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THE PREDICTIVE STRENGTH OF EMOTIONAL INTELLIGENCE
ON COACHING EFFICACY AND LEADERSHIP STYLE OF
HIGH SCHOOL BASKETBALL HEAD COACHES

presented by
Seunghyun Hwang

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TIIE PREDICTIVE STRENGTH OF EMOTIONAL INTELLIGENCE
ON COACHING EFFICACY AND LEADERSHIP STYLE OF
HIGH SCHOOL BASKETBALL HEAD COACHES

By

Seunghyun Hwang

A THESIS

Submitted to
Michigan State University
in partial ﬁilﬁllment of the requirements
for the degree of

MASTER OF SCIENCE

Department of Kinesiology

2008

ABSTRACT

THE PREDICTIVE STRENGTH OF EMOTIONAL INTELLIGENCE
ON COACHING EFFICACY AND LEADERSHIP STYLE OF
HIGH SCHOOL BASKETBALL HEAD COACHES
By

Seunghyun Hwang

The main purpose of this study was to investigate the relationships among
emotional intelligence, coaching efﬁcacy, and leadership behavior of high-school
basketball coaches on coaching effectiveness. High-school basketball coaches (N = 323)
in MI, OH, IN, and IL participated in an on-line survey. The ﬁndings suggest that
coaches’ emotional intelligence predict coaching efﬁcacy and leadership behaviors. More
speciﬁcally, the ability of regulating emotions signiﬁcantly predicts the game strategy (/2’
= .28), motivation (,8 = .34), technique (,8 = .30), and character building (,6 = .40) in
coaching efﬁcacy, and democratic (,8 = .20), positive feedback (ﬂ = .54), training &
instruction (,8 = .39), situational consideration (/I’ = .44), and social support (,8 = .28) in
leadership behaviors. The ability to appraise emotions signiﬁcantly predicts the
motivation (/3 = .12) in coaching efﬁcacy, and democratic (6 = .l2), training and
instruction (/1 = .12), situational consideration (,8 = .12), and social support (/3 = .20) in
leadership behaviors. Additionally, the four dimensions of coaching efﬁcacy Signiﬁcantly
predicted leadership behaviors, explaining 22 % of variance in situational consideration
behaviors, 25% of variance in positive feedback behaviors, and 36% in training and
instruction behavior. Those relationships were conﬁrmed by a structural equation

modeling test.

ACKNOWLEDGMENTS

First of all, I would like to acknowledge that Michigan State University has an
excellent master’s program to solve my research inquiry and enhance my ability as a
researcher in the area of Sport psychology.

This thesis could not be completed without others support. First, I would like to
acknowledge the support, guidance, and effort of my advisor, Dr. Deborah Feltz, for my
master’s program, particularly for her understanding of my circumstance as a newly
settled international student. I have to sincerely thank the members of my committee, Dr.
Dan Gould, Dr. Larry Lauer, and Teresa Hepler, for their helpful comments to make a
better thesis. Also, I would like to thank Jeong-Dae (JD) for giving me conﬁdence to be
adjusted as one of the members in the Department of Kinesiology.

I must extend my acknowledgements to Korea. I would like to thank all of my
supportive companies in Korea, particularly, Dr. Eun-Seok Yang for inspiring me to
study in the area of sport psychology, Dr. Ik-Soo Moon for broadening my view to see
the physical activity and Sport, and Dr. Jeong-Keun Park for introducing me to MSU.

I could not limit my acknowledgments to academics. I must thank my parents and
brother for their unconditional love. I could not ﬁnish this work without their support.
Finally, I would like to thank all those who have contributed to my thesis, particularly the

basketball coaches of Michigan, Illinois, Indiana, and Ohio.

iii

TABLE OF CONTENT

LIST OF TABLES .............................................................................. vi
LIST OF FIGURES .............................................................................. vii
CHAPTER]

INTRODUCTION ........................................................................................................ 1
Nature of the Problem .................................................................. 1
Purpose of the Study ................................................................... 1 l
Hypotheses and Proposed Model ..................................................... 1 l
Assumption ............................................................................. 12
Delimitations ........................................................................... 1 2
Limitations ............................................................................... 12
Deﬁnitions .............................................................................. 13

CHAPTER 2

LITERATURE REVIEW ....................................................................... 14
Emotional Intelligence ................................................................. 14
Approaches to the study of Emotional Intelligence ................................ 14

Ability Model .................................................................. l6

Trait Model ..................................................................... 2O

Emotional Intelligence and Effective Leaders ...................................... 23
Emotional Intelligence in Sport ...................................................... 24
Coaching Effectiveness ............................................................... 26
Coaching Efﬁcacy ............................................................ 30

Leadership Style for Sport ................................................... 35

Summary ................................................................................ 39

CHAPTER 3

METHOD ....................................................................................... 40
Participants ............................................................................. 40
Instruments ............................................................................. 41
Demographic Measures ...................................................... 41

Modiﬁed Version of Schutte Emotional Intelligence Scale ............ 41

Coaching Efﬁcacy Scale .................................................... 42

Revised Leadership Scale for Sport ....................................... 43

Data Collection Procedures .......................................................... 44

Data Analyses .......................................................................... 45

CHAPTER 4

RESULTS ....................................................................................... 47
Preliminary Analyses ................................................................. 47
Descriptive Statistics and Gender Differences .................................... 49
Correlations among the Variables ................................................... 50

iv

Tests of the Hypotheses ............................................................... 53

Structural Equation Modeling ........................................................ 60
Summary ................................................................................ 66
CHAPTER 5
DISCUSSION ................................................................................... 67
Implications ............................................................................. 74
Limitations and Future Direction .................................................... 75
APPENDICES ............................................................................. 77
REFERENCES .................................................................................. 9O

LIST OF TABLES

Table 1 Comparison Between Ability and Trait Model of Emotional Intelligence ....... 16
Table 2 Bar-On’s (1997) Five Dimensions and 15 Sub-Categories ......................... 21
Table 3 Coaches’ Ethnicity ....................................................................... 40
Table 4 Coaches’ Educational Level ............................................................ 41
Table 5 Summary Statistics for Normality and Internal Consistency ....................... 48
Table 6 Descriptive Statistics for Variables .................................................... 49

[able 7 Pearson Correlation Coefﬁcients between Emotional Intelligence and Coaching
Efﬁcacy and Leadership Behavior ...................................................... 51

Table 8 Pearson Correlation Coefficients between Coaching Efﬁcacy and Leadership
Behavior .................................................................................... 52

Table 9 Summary of the Canonical Correlation Analysis between Emotional Intelligence
and Coaching Efﬁcacy Subscales ...................................................... 53

Table 10 The Predictability of Emotional Intelligence on Coaching Efﬁcacy and
Leadership Behavior ................................................................... 54

Table 11 Summary of the Canonical Correlation Analysis between Emotional
Intelligence and Leadership Behavior Sub-factors ................................. 55

Table 12 Summary of the Canonical Correlation Analysis between Coaching Efﬁcacy

and Leadership Behavior Sub-factors ................................................ 58
Table 13 The Predictability of Coaching Efﬁcacy on Leadership Behavior ............... 59
Table 14 Parameter Summary of the First Model ............................................. 62
Table 15 Goodness of Fit Indexes Summary of the Final Model ........................... 64
Table 16 Parameter Summary of the Final Model ............................................. 65

vi

LIST OF FIGURES

Figure 1 A Working Model of Coaching Effectiveness (Horn, 2002) ..................... 7
Figure 2 Hypothesized Model of Emotional Intelligence, Coaching Efﬁcacy,

and Leadership Style ..................................................................... 11
Figure 3 Salovey and Mayer’s (1990) Model of Emotional Intelligence .................. 17
Figure 4 Conceptual Model of Coaching Efﬁcacy (Feltz et a1., 1999) ..................... 31
Figure 5 The Initially Proposed Model for Structural Equation Modeling ................ 61
Figure 6 The Final Model ........................................................................ 63

vii

CHAPTER 1

INTRODUCTION

Nature of the Problem

.lohn Wooden is reputed as the greatest basketball coach in NCAA history. He
reﬂects his leadership in his autography, noting ‘Emotion is Your Enemy’ (Wooden &
Jamison, 2005).

“Emotionalism destroys consistency. A leader who is ruled by emotions, whose
temperament is mercurial, produces a team whose trademark is the roller coaster-ups

and downs in performance; unpredictability and undependability in effort and
concentration; one day good, the next day bad. m 107) ”

He insisted that it is critical to keep emotions under control, and to know one’s
own emotional state in order to consistently lead a team, and not to lose respect from
athletes. That is, a coach should be emotionally intelligent to successfully coach a team.

Emotional intelligence (E1) has been conceptualized as the ability to perceive.
express, understand, and regulate emotions in oneself and others in general. E1 in the
business setting has been studied extensively over the last decade. The research indicates
that E1 is an important aspect of leadership effectiveness, and is one of the characteristics
of great leaders (Caruso, Mayer, & Salovey, 2002; Sosik & Megerain, 1999). A high
level of E1 allows leaders to be aware of their own emotions, to accurately identify the
emotions of the group and of the individual followers, and to control their own emotions.
Such leaders can generate enthusiastic circumstances for their tasks and motivate their

followers. In accordance, Mayer and Caruso (2002) said that leaders who are high in El

may be better equipped to develop stronger teams, and to communicate more effectively
with others.

Rosete and Ciarrochi (2005) studied 41 Australian public service managers to
explore the relationship between EI, personality, cognitive intelligence and leadership
effectiveness. They found that higher EI scores were associated with higher leadership
effectiveness. Kobe, Eiter—Palmon, and Rickers (2001) examined the power of both
emotional and social intelligence to account for variance in self- reported leadership
experiences through a regression analysis. The result showed that individuals who scored
high on a measure of El reported more participation in leadership experiences. Thus, the
leaders with a high level of El can be better equipped to promote effectiveness at all
levels in organizations, which, in turn, strongly inﬂuence their followers’ performance
outcome (Humphrey, 2002). That is, the ability of leaders to inﬂuence the emotional
climate can inﬂuence performance.

In addition to the above empirical studies, there are two distinct approaches to
conceptualizing El. One is the ability conception of E1, which was initially developed by
Salovey and Mayer (1990), and Mayer and Salovey (1997). They have emphasized E1 as
a cognitive-emotional ability within an ability framework that ought to be measured by a
maximum performance (IQ like) test, which consists of performance tasks requiring
responses that are evaluated against predetermined scoring criteria (e. g., expert scoring).
In the maximum performance test, respondents are instructed to choose the alternative
that would best describe their actual behavior in the given Situation (e. g., “You want to
celebrate your birthday with some friends, but they tell you that they have other plans”).

In contrast, the other conception of E1 is the trait ﬁamework, which is based on Bar-On’s

(1997) non-cognitive trait model of El. Bar-On (1997) deﬁned EI as “an array of non-
cognitive capabilities, competencies and skills that inﬂuence one’s ability to succeed in
coping with environmental demands and pressures.” (p.14). According to Petrides and
Fumham (2001), the trait approach places E1 in the domain of personality, encompassing
various behavioral dispositions and perceived abilities measured via self-report based on
psychometric theory (Likert scale). Petrides and Fumham also introduced the altemative
label, emotional self-eﬂzcacy, for the trait E1.

The present study was based on the trait conception of El because of the problems
of maximum performance test of ability E1. The test of ability EI cannot be objectively
scored because the subjectivity of emotional experience (6. g., Robinson & Clore, 2002;
Watson, 2000) undermines the development of valid maximum performance
measurement. C iarrochi, Chan, and Caputi (2000) criticized the ability measures because
of low reliabilities caused by two different scoring-methods. That is, in most cases, there
are no clear-cut criteria for what constitutes a correct response. Also, the nature of the
present study focused on the relationship between individuals’ characteristics, not
cognitive ability.

To measure the trait EI, Bar-On (1997) developed the Emotional Quotient
Inventory (EQ-i), which is a 133-item self-report measurement. With criticism about the
number of items and low validation, Schutte, Malouff, Hall, Haggerty, Cooper, Golden
and Domheim (1998) developed the Schutte Emotional Intelligence Scale (SEIS) with 33
items responded to on a 5—point Likert scale. The SEIS consists of three sub-scales: the
appraisal and expression of emotion, the regulations of emotion, and the utilization of

emotion.

Austin, Saklofske, Huang, and McKenney (2004) modiﬁed the SEIS because of a
lack of reverse-keyed items (Petrides & Fumham, 2000; Saklofske, Austin & Minski,
2003), which could potentially lead to a confounding of El score with acquiescent
responding (Austin et a1., 2004). The Modiﬁed Version of the SEIS (MVSEIS) consists
of 41 items having 20 forward-keyed and 21 reverse-keyed items. Austin and colleagues
reported that overall internal reliability is .85, and the sub-factors are following: .78 for
regulation of emotions, .68 for utilization of emotion, and .76 for appraisal of emotion.
They also reported that overall the MVSEIS was highly correlated (r=.66, p<.001) to the
Short version of Bar-On Emotional Quotient Inventory (EQ-izs, Bar-On, 1997).
Moreover, Austin and colleagues indicated this modiﬁed version is reasonably congruent
with most theoretical approaches to the trait E1.

However, despite a high level of scientiﬁc interest, the scientiﬁc development of
E1 is in its early years, and many key questions remain unanswered regarding conceptual
and psychometrical issues. Some psychometrical criticisms exist with E1: 1) there appears
little consensus over how EI Should be deﬁned and conceptualized; 2) it is uncertain how
El may be best measured, with various tests of the construct failing to converge; 3) the
practical utility of tests of E1 is limited by these conceptual and psychometric deﬁciencies
(Matthews, Zeidner, & Roberts, 2007). Still, researchers should put effort forth to draw
consensus between the ability model and trait model of El

Regardless of conceptual and psychometrical controversies, E1 in the sport
context is receiving growing attention from Sport psychologists as it relates to athletes,
but has been largely unexplored in terms of its potential inﬂuence on coaching

effectiveness. Despite many differences between the business setting and sport (e. g.,

nature of the goal and context), a number of similarities between the two contexts exist,
especially regarding the role of a coach in a team. Both athletic coaches and business
leaders, for example, involve motivating followers, encouraging mutual esteem.
evaluating follower’s performance, communicating effectively with followers.
encouraging performance results of followers, considering follower’s development, and
so on. Athletic coaches are also leaders of their teams just as business leaders. They
provide instruction, guide the practice of skills, and prepare for and lead competition
(Feltz, Chase, Moritz, & Sullivan, 1999). Coaches also have individual and group
relationships with their team members during practices and games. Therefore, just as E1
is perceived as an effective variable in the business setting, it also should be a powerful
variable in coaching effectiveness.

The majority of research regarding coaching effectiveness indicates that the
components of E1, which are perception, expression, understanding, and regulation of
emotions in self and in others, are characteristics of good coaches, and valuable to
coaching effectiveness. For instance, coaches have to accurately perceive their own
emotional state in order to sufﬁciently regulate certain emotions that may negatively
affect their athletes. At major competitions, for instance, some coaches may be nervous
and agitated. They have to regulate and sometimes cover their emotional state in order for
athletes not to be nervous and agitated. In an interview with James Counsilman, a
legendary Olympic swim coach, he recalled that he was often nervous at major
competitions but worked hard to not let his swimmers recognize his own stress (Kimiecik
& Gould, 1987). Gould, Guinan, Greenleaf, and Chung (2002) surveyed Olympic-level

coaches for coaching effectiveness, and identiﬁed that in Olympic competition, being in

control of one’s own emotional state and masking certain emotions from athletes are just
some aspects of a coach’s performance.

Also, coaches have to be able to identify and understand not only their own
emotional state but also their athletes’ needs and emotions so that they can successfully
deal with athletes’ needs and emotions during a competition, practice, and conference.
Regarding this capacity of coaches, Grace (1988) suggested that a successful coach
demonstrates sensitivity to an athlete’s needs both in and out of the competitive venue.
More speciﬁcally, Hanson and Gould (1988) reported that coaches clearly indicated that
being aware of their athletes’ anxiety levels is a matter of great concern. Also, Cote and
Sedgwick (2003) reported that, at the Olympic level, a coach’s interpersonal Skills and
ability to relate to athletes in a personal manner is essential for the coach’s success.

The only study in Sport to examine EI and coaching effectiveness was a doctoral
dissertation (VanSickle, 2004). VanSickle examined the relationship between the E1 of
Division I softball coaches and coaching effectiveness measured by win-loss records and
athletes’ satisfaction. E1 was measured using the Emotional Competence Inventory-2
(ECI-2: Sala, 2002), which measures 18 competencies organized into four clusters based
on trait model of E1: Self-Awareness, Self-Management, Social-Awareness, and
Relationship Management. The research discovered that a coach’s Self-Awareness was
related to win-loss records and athlete satisfaction. Social- Awareness was related to
current win-loss record. However, the study was focused on only the outcome of
coaching effectiveness. VanSickle recommended further study using potential variables.
such as coach’s behavior, personal characteristics, and organizational climate. which

contribute to coaching effectiveness.

Hom’s (2002) working model of coaching effectiveness Shows variables and links

between the variables related to coaching effectiveness. ‘Sociocultural context’,

‘organizational climate’ and ‘Coaches’ personal characteristics’ inﬂuence coaches’

expectation, which affect coaches’ behavior in practices and games. All of these factors

inﬂuence athletes’ behavior and performance in various ways (see Figure 1). According

to Horn (2002), the leadership style is one of the coaches’ behaviors to affect coaching

effectiveness, and coaching efﬁcacy is regarded as one of the coaches’ personal

characteristics to affect coaching effectiveness.

 

Sociocultural
context

 

 

I

 

climate

 

Organizational

 

 

 

 

‘ Coaches’
expectancies,
values, beliefs,
and goals

 

Athletes’
personal
characteristics

 

 

. _ Coaches"
* 7 ”behavior "

 

 

Coaches’
personal
characteristics

 

Figure 1

 

 

7

I.

 

Athletes’
performance
and behavior

 

 

A Working Model of Coaching Effectiveness (Horn, 2002)

I

 

 

Athletes’
perception,
interpretation, &
evaluation, of
their coaches’
behavior

 

I

 

/

 

Athletes’
self-perceptions.
beliefs, and
attitudes

 

 

I

 

 

Athletes’
level and type of
motivation

 

Note. From Coaching effectiveness in the sport domain. In T. S. Horn (Ed), Advances in
sport psychology (2”d.,p. 313), by T. S. Horn, 2002, Champaign, IL: Human Kinetics.
Copyright 2002 by the Human Kinetics. Reprinted with permission.

