity; able to reconcile inner conflicts and integrate paradoxes. Similar to Maslow’s description of the ‘self-actualised’ person who is growth motivated, seeking to actualize potential capacities, to understand her/his intrinsic nature, and to achieve integration and synergy within the self (Maslow, 1962).
Each ED stage is a unique structure that contributes to a person’s meaning-
making ability (Loevinger 1976). An individual’s developmental stage
“influences what they notice or can become aware of, and therefore, what they
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can describe, articulate, influence, and change” (Cook-Greuter 2004, p. 277).
An individual is not necessarily aware of or able to describe their current lens, or
developmental stage, particularly at the earlier stages, because it is the water in
which they are swimming. A person’s current meaning-making lens is the
‘subject’ of one’s reality. The existence of this meaning-making lens becomes
apparent only if it fails to adequately explain our experiences. At this point we
become aware of the lens and it is no longer the subject of our reality but rather
an observable, or objective, part of a larger unfolding reality. Simultaneously a
new lens emerges as our subjective truth. The ongoing process of subject
becoming object is known as developmental movement and occurs through a
process known as equilibration (Manners & Durkin 2000). When information
cannot be easily understood with the available meaning-making complexity, it is
experienced as disequilibrium or cognitive conflict (Murray 1983). In response,
an individual has the choice to assimilate or accommodate this incoherent
information, as Block (1982, p. 286) notes:
Assimilation is to be viewed as the invocation by the individual of existing adaptive structures, schemes or scripts to process experience, accommodation is to be viewed as the shift over by the individual to the formation of new (and the reformation of old) adaptive structures, schemes and scripts to process experience.
Each successive stage adds complexity to an individual’s capacity to identify,
understand and work with information from their environment. Whole new ways
of perceiving and interacting with the world are possible. This process has
specific characteristics as Cook-Greuter (2004, p. 277) has noted:
• Later stages are reached only by journeying though the earlier stages. Once a stage has been traversed, it remains a part of the individual’s response repertoire, even when more complex, later stages are adopted.
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• Each later stage includes and transcends the previous ones. That is, the earlier perspectives remain part of our current experience and knowledge (just as when a child learns to run, it doesn’t stop being able to walk).
• Each later stage in the sequence is more differentiated, integrated, flexible and capable of functioning optimally in a rapidly changing and complexifying world…
• …As development unfolds, autonomy, freedom, tolerance for difference and ambiguity, as well as flexibility, reflection and skill in interacting with the environment increase, while defenses decrease.
• A person who has reached a later stage can understand earlier world views, but a person at an earlier stage cannot understand later ones.
• Development occurs through the interplay between person and environment, not just by one or the other. It is a potential and can be encouraged and facilitated by appropriate support and challenge.
The inherent paradox is that the increasing complexity facilitates solving
problems that were impenetrable while, at the same time, enabling a
recognition of problems that was not possible before this ED stage. In short,
every ED stage has inherent strengths and limitations (Lichtenstein 1995).
Theoretically ED continues throughout the course of a person’s life. In reality it
appears ED “tends to increase with age throughout childhood and then
stabilizes as a function of age in adolescence and adulthood” (Newman,
Tellegen & Bouchard 1998, p. 985). Studies have found that most adults do not
progress to post-conventional levels of ego-development (Cook-Greuter 1990;
Loevinger, Wessler & Redmore 1970) and, in fact, stabilise at the self-aware
stage (Loevinger et al. 1985; Redmore & Loevinger 1979; Redmore 1983).
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A review of the literature exploring processes involved in adult ego-
development suggested individual differences may account for the extent to
which a person engages in the type of experiences that can lead to ego-
development (Manners & Durkin 2000, p. 504), including “self-acceptance, the
desire for challenge, and openness to new experiences”. While evidence
supports the theoretical assumption that some individual differences are
associated with key ED levels (Rozsnafszky 1981), the non-monotonic, or non-
linear, nature of ED is a key characteristic that differentiates it from many other
psychometrics that assume an increasing or decreasing linear relationship
between variables (Loevinger 1993).
A pattern of curvilinear relationships exist between ED and other individual
differences, such as defence mechanisms (Cramer 1999), friendliness,
conformity and compliance (Westenberg & Block 1993) and adjustment (Helson
& Wink 1987). As such, it is reasonable to suggest there is a relationship
between EI and ED. Specifically, the two constructs may be correlated. There
have been specific calls for research exploring the relationship between ED and
the socio-emotional domain (Manners & Durkin 2001) and how cognitive and
affective processes relate to one another at different stages of ED (Hauser
1993). However, to date, no studies have explored the relationship between EI
and ED.
In contrast to the empirical support for Loevinger’s (1976) theory and measure
of ED, two aspects of the theory have been challenged, namely:
1. The idea of ego as a master trait has not been consistently supported by
empirical data.
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2. While there is strong support for the sequential nature of the various ego
stages, there is also an indication of a possible regression of ego stage
transitions (Manners & Durkin 2001).
An examination of the structural nature of ED that used structural equation
modelling procedures to test alternate models, found the model in which ED
was a master trait was not the best fit to the data (Novy et al. 1994). However,
Novey et al. (1994) did caution that the results could have been confounded
due to limitations in finding appropriate measures to represent the sub-
constructs of ED. With regard to the theorised invariant forward nature of ED,
Loevinger (1976, p. 386) suggested some “regression in the form of
disorganization is required to make reorganization possible”, which has been
observed in longitudinal studies (Adams & Fitch 1982; Loevinger et al. 1985;
Redmore 1983).
Higher levels of ED are associated with indicators of individual effectiveness,
such as “increased intrinsic motivation, broader perception of ethical dilemmas,
greater self-acceptance following moral failure, richer conceptualization of a
moral self, and progressive integration of personal and moral identity”
(Giesbrecht & Walker 2000, p. 163). There are also positive associations
between the level of ED and aspects of maturity, such as tolerance of
ambiguity, affect regulating repression (Helson & Wink 1987) and the three
components of ego-resiliency (psychological mindedness, intellectualism and
resiliency) (Westenberg & Block 1993).
One of the criticisms of Loevinger’s measure of ED, the Washington University
Sentence Completion Test (WUSCT), is that it is limited in measuring higher ED
66
stages (Redmore & Waldman 1975; Sutton & Swensen 1983). The WUSCT is
discussed in more detail in Chapter 3. Cook-Greuter (2004) adapted the
measure to address these concerns and also changed the language to be more
specific to managerial-leadership settings. There are two commercially available
instruments that are based on Loevinger’s original measure, namely the Harthill
Leadership Development Profile (LDP) (Rooke & Torbet 1998; Torbert & Livne-
Tarandach 2009) and the SCTi Map, which was subsequently renamed as the
Leadership Maturity Framework (Cook-Greuter 2005). Loevinger’s theory and
measure has extensive application outside management. Yet despite its
widespread use within psychology, applications of CD and ED theories within
the field of MLE have been minimal. It has been argued by developmental
researchers that individuals at higher levels of ED will make qualitatively
different contributions in their fields of influence (Cook-Greuter 1994; Kegan &
Lahey 1984; Kegan 1995; Torbert 1987; Torbert & Fisher 1992; Torbert 1996).
This would logically extend to the roles played by managerial-leaders and the
profound influence that organisations have within the modern world. As such,
the next section reviews the role ED has played in our understanding of MLE.
2.6.3 MMS and MLE
The application of constructive developmental theory to managerial-leadership
was originally explored during the 1980s (Merron 1985; Merron, Fisher &
Torbert 1986; Merron, Fisher & Torbert 1987; Torbert 1985; Torbert 1987).
Since that time a number of writers have presented theories of MLE based on
constructive developmental theory, including Rooke and Torbert’s (2005)
internal action logic(s) model, Eigel and Kuhnert’s (2005, p. 259) Leadership
Development Level (LDL) model, which they defined as “the measurable
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capacity to understand ourselves, others, and our situations”, and Joiner and
Joseph’s (2007, p. 35) leadership agility model, which they defined as “the
ability to lead effectively under conditions of rapid change and high complexity”.
Empirical studies have found mixed support for a relationship between MMS
and a leader’s position within the organisational hierarchy (Gratch 1987; Smith
1980; Torbert 1994). In contrast, there appears to be a relationship between a
leader’s developmental level and the creation of high-involvement workplaces
and shared responsibility with subordinates (Joiner & Josephs 2007). In a
paper that applied Wilber’s (1993) spectrum of consciousness model, which is
based on CD theory, Young (2002b, p. 31) argued:
CEOs who operate at higher levels of consciousness will engage in more objective problem solving and will experience more affective social development, enabling them to make better or more effective business decisions than CEOs operating at lower levels of consciousness.
The results of a case study analysis of small business owner and operators
supported this hypothesis, as it was found that people at higher developmental
levels were able to notice more cues in their environment and to focus on
broader and more strategic issues (Hirsch 1988). Similarly, Rooke and Torbert
(1998) found CEOs with less complex MMS were not successful at leading
organisational transformation, in contrast to CEOs who were at higher levels of
MMS. They suggested a minimum MMS level is required if a person is to be
able to successfully effect organisational change.
Based on this research, it was logical to suggest developmental order is likely to
predict the use of transformational leadership behaviours (Kuhnert 1994;
Kuhnert & Lewis 1987). However, studies have found mixed support for this
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relationship (Mehltretter 1996; Spence 2005; Steeves 1997), although the
different sample sizes, measures and methods used in these studies have
limited their generalisability. Regardless, a review of the literature by McCauley
et al. (2006) concluded that there was support for a relationship between more
general measures of MLE and developmental order. In particular, they
suggested that post-conventional managerial-leaders:
have been found to be more likely to delegate, hold people accountable, influence through rewards and expertise (rather than coercive power), look for underlying causes of problems, act as change agents, and be more comfortable with conflict. (McCauley et al. 2006, p. 647)
It appears developmental order is related to more sophisticated and
collaborative means of enacting basic communication skills, problem solving,
handling conflict and managing performance (Fisher & Torbert 1991; Merron,
Fisher & Torbert 1987; Spillett 1995). Individuals with higher levels of MMS are
more likely to use altruism, anticipation, humour, consultation and to avoid the
use of coercive power (Smith 1980). MMS has also been found to be
significant in managerial-leaders’ decisions to request feedback and to explore
how behaviour change might improve their effectiveness (Merron & Torbert
1984 in Merron, 1987; Quinn & Torbert 1987; Torbert 1987). However, no
empirical studies have explored a potential relationship between developmental
order and competency based, behaviourally complex MLE.
Developmental order has also been found to be related to effectiveness as
perceived by colleagues (Harris 2005; Harris & Kuhnert 2008; Strang 2006).
West Point cadets with higher levels of MMS, for example, were rated by
superior officers as being more effective in carrying out their leadership
responsibilities (Lewis et al. 2005). Similarly, a comparison of high-performing
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group of principals and an average performing group found the former group
had significantly higher levels of MMS (Sweeney 1991). Empirical evidence
also supported a relationship between ED and consulting competence as
perceived by peers and expert raters (Bushe & Gibbs 1990). However, a study
of teachers acting in peer leadership positions, that used the MSF instrument of
Leader Behaviour Description Questionnaire (LBDQ) (Stogdill 1963) as the
measure of MLE, did not support such a relationship as the teachers’ MMS
explained only 1% of variance in two LBDQ subscales (initiating structure and
consideration) (Gammons 1993).
People with advanced MMSs are rare and such worldviews are thought to
require a considerable commitment to reflection and learning (Torbert & Fisher
1992). For example, in the studies that have been mentioned most had a modal
stage of E5 Self-Aware followed next by E6 Conscientious (Fisher & Torbert
1991; Gammons 1993; Gratch 1987; Hirsch 1988; Merron, Fisher & Torbert
1987; Quinn & Torbert 1987; Smith 1980; Torbert 1983 in Torbert, 1994). The
single exception is the study by Bushe & Gibbs (1990) where the sample was
drawn from Organisational Development consultants, not practising managerial-
leaders. In a five-year search for late stage managerial-leaders, Torbert (1996)
located a very small sample (n=6) at the magician stage (roughly equivalent to
Loevinger’s Autonomous stage) and described their behavioural complexity as
a manifestation of principles from chaos theory (Torbert 1996).
Every stage of developmental complexity makes a contribution, but also
generates potential problems in terms of meaning-making capacity. As such,
different levels of developmental complexity map to managerial-leadership
strengths and weaknesses (Drath 1990). For example, Drath (1990) provided a
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detailed discussion of the strengths and weaknesses that arose from two
specific contributions from the institutional stage (Kegan 1982) (roughly
equivalent to Loevinger’s Self-Aware stage). The first contribution, an ability to
perceive interpersonal relationships as objects, facilitates effective instrumental
relationships and the primacy of thinking over feeling in decision making, both
of which contribute to MLE in modern organisations. However, experiencing
relationships as objects also leads to difficulty with intimacy, addressing conflict,
and recognising and expressing emotion. The second contribution, a strong
internal sense of self, facilitates drive, ambition, willingness to be responsible
and accountable and being comfortable in working in hierarchies of authority. It
also leads to difficulty with accepting healthy criticism, relaxing and appreciating
and accepting others. The predominance of the institutional stage within
organisations and the competing demands of the related strengths and
weaknesses explain why there are so many “managers who would empower
others but cannot” (Drath 1990, p. 488).
2.6.4 MMS and EI
At a theoretical level, if one assumes MMS is a master trait, it is reasonable to
assert that it will have a relationship with other key constructs of individual
difference. At an empirical level, there is support for the relationship between
MMS and individual differences related to emotions.
Increased cognitive complexity is related to increased emotional range
(Sommers 1981). People at the post-conventional MMS levels have more
empathy than people at conformist or pre-conformist MMS levels (Carlozzi, Gaa
& Liberman 1983). MMS is significantly correlated with the Levels of Emotional
71
Awareness Scale (LEAS) (r= .40), a cognitive-developmental model of
emotional experience (Lane et al. 1990). Based on the concept of levels of
emotional awareness, Lane and Schwartz (1987) suggested CD theory might
explain a range of normal and abnormal emotional states.
MMS is also significantly correlated with the Range and Differentiation of
Emotional Experience Scale (RDEES) (r= .39), a measure of emotional
complexity (Kang & Shaver 2004). Kang and Shaver (2004) suggested the
RDEES would be most related to the first of the four branches of the MSCEITv2
(Mayer, Salovey & Caruso 2002) (i.e. the ability to perceive emotions in oneself
and in others). However, they did not test this hypothesis empirically. Indeed,
no studies have explored the relationship between ability-based EI models
(Mayer, Salovey & Caruso 2002) and MMS.
The previous discussion reviewed the concept of MMS by:
1. Grounding MMS within the domain of constructive developmental theory.
2. Operationalising MMS through Loevinger’s ED theory.
3. Summarising the application of MMS to the study of MLE.
4. Presenting support for the suggested relationship between EI and MMS.
The above review of the literature has established that ED is a valid construct
taken from developmental psychology and that it is distinct from personality or
IQ. However, there has been no empirical testing of the suggested relationship
between MMS and EI.
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The next section discusses the specific conceptualisation of the dependent
variable in the research: MLE as behavioural complexity.
2.7 MANAGERIAL-LEADERSHIP EFFECTIVENESS (MLE)
The dependent variable in the present study was managerial-leadership
effectiveness (MLE). Given the breadth and depth of ways that have been
suggested to conceptualise MLE, it is important to delineate the phenomenon of
interest within the present study. First, recognising that leadership can be a
formal role and also a process exercised by anyone in the organization (Raelin
2003), this study was concerned with the type of leadership exercised by
people in formal management roles. Further, acknowledging the contrasting
views about the differences and/or similarities of management versus
leadership, the present study assumed the two were distinct, but
complimentary, processes and that both are required to be effective (Bass
1990; Cacioppe & Albrecht 2000; Kotter 1990; Quinn et al. 1996). For this
reason the thesis used the term managerial-leadership coined by Vaill (1998,
p.4) to describe a phenomenon that is a:
blend of thought and action, of individual and group behaviour, of abstract and concrete focus, of problem solving and problem finding, of creativity and routine, of economics and humanities, of societal contribution and self-advancement.
Managerial-leaders operate in unprecedented complex and paradoxical
contexts that require multiple perspectives and skills (Handy 1994; Lewis 2000).
Evidence from prior research supports the view that effective managerial-
leaders think and act in complex ways by drawing on multiple cognitive models
and performing multiple roles (Denison, Hoojiberg & Quinn 1995; Hart, Hart &
73
Quinn 1993; Hooijberg & Quinn 1992). Based on the premise of competing and
sometimes contradictory demands, Quinn’s Competing Values Approach
(Quinn & McGrath 1982; Quinn & Rohrbaugh 1983) integrated a disparate
literature on managerial-leadership and organizational effectiveness. This
meta-theoretical model applied to understanding MLE was renamed the
Competing Values Framework (CVF) (Quinn 1984; Quinn 1988). Detailed
descriptions of the CVF model can be found elsewhere (see Denison, Hoojiberg
& Quinn 1995; Hart, Hart & Quinn 1993; Quinn 1988; Quinn et al. 1996) and so
only a brief description of the model is provided here.
At the heart of the CVF is the assumption that MLE requires an ability to work
with competing values and perspectives (i.e. an ability to deal with paradox).
The CVF has two dimensions that intersect. The vertical dimension represents
a range from flexibility to control, while the horizontal dimension represents a
shifting focus on external to internal aspects of the relevant system, as can be
seen in Figure 2.1. The resulting four competing and complementary models of
organisational theory represent different theoretical contributions to our
understanding MLE and have been termed the open systems model, the
rational goal model, the internal process model and the human relations model.
The bottom half of the CVF reflects the thinking at the beginning of the twentieth
Century, which is represented by the rational-goal model (with an emphasis on
productivity, profit and goal clarification), and internal process model (with an
emphasis on stability, control and documentation) (Quinn et al. 1996).
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Figure 2.1 The Competing Values Framework (Quinn 1996)
The upper half of the CVF reflects thinking about MLE in the latter part of the
Twentieth Century, which is represented in the human relations and the open
systems models. The human relations model emphasises commitment,
cohesion and morale (Quinn et al. 1996), while the open systems model
signalled a shift in organisational awareness of, responsiveness to and
symbiosis with, a dynamic external environment that emphasised adaptation,
creative problem solving, innovation and change management (Quinn et al.
1996).
Each of the four models in the CVF represents theoretical contributions drawn
from the evolution in our understanding of MLE. However, each on its own is
incomplete. Quinn (1996) labelled this context as the ‘both=and’ paradigm,
which requires leadership and management competencies from all four
quadrants of the CVF (Toor & Ofori 2008; Young & Dulewicz 2008; Yukl 1989).
Each model can be further delineated into two distinct managerial-leadership
roles (as is shown in Table 2.2), each of which represents a unique set of
values in its emphasis on specific means and ends. The implication is that
MLE comes from a range of competencies from each of these competing and
complimentary roles.
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Table 2.2 Implicit Values Within the CVF (Quinn 1996)
MODEL OF MLE
CRITERIA FOR EFFECTIVENESS
CVF ROLES IMPLICIT VALUES
RATIONAL GOAL
Productivity and Profit
Producer Productivity and Accomplishment
Director Direction and Goal Clarity
INTERNAL PROCESS
Stability and Continuity
Coordinator Stability and Control
Monitor Documentation and Info Management
HUMAN RELATIONS
Commitment, Cohesion, and
Morale
Facilitator Participation and Openness
Mentor Commitment and Morale
OPEN SYSTEMS
Adaptability, External Support
Innovator Innovation and Adaptation
Broker Growth and Resource Acquisition
The four quadrant structure of the CVF has been validated in many studies that
have explored both MLE (Buenger et al. 1996; Denison, Hoojiberg & Quinn
1995; Kalliath, Bluedorn & Strube 1999; Wyse & Vilkinas 2004) and
organisational culture (Howard 1998; Lamond 2003). However, some of these
studies have challenged the number (Hooijberg & Choi 2000) and the
placement (Denison, Hoojiberg & Quinn 1995) of some of the suggested roles.
The CVF inspired the introduction of a theory of behaviourally complex
managerial-leadership, premised on the idea that MLE requires an ability to
exercise paradoxical skills (Boal & Hooijberg 2000; Denison, Hoojiberg & Quinn
1995; Hooijberg & Quinn 1992; Hooijberg, Hunt & Dodge 1997). According to
this theory, MLE is “the ability to perform multiple roles and behaviours that
circumscribe the requisite variety implied by an organizational or environmental
context” (Denison, Hoojiberg & Quinn, p. 526). Behavioural complexity was
seen to consist of two facets, which were termed:
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1. A behavioural repertoire, which refers to the capacity or breadth of roles
a leader has an ability to enact.
2. Behavioural differentiation, which refers to managers’ ability to adjust
their leadership functions/ behaviours according to the context in which
they are operating (Denison, Hoojiberg & Quinn 1995; Hooijberg & Quinn
1992; Hoojiberg 1996).
A manager’s behavioural repertoire has been shown to correlate with measures
of individual MLE and with organisational effectiveness (Denison, Hoojiberg &
Quinn 1995; Hart, Hart & Quinn 1993; Hoojiberg 1996).
Much of the research that has examined behavioural complexity has used the
CVF model. Models of behavioural complexity that are based on the CVF
include the Integral Leadership and Management Model (ILMM) (Cacioppe &
Albrecht 2000; Cacioppe & Albrecht 2001) and the Integrated Competing
Values Framework (ICVF) (Vilkinas & Cartan 2006). Both models build on the
CVF and adapt the underlying structure in unique ways. For example, the ICVF
and the ILMM introduced a people/task dimension, which is important given the
behavioural theories found within the MLE literature. In addition, the ICVF
introduced an ‘integrator’ role as the behavioural control room for the other eight
operational roles (Vilkinas & Cartan 2001; Vilkinas & Cartan 2006). The
‘integrator role’ represents a managerial-leader’s capacity for critical
observation and reflective learning and added a further level of complexity to
the ICVF. The ILMM introduces a series of ‘Self-Qualities’ that were intended to
represent behavioural aspects of emotional intelligence. While both models
extended the theory of behavioural complexity in meaningful ways, further
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research is required to establish their validity as alternate conceptual
elaborations of this important construct.
2.8 CHAPTER SUMMARY AND HYPOTHESES
The present chapter presented the theoretical foundations for the key
constructs in the proposed research: Managerial-leadership effectiveness
(MLE), traditional intelligence (IQ), emotional intelligence (EI) and meaning-
making structure (MMS). The evolution in thinking about MLE was reviewed and
the re-emergence of interest in trait theories was discussed.
A review of the research suggested there was empirical support for a positive
relationship between IQ and ability-based EI, between IQ and MMS and
between MMS and various individual differences related to emotional range and
complexity. However, no studies were found that had explicitly tested the
relationship between these three constructs. As such, the hypothesis that the
individual difference constructs of MMS, EI and IQ are positively correlated and
have discriminant validity was suggested (H1). More specifically:
H1a: MMS, EI and IQ are positively correlated.
H1b: MMS, EI and IQ have discriminant validity from each other.
The literature review also found evidence to suggest IQ, EI and MMS are
predictors of MLE. However, no studies were found that have explored the
combined impact these constructs have on MLE. As such, the hypothesis that
the individual difference constructs of MMS, EI and IQ have incremental
predictive validity (H2) was suggested. More specifically:
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H2a: IQ is a significant predictor of MLE.
H2b: EI is a significant incremental predictor, beyond IQ, of MLE.
H2c: MMS is a significant incremental predictor, beyond IQ and EI, of MLE.
The measures and methods employed to examine these hypotheses are
discussed in detail in the next chapter.
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Chapter 3
MEASURES AND METHODS
3.1 CHAPTER OVERVIEW
In the previous chapter the relevant literature was reviewed and the suggested
hypotheses presented. The present chapter builds on this foundation and
presents the measures (section 3.2) and methods that were used in this study
(section 3.3). The chapter explains and justifies how the various constructs
were operationalised and how the data were collected, prepared and,
ultimately, analysed.
3.2 DEFINITIONS AND MEASURES
In the following sections the managerial-leadership effectiveness (section
3.2.1), ego-development (section 3.2.2), emotional intelligence (section 3.2.3)
and traditional intelligence (section 3.2.4) constructs are discussed in turn. Each
construct is operationally defined, the nature and number of items for each of
the scales that were used are outlined, the scales’ scoring method is described
and the psychometric properties of the measures are summarised.
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3.2.1 The Integral Leadership and Management Development Profile
The dependent variable in the research model was managerial-leadership
effectiveness (MLE). MLE can be defined as behavioural complexity or “the
ability to perform multiple roles and behaviours that circumscribe the requisite
variety implied by an organizational or environmental context” (Denison,
Hoojiberg & Quinn 1995, p. 526). MLE behavioural complexity was
operationalised in the present study as ratings from others on the Integral
Leadership and Management Development Profile (ILMDP) (Cacioppe &
Albrecht 2000; Cacioppe & Albrecht 2001). The ILMDP is based on a model of
behavioural complexity that integrates elements of Quinn’s (1996) Competing
Values Framework with integral theory (Wilber 1993; Wilber 1996). The ILMDP
has three nested domains of skills, which have been termed:
1. Self-Qualities.
2. Management and Leadership Functions and Roles.
3. Strategic Leadership Skills.
Domain 2, the Management and Leadership Functions and Roles, provided the
measure of behavioural complexity that was used in the study and which is
described in detail subsequently. A full description of the ILMDP’s other
domains can be found elsewhere (Cacioppe & Albrecht 2000; Cacioppe &
Albrecht 2001).
The ILMDP Management and Leadership Functions and Roles result from the
intersection of three dimensions of focus (relationship to task focus,
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organisation to individual focus, and internal versus external focus). The first
two dimensions intersect to create a four-quadrant model, which is shown in
Figure 3.1. The vertical axis is drawn from integral theory and represents a
range in focus from the unique or individual to the whole or organisation
(Cacioppe & Albrecht 2000; Rubin, Munz & Bommer 2005). MLE requires an
ability to link individual team members to the broader team and, ultimately, from
the team to the larger system (Cacioppe & Albrecht 2001). This dimension
represents a range in attention from a focus on the individual or the details
within the system, to a broad view of the whole system.
Figure 3.1 ILMDP Domain 2: Management and Leadership Functions
(adapted from Cacioppe, 2000)
The horizontal axis is drawn from MLE theory and represents a range in
intended action from focussing on relationship development, to focussing on
task or outcome completion. MLE requires both an ability to accomplish tasks
and to build relationships (Cacioppe & Albrecht 2000; Cacioppe & Albrecht
2001). This dimension represents the required balance of actions directed at
achieving results by building relationships with the people through whom these
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results will be accomplished. The resulting four quadrants represent two
functions of Leadership Effectiveness (i.e. Visionary Leadership and People
Leadership), and two functions of Management Effectiveness (i.e. Strategic
Goal Management and Performance Management). Visionary Leadership
requires the ability to develop an overall purpose and vision and the ability to
facilitate the changes to attain this vision. People Leadership requires the
ability to make other individuals feel valued and the ability to develop their
contribution to the organisation. Strategic Goal Management requires an ability
to define organisational goals and the ability to develop systems to attain these
goals. Finally, Performance Management requires the ability to initiate action
towards a specific goal and the ability to monitor progress.
The ILMDP employs a third dimension that represents a range in focus from the
external to the internal, as can be seen in Figure 3.2. This dimension identifies
an external and internal role for each function, resulting in eight MLE roles (i.e.
visioning, facilitating, brokering, directing, achieving, monitoring, stewarding,
and coaching) (Cacioppe & Albrecht 2000). The eight roles provide the ILMDP
version of behaviourally complex MLE. Each role can be categorised as
belonging to one of two areas as four of the roles contribute to management
effectiveness and four of the roles contribute to leadership effectiveness. These
various roles are described in turn in subsequent sections.
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Figure 3.2 Internal versus External Dimension of ILMDP Model
(Adapted from Cacioppe, 2000)
3.2.1.1 ILMDP Management Roles
In the ILMDP, management effectiveness consists of four roles, or skill sets (i.e.
achieving, brokering, monitoring and directing), which are discussed in turn.
The achieving role has an internal emphasis and asks one item for each of the
following four behaviours:
• I come up with good solutions when problems arise that might get in the
way of achieving our goals.
• I promote and support attempts to improve the standards of our products
and services.
• I provide people within our section with clear, accurate and timely
feedback on their performance.
• I create and develop new ideas and ways of approaching things.
The brokering role has an external emphasis and asks one item for each of
the following four behaviours:
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• I appropriately influence others to benefit the interests of our division,
section or workgroup.
• I effectively represent the interests and achievements of our work group
or section to higher levels of management.
• I negotiate effectively in order to obtain resources and outcomes which
help the overall success of our section.
• I am effective when speaking and presenting ideas at meetings or forums
held outside of our work group.
The directing role has an external focus and asks one item for each of the
following four behaviours:
• I keep a focus on important, high priority activities.
• I effectively delegate responsibility by giving people challenging jobs and
the freedom they need to do the job.
• I develop plans which clearly set out goals, tasks and timelines.
• I clearly communicate to people in the work group how their tasks and
activities fit into the broader organisational goals and objectives.
The monitoring role has an internal focus and asks one item for each of the
following four behaviours:
• I make sure that our decisions and actions comply with the organisation’s
policies, standards and rules.
• I make sure our section has good information about how we are
progressing toward our targets and goals.
• I monitor activities and procedures to ensure that our branch or section is
working as effectively and efficiently as possible.
• I follow up on decisions to make sure they are implemented.
3.2.1.2 ILMDP Leadership Roles
In the ILMDP, leadership effectiveness consists of four roles, or skill sets,
(visioning, facilitating, coaching and stewarding), which are discussed in turn.
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The visioning role has an external focus and asks one item for each of the
following four behaviours:
• I inspire others to believe in and have enthusiasm for the organisation’s
values and its vision.
• I communicate a long range vision for the section so people have a clear
sense of direction and purpose.
• I work at developing possibilities and new opportunities that contribute to
our vision or goals.
• I initiate and implement changes necessary to achieve our vision and
benefit the organisation as a whole.
The facilitating role has an internal focus and asks one item for each of the
following four behaviours:
• I manage meetings effectively (e.g. set realistic agendas, encourage
participation, keep to time, set action items).
• I use a participative style of management by involving others when
planning, goal setting, decision making etc.
• I effectively address and manage conflicts and disagreements as they
arise within our section.
• I facilitate group discussions effectively (e.g. clarify issues, help get
consensus, use brainstorming etc.).
The coaching role has an internal focus and asks one item for each of the
following four behaviours:
• I help and encourage people within the Division/Section develop their
skills and potential (e.g. share my skills, discuss training opportunities,
suggest ways to improve etc.).
• I demonstrate team leadership and build good team relations.
• I effectively manage people not performing to the required standard (e.g.
help them find solutions, set agreed outcomes, coaching etc.).
• I praise people for their positive contributions and achievements.
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The stewarding role has an external focus and asks one item for each of the
following four behaviours:
• I develop positive relations with people who use our services and/or
products (e.g. external clients, internal customers, etc.).
• I make sure our section gets feedback from our customers about how
well we are meeting their needs.
• I liaise and cooperate with other divisions, departments or sections so
the organisation, overall, provides good service.
• I demonstrate a strong commitment to customer satisfaction in my day-
to-day activities (e.g. respond to customer complaints and ideas on how
we can improve our service).
Each of the ILMDP items are scored on an bipolar 10-point Likert-type scale
that ranges from one (strongly disagree) to ten (agree totally), with a ‘not
applicable category on the side, as can be seen in Figure 3.3. Two anomalies
are noted in Cacioppe and Albrecht’s (2000) scale. Firstly, there is a positive
bias in the words used to anchor the numbers on the Likert scale. The most
neutral answer, ‘neither agree nor disagree’, is set at four rather than at a mid-
point in the scale. Secondly, there are more categories and as such different
adverbs used on the positive side of the scale (i.e. 6 = agree somewhat and 9 =
agree very strongly) than are used on the negative side. Justifications for the
use of an 10-point scale or the text anchors used are not detailed by the
developers of the scale (Cacioppe & Albrecht 2000; Cacioppe & Albrecht 2001).
0 1 2 3 4 5 6 7 8 9 10 Not
Applicable Disagree Strongly
Disagree Moderately
Disagree Slightly
Neither Agree nor Disagree
Agree Slightly
Agree Somewhat
Agree Moderately
Agree Strongly
Agree Very
Strongly
Agree Totally
Figure 3.3 ILMDP’s 10-Point Scale
Responses to the ILMDP are used to calculate 15 scores that are based on the
32-items (i.e. eight role scores, four function scores, a leadership score, a
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management score and a total MLE score), as can be seen in Table 3.1. Each
of the eight roles is scored by averaging the four-items that were used to
measure the relevant subscale. Each function score is an average of the eight-
items from the two relevant role scales. Similarly, the Leadership area score is
an average of the 16-items from the four leadership role scales and the
Management area score is an average of the 16-items from the four
management role scales. Finally, the total MLE score is the average of all of
the 32-items.
