Sports coaches’ interpersonal motivating styles: Longitudinal associations, change, and

multidimensionality

Andreas Stenling

Department of Psychology

Umeå 2016

Responsible publisher under swedish law: The Dean of the Faculty of Social

Sciences

This work is protected by the Swedish Copyright Legislation (Act 1960:729)

ISBN: 978-91-7601-565-0

Cover art: Inhousebyrån, Umeå University

Elektronisk version accessible at http://umu.diva-portal.org/

Printed by: UmU-tryckservice

Umeå, Sweden 2016

To A, B, & C with love

i

Table of Contents

Table of Contents i Abstract iii List of Papers iv Abbreviations v Figures and Tables vi Introduction 1 Epistemological and Ontological Positions 2 Leadership Research in Sport Psychology 5

Mediational Model of Leadership 5 Multidimensional Model of Leadership 7 Motivational Climate 8 Transformational Leadership 9 Self-Determination Theory 11 Why SDT as the Theoretical Framework? 13

A Self-Determination Theory-Based Model of the Coach–Athlete

Relationship 16 Autonomy-Supportive and Controlling Interpersonal Styles 18 Sports Coaches’ Interpersonal Styles 20 Cross-sectional studies 20 Longitudinal studies 21 Intervention studies 26 Antecedents of Coaches’ Interpersonal Styles 28 Multidimensional Nature of Coaches’ Interpersonal Styles 33

Purpose and Research Questions 36 Materials and Methods 37

Samples and Procedures 37 Measures 38 Data Analysis 38 Study 1: Latent difference score modeling 42 Study 2: Bayesian latent growth curve modeling 43 Study 3: Bifactor exploratory structural equation modeling 44

The Empirical Studies 46 Study 1: Changes in Perceived Autonomy Support, Need Satisfaction,

Motivation, and Well-Being in Young Elite Athletes 46 Background and aim 46 Method 46 Results and discussion 47 Study 2: Longitudinal Associations Between Athletes’ Controlled Motivation, Ill-

Being, and Perceptions of Controlling Coach Behaviors: A Bayesian Latent

Growth Curve Approach 48

ii

Background and aim 48 Method 49 Results and discussion 49 Study 3: Using Bifactor Exploratory Structural Equation Modeling to Examine

Global and Specific Factors in Measures of Sports Coaches’ Interpersonal Styles51 Background and aim 51 Method 52 Results and discussion 52

General Discussion 54 Coaches’ Interpersonal Styles and Athletes’ Motivation and Ill- and Well-being 54 The Multidimensional Nature of Measures of Coaches’ Interpersonal Styles 57 Limitations 60 Challenges of an Autonomy- or Need-Supportive Interpersonal Style 63 Implications for Research 65 Suggestions for Future Research 68

Some Final Remarks 69 References 71 Acknowledgements 103

iii

Abstract Coaches play a central role in shaping the sport environment for young

athletes. This thesis is focused on the leadership process in sports and how

coaches’ autonomy-supportive and controlling interpersonal styles

longitudinally are related to young athletes’ motivation and ill- and well-

being. The aim is also to examine psychometric multidimensionality in

measures of coaches’ need-supportive and controlling interpersonal styles.

Questionnaire data from young athletes were used in the empirical studies.

In Study 1, we examined an adaptive motivational process (i.e., longitudinal

associations between autonomy support, need satisfaction, self-determined

motivation, and well-being). The results showed that within-person changes

in perceived autonomy support, need satisfaction, self-determined

motivation, and well-being were all positively correlated. Higher self-

determined motivation and well-being early in the season longitudinally

predicted higher levels of perceived autonomy support from the coach.

Higher self-determined motivation was also a positive predictor of within-

person changes in perceived autonomy support and well-being over the

season. In Study 2, we examined a maladaptive motivational process (i.e.,

longitudinal associations between coaches’ controlling behaviors, controlled

motivation, and ill-being). The findings demonstrated that athletes who

perceived their coach as more controlling reported higher controlled

motivation at the end of the season and that higher controlled motivation

early in the season predicted higher ill-being at the end of the season.