 

 

 

Feltz et a1. (1999) prOposed a conceptual model of coaching efﬁcacy that includes
coaching—speciﬁc sources of efﬁcacy information as well as the effects or outcomes of
coaching efﬁcacy. Based on Bandura’s (1977, 1997) theory of self-efﬁcacy. coaching
efﬁcacy is deﬁned as the extent to which coaches believe they have the capacity to affect
the learning and performance of their athletes. As sources of coaching efﬁcacy, F eltz and
colleagues suggested coaching experience, prior success, perceived skill of athlete, and
school/ community support as variables that would inﬂuence coaching efﬁcacy beliefs.
As outcomes of coaching efﬁcacy, coaching behavior, player/ team satisfaction,
player/team performance, player/ team conﬁdence were suggested. Although Feltz et al.
did not include ‘emotional states’ as a source of coaching efﬁcacy information, Bandura
includes it as a source of self-efﬁcacy information. For instance, people who believe that
they can control their negative emotions have higher efﬁcacy beliefs that they can
perform a task under pressure Situations. It is, likewise, reasonable to suggest that
coaches who believe they have good control of their emotions will have more conﬁdence
in their ability to affect the learning and performances of their teams.

To measure coaching efﬁcacy, Feltz et a1. (1999) developed the Coaching
Efﬁcacy Scale, which consists of 24 items containing four dimensions: Game Strategy
Efﬁcacy, Motivation Efﬁcacy, Technique Efﬁcacy, and Character Building Efﬁcacy.
Game strategy efﬁcacy was deﬁned as the conﬁdence coaches have in their ability to
coach during competition and lead their team to a successful performance. Motivation
efﬁcacy was deﬁned as the conﬁdence coaches have in their ability to affect the
psychological Skills and states of their athletes. Technique efﬁcacy was deﬁned as the

belief coaches have in their instructional/diagnostic skills. The fourth factor, character-

building efﬁcacy, was the conﬁdence coaches have in their ability to inﬂuence a positive
attitude toward sport in their athletes. EI probably is associated with all four dimensions
of coaching efﬁcacy as coaches need to regulate emotions during games, in instruction, in
dealing with the emotional states of their athletes, and in inﬂuencing their positive
development. To sufﬁciently regulate emotions, it is also required for coaches to exactly
identify their own emotions, and express it well according to various situations.

To identify and measure coaches’ perceived leadership style, Chelladuri and
Saleh (1978, 1980) developed the Leadership Scale for Sport (LSS) with 40 items on a 5-
point scale. The LSS consists of ﬁve subscalesz two decision-making style factors
(Democratic and Autocratic Behavior), two motivational tendency factors (Social
Support and Positive Feedback), and one direct task factor (Training and Instruction).
The LSS has been mostly used in measuring coaching leadership style (Zhang, Jensen &
Mann, 1997). Amorose and Horn (2000) used the LSS to measure collegiate athletes’
perceptions of their coaches’ leadership style. The results supported the hypothesis that
athletes who perceived their coaches to exhibit a more democratic coaching style and to
respond to players’ performances with high levels of praise, encouragement, and
information-based feedback would exhibit higher intrinsic motivation than would athletes
who perceived their coaches to be more authoritarian in leadership style. As Amorose and
Horn reported, coaches’ behaviors inﬂuence athletes’ attitudes such as motivation.
Probably, coaches’ supportive and democratic behaviors are associated with E1. More
emotionally intelligent coaches would show more supportive and democratic behaviors
because they have behavioral tendencies to understand athletes’ need and emotion and

deal with it sufﬁciently.

Although LSS is the major measurement of perceived coaching leadership style. it
has been criticized for the following two reasons: (a) low internal reliability (.45) of
autocratic behavior; and (b) lack of support for the factor structure (Gordon, 1986;
Summers, 1983). Chelladurai (1990) also expressed two concerns with the LSS: (a) items
refer to the frequencies rather than the context of coaching leadership behavior; and (b)
items were derived from scales in business and industry rather than from coaches and
athletes.

Through changing items and increasing internal reliability, Zhang et a1. (1997)
revised the LSS. The Revised LSS has 60 items, which consists of six factors. including
one new factor, Situation Consideration.

With regard to the proposed relationship between EI and coaching effectiveness
(coaching efﬁcacy and coaches’ perceived leadership style), the following model was
established to see if the factors of E1 account for variance in coaching efﬁcacy and
coaches’ perceived leadership style and if coaching efﬁcacy accounts for variance in
leadership style of coaches (see Figure 2). This model is delimited to basketball coaches
because basketball is a major team sport that both boys and girls play and both males and

females coach.

10

 

Coaching Efﬁcacy
' Game Strategy

' Motivation

 

Emotional Intelligence ' Technique

' Character Building

 

 

' Regulation
' Utilization
. Appraisal Leadership Style

’ Democratic

 

 

 

 

 

' Social Support
’ Training & Instruction
‘ Positive Feedback

“Situational Consideration

 

 

 

Figure 2
Hypothesized Model of Emotional Intelligence, Coaching Efﬁcacy, and Leadership Style

Propose of the Study

The main purpose of this study was to examine the predictive strength of E1 of
coaches on coaching efﬁcacy and leadership style of coaches. Additionally, the predictive
strength of coaching efﬁcacy on leadership style of coaches was examined. Examining
these relationships will provide additional understanding of coaches’ personal
characteristics (coaching efficacy) and coaches’ behavior (leadership style of coaches) to

contribute to coaching effectiveness.

11

 

Hypotheses and Proposed Model

The following hypotheses are assessed within a proposed model in Figure 2

l. The E1 of coaches positively predicts their coaching efﬁcacy.
a. Regulation, utilization, and appraisal of emotion positively predict the
each of four dimensions of coaching efﬁcacy.
2. The E1 of coaches positively predicts their leadership style.
a. Regulation, utilization, and appraisal of emotion positively predict each of
ﬁve factors of leadership style.
3. Coaching efﬁcacy positively predicts the leadership style of coaches.
a. The four dimensions of coaching efﬁcacy positively predict each of ﬁve
factors of leadership style.
4. As illustrated in Figure 2, E1 of coaches directly predicts coaching efﬁcacy and
leadership style and indirectly predicts leadership style through coaching efﬁcacy.
Assumption
1. The measure of self-reported leadership style of coaching is assumed to reﬂect
leadership behavior.
Delimitations
l. The population was delimited to high school boys’ and girls’ basketball coaches
in Michigan, Indiana, Ohio, and, Illinois,
2. The E1 of coaches was measured by the self-report MVSEIS (Modiﬁed Version of

Shutte Emotional Intelligence Scale).

12

3. The coaching effectiveness of coaches was measured by the Coaching Efﬁcacy
Scale (CBS) and the Revised Leadership Scale for Sports (RLSS) based on Horn’s
(2002) working model of coaching effectiveness.

Limitations

1. The MVSEIS was designed for life in general, not for Sport settings.

2. Among the factors to affect coaching effectiveness, only the aspects of the
coaches’ characteristics and behaviors were considered to explain coaching
effectiveness based on Hom’s (2002) working model of coaching effectiveness,
C ES and LSS.

Definitions

1. Coaching Efﬁcacy: the extent to which coaches believe they have the capacity to
affect the learning and performance of their athletes (Feltz et a1., 1999).

2. Emotional intelligence (E1): in general, the ability to perceive, express,
understand, and regulate emotions in oneself and others. Academically, it is an
array of non-cognitive capabilities, competencies and skills that inﬂuence one’s
ability to succeed in coping with environmental demands and pressures (Bar-On.

1997)

13

CHAPTER II

LITERATURE REVIEW

Emotional Intelligence

Emotional intelligence (El) has been conceptualized as the ability to perceive,
express. understand, and regulate emotions in the self and others in general. Salovey and
Mayer (1990) created the ﬁrst formal model and deﬁnition, and conducted the ﬁrst
relevant empirical studies of El (Mayer, DiPaolo, & Salovey, 1990). Following this work.
Goleman’s (1995) inﬂuential book ‘Emotional Intelligence: Why it can Matter More
Than IQ’ strongly inﬂuenced most subsequent scientiﬁc conceptualizations and models
of El. Two fairly distinct approaches of E1 have been proposed, that is, ability model and
trait model. The following chapter provides a review of these two approaches of E1
regarding conceptualizations and measurements, followed by an overview of E1 in
leadership, E1 in sport, and an overview of the coaching effectiveness literature as it
pertains to coaching efﬁcacy and leadership style in Sport.

Approaches to the Study of Emotional Intelligence

Although the two EI models include both cognitive (e.g., understanding and
perceiving) and behavioral (e.g., expressing and regulation in others) components,
Petrides and F urnham (2001) emphasized the differentiation between trait E1 and ability
El. They proposed the trait approach places E1 in the domain of personality,
encompassing various behavioral dispositions and self-assessed abilities. By contrast, the
ability E1 is viewed as a cognitive-emotional ability within an ability framework that

ought to be measured by a maximum performance test. Like IQ tests. the maximum

14

performance test for El consists of correct answers and incorrect answers to the
emotionally laden interpersonal situation (e. g., “You want to celebrate your birthday
with some friends, but they tell you that they have other plans”). They also emphasized
the importance of using different terms for the ability El and the trait EI with the
following alternative labels: cognitive-emotional ability for the ability El and emotional
self-efﬁcacy for the trait E1.

The present study is based on the trait conception of E1 because of the problems
of maximum performance tests of ability E1. The tests of ability EI cannot be objectively
scored because the subjectivity of emotional experience (e. g., Robinson & Clore, 2002;
Watson, 2000) undermines the development of valid maximum performance
measurement. That is, in most cases, there are no clear-cut criteria for what constitutes a
correct response. Also, the nature of the present study focused on the relationship
between individuals’ characteristics, not cognitive ability.

For a broad discernment about two distinct EI models (see Table 1), Salovey and
Mayer’s (1990) original model of E1 and Mayer and Salovey’s (1997) revised ability
model of E1 are reviewed to understand what the ability model is. Bar-On’s (1997) the
ﬁrst non-cognitive trait model of E1 is reviewed for the trait model, and a modiﬁed
version of Schutte et al.’s(1998) measurement (Austin et a1., 2004) used for this study is

discussed as a updated measurement of the trait model.

15

Table 1
Comparison Between Ability and Trait Model of Emotional Intelligence

 

 

Ability Model Trait Model
Initial work Salovey and Mayer (1990) Goleman (1995)
Major MEIS (Mayer et a1., 1999) Bar-on s EQ-r (1997)
measurements MSCEIT (Mayer et a1., 2000) Schutte’s EIS (1998)
Conceptualization Cognitive ability Personality trait
Melasurement Maximum performance test Self-assessed
sty e
Scale Correct/ incorrect answers Likert scale
Alternative label Cognitive emotional ability Trait emotional self efﬁcacy

 

Ability models. Salovey and Mayer (1990) proposed the ﬁrst published,
conceptual model of El. According to Salovey and Mayer (1990), E1 comprises three
conceptually related mental processes involving emotional information. These processes
are (a) the appraisal and expression of emotion, (b) the regulation of emotion, and (c) the
utilization of emotion (see, Figure 3). In the Salovey and Mayer’s original model, two
branches (the appraisal and expression of emotion and the regulation of emotion) are
further subdivided into self and other. In the lower branch (the appraisal and expression),
the self and other are further subdivided according to a verbal versus a nonverbal domain.
Additionally, the upper left branch comprises four sub-factors, which assume that high EI
persons to be more ﬂexible in their utilization of emotions due to ﬂexible planning. more
creative thinking, the ability to (re-)direct attention, and a propensity to motivate
themselves and others. Furthermore, this model assumes that emotionally intelligent
individuals should be especially adept in the following certain domains: (a) perceiving
and appraising their own emotions accurately (e. g., to identify coach’s own emotional

status), (b) expressing and communicating them accurately to others when appropriate (e.

16

 

Flexible Creative Redirected Motivation In self In other
planning thinking attention

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Utilization of Regulation of
emotion emotion
El
Appraisal of
emotion
Self Other
Verbal Non-verbal Non-verbal Empathy
perception
Figure 3

Salovey and Mayer’s (1990) Model of Emotional Intelligence
Note. From Models of Emotional Intelligence. In R. Schulze and T. D. Roberts
(Eds), Emotional Intelligence: An International Handbook (p. 34), by A. C.

Neubauer and H. H. Freudenthaler, 2005, Cambridge, MA: Hogrefe & Huber.
Copyright 2005 by Hogrefe & Huber. Reprinted with permission.

g., to share the emotions that coaches and athletes have felt during the competition), (c)
recognizing the emotions in others accurately and responding to them with socially
adaptive behaviors (e. g., to appropriately encourage the discouraged athletes), (d)
regulating emotions in themselves and others effectively in order to meet particular goals
(e. g., to enhance coaches’ own and athletes’ mood to accomplish a goal), and (e) using
their own emotions in order to solve problems by motivating adaptive behaviors (e. g., to

Show enthusiasm to encourage athletes) (Neubauer & F reudenthaler, 2005).

17

Mayer and Salovey (1997) presented a revised and reﬁned model of E1. The two
factors ‘appraisal and expression of emotion’ and ‘regulation of emotion’ in the original
model remain in the ﬁrst branch and the fourth branch with more elaborated meanings.
Although the label of the factor ‘utilization of emotion’ was changed to ‘emotional
facilitation of thinking’, the meanings are similar to the original model in terms of
ﬂexible thinking and redirected attention with reasoning. However, the third branch in the
new model was newly added with a new component of understanding and analyzing
emotions with emotional knowledge (e. g., ability to label emotions and understand
complex feelings such as simultaneous feelings of love and hate)

In the revised model, Mayer and Salovey (1997) deﬁned El as a collection of
emotional abilities that can be divided into four branches. The ﬁrst branch (Perception,
Appraisal and Expression of Emotion) involves the receiving and recognizing of
emotional information and comprises the most basic emotion-related skills. These
components cover the ability to identify emotions in one’s self and in others. the ability to
discriminate between emotions, and the ability to express emotions accurately.

The second branch (Emotional Facilitation of Thinking) describes the use of
emotions to enhance reasoning and proposes various emotional events that assist in
intellectual processing, which is the ability to assimilate emotions into perceptual and.
cognitive processes. Included under this branch are emotions that direct attention to
important information and different kind of moods that may facilitate different forms of
reasoning.

The third branch (Understanding and Analyzing Emotion) involves cognitive

processing of emotions with emotional knowledge and comprises four representative

18

abilities involving abstract understanding and reasoning about emotions. These
components range from the ability to label emotions and recognize relations among the
words and the emotions themselves, to the ability to recognize likely transitions among
emotions.

The fourth branch (Reﬂective Regulation of Emotions) refers to the ability to
manage emotions in oneself, and in others, in order to enhance emotional and intellectual
growth. This ability comprises the most advanced skills, ranging from the ability to stay
open to feelings-both pleasant and unpleasant ones-to the ability to manage emotions in
oneself and others by enhancing pleasant emotions and moderating negative ones.

Based on this conceptual model, Mayer, Caruso and Salovey (1999) developed
the Multifactor Emotional Intelligence Scale (MEIS), which consists of 12 performance
tasks, includes 402 items and yields four subscales: Perception, Assimilation,
Understanding, and Managing Emotions. To validate the structure of MEIS, factorial
analyses (e. g., Mayer et a1., 1999; Roberts, Zeidner, & Matthews, 2001; Ciarrochi, Chan,
& Capupi, 2000) were conducted. Findings support only Branch 1 (perception) and IV
(management/regulation), and low reliabilities were revealed (Ciarrochi et a1., 2000).
Also, the correlations between consensus- and expert— scored subscales are much too low
to demonstrate satisfactory convergence between two scoring-method: (a) Group
consensus: each response is scored according to the proportion of participants who gave
the same answer), (b) Expert scoring: the correct answer is determined by asking experts
in the ﬁeld what the correct answer is (the ﬁrst two authors served as experts). To solve
some of these problems, as well as to improve the psychometric qualities of the MEIS.

the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) was developed

19

(Mayer, Salovey, & Caruso, 2000) with the improved scoring procedures: (a) a general
consensus criterion which is based on the answers of more than 2,000 participants, (b) an
expert-consensus criterion which is based on the assessments of 21 members of the
International Society of Research in Emotion (see Mayer, Salovey, Caruso, & Sitarenios.
2003). Mayer et a1. (2003) reported a surprisingly high correlation coefﬁcient of r = .91
between the two sets of scores, as well as improved reliabilities as compared to the MEIS.

Mayer and Salovey (1997) insisted that only performance tests of the proposed
emotion-related abilities, enabling discrimination between correct and incorrect responses,
can demonstrate and prove the existence of El. Nevertheless, Ciarrochi et a1. (2000)
criticized about the ability measures that the some of the ability measures are problematic
because of low reliabilities caused by two different scoring-methods.

Trait Model. In contrast to Mayer and Salovey’s ability conceptualization of E1,
the trait model (e.g., Bar-On, 1997; Cooper & Sawaf, 1997; Goleman, 1995, 1998;
Weisinger, 1998) does not exclusively refer E1 to emotion or intelligence. Instead. the
authors claim that E1 is often used as a label for a diverse group of personality
characteristics that might predict success in professional and everyday domains
(Neuhauer & Freudenthaler, 2005).

Bar-On (1997) deﬁned EI as “an array of non-cognitive capabilities,
competencies and skills that inﬂuence one’s ability to succeed in coping with
environmental demands and pressures.” (p.14). Bar-On identiﬁed ﬁve broad dimensions.
which are further subdivided into 15 subscales as key factors of El (see Table 2).

To measure it, EQ-i (Emotional Quotient Inventory) was developed with 133

items. The EQ-i is one of the most widely used measures of the trait E1 in the literature

20

(Perez. Petrides & Fumham, 2005). Empirically, however, there is no evidence fora
higher-order structure, as the questionnaire seems to be unifactorial (Petrides & F urnham.

2001). As another limitation, Peres et al. (2005) said that it includes several irrelevant

99 66 9’ 6‘

facets (e.g., “problem solving , reality testing , independence”) and neglects many

9, 6‘ 99 66

relevant ones (e.g., “emotion perception , emotion expression , emotion regulation”).

 

 

 

 

Table 2
Bar-On’s (1997) Five Dimensions and 15 Sub-Categories
5 Dimensions 15 Sub-Categories Contents
Being aware of, understanding and accepting
Self-regard oneself
Emotional Being aware of and understanding one’s
Intrapersonal self-awareness emotions
Skills Assertiveness (Egggzsmg one s emotrons, Ideas. needs and

 

Self-actualization Realizing one’s potential capacities

 

Being self-directed, self-controlled and free of

 

 

 

Inde endence .
p emotional dependency
Being aware of and understanding other’s
Empathy .
emotions
Interpersonal . . . . Demonstrating oneself as a constructive
Sk'll Socral responsrbrlrty , .
1 5 member of one S socralgroup
Interpersonal Forming and maintaining intimate
relationships relationships

 

Solving personal and social problems

Problem solving constructively

 

Adaptability Reality testing Validating one’s thinking and feelings

 

Adjusting one’s feelings, thoughts, and

Flex‘b'my behavior to changing conditions

 

Stress tolerance Actively and positively coping with stress

 

 

 

Stress Resist'n or dela in an im ulse or drive and
I ’ ,
Management Impulse control g , y g . p
controllmgone s emotions
' Feelin satisﬁed with one’s life
General Mood Happ mess . g. . . . .
Optimism Maintaining posrtrve attitudes

 

Note. From Models of Emotional Intelligence. In R. Schulze and T. D. Roberts (Eds),
Emotional Intelligence: An International Handbook (p. 41), by A. C. Neubauer and H. II.
Freudenthaler, 2005, Cambridge, MA: Hogrefe & Huber.Copyright 2005 by Ilogrefc &
Huber. Adapted with permission.