Table 3.1 Relationship Between Total, Area, Function and Role ILMDP Scores
(Adapted from Cacioppe & Albrecht 2001)
ILMDP Area Functions Roles
Total MLE
Management
Strategic Goal Management
Achieving (4-items) Brokering (4-items)
Performance Management
Directing (4-items) Monitoring (4-items)
Leadership
Visionary Leadership
Visioning (4-items) Facilitating (4-items)
People Leadership
Coaching (4-items) Servicing (4-items)
There has been limited empirical investigation of the ILMDP’s underlying
structure, although Cacioppe and Albrecht (2001, p. 130) noted that the ILMDP
items were “adapted and refined from the literature (e.g. Quinn et al., 1996),
have been tested with statistical methods and reflect feedback received from
over 500 managers as to what behaviours effective leaders and managers need
to demonstrate”. Cacioppe and Albrecht (2001, p. 131) also referred to the
results of a confirmatory factor analysis of 304 target managers ratings for their
super-ordinates on the ILMDP, although there is only a limited discussion of the
analysis. They examined:
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A number of statistics to see if they met accepted benchmark levels (ratio of chi-square to degrees of freedom being less than 2, the comparative fit index being greater than .93, the non-normed fit index exceeding .9 and the RMSEA confidence intervals falling below .08). All of the indices reached benchmark levels…We further established that the 8-factor model provided a superior fit to alternative 1-factor, 2-factor and 4-factor models…. The 2-factor model consisted of the left and right halves, the 4-factor model consisted of the four quadrants…..The results clearly support the validity of the 8-factor model of leadership and management.
This is the only published indication of the ILMDP’s measurement properties.
Another paper by the same authors updated this discussion with minor changes
and a more succinct explanation of the model (Cacioppe & Albrecht 2000). A
key change noted was the move from using a five-item scale for each role to a
four-item scale. However, there was no discussion of the statistical support for
such a shift.
In summary, the 32-items of the ILMDP used in the study provided a measure
of MLE behavioural complexity. It consists of eight four-item subscales, each of
which was measured on a 10-point bipolar Likert-type scale. The ILMDP is
structured as a multi-source feedback measure, with ratings from others in
addition to self-assessment, and provides a convenient sample of MLE
behavioural complexity. However, before the ILMDP role, function and total
scores can be used the constructs’ psychometric properties need to be
examined.
3.2.2 The Washington State Sentence Completion Test
MMS was measured through the Washington University Sentence Completion
Test (WUSCT, form 81) (Hy & Loevinger 1996). The WUSCT is a structured
projective paper and pencil test as can be seen in Table 3.2.
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Table 3.2 Form 81 of the Washington University Sentence Completion Test
(Hy & Loevinger, 1996, pgs 28-31)
1. When a child will not join in group activities… 2. Raising a family… 3. When I am criticised… 4. A man's job… 5. Being with other people… 6. The thing I like about myself is… 7. My mother and I… 8. What gets me into trouble is… 9. Education… 10. When people are helpless… 11. Women are lucky because… 12. A good father … 13. A girl has a right to… 14. When they talked about sex, I… 15. A wife should… 16. I feel sorry… 17. A man feels good when… 18. Rules are… 19. Crime and delinquency could be halted if… 20. Men are lucky because… 21. I just can’t stand people who… 22. At times he (she) worried about… 23. I am… 24. A woman feels good when… 25. My main problem is… 26. A husband has a right to … 27. The worst thing about being a man (woman)… 28. A good mother… 29. When I am with a woman (man) … 30. Sometimes he (she) wished that… 31. My father… 32. If I can’t get what I want… 33. Usually he (she) felt that sex… 34. For a woman a career is… 35. My conscience bothers me if… 36. A man (woman) should always…
The WUSCT assesses an individual’s overall MMS and assigns an ED stage.
It has 36 sentence stems, such as ‘When a child will not join in group
activities….’ and ‘Being with other people…’ There are both male and female
versions of the WUSCT, which are essentially the same except for variations in
relevant pronouns (i.e. he versus she) and nouns (i.e. man versus woman) in
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items 22, 27, 29, 30, 33 and 36. The equivalent wording for the female version
is shown in parenthesis in Table 3.6. Items 1 to 18 and 19 to 36 can be used
as alternative matched forms, but reliability is maximised when the 36 sentence
stems are included in the calculation of a person’s ED stage (Hy & Loevinger
1996).
A key advantage of the WUSCT is the existence of a feedback loop between
the theoretical model and the measure (Hauser 1976; Hauser 1993). This
includes the development of a comprehensive manual containing detailed
scoring guides for each of the 36 sentence stem items and instructions for
scoring an overall ED level score (Hy & Loevinger 1996). The scoring guide for
each sentence stem contains a text introduction, expected response categories
and examples of actual responses.
Item responses from a sample are pooled (i.e. all responses to item #1 are
collated) to ensure the scoring of each response occurs “without regard to
other responses or other data concerning the subject who made the response”
(Loevinger 1993, p. 7). Each response is assigned to one of the nine ED levels
by matching it with examples from the scoring guide. The scores for the 36
sentence stems are then collated for each individual and the collated
responses are used to assess the person’s core ego functioning level.
Three processes are used to triangulate the overall ED score. First, item
responses are used to arrive at an ED total item sum (TIS) score. Second, a
standard ogive scoring algorithm is used to determine an ED level score.
Finally, the overall protocol is reviewed visually by the researcher to assess the
impression of ED level indicated by the whole protocol. The three scores are
compared and a final ED level is assigned.
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The scoring manual contains a self-training guide with graduated exercises that
enable researchers to train themselves as WUSCT raters. The self-training
module produces raters who have high agreement among themselves and with
raters who were personally trained by Loevinger (median correlation between
raters for total protocol scores r = .86 and for agreement within a half stage
(r = .94) (Loevinger 1979). A substantial number of studies have consistently
found high levels of inter-rater reliability in various populations (Manners &
Durkin 2001). Such high levels of inter-rater reliability are a testament to the
underlying construct validity (Loevinger 1979). For example, “median complete
agreement between raters per item was 77%. The median correlation between
raters for total protocol scores was .86” (Loevinger 1979’, p. 285).
The WUSCT has been found to have high levels of split-half reliability (Novy &
Francis 1992; Redmore & Waldman 1975) and test-retest reliability (Redmore
& Waldman 1975; Weiss, Zilberg & Genevro 1989). Loevinger (1979) has
argued the open-ended and semi-structured format of the WUSCT provides
one form of assessing content validity as it invites the test-taker to articulate
their frame of reference, the very concept the test claims to measure.
Traditional means of assessing external validity are not always useful with
structural-developmental theories because the relationship between such
underlying structures and overt behaviour is often complex and non-linear
(Broughton 1978; Loevinger 1976; Loevinger 1993; Manners & Durkin 2001).
For example, initial studies exploring the construct validity of ED theory and the
WUSCT produced mixed results (Hauser 1976). Comprehensive reviews of
the literature have concluded there is strong evidence to support the construct
validity of the WUSCT (Hauser 1976; Hauser 1993; Loevinger 1979; Loevinger
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1993; Manners & Durkin 2001). Construct validity was found in studies
assuming non-monotonic relationships, where the importance of certain
aspects peaked with specific stages of development. For example,
delinquency occurred more frequently at lower ED levels (specifically the
impulsive stage) (Frank & Quinlan 1976); intellectualising and projection
increased with ED stages (Haan, Stroud & Holstein 1973); behavioural modes
of responsibility corresponded with ED levels (Blasi 1971) and conformity
maximally occurred at the conformist ED range (Hoppe 1972). Evidence for the
unitary structure of the WUSCT was found in a principal component analysis
(Loevinger, Wessler & Redmore 1970) and in two studies that failed to identify
item subsets within the SCT (Blasi 1971; Lambert 1972 in Loevinger, 1979
#565). Finally, the WUSCT has been found to correlate with other measures of
ED (Helson & Wink 1987; Rozsnafszky 1981; Sutton & Swensen 1983;
Westenberg & Block 1993). In their comprehensive review, Manners and
Durkin (2001, p. 548) reviewed evidence for the central tenets of ED theory
and the relationship of the WUSCT to alternate measures and concluded there
was “substantial support for the construct validity of ego development”.
With regard to the discriminant validity of the WUSCT, numerous studies have
confirmed that ED is distinct from, although moderately related to, key
personality traits, such as openness to experience, emotional security and
intimacy, and psychological-mindedness, and aspects of interpersonal
relations, such as greater nurturance, trust, valuing of individuality, and
responsibility (Hauser 1993). Positive correlations between ED levels, as
identified by the WUSCT, and IQ have been reported that range from .10 to
.50, with higher correlations for samples that included gifted students (Hauser
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1976; Loevinger 1976; McCrae & Costa 1980; Westenberg & Block 1993). The
results of a meta-analysis support the assertion that ED is distinct from verbal
intelligence with a weighted average correlation between these two constructs
of .32, being much lower than correlations between alternative tests of
traditional intelligence (r= .88) (Cohn & Westenberg 2004). The few studies
exploring the relationship between ED and socio-economic status (SES)
suggest there is a relationship, particularly for adolescents (Browning 1987;
Redmore & Loevinger 1979). However, it appears this relationship may be
less relevant for adults (Hansell et al. 1984) and, in some contexts, may be
irrelevant (Snarey & Lydens 1990). Rather than SES alone, broadening
experiences, such as higher education and work complexity, have consistently
been associated with higher levels of ED (Hansell et al. 1984; Snarey & Lydens
1990). In summary, ED is a distinct construct from personality, IQ and SES
(Manners & Durkin 2001).
The theoretical assumption of non-monotonic relationships between ED and
behaviour, which was mentioned earlier, means evidence for the predictive
validity of the WUSCT has been difficult to obtain and, consequently, is largely
based on probabilistic relationships (Manners & Durkin 2001). In a review of 13
studies, behavioural and group membership correlates were found to exist
between ED, as measured by the WUSCT, and conformity, delinquency,
responsibility and occupational choice (Loevinger 1979). As was noted by
Manners and Durkin (2001, p. 558), “further research is required before the
predictive validity of ego development can be said to have substantive empirical
support”.
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In summary, for this study the concept of MMS has been operationalised as an
individual’s ED score as measured by the WUSCT. The WUSCT is an
established measure with substantial support for inter-rater reliability, construct
validity and discriminant validity from the other constructs that were of interest in
the study.
3.2.3 The Mayer, Salovey and Caruso EI Test
EI was measured using Mayer, Salovey and Caruso’s (2002) Emotional
Intelligence Test (MSCEITv2). Specifically the four branch scores of Perceiving
Emotion, Facilitating Thought, Understanding Emotion and Managing Emotion
were employed. As was described in Chapter 2, the MSCEITv2 is an ability
measure of EI that is based on Mayer and Salovey’s (1997) four-branch ability
EI model. The MSCEITv2 is available in hard copy (question booklet and
answer sheet) and has an online version. The test takes approximately 40
minutes to 60 minutes to complete.
The MSCEITv2 has 141-items that are spread disproportionately over eight
tasks referred to as the Faces task, the Pictures task, the Sensations task, the
Facilitation task, the Blends task, the Changes task, the Emotion Management
task and the Emotional Relations task. Each of these tasks relate to one of four
EI abilities (i.e. Perceiving and Appraising Emotion, Using Emotion to Facilitate
Thought, Understanding Emotions, and Managing Emotions), as can be seen in
Figure 3.4.
Branch 1 (referred to as Perceive Emotion throughout this thesis) is measured
through Task A (Faces) and Task E (Pictures). Branch 2 (referred to as
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Facilitate Thought) is measured through Task B (Facilitation) and Task F
(Sensations). Branch 3 (referred to as Understand Emotion) is measured
through Task C (Changes) and Task G (Blends). Branch 4 (referred to as
Manage Emotion) is measured through Task D (Emotion Management) and
Task H (Emotional Relations). The four abilities map to two EI areas labelled
Experiential EI and Strategic EI. These two areas are combined to calculate
Total EI. The four branches and eight tasks with sample items are described in
the following sections.
Figure 3.4 Structure of MSCEITv2: Total EI, Area, Branch and Task Levels
(Mayer, Salovey & Caruso, 2002, pg 71)
3.2.3.1 Branch 1: Perceive and Appraise Emotion
The first branch of the MSCEITv2 (Perceive Emotion), refers to a person’s
ability to recognise and express how they are feeling and how those around
them are feeling. Indicative examples of the task items are displayed in Table
3.3.
This ability involves “paying attention to and accurately decoding emotional
signals in facial expressions, tone of voice, and artistic expressions” (Mayer,
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Salovey & Caruso 2002, pg 19). The MSCEITv2 assesses this ability through
the Faces (Task A) and Pictures (Task E) tasks. Task A and E require
respondents to view a face or picture and assess how much each of five
emotions are being expressed.
Table 3.3 Indicative Items for Faces and Pictures Tasks from MSCEITv2
Branch 1 Perceive and Appraise Emotion
Task A:
Faces
Instructions: How much is each feeling below expressed by this face?
Task E:
Pictures
Instructions: How much is each feeling below expressed by this picture?
(NOTE - these sample items have been created as indicative examples of format only in keeping with the request of the test publishers to avoid invalidating actual test items).
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3.2.3.2 Branch 2: Use Emotion to Facilitate Thought
The second branch of the MSCEITv2 (Facilitate Thought) refers to a person’s
ability to use emotions to facilitate their thinking and other cognitive processes.
The MSCEITv2 assesses this ability through the Facilitation (Task B) and the
Sensations (Task F) tasks. Indicative examples of the items for these tasks are
displayed in Table 3.4. Task B asks respondents to assess how different
moods would interact with and/or support thinking and reasoning. Task F asks
respondents to assess how different feelings are similar to other sensate
experiences.
Table 3.4 Indicative Items for Facilitation and Sensations Tasks from MSCEITv2
Branch 2: Facilitate Thought
Task B:
Facilitation
Instructions: Please select an answer for each item
What mood might be helpful when studying for an exam?
Task F:
Sensations
Instructions: For each item below, you are asked to imagine feeling a certain way. Answer as best as you can, even if you are unable to imagine the feeling.
Imagine feeling confused after having a conversation with your boss about your performance over the last year. How much is the feeling of confusion like each of the following?
(NOTE - these sample items have been created as indicative examples of format only in keeping with the request of the test publishers to avoid invalidating actual test items).
3.2.3.3 Branch 3: Understand Emotion
The third branch of the MSCEITv2 (Understand Emotion), refers to knowledge
of emotions including the stimuli for different emotional reactions, the range of
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specific emotions (i.e. from irritation to rage), and the relationship between
different emotions and how they can combine. The MSCEITv2 assesses this
ability through the Changes (Task C) and the Blends (Task G) tasks. Indicative
examples of the items for these tasks are displayed in Table 3.5.
Table 3.5 Indicative Items for Changes and Blends Tasks from the MSCEITv2
Branch 3: Understand Emotion
Task C:
Changes
Instructions: Select the best alternative for each of these questions.
Geoff felt irritated and began to feel extremely frustrated. If nothing changes, he will eventually feel _________.
a. Happy b. Disappointed c. Angry d. Disgusted e. Neutral
Task G:
Blends
Instructions: Select the best alternative for each of these questions.
A feeling of unease most closely combines the emotions of _____.
a. Love, anxiety, surprise, anger b. Surprise, pride, anger, fear c. Acceptance, anxiety, fear, anticipation d. Fear, joy, surprise, embarrassment e. Anxiety, caring, anticipation
(NOTE - these sample items have been created as indicative examples of format only in keeping with the request of the test publishers to avoid invalidating actual test items).
Task C asks respondents to assess how different emotions transition from one
to another. Task G asks respondents to assess how different feelings are
similar to other sensate experiences.
3.2.3.4 Branch 4: Manage Emotion
The fourth branch of the MSCEITv2 (Manage Emotion) refers to a person’s
ability to regulate emotions their own emotions and the emotions of others to
facilitate personal understanding and growth. The MSCEITv2 assesses this
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ability through the Emotion Management (Task D) and the Emotional Relations
(Task H) tasks. Indicative examples are displayed in Table 3.6.
Table 3.6 Indicative Items for Emotion Management and Emotional Relations
Tasks from the MSCEITv2 Branch 4 Manage Emotion
Task D:
Emotion management
Instructions: Please select a response for each action.
After a positive coaching session, David was feeling motivated and energised. How well would each action preserve his mood?
Action 1) He started doing some filing.
a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
Action 2) He made a list of what he wanted to accomplish this week and by the month end.
a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
Action 3) He decided to use the feeling to motivate himself to address a few things he had been procrastinating about.
a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
Action 4) He decided to go for coffee with a colleague who was frustrated with his job and try to cheer him up. a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
Task H:
Emotional relations
Instructions: Please select a response for each action.
Marie and Cindy are good friends. Recently, Marie felt betrayed by Cindy when she disclosed a confidence that Marie had specifically asked her to keep to herself. How effective would Marie be in maintaining a good relationship if she choose to respond in each of the following ways?
Response 1) She decided to ignore the situation.
a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
Response 2) She sent Marie a detailed email about how she felt. a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
Response 3) Marie shared her feelings with Cindy in person and asked for help in understanding why Cindy acted as she did.
a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
Response 4) Marie decided Cindy was not a trustworthy person and started to ignore her.
a. Very ineffective b. Somewhat ineffective c. Neutral d. Somewhat effective e. Very effective
(NOTE - these sample items have been created as indicative examples of format only in keeping with the request of the test publishers to avoid invalidating actual test items).
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Task D asks respondents to assess the effectiveness of four different actions in
regulating emotions to achieve a specific outcome. Task H asks respondents to
evaluate the effectiveness of different responses towards achieving specific
outcomes.
3.2.3.5 MSCEITv2 Scoring and Measurement Properties
The MSCEITv2 scores are based on 122 of the 141-items because a
“psychometric analysis on the normative sample suggested (the) exclusion of
19-items. These items were not deleted from the actual test so as to preserve a
balanced layout” (Lopes et al. 2004, p. 1021).
Each MSCEITv2 branch score is the average of two unadjusted raw task
scores. Similarly, each MSCEITv2 area score is the average of the four related
task scores. That is, Experiential EI consists of Tasks A, B, E and F and
Reasoning EI consists of Tasks C, D, G and H. Finally, the Total EI score is the
average of the eight unadjusted raw task scores.
The MSCEITv2 scoring algorithms are held by MHS, the test publisher, and
MSCEITv2 scores are obtained by sending completed forms, online or in hard
copy, to the MHS scoring service. While the algorithms are proprietorial, the
general protocol has been described as having six distinct stages (Mayer,
Salovey & Caruso 2002). The MSCEITv2 produces fifteen different scores
(eight task scores, four branch scores, two area scores and an overall EI score).
Each of the scores can be adjusted according to age, gender and/or ethnic
group. The scores are based on the task scores which are used to compute
branch, area and the total scores. Raw scores are converted to percentile
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scores. Percentile scores are converted to standard scores with a mean of 100
and a standard deviation of 15 (Mayer, Salovey & Caruso 2002). MHS also
provides a scatter score that is used to assess the amount of variation across
the eight tasks (the absolute value of the differential between each of the eight
task percentiles and the average, divided by eight) and a positive-negative bias
score that is based on responses to the pictorial stimuli that are used in the
MSCEITV2.
As was mentioned in Chapter 2, the MSCEITv2 uses a consensus scoring
method based on norms from the general population and/or from a panel of
experts (Mayer, Salovey & Caruso 2002). To compute the task scores, each
item response is “assigned a score based on the proportion of the consensus
sample (either general or expert) that selected that response” (Mayer, Salovey
& Caruso 2002, p. 67). The item scores are then used to compute an average
response score for each task, which is termed an unadjusted raw task score.
The expert consensus and the general population consensus have been found
to be highly correlated for the eight task scores (Palmer et al. 2005; Mayer,
Salovey & Caruso 2002). However, the use of consensus scoring continues to
be a key controversy for the MSCEITv2 (Keele & Bell 2009; MacCann et al.
2004; Roberts, Zeidner & Matthews 2001). There has been considerable
debate between those who feel large samples of individuals converge on
correct answers (Legree 1995) and those who feel consensual scores indicate
conformity and are not necessarily the correct answer (Roberts, Zeidner &
Matthews 2001).
In terms of construct validity, the original factor analysis on the MSCEIT
suggested three possible structures with increasing good fit indices. The
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solutions included a one factor structure, representing the overall EI score, a
two factor structure, representing the two area scores, and a four factor
structure, representing the four branch scores of the model (Mayer, Salovey &
Caruso 2000). Lopes et al. (2004) also used confirmatory factor analysis and
found support for the four-branch model. More recent studies, however, do not
support these results (Gignac 2005; Kafetsios 2004; Keele & Bell 2008; Palmer
et al. 2005; Rode et al. 2008). Specifically, fit indices in these later studies have
suggested a two factor structure (Day & Carroll 2004; Roberts et al. 2006) or a
three factor structure (Keele & Bell 2008; Palmer et al. 2005; Rode et al. 2008).
Another view is that three of the four MSCEITv2 EI abilities cascade from
Branch 1 (Perceive Emotion) to Branch 3 (Facilitate Thought) and then to
Branch 4 (Manage Emotion) (Joseph & Newman 2010).
The MSCEITv2 is said to have discriminant validity in relation to personality (J.
Ciarrochi, A.Y. Chan & P. Caputi 2000; Roberts, Zeidner & Matthews 2001;
Rosete & Ciarrochi 2005; J.V. Ciarrochi, A.Y. Chan & P. Caputi 2000). Low to
moderate correlations with IQ support the assertion that ability EI has
discriminant validity as an intelligence (Cote & Miners 2006; Lopes, Salovey &
Straus 2003; Roberts, Zeidner & Matthews 2001; Rode et al. 2007; Van Rooy,
Viswesvaran & Pluta 2005). In addition, low correlations between the
MSCEITv2 and mixed measures of EI, which are thought to be confounded with
personality measures, suggest the MSCEITv2 is not measuring personality
(Van Rooy, Alonso & Viswesvaran 2005). The MSCEITv2 subscales have
convergent validity in that the inter-correlations range from .27 to .51 (Mayer et
al. 2003).
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Pusey (2000) reported that the MSCEITv2 had good face validity and
concurrent validity is assumed from the moderate correlations between the
MSCEITv2 and psychological well-being scales (r= .28) (Brackett & Mayer
2003), measures of depression (r= -.33) and trait anxiety (r= -.29) (Head 2002
in Brackett, 2004 #642) , the JACBART (r= -.02 to .20) and the Vocal-I
(r= -.10 to .24) (Roberts et al. 2006).
The reliability of the MSCEITv2 scales vary. Good to high split-half reliability
scores have been reported when using both general and expert scoring for
Total EI score (r = .89 to .93), for the Strategic and Experiential Area scores (r =
.76 to .90) and for Branch 1 scale (Perceive Emotion) (r= .89 to .91) (Palmer et
al. 2005; Mayer, Salovey & Caruso 2002). The MSCEITv2 User’s Manual
reports good split-half reliability scores for Branch 2, 3 and 4, again using both
general and expert scoring (r= .76 to .79, .77 to .80 and .81 to .83 respectively)
(Mayer, Salovey & Caruso 2002). However, other studies have reported split-
half reliability scores lower than those reported for the normative sample. For
example, for Branch 2 (r= .63 to .80), Branch 3 (r= .56 to .73) and Branch 4 (r=
.60 to .76) (Clarke 2010; Lopes, Salovey & Straus 2003; Lopes et al. 2004;
Palmer et al. 2005; Weinberger 2003). The internal consistency of the
MSCEITv2 branch scales, when measured by Cronbach’s alpha, range from
moderate to good (α= .88, .65, .71 and .86 for Branch 1 through 4 respectively)
(Kafetsios 2004).
Follesdal (2009) has suggested Classical Test Theory measures of reliability,
such as split-half correlations and Cronbach’s alpha, are not appropriate given
the MSCEITv2’s multifaceted design and has argued for the use of G and D
studies from Generalisability Theory. Such an analysis of the MSCEITv2
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produced generalisability coefficients of the four branch scores that ranged from
.46 to .71, suggested low variance components associated with persons
(3% to 10%) and identified additional sources of variance that are not identified
in the current measurement design (Follesdal & Hagtvet 2009). However, this
analysis was based on the total 141-items, rather than on the subset of 122-
items that are used to produce the MSCEITv2 scores. The test-retest reliability
of the full-scale MSCEITv2 has been reported at r= .86 (Brackett & Mayer
2003).
A key issue debated in the literature concerns whether MSCEITv2 task scores,
branch scores, area scores or the total score are the most appropriate for use in
subsequent statistical analysis. This issue has both a theoretical and
measurement aspect. First, it is important to understand the theoretical basis as
to whether the dimensionality of EI is best viewed as a profile, an aggregate or
a latent construct (Law, Wong & Mobley 1998). If ability EI is comparable to
GMA, it could be considered to be a latent multidimensional construct (Wong &
Law 2002). Numerous studies have taken this approach (Cote & Miners 2006).
However, if the four branches represent individual aspects that are distinct from
each other, the score could be considered to be a profile score.
In summary, the MSCEITv2 is a relatively new measure of ability EI that seems
to have acceptable validity and varying levels of reliability. The MSCEITv2
continues to be one of the best measures of the ability model of EI (Mayer,
Caruso & Salovey 2000). Consequently, the MSCEITv2 Total EI score was
selected used in the present study. However, given the above discussion, it was
considered important to confirm the appropriate level of the construct hierarchy
to employ by examining the reliability of the MSCEITv2 subscales.
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3.2.1 The Wonderlic Personality Test
A critical control variable in the model is traditional intelligence (IQ). The
Wonderlic Personnel Test (WPT) (Wonderlic & Associates 2002) was used to
measure IQ in the present study. The WPT is a 12-minute timed test that
includes 50-items of increasing difficulty. The items represent a mixture of
numerical ability, reasoning, spatial ability, mathematical, logical and
geometrical questions. Scoring was done in two stages. First, an answer key
provided by the test publishers was used to identify correct and incorrect
responses on each protocol. Next, a score for each participant’s WPT was
calculated by subtracting the number of incorrect responses from the total
number of questions attempted/answered. For example, if a respondent
answered 35 of the 50 questions, five of which were incorrect, their WPT score
would be: 35 – 5 = 30.
The WPT has been found to be a reliable and valid measure of general mental
ability (GMA) or IQ (Dodrill 1983). Test-retest reliability has been estimated at
between .84 and .94 for up to a five-year period (Dodrill 1983). Split-half
reliability has been estimated at between .88 and .94 (Wonderlic & Associates
2002) and alternate form reliability has been estimated at between .73 and .95
(Schoenfeldt 1985). The concurrent validity of the WPT has been established
through correlations with the Wechsler Adult Intelligence Scale and the revised
Wechsler Adult Intelligence Scale (WAIS-revised), ranging from .84 to .93
(Dodrill 1983; Dodrill & Warner 1988; Hawkins et al. 1990). The WPT
interpretive guide suggests people who score 20 or more have managerial
potential and that people who score 28 or more have upper level management
potential, as can be seen in Appendix B.
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3.3 METHODS
3.3.1 Sample Sources
This research study was made possible through access to a convenient sample
of managerial-leaders undertaking leadership development at the AIM-UWA
Integral Leadership Center. Two samples were procured and employed. These
are each described in turn below.
3.3.1.1 The Separate ILMDP Sample
Given the limited published information on the measurement properties of the
ILMDP a separate sample of data was deemed necessary to test these
characteristics. This sample was extracted from the data-base at the AIM-UWA
Integral Leadership Center on 10 April 2005. The total population included 460
target managerial-leaders who had participated in a leadership development
program that included the use of the ILMDP as a multi-source feedback
developmental process. This data set, referred to throughout the remainder of
the thesis as ‘the separate ILMDP sample’, consisted of individual responses
from each ‘other-rater’ on all 32-items of the ILMDP.
3.3.1.2 The Main Sample
The main sample was collected from a pool of managerial-leaders undertaking
a leadership development program through the AIM-UWA Integral Leadership
Center from September 2003 to September 2006. Specifically, managerial-
leaders participating in seven programs, total population 615, were invited to
participate in the research. In response, 259 participants provided some
element of data towards the research representing a response rate of 42%.
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The eight variables of interest in the main sample include IQ, the four EI branch
scores (Perceive Emotion, Understand Emotion, Facilitate Thought and
Manage Emotion), meaning-making structure (MMS), and MLE. Unfortunately,
not all respondents provided a complete data set. For example, while
participants in the TAFE program provided complete data responses, the
program did not utilise the ILMDP as a measure of MLE and so participants
could not be included as complete cases to test the research hypotheses. The
final size of the main sample was n=169 complete cases representing a
response rate of 27.5%.
3.3.2 Data Collection for the Main Sample
During the period of September 2003 to September 2006, ILMDP reports
administered by the AIM-UWA Integral Leadership Center included an
information sheet introducing the researcher, the research topic, eligibility
requirements for participation, assurances of confidentiality and expected
benefits to participants (Appendix C). This was consistent with the
requirements outlined by The University of Western Australia’s Human
Research Ethics Committee. Benefits to participants included a personalised
feedback report and a group debrief of these results. An invitation and faxback
form, which are shown in Appendix D, were also included with the MSF
feedback and interested people were asked to complete the form and send it
back to the researchers by fax or email. By submitting the faxback form,
potential participants confirmed their availability and preference for a data
collection session. Potential participants were then contacted by email and/or
phone to confirm a date and time of the data collection session for which they
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registered. Travel and parking instructions were also provided for participants
who attended a session at The University of Western Australia.
The majority of data collection occurred in face-to-face sessions facilitated by
the researcher. The sessions varied in size from as few as one participant to as
many as 25 participants. Sessions were held at the UWA Business School or in
a private meeting room at the participant’s place of work. Measures were taken
to ensure the data collection rooms were set apart from other activity, provided
a quiet environment with no distractions, had appropriate lighting and enough
space for each participant. However, due to geographic limitations, it was
necessary to send some participants written instructions on how to complete the
WUSCT and the online MSCEITv2 and to arrange for them to undertake the
timed WPT at a later date (generally within 30 days of completing the
MSCEITv2 and WUSCT).
The same order of administration was followed for each face-to-face data
collection workshop. At the start, participants were given two copies of the
informed consent sheet, which can be seen in Appendix E. In keeping with
UWA Human Research Ethics procedures, participants were asked to read and
complete one copy and retain the second copy for their records. The signed
sheets provided informed consent for the researcher to access each
participant’s individual MSF feedback from the AIM-UWA Integral Leadership
Centre.
Once the informed consent sheets had been collected, participants were
reminded they would be completing three different tests. The WPT timed test
which was administered in accordance with the instructions in the user’s-
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manual was completed first (Wonderlic & Associates 2002). Next participants
completed the MSCEITv2 test. Where internet access was available,
participants completed the online version of the MSCEITv2. When this was not
possible, paper and pencil versions of the MSCEITv2 were used. In both cases,
the researcher briefed participants that the MSCEITv2 was to be completed
independently without input from others and in its entirety, in accordance with
the MSCEITv2 technical manual’s requirements (Mayer, Salovey & Caruso
2002). Comparison of results from the paper and pencil version of the
MSCEITv2 with the online version suggested the two were equivalent (Mayer et
al. 2003). Finally, a paper and pencil survey, which can be seen in Appendix F,
and which included the WUSCT, was handed to and completed by participants.
The average total administration time for the three instruments was
approximately one hour and 45 minutes.
3.3.3 Data Scoring and Entry of the Main Sample
As data from each construct came from different sources, a coding structure
was of critical importance. Each participant in the study was assigned a data
entry code number that facilitated collation and ensured the anonymity of
individual responses. Specific data handling, entry and coding procedures are
discussed in turn for each construct.
The ILMDP MSF data were downloaded from the AIM-UWA Integral Leadership
Centre database. A procedure was written by the Centre’s external database
designer to facilitate the process. The data were captured in Excel files that
required sorting to identify survey respondents. After this had been done, the
data files were collated to produce a format that was appropriate for exporting
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into the SPSS computer program that was used to undertake most of the
subsequent data analysis. As noted earlier in this chapter, the ILMDP uses a 0
to anchor the end point of the scale as ‘Not Applicable’. This was recognised
as a potential limitation in terms of calculating average other-rater scores. As
such, scores of 0 were identified as missing data to facilitate missing data
analysis.
The completed WPTs were scored by hand using a scoring key provided by the
test publisher. The WPT is scored by subtracting the number of incorrect
answers from the total number of questions attempted by the respondent. This
number is known as the raw WPT score. An age correction can also be applied
to produce an age-adjusted score. These calculations were initially performed
by hand on the back of each WPT and then double-checked by the researcher
at least one day after the initial calculation. The final raw score and age-
adjusted scores were entered into the SPSS computer program. The entered
numbers were again checked against the hard copies of the WPT to ensure
they matched and that no errors had been made in data entry.