Controlled motivation was also a positive predictor of athletes’ perceptions

of coaches’ controlling behaviors at the within-person level. Study 1 and 2

suggest that individual factors (e.g., motivation and well-being) seemed to

function as important determinants of how athletes perceived their coach

and future research should explore the underlying mechanisms through

which these processes occur. In Study 3, we examined psychometric

multidimensionality in measures of athletes’ perceptions of coaches’ need-

supportive (Interpersonal Supportiveness Scale-Coach [ISS-C]) and

controlling (Controlling Coach Behaviors Scale [CCBS]) interpersonal styles.

The analyses indicated that the ISS-C is not multidimensional; it appears to

comprise a single factor. Three of the four subscales of the CCBS appear to

share a common core, whereas the fourth subscale (i.e., controlling use of

rewards) seems to represent a slightly different aspect of a controlling

interpersonal style. These results bring into question the

multidimensionality in measures of athletes’ perceptions of coaches’

interpersonal styles. Neither measure displayed a coherent multidimensional

pattern, indicating a need for better alignment between theory and

measurement.

iv

List of Papers

1. Stenling, A., Lindwall, M., & Hassmén, P. (2015). Changes in

perceived autonomy support, need satisfaction, motivation, and

well-being in young elite athletes. Sport, Exercise, and Performance

Psychology, 4, 50–61. doi:10.1037/spy0000027

2. Stenling, A., Ivarsson, A., Hassmén, P., & Lindwall, M. (2016).

Longitudinal associations between athletes’ controlled motivation,

ill-being, and perceptions of controlling coach behaviors: A

Bayesian latent growth curve approach. Manuscript submitted for

publication.

3. Stenling, A., Ivarsson, A., Hassmén, P., & Lindwall, M. (2015). Using

bifactor exploratory structural equation modeling to examine global

and specific factors in measures of sports coaches’ interpersonal

styles. Frontiers in Psychology, 6:1303.

doi:10.3389/fpsyg.2015.01303

Paper 1 has been reproduced with permission from the copyright holder.

v

Abbreviations

BPNT Basic psychological needs theory

CBAS Coaching Behavior Assessment System

CCBS Controlling Coach Behaviors Scale

CET Cognitive evaluation theory

CETr Coach effectiveness training

CLPM Cross-lagged panel model

DIEM Dynamic integrated evaluation model

ESEM Exploratory structural equation modeling

FIML Full information maximum likelihood

GLS Generalized least squares

HCCQ Health Care Climate Questionnaire

HMIEM Hierarchical model of intrinsic and extrinsic motivation

ICM-CFA Independent clusters model confirmatory factor analysis

ISS-C Interpersonal Supportiveness Scale—Coach

LDS Latent difference score

LGCM Latent growth curve modeling

LSS Leadership Scale for Sports

MAC Mastery approach to coaching

ML Maximum likelihood

MLM Multilevel modeling

MNAR Missing not at random

OIT Organismic integration theory

SDT Self-determination theory

SEM Structural equation modeling

SIgA Secretory immunoglobulin A

vi

Figures and Tables

Figure 1. A modified version of Mageau and Vallerand’s (2003)

motivational model of the coach–athlete relationship……………………..

p. 17

Figure 2. Latent difference score model………………………………………… p. 47

Figure 3. Cross-lagged panel model………………………………………………. p. 48

Figure 4. Conditional latent growth curve model……………………………. p. 50

Figure 5. Unconditional latent growth curve model with time-varying

covariates……………………………………………………………………………………

p. 50

Figure 6a. Independent clusters model confirmatory factor

analysis……………………………………………………………………………………....