21

As a short measure of global trait El, Schutte et a1. (1998) developeled Schutte
Emotional Intelligence Scale (SEIS), which consists of three sub-scales with 33 items
responded to on a 5-point Likert Scale.

Despite being labeled a trait model, three of the factors, the appraisal and _
expression of emotion, the regulation of emotion, and the utilization of emotion, are
based on the original conceptual model of Salovey and Mayer (1990), which is an ability
model. Schutte and colleagues reported that the internal consistency was a Cronbach
alpha of .90 for the 33-item scale, and 2-week test-retest reliability was .78. According to
Schutte et a1. (1998), the scale showed evidence of validity: scores on the scale were
related to eight of nine measures predicted to be related to E1, furthermore, scores on the
scales differed between groups one would expect to differ on level of E1.

Although SEIS (Schutte Emotional Intelligence Scale) has been used a lot as a
measure of E1 in the trait mode, it has been criticized due to a small number of items.
Austin et a1. (2004) modiﬁed the SEIS because of a lack of reverse-keyed items (Petrides
& Fumham, 2000; Saklofske et a1., 2003), which could potentially lead to a confounding
of an El score with acquiescent responding (Austin et a1., 2004). The Modified Version
of the SEIS (MVSEIS) consists of 41 items having 20 forward-keyed and 21 reverse-
keyed items. Austin and colleagues reported that overall internal reliability is .85, and the
sub-factors are following: .78 for regulation of emotions, .68 for utilization of emotion,
and .76 for appraisal of emotion. They also reported that overall the MVSEIS was highly
correlated to the short version of Bar-On Emotional Quotient Inventory (EQ-i:S, Bar-On,
1997). In addition, Austin and colleagues indicated this modiﬁed version is reasonably

congruent with most theoretical approaches to trait EI.

22

Regardless the ability model and trait model on E1, the following psychometrical
criticisms exist of E1: (a) there appears little consensus over how EI should be deﬁned
and conceptualized; (b) it is uncertain how EI may be best measured, with various tests of
the construct failing to converge; (c) the practical utility of tests of E1 is limited by these
conceptual and psychometric deﬁciencies (Matthews et a1., 2007).

With regard to weaknesses, Matthews and colleagues (2007) suggested systematic
attempts to map the complete domain, to Show that there is some interrelationship and
coherence between different perspectives, and to ﬁnd the key components of E1 in order
to increase the reliability and the validity of E1 measurement, which may provide the
validated application into the growing public and scientiﬁc interest in El.

Emotional Intelligence and Effective Leaders

El has been identiﬁed as one of the effective leaders’ characteristics, regarding
leadership in the business setting. George (2000) theorized that EI facilitates dimensions
of leadership, including (a) the development of a uniﬁed sense of goals and objectives.
(b) inculcating the value of work in subordinates, (c) creating a climate of excitement,
enthusiasm, cooperation, optimism, and trust, (d) fostering adaptability to change, and (e)
creating and sustaining an identity for the organization. That is, E1 is a key factor in an
individual’s ability to be socially effective, and is viewed in leadership literatures as a
key determinant of effective leadership (George, 2000).

Rosete and Ciarrochi (2005) studied 41 Australian public service managers to
explore the relationship between El, personality, cognitive intelligence and leadership
effectiveness. He found that higher EI scores were associated with higher leadership

effectiveness. Kobe et a1. (2001) examined the power of both emotional and social

23

intelligence to account for variance in self- reported leadership experiences through a
regression analysis. The result showed that individuals who scored high on a measure of
E1 reported more participation in leadership experiences. Thus, the leaders with a high
level of El can be better equipped to promote effectiveness at all levels in organizations,
which, in turn, strongly inﬂuence their followers’ performance outcome (Humphrey,
2002). That is, the ability of leaders to inﬂuence the emotional climate can inﬂuence
performance.
Emotional Intelligence in Sport

While E1 has been widely researched from clinical and organizational
psychologists. it has not received widespread attention by sport psychologists in spite of
the possible relations to the domain of sport. In recent years, the interest is arousing. As
the interest reﬂects, researchers in the sport domain have tried to examine the relationship
between EI and performance. Zizzi, Deaner, and Hirshhom (2003) investigated the
relationship between EI measured by Schutte’s (1998) Emotional Intelligence Scale
(SEIS) and global measures of baseball performance in a sample of college baseball
players. For baseball performance, Zizzi and colleagues collected data of pitching
statistics (earned run, walks, hit, strikeouts, and wild pitches) and hitting statistics (hits,
doubles, walks, and strikeouts). Zizzi and colleagues reported that components of E1
appear to be moderately related to pitching performance, but not related to hitting
performance, indicating only modest support for the link between emotional skills (i.e.,
emotional awareness, control and utilization) and athletic performance.

Lane and Lowther (2005) investigated the relationship between El and

psychological skills of athletes in competition and practice. The ﬁnding showed that El

24

scores were Signiﬁcantly related to self-talk used in competition and practice. along with
goal-setting and imagery used in competition.

With the criticism about the delinquent behavior of collegiate athletes on and off
the ﬁeld, Crabbe (2007) hypothesized in his thesis that the higher the E1 in collegiate
athletes, the higher their ability to perceive, understand and manage emotions would be,
as well as the lower acts of delinquent behavior on and off the ﬁeld, and the better their
performance during games would be. He assessed El using the MSCEIT v. 2.0 (Mayer-
Salovey-Caruso Emotional Intelligence Test v. 2.0). However, none of the hypotheses
were supported.

Miller (2003) tried to qualitatively investigate the connection between El and
altruistic behavior of coaches through an interview with 15 collegiate level coaches. This
qualitative study had the primary purpose of exploring coaches’ perception of altruistic
leadership. A secondary purpose was to examine the connection between altruism and E1
by determining what factors of El emerged with the coaches’ perceptions of altruistic
leadership. The results Showed that several themes emerged from the interviews.
Coaches’ perceived character, consistency, balance, caring, and empowerment emerged
as altruistic leadership factors, and factors of El such as self-awareness, self-regulation,
social Skills, empathy, and self-motivation were reinforced in relation to altruistic
leadership. This result implicates that ﬁve factors of El identiﬁed by coaches’ interviews
are important competencies that may facilitate altruistic leadership behaviors.

One researcher used the concept of El for coaching effectiveness in a doctoral
dissertation. VanSickle (2004) examined the relationship between the emotional

intelligence of Division I softball coaches and coaching effectiveness measured by win-

25

loss records and athletes’ satisfaction. E1 was measured using the Emotional Competence
Inventory-2 (ECI-2: Sala, 2002), which measures 18 competencies organized into four
clusters: Self-Awareness, Self-Management, Social-Awareness, and Relationship
Management. The research discovered that a coach’s Self-Awareness was related to win-
loss records and athlete satisfaction. Social- Awareness was related to current win-loss
records. However, the study was focused on only the outcome of coaching effectiveness.
Van Sickle recommended further study using potential variables, such as coach’s
behavior, personal characteristic, and organizational climate. which contribute to
coaching effectiveness.

In addition to empirical studies, Meyer and Fletcher (2007) reviewed E1 models
and assessment inventories, and research on E1 in business, health, and sport in order to
identify directions for future research and professional practice in sport psychology. They
suggested for future research that regardless of the two models (ability and trait), a
cooperative approach with clear deﬁnition and construct would result in the testing of
appropriateness for sports of current assessment inventories, and eventually the
development of a sports-speciﬁc measure of El. They also suggested that one of the most
obvious applications of E1 in sports may be as a systematic and hierarchical framework
for enhancing the performance and satisfaction of athletes, teams, and coaches.
Coaching Effectiveness

In a team, a coach has number of roles as a leader, educator, instructor, supporter,
and inspirer for athletes. Thus, research on coaching effectiveness has been conducted
under the general assumption that coaches greatly inﬂuence not only the performance and

behavior of their athletes but also the athletes’ psychological and emotional well-being

26

(Horn, 2002). Most of the research that has been conducted on coaching effectiveness
within the last 3 decades has been motivated by a desire to identify the most effective
coaching characteristics, leadership styles, or behavior pattern (Horn, 2002). For
example, the Leadership Scale for Sports (LSS) was developed by C helladurai and Saleh
(1978, 1980) to measure coaches’ leadership style, and evaluates what behavioral pattern
is the most effective in teams. In addition, Smoll and Smith (1989) proposed a theoretical
model of leadership behavior that emphasizes relationships among situational, cognitive,
behavioral and individual difference variables. According to this model, a coach’s
behavior is inﬂuenced by his or her individual difference variable such as coaching goal
and motive and situational factors such as level of competition. Simultaneously, these
situational factors as well as players’ individual differences inﬂuence players’ perception
and recall.

According to Horn (2002), new perspectives regarding the inﬂuence of coaches
on their athletes’ psychosocial and emotional development Should be combined with
components of the models of Chelladurai’s (1978, 1990, 1993) and Smoll and Smith’s
(1989) sport leadership effectiveness. The new perspectives that Horn mentioned are
attribution theory (Weiner, 1986,1992), self-efficacy theory (Bandura, 1977,1986),
achievement goal theories (Ames, 1984, 1992a, 1992b; Dweck, 1986, 1999), competence
motivation theory (Harter, 1978. 1981), expectancy-value model (Eccles & Harold.
1991). self-determination theory (Deci &Ryan, 1985, 1991), and sport commitment
model (Scanlan, Carpenter, Schmidt, Simons & Keeler, 1993). With the previous model
of leadership effectiveness and the new perspectives, Horn (2002) created a working

model of coaching effectiveness (Figure 1).

27

Horn (2002) noted that as these aspects of the working model indicate,
researchers can no longer assume that one set of coaching behaviors will be effective for
all athletes and in all sport situations; rather, effective coaching behaviors will vary as a
function of the athlete and the sport context.

With regard to a working model of coaching effectiveness, Horn (2002)
summarized three major points of it. First, identiﬁable antecedent factors including
‘sociocultural context’, ‘organizational climate’, coaches’ personal characteristics’, and
‘coaches’ expectancies, values, beliefs, and goals’ explain the types of behaviors coaches
exhibit in the sport settings. For example, due to the team climate that a particular game
needs to be won, and given that coach’s personality, the coach may exhibit different and
complicated behavior than in another situation or setting (Horn, 2002).

The second point is that the coach’s behavior in practices and games affects
athletes’ performance and behavior indirectly as well as directly. The athletes’
performances and behaviors are indirectly affected by the interaction between the
following factors: ‘athletes’ personal characteristics’, ‘athletes’ perceptions,
interpretation, and evaluation of their coaches’ behavior’, ‘ athletes’ self-perceptions,
beliefs, and attitudes’ and ‘athletes’ level and type of motivation’ For example, although
a coach’s behavior is extremely autocratic for some reason, the athletes may exhibit
different behaviors, such as following or resisting their coach according to their
interpretation of the coach’s behaviors (Horn, 2002).

The last point is that the effectiveness of different types of coaching behaviors is
mediated by both situational and individual difference variables. Specially, the direct link

between a coach’s behavior and athletes’ performance and behavior is mediated by a

28

variety of sport contextual variables (e.g., competitive level, type of sport) and by a
variety of athlete variables (e.g., age, Skill level) (Horn, 2002).

According to Horn (2002), the leadership style is one of the coaches’ behaviors to
affect coaching effectiveness. In turn, coaching efﬁcacy is regarded as one of the
coaches’ personal characteristics and belief in coaching to affect coaching effectiveness.

As shown in Figure 1, there is a direct link between coaches’ behavior and
athletes’ performance and behavior. This means that a coach’s behavior during practice
and competitions will directly affect her or his athletes’ performance and their ability to
master the skills (Horn, 2002). The direct link between coaches’ behavior and their
athletes’ performance and behavior is mediated by athletes’ self-perceptions, beliefs.
attitudes and level and type of motivation. That is, the types of coaching behaviors are
most effective in facilitating the performance and behavior of the individual athlete
(Horn, 2002).

In addition to the relationship between coaches’ behaviors and athletes’
performance, such coaches’ behavior is also affected by their characteristics (e. g..
coaching efﬁcacy). According to the level of their conﬁdence in coaching, they will
differently behave. In this regard, using the Coaching Behavior Assessment System
(CBAS: Smith, Smoll, & Hunt, 1997), Feltz et a1. (1999) found that the fact that high-
efﬁcacy coaches demonstrated less instructional and organizational behavior than lower
efﬁcacy coaches did may be because lower efﬁcacy coaches were less efﬁcient and
therefore spent more time on organizing players for drills and practice. This result
supports that coaching efﬁcacy affects coaches’ behaviors and ﬁnally coaching

effectiveness.

29

Coaching Efficacy. According to Hom’s (2002) working model of coaching
effectiveness, a coach’s personal characteristics are one of the antecedent factors to affect
coaches’ expectancies, values, beliefs, and goals and ultimately coaching effectiveness.
Coaching efﬁcacy is deﬁned as the extent to which coaches believe they have the
capacity to affect the learning and performance of their athletes (Feltz et a1., 1999).

Using Denham and Michael’s (1981) multidimensional model of teacher efﬁcacy.
Bandura’s (1977, 1986) conceptualization of self-efﬁcacy, and Park’s (1992) initial
measure of coaching conﬁdence, Feltz et a1. (1999) proposed a multidimensional model
of coaching efﬁcacy that includes coaching-speciﬁc sources of efﬁcacy information as
well as the effects or outcomes of coaching efﬁcacy. With respect to the sources of
coaching efﬁcacy, Feltz and colleagues suggested coaching experience/preparation, prior
success, perceived skill of athletes, and school/community support as the most important
sources of coaching efﬁcacy information. Also, as outcomes of coaching efﬁcacy, Feltz
and colleagues mentioned coaching behavior, player/team satisfaction. player/team
performance, and player/team efﬁcacy (see Figure 4).

The Coaching Efﬁcacy Scale (F eltz et a1., 1999) consists of 24 items containing
four dimensions: Game Strategy Efﬁcacy (7 items), Motivation Efﬁcacy (7 items),

Technique Efﬁcacy (6 items), and Character Building Efﬁcacy (4 items). Game strategy

30

Sources of Coaching Coaching Efﬁcacy Outcomes of

 

 

 

 

 

 

 

 

 

 

 

Efﬁcacy Dimension Coaching Efficacy
Coaching experience/ Game strategy Coaching behavior
preparation

Motivation Player/ team
Prior success satisfaction
‘ Technique ‘
Perceived skill Player/ team
of athlete Character performance
building
School/ community Player/ team
support , efﬁcacy
Figure 4

Conceptual Model of Coaching Efﬁcacy (F eltz et a1., 1999)
Note. From “A Conceptual Model of Coaching Efﬁcacy: Preliminary Investigation
and Instrument Development,” by F eltz et a1., 1999, Journal of Educational

Psychology, 91, p. 766. Copyright 1999 by American Psychological Association.
Reprinted with permission.

efﬁcacy was deﬁned as coaches’ conﬁdence in their coaching during competition and
their ability to lead the team to a successful performance. Motivation efﬁcacy was
deﬁned as conﬁdence in the ability to change the psychological states and abilities of
athletes. Teaching technique efﬁcacy referred to the degree of conﬁdence coaches have in
their diagnostic and teaching skills. Finally, character building efﬁcacy involves coaches’
perception of their ability to inﬂuence their athletes’ personal maturation and positive
sporting attitudes.

Regarding research on the sources of coaching efﬁcacy, F eltz et a1. (1999) found
support for their model of coaching efﬁcacy in that canonical loadings showed years in

coaching (r = ~82), perceived community support (r = -.61), perceived team ability (r = -

31

 

.51). perceived parental support (r = -.44), and past winning percentage (r = -.43) to be
signiﬁcantly predictive of the dimensions of coaching efﬁcacy. The strongest predictors
of coaching efﬁcacy were years of coaching experience and community support for these
high school coaches. However, using collegiate coaches, Myers, Vargas-Tonsing, and
F eltz (2005) found that perceived team ability was the strongest predictor as explaining
that the major inﬂuence of perceived team ability has to do with the more competitive
nature of collegiate compared to high school sports. They also found gender difference in
the strength of sources. Female coaches reported that social support from the community
was a stronger source of character-building efﬁcacy compared to male coaches.

In recent years, efforts to specify and extend the sources of coaching efﬁcacy
have been attempted by several researchers (e.g., Chase, Feltz, Hayashi, & Hepler, 2005;
F eltz, Hepler, Roman, & Paiement, 2006; Short, Smiley, & Ross-Stewart, 2005; Sullivan,
Gee, & Feltz, 2006). Chase and colleagues (2005) proposed an extension to the sources in

the original model, outlined follows:

0 Extent of coaching experience and preparation:
0 Knowledge to prepare team
0 Past experience in coaching
0 Playing experience
0 Leadership skills
0 Coach’s development
Prior success (win/loss record)
Perceived skill of athletes
Player improvement
Support from the following:
c School students and teachers
Community
Parents
Athletic director
Players

0000

32

One potential source of efﬁcacy information for coaches is their perceived ability
to regulate, utilize, and appraise their emotions. As stated in Chapter 1, coaches who
believe they have good control of their emotions will have more conﬁdence in their
ability to affect the learning and performances of their teams. However, this hypothesis
has yet to be tested.

Perceived ability to regulate, utilize, and appraise emotions ﬁts with Bandura’s
(1977. 1997) sources of self-efﬁcacy information within his theory. Bandura (1997)
states that one way of altering efﬁcacy belief is by reducing “negative emotional
proclivities” (p. 106). This also includes mood states, such as anger, fear, anxiety, and joy.
Bandura explains that moods provide sources of efﬁcacy information “because they often
accompany changes in quality of functioning” (p. 111). That is, they can inﬂuence how
coaching situations are interpreted. Schunk (1995) suggested that emotional symptoms
that signal anxiety might be interpreted by an individual to mean that he or she lacks the
requisite skills to perform a certain task, which in turn inﬂuences efﬁcacy judgments. It
is. likewise, reasonable to expect that coaches who believe that they have good ability in
regulation of their emotions to keep a positive mood will have more conﬁdence in their
ability to affect the performances of their teams. That is, it may be more difﬁcult to attend
to the tasks of game strategizing, teaching, motivating, and instilling character when one
has less control of one’s emotions.

One of the major outcomes of coaching efﬁcacy is coaching behavior. F eltz et a1.
(1999) investigated the coaching behaviors and commitment of high- and low- efﬁcacy
coaches, using the CBAS (Smith et a1., 1997) which has 12 categories of coaching

behaviors such as ‘positive reinforcement in response to desirable player performance,’

33

‘mistake contingent encouragement in response to player mistakes,’ and ‘general
technical instruction.’ They found that the fact that high-efﬁcacy coaches demonstrated
less instructional and organizational behavior than lower efﬁcacy coaches did may be
because lower efﬁcacy coaches were less efﬁcient and therefore spent more time on
organizing players for drills and practice. Also, high-efﬁcacy coaches used instruction
that was paired with positive reinforcement more often than did low-efﬁcacy coaches
(Feltz et a1., 1999).