Approximately half of respondents completed the online version of the
MSCEITv2, which alleviated the need for data entry for these respondents. The
other half of the sample completed paper and pencil versions of the MSCEITv2
that the researcher entered onto the MHS website. Scoring of completed
MSCEITv2 protocols was performed on the website using the expert scoring
method. A single data file, provided in an Excel spreadsheet, was downloaded
from the MHS website on 4 June 2010. The 141-item response scores, and the
raw task, branch, area and Total EI scores, which were then imported into the
SPSS computer program.
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Two raters, including the researcher, were trained to score the WUSCT using
the self-training methodology that was provided in the WUSCT training manual
(Hy & Loevinger 1996). Both raters scored a sample of ten protocols collected
from another research project, independently. The results obtained were
debriefed. This process confirmed a high level of inter-rater agreement (80%)
and enabled the negotiation of coding procedures for the main sample. For the
main sample, both raters independently scored each item response by
applying both ego level (i.e. 2 through 9) and thematic codes (i.e. a, b, c etc.) to
a total of 6,588 item responses (183 protocols of 36-items each). The coding
from each rater was compared and areas of disagreement identified. These
responses were reviewed again, in turn, by each rater until a consensus
decision was reached. This iterative process was chosen to maximise learning
and the possibility of correctly assigning an ED level to each protocol.
3.3.4 Data Analysis
The data analysis had two distinct phases. In the first phase, preliminary data
analysis was used to obtain a feel for the data and to assess the constructs’
measurement properties. In the second stage, the hypotheses outlined in
Chapter 1 and Chapter 2 were examined.
For the separate ILMDP sample, preliminary data analysis began by ensuring
all cases had a minimum number of other-raters, identifying any missing data
and confirming the accuracy of data entry. Inter-rater similarity on each of the
items and ILMDP subscales was assessed to establish justification for
averaging other-rater scores. The other-rater average scores were tested for
the existence of outliers and the assumption of normality. Finally, the
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measurement properties of the ILMDP subscales were assessed using
confirmatory factor analysis.
Preliminary data analysis for the main sample included data preparation and
testing for normality. Once again, inter-rater agreement and the measurement
properties of the ILMDP items were assessed. In addition, inter-rater agreement
for the WUSCT coding and the reliabilities and inter-correlations of the
MSCEITv2 scales were assessed.
The second stage of data analysis involved testing the posited hypotheses
using the main sample. This required correlation analysis (H1) and hierarchical
regression analysis (H2).
Each of the above statistical processes are described in turn.
3.3.4.1 Data Preparation and Assumption Checking
Both samples were subjected to appropriate data preparation: ensuring
accuracy of data entry, identifying missing data, screening outliers and testing
the assumption of normality (Tabachnick & Fidell 2007). Univariate descriptive
statistics for each of the variables were examined to ensure accuracy of the
data file and identify any missing values. Screening for outliers was undertaken
by reviewing z scores and box-plots for each of the variables. Cases with
standardised scores in excess of 3.29 (p<.001) were identified as potential
outliers and considered for transformation and/or deletion (Tabachnick & Fidell
2007). The assumption of normality was assessed by both statistical and
graphical means: skewness, kurtosis, Kolmogorov-Smirnov statistic with a
Lilliefors significance level (p > .05), normal probability plots and detrended
113
normal plots (Tabachnick & Fidell 2007). Distributions were considered highly
skewed if the skewness statistic was greater than |1|, moderately skewed when
between |1 to .5| and approximately symmetrical when between |.5 to 0|
(Bulmer 1979). Normality was indicated by kurtosis statistics equal 0 and
Kolmogorov-Smirnov statistics below 2.
3.3.4.2 Inter-Rater Similarity
Issues of inter-rater similarity are relevant for both the ILMDP and the WUSCT
as both involve the use of ratings by more than one judge to arrive at a score
for the target managerial-leaders. Establishing inter-rater similarity for the
WUSCT scores provides evidence of construct validity and gives confidence in
the consistency of the assigned ratings (Loevinger 1979). Similarly, inter-rater
similarity for the groups of other-raters on the ILMDP is necessary to justify
combining the individual scores into an average other-rater score (James &
Brett 1984; James, Demaree & Wolf 1993). In addition, analysing the
measurement properties of the ILMDP using a data set with high levels of inter-
rater similarity eliminates a potential source of ‘noise’ and therefore assists
interpretation of results. Inter-rater similarity is assessed through both inter-
rater agreement (IRA) and inter-rater reliability (IRR), which are discussed in
turn.
Inter-rater agreement (IRA) refers to the degree to which different judges
provide similar scores for the target manager being rated; more specifically, it
attempts to provide an indication of how interchangeable the scores of the
other-raters are (Tinsley & Weiss 1975). While different IRA measures have
been found to yield highly convergent results, the use of multiple indices is
114
recommended to assist in interpreting such data (LeBreton & Senter 2008).
The IRA for the WUSCT coding was examined using percentage agreement
scores and Pearson correlations. In contrast, four indices were used assess
agreement on the ILMDP: the rWG and the rWG(J) (James & Brett 1984; James,
Demaree & Wolf 1993) and the ADM and the ADM(J) indexes (Burke & Dunlap
2002; Burke, Finkelstein & Dusig 1999).
The average deviation (AD) indices (i.e. ADM and ADM(J)) are pragmatic
measures of IRA that can be used when a single target is being rated (Burke,
Finkelstein & Dusig 1999). These indices represent a dispersion score that is
calculated around the mean for a group of other-raters on a single item scale
(ADM) or multi-item scales (ADM(J)). The ADM index is calculated as is shown in
equation 3.1, in which N is the number of judges assessing the target individual
on the single item j, xjk is the kth judge’s rating, and Xji is the mean of the judges’
ratings on item j.
ADM(J) is the average of the item ADM(j) scores as can be seen in equation 3.2.
Smaller scores indicate greater agreement. The recommended upper limit for
the AD indices is < c / 6, where c=the number of response options (Burke &
(3.2)
(3.1)
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Dunlap 2002). The ILMDP has 10 response options. Consequently, the
interpretive standard for examining IRA using ADM and ADM(J) is equal to or less
than 10/6 (i.e. < 1.67).
The rWG and rWG(J) indices are used to measure IRA for individual items and for
multi-item scales respectively. Both indices define agreement in terms of the
proportional reduction in error variance (LeBreton & Senter 2008). The rWG
index is appropriate when a researcher is interested in the agreement of
multiple judges on a single target for a single variable. This index is calculated
as is shown in equation 3.3, where Sx2 is the observed variance on variable X
from K raters and δE2 is the variance that would be expected when there was a
complete lack of agreement among a set of raters (James & Brett 1984; James,
Demaree & Wolf 1993).
In this study, a range of distributions for the completely random expected
variance were computed and these are reported as has been recommended by
Lebreton & Senter (2008). That is, distributions were computed for a uniform
(rWG UN), a slightly skewed (rWG_sskew) and a moderately skewed (rWG_Mskew) null
distribution (LeBreton & Senter 2008). The argument for using the uniform
distribution is based on an assumption that if raters are responding randomly
each response option has an equal chance of being selected (LeBreton &
Senter 2008). Alternatively, the restriction of variance hypothesis argues that
organisational development interventions, if even marginally successful, would
attenuate variance in job performance, resulting in a skew to the null distribution
(3.3)
116
(LeBreton et al. 2003). There is an extension of the above index appropriate for
situations where multiple judges assess a single target on multiple items. It is
calculated as shown in equation 3.4 where Sx2 is the mean of the observed
variance for J items (James & Brett 1984; James, Demaree & Wolf 1993):
When other-raters are in total agreement rWG and rWG(J)=1 and when other-
raters are in total lack of agreement rWG and rWG(J)=0 (James, Demaree & Wolf
1993). It is possible to obtain negative values for rWG and rWG(J) which are then
reset to 0 (James & Brett 1984). The commonly reported interpretive standard
for establishing moderate IRA is rWG= .70 (Lance, Butts & Michels 2006).
However the number of other-raters within the group can significantly attenuate
rWG (Kozlowski & Klein 2000). This effect can be avoided by increasing the
number of other-raters to at least ten, or increasing the number of items for
rWG(j). A more-inclusive set of guidelines for IRA interpretive standards are
intended to encourage researchers to “think more globally about the necessity
of high versus low within-group agreement based on their particular research
question and composition model” (LeBreton & Senter 2008). LeBreton and
Senter (2008) suggest moderate agreement can be assumed if the index is >
.51, strong agreement can be assumed if the index is > .71, and very strong
agreement can be assumed if the index is > .91. Given the small number of
other-raters for each of the target managers (that range from 3 to 12), the
hurdle for assuming a moderate level of IRA was set at r WG > .51.
(3.4)
117
Inter-rater reliability (IRR) refers to the consistency in ratings provided by
multiple other-raters of multiple target managerial-leaders (Bliese 2000; James,
Demaree & Wolf 1993; Kozlowski & Hattrup 1992; LeBreton et al. 2003).
Intraclass correlation (ICC) indices are widely used as a measure of IRR
(Shrout & Fleiss 1979). Deciding on which of the six different ICC indices is
appropriate depends on a number of factors, namely whether the analysis is
assumed to have:
• one way versus two way effects;
• random or fixed column effects;
• single or average measure;
• the existence of interaction effects (McGraw & Wong 1996).
Three different indices that are relevant within the context of this research will
be discussed in turn. ICC (1) is a one-way random effect ANOVA that provides
information on the consistency and consensus of individual judges’ ratings
(LeBreton & Senter 2008). It is computed as shown in equation 3.5 (McGraw &
Wong 1996):
In this formula MSR is the mean squares for the rows in the data file (or the
targets), MSW is the mean square within the target’s group of raters, and K is
the number of other-raters per target. ICC (1) is an estimate of the reliability of
ratings by individual judges and/or an effect size regarding how the judges
(3.5)
118
rating is affected by the managerial-leader they are evaluating (LeBreton &
Senter 2008). ICC (1) can be interpreted as an effect size with the standard of
ICC (1) > .01 representing a small effect, ICC (1) > .10 representing a medium
effect, and ICC (1) > .25 representing a large effect (Murphy, Myors & Wolach
1998 in James et al 2008).
However it is not an individual judge’s MSF scores that are of interest but rather
average MSF scores from a group of judges on a range of items, which was the
case in the present study. The ICC (K), a one-way random ANOVA, is
appropriate in this situation because each target individual in this study received
feedback from a different group of judges, which means the column effect does
not vary in a systematic way. ICC (K) assesses the stability of the average
ratings for a group of judges and is computed as is shown in equation 3.6
(McGraw & Wong 1996):
In contrast, IRA analysis of the WUSCT scores requires the ICC (A, 1) index,
which is a two way mixed effect ANOVA, because the same two judges scored
each of the target managerial-leaders. As such, the column effect does vary in
a systematic way. ICC (A, 1) is an estimate of the absolute agreement of
measurements made by the two judges and is computed as shown in equation
3.7 (McGraw & Wong 1996):
(3.6)
(3.7)
119
As was the case with ICC (1), ICC (A, 1) values are interpreted as the reliability
of individual judge’s scores and/or an estimate of effect size (LeBreton & Senter
2008). Traditionally, the minimum interpretive standard for all ICC indices has
been a score of .70 (Nunnally & Bernstein 1978). However, LeBreton & Senter.
(2008) express caution in this regard, as all ICC’s are measures of IRA + IRR.
Consequently, high values are only possible when both IRA and IRR are high,
while low values may indicate low IRA, low IRR or they may indicate both
(LeBreton & Senter 2008).
3.3.4.3 Measurement Properties
In order to confirm the appropriateness of employing the Total EI score, the
reliability of the each of the MSCEITv2 subscales was examined. This analysis
was undertaken on the 122-items identified as those being more valid items
(Mayer 2010). Split-half reliability coefficients, with a Spearman-Brown
correction, were calculated at the total, area and branch levels, given the item
heterogeneity for these scales (Mayer et al. 2003). In contrast, coefficient
alphas were calculated at the task level because items at this level have the
same response format (Mayer et al. 2003). All of the reliability coefficients were
calculated using MISCEITv2 raw scores.
The measurement properties of the ILMDP were analysed by examining the
construct validity, reliability and convergent validity of each of the subscales.
The ILMDP subscales were specified a priori and the AMOS structural equation
modelling program was used to undertake the confirmatory factor analysis
procedures that were used in this phase of the analysis. Construct validity can
be assessed by a range of goodness-of-fit (GOF) indices that fall roughly into
120
broad categories referred to as absolute, incremental or comparative, and
parsimony fit measures (Byrne 2010). Reporting multiple indices from different
categories is recommended as being an appropriate way to establish
acceptable fit and, hence, construct validity (Hair Jr et al. 1995).
Absolute fit measures indicate how well the hypothesised model is reflected in a
particular data set and include the Chi-Square Statistic (χ2) and the Root Mean
Square Error of Approximation (RMSEA), both of which were reported in the
present study (Hair Jr et al. 1995). The χ2 is the only statistically based SEM fit
measure that quantifies the difference between the observed and estimated
covariance matrices. Non-significant (large p-value) small Chi-Square Statistics
indicate that there is no significant difference between the two matrices (Hair Jr
et al. 1995). However, with sample sizes over 200 the p-values of the χ2
becomes less meaningful as they can suggest a poor fit for a trivial difference
between the two matrices, which leads to the need to report additional GOF
indices.
The RMSEA index, an alternate measure of absolute fit, indicates how well a
model with optimally chosen parameters fits a population’s covariance matrix
(Browne & Cudeck 1993). RMSEA is interpreted based on general rules of
thumb that vary depending on the number of variables, the sample size and the
value of the comparative fit index (the CFI), which is an incremental fit index
and is discussed below. To demonstrate GOF a model with less than 12
variables requires a RMSEA value that is less than .07 for sample sizes greater
than 250, and a RMSEA value that is less than .08 for sample sizes under 250,
both with a CFI that is greater than or equal to .97 (Hair Jr et al. 1995). The
advantages of the RMSEA index include sensitivity to model misspecification,
121
widely used and accepted interpretive guidelines and the ability to construct
confidence intervals around the RMSEA to see if it differs significantly from zero
(MacCallum & Austin 2000).
The second category, which includes incremental or comparative fit indices,
contrasts the hypothesised model with a baseline model, usually the null model
(Byrne 2010). Commonly used and widely accepted indices include the
Comparative Fit Index (CFI) and the Tucker Lewis Index (TLI) both of which are
reported in the present study (Hair Jr et al. 1995). Again, the CFI and TLI are
interpreted based on general rules of thumb that vary depending on the number
of variables and the sample size. For example, GOF for a model with less than
12 variables requires CFI and TLI values that are greater than .95 for sample
sizes that are larger than 250, and greater than .97 for sample sizes that are
under 250 (Hair Jr et al. 1995).
Construct reliability that is greater than or equal to .70 suggests that there is a
high level of internal consistency within a scale. Construct reliability represents
the degree to which a scale is free of measurement error (Hair Jr et al. 1995)
and is computed using equation 3.8.
Convergent validity is a measure of both how strongly related the items on a
scale are and also where there is more information than there is error in a
construct. Significant item loadings (i.e. greater than .6) are indicative of
convergence of items onto their relevant subscales and also of the scale’s GOF
(Bagozzi, Yi & Phillips 1991). Fornell and Larcker (1981b) developed the
(3.8)
122
average variance extracted (AVE) score as a way to assess convergent validity,
arguing scores of .50 or more imply there is more information than noise in the
construct of interest and, consequently, that a scale has convergent validity.
The AVE score is computed using equation 3.9.
3.3.4.4 Regression Analysis (H2)
To explore the potential predictive validity of the IQ, EI and ED constructs,
hierarchical regression analyses were estimated using the other-rater average
scores as the dependent variable. Based on a theoretical assumption that IQ is
the strongest single predictor of MLE, IQ was entered in the first stage of this
analysis. In the next two stages, EI and MMS were entered respectively based
on the theoretical assumption that EI would operate as a subset of MMS.
Variables needed to achieve a significant probability of .05 for them to be
considered in the forward step and entered into the regression model.
3.4 CHAPTER SUMMARY
The present chapter operationalised the constructs in the study’s research
model. As was noted, the MLE construct was measured using the ILMDP, the
EI construct was measured using the four branch scores of the MSCEITv2, IQ
was measured using the WPT and the MMS construct was measured using the
WUSCT. The type and number of items, the scoring approach that was used
and the psychometric properties of each of the measures was then discussed in
turn. Finally, an overview of the methods that were used to obtain participants,
(3.9)
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to collect data needed, to enter and score the data and to analyse the data was
provided. In the next chapter, the results obtained from the preliminary data
analysis phase are presented.
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Chapter 4
PRELIMINARY DATA ANALYSIS
4.1 CHAPTER OVERVIEW
In Chapter 3, the measures and methods that were used in the study were
described, including the two phases of data analysis that were undertaken (the
preliminary data analysis phase and the analysis of the hypothesised
relationships). This chapter presents the results of the preliminary data analysis
phase, which consists of an assessment of the following:
• The ILMDP’s measurement properties (section 4.2).
• The MSCEITv2’s measurement properties (section 4.3).
• The inter-rater agreement on the WUSCT coding (section 4.4).
As was noted in the previous chapter, the study’s research model included the
following constructs:
• Managerial-Leadership Effectiveness (MLE) measured by the Integral
Leadership and Management Development Profile (ILMDP).
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• Emotional intelligence (EI) measured by the Mayer-Salovey-Caruso
Emotional Intelligence Test version 2 (MSCEITv2).
• Intelligence (IQ) measured by the Wonderlic Personnel Test (WPT).
• Meaning-making structure (MMS) measured by the Washington
University Sentence Completion Test (WUSCT).
As was also discussed in Chapter 3, the measurement properties of the latter
two constructs are well established in the literature. In contrast, the ILMDP has
received limited attention beyond the work of the developers in their initial
presentation of the model (Cacioppe & Albrecht 2000; Cacioppe & Albrecht
2001). Similarly, the measurement properties of the MSCEITv2 have been the
subject of some debate within the literature, particularly with regard to which
level of the construct measurement hierarchy to employ. Consequently, the
measurement properties of the ILMDP and the MSCEITv2 were explored and
the results obtained are also discussed in this chapter. A summary of the results
obtained in the examination of the inter-rater reliability of the WUSCT scores is
also provided. This preliminary data analysis provides the foundation for the
second phase of data analysis, which is discussed in Chapter 5.
4.2 THE MEASUREMENT PROPERTIES OF THE ILMDP
Managerial-Leadership Effectiveness (MLE) was operationalised using the
ILMDP (Cacioppe & Albrecht 2000). The ILMDP is a multi-source feedback
(MSF) instrument that provides self-scores and scores from other-raters. Using
scores from other-raters alleviates issues of common method variance.
Consequently, these scores were used in the present study. However, in light of
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the limited published information on the measurement properties of the ILMDP
it was seen as critical to ensure that the construct was valid and reliable. As
was noted in section 3.3.1.1, a separate sample was used for this analysis
which is briefly described in the next section, which also includes a description
of the data preparation and screening undertaken prior to this analysis. The
results of the analysis of inter-rater similarity for each of the ILMDP’s items and
subscales are then described. These other-average scores for the ILMDP
items and subscales were used to analyse the ILMDP’s convergent and
discriminant validity. Finally, the results of the factor analysis procedures that
were used to assess the ILMDP’s factor structure are discussed.
4.2.1 The Sample and Data Preparation
The separate sample (n=460) that was used to test the measurement properties
of the ILMDP was initially examined to ensure there were a minimum number of
other-raters for each target manager and each case was screened for potential
data entry errors.
To begin, the number of other-raters was calculated for each case. Only cases
that had a minimum of three other-raters for all of the ILMDP’s items were
retained. This analysis resulted in a useable sample of 364 of the initial 460
managerial-leaders with a range of three to twelve other-raters for each of the
32-items on the ILMDP. This sample size is more than adequate for the
analysis of a scale’s measurement properties (Mendoza, Stafford & Stauffer
2000). The items’ scores in each of the 364 cases fell within the expected
ranges of 1 to 10, confirming no instances of missing data and suggesting input
errors were unlikely.
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4.2.2 Inter-Rater Agreement and Reliability
As was also described in Chapter 3, before the ILMDP data could be used in
the second phase of the analysis, it was important to establish the ILMDP items’
and subscales’ inter-rater agreement (IRA) (through the rWG and ADM indices)
and inter-rater reliability (through the ICC (1) and ICC (K) indices). The IRA
analysis and the IRR analysis were undertaken using the SPSS program and
the results obtained are described in subsequent sections.
4.2.2.1 IRA and IRR for the ILMDP Items
As was noted in Chapter 3, the rWG statistic for each of the raters on each of the
32-items were computed using the uniform (rWG_UN), slightly skewed (rWG_skew)
and moderately skewed (rWG_Mskew) distributions. Table 4.1 provides a summary
of the mean scores for rWG_UN, rWG_skew, rWG_Mskew, ADM, ICC (1) and ICC (K) for
each of the 32-items. As was recommended by LeBreton and Senter (2008),
measures of central tendency and dispersion for each item were computed and
reported for rWG_UN in Appendix G, for rWG_skew in Appendix H and for rWG_Mskew
in Appendix I.
The rWG statistics for the individual raters on all 32-items ranged from perfect
agreement to perfect non-agreement (i.e. from 0 to 1, with negative values reset
to 0 for the reasons noted in Chapter 3). Despite the variance in the scores, the
mean rWG_UN values for each of the 32-items ranged from .65 to .81 and all were
above the recommended .51 interpretive standard. Assuming a uniform null
distribution, there was an acceptable level of inter-rater agreement between the
other-raters about their managerial-leaders.
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Table 4.1 Mean IRR and IRA Statistics for the ILMDP Items
ITEM Agreement Reliability
rWG_UN rWG_skew rWG_Mskew ADM Mean ICC (1) Mean ICC (K)
1 .71 .62 .53 1.05 .15 .51 2 .76 .68 .61 .95 .14 .50 3 .80 .74 .68 .85 .18 .55 4 .81 .75 .69 .84 .14 .52 5 .81 .75 .69 .87 .12 .47 6 .71 .62 .54 1.05 .21 .63 7 .72 .64 .55 1.03 .13 .50 8 .72 .64 .55 1.03 .13 .49 9 .75 .67 .59 .98 .08 .36 10 .80 .74 .68 .87 .11 .43 11 .67 .57 .47 1.14 .09 .38 12 .65 .54 .43 1.17 .08 -.11 13 .78 .72 .65 .91 .18 .58 14 .76 .69 .61 .96 .10 .39 15 .69 .60 .50 1.09 .12 .45 16 .74 .66 .58 .99 .12 .43 17 .72 .63 .55 1.03 .09 .38 18 .79 .72 .65 .88 .13 .49 19 .78 .71 .64 .93 .11 .46 20 .77 .70 .63 .94 .08 .21 21 .77 .70 .62 .93 .09 .37 22 .76 .68 .61 .95 .19 .50 23 .70 .61 .52 1.09 .12 .42 24 .71 .62 .53 1.05 .10 .31 25 .75 .67 .60 .97 .19 .60 26 .69 .60 .51 1.08 .13 .44 27 .74 .66 .58 1.01 .14 .52 28 .76 .68 .61 .95 .27 .59 29 .78 .71 .64 .92 .18 .55 30 .72 .63 .54 1.03 .12 .44 31 .76 .69 .61 .95 .18 .59 32 .78 .71 .65 .92 .09 .38
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The same analysis was repeated for the rWG_skew and rWG_Mskew null distributions.
The mean rWG_skew scores for the items ranged from .54 to .75. Once again, all
of the mean scores were above the recommended interpretive standard. The
mean rWG_Mskew scores ranged from .43 to .69. In this case, three of the items
fell below the recommended interpretive standard. If a slightly-skewed null
distribution is assumed, there was an acceptable level of inter-rater agreement
between the other-raters regarding their managerial-leaders. With the exception
of three items, this was also true for a moderately-skewed null distribution.
The mean ADM indexes for the 32-items ranged from .84 to 1.17 and all were
below the 1.67 recommended hurdle. Based on the ADM index, it can also be
seen that there was an acceptable level of inter-rater agreement in the present
sample.
The ICC (1) scores ranged from .08 to .27, indicating a medium to large effect
at an individual level. The implication is that group membership, has only a
moderate effect on the scores provided by other-raters and that mean scores
between target managerial-leaders can be compared. However, the ICC (K)
scores ranged from -.11 to .63, suggesting the item means were not as stable
as had been hoped.
Given the variation in these results, the percentage of indices meeting the
respective interpretive standards were determined. The number of items
meeting the rWG hurdle ( > .51) and the ADM hurdle ( < 1.67) was summed for
each case, for each of the three null distributions. The results that were
obtained indicated the modal case in the sample had 100% of the 32-items
meeting all of the IRA hurdles, as can be seen in Table 4.2.
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Table 4.2 Central Tendency and Dispersion Total # of ILMDP Items Meeting IRA
Interpretive Standards
# Items meeting ADM standard
# Items meeting rwg standard for
uniform null distribution
slightly skewed null distribution
moderately skewed null distribution
# % # % # % # %
Mean 30 97 28 86 25 79 23 72 Median 32 100 31 97 28 88 26 80 Mode 32 100 32 100 32 100 32 100 Std. Deviation 4 13 6 20 7 23 8 26 Percentile 25 29 91 25 79 21 66 18 56
50 32 100 31 97 28 88 26 80 75 32 100 32 100 31 97 30 94
As can also be seen in Table 4.2, the mean number of items (and their
percentage out of 32 total items) ranged from ADM=30 (97%), rWG_UN=28 (86%),
rWG_skew=25 (79%), to rWG_Mskew=23 (72%). Further examination of the dispersion
showed that the lowest 25th percentile of the sample had large numbers (and
percentage) of items meeting the interpretive standard: ADM=29 (91%),
rWG_UN=25 (79%), rWG_skew=21 (66%), and rWG_Mskew=18 (56%).
The analysis of the mean rWG, ADM and ICC statistics suggested that the
sample of other-raters had moderate to high levels of IRA and IRR on each of
the 32-items within the ILMDP. Consequently, all of the cases and items in the
present sample were included in the subsequent analysis.
4.2.2.2 IRA and IRR for the ILMDP Scales
As was also noted in Chapter 3, the ILMDP has a total of eight role subscales
and a number of aggregations of these scales (i.e. the Management Function
Scale, the Leadership Function Scale, and the total MLE Scale). Given the
limited information on the measurement properties of the ILMDP, it was decided
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to examine all of these scales at this early stage in the analysis. Consequently
the rWG(J), ADM(J), ICC (1) and ICC (K) statistics were computed for the eleven
scales. Once again, the rWG(J) statistics were computed using the uniform,
slightly skewed and moderately skewed distributions. The mean rWG(J)_UN for
each the eleven scales was above the recommended interpretive standard,
ranging from .87 to .98. The mean rWG(J)_skew for 10 of the 11 scales was also
above the recommended interpretive standard, ranging from .68 to 1.00. The
exception was the Coaching Scale, which had an rWG(J)_skew score of -.04.
The mean rWG(J)_Mskew was above the recommended interpretive standard for
eight of the 11 scales, with these scores ranging from .61 to .98. The three
exceptions were the Monitoring Scale (rWG(J)_Mskew = .43), the Facilitating Scale
(rWG(J)_Mskew = .23) and, once again, the Coaching Scale (rWG(J)_Mskew = .19). It is
not unusual for rWG(J) scores to be larger than rWG scores, as the process of
averaging parallel items reduces the influence of measurement error (James &
Brett 1984). These results can be seen in Table 4.3. The minimum, maximum
and standard deviations for each scale were again computed and, for rWG_UN the
results are reported in Appendix J, for rWG_skew the results are reported in
Appendix K, and for rWG_Mskew the results are reported in Appendix L.
The eleven scales’ ADM(J) scores met the recommended standard, with values
ranging from .09 to 1.06. Similarly, the ICC (1) scores ranged from .48 to .67
and the ICC (K) scores ranged from .79 to .97, indicating high levels of rater
consistency and stability in the mean ratings respectively. The analysis of the
mean rWG(J) and the ICC (1) and ICC (K) statistics suggested the sample of
other-raters had moderate to high levels of IRA and IRR for the ILMDP scales,
with the three possible exceptions noted. The potential problem of varying IRA
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with the Coaching, Monitoring and Facilitating scales is noted and may
contribute to the decision to ultimately exclude them. In the interest of exploring
the measurement properties of the ILMDP further, they are currently retained for
further analysis.
Table 4.3 Mean IRA and IRR Statistics for the ILMDP Scales
Scale Agreement Reliability
rWG_UN rWG_skew rWG_Mskew ADM(J)
Mean ICC (1)
Mean ICC (K)
1 Brokering .90 .84 .92 .96 .63 .87 2 Directing .88 .68 .83 1.00 .51 .81 3 Achieving .92 .86 .61 .94 .48 .79 4 Monitoring .92 .81 .43 .93 .55 .83 5 Facilitating .89 .78 .23 1.05 .54 .82 6 Visioning .89 .85 .85 1.00 .67 .89 7 Stewarding .90 .90 .90 .92 .56 .84 8 Coaching .87 -.04 .19 1.06 .58 .85 9 Management .96 .98 .94 .96 .54 .95 10 Leadership .97 .95 .94 1.01 .55 .95 11 32-item Scale .98 1.01 .98 .98 .53 .97
4.2.3 Testing for Outliers and Normality
Prior to further analysis, the other-average scores were examined to identify
outliers and see whether they were normally distributed. Outliers were
identified by examining boxplots and z scores for each of the items. A total of
49 cases were identified as outliers on one or more items. A total of 14 cases
were deleted as they appeared to be outliers on more than three of the 32
items. The remaining sample (n=350) was then examined for normality.
Normality was examined by looking at histograms, normal and de-trended
normal plots, Kolmogorov-Smirnov z-statistics and the skewness of each of the
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32 items (Tabachnick & Fidell 2007). The skewness, and the Kolmogorov-
Smirnov statistics for the 32 items can be seen in Table 4.4.
Table 4.4 Descriptive statistics for the IMLDP Items
Item # Mean Std Dev Skewness Statistic
Kolmogorov-Smirnov Z
Statistic Sig.
1 7.65 .86 -.40 1.24 .10 2 7.89 .86 -.78 2.01 .00 3 8.24 .74 -.48 1.56 .02 4 8.33 .71 -.67 1.28 .08 5 8.11 .66 -.55 1.33 .06 6 7.79 .92 -.45 1.59 .01 7 7.77 .87 -.43 1.13 .16 8 7.54 .84 -.31 1.35 .05 9 7.74 .78 -.29 1.43 .03
10 8.18 .67 -.32 1.99 .00 11 7.21 .94 -.37 0.98 .30 12 7.25 .91 -.37 1.35 .05 13 8.11 .70 -.78 1.23 .10 14 7.79 .70 -.42 1.22 .10 15 7.40 .86 -.36 1.65 .01 16 7.98 .87 -.46 1.21 .11 17 7.86 .79 -.39 1.35 .05 18 8.14 .76 -.57 1.89 .00 19 7.98 .73 -.38 1.14 .15 20 7.96 .74 -.25 1.10 .18 21 8.17 .73 -.49 1.36 .05 22 7.74 .76 -.18 1.21 .11 23 7.59 .91 -.22 1.00 .27 24 7.72 .87 -.35 1.23 .10 25 7.95 .78 -.39 1.52 .02 26 7.56 .84 -.14 1.25 .09 27 7.74 .83 -.43 1.42 .04 28 7.87 .74 -.34 1.52 .02 29 7.86 .75 -.18 0.89 .41 30 7.53 .79 -.32 1.08 .19 31 8.11 .85 -.26 1.31 .06 32 7.85 .72 -.20 1.29 .07
The skewness statistics for most items (27 of 32) suggested they were
approximately symmetrical, as they were between |.5 to 0|. The remaining five
items had only a moderate level of skewness. In addition the Komolgorov-
Smirnoff z-scores for most items (22 of 32) suggest normality could be assumed
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(p>.05). The moderate skewness is likely the cause of the test failure, but it
was not seen to be a major concern. Consequently, all of the items in the
ILMDP were included in the next stage of the analysis.
4.2.4 Measurement Properties of the ILMDP Subscales
As was noted in Chapter 3, the measurement properties of each of the eight
ILMDP role subscales were analysed by examining their unidimensionality,
reliability and convergent validity. The four-item subscales were specified as
suggested by Cacioppe and Albrecht (2000) and the AMOS Structural Equation
Modelling (SEM) program’s Maximum Likelihood Analysis procedure was used
to estimate the relevant confirmatory factor analysis (CFA) for each. An
iterative process, described in detail subsequently, was followed if the initially
suggested model did not fit the data.
4.2.4.1 The Brokering Subscale
The four items that were used to measure the ILMDP Brokering role were:
I appropriately influence others to benefit the interests of our division,
section or workgroup.