p. 53

Figure 6b. Exploratory structural equation model………………………….. p. 53

Figure 6c. Bifactor exploratory structural equation model………………. p. 53

Table 1. Overview of the three studies…………………………………………… p. 39

1

Introduction This thesis is focused on the leadership process in sports and how coaches’

autonomy-supportive and controlling interpersonal styles longitudinally are

related to young athletes’ motivation and ill- and well-being. Leadership can,

however, be conceptualized in many ways. My definition stems from

Northouse (2016): “Leadership is a process whereby an individual influences

a group of individuals to achieve a common goal” (p. 6). Defining leadership

as a process means that it is not a trait within the leader or a unidirectional

influence from the leaders to the followers; a process implies a transactional

event whereby the leader and followers are influencing each other. Many

theories can be applied to understand the leadership process in sports,

ranging from traditional leadership theories, such as the multidimensional

model of leadership (Chelladurai, 1990) and transformational leadership

theory (Bass, 1985; Bass & Riggio, 2006) to coach–athlete relationship

theories (Jowett & Poczwardowski, 2007) to motivation-oriented theories,

such as achievement goal theory (Ames, 1992a, 1992b; Nicholls, 1989) and

self-determination theory (SDT; Deci & Ryan, 1985, 2000). The present

thesis is guided by SDT, which is a metatheory of human motivation and

personality.

Organized leisure activities have been suggested as an effective vehicle to

promote positive development (Larson, 2000), and the most popular

organized leisure activity among young people in Sweden is sports (Statistics

Sweden, 2009). Not all young people, however, have positive experiences

from sports participation, and sports involvement has been associated with

both positive (e.g., increased self-esteem and well-being) and negative (e.g.,

burnout and stress) experiences and outcomes (Côté, Bruner, Erickson,

Strachan, & Fraser-Thomas, 2010). Researchers in sport (e.g., Côté et al.,

2010) and developmental psychology (e.g., Eccles, Barber, Stone, & Hunt,

2003) have emphasized the importance of supportive relationships,

relationships with adults, and appropriate role models as facilitating factors

of young athletes’ healthy development. There is also some evidence

suggesting that the influence of these various sources on young athletes’

healthy development changes across adolescence. Specifically, the

importance of parents’ feedback seems to decline, whereas the importance of

coaches appears to increase during adolescence (Horn & Butt, 2014).

Coaches are considered to be one of the most influential motivational

factors for young athletes and play a central role in shaping their sporting

environment (Côté et al., 2010; Mageau & Vallerand, 2003; Ntoumanis,

2012; Reimer, 2007). The central role that sports coaches are assumed to

play in shaping young athletes’ experiences has stimulated several lines of

research on the coach–athlete relationship, and athlete outcomes have

received much attention in sport psychology research. One of the key

2

outcomes is athlete well-being, which is a central component for positive

experiences through sports participation (Gagné & Blanchard, 2007; Smoll &

Smith, 2002). The present thesis is focused on the leadership process in

sports and specifically how coaches’ autonomy-supportive and controlling

interpersonal styles longitudinally are related to young athletes’ motivation

and ill- and well-being. Of particular interest are the dynamic and potentially

reciprocal or reversed associations over time (see Study 1 and Study 2),

which generally have received little attention in leadership research (Avolio,

2007) as well as in SDT-based research on sports coaches (Ntoumanis,

2012). My aim is also to go beyond the unidimensional conceptualization of

coaches’ autonomy-supportive and controlling interpersonal styles and

examine multidimensionality in measures of coaches’ controlling and need-

supportive interpersonal styles (see Study 3).

The present thesis is organized into five parts. First, I will explicate my

epistemological and ontological positions. Second, I will provide a brief

overview of some of the most common leadership frameworks in the sport

psychology literature and contrast them with SDT. Third, I will provide an

overview of SDT-based research on leadership in sports. Fourth, I will

provide a summary of the empirical studies. And fifth, I will synthesize the

findings in the discussion.

Epistemological and Ontological Positions Sport and exercise psychology have gone through a paradigmatic evolution

(see Kuhn, 2012, for a discussion about paradigms) during the 20th century,

an evolution nicely summarized by Vealy (2006):

The “box” evolved from the “subjectivity” of philosophy, to the

“hardscience” of experimental psychology, to motor behavior,

and finally to social psychology so that more contextually

relevant knowledge about sport and exercise could be pursued.