Using multiple regression analysis, Sullivan and Kent (2003) examined the
relationship between the coaching efﬁcacy of intercollegiate coaches and their leadership
style measured by the Leadership Scale for Sport (LSS: Chelladurai & Saleh, 1978,
1980). The LSS consists of ﬁve subscales: two decision-making style factors (Democratic
and Autocratic Behavior), two motivational tendency factors (Social Support and Positive
Feedback). and one direct task factor (Training and Instruction). Sullivan and Kent
hypothesized that technique and game strategy efﬁcacy would predict training and
instruction leadership behavior, that motivation and character building efﬁcacy would
predict social support, and that game strategy efﬁcacy would also predict democratic and
autocratic behavior. However, results Showed that training and instruction and positive
feedback were both predicted by motivation and technique efﬁcacy. Both models
accounted for large amounts of variation in leadership style (28 and 42%, respectively),
and both were based on positive relationships between leadership style and efﬁcacy
(Sullivan & Kent, 2003). Sullivan and Kent concluded that as coaches were more

conﬁdent in their roles as motivators and teachers. they were closer to their image of the

34

ideal leader with respect to using positive feedback and appropriate training and
instruction, and engaged in these behaviors to a greater extent.

Furthermore, Vargas-Tonsing, Wamers, and Feltz (2003) examined the
relationship between coaching efﬁcacy and player and team efﬁcacy in high school
volleyball teams. The regression analyses showed coaching efﬁcacy to signiﬁcantly
predict team efﬁcacy, with no meaningful association with player efﬁcacy, and character
building efﬁcacy were shown to be the strongest predictors of team efﬁcacy (Vargas-
Tonsing et a1., 2003). They implied that coaches need to be cognizant that their sense of
efﬁcacy can impact their team as a whole. Specially, coaches’ efﬁcacy beliefs may be
beneﬁcial to the team when trying to increase the team’s conﬁdence (Vargas-Tonsing et
al.. 2003).

Leadership stylefor sports. To identify, evaluate and measure perceived coaches’
leadership behavior (or style), several instruments have been developed and used:
Leadership Scale for Sports (LSS: Chelladurai, 1993; Chelladurai & Riemer, I998;
Chelladurai & Saleh, 1978, 1980), Decision-Style Questionnaires (DSC: Chelladurai &
Amott, 1985; Chelladurai & Quek, 1991), Perceptions of Coaches’ Interpersonal Style
(PCIS: Pelletier & Vallerand, 1985), Perceived Motivational Climate in Sport
Questionnaire-2 (PMCSQ-2: Duda & Whirehead, 1998; Newton & Duda; 1993), and
Coaching Behavior Assessment System (CBAS: Smith, Smoll, & Curtis. 1978; Smith.
Smoll, & Hunt, 1997).

Among those instruments, the LSS is the most often used inventory to measure
coaches’ leadership style. Chelladuri and Saleh (1978, 1980) developed this inventory

with 40 items on a 5-point scale. The LSS consists of ﬁve subscales: two decision-

35

making style factors (Democratic and Autocratic Behavior), two motivational factors
(Social Support and Positive Feedback), and one direct task factor (Training and
Instruction). The LSS has three versions: athlete preference, athlete perception, and coach
self-evaluation.

The major ﬁndings of athletes’ perceptions of coach leadership behavior using
LSS were that the subscale factors were consistently affected by the performance and
satisfaction of the athletes’ level of readiness (Chelladurai & Carron, 1983) and cultures
or nationalities (Chelladurai, Imamura. Yamaguchi, Oinuma, & Miyauchi, 1988). In
addition, the perceived coach social support behavior or relationship behavior was the
most predictive factor in satisfying the needs of the athletes (Chelladurai, 1984).

Amorose and Horn (2000) used the LSS to measure collegiate athletes’
perceptions of their coaches’ leadership style. The results supported the hypothesis that
athletes who perceived their coaches to exhibit a more democratic coaching style and to
respond to players’ performances with high levels of praise, encouragement, and
information-based feedback would exhibit higher intrinsic motivation than would athletes
who perceived their coaches to be more authoritarian in leadership style (Horn 2002).

However, the LSS has received criticism due to quality of measurement.
According to Chelladurai and Saleh (1980), the measurement properties of the coaching
self-evaluation version were not tested, and the development of LSS was ﬁrst started in
industry and business areas. Zhang and colleagues (1997) claimed that the LSS is not
directly appropriate to sport because the sport setting has the following unique

characteristics: (a) athletic training requires much more time to prepare for competition;

36

(b) athletic winning is always accompanied by losing; and (c) athletic teams exist for a
speciﬁed time period.

Zhang and colleagues (1997) revised the three versions of the LSS to improve
psychometric properties. According to them, the result Shows that the Revised Leadership
Scale for Sport (RLSS) has improved the measurement characteristics of the LSS in
several ways: (a) the items were generated through interviewing the coaches. hence, they
are sports speciﬁc; (b) the study was conducted in the United States and regulations of
the National Collegiate Athletic Association were considered, thus, the scale is more
culturally speciﬁc to the United States; (c) involvement of large samples of subjects in a
variety of sports improves the generalization and the application of the scale; ((1) the
measurement properties of the coaching self-evaluation version were tested and
improved; and (e) overall factor structures in determining the constructs of the scale were
notably improved.

The RLSS contains 60 items on a 5-point Likert scale, which consists of six
factors: Training and Instruction (10 items), Democratic Behavior (12 items), Autocratic
Behavior (8 items), Social Support Behavior (8 items), Positive Feedback Behavior (12
items), Situation Consideration (10 items). Zhang and colleagues (1997) reported that
coefﬁcient alpha for each sub-factors were signiﬁcantly (p<.05) greater than .70, with
exception of the ‘Autocratic Behavior’: .93 for democratic behaviors, .85 for positive
feedback behaviors, .81 for situation consideration behavior, .83 for teaching and
instruction behavior, .81 for social support behaviors, and .35 for autocratic behaviors.

Using RLSS, Beam, Serwatka, and Wilson (2004) examined the differences of

student-athletes’ preferred leadership behavior for their coaches based on gender,

37

competition level, task dependence, and task variability. Four hundred and eight male and
female student-athletes from four NCAA Division I and six Division 11 universities
expressed their preferred coaching behaviors. As a result, male athletes showed
signiﬁcantly greater preferences for autocratic and social support behaviors, while female
athletes preferred situational consideration and training and instruction behaviors. This
result was consistent with Chelladurai and Saleh’s (1978) result in which male athletes
more preferred coach’s autocratic and social support behaviors than female athletes.

E1 is one variable that also should be predictive of leadership style in sport.
Coaches with higher levels of El should demonstrate greater coaching effectiveness
through their democratic approach, greater signs of social support, more positive training
and feedback. and better attention to situational considerations. Thus, coaches’

leadership styles should be inﬂuenced by both coaches’ efﬁcacy and beliefs and their El.

38

Summary

El has been conceptualized as the ability to perceive, express, understand, and
regulate emotions in the self and others. It has been identiﬁed as one Of the effective
leaders’ characteristics in business settings because it contributes to effective
performance at work. Researches in the business setting indicate that a high level of El
promotes effectiveness at all level in organizations, which, in turn, enhances followers’
performance.

Athletic coaches are also leaders of their team. In terms of coaching effectiveness.
coaches play a key role to inﬂuence athlete performance and behavior. According to
Horn’s (2002) working model of coaching effectiveness, many factors inﬂuence coaching
effectiveness and athletes’ performance. Although many factors affect coaching
effectiveness, in this study, the perspectives of coaches’ characteristic and behaviors are
considered to explain the coaching effectiveness. The leadership style is one of the
coaches’ behaviors to affect coaching effectiveness, and the coaching efﬁcacy beliefs and
EI are regarded as two of the coaches’ personal ~characteristics. Those have a potential
impact on coaching effectiveness as measured by leadership style. Further, E1 is

considered a potential source of coaching efﬁcacy information.

39

CHAPTER 3
METHOD

Participants

The targeted population for this study was high school boys’ and girls’ basketball
head coaches in Michigan, Ohio, Indiana, and Illinois. The total number of high school
basketball programs in those states is approximately 5,162, in which boys’ teams are
2.601 and girls’ teams are 2,562. Participants for this study were 323 head coaches in
high schools (280 male, 42 female, and 1 unidentiﬁed), which represents 6.3% of the
targeted population. One hundred and seventy eight (55.3%) coaches were involved with
boys’ teams, and 144 (44.7%) were involved with girls’ teams. All female coaches were
involved with girls’ teams. Table 3 and 4 summarize the information about the ethnicity
and educational level of the participating coaches. Participants ranged in age from 22 to
68 years old (M=39.77, SD=10.23). More than 93% of the coaches were white/
Caucasian. About 68% of the coaches participated in at least one master or doctoral

degree program.

 

 

Table 3
Coaches’ Ethnicity
Types of Ethnicity Frequency Percent
White/ Caucasian 302 93.5
Black/ African American 10 3.1
Hispanic 5 1.5
American Indian/ Alaska

. 2 .6
Natrve
Other 3 .9
Missing data 1 .3
Total 323 100

 

4O

Table 4
Coaches’ Educational Level

 

Level of Education Frequency Percent
High School Graduate 13 4.0
Less Than 2 Years College 14 4.3
2 or More Years College 18 5.6
Bachelor Degree 5 5 17.0
Some Master Level Work 68 21.1
Master Degree 144 44.6
Some Doctoral Level Work 8 2.5
Completed Doctorate 1 .3
Other 2 .6
Total 323 100.0

 

The majority of coaches (n = 239, 74%) had a teaching certiﬁcation, and 216
(66.9%) participated in at least one coaching education program. The mean years of
coaching experience as a head coach was 14.42 years (SD=9.36), and the average length

of the coaches’ previous playing experience was 12.95 (SD=7.68).

Instruments

Demographic measures( See Appendix B). The demographics collected were the
coaches’ gender, team’s gender, age, ethnicity, educational background, current
occupation, experience of a coaching education program, years of coaching basketball,
years playing basketball, and hours spent coaching basketball.

Modified Version of Schutte Emotional Intelligence Scale (MVSEIS; See Appendix
C). To evaluate the El level of coaches, the MVSEIS (Austin et a1., 2004) was used
because it is reasonably congruent with most theoretical approaches of the trait approach

to E1. It has 41 items on a 5-point Likert scale; however, the short version (28 items) was

41

used for this study. The 5-point scale is as follows: 1= Strongly Disagree, 2=Disagree,
3=Neutral, 4=Agree, 5= Strongly Agree. It has the overall EI and three factors, which are
regulation of emotions (e. g., I use good moods to help myself keep trying in the face of
obstacles), utilization of emotions (e. g., When my mood changes, I see new
possibilities), and appraisal of emotions (e. g., I am aware of the non-verbal messages
other people send). Austin et a1. (2004) reported that overall internal reliability is .85,
and the sub-factors are following: .78 for regulation of emotions, .68 for utilization of
emotion, and .76 for appraisal of emotion. They also reported that overall the MVSEIS
was highly correlated (r=.66, p<.001) to the short version of Bar-On Emotional Quotient
Inventory (EQ-izs, Bar-On, 1997).

The present study revealed that the coefficient alphas for each subscale and
overall EI were acceptable, except for utilization of emotion: .72 for regulation of
emotion, .53 for utilization of emotion, .76 for appraisal of emotion, .74 for overall EI.
Utilization of emotion was not used to test any hypotheses due to low internal
consistency. A list of the 28 items is contained in the Appendix C.

Coaching Efficacy Scale (CES; See Appendix D). In order to assess coaching
efﬁcacy, the CES (Feltz et a1., 1999) was used. The CBS is a 24-item self-report measure.
In the present study, the 5-point Likert scale form was used from 1 (no conﬁdence) to 5
(complete conﬁdence) (Myers, Feltz, & Wolfe, in press). Myers et al. validated this
category structure and found it to provide a better ﬁt to the factor structure than the
original IO-category structure.

The CBS consists of four subscales: Game Strategy Efficacy (GSE: 7 items),

Motivation Efﬁcacy (ME: 7 items), Technique Efﬁcacy (TE: 6 items), and Character

42

Building Efficacy (CBE: 4 items). Feltz et al. (1999) reported a conﬁrmatory factor
analysis (CFA) that revealed all signiﬁcant parameter estimates (p<.01) for the ﬁrst order
factor structure, all R2 values surpassed .40, and the results indicated an acceptable ﬁt of
the model using appropriate global indices of non-normed ﬁt index (NNFI) = .88,
comparative ﬁt index (CF I) = .89, root-mean-square residual error of approximation
(RMREA) = .08. Although the second-order CFA indices were slightly different, they
were marginally acceptable.

For internal reliability, Feltz et a1. (1999) reported that the coefﬁcient alphas and
test-retest coefﬁcients for each subscale were quite acceptable with respective values of
.88 and .77 for character building, .89 and .78 for technique, .91 and .83 for motivation,
and .88 and .84 for strategy. The coefﬁcient alpha for the total CES scale was .95, and the
test-retest coefﬁcient was .82 (Feltz et a1., 1999).

The present study revealed that the coefficient alphas for each subscale and total
coaching efﬁcacy were quite acceptable: .89 for GSE, .91 for ME, .86 for TE, .90 for
CBE, and .94 for total coaching efﬁcacy.

Revised Leadership Scale for Sport (RLSS; See Appendix E). In order to measure
the leadership style of the coaches, the RLSS (Zhang et a1., 1997) was used. This
instrument, measuring a broad spectrum of leadership behaviors, contains 60 items on a
5-point Likert scale, which consists of six factors: Training and Instruction Behavior
(TIB: 10 items), Democratic Behavior (DB: 12 items), Autocratic Behavior (AB: 8
items), Social Support Behavior (SSB: 8 items), Positive Feedback Behavior (PF B: 12
items), Situation Consideration Behavior (SCB: 10 items). The Likert 5-point scale is

accompanied with the following wording and quantiﬁcation: Always (100% of time),

43

Often (75% of time), Occasionally (50% of time), Seldom (25% of time), and Never (0%
of time). It has three versions: athlete preference, athlete perception, and coach self-
evaluation. For the present study, the version of coach self-evaluation was used to
evaluate their own perception of their coaching behavior. Zhang et a1. (1997) reported
that coefﬁcient alphas for each sub-factors were signiﬁcantly (p<.05) greater than .70,
with exception of the ‘Autocratic Behavior’ (.35). The other sub-factors were .93 for
democratic behaviors, .85 for positive feedback behaviors, .81 for situation consideration
behavior, .83 for teaching and instruction behavior, and .81 for social support behaviors.
Due to low internal consistency, the items of autocratic behaviors were not included in
the questionnaire. The present study revealed that the coefﬁcient alphas for each factor
were acceptable: .85 for democratic behavior, .81 for positive feedback behavior, .78 for
teaching and instructional behavior, .69 for situational consideration behavior, and .72 for

social support.

Data Collection Procedures

I conducted an online survey for the study. Permission to use human subjects for
this study was obtained from the Institutional Review Board at Michigan State University.
I contacted the coaches in Illinois and Ohio through e-mail or director of basketball
coaches association in Michigan, asking for cooperation and sending an email out to
every basketball coach in Michigan. Also I contacted the athletic directors of high
schools in Michigan and Indiana to ask to forward emails to basketball coaches of their
schools. The emails contained the purpose and procedure of the research as well as asked
for participation in this study, along with web address for direct link. The ‘QuestionPro’

software was used to create an online based questionnaire which contains one consent

44

form, demographic questions, the MVSEIS, the CBS, and the RLSS. The follow-up e-
mails were sent to encourage participation, and remind participants of the deadline.
Furthermore, all participating coaches were told that all participants who complete and
submit a questionnaire will be entered in a drawing. Five participants were randomly
selected to win $50 as a reward.

Data Analyses

Data analysis was conducted in a two-step process. In the ﬁrst step of data
analysis, the calculation of descriptive statistics for the used variables was conducted.
Pearson correlations were calculated for all variables of E1, coaching efﬁcacy, and
leadership behaviors to determine if there was a sufﬁcient relationship between each
variable.

In the second step, the following statistical methods were used to test the
hypotheses with SPSS 15.0. The ﬁrst three hypotheses were tested with separate
canonical correlation, and a multivariate multiple regression (MMR) analyses. In the case
of a signiﬁcant overall multivariate effect, follow-up univariate multiple regression
(U MR) analyses were conducted on each efﬁcacy dimensions and leadership behaviors.
Alpha level for statistical signiﬁcance for each test was set at 0.05.

For the modeling test, the primary statistical procedure used in the study was
Structural Equation Modeling (SEM). SEM was conducted using EQS 6.0 to test the
overall model ﬁt. The ﬁt indices included chi-square statistic (12), the Non-normed Fit
(NNFI), the Comparative Fit (CFI), and Root Mean Square Residual Error of
Approximation (RMSEA). Before the SEM test, preliminary data analyses, such as

multivariate normality, homoscedasticity, univariate normality, outliers, and

45

mulicollinearity were conducted as required to screen data before the examination (Kline,

1998)

46

CHAPTER 4

RESULTS

The results are presented in three sections. First, the preliminary analysis is
presented to evaluate the accuracy and normality of the variables. The second section
presents descriptive information of the variables and correlations among the variables.
The last section presents the results of testing hypotheses with a canonical correlation,
MMR, and the further UMR for the predictability of the variables and the SEM results for

testing the proposed conceptual model in the study.

l’rclimimrry analyses

The preliminary analyses were conducted to assess the normality and reliability of
the valuable. For the assumption of normality of variables, skewness and kurtosis values
for each of variables were assessed (see Table 5). The skewness values of each variable
ranged from -.35 (regulation of emotion) to .12 (appraisal of emotion), and kurtosis
values ranged from -.63 (technique efﬁcacy) to .64 (regulation of emotion), except for
character building efﬁcacy, which was -1 .36 for skewness, and 2.63 for kurtosis.
Although all values of skewness and kurtosis were different from zero, indicating
nonperfect normal distributions, the assumption of normality could be made if the value
of skewness ranges from -1 to +1, and the values of kurtosis range from -1 to +2 (Huck,
2004). Thus, reasonable assumptions about normality could be established, except for

character building efﬁcacy.