I effectively represent the interests and achievements of our work group or section to higher levels of management.
I negotiate effectively in order to obtain resources and outcomes
which help the overall success of our section.
I am effective when speaking and presenting ideas at meetings or
forums held outside of our work group.
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The results of the CFA for these four items suggested the model was a good fit
to the data (χ2=2.80, df=2, p<.25, RMSEA= .03, CFI=1.00, TLI=1.00), with
loadings ranging from .71 to .84 as can be seen in Figure 4.1. The subscale’s
construct reliability was .87 and its AVE score was .63, suggesting the construct
was reliable and had convergent validity. Consequently the four-item Brokering
subscale was retained for subsequent analysis.
* item loading
Figure 4.1 ILMDP Brokering Subscale Item Loadings and Fit Statistics
4.2.4.2 The Directing Subscale
The four items that were used to measure the ILMDP Directing role were:
I keep a focus on important, high priority activities.
I effectively delegate responsibility by giving people challenging jobs
and the freedom they need to do the job.
I develop plans which clearly set out goals, tasks and timelines.
I clearly communicate to people in the work group how their tasks and
activities fit into the broader organisational goals and objectives.
Influence others
Represent the workgroup
Negotiate for the section
Speaking and presenting
.84*
.81*
.83*
.71*
Brokering
chi-square = 2.80 df = 2 p < .25 rmsea = .03 cfi = 1.00 tli = 1.00
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The results of the CFA suggested the model was not a good fit to the data
(χ2=26.51, df=2, p<.01), despite item loadings that ranged between .69 and .79.
An examination of the modification indices suggested this was due to the error
term for the first item (I keep a focus on important, high priority activities) and
the error term for third item (I develop plans which clearly set out goals, tasks
and timelines) being correlated. Correlated errors can cause problems in
subsequent analysis and, as Byrne (2001, p. 110) has noted, “the specification
of correlated errors for purposes of achieving a better fit is not an acceptable
practice.”
Consequently, following Simon and Usunier’s (2007) suggestion, it was decided
to remove one of the items. This change made theoretical sense as the two
items were similar as they both relate to priorities and planning. To this end, the
model was re-run with the correlated error and the item with the lowest loading
was deleted. However, its removal left no degrees of freedom with which to
assess model fit. An examination of the error variances suggested two could be
made equal without affecting model fit greatly, which provided the degree of
freedom required to examine the construct’s measurement properties.
The resulting CFA had item loadings ranging from to .69 to .87 and an
acceptable fit (χ2= .01, df=1, p<.91, RMSEA= .00, CFI=1.00, TLI=1.01), as can
be seen in Figure 4.2. Construct reliability in this case was .81 and the AVE was
score was .59, suggesting the revised construct was reliable and had
convergent validity. Consequently, the revised three-item Directing subscale
was retained for subsequent analysis.
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* item loading
Figure 4.2 ILMDP Directing Subscale Item Loadings and Fit Statistics
4.2.4.3 The Monitoring Subscale
The four items used to measure the ILMDP Monitoring role were:
I make sure that our decisions and actions comply with the
organisation’s policies, standards and rules.
I make sure our section has good information about how we are
progressing toward our targets and goals.
I monitor activities and procedures to ensure that our branch or
section is working as effectively and efficiently as possible.
I follow up on decisions to make sure they are implemented.
The results of the CFA suggested the four-item model was not a good fit to the
data (χ2=12.90, df=2, p<.01), although the item loadings ranged from .61 to .84.
The poor fit was again due to correlations between some of the error terms. An
examination of the modification indices indicated the correlated error between
the first item (I make sure that our decisions and actions comply with the
organisation’s policies, standards and rules) and the second item (I make sure
our section has good information about how we are progressing toward our
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targets and goals) was the issue. Consistent with the process described in
Section 4.2.4.2, the model was re-run with this correlation included in order to
identify an item to delete. This change made theoretical sense as the two items
relate to external standards. The first item had the lowest loading and was
deleted, again leaving no degrees of freedom to assess model fit.
An examination of the remaining error variances suggested two could be made
equal, providing the degree of freedom needed to examine the construct’s
measurement properties. The construct had an acceptable fit
(χ2= .80, df=1, p< .37, RMSEA= .00, CFI=1.00, TLI=1.00), with loadings ranging
from .80 to .82, as can be seen in Figure 4.3. Construct reliability was .86 and
the AVE score was .66, suggesting the construct was reliable and that it had
convergent validity. Consequently the revised three-item Monitoring subscale
was retained for subsequent analysis.
* item loading
Figure 4.3 ILMDP Monitoring Subscale Item Loadings and Fit Statistics
4.2.4.4 The Achieving Subscale
The four items that were used to measure the ILMDP Achieving scale were:
I come up with good solutions when problems arise that might get in
the way of achieving our goals.
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I promote and support attempts to improve the standards of our
products and services.
I provide people within our section with clear, accurate and timely
feedback on their performance.
I create and develop new ideas and ways of approaching things.
The results of the CFA suggested the model was not an acceptable fit to the
data (χ2=19.58, df=2, p<.01), despite items loading ranging between
.60 and .85. The poor fit was again due to correlations between some of the
error terms. An examination of the modification indices suggested the
correlation between the error term for the second item (I promote and support
attempts to improve the standards of our products and services) and the error
term for the third item (I provide people within our section with clear, accurate
and timely feedback on their performance) was the problem. Consistent with
the process described above in Section 4.2.4.2, the model was re-run with this
correlation. This change made theoretical sense, as both related to achieving
results by enabling others. The third item had the lowest loading and was
deleted, leaving no degrees of freedom left to assess model fit.
An examination of the remaining error variances suggested two of the items
could be made equal, which provided the degree of freedom required to
examine the construct’s measurement properties. The resulting CFA had an
acceptable fit (χ2=1.55, df=1, p< .21, RMSEA= .04, CFI=1.00, TLI=1.00), with
loadings ranging from to .79 to .88, as scan be seen in Figure 4.4. The
construct reliability was .86 and the AVE score was .67, suggesting the
construct was reliable and had convergent validity. Consequently, the revised
three-item Achieving subscale was retained for subsequent analysis.
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* item loading
Figure 4.4 ILMDP Achieving Subscale Item Loadings and Fit Statistics
4.2.4.5 The Stewarding Subscale
The four items that were used to measure the ILMDP Stewarding role were:
I develop positive relations with people who use our services and/or
products (e.g. external clients, internal customers, etc.)
I make sure our section gets feedback from our customers about how
well we are meeting their needs.
I liaise and cooperate with other divisions, departments or sections so
the organisation, overall, provides good service.
I demonstrate a strong commitment to customer satisfaction in my
day-to-day activities (e.g. respond to customer complaints and ideas
on how we can improve our service).
The results of the CFA suggested the model was not a good fit to the data
(χ2 =18.91, df=2, p<.01), although the item loadings ranged from .68 to .85. The
poor fit was again due to correlations between some of the error terms. An
examination of the modification indices suggested the correlation between the
error term for the second item (I make sure our section gets feedback from our
customers about how well we are meeting their needs) and the error term for
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the fourth item (I demonstrate a strong commitment to customer satisfaction in
my day-to-day activities) was the problem. Consistent with the process
described in Section 4.2.4.2, the model was re-run with this correlation. The
second item had the lowest loading and was deleted, leaving no degrees of
freedom left to assess model fit. This change made theoretical sense, as the
two items ask about engaging with and responding to customer complaints.
An examination of the remaining error variances suggested two could be made
equal, providing the degree of freedom required to examine the construct’s
measurement properties. The resulting CFA had an acceptable fit
(χ2=2.62, df=1, p<.10, RMSEA= .07, CFI=1.00, TLI=1.00), with loadings ranging
from to .76 to .85, as can be seen in Figure 4.5. Construct reliability was .85 and
the AVE score was .65, suggesting the construct was reliable and had
convergent validity. The revised three-item Stewarding subscale was therefore
retained for subsequent analysis.
* item loading
Figure 4.5 ILMDP Stewarding Subscale Item Loadings and Fit Statistics
4.2.4.6 The Coaching Subscale
The four items that were used to measure the ILMDP Coaching role were:
143
I help and encourage people within the Division/Section develop their
skills and potential (e.g. share my skills, discuss training opportunities,
suggest ways to improve etc.).
I demonstrate team leadership and build good team relations.
I effectively manage people not performing to the required standard
(e.g. help them find solutions, set agreed outcomes, coaching etc.).
I praise people for their positive contributions and achievements.
The results of the CFA suggested the model was not a good fit to the data
(χ2=7.50, df=2, p<.02), despite item loadings ranging from .75 to .83. The poor
fit was again due to correlations between some of the error terms. An
examination of the modification indices suggested the correlation between the
error term for the third item (I effectively manage people not performing to the
required standard) and the error term for the fourth item (I praise people for their
positive contributions and achievements) was the problem. Consistent with the
process described in Section 4.2.4.2, the model was re-run with this correlation
in place. The third item had the lowest loading and was deleted, which meant
there were no degrees of freedom left to assess model fit. This change made
theoretical sense, as the two items are concerned with aspects of performance
management.
An examination of the remaining error variances suggested two could be made
equal, which provided the degree of freedom required to examine the
construct’s measurement properties. The resulting CFA had an acceptable fit
(χ2= .08, df=1, p<.78, RMSEA= .00, CFI=1.00, TLI=1.01), with loadings ranging
from to .80 to .83, as can be seen in Figure 4.6. Construct reliability was .85
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and the construct’s AVE score was .66, suggesting the construct was reliable
and had convergent validity. Consequently the revised three-item Coaching
subscale was retained for subsequent analysis.
* item loading
Figure 4.6 ILMDP Coaching Subscale Item Loadings and Fit Statistics
4.2.4.7 The Facilitating Subscale
The four items that were used to measure the ILMDP Facilitating role were:
I manage meetings effectively (e.g. set realistic agendas, encourage
participation, keep to time, set action items).
I use a participative style of management by involving others when
planning, goal setting, decision making etc.
I effectively address and manage conflicts and disagreements as they
arise within our section.
I facilitate group discussions effectively (e.g. clarify issues, help get
consensus, use brainstorming etc.).
The results of the CFA suggested the model was a good fit to the data
(χ2=2.65, df=2, p<.27, RMSEA= .03, CFI=1.00, TLI=1.00), with item loadings
ranging from .69 to .85, as can be seen in Figure 4.7. Construct reliability was
Encourage people to develop
Team leadership
Praise positive contributions
.80*
.83*
.81* Coaching
chi-square = .08 df = 1 p < .78 rmsea = .00 cfi = 1.00 tli = 1.01
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.85 and the AVE score was .60, suggesting the construct was reliable and had
convergent validity. The four-item Facilitating subscale was therefore retained
for subsequent analysis.
* item loading
Figure 4.7 ILMDP Facilitating Subscale Item Loadings and Fit Statistics
4.2.4.8 The Visioning Subscale
The four items that were used to measure the ILMDP Visioning role were:
I inspire others to believe in and have enthusiasm for the organisation’s
values and its vision.
I communicate a long range vision for the section so people have a
clear sense of direction and purpose.
I work at developing possibilities and new opportunities that
contribute to our vision or goals.
I initiate and implement changes necessary to achieve our vision and
benefit the organisation as a whole.
The results of the CFA suggested the model was a good fit to the data
(χ2=5.61, df=2, p<.06, RMSEA= .07, CFI=1.00, TLI=1.00), with item loadings
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ranging from .77 to .86, as can be seen in Figure 4.8. Construct reliability was
.77 and the AVE score was .68, suggesting the construct was reliable and had
convergent validity. The four-item Visioning subscale was therefore retained for
subsequent analysis.
* item loading
Figure 4.8 ILMDP Visioning Subscale Item Loadings and Fit Statistics
4.2.4.9 Summary Statistics and Correlation Analysis with Original Scales
The results of the eight CFAs described above are summarised in Table 4.5,
which includes the fit statistics, the final number of items retained, the range of
item loadings, the reliability and the AVE score for each scale. It can be seen
that each of the eight ILMDP subscales can be modelled as a one-factor
congeneric model. Each of the subscales met the required criteria for
unidimensionality, reliability and convergent validity; although in some cases
items had to be deleted from the analysis to achieve this outcome. To ensure
there had been no loss in information in reducing the number of items,
correlations were computed between the revised scales and the initially
suggested scales, as suggested by Thomas, Soutar and Ryan (2001). As can
be seen in Table 4.5, these correlations ranged from 1.00 (for the scales in
147
which the four items were retained) to .96, suggesting no information was lost in
ensuring appropriate measurement properties in the present study.
Table 4.5 CFA Summary Statistics for the Eight ILMDP Subscales
Chi square
Sig df # Items
Item loadings
AVE Construct Reliability
Correlation with Original Subscale
Brokering 2.80 .25 2 4 .71 to .84 .63 .87 1.00**
Directing .01 .91 1 3 .72 to .87 .59 .81 .97**
Monitoring .080 .37 1 3 .80 to .82 .66 .86 .96**
Achieving 1.55 .21 1 3 .79 to .88 .67 .86 .97**
Stewarding 2.62 .10 1 3 .76 to .85 .65 .85 .97**
Coaching .08 .78 1 3 .80 to .83 .66 .85 .98**
Facilitating 2.65 .27 2 4 .69 to .75 .60 .85 1.00**
Visioning 5.61 .06 2 4 .77 to .86 .68 .77 1.00**
4.2.5 Discriminant Validity of the ILMDP Subscales
Discriminant validity is the extent to which different constructs (in this case, the
ILMDP subscales) are distinct from one another and provide unique information.
It can be assessed by comparing the squared correlations between the various
construct pairs with their AVE scores (Fornell & Larcker 1981a). Discriminant
validity can be assumed if the squared correlation between two constructs is
less than the AVE scores of the individual constructs.
The AVE scores for the various roles ranged from .59 to .68. However, many of
the subscales’ AVE scores were less than the squared correlations between
them as these correlations ranged from .51 to .78. These results are reported in
Table 4.6, in which the AVE scores are shown in the main diagonal and the
squared correlations are shown in the upper triangle of the table.
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Table 4.6 AVE and Squared Correlations for the ILMDP Subscales
Broker Director Monitor Achieve Steward Coach Facilitate Vision
Broker .64 .70 .60 .73 .66 .62 .65 .77
Director .59 .65 .72 .59 .65 .70 .75
Monitor .66 .72 .59 .51 .56 .57
Achiever .67 .63 .65 .61 .78
Steward .65 .65 .62 .63
Coach .66 .77 .66
Facilitate .60 .60
Vision .68
Thus, while the eight role subscales had convergent validity and reliability, they
did not seem to have discriminant validity. The eight factor structure suggested
by the scale’s developers does not appear to be appropriate to the data that
were obtained in the present study. Consequently, the structure of the ILMDP in
the present context was explored further and the results obtained in this phase
of the analysis are discussed in the next section.
4.2.6 Exploring the ILMDP’S Structure in the Present Study
As the hypothesised structure of the ILMDP is a circumplex, it was decided the
underlying structure should be first analysed using the PREFSCAL program
contained in SPSS (Busing 2006) to see if this was the case. However, this
analysis led to a degenerate solution suggesting the ILMDP is not circular, as
suggested by Cacioppe and Albrecht (2000; 2001), at least in the current
research context. Consequently, exploratory factor analysis (EFA), using the
same 350 respondents, was undertaken to identify the underlying structure of
149
the ILMDP in the present study. The Kaiser–Meyer–Olkin measure of sampling
adequacy, which provides an indication of the presence of underlying factors,
was .97, which strongly suggested there were factors and that undertaking a
factor analysis would be useful (Hair Jr et al. 1995). Contrary to the model
suggested by the ILMDP’s developers, the EFA results did not support an eight-
component structure, as only three factors had eigenvalues greater than one,
which is the most common rule for determining the number of factors (Hair Jr et
al. 1995). Further, an examination of the scree diagram suggested there may
be only two components, as can be seen in Figure 4.9.
Figure 4.9 Scree Diagram of ILMDP Items
The two-component solution was therefore initially rotated to obtain simple
structure using the oblimin oblique rotation procedure. However, as the two
components were not correlated (-.002), the EFA was re-estimated using the
varimax rotation procedure, with the resulting factor loadings being shown in
Table 4.7. The analysis suggested the rotated components explained similar
amounts of the variance in the data set (33.3% component one and 32% for
component two) and this solution was accepted as a revised measure of MLE.
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Table 4.7 Rotated Factor Loadings for the ILMDP Items
Leadership Effectiveness
Managerial Effectiveness
Team Leadership .83
Participative Style .83
Positive Relationships with Customers .74
Inspires Vision .74
Praises Positive Contributions .74
Manages Conflicts Well .73
Encourages People’s Development .73
Delegate_Responsibility .72
Manage_Not_Performing .70
Communicate_Fit .69
Facilitate_Discussion .69
Influence_Others .64 .59 Provide_Feedback .63 .51 Liase_Cooperate .62 .50 Feedback_From_Cust .59
Represent_Workgroup .59 .54 Follow_Up .78 Focu_High_Priority .75 Monitor_Activities .74 Support_Att_Improve .72 Develop_New_Ideas .72 Negotiate_Effectively .72 Plans_Goals_Tasks .71 Good_Solutions .71 Dev_New_Opportunities .71 Initiate_Changes .70 Comply_With_Policies .66 Info_Progressing .65 Communicate_Vision .55 .65 Commitment_Customer .51 .59 Manage_Meetings .56 Speaking_Presenting .54
As can be seen in Table 4.7, the first component is most closely related to items
that asked about the target manager’s leadership style (e.g. provides team
leadership, inspires a vision and has a participative style). On the other hand,
151
the second component was most closely related to items that asked about the
managerial side of the task (e.g. follows up, focus on high priority tasks and
monitors activities). These relationships suggested the two components should
be labelled Leadership Effectiveness (LE) and Management Effectiveness (ME)
and that the ILMDP scale should be examined further using these two aspects.
Only items loading highly on one component (greater than .60) were retained
for this further analysis, which is discussed in the next section.
4.2.7 Confirming the Revised MLE Scales in the Present Study
As was the case with the original eight subscales, a CFA was used to examine
the revised LE and revised ME scale’s measurement properties. In this case, a
different data set was used to ensure there were no biases in the resulting
constructs. The data set in this case was obtained from the 169 respondents
who had answered the IQ, EI and MMS questions and who had not been
included in the initial data set from which the eight factors’ measurement
properties had previously been assessed. Data for 31 of the 169 cases were
only available as the average other-rater scores, as this was the way the data
were provided to the researcher. However an IRA analysis was undertaken on
the ME and LE data for which actual other-rater scores were available (n=127)
before the CFA analysis was undertaken using all of the other-rater average
scores (n=169). The IRA and CFA results are reported for the revised LE and
ME scales in turn and more detail on the main sample characteristics is
provided in Chapter 5.
152
4.2.7.1 The Revised LE Scale
Based on the decisions about the items that were retained, the revised
Leadership Effectiveness (LE) scale was measured using 11 items:
I demonstrate team leadership and build good team relations.
I use a participative style of management by involving others when
planning, goal setting, decision making etc.
I develop positive relations with people who use our services and/or
products (e.g. external clients, internal customers, etc.).
I inspire others to believe in and have enthusiasm for the organisation’s
values and its vision.
I praise people for their positive contributions and achievements.
I effectively address and manage conflicts and disagreements as they
arise within our section.
I help and encourage people within the Division/Section develop their
skills and potential (e.g. share my skills, discuss training opportunities,
suggest ways to improve etc.).
I effectively delegate responsibility by giving people challenging jobs
and the freedom they need to do the job.
I effectively manage people not performing to the required standard
(e.g. help them find solutions, set agreed outcomes, coaching etc.).
I clearly communicate to people in the work group how their tasks and
activities fit into the broader organisational goals and objectives.
I facilitate group discussions effectively (e.g. clarify issues, help get
consensus, use brainstorming etc.).
153
As was noted in Chapter 3, before averaging the other rater scores for the
above 11 items, it was important to ensure there was acceptable inter-rater
agreement. The results of the analysis undertaken to ensure this was the case
are described in the next section.
4.2.7.1.1 IRA: The revised 11-item LE scale
The ADM and the rWG (rWG_UN, rWG_skew and rWG_Mskew) statistics for each of the
raters on each of the above items were computed. Table 4.8 provides a
summary of the mean scores for all three distributions for each of the 11-items.
Table 4.8 Mean IRR Statistic for Items in Revised LE Scale
rWG_UN rWG_skew rWG_Mskew ADM
Team leadership .67 .56 .46 1.12
Participative style .67 .56 .46 1.12
Positive relations customers .83 .77 .72 .81
Inspire vision .68 .59 .49 1.09
Praise positive contributions .70 .61 .52 1.02
Manage conflicts .69 .60 .50 1.08
Encourage people develop .71 .62 .52 1.07
Delegate responsibility .67 .56 .46 1.10
Manage not performing .68 .57 .47 1.06
Communicate fit .72 .63 .54 1.02
Facilitate discussions .71 .62 .54 1.05
n = 127
The rWG statistics for the individual raters on all 11-items ranged from perfect
agreement to perfect non-agreement (i.e. from 0 to 1, with negative values reset
to 0). Despite the variance in the scores, the mean rWG_UN values, ranging from
.66 to .83, and the mean rWG_skew values, ranging from .56 to .83, were above
the recommended .51 hurdle. The mean rWG_Mskew ranged from .45 to .77 with
three of the items just below the recommended hurdle. As was recommended
by LeBreton and Senter (2008), measures of central tendency and dispersion
for each item were computed for rWG_UN, rWG_skew, and for rWG_Mskew, as reported
154
in Appendices O, P and Q respectively. Finally, mean ADM indexes for all 11-
items were above the recommended 1.67 recommended hurdle.
Given the variation in these results, the percentage of indices meeting the
respective interpretive standards were determined. The number of items
meeting the rWG hurdle ( > .51) and the ADM hurdle ( < 1.67) was summed for
each case, for each of the three null distributions. The modal case in the sample
had 100% of the 32-items meeting all of the IRA hurdles, as can be seen in
Table 4.9.
Based on the above analysis, it seems there was acceptable inter-rater
agreement between the other-raters in the main sample on the 11-items of the
revised LE scale. Consequently, the other-rater scores for these 11-items were
averaged and used in the CFA.
Table 4.9 Central Tendency and Dispersion of Total # of Revised LE Scale Items
Meeting IRA Interpretive Standards
# Items
meeting ADM standard
# Items meeting rwg standard for
uniform null distribution
slightly skewed null distribution
moderately skewed null distribution
# % # % # % # %
Mean 10 91 9 79 8 71 7 64
Median 11 100 10 91 9 82 8 73
Mode 11 100 11 100 11 100 8a 73
Std. Deviation 2 18 2 18 3 29 3 30
Percentile
25 10 91 8 73 6 55 4 36
50 11 100 10 91 9 82 8 73
75 11 100 11 100 11 100 10 91 n = 127 a lowest of multiple modes
155
4.2.7.1.2 CFA: The revised 11-item LE scale
The CFA suggested the 11 items were not a good fit to the data
(χ2=241.66, df=44, p<.01) despite item loadings ranging from .78 to .86. The
same iterative process that was used to examine the previous subscales (i.e.
examining modification indices and item loadings) was followed to improve the
subscale’s fit. This approach led to a significant reduction in the number of
items, as only four of the 11 original items were retained. However, the
resulting scale had an acceptable fit
(χ2=6.01, df=2, p<.35, RMSEA= .03, CFI= .99, TLI= .99), with loadings ranging
from to .82 to .91, as can be seen in Figure 4.10. Construct reliability in this
case was .94 and the scale’s AVE score was .72, suggesting the construct was
reliable and had convergent validity. The revised four-item scale for the LE
construct was therefore retained for subsequent analysis.
* item loading n=169
Figure 4.10 Revised LE Scale Item Loadings and Fit Statistics
4.2.7.2 Revised ME Scale
Based on the decisions about the items that were retained, the revised
Management Effectiveness (ME) scale was measured using 12 items:
Team Leadership
Participation Style
Delegate Responsibility
Communicate Fit
.91*
.88*
.82*
.82*
LE
chi-square = 6.01 df = 2 p < .35 Rmsea = .03 cfi = .99 tli = .99
156
I follow up on decisions to make sure they are implemented.
I keep a focus on important, high priority activities.
I monitor activities and procedures to ensure that our branch or
section is working as effectively and efficiently as possible.
I promote and support attempts to improve the standards of our
products and services.
I create and develop new ideas and ways of approaching things.
I negotiate effectively in order to obtain resources and outcomes
which help the overall success of our section.
I develop plans which clearly set out goals, tasks and timelines.
I come up with good solutions when problems arise that might get in
the way of achieving our goals.
I work at developing possibilities and new opportunities that
contribute to our vision or goals.
I initiate and implement changes necessary to achieve our vision and
benefit the organisation as a whole.
I make sure that our decisions and actions comply with the
organisations policies, standards and rules.
I make sure our section has good information about how we are
progressing toward our targets and goals.
Once again, before averaging the other rater scores for these 12 items, it was
important to ensure there was acceptable inter-rater agreement. The results of
this analysis are described in the next section.
157
4.2.7.2.1 IRA: revised 12-item ME scale
Prior to averaging the other-rater scores in the main sample, the ADM and the
rWG (rWG_UN, rWG_skew and rWG_Mskew) statistics for each of the raters on each of
the above items were computed. Table 4.10 provides a summary of the mean
scores for all three distributions for each of the items. The rWG statistics for the
individual raters on all items ranged from perfect agreement to perfect non-
agreement (i.e. from 0 to 1, with negative values reset to 0). Despite the
variance in the scores, the mean rWG_UN values, ranging from .70 to .82, the
mean rWG_skew values, ranging from .61 to .76, and the mean rWG_Mskew ranged
from .52 to .70 were all above the recommended .51 hurdle. As was
recommended by LeBreton and Senter (2008), measures of central tendency
and dispersion for each item were computed for rWG_UN, rWG_skew, and for
rWG_Mskew, as reported in Appendices R, S and T respectively. Finally, mean
ADM indexes for all items were above the recommended 1.67 recommended
hurdle.
Table 4.10 Mean IRR Statistic for Items in Revised ME Scale
rWG_UN rWG_skew rWG_Mskew ADM
Follow up decisions .78 .71 .64 .92 High Priority .75 .68 .60 .94 Monitor activities .75 .67 .60 .96 Support attempts improve .82 .76 .70 .81 Develop new ideas .78 .72 .65 .89 Negotiate effectively .77 .70 .63 .91 Plans goals, tasks and timelines .71 .61 .52 1.07 Good solutions problems .80 .74 .67 .86 Developing new opportunities .79 .72 .66 .91 Initiative changes .77 .69 .62 .93 Comply organisation policies .82 .76 .70 .85 Information about progress .76 .69 .61 .92 n = 127
158
The number of items meeting the rWG hurdle ( > .51) and the ADM hurdle
( < 1.67) was summed for each case, for each of the three null distributions.
The modal case in the sample had 100% of the 12-items meeting all of the IRA
hurdles, as can be seen in Table 4.11.
Table 4.11 Central Tendency and Dispersion of Total # of Revised ME Scale Items Meeting IRA Interpretive Standards
# Items meeting ADM standard
# Items meeting rwg standard for uniform null distribution
slightly skewed null distribution
moderately skewed null distribution
# % # % # % # % Mean 11 94 11 92 10 81 9 76
Median 12 100 11 92 11 92 10 83
Mode 12 100 12 100 12 100 12 100
Std. Deviation 1 8 3 22 2 16 3 22
Percentile
25 12 100 10 83 9 75 8 67
50 12 100 11 92 11 92 10 83 75 12 100 12 100 12 100 12 100
n = 127
Based on the above analysis, it was assumed there was an acceptable level of
inter-rater agreement between the other-raters in the main sample on all items
of the revised ME scale. Consequently, the other-rater scores for these items
were averaged and used in the CFA.
4.2.7.2.2 CFA: revised 12-item ME scale
The CFA again suggested that the 12 items were not a good fit to the data
(χ2=380.76, df = 44, p<.01), despite item loadings ranging from .74 to .84. The
same iterative process was followed to improve the scale’s fit. This approach
again led to a significant reduction in the number of items, as only five of the
original twelve items were retained. However, the resulting scale had an
acceptable fit (χ2=6.01, df=5, p= .31, RMSEA= .03, CFI= .99, TLI= .99), with
loadings ranging from to .77 to .87, as can be seen in Figure 4.11. Construct
159
reliability in this case was .92 and the scale’s AVE score was .69, suggesting
the construct was reliable and had convergent validity. Consequently, the
revised five-item ME construct was retained for subsequent analysis.
* item loading n = 169
Figure 4.11 Revised Management Effectiveness Scale Item Loadings and Fit Statistics
To ensure there had been no loss in information in reducing the number of
items, correlations were computed between the revised scales and the initially
suggested scales (Thomas, Soutar & Ryan 2001). In this case, the correlation
between the two leadership effectiveness scores was .96 and the correlation
between the two management effectiveness scores was .97, suggesting no
information was lost in ensuring the two scales had appropriate measurement
properties. Finally, the revised leadership effectiveness and management
effectiveness scales were analysed for discriminant validity. The squared
correlation between the two constructs (r= .65) was below both AVE scores.
Consequently, discriminant validity can be assumed and the revised Leadership
Effectiveness (LE) and Management Effectiveness (ME) Scales were retained
for use as the dependent variables in the subsequent analysis.
Initiate Changes
Developing New Opportunties
Focus on High Priority Issues
. 89*
. 85*
.72* ME
chi-square = 5.72 df = 5 p < .33 cfi = 1.00 tli = 1.00
Good Solutions
Negotiate Effectively
.81*
.88*
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4.3 THE RELIABILITY OF THE MSCEITV2
This section reports the results of the analyses that were undertaken to
examine the reliability of the MSCEIT branch scales that were used in this
study. The subsection of the main sample that was used in this analysis, and
which is described in more detail in the next chapter, included 255 respondents.
Inter-correlations were first computed at the task level and the results obtained
are summarised in Table 4.13. It was expected that the two tasks that were
used for each branch would have the highest correlations. For Branch 1
(Perceive Emotion) the correlation between Task A (Faces) and Task E
(Pictures) was r = .24 (p < .01). However, Task A (Faces) had a slightly higher
correlation with Task B (Facilitation) (r = .26, p < .01) and similarly, Task E had
a higher correlation with Task F (Sensations) (r = .28, p < .01).
For Branch 2 (Facilitate Thought) the correlation between Task B (Facilitation)
and Task F (Sensations) was r = .31 (p < .01). Again, however, there was a
higher correlation between Task F (Sensations) and Task C (Changes)
(r = .33, p < .01). For Branch 3 (Understand Emotion) the correlation between
Task C (Changes) and Task G (Blends) was the highest (r = .40, p < .01). This
was also true for Branch 4 (Manage Emotion) as the correlation between Task
D (Emotion Management) and Task H (Emotion Relations) was the highest
(r = .31, p < .01). With the three exceptions that were noted, which may be due
to sampling error alone, this pattern of correlations was consistent with the
theory underlying the MSCEITv2 and with previous results (Palmer et al. 2005).
161
Table 4.12 MSCEITv2 Task Level Correlations
Branch 1
Perceive Emotion
Branch 2
Facilitate Thought
Branch 3
Understand Emotion
Branch 4
Manage Emotion
Task A Task E Task B Task F Task C Task G Task D
Branch 1: Task_E_Pictures .24**
Branch 2:
Task_B_Facilitation .26** .24**
Branch 2: Task_F_Sensations .18* .28** .31**
Branch 3 Task_C_Changes .19* .24** .19* .33**
Branch 3: Task_G_Blends .10 .21** .01 .28** .40**
Branch 4: Task_D_Emotion Management
.14
.22**
.15*
.15*
.16*
.05
Branch 4: Task_H_Emotion Relations
.09 .22** .15 .11 .05 .10 .31**
**p<.01; *p<.05
The correlations between the four branch scores of the MSCEITv2 ranged from
.12 to .33, as can be seen in Table 4.14, suggesting the branches are related,
but distinct, factors. The correlation between the MSCEITv2 area scores of
Strategic EI and Experiential EI was .40 (p < .01), again indicating a positive
relationship, but not so strong as to suggest they might be redundant
constructs.
Table 4.13 MSCEITv2 Branch Level Correlations
MSCEITv2 Branch Perceive Emotion
Facilitate Thought
Understand Emotion
Branch 2: Facilitate Thought .33**
Branch 3: Understand Emotion .24** .30**
Branch 4: Manage Emotion .26** .19** .12**
**p<.01; *p<.05
162
The reliability of the MSCEITv2 was assessed at the task, branch, area and
total levels. Scale reliability for the Total EI score was .70, while the scale
reliabilities for Experiential EI and Strategic EI were .88 and .53 respectively.