47

Table 5
Summary Statistics for Normality and Internal Consistency

Skewness

 

Variables N (Skewnee/SE) Kurtosrs Cronbach a
Game strategy efﬁcacy 323 -.23 (-l.71) -.31 .89
Motivation efﬁcacy 323 -.26 (-1.93) -.01 .91
Technique efﬁcacy 323 -.33 (-2.40) -.63 .86
Character building efﬁcacy 323 -1.36 (-10.02) 2.63 .90
Total coaching efﬁcacy 323 -.26 (-1.88) -.39 .94
Regulation of emotion 323 -.35 (-2.57) .64 .72
Utilization of emotion 323 -.02 (-.14) -.08 .53
Appraisal of emotion 323 .12 (.85) -.32 .76
Overall emotional intelligence 323 .06 (.43) .18 .74
Democratic behavior 323 -.01 (-.07) -.13 .85
Positive feedback behavior 323 —.20 (-1.43) .03 .81
Teaching & instruction behavior 323 -.26 (-1.94) -.46 .78
Situational consrderatron 323 .09 (. 68) _. 60 . 69
behavror

Social support behavior 323 -.22 (-1.60) -.19 .72

 

Note. SE: Standard Error

For reliability of variables, the Cronbach’ alpha values were calculated to evaluate
the internal consistency of a variable (see Table 5). Values ranged from .69 (Situational
consideration behavior) to .94 (overall coaching efﬁcacy), except for .53 of utilization of
emotion. Since Nunnaly (1978) has indicated 0.7 to be an acceptable reliability
coefﬁcient, I concluded that all variables were internally consistent, except for situational
consideration behavior (.69) and utilization of emotion (.53). I accepted the value of .69
for situational consideration behavior as an acceptable reliability coefﬁcient because it is
very close to .70. But, .53 for utilization of emotion was not accepted as an internally
consistent variable, which means that it is not appropriate psychometrically to test a
hypothesis. Thus, this variable was eliminated for further analysis as a dependent or

independent variable.

48

Descriptive Statistics and Gender Differences

Descriptive statistics for the variables of emotional intelligence, coaching

efﬁcacy, and leadership behavior are presented in Table 6. All scales are 5-point scales

from 1 to 5.

For the coaching efﬁcacy variables, the highest mean is of character building

efficacy indicating that the coaches perceived that they had the highest conﬁdence in

character building of their athletes, while motivation efﬁcacy was the lowest mean.

However, the overall descriptive scores of coaching efﬁcacy, high mean and low

deviation, shows that the coaches perceived that they are highly conﬁdent in basketball

coaching.

For the emotional intelligence variables, regulation of emotion was the highest

while utilization was the lowest mean. Regulation of emotion was highly rated, compared

to utilization of emotion, which means that the coaches highly perceived their emotional

ability in regulating emotion when interacting with athletes.

 

 

Table 6

Descriptive Statistics for Variables

Variables N M SD Minimum Maximum
Game strategy efﬁcacy 323 4.15 .53 2.43 5.00
Motivation efficacy 323 3.94 .58 2.14 5.00
Technique efﬁcacy 323 4.31 .50 2.83 5.00
Character building efﬁcacy 323 4.57 .52 2.00 5.00
Total coaching efﬁcacy 323 4.24 .42 2.92 5.00
Regulation of emotion ' 323 4.02 .36 2.58 5.00
Utilization of emotion 323 3.50 .51 2.00 5.00
Appraisal of emotion 323 3.93 .46 2.70 5.00
Overall El 323 3.98 .34 2.64 4.83
Democratic behavior 323 2.92 .52 1.42 4.33
Positive feedback behavior 323 4.16 .41 2.50 5.00
Teaching & instruction behavior 323 4.23 .37 3.10 5.00
Situational consideration behavior 323 4.08 .36 3.30 4.90
Social support behavior 323 3.78 .49 2.38 5.00

 

49

For the variables of the leadership behavior, democratic behavior was rated
considerably lower than other leadership behaviors. The coaches thought that they did not
exhibit democratic behavior much, compared to other leadership behaviors.

Although coaches’ gender differences could not be investigated due to the low
rate of female coaches’ participation (i. e., 87% male coaches versus 13% female
coaches), the differences of teams’ gender on the variables could be investigated with
independent t-tests However, no differences between head coaches of boys’ teams and

girls’ teams on the all variables were found.

Correlations among the variables

Pearson correlations were calculated in order to ﬁnd which predictors of E1
showed sufﬁcient relationship with coaching efﬁcacy and leadership behaviors (see Table
7), and between dependent variables (see Table 8). Additionally, the relationships
between the demographic variables of coaches such as age, education level, coaching and
playing years, and coaching hours and EI, coaching efﬁcacy, and leadership behavior
were investigated with Pearson correlations (see Appendix F).

Table 7 displays the correlations of E1, coaching efficacy, and leadership
behavior, indicating that regulation and appraisal of emotion correlated signiﬁcantly with
the four dimension of coaching efﬁcacy, total coaching efﬁcacy, and ﬁve sub-domains of
leadership behavior. Overall, regulation of emotion produced higher correlations with
coaching efﬁcacy and leadership behavior, as compared to other dimensions of E1.

Especially, regulation of emotion was highly correlated with CBE (.43), TCE (.45), PFB

50

Table 7
Pearson Correlation Coefﬁcients between Emotional Intelligence and Coaching
Efficacy and Leadership Behavior

 

. Coaching Efﬁcacy Leadership Behavior
Emotronal

Intelligence GSE ME TE CBE TCE DB PFB TIB SCB SSB

 

Regulation .30** .38** .31** .43** .45** .24** .55** .43** .48** .34**
Appraisal .14" .23** .17** .23** .25** .19** .21** .24** .26** .28**
Overall .26** .36" .28" .39" .41** .26** .44** .39** .43** .38**

 

** p < .01, * p < .05 (2-tailed)

Note. GSE = Game Strategy Efﬁcacy; ME = Motivation Efﬁcacy; TE = Technique
Efﬁcacy; CBE = Character Building Efﬁcacy; TCE = Total Coaching Efﬁcacy; DB =
Democratic Behavior; PFB = Positive Feedback Behavior; TIB = Training & Instruction
Behavior; SCB = Situational Consideration Behavior; SSB = Social Support Behavior.
(.55), TIB (.43), and SCB (.48). It is estimated that coaches’ higher perceived ability to
regulate emotion tends to Show more conﬁdence in character building and overall
coaching skill, and more positive feedback behavior, teaching and instructional behavior,
and Situational consideration behavior during games and practice.

Table 8 displays the correlations between coaching efﬁcacy and leadership
behavior. It indicates that all variables of coaching efficacy are correlated Signiﬁcantly
with and ﬁve sub-domains of leadership behavior, except for the relationship between
GSE and DB, and TE and DB. Overall, teaching & instructional behavior (TIB) produced
higher correlations with coaching efﬁcacy, as compared to other dimensions of leadership
behavior, which indicates that teaching and instructional behavior of coaches are highly
associated with coaches’ conﬁdence in game strategy, motivation, technique, character
building for athlete. Additionally, positive feedback behavior is highly correlated with
C BE (.43), and TCE (.49), situational consideration behavior are highly correlated with

GSE (.42), and TCE (.47).

51

Table 8
Pearson Correlation Coefﬁcients between Coaching Efﬁcacy and Leadership Behavior

 

Leadership Behavior

 

 

Coaching
Efﬁcacy DB PF B TIB SCB SSB
GSE .05 .26** .51** .42"‘* .24**
ME .l7** .39** .45** .37** .31**
TB .02 .34" .49** .36** .18**
CBE .13* .43** .42** .33** .30**
TCE .12* .45** .59** .47** .33**

 

** p < .01 , * p < .05 (2-tailed)
Note. GSE = Game Strategy Efﬁcacy; ME = Motivation Efﬁcacy; TE = Technique
Efﬁcacy; CBE = Character Building Efﬁcacy; TCE = Total Coaching Efﬁcacy; DB =
Democratic Behavior; PFB = Positive Feedback Behavior; TIB = Training & Instruction
Behavior; SCB = Situational Consideration Behavior; SSB = Social Support Behavior.
Regarding the relationship with the demographic variables (see Appendix F),
coaches’ age is positively correlated with ME (.13), CEB (.14), TCE (.12), and TB (.1 l),
but negatively with appraisal of emotion (-.25) and overall El (-.18). Coaching years is
also positively correlated with GSE (.24), ME (.14), TE (.13), CBE (.16), TC E (.21 ), TB
(.19), and SCB (.12) but negatively with appraisal of emotion (-.16).

Educational level (.11) and playing years of coaches (. 14) are correlated with TE,

and coaching hours per a day is correlated with GSE (.14) and appraisal of emotion (.11).

52

Tests of the Hypotheses

The ﬁrst hypothesis tested the predictive strength of E1 on the four dimensions of
coaching efﬁcacy and was tested in a MMR analysis and four UMRS using regulation and
appraisal of emotion as two predictors and GSE, ME, TE, and CBE as the criterion
variables. Also, the multivariate relationship between two perceptual sets (i. e., EI’S and
coaching efﬁcacy’s subscales) was tested through canonical correlation analysis.

The result of the MMR indicated that E1 was a signiﬁcant predictor of the
coaching efﬁcacy’s subscales, Wilks’s lambda = .75, H3534) = 12.06, p < .001. Also, the
canonical relationship was found to be signiﬁcant (Rc = .50, RC2 = .25, p<.001). The
redundancy index that measured the arnount of variance predicted in coaching efﬁcacy
from the set of E1 was 14%. A redundancy index of 10% or higher is considered to be
meaningful (Pedhazur, 1982). Table 9 presents a summary of the canonical correlations
Table 9

Summary of the Canonical Correlation Analysis between Emotional Intelligence and
Coaching Efﬁcacy Subscales

 

 

Canonical Standardized canonical
Variables loadings coefﬁcients
’EI
Regulation -.97 -.88
Appraisal -.55 -.26
Percentage of variance 15.18
Redundancy .15
Coaching efﬁcacy subscales
Game strategy -.61 .04
Motivation -.79 -.41
Technique -.64 -.23
Character building -.89 -.61
Percentage of variance 55.89
Redundancy .14
Canonical correlation .50

 

53

analysis. Canonical loadings were calculated to determine the speciﬁc variables that
contributed to the multivariate relationship between the two sets. Considering the rule of
thumb (i. e., loadings greater than .30), both predictors in B] were important contributors
to the relationship. Regulation’s contribution was bigger than appraisal's. Thus, coaches
who perceived that they were higher in ability in regulating and appraising of emotion
were more conﬁdent in their coaching, especially in the areas of motivation and character
building.

In addition to multivariate analysis, the UMR (see Table 10) results indicated that
regulation and appraisal of emotion signiﬁcantly predicted GSE (F (3,320) = 16.23;p<.001),
ME (F0320) = 29.27; p<.001), TE (F(3,320)= l7.69;p<.001), and CBE (F9330) = 38.50;
p<.001) with a considerably strong predictive relationship with ME (I?2 = .15). and C BE
(.19).

Table 10

The Predictability of Emotional Intelligence on Coaching Efﬁcacy and Leadership
Behavior

 

 

 

 

 

 

Coaching Efﬁcacy
Predictors
GSE ME TE CBE
Regulation (,6.) .28** .34** .30** .40**
Appraisal ([32) .05 .12* .08 .10 (p=.051)
Leadership Behavior
Predictors
DB PFB TIB SCB SSB
Regulation r/i.) .20** .54** .39** ' .44** .28**
Appraisal (.32) .12* .03 .12* .12* .20**

 

** p < .01, *p < .05 (2-tailed)

Note. GSE = Game Strategy Efﬁcacy; ME = Motivation Efﬁcacy; TE = Technique
Efﬁcacy; CBE = Character Building Efﬁcacy; TCE = Total Coaching Efﬁcacy; DB =
Democratic Behavior; PFB = Positive Feedback Behavior; TIB = Training & Instruction
Behavior; SCB = Situational Consideration Behavior; SSB = Social Support Behavior.

54

The factors of El (regulation and appraisal) accounted for 15% of variance in ME
and 19% in CBE. Regulation of emotion is a stronger predictor than appraisal of emotion
within coaching efﬁcacy. Speciﬁcally, the predictive strength of regulation was strong
within ME (,6 = .34) and CBE (.40). Thus the ﬁrst hypothesis was supported, implying
that coaches’ perceived ability to regulate their own emotion and appraise their own and
athlete’s emotion is the source of the overall coaching efﬁcacy, especially motivation and
character building efﬁcacies.

The second hypothesis to test the predictive strength of E1 on the ﬁve sub
domains of leadership behavior was tested in a MMR and ﬁve UMRS using regulation
and appraisal of emotion as two predictors and DB, PFB, TIB, SCB, and SSB as the
criterion variables (see Table 10). Also, the multivariate relationship between two
perceptual sets (i. e., El’s and leadership behavior subscales) was tested through
canonical correlation analysis (see Table 11).

Table 11

Summary of the Canonical Correlation Analysis between Emotional Intelligence and
Leadership Behaviors Sub-Factors

 

 

Canonical Standardized canonical
Variables loadings coefﬁcients
E1
Regulation -.99 -.94
Appraisal -.46 -. 16
Percentage of variance 21.68
Redundancy .22
Leadership behavior
sub-factors
Democratic -.42 -.03
Positive feedback -.91 -.62
Training & instruction -.72 -.10
Situational consideration -.81 -.37
Social support -.60 -.09
Percentage of variance 51.00
Redundancy .19
Canonical correlation .60

 

55

The result of MMR indicated that the predictiveness of E1 was signiﬁcant in
relation to the leadership behavior’s subscales, Wilks’s lambda = .61, P110632, = 17.50. p
< .001. Also, the canonical relationship was found to be signiﬁcant (Rc = .60, Rc2 = .36,
p<.001). The redundancy index that measured the amount of variance predicted in
leadership behavior from the set of E1 was 19%. Table 11 presents a summary of the
canonical correlation analysis. Canonical loadings were calculated to determine the
speciﬁc variables that contributed to the multivariate relationship between the two sets.
Both predictors in El were important contributors to the relationship. Regulation’s
contribution was bigger than appraisal’s. Thus, coaches who perceived that they were
higher in ability for regulating and appraising emotion tended to think that they Showed
more leadership behaviors, such as democratic, positive feedback, training and
instruction, situational consideration, and social support behaviors.

In addition to multivariate analysis, the UMR (See Table 10) results indicated that
regulation and appraisal of emotion signiﬁcantly predicted DB (H3320) = 12.10; p<.001),
PFB (123,330,: 69.98;p<.001), TIB (1:13.320): 38.13; p<.001 ), SCB (H3320): 51 .00;
p<.001), and SSB (F (3,320) = 27.96; p<.001) with a considerably strong predictive
relationship with TIB (R2 = .19), SCB (.24), and PFB (.30). The factors of El (regulation
and appraisal) accounted for 19% of variance in TIB, 24% in SCB, and 30% in PF B.
Regulation of emotion is a stronger predictor than appraisal of emotion within leadership
behaviors. Speciﬁcally, the predictive strength of regulation was strong within TIB ([3 =
.39), SCB (.44), and PFB (.54). Thus the second hypothesis was supported, implying that

coaches perceiving more ability to control their own emotion Show more positive

56

feedback behavior, situational consideration behavior and teaching and instructional
behavior.

For the third hypothesis, the multivariate relationship between two perceptual sets
(i. e., coaching efﬁcacy’s and leadership behavior’s subscales) was tested through
canonical correlation analysis (see Table 12). Also, the predictability of coaching efﬁcacy
on the ﬁve sub-domains of leadership behavior was tested in a MMR and ﬁve UMRS
using four dimensions of coaching efﬁcacy as four predictors and DB. PFB. TIB. SCB.
and SSB as the criterion variables (see Table 13).

The result of MMR indicated that the predictiveness of coaching efﬁcacy was
signiﬁcantly related to the sub-factors of leadership behaviors, Wilks’s lambda = .53.
12201042) = 10.93, p < .001. Also, the canonical relationship was found to be signiﬁcant
(Rc = .62, RC2 = .39, p<.001). The redundancy index that measured the amount of
variance predicted in leadership behavior from the set of coaching efﬁcacy was 18%.
Table 12 presents a summary of the canonical correlations analysis. Canonical loadings
were calculated to determine the speciﬁc variables that contributed to the multivariate
relationship between the two sets. All four predictors in coaching efﬁcacy were important
contributors to the relationship (i. e., loadings greater than .30). Thus, coaches who
perceived that they were conﬁdent in coaching tended to think of them that they Show
more leadership behaviors, such as democratic, positive feedback, training and

instruction, situational consideration, and social support behaviors.

57

Table 12
Summary of the Canonical Correlation Analysis between Coaching Efﬁcacy and
Leadership Behaviors Sub-Factors

 

 

Canonical Standardized canonical
Variables loadings coefﬁcients
Coaching efﬁcacy subscales
Game strategy .80 .23
Motivation .77 .28
Technique .81 .39
Character building .76 .37
Percentage of variance 24.11
Redundancy .240
Leadership behavior
sub-factors
Democratic . l 8 -.14
Positive feedback .74 .35
Training & instruction .95 .72
Situational consideration .74 .1 1
Social support .52 .01
Percentage of variance 45.84
Redundancy .1 8
Canonical correlation .62

 

In addition to multivariate analysis, in univariate analysis (see Table 13), The
result indicated that the four dimensions of coaching efﬁcacy Signiﬁcantly predicted DB
(Fl-1.318) = 3.21 ;p= .013), PFB (F(4,318) = 26.61;p<.001), TIB (34,318, = 43.96;p<.001),
SCB (FM, 313) = 22.50; p<.001), and SSB (Fm. 3.3) = 11.48; p<.001) with a considerably
strong predictive relationship with SCB (R2 = .22), PFB (.25), and TIB (.36), and the

moderate relationship with SSB (.17).

58

Table 13

The Predictive Strength of Coaching Efﬁcacy on Leadership Behavior

 

Leadership Behavior

 

 

Predictors
DB PFB TIB SCB SSB
GSE (3,) -.06 -.13 .21** .22** .06
ME ([32) .18** .23** .15* .13* .19**
TE (I33) -.07 .20** .22** .10 -.01
CBE (134) .09 .30** .18** .13* .19“

 

** p < .01, * p < .05 (2-tailed)

Note. GSE = Game Strategy Efﬁcacy; ME = Motivation Efﬁcacy; TE = Technique
Efﬁcacy; CBE = Character Building Efﬁcacy; TCE = Total Coaching Efﬁcacy; DB =
Democratic Behavior; PFB = Positive Feedback Behavior; TIB = Training & Instruction
Behavior; SCB = Situational Consideration Behavior; SSB = Social Support Behavior.

The four dimensions of coaching efﬁcacy accounted for 22% of variance in SC B,

25% in PFB, 36% in TIB, and 17% in SSB. GSE predicted TIB (ﬂ = .21), and SCB (.22).

ME predicted DB (.18), PFB (.23), TIB (.15), SCB (.13), and SSB (.19). TE predicted

PFB (.20), and TIB (.22). CBE predicted PFB (.30), TIB (.18), SCB (.13). and SSB (.19).

The strongest predictor was ME (,8 == .18) in DB, CEB (ﬂ = .30) in PFB, TE (ﬂ = .22) in

TIB, GSE (e = .22) in SCB, and ME and CBE (p = .19) in SSB. Thus the third hypothesis

was supported. It implies that coaches who have conﬁdence in game strategy, motivation,

technique, and character building tend to Show more leadership behaviors, especially

training and instructional behavior, positive feedback behavior. situational consideration

behavior, and social support behavior.

59

Structural Equation Modeling

For the modeling test, the primary statistical procedure used in the study was
Structural Equation Modeling (SEM). Before the SEM test, preliminary data analyses
such as multivariate normality, homoscedasticity, univariate normality, outliers, and
mulicollinearity were conducted as required to screen data before the examination (Kline.
2005). AS a result, nothing was found to be problematic.

The hjpothesized model. At the ﬁrst stage of the modeling examination, the
emotional intelligence (E1), the coaching efficacy (CE), and the leadership behavior (LB)
were hypothesized as ‘latent variables’ in the model as were represented with sub-
categories. In the model, the sub-categories were called sub-factors and applied as
‘indicators’ to the latent variables. At the same time, characters of the variables in
relation were also determined with the terms, ‘exogenous’ and ‘endogenous’. In the
study, E1 was employed as an ‘exogenous’ variable because it was used as equivalent to
an independent variable. Then, CE and LB were deﬁned as endogenous variables because
they were used as equivalent to dependent variables. Using EQS 6.0, the hypothesized
model was entered into the relationships between the variables; ( 1) E1 ——> CF, (2) El —+
LB, and (3) CF —> LB. The relationships also applied sub-categories of the major
variables. In Figure 5, correlation coefﬁcients of the model are reported. They

demonstrated strengths of the relationships between and within variables.

60

 

.803* GSE

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.71 2*
CE * ME
.602* (R2= 36) .727
° TE
.600*
335*
* * 579* DB
.768 .424 .3751: p133
LB
Regulation Appraisal 2= .635"
(R .68) TIB
.812*
804, SCB
.622* SSB

 

 

 

Figure 5
The Initially Proposed Model for Structural Equation Modeling
(* Statistically signiﬁcant correlation coefﬁcients)

The ovals in the model demonstrate the latent variables, and the rectangles
represent the sub-factors. In the diagram of Figure 5, I found statistical signiﬁcance on
every relationship between variables; (1) E1 and CE, ﬂ = .602, (2) El and LB, )3 = .579
and (3) CE and LB, )3 = .335. All the sub-categories also showed statistical signiﬁcance
under each variable.

Along with the relationships between variables in the hypothesized model, the
SEM test reported the goodness of ﬁt indexes to demonstrate whether the model ﬁt the
data. According to the results, this model could not obtain acceptable values of ﬁt
indexes; the goodness of ﬁt indexes summary indicated that any of the indexes did not

meet the recommended ﬁt criteria; KM: 180.950 (df= 41 , p <. 01),er = .867, NNFI =

61

.857, CFI = .893, RMSEA = .103. The recommendation for the cut-off value of NF 1,
NNFI, and CF I was .90, and RMSEA was .10 (Kline, 1998).

Table 14 summarized parameter, estimate, and standard errors of the ﬁrst model
test. In Table 14, ‘parameter’ indicates the relationships between the variables, and
‘estimate’ demonstrates the correlation coefficients. In addition, ‘SE’ represents the
standard errors of the variables, and R2 demonstrates the proportion of variability that can
be explained in the model. Overall, it was suggested that the hypothesized model failed to
reach all the satisfactory goodness of ﬁt index criteria. Therefore, a modiﬁcation was
recommended. In the modiﬁcation, reassessment of correlations coefﬁcients among

major variables was suggested.

 

 

 

 

Table 14
Parameter Summary of the First Model

Parameter Estimate“ SE Parameter Estimate SE

Direct Effects Measurement Error Variances &
Disturbance
E1 (F1) —) CE (F2) .602 .177 DFZ .799 .020
E1 (F1) —) LB (F3) .579 .120 ng, .563 .004
CE (F2) —> LB (F3) .335 .052
Indicators

El (F1) —> Regulation (V1) .768 - Ev. .640 .012
El (F 1) —> Appraisal (V2) .424 .124 By; .906 .015
CE (F2) —> GSE (V3) .803 - Ev3 .803 .012
CE (F2) —> ME (V4) .712 .079 EM .712 .017
CE (F 2) —) TE (V 5) .727 .068 Bus .687 .012
CE (F2) —> CBE (V6) .600 .072 Ew, .800 .016
LB (F3) —> DB (V7) .375 - Ev7 .927 .018
LB (F3) —> PFB (V8) .685 .236 Evg .728 .008
LB (F 3) —> TIB (V9) .812 .240 Evo .583 .005
LB (F3) —) SCB (V10) .804 .232 Ev“) .595 .005
LB (F3) —> SSB (V l 1) .622 .262 Evn .783 .013

 

62

 

 

.849* GSE
.674*

’ .747*

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

.668*
.772* 421* 533* R/ PFB
*
Regulation Appraisal ZLB '827 TIB
(R “'68) .806*
SCB
.605*
SSB
Figure 6

The Final Model (* Statistically signiﬁcant correlation coefﬁcients)

The final model. As suggested in the ﬁrst model test, all the correlation
coefﬁcients in the ﬁrst model were reassessed. This assessment showed that CBE in CB
and DB in LS had the lowest correlation coefﬁcients (,6 = .600 and 375 respectively, see
Figure 5) under each of their latent variables. Therefore, I decided to exclude these two
sub-factors from the model. The following model shows the ﬁnal model.

Along with the modiﬁcations, the researchers found that all the correlation
coefficients among the latent variables were statistically signiﬁcant (p< .05) in the model
(see Figure 6); (1) E1 and CE,,B = .522 and (2) El and LB,)3 = .583, and (3) CE and LB,/J’
= .353 CE. In addition, every correlation coefficient of all the sub-factor were also
statistically signiﬁcant (p< .05): (1) CE and GSE,ﬂ =.849, (2) CE and ME,fi = .674, (3)
CE and TE,)3 = .747 (4) LS and PFB,,B = .668, (5) LS and TIB,,B = .827, (6) LS and SCB,

)3 = .806, and (7) LS and SSB,)3 = .605.

63

In terms of the goodness of ﬁt indexes, we found fairly considerable improvement
in the second model. The model reported; 12,” = 101.398 (df= 24, p < .01), NFI = .911,
NNFI = .894, CFI = .930, RMSEA = .10. The values of NFI, CPI, and RMSEA met the
ﬁt criteria as they were above .90 and .10 (for RMSEA). However, the value of NNFI did
not meet the ﬁt criteria as it was below .90. This model also was compared to the
previous model. The following table demonstrates the goodness of ﬁt indexes of the
second model and comparisons with the ﬁrst model.

In the Table 15, chi-squares and Akaike Information Criterions (AIC) were
reported to compare the ﬁrst and second models. In comparison, it was suggested that the
second model was signiﬁcantly different from the model; A12 = 79.552 (df= 17, p < 0.01).

In the examination for AICs of the two models, the researchers found that the AIC
value of the second was lower than that of the ﬁrst model; AICM2 (53.398) < AICM.
(98.950). Reﬂecting these model comparisons, the second model was suggested as a
better model than the ﬁrst model. Table 16 summarized parameters, estimates, and

standard errors that were used for the second model test.

Table 15
Goodness of Fit Indexes Summary of the Final Model

 

)5 df j/df NFI NNFI CFI RMSEA A/ AIC

 

Final 101.398 24 4.224 .911 .894 .930 .100 79.552 53.398
Model

St

1 180.950 41 4.413 .867 .857 .894 .103 — 98.950
Model

 

64

Table 16
Parameter Summary of the Final Model

 

Parameter Estimate“ SE Parameter Estimate SE

 

Measurement EI’I’OI‘ Variances

 

 

 

Direct Effects & Disturbance
El (F1) —-> CE (F2) .522 .175 DF2 .853 .022
El (Fl) —) LB (F3) .583 .140 DF3 .583 .006
CE (F2) —9 LB (F3) .353 .056
Indicators

El (Fl) ——> Regulation (V1) .772 - Ev] .635 .013
E1 (F1) —-) Appraisal (V2) .421 .128 By; .907 .015
CE (F2) —> GSE (V3) .849 - Ev3 .528 .013
CE (F2) —9 ME (V4) .674 .075 EM .739 .018
CE (F2) —> TE(V5) .747 .065 Ev5 .665 .012
LB (F3) —> PFB(V8) .668 - Eva .744 .008
LB (F3) —> TIB (V9) .827 .091 EN .562 .005
LB (F3) —> SCB (V10) .806 .087 Evg .592 .005
LB (F3) —) SSB (V11) .605 .113 Eye .796 .013

 

" Refers to standardized solutions

65

Summary

Coaches’ El signiﬁcantly predicts coaching efﬁcacy and leadership behaviors in
this sample of coaches. More speciﬁcally, the ability of regulating emotions signiﬁcantly
predicts the game strategy (/3 = .28), motivation (,b’ = .34), technique (6 = .30), character
building (,[I = .40) coaching efﬁcacy, and democratic (,8 = .20), positive feedback (/)’ =
.54), training & instruction (3 = .39), Situational consideration (8 = .44), and social
support (,6 = .28) in leadership behaviors. The ability to appraise emotions signiﬁcantly
predicts the motivation (,6 = .12) coaching efﬁcacy, and democratic (,8 = .12), training and
instruction (,3 = .12), situational consideration (,6 = .12), and social support (,6 = .20) in
leadership behaviors. Additionally, the relationship between coaching efﬁcacy and
leadership behaviors was found. The four dimensions of coaching efﬁcacy Si gniﬁcantly
predicted leadership behaviors, explaining 22 % of variance in Situational consideration
behaviors, 25% of variance in positive feedback behaviors, and 36% in training and
instruction behavior. Those relationships were conﬁrmed by a structural equation

modeling test.

66

CHAPTER 5

DISCUSSION

The main purpose of this study was to investigate the predictive strength of E1 of
coaches on coaching efﬁcacy and leadership behavior of coaches. The ﬁrst hypothesis,
that the four dimensions of coaching efﬁcacy would be predicted by E1 of coaches, was
supported. The factors of E1 (regulation and appraisal) accounted for 15% of variance in
ME and 19% in CBE. Thus, the E1 of coaches was particularly predictive of coaches’
motivational and character—building efﬁcacy beliefs. This makes sense that coaches’
perceived ability to regulate and be aware of their own and athletes’ emotions would
inform their sense of being able to motivate and build the character of their athletes
during practices, conferences, and competitions. For example, if coaches appropriately
regulate the agitated situation, they would think that these behaviors positively affect the
attitude of athletes in sport participation and athletes’ character development. If coaches
are good at being aware of athletes’ emotional states (e. g., bum-out, boredom, and
anxiety level), they could effectively motivate the athletes with conﬁdence. Hanson and
Gould (1998) emphasized the importance of coaches’ ability to estimate athletes’ anxiety
levels to the athetes’ motivation.

Although there is no previous research linking El and coaching efﬁcacy in sport,
there is research in education that has examined the relationship between teaching
efﬁcacy and E1 of teachers (e. g., Chan, 2004; Okech, 2004; Penrose, Perry, & Ball,
2007). Those results were consistent with the results of this study. Okech (2004) found a
signiﬁcant positive relationship between El and teacher self-efﬁcacy. Chan (2004) found

that the component of El (positive regulation and empathic sensitivity) signiﬁcantly

67

predicted efﬁcacy beliefs of teachers. Penrose et a1. (2007) reported that 14% of the
variation in personal teaching efﬁcacy could be explained by taking EI into account.

Such association originated from Bandura’s self-efﬁcacy theory (1997) that
physiological and emotional states work together as a source of efﬁcacy beliefs. Aside
from the physiological state, perceived ability to regulate, utilize, and appraise emotions
ﬁts with Bandura’s (1977, 1997) sources of self-efﬁcacy information within his theory.
Bandura (1997) states that one way of altering efﬁcacy belief is by reducing “negative
emotional proclivities” (p. 106). This also includes mood states, such as anger, fear,
anxiety, and joy. Bandura explains that moods provide sources of efﬁcacy information
“because they often accompany changes in quality of functioning” (p. 111). That is, they
can inﬂuence how coaching situations are interpreted. Schunk (1995) suggested that
emotional symptoms that signal anxiety might be interpreted by an individual to mean
that he or she lacks the requisite skills to perform a certain task, which in turn inﬂuences
efﬁcacy judgments. It is, likewise, reasonable to expect that coaches who believe that
they have good ability in regulation of their emotions to keep a positive mood will have
more conﬁdence in their ability to affect the performances of their teams. That is, it may
be more difﬁcult to attend to the tasks of game strategizing, teaching, motivating, and
instilling character when one is less control of one’s emotions.

Regarding E1 of coaches and leadership behavior, the second hypothesis, that
each of the ﬁve sub-factors of leadership behavior would be predicted by E1 of coaches,
was also supported. These results suggest that coaches who have higher perception in
regulating their own emotion to keep a positive mood are likely to demonstrate positive

feedback behaviors (e. 1., encouraging an athlete after a mistake and complimenting

68

properly), situational consideration behaviors (e. g., selecting an athlete for the
appropriate game position or line up and differentiating coaching methods at different
maturity stages and skill levels), and training and instructional behavior (e. g., instructing
the athletes in the skills, techniques, and the tactics of the sport, and planning training
practices and evaluating the performance of the athletes).

In addition to regulation of emotion, the appraisal of emotion was a signiﬁcant
predictor of social support behavior, democratic behavior, situational consideration, and
training and instructional behavior. Although it was not a strong predictor, as compared
to regulation of emotion, the coaches’ perceived ability to be aware of their own and
athletes’ emotional states contributed to their perceptions of their coaching behavior. For
instance, if coaches are good at being aware of athletes’ emotional states (e. g., bum-out.
boredom, and anxiety level), they could demonstrate different behaviors considering
athletes’ and groups’ feelings and climates. Those coaches are more likely to select an
athlete for the appropriate game position, and consider situational factors, such as
emotions, time, environment, individual, and health condition. The coaches also would
touch and relieve the non-starting members’ feelings and disappointment by the
recognition of the athletes that they are supporting.

These results are consistent with Goleman’s (1995) primary book in El
‘Emotional intelligence: Why it can matter more than IQ’ asserting that E1 is an essential
contributor to effective leadership, and other empirical studies in business (e. g., Caruso
et a1., 2002; Humphrey, 2002; Kobe et a1., 2001; Mayer & Caruso, 2002; Rosete &
Ciarrochi, 2005; Sosik & Megerain, 1999) indicating that leaders’ E1 promoted

effectiveness in organization.

69

Regarding the relation to leadership behaviors in coaching, the result is aligned
with Miller’s (2003) qualitative study on the relationship between altruistic leadership
and E1 of coaches. Through interviews with 15 collegiate coaches, she found that the
factors of E1 (self-awareness, self-regulation, social skills, empathy. and self-motivation)
were reinforced in relation to altruistic leadership, and suggested that the ﬁve factors of
El were possible factors of altruistic leadership. Speciﬁcally, regarding self-regulation,
some coaches’ perceptions of altruistic leadership included controlling emotions.
controlling language, and remaining consistent with athletes (Miller, 2003).

The fact that EI Signiﬁcantly predicted the technique efﬁcacy and the training and
instructional behavior is interesting. Given the theoretical tenant of E1, it seemed to take
account only for motivation and character building efﬁcacy of athlete and positive
feedback and social support behavior, not related to technique instruction. But, the result
indicated that emotionally intelligent coaches are more likely to perceive that they have
more technique coaching conﬁdence and show more instructional behavior effectively.
Because they are equipped with the ability to regulate their emotion without agitation or
anger during the instruction, they know how to lead the instruction with conﬁdence and
reasonable behavior. 3

Based on Salovey and Mayer’s (1990) model, utilization of emotion means that
using emotion and not being disrupted by emotion. An individual high in utilization of
emotion shows more efﬁciency in doing ﬂexible planning, creative thinking, redirected
attention, and motivating. In coaching situations, coaches Should be able to purposely
Show their own enthusiasm toward their team in order to motivate athletes after

identifying and regulating their own emotions. However, despite this important property

70

of an individual’s E1, the utilization of emotion was not used for this study because of
low internal consistency. This low internal consistency may have been the result of the
following: (a) relatively small number of items (Six items) as compared with other factors
of E1, (b) unclear items for coaches to think about in basketball coaching situations (e. g.,
emotions don’t play a big part in howl deal with problems), and (c) items’ low ﬁt to the
sport setting (e. g., I don’t believe that my emotions give any help in coming up with new
ideas).

The additional hypothesis was that efﬁcacious belief in coaching would predict
perceived leadership behaviors of coaches. This was based on Feltz and colleagues’
(1. 999) coaching efﬁcacy model indicating that coaching behavior is the major outcome
of coaching efﬁcacy. The coaches’ efﬁcacious beliefs in coaching explained 36%
variance of training and instructional behaviors, 25% of positive feedback behaviors.
22% of situational considering behaviors, and 17% of social support behaviors. These
results partially support Feltz and colleagues’ (1999) ﬁnding that higher-efﬁcacy coaches
used more praise and encouragement behavior. However, the fact that the positive linear
relationship between the coaching efﬁcacy and training and instructional behaviors is
inconsistent with Feltz and colleagues’ (1999) ﬁnding that less conﬁdent coaches
demonstrated more instructing organizing behavior. However, Sullivan and Kent (2003)
reported the positive relationship between technique efﬁcacy and training and instruction
leadership behaviors, using the self-reported Leadership Scale for Sport (LSS: Chelldurai
&Saleh, 1980).

The big difference between Feltz and colleague’s (1999) study and this study

including Sullivan and Kent’s (2003) study is the measurement style. Feltz and

71

colleagues (1999) used the CBAS (1978, 1997) to measure coaches’ behavior in which
coaches’ behavior was observed and assessed by others who had been trained in using the
C BAS (Smith et a1., 1978, 1997). By contrast, this study and the Sullivan and Kent’
(2003) study used the self-reported measurement style to assess coaches’ behavior.

For more speciﬁc predictors of leadership behaviors in coaching, Sullivan and
Kent (2003) reported motivation and technique efﬁcacy beliefs predicted training and
instruction and positive feedback behaviors. However, as compared to Sullivan and
Kent’s study, the present study revealed many predictors: (a) game strategy efﬁcacy
beliefs predicted training and instruction behaviors and situational consideration
behaviors, (b) motivation efficacy beliefs predicted democratic behaviors, positive
feedback behaviors, training and instruction behaviors, situational consideration
behaviors, and social support behaviors, (c) technique efficacy belief predicted positive
feedback behaviors, and training and instruction behaviors, ((1) character building
efﬁcacy belief predicted positive feedback behaviors, training and instruction behaviors,
situational consideration behaviors, and social support behaviors. Although it was
expected that the game strategy and technique efﬁcacy would predict training and
instruction and situational consideration behaviors, and the motivation and character
building efﬁcacy would predict social support and positive feedback behaviors, those
were only partially supported. That is, efﬁcacy belief in the four dimensions predicted
leadership behaviors in various ways. Speciﬁcally, in the case of motivation efﬁcacy, as
coaches were more conﬁdent in motivating their athletes, they were more concerned with
all ﬁve sub-factors of leadership (i. e., predicted democratic behaviors, positive feedback

behaviors, training and instruction behaviors, situational consideration behaviors, and

72

social support behaviors). This result is comparable with Sullivan and Kent’s study
ﬁnding that motivation efﬁcacy predicted positive feedback and training and instruction
behaviors. In the present study, it appears that the various beliefs in coaching inﬂuence
the coaching behaviors in various ways. However, the fact that technique efﬁcacy
predicted positive feedback behaviors, and training and instruction behaviors, is
consistent to Sullivan and Kent’s study.