The scale reliabilities of each of the four branch scores ranged from .50 to .88,
while the scale reliabilities of each of the MSCEITv2 task scores ranged from
.21 to .90, as can be seen in Table 4.15. Some of these reliabilities are lower
than those reported by the test’s authors (Mayer et al. 2003). While the
reliability of Branch 3 (Facilitate Thought), which clearly follows from the lower
reliability of the two tasks on which it is based, is of particular concern, it will be
retained for further analysis.
TABLE 4.14 Comparison of the Reliabilities for the MSCEITv2 Total EI,
Branch and Task Subscales across Three Studies
The Current Study
Fiori et al (2011)
Maul (2010)
Palmer et al. (2005)
Mayer et al. (2003)
Total EI .70 .66 .85 .89 .91
Area Scores
Experiential .88 --- --- .90 .90
Strategic .53 --- --- .76 .86
Branch Scores
1 Perceive Emotion .88 .53 .87 .89 .90
2 Facilitate Thought .70 .63 .62 .67 .76
3 Understand Emotion .50 .73 .62 .69 .77
4 Manage Emotion .69 .77 .66 .66 .81
Branch 1 Tasks
A Faces .90 --- .76 .84 .82
E Pictures .81 --- .82 .85 .87
Branch 2 Tasks
B Facilitation .63 --- .56 .48 .62
F Sensations .57 --- .50 .48 .56
Branch 3 Tasks
C Changes .21 --- .45 .60 .68
G Blends .35 --- .49 .54 .62
Branch 4 Tasks
D Emotion Mgmt .56 --- .55 .48 .64
H Emotion Relations .47 --- .55 .51 .64
163
However, the lower reliabilities reported in the present study are consistent with
results obtained by Palmer et al. (2005), although some of the subscales’
reliabilities in the current study are lower than those reported by Palmer et al.
(2005) (i.e. the Total EI score, the Strategic EI area score, the Understand
Emotion branch and the Changes and Blends tasks). Given these results, a
further analysis of the MSCEITv2 measurement properties was undertaken
using Maximum Likelihood Confirmatory Factor Analysis. However, the results
of this analysis did not lead to an improvement in the subscale’s reliabilities, as
can be seen in Appendix M. Consequently, a decision was made to use the
MSCEITv2 branch scales in the subsequent analysis. The low reliabilities of the
MSCEITv2 task subscales are a limitation of the present study and it is clear
more work is needed to understand why such results were obtained in the
present research context.
4.4 THE INTER-RATER SIMILARITY OF THE WUSCT SCORES
This section reports the results of the analysis undertaken to assess the inter-
rater similarity of the WUSCT scores that were used in this study. The
subsection of the main sample used for this analysis, which is described in
more detail in the next chapter, included 183 respondents.
As was described in Chapter 3, the WUSCT is made up of 36-items. Each of
the 6,588 item responses (183 protocols x 36 items=6,588 item responses) was
scored by two raters. Inter-rater agreement on the first round of scoring was
moderately high, with the two raters independently agreeing on the score for
75% of the individual item responses (4,924 item responses) and 73% of the
164
total protocol ratings of respondents’ ED levels (135 total protocol ratings).
Similarly, the correlation between the raters on individual items was .76 and on
total protocol rating (or ED level) was .86 (p<.001). These results are similar to
those reported elsewhere in the literature as noted in Chapter 3.
The ICC (A,1) for this first round of scoring was above .74 for all of the items
except item seven, as can be seen in Appendix N. For item seven, the total
number of responses that raters disagreed upon was consistent with the other
35 items on the WUSCT. However, where the raters disagreed on item seven
responses, the mean difference in the assigned scores was larger, which in turn
led to a decrease in ICC for this item. Regardless, the iterative process of
assessing disagreements mitigated this as a concern.
To maximise the possibility of correctly assigning an ED level to the items and
total protocols an iterative discussion process that was described in Chapter 3
(p. 101) was followed that enabled the raters to reach agreement on the
remaining items. Agreement was reached on a further 20% of the items in the
second round, 3% in the third round and 2% in the fourth and final round. For
148 protocols the final TPR was the same using both the ogive and the item
sum method. The remaining 35 protocols were reviewed visually by both raters
and assigned a final TPR.
4.5 CHAPTER SUMMARY
The present chapter presented results from the preliminary data analysis phase.
First, the measurement properties of the ILMDP were assessed, with the results
suggesting the eight-factor model proposed by Cacioppe and Albrecht (2001)
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was not a good fit to the data in the present study. As such, an exploratory
factor analysis was undertaken that suggested a two-factor model. The items
that were related to the two factors, which were labelled Leadership
Effectiveness (LE) and Management Effectiveness (ME), were analysed and
revised to ensure they were unidimensional and reliable and that they had
convergent and discriminant validity. These constructs were alternately used as
the dependent variable of MLE.
Results of the examination of the internal consistency of the MSCEITv2 total,
area, branch and task scales were then outlined. A lower than previously
reported set of internal reliabilities were identified, which was seen to be a
limitation in the present study. Finally, the inter-rater similarity of the two-person
WUSCT rating team was examined. This analysis suggested there was a high
level of reliability and agreement in the raters’ scoring of the WUSCT item
responses and of the total protocol ratings.
In the next chapter, these constructs were used in the second data analysis
phase that was undertaken to examine the hypotheses that arose from the
literature review and that were discussed in Chapter 2.
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Chapter 5
RESULTS
5.1 INTRODUCTION
The previous chapter presented the results of the preliminary data analysis phase
of the present study. This phase included an analysis of the measurement
properties for the measure of MLE, the reliability of the MSCEITv2 branch scores
and the inter-rater reliability on the MMS data. This chapter reports the results of
the data analysis that was undertaken to examine the hypotheses that were
developed in Chapter 2. The chapter begins with a discussion of the initial analysis
of the main research sample (section 5.2) and of its background characteristics
(section 5.3). Next a summary of the descriptive statistics for the model’s
constructs is provided (section 5.4) including the correlations between each of the
constructs. The final sections of the chapter summarise the results that were
obtained from testing the study’s various hypotheses (section 5.5 to section 5.9).
5.2 THE INITIAL ANALYSIS OF THE MAIN SAMPLE
The main research sample, which is described in Chapter 3 (section 3.3.1.2 The
Main Research Sample), was examined first. The ways in which respondents were
approached and the data were collected were discussed in detail in Section 3.3.2.
This process led to a final sample of 169 managers that was used to examine the
study’s various research hypotheses.
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Data preparation of the main sample was undertaken as described in section
3.3.4.1. As already noted, the sample was checked for the existence missing data.
However, there were no missing data in this sample. An examination of univariate
descriptive statistics for each variable found scores that fell within the expected
ranges, suggesting input errors were unlikely. Z-scores for all the constructs were
below the recommended hurdle of 3.29 (Tabachnick & Fidell 2007) with very few
exceptions. Consequently, all of the cases were retained. Skewness, kurtosis and
Kolmogorov-Smirnov Z-scores for each of the constructs can be seen in Table 5.1.
Table 5.1 Normality Statistics for Main Research Sample
Skewness Statistic
Kurtosis Statistic
Kolmogorov-Smirnov Z
Statistic Sig.
IQ -.08 -.55 1.17 .13 MMS – total item score .26 .45 .99 .28 Perceive Emotion (Branch 1) -.20 -1.03 1.31 .06 Facilitate Thought (Branch 2) -1.08 .81 1.83 .00 Understand Emotion (Branch 3) -.60 -.10 1.20 .11 Manage Emotion (Branch 4) -.88 1.35 1.14 .15 LE -.57 .30 .98 .29 ME -.34 -.02 .68 .75
n=169
The skewness for the IQ, MMS, and Perceive Emotion data suggested the
distribution was approximately normal. The constructs of Understand Emotion,
Manage Emotion and LE exhibited a moderate level of skewness. Facilitate
Thought exhibited a significant amount of skewness. However, the kurtosis was
within an acceptable level. The Kolmorgorov-Smirnov Z-scores suggested all but
one of the constructs (Facilitate Thought) could be considered to be normal. The
skewness for Facilitate Thought is likely the cause of its non-normality. Given the
skewness of this construct, it was decided to drop Facilitate Thought from
subsequent analysis. Consequently, this analysis was based on the MLE
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antecedents of IQ, MMS, Perceive Emotion, Understand Emotion and Manage
Emotion.
5.3 SAMPLE BACKGROUND CHARACTERISTICS
The respondents’ background characteristics can be seen in Table 5.2. There
were 103 males (61%) and 66 females (39%) in the sample, which is similar to the
gender break-down of full-time employed professionals in Australia for the period
from 2003-2005 (56% males and 44% females respectively) (ABS 2003 - 2005).
Respondents’ ages ranged from 25 to 58 years of age, with a mean age of 39 and
a standard deviation of 7.7 years. The sample was predominantly Caucasian
(90%), but also included a small number of Asian (7%) and Middle Eastern (1%)
participants.
Respondents’ education attainment ranged from primary school to having a post-
graduate qualifications. The sample was generally well educated, as the modal
response was a post-graduate degree (46% of the sample), while the next most
common response was an undergraduate qualification (Bachelor’s degree) (36% of
the sample). Most respondents were employed, with 81% working full-time and 8%
working part-time. The remaining 10% were not currently employed and were
pursuing an MBA degree. Respondents’ work experience ranged from a minimum
of 2.5 years to a maximum of 41 years, with a mean work experience of 18 years
and a standard deviation of nine years.
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Table 5.2 Background Characteristics of the Sample
n % Gender Male 103 61
Female 66 39 Age Under 25 1 1
26-30 years 23 14 31-35 years 42 25 36-40 years 40 23 41-45 years 25 15 46-50 years 25 15 51-55 years 9 5 56-60 years 4 2
Ethnicity Asian 11 7 Caucasian 152 90 Middle Eastern 2 1 Missing 4 2
Education Primary School 1 1 3 years or less high school 3 2 Completed high school 8 5 Business college 9 5 Technical college 4 2 Trade qualification 3 2 Undergrad/Bachelor’s degree
61 36 Post-graduate/Master’s degree
79 46 Missing 1 1
Employment Status
Employed full-time 137 81 Employed part-time 13 8 Not currently employed 18 10 Missing 1 1
Years of Work Experience 2.5 – 4 9 5
5 – 9 18 11 10 – 14 31 18 15 – 19 30 18 20 – 24 33 20 25 + 46 27 Missing 2 1
Manage Staff Yes 122 72 No 46 27 Missing 1 1
Years Managing Staff
0 11 7 1 – 5 61 36 6 - 10 39 23 11 – 19 39 23 20 + 18 11 Missing 1 1
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Most participants had responsibility for managing staff (72%) and there was a wide
range (zero to 40 years) of managerial experience within the sample. A more
detailed look at the eleven individuals with zero years of management experience
found they were in professional roles that enabled multi-source feedback to be
provided on the skills within the ILMDP. The mean managerial experience was nine
years, with a standard deviation of eight years.
As can be seen in Table 5.3, respondents came from a range of industries. The
property and business services, government and health-community sectors
employed a third of the sample, while the mining and utility industries were also
strongly represented, which is not surprising as respondents were from Western
Australia.
Table 5.3 Industries Represented in the Sample
n % Agriculture, Forestry, Fishing and Hunting 5 3 Mining 12 7 Manufacturing 6 3 Electricity, Gas and Water Supply 17 10 Construction 5 3 Retail Trade 4 2 Accommodation, Cafes and Restaurants 9 5 Transport and Storage 3 2 Communication Services 3 2 Finance and Insurance 10 6 Property and Business Services 37 22 Government Administration and Defence 26 15 Education 10 6 Health and Community Services 18 11 Cultural and Recreational Services 1 1 Personal and Other Services 1 1 Missing 2 1 TOTAL 169 100
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As can be seen in Table 5.4, the sample included a broad cross section of
executives, senior managers, managers and non-managerial professionals.
However, senior managers were the largest group (31%) and managers were the
second largest group (29%).
Table 5.4 Frequency of Job Titles
n %
Managing Director/CEO/Director 19 11
Senior Manager 53 31
Manager 49 29
Professional Specialist 36 21
Student 6 4
Missing 6 4
5.4 DESCRIPTIVE STATISTICS
Descriptive statistics, including the range, mean and standard deviation were
calculated for each of the key constructs. These included MLE (measured using
the revised Leadership Effectiveness scale and the revised Management
Effectiveness scale), IQ, MMS (measured as the ED total item sum using the
WUSCT) and the three MSCEITv2 branch scores (Perceive Emotion, Understand
Emotion and Manage Emotion). The results obtained for the various constructs are
discussed below and are summarised in Table 5.5 and Table 5.6.
Respondents’ scores on the revised Leadership Effectiveness scale ranged from
4.99 to 9.25, while scores on the Management Effectiveness scale ranged from
5.82 to 9.30. The mean Leadership Effectiveness score was 7.59, with a standard
deviation of .89, while the mean Management Effectiveness score was 7.84, with a
standard deviation of .71.
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Table 5.5 Descriptive Statistics for IQ, ED and MLE Variables
N Minimum Maximum Mean SD
IQ 169 17 39 28.3 5.13
MMS (TIS)1 169 159 244 199 15
LE2 169 4.99 9.25 7.59 .89
ME2 169 5.82 9.30 7.84 .71
1 MMS operationalised as Ego Development total item score (TIS); 2 MLE operationalised as Management Effectiveness (ME) and Leadership Effectiveness (LE).
As was noted in Chapter 3, IQ was measured using the Wonderlic Personality Test
(WPT) (Wonderlic & Associates, 2002). Scores ranged from 17 to 39, with a mean
score of 28.3 and a standard deviation of 5.13. Given the education and
management experience of the present sample, the mean score is consistent with
norms obtained in previous studies that used the WPT, as was noted in Chapter 3.
MMS was operationalised as Ego-development (ED), which was measured using
the Washington University Sentence Completion Test (WUSCT) (Loevinger, 1998).
As was noted in Chapter 3, WUSCT scores can be used to determine a person’s
ED level or can be used to compute an ED total item sum score. The ED levels in
the present sample which are shown in Table 5.6 and ranged from four to nine,
with a mean level of six. The ED total item sum scores (TIS) ranged from 159 to
244, with a mean of 199 and a standard deviation of 15, which was consistent with
prior research that had suggested 80% of the population is within a similar range
(Cook-Greuter, 2000). As was expected, ED (TIS) and ED level in the present
sample had a correlation of .89 (p<.01) and as such, only ED (TIS) is reported
throughout the remainder of the thesis.
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Table 5.6 Frequency of ED Level
Current Study Rooke (2005) 1
N % %
3 0 0 5
4 1 1 12
5 15 9 38
6 93 55 30
7 48 28 10
8 10 6 4
9 2 1 1 1 Based population of “thousands of managers and professionals, most between the ages of 25 and 55’ (pg 68).
Emotional Intelligence (EI) was measured using the Mayer, Salovey and Caruso
(2002) Emotional Intelligence Test version 2 (MSCEITv2). As was noted in
Chapter 3, raw scores, rather than percentiles, were used in this study. The raw
scores for the Perceive Emotion Branch (Branch 1) ranged from .30 to .75, with a
mean score of .55 and standard deviation of .12. The raw scores for the
Understand Emotion Branch (Branch 3) ranged from .38 to .81, with a mean of .66
and standard deviation of .09. The raw scores for the Manage Emotion Branch
(Branch 4) ranged from .25 to .57, with a mean of .46 and standard deviation of
.06. As can be seen in Table 5.7, the mean score for each of the MSCEITv2
branch scales in this study were slightly higher than were the mean scores that
were reported by the test authors in their norming data set (Mayer et al., 2003).
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Table 5.7 Descriptives on MSCEITv2 Subscales and Comparison to Norming Sample
Current Study
MSCEITv2 Norming Sample1
Min Max Mean SD Mean SD
Perceive Emotion (Branch 1) .30 .75 .55 .12 .49 .11
Understand Emotion (Branch 2) .38 .81 .66 .09 .62 .11
Manage Emotion (Branch 4) .25 .57 .46 .06 .41 .07
1 (Mayer et al., 2003)
5.5 TESTING HYPOTHESIS ONE
Hypothesis one (H1) suggested the individual difference constructs of EI, MMS and
IQ would be positively correlated, but that they would have discriminant validity.
That is:
H1a: IQ, EI and MMS are positively correlated.
H1b: IQ, EI and MMS have discriminant validity from each other.
In order to test H1a, correlations were computed to investigate the nature of the
bivariate relationships between the three independent variables. The results
obtained can be seen in Table 5.8. IQ was positively correlated with Understand
Emotion (r= .30; p<.01). Perceive Emotion was positively correlated with the other
MSCEITv2 branch Understand Emotion (r= .26; p<.01) and also Manage Emotion
(r= .27; p<.01)]. However, Understand Emotion and Manage Emotion were not
significantly related.
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Table 5.8 Correlations of Independent Variables
IQ MMS
(TIS)1 Perceive 2 Understand 4
MMS (TIS) 1 .07
Perceive Emotion 2 .13 -.10
Understand Emotion 4 .30** .06 .26**
Manage Emotion 5 .03 .04 .27** .13
1 MMS operationalised as Ego Development (ED) total item score (TIS); 2 MSCEIT Branch 1 Perceive Emotion; 3 MSCEIT Branch 2 Facilitate Thought; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
While IQ was significantly correlated with Understand Emotion, as was
hypothesised, it was not significantly correlated to any of the other independent
variables that were included in the study. Further, MMS was not significantly
related to IQ or to any of the MSCEITv2 branches. Based on this analysis, H1a was
rejected at an overall sample level. However, the lack of significant relationships
between the independent variables, combined with significant correlations between
the two ED scoring methods and between the MSCEIT branches, provides partial
evidence of discriminant validity. Based on this analysis, H1b was accepted at an
overall sample level.
5.6 TESTING HYPOTHESIS TWO
Hypothesis two (H2) had suggested the individual difference constructs of IQ, EI
and MMS would have incremental predictive validity. That is:
H2a: IQ is a significant predictor of MLE.
H2b: EI is a significant incremental predictor of MLE, beyond IQ.
H2c: MMS is a significant incremental predictor of MLE, beyond IQ and EI.
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Hierarchical regression analyses were used to explore the potential predictive
validity of the IQ, EI and MMS constructs, using the other-rater average scores on
the revised ME and LE scales as the dependent variables. Based on a theoretical
assumption that IQ is the strongest single predictor of MLE (Van Rooy &
Viswesvaran 2004), IQ was entered in the first stage of this analysis. In the next
two stages, EI and MMS were entered respectively based on the theoretical
assumption that EI would operate as a subset of MMS (Lane & Schwartz 1987).
The regression analysis found that none of the variables were significantly related
to the dependent variable of management effectiveness with the final equation
explaining almost none of the variation in ME scores (R2= .03, F=1.00, p<.43).
Similar results were obtained when the same analysis was undertaken to examine
the relationship between IQ, EI, MMS and leadership effectiveness. The regression
analysis found that none of the variables were significantly related to the dependent
variable of leadership effectiveness, with the final equation explaining almost none
of the variation in LE scores (R2= .04, F=1.02, p<.41). Consequently, the suggested
relationships were not evident in the sample obtained for the present study and
both H2a and H2b were rejected at an overall sample level.
5.7 TESTING FOR SUBGROUPS
Despite the theoretical underpinnings that were outlined in Chapter 2, the
regression analyses failed to support the hypothesised relationships. One possible
explanation of these results is the issue of unobserved heterogeneity. As Sarsted
(2008, p. 228), noted, regression analysis :
…is usually based on the assumption that the analysed data originate from a single population, that is, a unique global model represents all the observations well. In many real-world
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applications however, this assumption of homogeneity is unrealistic, as individuals (or groups) are likely to be heterogeneous.
However, there may be unobserved subgroups for whom the relationships of
interest differ, which would explain the results obtained from the sample as a
whole. Sometimes this heterogeneity is dealt with in an a prior fashion by
estimating regressions at a subgroup level based on demographic or other
background information, but this may not be sufficient to explain the sample’s
heterogeneity. As Coltman, Devinney and Midgley (2003, p. 2) pointed out, what is
often required is ‘a more sophisticated approach that moves beyond data pooling
and aggregation techniques towards approaches that enable us to capture the
heterogeneity that actually exists’.
The mixture regression or latent class (LC) regression approach overcomes this
problem, as it assumes the data comes from a number of subgroups (or segments)
‘that are homogeneous with respect to the within-segment regression
coefficients…(and) identifies latent (unobserved) groups of individuals who differ in
the effects of predictor variables on outcome’ (Jaccard 2012, p. 63). In LC
regression ‘the latent variable is a predictor that interacts with the observed
predictors which means that it serves as a moderator variable’ (Magidson &
Vermunt 2004, p. 18) as can be seen in Figure 5.1.
This approach assumes the relationships of interest cannot be explained “with a
single distribution of probabilities; rather, it requires a mixture of them…It is
necessary to separate the samples by identifying the number of segments and
estimating the parameters (regression coefficients, in this case) that define each of
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them” (Castro, Martín Armario & Martín Ruiz 2007, pp. 179-180), which is seen as
advantageous, as the subgroups are not determined by the researcher.
Figure 5.1: A Representation of Latent Class Regression
The goal of LC regression is to determine the smallest number of latent classes
that are sufficient to explain the variance in a dependent variable within each of the
subgroups (Magidson & Vermunt 2002). “This ability to separate and extract
segments and then determine a statistical (regression) model for each one,
enables…researchers to develop more realistic and complex theoretical models,
while maintaining parsimony” (Taylor, Garver & Williams 2010, p. 217).
Consequently, it was decided to follow this approach to see whether there were
subgroups in the present research context that had led to the lack of significant
results at an overall sample level.
The analysis typically begins by fitting a single class baseline model (which
suggests there are no subgroups and that the relationship between a set of
predictor variables and a dependent variable is the same for the sample as a
whole). Assuming the null model does not provide an adequate fit to the data, a LC
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regression model with two classes is fitted to the data to see whether there is a
significant improvement in fit. This process continues by fitting successive LC
models with more classes (or subgroups) to the data, until the simplest model that
provides an adequate fit is found (Magidson & Vermunt 2002). Choosing the
correct number of classes requires balancing the amount of heterogeneity that is
explained with model parsimony. To assist with this, log-likelihood and a number of
chi-squared based statistics provide information on the fit of a particular model
(Vermunt & Magidson 2005). In particular:
• The L-squared (L2), or likelihood–ratio, is a goodness of fit value for the
current model. When there is perfect fit, this statistic will equal zero. A
model with a higher value of L2 is a poorer model in terms of explaining less
of the association in the data
• The Bayesian Information Criterion (BIC) is based on the L2 but also takes
model parsimony into account. Again, when comparing models, a lower BIC
is indicative of a better fit.
• The Akaike Information Criterion (AIC) is also based on the L2 statistic. The
lower the value the better the model fit.
Classification statistics provide information about how well the model classifies
individual cases into the relevant classes (Vermunt & Magidson 2005):
• Classification Errors measure the proportion of cases that are misclassified.
As such, the closer to zero the better.
• Reduction of Errors (R2) indicates how well a model predicts class
membership. The closer to one the better the prediction.
Finally, output from a LC regression analysis includes a number of parameter
estimates (Vermunt & Magidson 2005). In particular:
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• Betas, which are indicators of the class-specific effect each predictor
variable has on the dependent variable.
• Sigmas, which are the error variances for a continuous dependent variable.
• Two Wald statistics, Wald and Wald (=), are provided each with an
associated p-value. The Wald statistic assesses the statistical significance of
the set of parameter estimates associated with a given variable. Wald (=)
assesses the equality of each set of regression effects across classes. As
such Wald (=) statistics that are not statistically different suggest that
different suggest the betas for that variable are same across the classes.
The Latent Gold 4.0 computer program (Vermunt and Magidson, 2005), was used
to estimate the various regressions, using the random seed default, which meant
ten starting points were randomly selected for each analysis which is “best
practice” as it minimises the chance of obtaining a local solution (Garver, Williams
& Taylor 2008). Following Vermunt and Magidson’s (2000) suggestion, and as
noted earlier, a 1-class model was initially estimated, after which additional models
that had more classes were estimated, until the simplest acceptable model was
found.
As LC regression cannot be undertaken in a hierarchical manner all of the
independent variables (i.e. IQ, the three EI branches and MMS) were included in
this phase of the analysis. The two aspects of MLE (i.e. management
effectiveness and leadership effectiveness) were again examined separately and
are discussed in turn. Further, as the number of cases in the smallest subgroups
were very low when the number of groups were greater than four for both the ME
and LE analysis (less than 20 in each case), it was decided to look only at the
results for the one, two, three and four-group solutions.
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5.7.1 Latent Class Regression: LE
The fit statistics obtained for the leadership effectiveness (LE) analysis are shown
in Table 5.9. As can be seen from the Table, the three group solution seems to be
the best, as there was a clear slowing in the improvements in the various fit
statistics, the entropy R2 statistic only changed by .03 and the R2 statistic only
changed by .05 from the three group to the four group solution and the number of
parameters that had to be estimated increased by 8. As the smallest group
included 43 respondents, the three-group solution was accepted in this case.
Table 5.9 Latent Class Regression Fit Statistics: Leadership Effectiveness
Number of Segments
Log Likelihood
BIC AIC Number of Parameters
Entropy R2
R2
1 -217.99 471.88 449.98 7 .04
2 -203.45 483.84 436.89 15 .40 .48
3 -189.38 496.74 424.76 23 .54 .80
4 -176.48 511.99 414.96 31 .57 .85
5.7.2 Latent Class Regression: ME
The fit statistics obtained for the managerial effectiveness analysis (ME) are shown
in Table 5.10. As can be seen from the Table, the four-group solution seems to be
the best, as while the BIC scores increased across the various solutions, the log
likelihood and AIC statistics were minimised and the entropy R2 and R2 statistics
were maximised at this point. However, in this case, the fourth group had only 27
respondents, which meant it was too small to be used in any subsequent analysis.
Consequently, it was decided to use the three larger groups suggested within the
four group solution and to treat the members of the fourth group as outliers and not
include these respondents in any of the subsequent analyses. This seemed
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especially appropriate as an examination their backgrounds suggested they did not
have any unique characteristics. A series of chi-squared tests suggested the group
had similar distributions of gender, age and years of managerial experience to the
rest of the sample.
Table 5.10 Latent Class Regression Fit Statistics: Managerial Effectiveness
Number of Segments
Log Likelihood
BIC AIC Number of Parameters
Entropy R2
R2
1 -178.76 393.44 371.53 7 .03
2 -163.71 414.63 359.46 15 .36 .59
3 -142.45 418.28 360.10 23 .46 .82
4 -130.97 441.48 355.17 31 .63 .90
In summary, results of the LC regression analysis suggested there were in fact
subgroups within the sample. The optimal solution for the LE dependent variable
suggested three subgroups. The optimal solution for the ME data suggested four
subgroups, although the fourth is too small for further statistical analysis and so
these cases are excluded as outliers. Consequently, the remaining group sizes
ranged from 35 to 80 as can be seen in Table 5.11.
Table 5.11 Sizes of the Latent Classes
MLE Scale Group # n %
Management Effectiveness Scale 1 55 32.5 2 52 30.8 3 35 20.7
4 27 16.0
Leadership Effectiveness Scale 1 80 47.3 2 46 27.2 3 43 25.4
The existence of subgroups within the sample would in fact have an impact on the
results obtained in the initial regression. However, before exploring the regressions
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at the subgroup level, the background characteristics and mean scores on the
variables of interest were examined further so as to better understand the
differences between the subgroups.
5.7.3 Mean Differences within the LE Subgroups
Descriptive statistics for the constructs and the univariate analysis of variance
results for the Leadership Effectiveness (LE) subgroups can be seen in Table 5.12.
Table 5.12 Descriptive Statistics on Variables for Leadership Effectiveness Groups
LE Group One (LE1)
LE Group Two (LE2)
LE Group Three (LE3) F
Statistic Sig.
Mean Std. Dev Mean Std. Dev Mean Std. Dev IQ 29.21 5.32 27.73 4.94 28.16 5.14 1.26 .29 MMS (TIS) 1 201 14 199 17 199 13 .26 .79 MLE2 6.82 .88 7.97 .57 7.92 .73 44.23 .00 Perceive Emotions3 .55 .12 .55 .12 .55 .13 .02 .98 Understand Emotion4 .66 .09 .64 .10 .67 .08 1.22 .30 Manage Emotion5 .46 .05 .46 .06 .45 .06 .21 .81 1 MMS operationalised as Ego Development (ED) total item score (TIS); 2 MLE operationalised as Leadership Effectiveness
(LE); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
As can be seen in the Table, the only variable for which there was a significant
difference across the three groups was the effectiveness variable itself, suggesting
the differences in the various relationships were not due to differences in the mean
scores of the predictor variables.
5.7.4 Mean Differences within the ME Subgroups
Descriptive statistics for the constructs and the univariate analysis of variance
results in the Management Effectiveness subgroups can be seen in Table 5.13. As
can be seen in the Table, the only variable for which there was a significant
difference across the three groups was the effectiveness variable itself, suggesting
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that, once again, differences in the various relationships were not due to
differences in the mean scores of the predictor variables.
Table 5.13 Descriptive Statistics on Variables for Management Effectiveness Groups
ME Group One (ME1)
ME Group Two (ME2)
ME Group Three (ME3 )
F Statistic
Sig.
Mean Std. Dev Mean Std. Dev Mean Std. Dev
IQ 28.40 4.79 27.43 5.09 29.13 5.93 1.07 .34 MMS (TIS) 1 202 16 196 13 196 15 2.48 .09 MLE2 7.97 .55 7.85 .89 6.91 .31 11.48 .00 Perceive Emotions3 .56 .11 .56 .13 .52 .13 1.11 .33 Understand Emotion4 .65 .10 .67 .09 .65 .10 .63 .53 Manage Emotion5 .46 .06 .45 .06 .46 .05 .41 .66 1 MMS operationalised as Ego Development (ED) total item score (TIS); 2 MLE operationalised as Management Effectiveness (ME); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
Given the existence of subgroups and a lack of subgroup differences between the
mean scores of the independent variables within the sample, it was considered
necessary to re-examine H1a, H1b, H2a and H2b at a subgroup level. The intention
was to explore the nature of the relationships between the independent variables
and their incremental validity in predicting MLE for each of the subgroups. The
results obtained are discussed next.
5.8 TESTING THE HYPOTHESES FOR THE SUBGROUPS
As was described earlier in this chapter, correlation analysis was used to test H1a
(IQ, EI and MMS are positively related) and H1b (IQ, EI and MMS have discriminant
validity), while regression analysis was used to test H2a (IQ predicts MLE), H2b (EI
has incremental validity in predicting MLE, beyond IQ), and H2c (MMS has
incremental validity in predicting MLE, beyond IQ and EI). These processes were
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repeated at a subgroup level and the results obtained from these analyses are
reported in subsequent sections.
5.8.1 H1 and H2 for Leadership Effectiveness Group One
The results of the correlation analysis for Leadership Effectiveness Group One
(LE1) can be seen in Table 5.14. For LE1 managerial-leaders there were significant
relationships between all three of the EI variables and MLE: Perceive Emotion
(r= -.36, p<.01), Understand Emotion (r= .34, p<.01) and between Manage Emotion
(r= .34, p<.01). IQ was not significantly correlated with any other constructs. MMS
correlated positively with Manage Emotion (r= .23, p<.05). Two of the three
MSCEITv2 branch correlations were significant with a non-significant correlation
between Understand Emotion and Manage Emotion.
Table 5.14 Correlations for Leadership Effectiveness Group One
MLE1 IQ MMS2 (TIS)
Perceive Emotion3
Understand Emotion4
IQ .16
MMS (TIS)2 -.05 .11
Perceive Emotion3 -.36** .19 -.10
Understand Emotion4 .34** .19 .10 .24*
Manage Emotion5 .34** -.02 .23* .27* .07
1 MLE operationalised as Leadership Effectiveness (LE); 2 MMS operationalised as Ego Development (ED) total item score (TIS); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
The regression analysis undertaken for this group suggested all of the independent
variables were significantly related to respondents’ perceptions of MLE
(R2= .62; F=28.88; p<.01). The standardised regression coefficients obtained in this
case can be seen in Table 5.15. For this subgroup of managers, Perceive Emotion
(β= -.70; p<.01) was the single largest negative predictor of MLE and Manage
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Emotion (β= .58; p<.01) was the single largest positive predictor. The remaining
relationships were moderately strong and also varied in direction: positive between
MLE and IQ (β= .24; p<.01) and Understand Emotion (β= .45; p<.01), and negative
between MLE and MMS (β= -.32; p<.01).