The big difference between the predictive strength of coaching efﬁcacy in this
study and Sullivan and Kent’s study may result from using the psychometrically
improved measurement, the diversity of sports, and the coaches’ level. Instead of LSS
(Chelldurai & Saleh, 1980), this study used the Revised Leadership Scale for Sport
(Zhang, et al, 1997), which was psychometrically improved in several ways. In terms of
the subjects, Sullivan and Kent used intercollegiate coaches in various sports while only
basketball coaches in high-schools participated in this study.

The relationships among El, coaching efﬁcacy, and leadership behavior in the
sport setting were conﬁrmed by SEM, indicating that coaches’ E1 inﬂuences coaching
efﬁcacy beliefs and leadership behaviors excluding democratic behaviors. The coaching
efﬁcacy beliefs inﬂuence leadership behaviors excluding democratic behaviors. The
ﬁtness of the model suggests that the concept of El ﬁts in the contents of coaching
efﬁcacy and leadership behaviors of the coaching effectiveness. Research regarding E]

can be developed in sports investigating coaching effectiveness.

Implications

Given the lack of research on E1 in sport, the results of this investigation are

important in several ways. First, given that coaching conﬁdence and coaching behaviors

73

affect coaching effectiveness, according to Horn (2002)’s model, E1 contributes to the
coaching effectiveness as a characteristic of a coach. Emotionally intelligent coaches are
more conﬁdent in coaching, and perceive that they show more effective leadership
behaviors (i. e., positive feedback, situational consideration, social support, and training
instruction behaviors). The reason that those coaching behaviors are called effective
behaviors is due to Chelladurai (1984)’s contention that coaches’ social support behavior,
or relationship behavior, was the most productive factor in satisfying the needs of the
athletes. In an empirical study, Amorose and Horn (2000) reported that athletes who
perceived their coaches to exhibit a more democratic coaching style and to respond to
players’ performances with high levels of praise, encouragement, and information-based
feedback would exhibit higher intrinsic motivation than would athletes who perceived
their coaches to be more authoritarian in leadership style. Therefore, E1 is one of the
qualities of an effective coach, as Humphrey (2002) indicated that the leaders with a high
level of El can be better equipped to promote effectiveness at all levels in organizations.
which. in turn, strongly inﬂuences their followers’ performance outcome.

Second, coaches’ perceived emotional functioning is another source Of coaches’
efﬁcacious belief in coaching. The fact that coaches’ perceived ability to regulate and
appraise their emotion inﬂuences their coaching conﬁdence is evidence to add it as a
source of coaching efﬁcacy, along with coaching experience, prior success, perceived
skill of athletes, and school/community support.

Third, E1 is valuable enough to be applied to Sport settings. Coaches’ emotions in
relation with athletes have received little attention as compared to athletes’ emotions.

Studies regarding coaches’ emotions indicated that coaches need the ability to regulate

74

emotions for themselves and athletes. They should regulate their emotion not to be
agitated, to appraise it to know their own and athlete’s emotional status, and to use it for a
good relationship with athletes. In addition to the nature of E1, researchers should
consider psychological characteristics of coaches as well as apparent variables such as
coaching behaviors. The major theme of studies about coaching is coaching behaviors (i.
e.. leadership style). But researchers need to describe coaches psychologically and
emotionally, because coaches’ personal characteristics affect coaching behaviors,
coaching effectiveness, and athletes’ outcomes in various ways. This suggestion is
aligned with F eltz’s efforts with the coaching efﬁcacy model, trying to explain coaches’

psychological belief in coaching.

Limitations and Future Direction

The major weakness of the study was the unavailability of an adequate scale for
E1 in sport settings. The self-report MVSEIS is designed for life in general, not for a sport
setting. It was developed in clinical psychology for the well-being of individuals. The
major concern in sport psychology is with the content validity. Despite the lack of the
content validity, it was deemed sufﬁcient for our purpose when considering items such as
‘I am aware of the non-verbal messages other people send.’ and ‘my mood has little
effect on howl deal with problems.’ Additionally, the problematic nature of the self-
reported measurement exists. It is totally based on respondents’ perceptions. The score
may be over estimated or under estimated, or others may think of them differently in their
emotional ability. Therefore, the development of the speciﬁc scale for emotional
intelligence, competence, efﬁcacy, or ability in the sport setting is recommended where

items are developed that are suited for the sport setting.

75

In terms of coaching effectiveness, this study regarded only coaching efﬁcacy and
leadership behavior as the major factors of coaching effectiveness. We can infer that E1
of a coach contributes to coaching effectiveness from the study’s results. However. a
study about association with actual outcome of coaching effectiveness, such as coaching
behavior and performance (perhaps using the CBAS, Smith et a1., 1978, 1997), win-loss
record, and athlete’s perceived motivational climate and satisfaction, are needed for
practical studies of El. VanSickle (2004) has reported that coaches’ self-awareness of
emotion predicted athletes’ satisfaction.

The current study lacks variety in samples. Only basketball coaches in hi gh-
school participated in the study, which means that it is hard to generalize the results to all
coaches. For that reason, the following future research is recommended that (a) involves
coaches in various types of Sports to compare those of team and individual sports. (b)
involves coaches in various coaching levels such as professional, intercollegiate, high
school, and recreational coaches to compare coaching levels, and (c) involves male and
female coaches, and male and female teams with similar rate of participation to see
coaches’ gender differences, teams’ gender differences, and the interaction of them. I
failed to compare the gender differences that had been hypothesized because of the low
rate of female coaches’ participation (i. e., 87% male coaches versus 13% female
coaches).

Although this study focused only on the personal characteristics of coaches, the
studies investigating whether E1 of coaches and athletes affect the group dynamics in a
team, such as coach-athletes relationship, group cohesion, communication, and collective

efﬁcacy, are recommended.

76

APPENDICES

77

APPENDIX A

Research Participant Information and Consent Form

You are being asked to participate in a study conducted by graduate student Seunghyun '
Hwang under the supervision of Deborah Feltz, Ph.D., from Michigan State University. The
purpose of this study is to examine the relationship between emotional intelligence and coaching
effectiveness. You have been selected as a potential participant in this study because you are
currently a high school basketball coach.

As part of this research, you will be asked to complete 3 different surveys regarding your
emotional intelligence and coaching effectiveness. Your participation will require approximately
30 minutes. Participation is voluntary and you have the right to choose not to participate,
withdraw from the study at any time, or refuse to answer any question(s) without penalty.

Your participation in this study will remain completely conﬁdential, as no one except the
principal investigators will have access to these responses or to participation records. All data will
be kept in a locked ﬁle cabinet and a password protected computer. At the end of the project,
responses will be presented at the group level to ensure the conﬁdentiality and anonymity of
individual responses. Group-based ﬁndings will be made available to those who are interested.
Your privacy will be protected to the maximum extent allowable by law.

In exchange for you participation, we will provide you with a brief summary of the results of
the study. Additionally, your participation in this study may serve to further our knowledge about
coaching effectiveness. There are no foreseeable risks involved with participation in this study.

You are not guaranteed to receive any form of monetary compensation for participating in
this study. However, all participants who complete and submit a questionnaire will be entered in a
drawing. Five participants will be randomly selected to win $50 as a reward. 230 people were
invited to participate in this research. If all participants respond, you have a 1 in 46 chance of
Winning.

If you have any questions about this study, such as scientiﬁc issues, how to do any part of it,
or to report an injury, please contact Seunghyun Hwang, (hwangse6@msu.edu, or 224-688-1500)
or Deborah L. Feltz (dfeltz@msu.edu or 517-355-4732).

If you have any questions about your role and rights as a research participant, or would like to
register a complaint about this study, you may contact, anonymously if you wish, the Director of
MSU’s Human Research Protection Programs, Dr. Peter Vasilenko, 517-355-2180, FAX 517-
432-4503, e-mail irb@msu.edu, or regular mail at: 202 Olds Hall, MSU, East Lansing, MI 48824.

Thank you for your time and cooperation.

Seunghyun Hwang
Deborah L. Feltz

1, (Type your initials), have been informed of and voluntary agree to participate in
the above-mentioned study.

 

Note: Typing initials means that you agree to voluntarily participate in this study.

78

APPENDIX B

Demographic Response Form

Please complete the following questions:

1. What is your gender? (Male Female)

2. What is the gender of your team? (Boy’s Basketball Team Girl’s Basketball Team)

3. What is your age? ( )

4. What is your ethnicity?
1) White/ Caucasian
2) Black/ African American
3) Hispanic
4) American Indian! Alaska Native
5) Hawaiian/ Paciﬁc Islander
6) Asian American
7) Other ( )

5. What is your educational background?
1) High School
2) Less Than College/ Tech
3) 2 or More Years College
4) Bachelor Degree
5) Some Master Level Work
6) Master Degree
7) Some Doctoral Level Work
8) Completed Doctorate
9) Other ( )

6. If you attended college or graduate school,

What is your undergraduate major? (if any) ( )

What is your master’s major? (if any) ( )

What is your doctoral major? (if any) ( )
7. Do you have a teaching certiﬁcate? (Yes No)

79

8. What is your current occupation? ( )

9. How many years have you been coaching basketball, including
this year? years

 

10. How many years had you been playing basketball before becoming a coach?
years

11. How many hours do you spend for coaching basketball per a week?
hours

12. Have you ever attended a coaching education program?
1) No
2) Yes, then how many programs did you attend? ( )

80

APPENDIX C
Modiﬁed Version of Schutte Emotional Intelligence Scale (MVSEIS)-Short Version

Y Circle the answer that best matches your response to each statement.

EX)Iknow how to speak to others. Strongly disagree Disagree Neutral Agr e Strongly agree
a o e) (p e

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

No. Items 3:23;: Disagree Neutral Agree 53:23:”

When I am faced with obstacles, I remember times

1 when I faced similar obstacles and overcame them. CD ® ® @ ®
My mood has little effect on how I deal with

2 problems. GD @
I ﬁnd it hard to understand the non-verbal messages

3 of other people. ® ® ® @ ®
Some of the major events of my life have led me to

4 re-evaluate what is important and not important. ® ® ® @ C5)
I sometimes can’t tell whether someone I am

5 conversing with is serious or joking. G) ® ® @ 6)

6 When my mood changes I see new possibilities. (D ® C5) @ ®

7 ﬁgotions don’t have much effect on my quality of ® ® <3) @ 6)

8 I generally don’t expect good things to happen. ® ® CD CD (3
When I experience a positive emotion, I know how

9 to make it last. ® ® ® @ 6)
I quite often misread what is going on in social

10 situations. ® ® ® @ ®

11 I seek out activities that make me happy. ® ® ® @ (3
When I am in a positive mood, solving problems is

12 easy for me. G) ® ® @ ®

13 I tend to misread people’s facial expressions. Q) ® (3) @ 6)
I don’t believe that my emotions give any help in

14 coming up with new ideas. ® ® ® @ ®

15 I ﬁnd it hard to control my emotions. Q) ® G) GD 6)

16 People have told me that I am difﬁcult to talk to. 6,) ® C3) (4) ®
I motivate myself by imagining a good outcome to

17 task I take on. ® ® ® @ ®

 

 

 

 

 

 

 

81

 

 

I compliment others when they have done something

 

 

 

 

 

 

 

 

 

 

 

‘8 well. CD <23 ® (4) 6)
- 1

19 :32 aware of the non verbal messages other peop e GD ® 6) © 6
When I feel a change in emotions, I tend to come up

20 with new ideas. CD ® ® @ 6)
Emotions don’t play a big part in how I deal with

21 problems. ® ® ® @ 6)
When I am faced with a challenge, I give up because

22 I believe I will fail. ® ® ® @ 6)

23 ;tktrlliow what other people are feeling just by looking GD ® ® @ (9

cm.

24 I help other people feel better when they are down. (D ® 6) GD ®
I use good moods to help myself keep trying in the

25 face of obstacles. ® ® ® @ ®
I ﬁnd it hard to tell how someone is feeling from

26 their tone of voice. Q) ® ® @ <5)
It is difﬁcult for me to understand why people feel

27 the way they do. ® ® ® @ ®

28 I ﬁnd it hard to make close friendships. ® ® (3) CD CD

 

 

 

 

 

 

 

82

 

APPENDIX D
Coaching Efﬁcacy Scale (CES)

Y Circle the answer that best matches your response to each statement.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Ex) How conﬁdent are you in your ability to cook. 0
1 2 {£7 4 5
No Completed
Conﬁdence Conﬁdence
No. ITEMS

How conﬁdent are you in your ability to recognize opposing team’s strengths.

‘ 1 2 3 4 5
How conﬁdent are you in your ability to understand competitive strategies.

2 1 2 3 4 5
How conﬁdent are you in your ability to adapt to different game/meet situations.

3 1 2 3 4 5
How conﬁdent are you in your ability to recognize opposing team’s weakness during

4 competition. 1 2 3 4 5
How conﬁdent are you in your ability to make critical decisions during competition.

5 1 2 3 4 5
How conﬁdent are you in your ability to maximize your team’s strengths during competition.

6 1 2 3 4 5
How conﬁdent are you in your ability to adjust your game/meet strategy to ﬁt your team’s talents

7 l 2 3 4 5
How conﬁdent are you in your ability to maintain conﬁdence in your athletes.

8 1 2 3 4 5
How conﬁdent are you in your ability to mentally prepare athletes for game meet strategies.

9 1 2 3 4 5
How conﬁdent are you in your ability to build the self-esteem of your athletes.

10 1 2 3 4 5
How conﬁdent are you in your ability to motivate your athletes.

11 1 2 3 4 5
How conﬁdent are you in your ability to build team cohesion.

12 1 2 3 4 5
How conﬁdent are you in your ability to build the self-conﬁdence of your athletes.

13 1 2 3 4 5

 

 

 

 

 

83

 

How conﬁdent are you in your ability to build team conﬁdence.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

14 I 2 3 4
How conﬁdent are you in your ability to demonstrate the skills of your sports.

15 1 2 3 4
How conﬁdent are you in your ability to coach individual athletes on technique.

16 1 2 3 4
How conﬁdent are you in your ability to develop athletes’ abilities.

l7 1 2 3 4
How conﬁdent are you in your ability to recognize talent in athletes.

18 1 2 3 4
How conﬁdent are you in your ability to detect skill errors.

19 1 2 3 4
How conﬁdent are you in your ability to teach the skills of your sport.

20 1 2 3 4
How conﬁdent are you in your ability to instill an attitude of good moral character.

21 1 2 3 4
How conﬁdent are you in your ability to instill an attitude of fair play among your athletes.

22 1 2 3 4
How conﬁdent are you in your ability to promote good sportsmanship.

23 l 2 3 4
How conﬁdent are you in your ability to instill an attitude of respect for others.

24 1 2 Q ,1

 

 

 

84

 

APPENDIX E

Revised Leadership Scale for Sports (LSS)
Y Circle the answer that best matches your response to each statement.

EX) In coaching, I let athletes work at their speed

Never(0 % of time) Seldom(25%) Occa.(50%) O

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

he I 5%) Always( 100%)
(D Q) (3) (5)
N S Id Oc . OR A] a
No. Items 0% :fthifne grim 50E: 75°64n 133on
In coaching. I get input from the athletes at daily team
1 meetings. ® ® ® @ (5)
In coaching, I use alternative methods when the efforts of
2 the athletes are not working well in practice or CD ® C3) Q) (3
in competition.
3 In coaching, I congratulate an athlete after a good play. CD ® C3 C1) (5)
4 In coaching, I use a variety of drills for a practice. Cl) ® C3) @ ®
5 In coaching, I remain sensitive to the needs of the athletes. (1) ® (39 C1) C9
In coaching, I put the suggestions made by the team
6 members into operation. ® ® ® @ C5)
7 In coaching, l pat an athlete after a good performance. G) ® C3) C1) ®
8 In coaching, I perform personal favors for the athletes. (1) ® C3) (4) ®
9 In coaching, I give credit when it is due. G) ® C3) C1) C5)
10 In coaching, I let the athletes set their own goals. Q) ® (3) CD 6)
11 In coaching, I adapt coaching style to suit the situation. ® ® 6) C4,) (5)
In coaching, I express appreciation when an athlete
12 performs well. ® ® C3) @ <5)
13 In coaching, 1 encourage the athletes to conﬁde in the ® ® 6) GD ®
coach.
14 In coaching, I supervise athletes’ drills closely. (1) C2) C3) @ 6)
In coaching, I give the athletes freedom to determine the
15 details of conducting a drill. ® ® ® @ (S)
In coaching, I put an athlete into different positions
16 depending on the needs of the situation. CD ® ® @ ®
17 In coaching, I Show ‘O.K.’ or ‘Thumbs UP’ gesture to the ® ® ® @ ®
athletes.
In coaching, I let the athletes decided on plays to be used ,,,
18 in a competition. CD <2) <0) @ 6)

 

85

 

 

 

 

 

 

 

Never

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

No. Items 03:}? 8:13?“ (303: ' 912;." Aisha/IS

19 In coaching, I clap hands when an athlete does well. G) ® C1)

20 In coaching,althpl:yessp:lci:atla:t::ntion to correcting ® Q9

21 In coach ing,pI atrellcl: :lriaraéhlgegce) 331;? the athlete does a ® ® ® @ 6)
22 '“°°’°"’“g’iiiiflilais’ii$333332?” 9 ® ‘3 Q” ‘5’
23 In coaching, I stress the mastery of greater skills. ® ® C3) (4) C5)
24 In coaching, ﬁl, elet :3; 20:01:ng their own way even if ® ® ® @ (5)
zs “3......“ <9 8 a 8 <9
2. 1" 33.42233213122353’. '1‘: GD 3 e e a
27 ac...“ 02> e a @ <8
28 In coaching,li1nj;:tpthe appropriate athletes in the Q) ® Q) @ ®
29 In coaching,ﬂ1‘;lnarify training priorities and work on ® ® ® (,9 C5)
30 In coaChing,irln:s:rtt:;tt::a:f-li’1: 1:212: 21;: athletes on (1) ® ® @ ®
31 In coaching, I possess good knowledge of the sport. ® ® C3) @ C5)
32 In coaching, I alter plans due to unforeseen events. CI) ® (3 CD (5)
33 I"°°’°“”g’leZZ§§2’r’§J§filli’irfrié’i’b"”°""°’h" 9 9 ® @ ‘5’
34 In coaching,al1:xt:l:iirclst:fe:;:r:ﬂilete the techniques ® ® ® ® ®
35 In coaching,alﬂlrl)eotlecsout for the personal welfare of the G) ® C3 C1) 6)
36 In coaching,nli;:vlv]::ll-an athlete as long as the athlete (1) ® ® @ 6)
37 In coach ing,g10:ll:rtig ioeagsthalneileihe paths to reach the 0) ® ® @ (5)
38 In coach ing,ﬂll es)eed Elf}: :nfegltr: pnf i1thietes’ ideas when CD ® ® @ 6)
39 In coaching,elv:is:art)il;j:ctive measurements for ® ® ® @ ®
40 In coaching, I set goals that are compatible with the CI) ® ® @ 6)

 

athletes’ ability.

 

 

 

 

 

 

86

 

 

Never

 

 

 

 

 

 

 

 

 

 

 

 

 

u... a... 0,;ng 22$:
41 In coaChing’slu:gzgtlibiriefgievff;kefsscgnilluﬁepractices. ® ® ® ® (5)
42 In coaching,fll:§:rd‘t:;ttplrsoper progressions in teaching (1) ®

43 In coachmg,l:e:[:agyoi?$ree:::(lleit:sthe personal well- ® ® ® @ (5)
44 I"°°’°"’"g’riniita’fiﬁiilelf‘i‘a”;5332223;. ® ® ® ‘9 6’
45 In coach ingEgg-alt;rtlhgeaailrﬁgz’tiﬁzgfl performance (1;) ® ® CI) 6)
.. “are <3 e a e a
47 In coaching, aIn if; :1]? ;ﬂ;:::: 31:: in decision making G) ® ® (4) 6)
48 In coaching,plr2::2nt11;e athletes with their personal ® ® ® @ ®
49 In coaching,a1th vliestigswith the parents/ guardians of the CD ® ® @ ®
so m 23:22: assists °" <2 e a e a
51 In coaching,"1l :lzrecsoumriasgtzk :r; athlete when the athlete GD ® ® @ (5)
52 In coaching, I coach to the level of the athletes. Q) Q) (3 CD ®

 

 

 

 

 

 

 

87

 

APPENDIX F

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88

REFERENCES

89

REFERENCES

Ames, C. (1984). Competitive, cooperative, and individualistic goal structures: A
cognitive-motivational analysis. In R. Ames & C. Ames (Eds.), Research on
motivation in education Vol. 1 : Student motivation (pp. 177-207). Orlando, FL:
Academic Press.

Ames, C. (1992a). Classrooms: Goals, structures, and student motivation. Journal of
Educational Psychology, 84, 261-271.

Ames, C. (1992b). Achievement goals, motivational climate, and motivational processes.
In G. C. Roberts (Ed), Motivation in sport and exercise (pp. 161-176).
Champaign, IL: Human Kinetics.

Amorose, A. L., & Horn, T. S. (2000). Intrinsic motivation: Relationships with collegiate
athletes’ gender, scholarship status, and perceptions of their coaches’ behavior.
Journal of Sport and Exercise Psychology, 22, 63-84.

Austin, E. J ., Saklofske, D. H., Huang, S. H. S., & McKenney, D. (2004). Measurement
of trait emotional intelligence: testing and cross-validating a modiﬁed version of
Schutte et al.’s (1998) measure. Personality and Individual Differences, 36,
555-562.

Bandura, A. (1977). Self-efﬁcacy: Toward a unifying theory of behavioral change.
Psychological Review, 84, 191-215.

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory.
Englewood Cliffs, NJ: Prentice-Hall.

Bandura, A. (1997). Self-eﬂicacy: The exercise of control. New York: Freeman.

Bar-On, R. (1997). EQ—I. Bar-0n emotional quotient inventory: A measure of emotional
Intelligence. Toronto: Multi-Health Systems Inc.

Beam, J. W., Serwatka, T. S., & Wilson, W. J. (2004). Preferred leadership of NCAA
Division I and II intercollegiate student-athletes. Journal of Sport Behavior,
27(1), 3-18.

Caruso, D.R., Mayer, J.D., & Salovey, P. (2002). Emotional intelligence and emotional
leadership. In RE. Riggio, S.E. Murphy, & F. J. Pirozzolo (Eds), Muliple
intelligences and leadership. (p. 55-74), Mahwah, N.J.: Lawrence Erlbaum
Associates.

Chan, D. W. (2004). Perceived emotional intelligence and self-efﬁcacy among Chinese

secondary school teachers in Hong Kong. Personality and Individual
Differences, 36, 1781-1795.

90

Chase, M. A., F eltz, D. L., Hayashi, S. W., & Hepler, T. J. (2005). Sources of coaching
efﬁcacy: The coaches’ perspective. International Journal of Exercise and Sport
Psychology, 1, 7-25.

Chelladurai. P. (1984). Discrepancy between preferences and perceptions of leadership
behavior and satisfaction of athletes in varying sport. Journal of Sport
Psychology, 6, 27-41.

Chelladurai, P. (1990). Leadership in sports: A review. International Journal of Sport
Psychology, 21, 328-354.

Chelladurai, P. (1993). Leadership. In RN. Singer, M. Murphey, & L.K. Tennant (Eds.),
Handbook of research on sport psychology (pp. 647-671). New York:
Macmillan.

Chelladurai, P., & Amott, M. (1985). Decision style in coaching: preferences of
basketball players. Research Quarterly for Exercise and Sport, 56, 15-24.

Chelladurai, P. & Carron, A. V. (1983). Athletic maturity and preferred leadership.
Journal of Sports Psychology. 5. 371-3 80.

Chelladurai. P., Imamura, H., Yamaguchi. Y., Oinuma. Y., & Miyauchi. T. (1988). Sport
leadership in cross-national setting: The case of Japanese and Canadian
university athletes. Journal of Sports & Exercise Psychology. 10. 374-3 89.

Chelladurai, P., & Quek, CB. (1991). Decision style choices of high school basketball
coaches: The effects of situational and coach characteristics. Journal of Sport
Behavior, 18, 91-108.

Chelladurai, P., & Riemer, HA. (1998). Measurement of leadership. In J .L. Duda (Ed.),
Advances in sport and exercise psychology (pp. 227-253). Morgantown, WV:
Fitness Information Technology.

Chelladurai, P., & Saleh, S. (1978). Preferred leadership in sports. Canadian Journal of
Applied Sport Sciences, 3, 85-92.

Chelladurai, P., & Saleh, S. (1980). Dimensions of leader behavior in sports:
Development of a leadership scale. Journal of Sport Psychology, 2, 34—45.

Ciarrochi, J., Chan, A. Y. C., & Caputi, P. (2000). A critical evaluation of the emotional
intelligence construct. Personality and Individual Differences, 28, 539-561.

Cooper, R. K., & Sawaf, A. (1997). Executive EQ.‘ Emotional intelligence in leadership
and organizations. New York: Grosset/Putnam.

91

Cote, J ., & Sedgwick, W. A. (2003). Effective behaviors of expert rowing coaches: A
qualitative investigation of Canadian athletes and coaches International Sports
Journal, 7(1), 62-77.

Crabbe, R. C. (2007). Emotions in sports. Unpublished thesis, Virginia Tech University,
Blacksburg.

Deci, E.L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human
behavior. New York: Plenum Press.

Deci, E.L., & Ryan, R. M. (1991). A motivational approach to self: Integration in
personality. In R. Dientsbier (Ed.), Nebraska symposium on motivation: Vol. 38.
Perspectives on motivation (pp. 237-288). Lincoln: University of Nebraska
Press.

Denham, C.H., & Michael, J .J . (1981). Teacher sense of efﬁcacy: A deﬁnition of the
construct and a model for further research. Educational research Quarterly, 5,
39-63.

Duda, J .L., & Whitehead, J. (1998). Measurement of goal perspectives in the physical
domain. In J .L. Duda (Ed.), Advances in sport and exercise psychology (pp.
227-253). Morgantown, WV: Fitness Information Technology.

Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist,
41, 1040-1048.

Dweck, C. S. (1999). Self-theories: Their role in motivation, personality, and
development. Philadelphia: Psychology Press.

Eccles, J. S., & Harold, R. D. (1991). Gender differences in sport involvement: Applying
the Eccles’ expectancy value model. Journal of Applied Sport Psychology, 3, 7-
35.

Feltz, D. L., Chase, M. A., Moritz, S. E., & Sullivan, P. J. (1999). A conceptual model of
coaching efﬁcacy: preliminary investigation and instrument development.
Journal of Educational Psychology, 91, 765-776.

F eltz, D. L., Hepler, T. J ., Roman, N., & Paiement, C. A. (2006). Coaching efﬁcacy of
youth sport coaches: Extending validity evidence for the Coaching Efﬁcacy
Scale. Unpublished manuscript, Michigan State University, East Lansing.

George, J .M. (2000). Emotions and leadership: the role of emotional intelligence.
HumanRelations, 53, 1027-55.

Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ. New York:
Bantam Books.

92

Goleman, D. (1998). Working with emotional intelligence. New York: Bantam Books.

Gordon, S. (1986). Decision styles and coaching effectiveness in university soccer.
Canadian Journal of Sport Sciences, 13(1), 56-65.

Gould, D., Guinan D., Greenleaf, C., & Chung, Y. (2002) A survey of US. Olympic
coaches: variables perceived to have inﬂuenced athlete performances and coach

effectiveness. The Sport Psychologist, 16, 229-250.

Grace, K. (1988). Successﬁrl vs. unsuccessful coaches and athletes. Track Technique, 104,
3325.

Hanson, W., & Gould, D. (1988). Factors affecting the ability of coaches to estimate their
athletes’ trait and state anxiety levels. The Psychologist, 2, 298-313.

Harter, S. (1978). Effectance motivation reconsidered. Human Development, 21, 34-64.

Harter, S. (1981). A model of intrinsic mastery motivation in children: Individual
differences and developmental change. In W. A. Collins (Ed), Minnesota
symposium on child psychology (Vol. 14, pp. 215-255).

Humphrey, RH. (2002). The many faces of emotional leadership. The Leadership
Quarterly, 13, 493-504.

Horn, T. S. (2002). Coaching effectiveness in the sport domain. In T. S. Horn (Ed.),
Advances in sport psychology (2“ ed., pp. 309-354). Champaign, IL: Human
Kinetics.

Huck, S. (2004). Reading statistics and research (4.}. ed). Upper Saddle River, NJ: Ally
& Bacon.

Kimiecik, J., & Gould, D. (1987). Coaching psychology: the case of Jame “Doc”
Counsilman. The Sport Psychologist, I, 350-358.

Kline, R. B. (1998). Principles and practice of structural equation modeling. New York:
Guilford Press

Kobe, L.M., Reiter-Palmon, R., & Rickers, J .D. (2001). Self-Reported leadership
experiences in relation to inventoried social and emotional intelligence. Current
Psychology, 20, 154-163.

Lane, A., & Lowther, J. (2005). Relationships between emotional intelligence and
psychological skills among athletes ‘Annual conference of the British

Association of Sport and Exercise Sciences’. Journal of Sports Sciences, 23,
1253-1254.

93

Matthews, G., Zeidner, M., & Roberts, R D. (2007). Emotional intelligence: consensus,
controversies, and questions. In G. Matthews, M. Zeidner., & R. D. Roberts
(Eds), The science of emotional intelligence: knows and unknowns (pp. 3-46).
New York: Oxford University Press.

Mayer, J. D. & Caruso, D. R. (2002). The effective leader: Understanding and applying
emotional intelligence. Ivey Business Journal November/December, 1-5.

Mayer, J. D., Caruso, D., & Salovey, P. (1999). Emotional intelligence meets traditional
standards for an intelligence. Intelligence, 27, 267-298.

Mayer, J. D., DiPaolo, M., & Salovey, P. (1990). Perceiving affective content in
ambiguous visual stimuli: A component of emotional intelligence. Journal of
Personality Assessment, 54, 772-781.

Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. J.
Sluyter (Eds.), Emotional development and emotional intelligence: Educational
implications (pp. 3-31). New York: Basic Books.

Mayer, J. D., Salovey, P., & Caruso, D. R (2000). Models of emotional intelligence. In R.
J. Stemberg (Ed.), Handbook of intelligence (pp. 396-420). Cambridge:
Cambridge University Press.

Mayer, J. D., Salovey, P., Caruso, D. R, & Sitarenios, G. (2003). Measuring emotional
intelligence with the MSCEIT V2.0. Emotion, 3, 97-105.

Meyer, B. B., & Fletcher, T. B. (2007). Emotional intelligence: a theoretical overview
and implications for research and professional practice in sport psychology.
Journal of Applied Sport Psychology, 19, 1-15.

Miller, L. M. (2003). Qualitative investigation of intercollegiate coaches ’ perceptions of
altruistic leadership. Unpublished doctoral dissertation, Ohio State University,
Columbus. .

Myers, N. D., Feltz, D. L., & Wolfe, E. W. (in press). A Conﬁrmatory study of rating
scale category effectiveness for the coaching efﬁcacy scale. Research Quarterly
for Exercise and Sport.

Myers, N. D., Vargas-Tonsing, T. M., & Feltz, D. L. (2004). Coaching efﬁcacy in
intercollegiate coaches: Sources, coaching behavior, and team variables.
Psychology of Sport and Exercise, 6, 129-143.

Neubauer, A. C., & Freudenthaler, H. H. (2005). Models of emotional intelligence. In R.

Schulze & R. D. Roberts (Eds.). Emotional intelligence: an international
handbook (pp. 31-50). Cambridge, MA: Hogrefe & Huber.

94

Newton, M.L., & Duda, J .L. (1993). The Perceived Motivational climate in Sport
Questionnaire-2: construct and predictive validity. Journal of Sport and
Exercise Psychology, 15(Suppl.), SS9.

Nunnaly, J. (1978). Psychometric theory. New York: McGraw—Hill.

Okech, A. P. (2004). An exploratory examination of the relationship among emotional
intelligence, elementary school science teacher self-eﬁ‘icacy, length of teaching
experience, race/ ethnicity, gender, and age. Unpublished doctoral dissertation.
Texas A&M University, Kingsville.

Park, J .K. (1992). Construction of the Coaching Conﬁdence Scale. Unpublished doctoral
dissertation. Michigan State University, East Lansing.

Pedhazur, E. J. (1982). Multiple regression and behavioral research. New York: Holt,
Rinehart & Winston.

Pelletier, L.G., & Vallerand, R.J. (1985). Effects of coaches ’ interpersonal behavior on
athletes’ motivational level. Paper presented at the annual conference of the
Canadian Society for Psychomotor Learning and Sport Psychology, Montreal.

Penrose, A., Perry, C. & Ball, 1. (2007). Emotional intelligence and teacher self efﬁcacy:
The contribution of teacher status and length of experience. Issues in
Educational Research, 17(1), 107-126.

Petrides, K. V., & F umham, A. (2000). On the dimensional structure of emotional
intelligence. Personality and Individual Diﬂerences, 29, 3 1 3-3 20.

Petrides, K. V., & F urnham, A. (2001). Trait emotional intelligence: Psychometric
investigation with reference to established trait taxonomies. European Journal
of Personality, 15, 425-448.

Perez, J. C., Petrides, K. V., & Fumham, A. (2005). Measuring Trait Emotional
Intelligence. In R. Schulze & R D. Roberts (Eds). Emotional intelligence: an
international handbook (pp. 31-50). Cambridge: Hogrefe & Huber.

Roberts, R. D., Zeidner, M., & Matthews, G. (2001). Does emotional intelligence meet
traditional standards for an intelligence? Some new data and conclusions.
Emotion, 1, 196-231.

Robinson, M. D., & Clore, G. L. (2002). Belief and feeling: Evidence for an accessibility
model of emotional self-report. Psychological Bulletin, 128, 934-960.

Rosete, E., & Ciarrochi, F. (2005). Emotional intelligence and its relationship to

workplaceper formance outcomes of leadership effectiveness. Leadership &
Organization Development Journal, 26, 388-399.

95

Saklofske, D. H., Austin, E. J ., & Minski, P. (2003). Factor structure and validity of a
trait emotional intelligence measure, Personality and Individual Diﬂerences, 34,
707-721.

Sala, F. (2002). EC! Technical Manual. Hay Acquisition Company.

Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagination, Cognition and
Personality, 9, 185-211.

Scanlan, T. K., Carpenter, P. J ., Schmidt, G. W., Simons, J. P., & Keeler, B. (1993). An
introduction to the sport commitment model. Journal of Sport and Exercise
Psychology, 15, 1-15.

Schunk, D. H. (1995). Self-efﬁcacy and education and instruction. In J .E. Maddux (Ed.),
Self-eﬂicacy, adaptation, and adjustment: Theory, research, and application (pp.
281-303). New York: Plenum Press.

Schutte, N. S., Malouff, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T., Golden, C. J., &
Domheim, L. (1998). Development and validation of a measure of emotional
intelligence. Personality and Individual Differences, 25, 167-177.

Short, S. E., Smiley, M., & Ross-Stewart, L. (2005). The relationship among imagery use
and efﬁcacy beliefs in coaches. The Sport Psychologist, 19, 380-394.

Smith, R.E., Smoll, R.F., & Curtis, B. (1978). Coaching behaviors in Little League
baseball. In F .L. Smoll & R. E. Smith (Eds), Psychological perspectives in
youth sports (pp. 173-201). Washington, DC: Hemisphere.

Smith, R., Smoll, F., & Hunt, E. (1997). A system for the behavioral assessment of
athletic coaches. Research Quarterly, 48, 401-407.

Smoll, F. L., & Smith, R. E. (1989). Leadership behaviors in sport: A theoretical model
and research paradigm. Journal of Applied Social Psychology, 19, 1522-1551.

Sosik, J .J . & Megerian, LE. (1999). Understanding leader emotional intelligence and
performance: The role of self-other assessment on transformational leadership.
Group and Organization Management 24, 367-390.

Sullivan, P. J ., Gee, C. J ., & Feltz, D. L. (2006). Playing experience: The content

knowledge source of coaching efﬁcacy beliefs. In A. A. Mitel (Ed.), Trends in
Educational Psychology. New York: Nova Publishers.

Sullivan, P. J ., & Kent, A. (2003). Coaching efﬁcacy as a predictor of leadership style in
intercollegiate athletics. Journal of Applied Sport Psychology, 15, 1-11.

96

Summers, R. J. (1983). A study of leadership in sport setting. Unpublished master’s thesis.
University of Waterloo, Canada.

Vargas—Tonsing, T. M., Wamers, A. L. & F eltz, D. L. (2003). The predictability of
coaching efﬁcacy on team efﬁcacy and player efﬁcacy in Volleyball. Journal of
Sport Behavior, 26, 396-470.

VanSickle, J .L. (2004). The relationship between emotional intelligence and coaching
eﬂ'ctiveness in Division I head softball coaches. Unpublished doctoral
dissertation. University of Kentucky, Lexington.

Watson, D. (2000). Mood and temperament. New York: Guilford.

Weiner, B. (1986). An attributional theory of motivation and emotion. New York:
Springer-Verlag.

Weiner, B. (1992). Human motivation: Metaphors, theories, and research. Newbury Park,
CA: Sage.

Weisinger, H. (1998). Emotional intelligence at work: The untapped edge for success.
San Francisco: Jossey-Bass.

Wooden, J ., & Jamison, S. (2005). Wooden on leadership. New York: McGraw-Hill.

Zhang, J ., Jensen, B. E. & Mann, B. L. (1997). Modiﬁcation and revision of the
leadership scale for sport. Journal of Sport Behavior, 20(1), 105-122.

Zizzi, S. J ., Deaner, H. R., & Hirshhorn, D. K. (2003). The relationship between

emotional intelligence and performance among college baseball players.
Journal of Applied Sport Psychology, 15, 262-269.

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