Table 5.15 Regression for Leadership Effectiveness Group One
Standardised Coefficient
Sig. Collinearity Statistics
Beta Tolerance VIF
IQ .24 .00 .92 1.09
MMS (TIS) -.32 .00 .88 1.13
Perceive Emotion -.70 .00 .81 1.23
Understand Emotion .45 .00 .91 1.10
Manage Emotion .58 .00 .85 1.18
5.8.2 and H2 for Leadership Effectiveness Group Two
The results of the correlation analysis for Leadership Effectiveness Group Two
(LE2) can be seen in Table 5.16. For this group of managerial-leaders, there was
a significant positive relationship between MLE and Perceive Emotion
(r= .43, p<.01) and between IQ and Understand Emotion (r= .42, p<.01). MMS was
negatively correlated with Perceive Emotion (r= -.32, p<.01) and Manage Emotion
(r= -.41, p<.01). Two of the three MSCEITv2 branch correlations were significant
with again the non-significant correlation between Understand Emotion and
Manage_Emotion.
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Table 5.16 Correlations for Leadership Effectiveness Group Two
MLE1 IQ MMS2 (TIS)
Perceive Emotion3
Understand Emotion4
IQ .08
MMS (TIS)2 -.15 -.02
Perceive Emotion3 .43** .23 -.32**
Understand Emotion4 -.06 .42** -.12 .45**
Manage Emotion5 -.25 .03 -.41** .39** .19
1 MLE operationalised as Leadership Effectiveness (LE); 2 MMS operationalised as Ego Development (ED) total item score (TIS); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5
MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
The regression analysis undertaken for the second subgroup suggested only three
of the independent variables were significantly related to perceptions of MLE
(R2= .45; F=8.34; p<.01). The standardised regression coefficients that were
obtained in this case can be seen in Table 5.17. For this subgroup of managerial-
leaders, IQ and MMS are not significant predictors. Perceive Emotion
(β= .73; p<.01) is the strongest positive indicator and Manage Emotion
(β= -.55; p<.01) is the strongest negative indicator. Understand Emotion
(β= -.34; p<.02) was also negatively related to perceptions of MLE.
Table 5.17 Regression for Leadership Effectiveness Group Two
Standardised Coefficient
Sig. Collinearity Statistics
Beta Tolerance VIF
IQ .07 .60 .82 1.22
MMS (TIS) -.18 .15 .80 1.25
Perceive Emotion .73 .00 .67 1.50
Understand Emotion -.34 .02 .69 1.45
Manage Emotion -.55 .00 .75 1.33
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5.8.3 H1 and H2 for Leadership Effectiveness Group Three
The results of the correlation analysis for the Leadership Effectiveness Group
Three (LE3) can be seen in Table 5.18.
Table 5.18 Correlations for Leadership Effectiveness Group Three
MLE1 IQ MMS2 (TIS)
Perceive Emotion3
Understand Emotion4
IQ .30*
MMS (TIS)2 .20 .10
Perceive Emotion3 .33 .00 .10
Understand Emotion4 -.55** .36* .19 .16
Manage Emotion5 -.36* .09 .03 .17 .21
1 MLE operationalised as Leadership Effectiveness (LE); 2 MMS operationalised as Ego Development (ED) total item score (TIS); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
For this group of managerial-leaders there was a significant positive relationship
between MLE and IQ (r= .30, p<.05) and a significant negative relationship
between MLE and both Understand Emotion (r= -.55, p<.01) and Manage Emotion
(r= -.36, p<.05). IQ was positively correlation with Understand Emotion
(r= .36, p<.05). For this group of managerial-leaders there were no significant
correlations with MMS or between the MSCEITv2 subscales.
The model summary of the regression analysis undertaken for the third subgroup
suggested all of the independent variables were significantly related to perceptions
of MLE (R2= .95; F=176.62; p<.01). The standardised regression coefficients
obtained in this case can be seen in Table 5.19.
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Table 5.19 Regression for Leadership Effectiveness Group Three
Standardised Coefficient
Sig. Collinearity Statistics
Beta Tolerance VIF
IQ .60 .00 .87 1.15
MMS (TIS) .25 .00 .96 1.04
Perceive Emotion .49 .00 .95 1.06
Understand Emotion -.82 .00 .81 1.24
Manage Emotion -.33 .00 .94 1.07
For this subgroup of managerial-leaders, the strongest positive predictor of MLE is
IQ (β= .60; p<.01) followed by Perceive Emotion (β= .49; p<.01) and then MMS
(β= .25; p<.01). Understand Emotion (β= -.82; p<.02) and Manage Emotion
(β= .33; p<.01) were both negatively related to perceptions of MLE.
5.8.4 H1 and H2 for Management Effectiveness Group One
The results of the correlation analysis for Management Effectiveness Group One
(ME1) can be seen in Table 5.20. There were significant relationships between all
three of the EI variables and MLE: Perceive Emotion (r= -.45, p<.01), Understand
Emotion (r= .51, p<.01) and Manage Emotion (r= .27, p<.01). There was a
significant positive relationship between MMS (TIS) and Perceive Emotion
(r= .38, p<.01). For this group of managerial-leaders there were no significant
correlations with IQ or between the MSCEITv2 subscales.
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Table 5.20 Correlations for Management Effectiveness Group One
MLE1 IQ MMS2 (TIS)
Perceive Emotion3
Understand Emotion4
IQ -.07
MMS (TIS)2 -.02 .07
Perceive Emotion3 -.45** .25 .38**
Understand Emotion4 .51** .25 .17 .04
Manage Emotion5 .27** .12 .11 .23 .23
1 MLE operationalised as Management Effectiveness (ME); 2 MMS operationalised as Ego Development (ED) total item score (TIS); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
The regression analysis undertaken for the first ME subgroup suggested four of the
independent variables were significantly related to perceptions of MLE
(R2= .67; F=22.87; p<.01). The standardised regression coefficients obtained in
this case can be seen in Table 5.21.
Table 5.21 Regression for Management Effectiveness Group One
Standardised Coefficient
Sig. Collinearity Statistics
Beta Tolerance VIF
IQ -.04 .62 .86 1.16
MMS (TIS) -.41 .00 .78 1.28
Perceive Emotion -.70 .00 .72 1.38
Understand Emotion .54 .00 .88 1.14
Manage Emotion .36 .00 .87 1.15
The independent variables significantly related to perceptions of MLE differed in the
direction and strength of these relationships. Both MMS (β= -.41; p<.01) and
Perceive Emotion (β= -.70 p<.01) were negatively related to perceptions of MLE. In
contrast, Understand Emotion (β= .54, p<.01) and Manage Emotion (β= .36, p<.01)
were positively related to perceptions of MLE. IQ was not significant for this
subgroup of managers.
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5.8.5 H1 and H2 for Management Effectiveness Group Two
The results of the correlation analysis for the Management Effectiveness Group
Two (ME2) can be seen in Table 5.22. For ME2 managerial-leaders, there was a
significant relationship between MLE and MMS (r= -.33, p<.05), Understand
Emotion (r= -.37, p<.01) and Manage Emotion (r= .45, p<.01). A significant positive
relationship existed between IQ and Understand Emotion (r= .42, p<.01). Perceive
Emotion was positively and significantly related to Understand Emotion
(r= .45, p<.01) and Management Emotion (r= .35, p<.05).
Table 5.22 Correlations for Management Effectiveness Group Two
MLE1 IQ MMS2 (TIS)
Perceive Emotion3
Understand Emotion4
IQ -.06
MMS (TIS)2 -.33* -.15
Perceive Emotion3 .27 .16 -.02
Understand Emotion4 -.37** .42** -.08 .45**
Manage Emotion5 .45** .14 .04 .35* .10
1 MLE operationalised as Management Effectiveness (ME); 2 MMS operationalised as Ego Development (ED) total item score (TIS); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
The regression analysis undertaken for the second ME subgroup suggested all the
independent variables were significantly related to perceptions of MLE
(R2= .90; F=91.04; p<.01). The standardised regression coefficients that were
obtained in this case can be seen in Table 5.23.
As was the case with the first group, while each of the independent variables was
significantly related to perceptions of MLE, for this subgroup, they differed in the
direction and strength of these relationships. MMS was again negatively related to
MLE perceptions in this subgroup (β= -.32, p<.01). In this subgroup, Understand
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Emotion (β= -.77, p<.01) and Manage Emotion (β= -.68, p<.01) were negatively
related to MLE, while Perceive Emotion was positively related to MLE
(β= .82, p<.01). This was opposite to the results that were obtained for subgroup
ME1, providing one reason for the lack of significant relationships in the overall
sample. Another difference for this subgroup was that IQ was significantly and
positively related to respondents’ perceptions of MLE (β= .19, p<.01).
Table 5.23 Regression for Management Effectiveness Group Two
Standardised Coefficient
Sig. Collinearity Statistics
Beta Tolerance VIF
IQ .19 .00 .79 1.26
MMS (TIS) -.32 .00 .97 1.03
Perceive Emotion .82 .00 .70 1.43
Understand Emotion -.77 .00 .67 1.50
Manage Emotion -.68 .00 .85 1.17
5.8.6 H1 and H2 for Management Effectiveness Group Three
The results of the correlation analysis for the Management Effectiveness Group
Three (ME3) can be seen in Table 5.24. For the ME3 group of managerial-leaders
there are only two significant relationships, which are both positive: between MLE
and both IQ (r= .38, p<.05) and Perceive Emotion (r= .58, p<.01).
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Figure 5.24 Correlations for Management Effectiveness Group Three
MLE1 IQ MMS2 (TIS)
Perceive Emotion3
Understand Emotion4
IQ .38*
MMS (TIS)2 -.21 .07
Perceive Emotion3 .58** .08 .18
Understand Emotion4 -.31 .30 .10 .10
Manage Emotion5 -.22 -.12 .28 .10 .01
1 MLE operationalised as Management Effectiveness (ME); 2 MMS operationalised as Ego Development (ED) total item score (TIS); 3 MSCEIT Branch 1 Perceive Emotion; 4 MSCEIT Branch 3 Understand Emotion; 5 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
The regression analysis undertaken for the third ME subgroup suggested all but
one of the independent variables were significantly related to perceptions of MLE
(R2= .76; F=22.63; p<.01). The standardised regression coefficients that were
obtained in this case can be seen in Table 5.25.
Table 5.25 Regression for Management Effectiveness Group Three
Standardised Coefficient
Sig. Collinearity Statistics
Beta Tolerance VIF
IQ .48 .00 .89 1.13
MMS (TIS) -.27 .01 .89 1.13
Perceive Emotion .65 .00 .96 1.05
Understand Emotion -.50 .00 .90 1.11
Manage Emotion -.14 .13 .90 1.11
For this group of managers, Perceive Emotion (β= .65, p<.01) and IQ
(β= .48, p<.01) had positive relationships with perceptions of MLE. Understand
Emotion (β= -.50, p<.01) and MMS (β= -.27, p<.01) were negatively related to MLE
perceptions. Manage Emotion was not significant.
In summary, the independent variables in different combinations accounted for
between .45 to .95 of the variance in respondents’ perceptions of MLE as
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measured by the revised Management Effectiveness and Leadership Effectiveness
scales. However, the different independent variables varied in the strength and the
direction of their relationship with both dependent variables across the sub-groups.
A discussion of the significant regression coefficients relevant to each of the sub-
groups with sufficient numbers to undertake the regression analysis is provided in
Chapter 6. However, before these results were interpreted in more detail, it was
considered important to see if there were background variables that differed across
the various groups. The analysis that was undertaken to see if this was the case
and the results obtained from this analysis are discussed in the next section.
5.9 SUBGROUP BACKGROUND DIFFERENCES
In order to examine the background differences between the groups in a
multivariate way, a discriminant analysis was undertaken in which the background
characteristics were included as potential group differentiators (Klecka 1980). In
addition to age, years of work experience and years of managing staff, a number of
nominally scaled characteristics were obtained from respondents, as can be seen
in Table 5.26. These nominal variables were converted into dummy (i.e. zero-one)
variables before being included in the discriminant analysis.
As two sets of groups had been obtained (a set of three groups for Leadership
Effectiveness and a set of three useable groups for Management Effectiveness), a
discriminant analysis was estimated for both the LE and ME contexts. The results
that were obtained in each analysis are discussed in turn.
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5.9.1 Background Differences in the LE Groups
As was also noted earlier in Section 5.6, three groups were found that differed in
the way MLE was predicted when measured with the revised Leadership
Effectiveness scale. The means of these three groups’ background variables can
be seen in Table 5.27, which also shows the F-statistics that were obtained from
the equivalent of a one-way ANOVA on each of these constructs (Soutar & Clarke
1981).
As can be seen in the Table, there were a number of statistically significant
differences between the groups. For example, while the mean scores for years of
work experience were all greater than 15, group two had the least such experience
(16 years), while group three had the greatest such experience (21 years).
Table 5.26 Nominally Scaled Background Variables
Gender Male
Female
Ethnicity Asian Caucasian Middle Eastern
Education All Other Undergrad/Bachelor’s degree Post-graduate/Master’s degree
Employment Status
Employed full-time Employed part-time Not currently employed
Manage Staff Yes No
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Consequently, it was seen as worthwhile to examine these background differences
in a multivariate way.
Table 5.27 Frequency (or Mean) of Background Variables for the LE Groups
Background Characteristic LE 1 n=53
LE 2 n=66
LE 3 n= 50
F- Statistic Sig.
Caucasian Ethnicity 51 55 46 3.00 .05
Currently Working 49 56 45 1.17 .31
Currently Managing Staff 42 41 39 3.25 .04
Education Level
All other 9 10 9 .09 .91
Bachelor Degree 21 20 20 .76 .47
Masters/PG 23 36 20 1.12 .33
Gender M
F
33
20
36
30
34
16
.86
-------
.42
-------
Age1 39.5 37.0 40.3 2.77 .06
Work Experience1 18.8 16.3 20.6 3.38 .03
Management Experience1 8.5 7.7 11.6 3.79 .03
1 Figures represent the group mean measured in years.
Two significant discriminant functions were found that the I2 statistic (Peterson &
Mahajan 1976) suggested explained eight per cent of the differences between the
three groups. Clearly, any multivariate differences are marginal and this needs to
be kept in mind. However, it was decided to examine these differences further to
see if they provided any useful insights.
Consequently, the structural correlations between the background characteristics
and the estimated discriminant functions were computed and varimax rotated to
better understand the groups’ differences (Clarke-Murphy & Soutar 2005). The
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resulting structural correlations, which are ordered in terms of the strength of the
relationships with the two discriminant functions, are shown in Table 5.28.
Table 5.28 Structural Correlations After Varimax Rotation
Background Characteristic Discriminant Function
1 2
Caucasian Ethnicity .77 .11
Managing Staff .63 .41
Currently Working .32 .24
Education: Bachelor Degree .03 -.001
Management Experience -.04 .98
Years Work Experience .09 .76
Age .03 .72
Education: All Others .08 .25
Gender: Male .13 .24
Education: Masters/Post-Graduate -.00 -.19
As can be seen in the Table, the first function was most related to whether the
leader had a Caucasian background and whether they managed staff. The second
function was most related to the leader’s years of management and work
experience and age. Consequently, the first function was termed Caucasian
Managers, while the second function was termed Experience.
The three groups’ positions in the space created by the estimated functions can be
shown by their centroid (or average) values (Johnson 1977). These values are
shown in Table 5.29. As can be seen in the Table, members of LE1 and LE2 were
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more likely to be Caucasian managers, while LE3 members were more likely to be
experienced managers.
Table 5.29 Group Centroid Values
Leadership Effectiveness Group Caucasian Managers Experience
1 .76 -.09
2 .64 .17
3 -.36 .96
5.9.2 Background Differences in the ME Groups
As was also noted earlier, four groups were found that differed in the way MLE was
predicted when measured with the revised Management Effectiveness scale.
However, only three groups had sufficient numbers to be used in subsequent
analysis. Consequently, only these three groups were included in the discriminant
analysis. The means of the three groups’ background variables can be seen in
Table 5.30, which also shows the F-statistics obtained from the equivalent of a one-
way ANOVA on each of these constructs.
As can be seen in the Table, there were no statistically significant differences
between the Management Effectiveness groups’ background variables.
Differences in the estimated regression coefficients of the independent variables for
each of the Management Effectiveness groups were not a result of differences in
these background variables.
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Table 5.30 Frequency (or Mean) of Background Variables for the ME Groups
Background Characteristic
ME 1 n=81
ME 2 n=46
ME 3 n= 31
F- Statistic Sig.
Caucasian Ethnicity 71 42 29 .50 .61
Currently Working 72 41 27 .16 .85
Managing Staff 56 35 24 .78 .46
Education Level
All other 15 9 4 .34 .71 Bachelor Degree 28 17 13 .23 .79
Masters/PG 38 19 14 .09 .91
Gender M
F
45 36
32 14
13 18 .99 .37
Age1 38.3 39.7 40 .68 .51
Work Experience1 18.0 19.8 18.9 .59 .55
Management Experience1 8.9 10.4 8.2 .81 .45
1 Figures represent the group mean and is measured in years.
In summary, the discriminant analyses suggested there were some marginal
differences in the Leadership Effectiveness groups’ backgrounds, as there were
significant differences in the groups’ ethnicity and work experience. LE1 and LE2
members were more likely to be Caucasian managers, while LE3 members were
more likely to be experienced managers. However, there were no differences in the
backgrounds of the Management Effectiveness groups.
5.10 SUMMARY
This chapter outlined the results of testing the study’s three research hypotheses.
In H1a it was posited that there was a positive relationship between the predictor
variables of IQ, EI and MMS. H1a was tested using correlational analysis and the
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results suggested the hypothesis should be rejected at a full sample level for this
data set. In H1b it was posited that there would be discriminant validity between the
IQ, EI and MMS constructs. H1b was also tested using correlational analysis and
results suggested there was support for this hypothesis.
Hierarchical regression analysis was then used to test H2a (IQ predicts MLE), H2b
(EI has incremental validity in predicting MLE, beyond IQ), and H2c (MMS has
incremental validity in predicting MLE, beyond IQ and EI). However, this analysis
found that none of the variables were significantly related to either measure of MLE
(i.e. ME or LE). It was thought this outcome may have been due to the presence of
subgroups in the sample and it was decided to undertake a post-hoc Latent Class
Regression Analysis to see if this was the case.
This analysis suggested subgroups did exist: three were suggested for the
dependent variable of LE and four were suggested for the dependent variable of
ME, although one of these groups was too small to examine further.
Consequently, differences between the subgroups were examined by testing H2a,
H2b and H2c for each of the subgroups. The results obtained from this analysis
found the independent variables accounted for between 45% and 95% of the
variance in respondents’ perceptions for the ME and LE subgroups. Further, the
independent constructs varied in the combination, strength and direction of their
relationships, with perceptions of ME and LE across the subgroups. In summary, at
a subgroup level, support was found for H2.
In order to see whether subgroups differed in their background characteristics
within the sample, a discriminant analysis was undertaken for both the LE and ME
subgroups. This analysis suggested some differences for the LE groups. Members
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of LE1 and LE2 were more likely to have a Caucasian background, while members
of LE3 tended to have more years of work experience. There were no statistically
significant differences between the ME groups in terms of the background
variables. In summary, the differences between the subgroups on the background
characteristics were marginal to non-existent and so can be ruled out as the source
of differences between the subgroups. In the next, and final, chapter, the results
from these analyses are discussed and conclusions drawn for the research as a
whole.
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Chapter 6
DISCUSSION
6.1 INTRODUCTION
The previous chapter presented the results of the analysis that examined the core
hypotheses that were outlined in Chapter 3. This chapter discusses these results
and provides a conclusion to the thesis. This discussion begins with a summary of
the thesis (section 6.2) and the inferences that were drawn from the analysis of the
data that were collected within the study (section 6.3). The chapter then explores
the study’s implications for practice (section 6.4) and for future research and theory
(section 6.5). Finally, some limitations of the research are outlined (section 6.6)
and the chapter is concluded (section 6.7).
6.2 A SUMMARY OF THE THESIS
The present study was undertaken to improve our understanding of the
relationships between emotional intelligence (EI), meaning-making structure
(MMS), intelligence (IQ) and managerial-leadership effectiveness (MLE). It has
been argued that today’s managerial-leaders need emotional abilities if they are to
successfully navigate the diverse and constantly changing context of modern
organisational life (Pfeiffer 2001; Price 2003; Quatro, Waldman & Galvin 2007;
Reams 2005; Zohar & Marshall 2000). Further, significant amounts of time and
money are being invested in leadership development initiatives that focus on
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developing managerial-leaders’ EI capacities. However, the role EI plays in MLE
continues to be poorly understood (Antonakis, Ashkanasy & Dasborough 2009;
Walter, Cole & Humphrey 2011). In addition, other aspects of personality, such as
capacity for meaning-making, may contribute to individual and leadership
effectiveness beyond EI (Rooke & Torbert 2005; Raelin 2006). Modern
organisational life is becoming increasingly complex and, consequently,
managerial-leaders who have a greater capacity to deal with the complexity of
current and emerging meaning-making structures will have an advantage in all
aspects of perception, cognition and processing (Caruso & Wolfe 2004; George
2000; Humphrey, Pollack & Hawver 2008; Young 2002b).
Despite this, very few empirical studies have examined the relationship between
ability EI and MLE (i.e. studies with valid samples of managerial-leaders and ability-
based measures of EI that have addressed issues of common method variance)
(Antonakis, Ashkanasy & Dasborough 2009; Walter, Cole & Humphrey 2011).
Further, to the researcher’s knowledge, no published studies have explored the
relationship between ability EI and MMS using a sample of practising managerial-
leaders, which made the present study an important one. Based on a review of the
literature, three hypotheses were suggested that were tested within the study,
namely:
H1: The individual difference constructs of EI, MMS and IQ are positively
correlated and have discriminant validity. More specifically:
H1a EI, MMS and IQ are positively correlated.
H1b EI, MMS and IQ have discriminant validity from each other.
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H2: The individual difference constructs of EI and MMS have incremental
predictive validity beyond IQ. More specifically:
H2a IQ is a significant predictor of MLE.
H2b EI is significant incremental predictor of MLE, beyond IQ.
H2c MMS is significant incremental predictor of MLE, beyond IQ and EI.
Based on a review of the literature, three existing measures were selected to
measure the independent variables included in the research model. Mayer,
Salovey, and Caruso’s (2002) Emotional Intelligence Test version 2 (MSCEITv2)
was used to measure ability Emotional Intelligence, the Washington University
Sentence Completion Test (WUSCT) (Hy and Loevinger, 1996) was used to
measure meaning-making structure and IQ was measured using the Wonderlic
Personality Test (WPT) (Wonderlic & Associates, 2002). The measure used to
operationalise the dependent MLE construct was the Integral Leadership and
Management Development Profile (ILMDP) (Cacioppe, 2000).
The study was made possible when access was made available to two
convenience samples of managerial-leaders who were undertaking leadership
development at the AIM-UWA Integral Leadership Centre. The first sample was
obtained from a group of 460 target managerial-leaders who had participated in a
leadership development program that included the ILMDP as a multi-source
feedback assessment. This data set, referred to throughout the thesis as ‘the
separate ILMDP sample’, included individual responses from each ‘other-rater’ on
the 32 items that make up the ILMDP. After removing respondents who had
missing data, a total of 364 cases were retained. The second sample, referred to
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as the main research sample, also included managerial-leaders who had
undertaken a leadership development program through the AIM-UWA Integral
Leadership Centre. This sample included part or whole responses from 259
managerial-leaders. However, after removing respondents with missing data, the
main sample included 169 complete cases.
The initial phase of data analysis examined the measurement properties of the
ILMDP, the internal consistency of the MSCEITv2 scales and the inter-rater
similarity for the WUSCT data. The separate sample of 364 target managerial-
leaders, who had three to twelve other-raters, was used to examine the
measurement properties of the ILMDP. The analysis of the ILMDP measurement
properties suggested the proposed eight-factor model was not a good fit to the data
in the present study. Consequently, an exploratory factor analysis was undertaken
that resulted in a two factor MLE model that included:
1. An 11-item Leadership Effectiveness (LE) factor and
2. A 12-item Management Effectiveness (ME) factor.
Further examination of these factors using confirmatory factor analysis and the
data in the main sample identified a one-factor congeneric model for each. The
one-factor congeneric models for the revised LE and the revised ME scales had
appropriate unidimensionality, reliability and validity. However, each scale had a
smaller number of items than had been the case at the beginning of the analysis.
To ensure there had been no loss in information in reducing the number of items,
correlations were computed between the revised scales and the initially suggested
scales (Thomas, Soutar & Ryan 2001). In this case, the correlation between the
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two leadership effectiveness scores was .96 and the correlation between the two
management effectiveness scores was .97, suggesting no information was lost in
ensuring that the two scales had appropriate measurement properties. The four-
item LE scale and the five-item ME scale had discriminant validity and,
consequently, were retained for use as dependent variables in the subsequent
analysis.
A subsection of the main research sample, consisting of 255 completed surveys,
was used to analyse the MSCEITv2 reliability. The reliability estimates for the
MSCEITv2 Scales ranged from an acceptable reliability of .88 for Perceive Emotion
through to an unacceptable reliability of .50 for Understanding Emotion. The
reliabilities for the Strategic EI area score and many of the task level scores were
also unacceptably low. However, a further analysis of the MSCEITv2 subscales
using confirmatory factor analysis failed to suggest a better alternate structure.
Consequently, the original MSCEITv2 branch scales were retained for use as the
ability-measure of EI and the reliability of the EI measures was noted as a limitation
to the present study.
Another subsection of the main research sample, consisting of 183 completed
protocols, was used to analyse the WUSCT inter-rater similarity. The inter-rater
similarity on the WUSCT data suggested there was a high level of reliability and
agreement in the scoring of the item-responses and the total protocol ratings and
that, consequently, they could be used in the subsequent analysis with confidence.
The main research sample, consisting of 169 LE and ME scores, the MSCEITv2
branch scores, the WUSCT and the WPT scores, was used to test H1, and H2,
which were not supported, despite the theoretical foundation that had led to their
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suggestion. These findings were inconsistent with previous research that had
supported the existence of a relationship between IQ and EI (Cote & Miners 2006;
Rode et al. 2007; Rosete & Ciarrochi 2005; Van Rooy, Viswesvaran & Pluta 2005)
between IQ and MMS (Hauser 1976; Loevinger 1976; McCrae & Costa 1980;
Westenberg & Block 1993) and between EI and MLE (Byron 2007; Kerr et al. 2006;
Rosete & Ciarrochi 2005).
One possible explanation for these results was thought to be unobserved
heterogeneity (Sarstead 2008). Consequently, the data were analysed using Latent
Class (LC) Regression Analysis, which can be used to determine the smallest
number of latent classes that are sufficient to explain the variance in a dependent
variable within each of the subgroups (Magidson & Vermunt 2002). In the current
research, the LC Regression Analysis results suggested there were three
subgroups that differed in the way the independent variables related to perceptions
of LE and four subgroups that differed in the way these same variables related to
perceptions of ME. However, the fourth ME subgroup was too small to be retained
for further analysis. The remaining six subgroups (three in the LE case and three in
the ME case) were used to re-examine H1 and H2 at a subgroup level.
6.3 DISCUSSION
6.3.1 The Relationships Between IQ, EI and MMS (H1)
The results of the correlation analysis found limited support for the hypothesised
relationships between EI, MMS and IQ. The only significant correlation at the
overall sample level was a positive relationship between IQ and Understand
Emotion which replicates results found by others (David 2005’, in Brackett, 2006,
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Lopes et al., 2003; Lam & Kirby 2002). In contrast MMS, which was measured by
as ED (TIS), was not significantly related to any of the other independent variables.
However, this was not the case at a subgroup level.
Using the LE subgroups identified through the Latent Class Regression Analysis, a
number of significant relationships were identified, as can be seen in Table 6.1.
For the LE groups, significant relationships existed between MLE and IQ (for
subgroup LE3), Perceive Emotion (for subgroups LE1 and LE2), Understand
Emotion (for subgroups L1 and L3) and Manage Emotion (for subgroups LE1 and
LE3). IQ was also significantly related to Understand Emotion for two subgroups
(for subgroups LE2 and LE3). Finally, MMS was significantly related to Perceive
Emotion for subgroup LE2 and to Manage Emotion for subgroup LE1 and LE2.
Interestingly, Understand Emotion and Manage Emotion were not significantly
correlated for any of the subgroups and none of the EI branch scores were
correlated for subgroup LE3.
Table 6.1 Summary of Correlations for the Leadership Effectiveness Subgroups
IQ MMS MLE Perceive Understand
IQ1
MMS2
MLE3 LE3: .30*
Perceive4 LE2: .32** LE1: -.36** LE2: .43**
Understand5 LE2: .42** LE1: .34** LE1: .24* LE3: .36* LE3: -.55** LE2: .45**
Manage5 LE1: .23* LE1: .34** LE1: .27* LE2: -.41** LE3: -.36* LE2: .39** 1 Traditional intelligence; 2 MMS operationalised as Ego Development total item score (TIS); 3 MLE operationalised as Leadership Effectiveness (LE); 4 MSCEIT Branch 1 Perceive Emotion; 5 MSCEIT Branch 3 Understanding Emotion; 6MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
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Similarly, using the ME subgroups identified through the Latent Class Regression
Analysis, a number of significant relationships were identified, as can be seen in
Table 6.2. Significant positive correlations existed between MLE and IQ (for
subgroups ME1 and ME3), MLE and MMS (for subgroup ME2), MLE and Perceive
Emotion (for subgroup ME3), MLE and both Understand Emotion and Manage
Emotion (for subgroup ME2). MMS was significantly correlation with Perceive
Emotion for subgroup ME1. Significant correlations also existed between IQ and
Understand Emotion (ME1 and ME2) and Manage Emotion (ME1). Once again,
Understand Emotion and Manage Emotion were not significantly correlated for any
of the subgroups. Finally, none of the EI branch scores were correlated for ME1 or
ME3.
Table 6.2 Summary of Correlations for the Management Effectiveness Subgroups
IQ MMS MLE Perceive Understand
IQ1
MMS2
MLE3 ME1: -.45** ME2: -.33*
ME3: .38*
Perceive4 ME1: .38** ME3: .58**
Understand5 ME1: .51** ME2: -.37** ME2: .45** ME2: .42**
Manage6 ME1: .27** ME2: .45** ME2: .35* 1 Traditional intelligence; 2 MMS operationalised as Ego Development total item score (TIS); 3 MLE operationalised as Management Effectiveness (ME); 4 MSCEIT Branch 1 Perceive Emotion; 5 MSCEIT Branch 3 Understanding Emotion; 6 MSCEIT Branch 4 Manage Emotion; **p<.01; *p<.05
In summary, a correlation analysis of the research constructs at a subgroup level
suggested there was partial support for H1a. The constructs of IQ, EI and MMS
were sometimes moderately correlated. However, the strength and direction of
these relationships varied across the subgroups. Further, the pattern and strength
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of correlations suggest the constructs of IQ, EI, MMS and MLE are distinct and
thus, provide support for H1b.
6.3.2 Incremental Predictive Validity of MMS (H2)
The results of the regression analysis provided partial support for the hypothesis
that MMS, operationalised as ED, had incremental predictive validity beyond IQ
and EI in predicting perceptions of MLE. While the regression at an overall sample
level did not suggest any significant relationships, at a subgroup level there were
differences between the predictor variables in the model and their patterns of
relationships with the dependent variables, as can be seen in Table 6.3. However,
before discussing the results at the construct and subgroup level, larger scale
patterns that emerged are described.
The first pattern to emerge was the positive and negative polarity of the regression
coefficients. With the exception of IQ, the explanatory variables had both positive
and negative instances of polarity in different subgroups. It is interesting that some
of the relationships were negative. At least three explanations can be offered,
namely:
1. The ability constructs (IQ, EI or MMS) may not be effective in all managerial
contexts.
2. The skill may not be done well, although the use of ability measures should
mitigate this issue
3. An ability may be over-used by a manager.
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Table 6.3 Summary of Standardised Regression Coefficients for all Subgroups
Leadership Effectiveness Management Effectiveness
Construct Group 1 Group 2 Group 3 Group 1 Group 2 Group 3
IQ .24** --- .60** --- .19** .48**
MMS -.32** --- .25** -.41** -.32** -.27**
Perceive Emotion -.70** .73** .49** -.70** .82** .65**
Understand Emotion .45** -.34** -.82** .54** -.77** -.50**
Manage Emotion .58** -.55** -.33** .36** -.68** --- **p<.01; *p<.05
There was a pattern to the polarity between the first and second strongest predictor
variables. If the strongest predictor variable was positive, the next strongest
predictor variable was negative, and visa versa. Two additional patterns regarding
polarity of the independent predictor variables were evident, as:
1. The Perceive Emotion and Understand Emotion coefficients had consistently
opposite polarities.
2. The Understand Emotion and Manage Emotion coefficients had consistently
the same polarity.
Theoretically, it is understandable that the Understand Emotion and Manage
Emotion abilities vary similarly as they combine to form the MSCEITv2 Strategic
Reasoning Area of emotional Intelligence (Mayer, Salovey & Caruso 2002).
However, why these abilities are opposite to Perceive Emotion is not clear. What is
clear is that when a relationship is positive, the independent variable contributes to
effectiveness and when it is negative, it detracts from effectiveness. We first look at
the LC Regression Analysis results through the lens of the independent predictor
variables, including the significance and polarity of each.
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IQ was significant and positive for four groups of managerial-leaders
(β= .19 to .60, p<.01). It was the only construct to be consistent in the direction of
the relationship with MLE. The interpretation is that IQ is either not a significant
predictor or is a positive predictor of MLE. These results support both sides of the
IQ versus EI debate, as IQ was a significant antecedent of perceived effectiveness
in some, but not all, of the subgroups. IQ is important in some cases, but it is not
the whole story (Kellet, Humphrey & Sleeth 2006; Kellet, Humphrey & Sleeth 2002;
Riggio 2002).
MMS was significant for five of the subgroups, being positive for one and negative
for four. This suggests that, for many managerial-leaders, an increasing capacity to
think and act with more complexity detracts from their perceived effectiveness. At
an absolute level, the strength of the significant standardised betas was relatively
consistent, varying from |.25| to |.41|. One interpretation of the varied direction in
the relationship between MMS and MLE is the ‘match’ between the managerial-
leaders capacity for meaning-making and the context in which they operate. As
such, where MMS negatively predicts MLE, there may be a mismatch between the
target managerial-leader’s current capacity for meaning-making and the
requirement of their context. There is empirical evidence, albeit limited, to support
the need for such a match between the MMS and a managerial-leader’s context
(Rooke & Torbet 1998). A lack of a significant relationship between MMS and MLE
might indicate a neutral effect of the capacity for meaning-making in the current
context. Regardless of whether it was in synch or not with the context it was not
relevant. Finally, when the relationship was positive, the managerial-leader was
able to leverage their current MMS into MLE.
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At a conceptual level, it was not expected that some of the EI abilities would have
negative relationships with perceptions of MLE. The obtained results contribute to
the ongoing debate about the existence and nature of the relationship between EI
and managerial-leadership. Much of the literature exploring the link between EI and
MLE has assumed a positive relationship (George 2000; Lam & Kirby 2002; Rosete
& Ciarrochi 2005). This belief continues to hold despite the mixed empirical
evidence. However, it may be the case that the direction of the relationship is both
ability and context specific (Karim & Weisz 2010; Palmer et al. 2008). Alternative
explanations for inconsistent results regarding the EI and MLE relationship include
compensatory effects of IQ or other personality constructs. As was discussed in
Chapter 2, Cote and Miners (2006, p. 2) suggested ‘emotional intelligence should
predict job performance only some of the time, depending on the other ability that
individuals possess.’ This is explored further in sections 6.3.3 and 6.3.4. However,
the patterns of relationships that emerged in the results are described first.
Perceive Emotion and Understand Emotion were significantly related to the
dependent variable for all of the subgroups. Further, Perceive Emotion was the
strongest absolute predictor in five of the six subgroups. At a theoretical level, the
Perceive Emotion results are consistent with the foundational nature of this EI
ability (Joseph & Newman 2010; Mayer, Roberts & Barsade 2008) and replicate
results obtained in other studies (e.g. Rosete and Ciarrochi, 2005). At an
operational level, this may result from a higher internal validity for this subscale that
was found in both the present study and within the broader range of studies that
have used the MSCEITv2 measure (Maul 2011; Karim & Weisz 2010; Mayer et al.
2003; Palmer et al. 2005). For two of the groups, the ability to Perceive Emotion
was negatively related to people’s perceptions of effectiveness while, for the
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remaining groups the relationship was positive. When Perceive Emotion was a
positive predictor, the implication was managerial-leaders were perceived as more
effective when they had a greater ability to read the emotional landscape.
Conversely, when Perceive Emotion was a negative predictor, it suggested
managerial leaders were perceived as less effective when they had a greater ability
to read the emotional landscape. Job requirements may expose them to emotional
information that is best disregarded in order to be effective. This is one of many
possible explanations and further research is required to gain a better
understanding of this issue.
Understand Emotion was the strongest predictor for one subgroup and the second
strongest predictor for three of the remaining five subgroups. When Understand
Emotion was a positive predictor, the implication was managerial-leaders were
perceived as more effective when they had a great knowledge of the stimuli for
different emotional reactions, the relationship between emotions and how they can
combine. Conversely, when Understand Emotion was a negative predictor, it may
be the case that this ability is not effective, the ability is not effectively used and/or
the skill is over-used by the manager in their work context.
The ability to Manage Emotion was significant for five subgroups, positive for two
and negative for three. As already mentioned, the polarity of Manage Emotion
consistently mirrored that of Understand Emotion. This is evidence of the
theoretical assumption that the two abilities work in tandem as the two aspects of
Strategic EI (Mayer & Salovey 1997). Manage Emotion refers to a person’s ability
to regulate their own emotions, and the emotions of others, towards growth and
understanding. When the relationship with MLE is negative, the suggestion is that
an ability to Manage Emotion detracted from MLE. Again, it may be the case that
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the ability to Manage Emotion is not effective, the ability is not effectively used
and/or the skill is over-used by the manager in their work context.
It is interesting that the direction of the coefficients for these two abilities was
consistently opposite to that of Perceive Emotion. It may be the case that an
increased ability to Perceive Emotion may lead to managerial-leaders being
overwhelmed with emotional information and, thus, being less effective at
Understanding Emotion or Managing Emotion. For most of the groups the
coefficients were negative. Again, a negative relationship suggested these two
abilities detracted from the managerial-leader being perceived as effective within
their work environment.
In summary, each of the independent variables had a range of significant
relationships when predicting perceptions of Management Effectiveness and
Leadership Effectiveness. The results of the LC Regression Analyses at a
subgroup level revealed partial support for H2a, H2b, and H2c. The IQ, EI and MMS
constructs were incrementally significant in predicting MLE at the subgroup level.
However, the strength and direction of these relationships varied. Consequently,
the unique patterns of the relationships within the different groups identified were
explored to obtain further insight.
6.3.3 The Three Leadership Effectiveness Groups
The results of the LC Regression Analysis suggested the existence of three
statistically different groups of managerial-leaders that differed in terms of the ways
EI, MMS and IQ acted as antecedents to perceptions of MLE. Based on the
pattern of relationships with the independent variables, these groups were renamed
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Manage the Emotional Landscape Leaders, Read the Emotional Landscape
Leaders, and Intelligent Ego Leaders.
6.3.3.1 Manage the Emotional Landscape Leaders
The perceived effectiveness of the Manage the Emotional Landscape Leaders was
a function of all the independent predictor variables. However, for this group of
managerial-leaders the strongest positive predictor of perceptions of MLE was their
ability to Manage Emotion and so the label Manage the Emotional Landscape
Leaders was used. The next strongest relationship for Manage the Emotional
Landscape Leaders was Perceive Emotion, which was a negative predictor. For
this subgroup, IQ and the ability to Understand Emotion were weak to moderate
positive predictors of MLE. In contrast, increases in MMS detracted from being
seen as effective in their environment.
Manage the Emotional Landscape Leaders were rated as more effective when they
engaged their ability to regulate emotions, their own emotions and the emotions of
others, to facilitate personal understanding and growth. However, their ability to
accurately observe the emotional content in this landscape detracted from their
effectiveness. This was not due to mean differences in ability between the groups
for any of the independent variables. Something else created a negative
relationship between their ability to Perceive Emotion and how effective they are
perceived to be. One possible explanation could be the context for Manage the
Emotional Landscape Leaders. It may be rife with emotional information that is
considered superfluous, requiring them to disregard such information in order to be
effective. Regardless, based on the results of this research, the Manage the
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Emotional Landscape Leaders subgroup would be best advised to downplay their
ability to Perceive Emotion and exercise their ability to Manage Emotion.
Manage the Emotional Landscape Leaders would benefit from understanding how
their worldview (MMS) could better leverage perceptions of their MLE. For some
reason, the MMS level of Manage the Emotional Landscape Leaders was
negatively related to ratings of MLE. We do not have enough information to identify
the nature of the mismatch between the work context and the managerial-leader’s
worldview. However, given the negative relationship, we could infer the managerial-
leader’s worldview is not leveraging their effectiveness. It may be the worldview is
either more, or less, complex than what is required in their work context. This
could be either a positive or negative experience for them.
A more complex worldview would facilitate greater awareness and complexity in
thinking, which would include an increased capacity to Perceive Emotion and other
subtle dynamics (Young 2002b). In contexts where this is valued, an individual
managerial-leader would flourish. However, in contexts where this is not supported,
where the managerial-leader is not empowered or appreciated, or where the
preference is to view things simplistically, an increased capacity for complexity
could be seen as a negative attribute. Alternatively, the managerial-leader may
have a worldview that is less complex than their work context. This too could be
experienced as a challenge or a threat depending on the level of support for the
managerial-leader. Finally, the managerial-leader may be aligned with their work
context, but their ‘other-raters’ may not have a matching worldview and/or things
may be rapidly changing. Regardless of the root cause, the level of MMS is
detracting from the managerial-leader’s perceived MLE.
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6.3.3.2 Read the Emotional Landscape Leaders
For the Read the Emotional Landscape Leaders, perceptions of MLE were most
strongly, and positively, predicted by their ability to Perceive Emotion. This ability
involves ‘paying attention to and accurately decoding emotional signals in facial
expressions, tone of voice and artistic expressions’ (Mayer, Salovey & Caruso
2002, p. 19). An increasing ability to Manage Emotion was the next strongest, but
negative, predictor. These were opposite results than for the Manage Emotion
Leaders subgroup which was described in the previous section.
This group of managerial-leaders was seen as more effective when they had a
greater ability to pick up on the emotional cues around them, but less effective
when they had a greater ability to manage emotions. As was noted in Chapter 5,
this is not due to mean differences in EI abilities between the groups. Rather,
something in these managerial-leaders’ contexts contributed to the negative
relationship between their ability to Manage Emotion and perceptions of MLE
(Salovey & Grewal 2005).
6.3.3.3 Intelligent Ego Leaders
The strongest positive predictor of MLE for Intelligent Ego Leaders was IQ. In
addition, this was the only group of managerial-leaders for whom MMS positively
predicted perceptions of Leadership Effectiveness. The MLE of Intelligent Ego
Leaders was positively related to IQ, MMS and the ability to Perceive Emotion.
Increasing levels of IQ facilitate speed of cognitive processing. Increasing levels of
MMS facilitate a person’s ability to think with more complexity and increasing levels
of the ability to Perceive Emotion enable more information about the emotional
environment to be considered. It is also possible IQ, MMS and Perceive Emotion
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mutually reinforce each other (Cote & Miners 2006). This combination of
antecedents suggests the context in which Intelligent Ego Leaders operate requires
them to have a broader cognitive skill set to be perceived as effective.
As already discussed, we do not know whether Intelligent Ego Leaders’ personal
MMS are in sync or out of step with their context. Rooke and Torbert’s (1998)
research into CEOs and organisational transformation would suggest it is the
former. Regardless, the positive relationship between MMS and MLE suggests
Intelligent Ego Leaders are coping in their environment and can leverage
differences between their capacity and their environment. It may be the case that
the greater importance of IQ facilitates this process. The abilities to Understand
Emotion and Manage Emotion detracted from the perceived effectiveness of
Intelligent Ego Leaders, which also contrasts with the Manage Emotion Leaders.
Despite differences in the strength and direction of the relationships with the
predictor variables, the similarity in the mean MLE ratings between the statistically
distinct Intelligent Ego Leaders and Read the Emotional Landscape Leaders
groups supports the view that there are multiple paths towards excellence as a
leader (Quinn et al. 1996).
6.3.4 The Three Management Effectiveness Groups
The results of the LC Regression Analysis suggested the existence of three
statistically different groups of managerial-leaders that differed in terms of the way
EI, MMS and IQ act as antecedents of perceptions of MLE. Based on the pattern
of relationships with the independent variables, these groups were renamed
Strategise the Emotional Landscape Managers, Experience Emotion Managers
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and I See But I Don’t Understand Managers. Each of these groups is discussed in
turn.
6.3.4.1 Strategise the Emotional Environment Managers
The strongest positive predictor of MLE for Strategise the Emotional Environment
Managers was their ability to Understand Emotion. In addition, Manage Emotion
was a positive predictor. These two abilities form the Strategic Reasoning area of
the ability model of EI (Mayer, Salovey & Caruso 2002). Perceive Emotion and
MMS were negative predictors of MLE. The antecedent profile for Strategise the
Emotional Environment Managers was similar to that of the Manage Emotion
Leaders with a few key exceptions. For the Strategise the Emotional Environment
Managers IQ was not a significant predictor, Understand Emotion was a slightly
stronger predictor and Manage Emotion was a slightly weaker predictor. Based on
these results, Strategise the Emotional Environment Managers would do well to
use their ability to Understand Emotion and Manage Emotions while downplaying
their ability to Perceive Emotion. More abstractly, using ability-EI theory (Mayer,
Salovey & Caruso 2002), Strategise the Emotional Environment Managers should
emphasise their strategic reasoning with and minimise their experiential use of
emotion (more thinking less feeling).
6.3.4.2 Intelligent Perception Managers
For the Intelligent Perception Managers, all of the hypothesised antecedents were
significant predictors of perceptions of MLE. Perceive Emotion and IQ were the
only positive predictors of MLE with the ability to Perceive Emotion being the
strongest predictor overall. Exactly opposite of the previous Strategise the
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Emotional Landscape Managers subgroup, this subgroup should do less thinking
and use their IQ to do more feeling.
These managerial-leaders were rated as being more effective when they had a
greater ability to pick up on the emotional cues around them. Understand Emotion
and Manage Emotion, which again form the Strategic Reasoning area of EI (Mayer
et al., 2002), were both significant and negatively related to perceptions of
effectiveness, suggesting increasing levels of MMS detracted from the Intelligent
Perception Managers’ rated effectiveness.
Again, the similarity in the mean ratings of effectiveness between Strategise the
Emotional Environment Managers and Intelligent Perception Managers despite the
very different profiles of the importance of the predictor variables, supported the
view that there are multiple paths towards excellence as a manager.
6.3.4.3 I See It But I Don’t Understand It Managers
For the I See It But I Don’t Understand It Managers, increases in perceptions of
MLE were strongly predicted by increases in IQ and the ability to Perceive Emotion.
However, the ability to Understand Emotion detracted from perceptions of
Management Effectiveness. The pattern of antecedents for this group was similar
to that of the statistically different Intelligent Perception Managers group, with the
exception that for this group IQ was a much stronger predictor, Perceive Emotion
and Understand Emotion were slightly weaker and Manage Emotion was not
significant. Once again, MMS was a negative predictor of MLE. This group of
managerial-leaders was on average perceived as less effective than the other two
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groups, supporting the view that IQ is important but may be more of a threshold
ability. MLE also requires emotional and meaning-making abilities.
6.4 IMPLICATIONS FOR PRACTICE
As is mentioned in the subsequent limitations section, this study was conducted
using a convenience sample drawn from a pool of managerial-leaders who had
participated in leadership development programs through the AIM–UWA Integral
Leadership Centre in Western Australia. As such, the results and implications of
this study are not generalisable beyond this population. However, results from this
study do have implications for practice, including providing:
1. Support for focussing on multiple abilities in leadership development,
including, in varying degrees, IQ, EI and MMS.
2. Evidence of the relevance and potential untapped resource of MMS for MLE.
3. Additional information about the reliability of the MSCEITv2, and supporting
evidence of the importance of ensuring fit for purpose when using the
MSCEITv2.
4. Additional information about the psychometric properties of the ILMDP for
HRM practitioners using this measure of MLE.
First and foremost, support for H1 and H2 at the subgroup level provides support for
the importance of individual differences beyond IQ, such as EI and MMS, in
understanding MLE. Further, the existence of subgroups, with different patterns
between these constructs and MLE, suggests the importance understanding the
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context within which individual managerial-leaders operate (Zaccaro 2007). This is
particularly relevant for leadership selection and development. We know that
without consideration of the match between skills and context requirements, some
managerial-leaders find it hard to succeed. In addition, the context in which
managerial-leaders operate may preclude an opportunity to apply new skills and
abilities that are important to leadership development. In contrast, recognising the
existence of different groups of managerial-leaders can help in the design of more
effective placement and leadership development programs.
A second implication worth further consideration by practitioners concerns the
relationship between MMS and perceptions of MLE. The findings in this study
suggest MMS varies in its relationship with MLE. It can be negatively related,
positively related or not significant at all. In this study, MMS was more commonly
negatively related to perceptions of MLE. The implication is that simplicity in
thinking and acting is valued. Whether or not this is productive is another question.
It may be the case that there is an untapped potential within managerial-leaders
MMS. However, further research is required to ascertain this. HRM practitioners
are uniquely placed to identify and diagnose situations in which managerial-leaders
require more support to leverage their MMS. It may be the case that ‘difficult’
employees represent a potential contribution in terms of their meaning-making
capacity.
Another implication from this study is that the results lend support to an ongoing
concern about the commercial use of the MSCEITv2. As was discussed in Chapter
1 and 2, the EI construct has enjoyed widespread popularity with individuals and
organisations in recent years, resulting in a demand for usable EI measures. The
MSCEITv2 is widely seen as the most scientifically valid measure available. HR
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practitioners have chosen to use the MSCEITv2, believing it is the gold standard in
measuring EI. However, this study contributes to a growing body of research that
suggests the limitations of the MSCEITv2 may outweigh its benefits. First, the
MSCEITv2 reliabilities appear to be lower than reported by the test’s authors.
Second, the inter-correlations of the four abilities measured by the MSCEITv2 vary
and contribute differently to perceived effectiveness, depending on the specific
context of the target managerial-leader. There are situations in which the
MSCEITv2 will be the best fit for the leadership development purpose, including
when it is used as part of a battery of tests. However, given the cost of the
MSCEITv2 and mixed support for its reliability, it may be prudent for HR
practitioners to choose additional or alternative ways of measuring EI until some of
these problems are overcome.
Finally, this study contributes to our understanding of the measurement properties
of the ILMDP. It would be possible to improve the measurement properties of the
ILMDP by revising the items and the scale overall. The revised ME and LE scales
identified in this study provide a good start for the latter. However, practitioners
might argue for a full array items and feel the ILMDP as a circumplex model that
includes the multiple roles of managerial-leadership provides a useful map for
teaching behaviourally complex MLE.
6.5 IMPLICATIONS FOR THEORY AND FUTURE RESEARCH
This research also has implications for theory and future research. In terms of
theory, the study’s results have implications for:
1. Theories of MLE based on individual-differences.
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2. The ability-based theory of EI.
3. The application of constructive developmental theory to understanding MLE.
4. The use of the ILMDP.
This research contributes support to the important role that individual differences
play in predicting MLE. The study contributes to the suggestion that multivariate
antecedents and the context within which managerial-leaders operate are important
in building an appropriate MLE theory (Zaccaro 2007). Both EI and MMS were
antecedents to MLE, but their significance, relative strength and the direction of
their relationship to MLE differed for broad groups of managerial-leaders. Again,
the importance of context to the relationship between IQ, EI and MMS with MLE is
stressed (Karim & Weisz 2010; Parker et al. 2005).
This research also has implications for EI theory. Specifically, the results from this
research support the increasing call for a better understanding of ability-based EI.
There appears to be an uncritical acceptance of a positive relationship between
overall EI and MLE. However, it is clear EI abilities ‘cannot exist outside of the
social context in which they operate’ (Salovey & Grewal 2005, pg 282). Initial
results from this research replicated some other studies, as none of the MSCEIT
scores were significantly related to perceptions of MLE (Weinberger 2009; Byron
2007). Further, it is important to examine EI at a branch level given the individual
and changing contributions each branch makes in different managerial-leader
contexts. In addition to theoretical considerations, there are a number of
implications for future EI research. First, future researchers interested in exploring
relationships between EI and other constructs should be vigilant in choosing their
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measure of EI and heed concerns about the MSCEITv2 (Antonakis, Ashkanasy &
Dasborough 2009; Fiori & Antonakis 2011; Maul 2011). This is a critical issue in
the field as noted by Fiori and Antonakis (2011): ‘a practical problem that
researcher willing to study EI as an ability are confronted with is that there are
basically no alternative to the MSCEIT’ (pg 333). The MSCEITv2 has contributed
significantly to our ability to explore and understand the construct of EI. However, if
future research is to advance the field, a measure with better measurement
properties is required. Future research should:
• Use alternate measures of ability-based EI.
• Rely on the Perceive Emotion branch of the MSCEITv2 as a measure of
ability-based EI, given the higher reliabilities for this scale.
• Explore alternate scoring methodologies (i.e. optimal scoring) for the existing
MSCEITv2 (Keele & Bell 2009).
• Design new ability-based measures of EI taking advantage of the knowledge
we have gained from the use of the MSCEITv2 over the past eight years.
This research has implications for furthering the application of constructive
developmental theory to MLE. Differential results for the relationship between
MMS and MLE, despite no mean differences on the independent variables between
the groups, challenges the assumption argued in the literature that higher levels of
MMS will translate into MLE. Instead, it seems this relationship is more complex
and depends on other aspects, such as other individual differences and the
requisite skills for particular work contexts. This result is more in line with the
228
theoretical argument that each level of MMS has contributions and limitations
(Drath 1990). As such, it is important for theory to explore both the context in
which level of MMS operates most effectively and the support structures (i.e.
coaching, job-design, leadership-development) that leverage any differences
between MMS and the required context.
Further qualitative exploration of the impact MMS level has on MLE, particularly
with managerial-leaders at the higher stages of MMS, would improve our
understanding of the contextual factors that contribute to this becoming a negative
relationship. Why and when does MMS detract from perceptions of MLE? Is it that
complex thinking is not valued, or does complex thinking require mediation with
other abilities or skills? Alternatively, what is the relationship between EI and
MMS? What does EI look like at different levels of MMS? Unfortunately, the
WUSCT is such a labour intensive instrument that it is difficult to use with large
samples.
Finally, the examination of the measurement properties of the ILMDP suggested
many of the roles did not have discriminant validity. Consequently, researchers
interested in exploring MLE from a behavioural complexity perspective need to be
aware that the ILMDP may not have acceptable measurement properties in its
current form. Further research is needed to create an underlying structure that
reflects the suggested circumplex, as well as refinement of some of the items that
may be ambiguous.
229
6.6 LIMITATIONS TO THE PRESENT STUDY
The present study had a number of limitations. The scale that was used to
measure managerial-leadership effectiveness was both a contribution and a
limitation. The decision to use the Integral Leadership and Management
Development Profile (ILMDP) as a measure of managerial-leadership effectiveness
was made because of the availability of a sample of managerial-leaders who had
used the ILMDP. However, the ILMDP’s measurement properties have received
limited academic attention, which represented both an opportunity and a risk. The
specific limitations of the ILMDP include the double-barrelled nature of some of the
items and the use of an uneven 10-point bi-polar scale. The opportunity was to
contribute to the discussion of EI and managerial-leadership beyond the Multi-
Factor Leadership Questionnaire (Avolio, Bass & Jung 1995). The limited use of
alternate MLE measures is recognised as a limitation in the EI research (Walter,
Cole & Humphrey 2011). Further, the use of other-average scores from multiple-
source feedback alleviated issues of common method variance.
The use of a convenience sample was also a limitation. While the sample included
a variety of managerial-leaders at different levels and from different organisations,
participants were approached after completing the multiple-source feedback and so
there may be aspects of self-selection at play.
The sample size at the whole sample level was more than adequate to test the
hypotheses. However, when the analysis shifted to the subgroup level, the
subgroup sizes ranged from 35 to 80, which was a limiting factor.
Another potential limitation was the length of the data collection process itself, as
this may have contributed to cognitive fatigue (Rode et al. 2008). Most participants
230
completed all of the measures in one setting that lasted approximately two hours.
Breaks were built into the process to mitigate this concern, but it may have led to a
lack of motivation in some cases.
The lack of a personality measure, such as the Big Five, could also be perceived
as a limitation to the current study. Because the primary interest was in MMS and
EI and the length of time required to complete these measures, it was decided not
to include additional constructs. However, the inclusion of a personality measure
may have provided additional information.
The measure used to operationalise emotional intelligence (the Mayer-Salovey-
Caruso-Emotional-Intelligence-Test version 2 (MSCEITv2)) (Mayer, Salovey &
Caruso 2002) was also a limitation and may have impacted on the study’s results.
The MSCEITv2 is not without controversy (as was discussed in detail in Chapter 2).
At the time the data were collected, the MSCEITv2 was seen as the best measure
of ability EI available. It is still widely used in research and practice, although this
research contributes to the growing debate about the measure. However, the
reliabilities of the MSCEITv2 subscales in the present study were not as strong as
had been hoped. The difference in the administration methods of the MSCEITv2
was noted (i.e. online versus paper-and-pencil), but this was not considered to be a
limitation as the two methods have been shown to be virtually identical (Mayer et
al. 2003).
A limitation for much of the research on meaning-making structure is that samples
often have a restricted range of development orders (McCauley et al. 2006). In
order to manage this issue a sample size of more than 150 was targeted.
However, the range of MMS levels was still restricted and may have contributed to
231
the present results. Finally, the cross-sectional design of this study is a limitation
(Locke 2005), as it limited the interpretation of causality in the identified
relationships.
6.7 CONCLUSIONS
The growing importance of emotions and meaning-making within organisational life
has meant EI and MMS are seen as increasingly relevant to understanding
managerial-leadership effectiveness. The present study provides some insights
into the relationship between EI, MMS, IQ and followers’ perceptions of MLE. The
present results suggest the relationships between these constructs are not
universal phenomena. Each construct is relevant. However, a managerial-leaders’
work context impacts on the relative importance these antecedents have in
predicting people’s perceptions of MLE. This is also true for the abilities of EI
(Perceive Emotion, Understand Emotion and Manage Emotion). The existence of
groups of managerial-leaders with similarities in how EI, MMS and IQ contribute to
MLE is consistent with the reality underpinning the theory of behaviourally complex
MLE. There is great importance in recognising the uniqueness of an individual
managerial-leader’s situation and the existence of similar groups of managerial-
leaders. Finally, the results of the present research support concerns about the
MSCEITv2’s effectiveness in measuring EI.
Both the scientific and the practitioner communities continue to grapple with the EI
construct, its relationship to meaning-making and, ultimately, its impact on MLE. It
is hoped the present study has contributed to the empirical evidence of this
important issue and suggested the need for more research into heterogeneity as
this seems to be crucial to our understanding of these relationships.
232
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APPENDICES
Appendix A Studies Exploring Ability-EI and MLE
Appendix B Wonderlic Personnel Test Norms
Appendix C Information Sheet for Participants
Appendix D Invitation and Faxback Form
Appendix E Informed Consent Form
Appendix F Survey
Appendix G Central Tendency and Dispersion of rWG_un for 32 ILMDP Items
Appendix H Central Tendency and Dispersion of rWG_skew for 32 ILMDP Items
Appendix I Central Tendency and Dispersion of rWG_Mskew for 32 ILMDP Items
Appendix J Central Tendency and Dispersion of rWG_UN for the ILMDP Scales: 8 role scales, 2 function scales and a 32-item scale.
Appendix K Central Tendency and Dispersion of rWG_skew for the ILMDP Scales: 8 role scales, 2 function scales and a 32-item scale.
Appendix L Central Tendency and Dispersion of rWG_Mskew for the ILMDP Scales: 8 role scales, 2 function scales and a 32-item scale.
Appendix M Exploratory Factor Analysis of the MSCEITv2
Appendix N IRA on WUSCT Items
Appendix O Central Tendency and Dispersion of rwg_UN for Items in Revised LE Scale
Appendix P Central Tendency and Dispersion of rwg_skew for Items in Revised LE Scale
Appendix Q Central Tendency and Dispersion of rwg_Mskew for Items in Revised LE Scale
Appendix R Central Tendency and Dispersion of rwg_UN for Items in Revised ME Scale
Appendix S Central Tendency and Dispersion of rwg_skew for Items in Revised ME Scale
Appendix T Central Tendency and Dispersion of rwg_Mskew for Items in Revised ME Scale
260
Studies Exploring Ability-EI and MLE
Study EI Measure
Sample Measure of Leadership Results of testing EI and MLE relationship
Collins (2001)
MSCEIT N= 91 Managers
Customised 360o feedback Relationship not supported
Leban & Zulauf (2004)
MSCEIT N=24
Project Managers
Transformational leadership (MLQ) other-raters average scores
Partial support
Rubin et al (2005)
DANVA
N=177 Transformational leadership style (MLQ) subordinate scores
Partial support
Rosete & Ciarriochi (2005)
MSCEIT N=41
Managers
Customised 360o feedback (supervisors)
Partial support
Kerr et al (2006)
MSCEIT N=38
Supervisors
Customised 360o feedback (subordinates)
Partial support
Byron (2007) DANVA N=112
Managers
Satisfaction with supervisor (Hackman and Oldham 1975) (subordinates)
Managerial performance (Mount 1984) (supervisors)
Direct relationship not supported.
Support for relationship when mediated by managers’ supportiveness and for relationships when mediated by managers’ persuasiveness.
Partial support for relationship when moderated by gender – holds for female managers only.
Jin et al (2008)
MSCEIT N=178
Managers
Transformational leadership (MLQ) (subordinate)
Support for relationship
Weinberger (2009)
MSCEIT
N=151
Managers
Transformational leadership (MLQ) (subordinate)
Relationship not supported
Cote et al (2010)
MSCEIT
N=138
(study 1)
N=165 (study 2)
Undergrad students
Leadership emergence (peers)
Support for relationship
Clarke (2010) MSCEIT N=67
Project Managers
Transformational leadership (MLQ) (self ratings)
Project manager competencies (self-ratings)
Support for relationship
Appendix A
261
Wonderlic Personnel Test Norms
Test Scores Job Potential Education Potential
28 and over
Upper level management; only upper 17% of population score within this range.
College graduate mean IQ 120; WPT 29. Central tendency for graduate students is WPT 30.
26 to 30
Managerial potential and upper level clerical positions; 24% of the population score within this range; gathers information; analyses and makes decisions from a limited number of choices.
May enter college; mean score for college freshmen 1Q 115; WPT 24
20 to 26
General clerical and first line supervisors; able to train other for routine positions; gather information; may require help with making decisions. 29% of the population score within this range.
Mean for High School grads is IQ 110; WPT 21; Central tendency for College Freshmen WPT 24 - have a better than average chance of completing High School curriculum, 50/50 change of graduating from college.
16 to 22
Routine office worker can run routinized equipment; 27% of the population scores in this range. Given enough time can learn and perform jobs with length routinized steps; perform simple operations with lists of names and numbers.
May enter High School; will probably select classes which are on a less academic track; Central tendency for High School Junior, WPT 16.
10 to 17
Operate simple process equipment 21% of the population score within this range. Given ample time can learn limited number of steps for routinized jobs; if deviations occur on the job will have difficulty establishing or using contingencies.
Slightly better than average chance of reaching the 9th grade or entering high school. Central tendency for High School Sophmore is WPT 14, High School Freshmen WPT 13, 8th grade WPT 11.
12 or less
Use very simple tools and equipment; repair furniture; assist electrician; simple carpentry; domestic work. 13 % of the population score within this range. '
Armed forces IQ cut off score between 75-80. Central tendency for 7th grade WPT 9.
Appendix B
262
INFORMATION SHEET
The Graduate School of Management The University of Western Australia 35 Stirling Highway CRAWLEY WA 6009 AUSTRALIA
RESEARCH PROJECT: Multiple Intelligences Underpinning Effective Leadership
The intention of this research is to explore the relationships between traditional intelligence (IQ), emotional intelligence (EI), spiritual intelligence (SI) and effective leadership. It is also intended to explore the concept of spiritual intelligence in more detail.
Who are the researchers in this study? This study is being conducted by Stacie Chappell, Lecturer and Doctoral Student at the GSM and Dr. Renu Burr, Lecturer at the Graduate School of Management (GSM).
Who can participate in this study? Participation in this study is limited to clients of the AIM-UWA Senior Management Centre (SMC) who have completed the Integral Leadership and Management Development 360 Profile (ILMDP 360). The ILMDP 360 results will provide a standard measure of leadership effectiveness for the testing of the research hypothesis.
What about confidentiality and security of the data? All data collected will be held in the strictest confidence adhering to UWA ethics standards, kept in secure premises at UWA and accessed only by the researcher and her supervisor. Upon completion of the research project, the data will be destroyed. All data will be coded to eliminate the need for using specific names in the database.
What do I have to do to ‘participate?’ Participants in this study will participate in one a 2hr data collection workshop. Over the course of the 2 hours, participants will complete:
• the Wonderlic Personnel Test, a measure of traditional cognitive intelligence (IQ);
• Mayer, Salovey and Caruso’s Emotional Intelligence Test (MSCEIT), a measure of emotional intelligence (EI);
• Loevinger’s Sentence Completion Test, a measure of ego development;
• Psychomatrix Spirituality Indicator, (PSI) a profile of if/how you experience / incorporate spirituality into your life.
In addition, you will be asked to release the results of your ILMDP 360 for the purposes of this study.
When and where are the data collection workshops? The data collection workshop will be held at the UWA Graduate School of Management and will be scheduled at a time that suits you.
Appendix C Page 1 of 2
263
What’s in it for me? If you are interested in your own leadership development, participating in this study will provide you with a number of direct benefits:
• You will receive a validated rating of your traditional IQ using one of the most common measures employed by corporate recruiters;
• You will receive a validated rating and individualised feedback report on your EI profile using one of the leading measures of EI;
• You will receive a validated rating and individualised feedback report on your SI profile; and
• You will receive a complimentary seat for the ½ day workshop titled ‘Leveraging EI and SI for Effective Leadership’. This workshop will be presented in the first ½ of 2004 and will debrief the results of the research and explore strategies for developing and leveraging your current levels of EI and SI.
As well, by participating in this study you will be contributing to the growing body of knowledge on EI and SI. It is important that we continue to explore these new concepts through research in order for them to become useful to us as practitioners. As a participant in this study, you will receive a summary report of the findings from the research.
Can I say yes now, and change my mind? Yes! It is important that you know that your participation in this study should be completely voluntary and you can withdraw your participation at any point during the process. If you should withdraw from the study, the data collected from you, if any, will be destroyed. Your participation in this study does not prejudice any right to compensation, which you may have under statute or common law.
Can I ask a few more questions? Yes! We would be happy to answer any and all questions you might have on this research project. For more information, or to register your interest in participating, please contact:
Stacie Chappell
Lecturer and Doctoral Student
UWA Graduate School of Management
[email protected] 61 8 6488 1019 phone
61 8 6488 1072 fax
Thanks in advance for your consideration!
Appendix C Page 2of 2
Invitation and Faxback Form
The Graduate School of Management The University of Western Australia 35 Stirling Highway CRAWLEY WA 6009 AUSTRALIA PH: 61 8 6488 1019 FX: 61 8 6488 1072
Dear ILMDP 360 Client,
Invitation to Participate in Research Study on Multiple Intelligences and Leadership Effectiveness
The UWA Graduate School of Management is conducting a study on the role of multiple intelligence and effective leadership. Specifically, we want to explore the role of traditional intelligence, emotional intelligence and spiritual intelligence on leadership effectiveness. Because you have recently completed the Integral Leadership and Management 360 Profile (ILMDP), we would like to invite you to participate in this study. This would consist of:
• participating in a 2 hr data collection workshop; and • enabling us to access your ILMDP profile for the purposes of this research study.
PLEASE NOTE: All information gathered for the use of this study will be held in the strictest of confidence. Best practice security measures are used in the storage of hard copy and soft copy data. All data is coded and specific reference to individuals is not necessary.
In addition to contributing to our understanding of multiple intelligence and leadership effectiveness, you would gain:
• individualised feedback on each of the three intelligences measured; • access to a free ½ day workshop on multiple intelligences and leadership
effectiveness; and • a summary report of the research findings.
If you would like to sign up to participate, or would like more information on the proposed study, please complete the enclosed faxback form and return it to us. Alternatively, you can contact us via Stacie Chappell at [email protected] or 6488 1019. Thanks in advance for your consideration and time. Renu Burr Stacie Chappell Lecturer, UWA GSM Doctoral Student, UWA GSM
Appendix D Page 1 of 2
265
The Graduate School of Management The University of Western Australia 35 Stirling Highway CRAWLEY WA 6009 AUSTRALIA
Fax Back Form To: Stacie Chappell and Renu Burr
UWA Graduate School of Management Fax: 9380 1072
From: Date:
Pages: 1
Re: Research Study on Multiple Intelligences and Effective Leadership
I would like to participate in this study, please reserve a spot for me in the following data collection workshop (Circle one):
Tuesday 23 March 3-5pm Tuesday 23 March 6-8pm
Tuesday 30 March 3-5pm Tuesday 30 March 6-8pm
Tuesday 6 April 3-5pm Tuesday 6 April 6-8pm
Tuesday 13 April 3-5pm Tuesday 13 April 6-8pm
Tuesday 20 April 3-5pm Tuesday 20 April 6-8pm
Tuesday 27 April 3-5pm Tuesday 27 April 6-8pm
Tuesday 4 May 3-5pm Tuesday 4 May 6-8pm
Tuesday 18 May 3-5pm Tuesday 18 May 6-8pm
Tuesday 25 May 3-5pm Tuesday 25 May 6-8pm
Tuesday 1 June 3-5pm Tuesday 1 June 6-8pm
Tuesday 8 June 3-5pm Tuesday 8 June 6-8pm
Tuesday 22 June 3-5pm Tuesday 22 June 6-8pm
Tuesday 29 June 3-5pm Tuesday 29 June 6-8pm NOTE: spaces are limited to 10 people per session. As such, we will confirm your spot (via phone or email) or contact you to reschedule if your preference is not available.
My contact details are as follows:
Daytime phone contact: Email:
I would like to speak with someone in more detail about this research project.
Appendix D Page 2 of 2
CONSENT FORM
UWA Business School The University of Western Australia 35 Stirling Highway CRAWLEY WA 6009 AUSTRALIA
VOLUNTARY PARTICIPATION IN RESEARCH STUDY:
Multiple Intelligences Underpinning Effective Leadership I (the participant) have read the information provided and any questions I have asked
have been answered to my satisfaction. I agree to participate in this activity, realising that I may withdraw at any time without reason and without prejudice.
I have given permission for the researcher to access my 360 feedback and/or MSCEIT
results. I understand that all information provided is treated as strictly confidential and will not be released by the investigator unless required to by law. I have been advised as to what data is being collected, what the purpose is, and what will be done with the data upon completion of the research.
I agree that research data gathered for the study may be published provided my name or other identifying information is not used.
____________________________________________________________________ Participant Name Signature Date
(Please note that as this document is not a contract between parties, it is not necessary that the researcher sign it. Nor is it necessary to have a witness.)
The Human Research Ethics Committee at the University of Western Australia requires that all participants are informed that, if they have any complaint regarding the manner, in which a research project is conducted, it may be given to the researcher or, alternatively to the Secretary, Human Research Ethics Committee, Registrar’s Office, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009 (telephone number 6488-3703). All study participants will be provided with a copy of the Information Sheet and Consent Form for their personal records.
Copies of this form are available for you to take home.
For more information on this research project, please contact:
Stacie Chappell Lecturer and Doctoral Student
UWA Business School [email protected]
61 8 6488 1019 phone 61 8 6488 1004 fax
Appendix E
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Appendix F
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Appendix F
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276
Central Tendency and Dispersion of rWG_un for ILMDP 32-Items
ILMDP Item # Mean Median Mode Std.
Deviation Minimum Maximum
1 .71 .80 .87 .27 -.66 1.00
2 .76 .85 .92 .25 -.66 1.00
3 .80 .88 .90 .23 -.73 1.00
4 .81 .89 .92 .21 -.54 1.00
5 .81 .87 .92* .18 -.43 1.00
6 .71 .83 .92 .28 -.62 1.00
7 .72 .82 .92 .26 -.36 1.00
8 .72 .81 .96 .26 -.58 1.00
9 .75 .82 .92 .24 -.31 1.00
10 .80 .86 .92 .19 -.07 1.00
11 .67 .76 .92 .28 -.54 1.00
12 .65 .73 .92 .30 -.89 1.00
13 .78 .85 .92 .22 -.54 1.00
14 .76 .83 .92* .20 -.18 .98
15 .69 .79 .92 .27 -.66 1.00
16 .74 .84 .85 .25 -.48 1.00
17 .72 .81 .92 .28 -1.02 1.00
18 .79 .88 .92 .25 -.74 1.00
19 .78 .84 .92 .20 -.15 1.00
20 .77 .83 .92 .21 -.09 1.00
21 .77 .85 .92 .24 -.70 1.00
22 .76 .83 .92 .24 -.49 1.00
23 .70 .78 .92 .27 -.72 1.00
24 .71 .81 .89 .28 -.58 1.00
25 .75 .83 .92 .25 -.40 1.00
26 .70 .79 .79 .29 -.66 1.00
27 .74 .82 .90* .24 -.25 1.00
28 .76 .84 .90* .24 -.66 1.00
29 .78 .84 .90* .22 -.64 1.00
30 .72 .81 .97 .27 -.66 1.00
31 .76 .85 .90 .26 -.81 1.00
32 .78 .84 .84 .21 -.45 1.00
* Multiple modes exist. The smallest value is shown.
Appendix G
277
Central Tendency and Dispersion of rWG_skew for ILMDP 32-Items
ILMDP Item # Mean Median Mode Std.
Deviation Minimum Maximum
1 .62 .74 .83 .36 -1.17 1.00
2 .68 .80 .89 .33 -1.17 1.00
3 .74 .85 .87 .30 -1.27 1.00
4 .75 .85 .89 .28 -1.02 1.00
5 .75 .82 .89* .23 -.87 1.00
6 .62 .78 .89 .37 -1.12 1.00
7 .64 .76 .89 .34 -.78 1.00
8 .64 .75 .95 .34 -1.06 1.00
9 .67 .76 .89 .31 -.71 1.00
10 .74 .82 .89 .25 -.40 1.00
11 .57 .68 .89 .37 -1.02 1.00
12 .54 .65 .89 .40 -1.47 1.00
13 .72 .81 .89 .29 -1.02 1.00
14 .69 .78 .89* .27 -.55 .97
15 .60 .73 .89 .36 -1.17 1.00
16 .66 .79 .81 .33 -.94 1.00
17 .63 .75 .89 .37 -1.65 1.00
18 .72 .84 .89 .33 -1.28 1.00
19 .71 .79 .89 .26 -.50 1.00
20 .70 .78 .89 .28 -.43 1.00
21 .70 .81 .89 .32 -1.22 1.00
22 .68 .78 .89 .31 -.96 1.00
23 .61 .71 .89 .35 -1.25 1.00
24 .62 .75 .85 .37 -1.06 1.00
25 .67 .78 .89 .33 -.84 1.00
26 .60 .73 .73 .38 -1.17 1.00
27 .66 .77 .87* .31 -.64 1.00
28 .68 .79 .87* .32 -1.17 1.00
29 .71 .79 .87* .28 -1.14 1.00
30 .63 .75 .96 .36 -1.17 1.00
31 .69 .81 .87 .34 -1.37 1.00
32 .71 .79 .79 .28 -.90 1.00
* Multiple modes exist. The smallest value is shown.
Appendix H
278
Central Tendency and Dispersion of rWG_Mskew for ILMDP 32-Items
ILMDP Item # Mean Median Mode Std.
Deviation Minimum Maximum
1 .53 .68 .79 .44 -1.69 1.00
2 .61 .75 .87 .40 -1.69 1.00
3 .68 .81 .84 .38 -1.81 1.00
4 .69 .81 .87 .34 -1.50 1.00
5 .69 .78 .87* .29 -1.32 1.00
6 .54 .73 .87 .45 -1.63 1.00
7 .55 .71 .86 .43 -1.20 1.00
8 .55 .69 .94 .43 -1.55 1.00
9 .59 .71 .87 .39 -1.12 1.00
10 .68 .78 .87 .31 -.73 1.00
11 .47 .61 .86 .46 -1.50 1.00
12 .43 .56 .87 .49 -2.06 1.00
13 .65 .76 .86 .35 -1.50 1.00
14 .61 .73 .86* .33 -.92 .97
15 .50 .66 .87 .44 -1.69 1.00
16 .58 .74 .76 .41 -1.40 1.00
17 .55 .69 .86 .45 -2.27 1.00
18 .65 .80 .87 .41 -1.82 1.00
19 .64 .74 .86 .32 -.86 1.00
20 .63 .73 .87 .34 -.77 1.00
21 .62 .76 .86 .40 -1.75 1.00
22 .61 .73 .86 .38 -1.42 1.00
23 .52 .64 .86 .43 -1.79 1.00
24 .53 .69 .82 .45 -1.55 1.00
25 .60 .73 .87 .41 -1.28 1.00
26 .51 .67 .67 .47 -1.69 1.00
27 .58 .71 .84* .39 -1.03 1.00
28 .61 .74 .84* .39 -1.69 1.00
29 .64 .74 .84* .35 -1.65 1.00
30 .54 .69 .95 .44 -1.69 1.00
31 .61 .76 .84 .42 -1.93 1.00
32 .65 .74 .74 .35 -1.36 1.00
* Multiple modes exist. The smallest value is shown.
Appendix I
279
Central Tendency and Dispersion of rWG_UN for ILMDP Subscales: eight roles,
two functions and the full 32-item scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Management Function .95 .97 .95* .07 .03 1.00
Broker .90 .94 .98* .15 -1.00 1.00
Director .88 .94 .85* .28 -3.72 .99
Achiever .92 .95 .95* .09 .33 .99
Monitor .92 .95 .95 .10 .00 .99
Leadership Function .94 .97 .96* .11 -.15 1.00
Facilitator .89 .93 .93 .22 -.54 3.54
Visioner .89 .94 .97 .15 -.20 .99
Steward .90 .95 .96 .25 -2.56 1.00
Coach .87 .93 .93* .26 -2.58 .99
32-Item Scale .98 .99 .99* .05 .12 1.00
* Multiple modes exist. The smallest value is shown.
Appendix J
280
Central Tendency and Dispersion of rWG_skew for ILMDP Subscales: eight roles,
two functions and the full 32-item scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Management Function .91 .95 .91* .23 -1.67 3.12
Broker .84 .92 .97* .44 -3.88 4.06
Director .68 .92 .77* 3.46 -59.76 16.61
Achiever .86 .93 .93* .23 -1.31 .99
Monitor .81 .93 .93 1.01 -17.33 .99
Leadership Function .90 .95 .93* .48 -6.06 3.81
Facilitator .78 .90 .9 1.19 -11.59 13.78
Visioner .85 .91 .97 1.52 -17.33 16.61
Steward .90 .94 .95 .49 -1.82 7.62
Coach -.04 .91 .91* 17.60 -334.67 6.67
32 Item Scale 1.01 .99 .99* .34 .64 7.15
* Multiple modes exist. The smallest value is shown.
Appendix K
281
Central Tendency and Dispersion of rWG_Mkew for ILMDP Subscales: eight roles,
two functions and the full 32-item scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Management Function .96 .97 .95 .29 -2.37 3.59
Broker .95 .91 .96 1.73 -1.79 26.94
Director .87 .90 .66 1.05 -5.93 16.76
Achiever .90 .91 .91 1.23 -6.56 19.93
Monitor .84 .91 .91 .50 -7.09 3.40
Leadership Function .94 .97 .96 .29 -3.62 2.00
Facilitator .19 .89 .86 1.74 -192.57 11.94
Visioner .81 .90 .96 1.70 -27.55 9.41
Steward .89 .92 .93 .46 -2.85 5.97
Coach .69 .88 .88 1.04 -15.22 2.74
32-Item Scale .98 .99 .98 .08 .54 2.04
* Multiple modes exist. The smallest value is shown.
Appendix L
Exploratory Factor Analysis on MSCEIT Task A
# items
chi-square
df p< TLI CFI RMSEA
NOTES
Step 1: 20 640.851 170 .001 .680 .714 .104 deleted items < .4 (#1, 7, 12 & 16) run with all items
Step 2: 16 499.314 104 .001 .699 .739 .112 std resid over 2.58 for: 8&13, 11&13, 17&19, 19&20, 17&18, 17&9, 2&4, 11&15
deleted items #1, 7, 12 & 16
MI highest for 19&20
covary 19&20 and deleted 19 Bc lowest loading
Step 3: 15 397.907 90 .001 .742 .779 .112 std resid over 2.58 for: 8&13, 11/&13, 17&18, 18&20, 17&20, 15&11, 2&4
deleted 19
MI highest for 17&18
covary 17&18 and deleted 17 Bc lowest loading
Step 4: 14 288.868 77 .001 .787 .819 .104 std resid over 2.58 for: 8&13, 11&13, 18&20, 15&11, 2&4 deleted 17
MI for 18&20 highest
covary 18&20 and deleted 18 bc lowest loading Step 5: 13 243.734 65 .001 .799 .832 .104 std resid over 2.58 for: 8&13, 11&13, 15&11, 2&/4 deleted 18
MI for 11&15 highest
covary 11&15 and deleted 11 bc lowest loading Step 6: 12 186.866 54 .001 .831 .862 .098 std resid over 2.58 for: 8&13, 2&4 deleted 11
MI for 2&4 highest
covary 2&4 and deleted 2 bc lowest loading Step 7: 11 131.898 44 .001 .871 .897 .089 std resid over 2.58 for: 8&13 deleted 2
MI for 8&13 highest
covary8&13 and deleted 13 bc lowest loading Step 8: 10 99.087 35 .001 .895 .918 .085 std resid all < 2.58 deleted 13
MI for 8&9 highest
covary 8&9 and deleted 8 bc lowest loading Step 9: 9 74.166 27 .001 .908 .931 .083 std resid all < 2.58 deleted 8
MI for 9&15 highest
covary 9&15 and deleted 15 bc lowest loading Step 10: 8 47.386 20 .001 .935 .954 .073 std resid all < 2.58 deleted 15
MI for 3&10 highest
covary 3&10and deleted 10 bc lowest loading Step 11 7 25.478 14 .030 .964 .976 .057 std resid all < 2.58 deleted 10
MI for 4&6highest
covary 4&6 and deleted 6 bc lowest loading Step 12: 6 1.534 9 .309 .994 .996 .026 accept fit deleted 6
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 1 of 8
283
Exploratory Factor Analysis on MSCEIT Task B
# items
chi-square df p< TLI CFI RMSEA
NOTES
Step 1: 15 177.09 90 .001 .564 .626 .062
deleted items <.3 loading (3,4, 5, 9, 10, 11, 12, 13, 14)
Run with all items
Step 2: 6 21.809 9 .010 .835 .901 .075 std resid over 2.58 for 1&7 (2.613) - deleted items 3,4, 5, 9, 10,
MI for 1&7 highest
11, 12, 13, 14
Covary 1& 7 and deleted #7
Step 3:
Deleted 7 5 4.822 5 .438 1.003 1.000 .000 accept model fit - 1 Step 4:
deleted 1 4 .179 2 .914 1.068 1.000 .000 accept model fit - 2 Step 5:
deleted 15 replaced 1 4 3.65 2 .161 .940 .980 .057 accept model fit - 3 Step 6:
deleted 1&15, 3 1.836 1 .175 .953 .984 .057 accept model fit - 4 error on 2&6 set to unity
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 2 of 8
284
Exploratory Factor Analysis on MSCEIT Task C
# items
chi-square df p< TLI CFI RMSEA
NOTES
Step 1: 20 212.66 170 .015 .798 .820 .031 deleted items loading < .3 (2, 3, 5, 8,
Run with all items 11, 13, 16, 17, 18 and 19) Step 2: 9 33.401 27 .184 .944 .958 .031 no std resid > 2.5ish 2, 3, 5, 8, 11, 12, 13, 16,
MI for 1-14 highest
17, 18 and 19 deleted
delete # 1 with lower std reg weight
Step 3: 8 24.093 20 .238 .959 .971 .028 no std resid > 2.5ish Deleted 1
MI for 7-15 highest
delete #7 with lower std reg weight Step 4: 7 16.226 14 .300 .971 .981 .025 no std resid > 2.5ish Deleted 7
MI for 4 - 20 highest
delete # 20 with lower std reg weight
Step 5: 6 8.199 9 .514 1.014 1.000 .000 no std resid > 2.5ish Deleted 20
MI for 4-9 highest
delete 9 with lower std reg weight Step 6: 5 2.35 5 .799 1.065 1.000 .000 no std resid > 2.5ish Deleted 9
MI for 4-10 highest
delete #4 with lower std reg weight
Step 7: 4 .604 2 .793 1.061 1.000 .000 Accept model fit - 1 Deleted 4
no std resid > 2.5ishMI for 10-15 highest
delete #10 with lower std reg weight
Step 8: 3 1.624 1 .203 .967 .989 .050 Accept model fit - 2 Deleted 10, set variance of
6&15 to unity
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 3 of 8
285
Exploratory Factor Analysis on MSCEIT Task D
# items
chi-square df p< TLI CFI RMSEA
NOTES
Step 1: 20 262.35 170 .001 .496 .549 .046 deleted items with < .3 loading (#1, 2, 4, 5, 6, 7, 9,
10, 11, 12, 13, 18, 19) Run with all items
Step 2: 7 26.754 14 .021 .813 .875 .060 std resid > 2.5ish for 15/16 (3.037)
Deleted: 1,2,4,5,6,7,9,10, 11,
MI for 15&16highest
12,13,18,19
covary 15&16 and deleted 15 bc lowest loading Step 3: 6 5.971 9 .743 1.068 1.000 .000 no std resid > 2.5ish Deleted #15
MI for 3&8 highest
covary 3&8 and deleted 3 bc lowest loading Step 4: 5 2.083 5 .838 1.094 1.000 .000 no std resid > 2.5ish Deleted #3
MI for 16&20 highest
covary 16&20 and deleted 20 bc lowest loading
Step 5: 4 1.046 2 .593 1.054 1.000 .000 Accept Model Fit- 1 Deleted 20
no std resid > 2.5ish, MI for 8&14 highest
covary 8&14 and deleted 14 bc lowest loading Step 6: 3 7.262 1 .007 .585 .862 .157
Deleted 14
set variance on 8&16 equal
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 4 of 8
286
Exploratory Factor Analysis on MSCEIT Task E
# items
chi-square df p< TLI CFI RMSEA
NOTES
Step 1: 30 1111.27 405 .001 .592 .620 .083 Deleted items < .40 (#1,2,4,5,6, 8,10,11,18,21,26,27)
run with all items
Step 2: 18 461.13 135 .001 .723 .755 .097 std resid > 2.518 for 28&30, 24&25, 22&25, 14&29, 7&9 and 13&14
#1,2,4,5,,6,8,10,11,18,21,26,27
MI for 7&9 highest deleted above items
delete # 9 with lower std reg weight & reran model
Step 3: 17 338.027 119 .001 .787 .813 .085 std resid > 2.518 for 28&30, and 14&/29 deleted #9
MI for 28&30 highest
delete #28 with lower std reg weight & reran model
Step 4: 16 271.733 104 .001 .811 .836 .080 std resid > 2.518 for 14&29 deleted #28
MI for 14&29 highest
delete #29 with lower std reg weight & reran model
Step 5: 15 223.1 90 .001 .837 .860 .076 no std resid > 2.5ish deleted #29
MI for 13&14 highest
delete #14 with lower std reg weight & reran model
Step 6: 14 181.453 77 .001 .859 .880 .073 no std resid > 2.5ish deleted #14
MI for 20&22 highest
delete #22 with lower std reg weight & reran model
Step 7: 13 145.962 65 .001 .880 .900 .070 no std resid > 2.5ish deleted #22
MI for 24&25 highest
delete #25 with lower std reg weight & reran model
Step 8: 12 105.937 54 .001 .914 .929 .061 no std resid > 2.5ish deleted #25
MI for 23&24 highest
delete #24 with lower std reg weight & reran model
Step 9: 11 76.096 44 .002 .933 .947 .053 no std resid > 2.5ish deleted #24
MI for 19&23 highest
delete #19 with lower std reg weight & reran model
Step 10: 10 56.447 35 .012 .951 .962 .049 no std resid > 2.5ish deleted #19
MI for 13&17 highest
delete #13 with lower std reg weight & reran model
Step 11: 9 33.58 27 .179 .980 .985 .031 Accepted Model Fit - 1 deleted #13
deleted items < .50 (#3, 15 & 16)
Step 12 6 11.994 9 .214 .984 .990 .036 Accept model fit - 2 deleted #3, 15, 16
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 5 of 8
287
Exploratory Factor Analysis on MSCEIT Task F
#
items chi-
square df p< TLI CFI RMSEA
NOTES
Step 1: 15 158.868 90 .001 .503 .574 .055 deleted items <.3 (1,2,3,4,5,7,8,10,11,13) Run with all items
Step 2 5 3.112 5 .683 1.049 1.000 .000 no std resid > 2.5ish deleted #1, 2, 3, 4, 5, 7, 8, 10, 11, 13
Accept model fit -1
Ave low so deleted item<.4 (#12)
Step 3: 4 2.327 2 .312 .985 .995 .025 Accept model fit -2 deleted #12
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 6 of 8
288
Exploratory Factor Analysis on MSCEIT Task G
# items
chi-square
df p< TLI CFI RMSEA
NOTES
Step 1: 12 72.301 54 .049 .740 .787 .036 deleted items < .3 loading (1,2,3,5,7,10) Run with all items Step 2: 6 8.292 9 .505 1.021 1.000 .000 no std resid > 2.5ish
deleted 1,2,3,5,7,10
MI for 4&9 highest
delete #9 with lower std reg weight & reran model
Step 3: 5 3.04 5 .694 1.085 1.000 .000 no std resid > 2.5ish deleted #9
MI for 6&8 highest
delete # 8 with lower std reg weight & reran model
Step 4: 4 1.145 2 .564 1.065 1.000 .000 no std resid > 2.5ish deleted #8
MI for 4&11 highest
delete #11 with lower std reg weight & reran model
Step 5: 4 .215 1 .643 1.068 1.000 .000 Accept model fit deleted 11
set error 6&12 to equal
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 7 of 8
289
Exploratory Factor Analysis on MSCEIT Task H
# items
chi-square df p< TLI CFI RMSEA
NOTES
Step 1: 9 25.559 27 .543 1.024 1.000 .000 Delete items loading <.30 (#4, 7, 8 &9)
run with all items
Step 2: 5 10.05 5 .074 .865 .932 .063 no std resid > 2.5ish
deleted 4,7 8 &9
MI for 5&6 highest
delete #6 with lower std reg weight & reran model
Step 3: 4 .47 2 .791 1.088 1.000 .000 accept model fit - 1
deleted 6
Low AVE so deleted low items #3
Step 4: 3 1.567 1 .211 .949 .983 .047 accept model fit - 2 deleted #3 set 2&5 to unity
Step 5:
deleted #5, replace 3 3 10.78 1 .001 .144 .715 .196 reject model fit - 3
* EFA steps and cut-off criteria were based on processes outlined by Byrne (2010)
Appendix M Page 8 of 8
290
Inter-rater Agreement on 32 WUSCT Items
Item
Cron Alpha
1 .95 2 .92 3 .85 4 .87 5 .89 6 .86 7 .45 8 .87 9 .92
10 .92 11 .86 12 .89 13 .88 14 .92 15 .88 16 .74 17 .91 18 .90 19 .80 20 .90 21 .89 22 .87 23 .90 24 .92 25 .79 26 .87 27 .82 28 .93 29 .80 30 .90 31 .91 32 .88 33 .86 34 .89 35 .87 36 .81
Appendix N
291
Central Tendency and Dispersion of rWG_un for Items in Revised LE Scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Team leadership .67 .77 .89 .32 -‐.94 1.00
Participative style .67 .76 .92 .35 -‐1.18 1.00
Positive relations customer .83 .89 .96 .19 -‐.17 1.00
Inspire vision .68 .77 .92a .30 -‐.49 1.00
Praise positive contribution .70 .83 .96 .37 -‐1.21 .98
Manage conflicts .69 .78 .76 .29 -‐.61 1.00
Encourage people develop .71 .81 .92 .30 -‐.97 .98
Delegate responsibility .67 .79 .96 .35 -‐1.18 1.00
Manage not performing .68 .79 .76 .30 -‐.52 1.00
Communicate fit .72 .82 .96 .26 -‐.37 1.00
Facilitate discussions .71 .81 .84a .28 -‐.54 1.00
* Multiple modes exist. The smallest value is shown.
Appendix O
292
Central Tendency and Dispersion of rWG_sskew for Items in Revised LE Scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Team leadership .56 .70 .85 .41 -‐1.54 1.00
Participative style .56 .69 .89 .46 -‐1.86 1.00
Positive relations customer .77 .86 .95 .25 -‐.53 1.00
Inspire vision .59 .70 .89a .40 -‐.96 1.00
Praise positive contribution .61 .78 .95 .49 -‐1.90 .97
Manage conflicts .60 .71 .68 .37 -‐1.11 1.00
Encourage people develop .62 .75 .89 .39 -‐1.58 .98
Delegate responsibility .56 .73 .95 .45 -‐1.86 1.00
Manage not performing .57 .72 .68 .40 -‐.98 1.00
Communicate fit .63 .76 .95 .34 -‐.80 1.00
Facilitate discussions .62 .75 .79 .36 -‐1.01 1.00
* Multiple modes exist. The smallest value is shown.
1
Appendix P
2
293
Central Tendency and Dispersion of rWG_Mskew for Items in Revised LE Scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Team leadership .46 .63 .82 .51 -‐2.14 1.00
Participative style .46 .61 .87 .57 -‐2.54 1.00
Positive relations customer .72 .82 .95 .31 -‐.90 1.00
Inspire vision .49 .63 .86a .49 -‐1.42 1.00
Praise positive contribution .52 .73 .94 .61 -‐2.59 .97
Manage conflicts .50 .64 .61 .46 -‐1.61 1.00
Encourage people develop .52 .69 .87 .48 -‐2.19 .97
Delegate responsibility .46 .67 .93 .56 -‐2.54 1.00
Manage not performing .47 .65 .61 .49 -‐1.46 1.00
Communicate fit .54 .71 .93 .42 -‐1.23 1.00
Facilitate discussions .54 .69 .74a .45 -‐1.49 1.00
* Multiple modes exist. The smallest value is shown.
Appendix Q
294
Central Tendency and Dispersion of rWG_un for Items in Revised ME Scale
Mean Median Mode Std.
Deviation Minimum Maximum
Follow up decisions .78 .87 .88 .22 -‐.40 1.00
High priority .75 .87 .96 .28 -‐.43 .98
Monitor activities .75 .86 .89a .27 -‐.82 1.00
Support attempts improve .82 .88 .92 .29 -‐1.97 1.00
Develop new ideas .78 .85 .92 .23 -‐.66 1.00
Negotiate effectively .77 .85 .86a .27 -‐1.18 1.00
Plans goals, task and timelines .71 .80 .76a .30 -‐.97 .98
Good solutions problems .80 .87 .90 .21 -‐.32 1.00
Developing new opportunities .79 .85 .92a .20 -‐.45 1.00
Initiate changes .77 .83 .92a .22 -‐.05 1.00
Comply organisation policies .82 .88 .90a .18 -‐.03 1.00
Information about progress .76 .84 .96 .29 -‐.98 1.00
* Multiple modes exist. The smallest value is shown.
Appendix R
295
Central Tendency and Dispersion of rWG_sskew for Items in Revised ME Scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Follow up decisions .71 .83 .84 .29 -‐.84 1.00
High priority .68 .83 .95 .37 -‐.87 .98
Monitor activities .67 .82 .85a .36 -‐1.38 1.00
Support attempts improve .76 .84 .89 .38 -‐2.89 1.00
Develop new ideas .72 .80 .89 .31 -‐1.17 1.00
Negotiate effectively .70 .81 .82a .36 -‐1.86 1.00
Plans goals, task and timelines .61 .74 .68a .39 -‐1.58 .97
Good solutions problems .74 .83 .87 .27 -‐.73 1.00
Developing new opportunities .72 .81 .89a .26 -‐.90 1.00
Initiate changes .69 .78 .89a .29 -‐.38 1.00
Comply organisation policies .76 .84 .87a .24 -‐.35 1.00
Information about progress .69 .79 .95 .38 -‐1.59 1.00
* Multiple modes exist. The smallest value is shown.
Appendix S
296
Central Tendency and Dispersion of rWG_Mskew for Items in Revised ME Scale
Mean Median Mode
Std. Deviation
Minimum Maximum
Follow up decisions .64 .79 .80 .36 -‐1.28 1.00
High priority .60 .79 .86a .46 -‐1.32 .97
Monitor activities .60 .78 .82 .44 -‐1.95 1.00
Support attempts improve .70 .80 .87 .47 -‐3.81 1.00
Develop new ideas .65 .75 .87 .38 -‐1.69 1.00
Negotiate effectively .63 .76 .78 .45 -‐2.54 1.00
Plans goals, task and timelines .52 .68 .61a .49 -‐2.19 .97
Good solutions problems .67 .79 .84 .33 -‐1.14 1.00
Developing new opportunities .66 .76 .87a .33 -‐1.36 1.00
Initiate changes .62 .72 .87a .35 -‐.70 1.00
Comply organisation policies .70 .80 .86 .29 -‐.67 1.00
Information about progress .61 .74 .74a .47 -‐2.21 1.00
* Multiple modes exist. The smallest value is shown.
Appendix T
297