Theoretically, the “box” moved from perception and sensation,

to traits, to dispositions, to cognitions, to socially constructed

cognitions within unique social contexts. And finally, the “box”

moved from positivism to what seems to now be a post-

positivistic, modernist era with some movement toward

constructivism. (p. 148)

The box Vealy referred to represents the dominant paradigm that serves as a

model for research and application as it evolves through successive historical

eras (Kuhn, 2012). Vealy (2006) also pointed out that the field is in an era of

diversification in terms of theories, research, and methods and that a strong

3

knowledge base is being identified, established, used, and disseminated.

Despite this, a recent review of qualitative research between 2000 and 2009

in three sport and exercise psychology journals revealed that positivist/post-

positivist approaches maintain a predominant position in sport and exercise

psychology research (Culver, Gilbert, & Sparkes, 2012). Of the 183 articles

included in the review, only 13.7% of the authors took an epistemological

stance, and this percentage is probably lower among articles presenting

quantitative data. Culver et al. (2012) suggested that:

positivists/postpositivists have a privileged stance in sport

psychology, which may lead them to disregard the need to

identify their epistemology, it being assumed. (p. 278)

To highly generalize, within the positivist/post-positivist paradigm, an

ontological assumption is that there exists one objective reality and an

epistemological belief in science’s ability to capture something about this

reality (Giacobbi, Poczwardowski, & Hager, 2005; Martela, 2015).

Another philosophy of science that recently has regained attention in the

social sciences is pragmatism. A pragmatic philosophy of science is viewed as

an alternative to both positivist/post-positivist and constructivist

philosophies that potentially can mediate or overcome the conflict between

positivism and constructivism (Giacobbi et al., 2005; Martela, 2015; Zyphur

& Pierides, 2015). Originating from Dewey (1908, 1938), James (1907), and

Peirce (1931), pragmatism relies ontologically on ontological

experientialism, meaning that humans are embedded in a world of

experiencing that they can never escape and that humans are living in a

world in which they need to act. In line with this ontological position, the

world is not something to be “objectively” observed but something in which

humans already aim to live their lives as best they can (Martela, 2015). A

pragmatist epistemology can be described as a fallibilistic instrumentalist

epistemology relying on an assumption that all one’s beliefs are future-

oriented rules for action (James, 1907). Embracing the notion of fallibilism is

to say that one cannot be absolutely sure of anything, that is, one cannot

reach perfect certitude. Instead, one’s knowledge always consists of

uncertainty and indeterminacy and can always be subject to change in the

future (Martela, 2015; Peirce, 1931). Instead of trying to find an objective

reality and seeking correspondence, pragmatists have an ends-in-view

approach (Dewey, 1938) and are trying to find practical solutions to worldly

problems that matter (Zyphur & Pierides, 2015).

Dewey (1938) described inquiry as a process of active engagement

through the construction of various forms of knowledge and experience

resulting from collective activities. Dewey proposed that ways of

investigating phenomena give shape to a set of strategies to solve problems,

4

whether they be practical (i.e., situations that arise in daily life), theoretical

(i.e., scientific problems), factual (e.g., describing an entity or process), or of

value (i.e., what to do in a particular situation). Recently, Zyphur and

Pierides (2015) proposed a pragmatist theory of quantitative research

grounded in the work by early pragmatists such as Dewey (1938), James

(1907), and Mead (1929) as well as more recent work by Martela (2015) and

Rorty (1982). It starts with a simple assertion that research is a kind of work

done by people and that adhering to this pragmatic approach means valuing

the practical effects of doing quantitative research. To briefly describe

Zyphur and Pierides’s (2015) pragmatic theory of quantitative research, they

present five “themes” for their theory—tools, socialization, habits,

experience, and truth and knowledge—as a way to grapple with quantitative

research as a kind of work. The basic notion is that humans did not evolve to

be abstract thinkers and observers of a singular external reality, but instead,

humans survive by organizing activities that put thinking, language, and

objects to work as tools in specific environments (Wittgenstein, 1973).

Furthermore, people do not exist in a vacuum but seem to be inclined to act

with and in relation to other people (Baumeister & Leary, 1995).

Socialization processes make it possible to create and to enter into a

community, which requires learning specific ways of thinking and using

language to coordinate action. Hence, researchers socialize into specific

communities of practice, and within these, they create ideas about what the

world is like and how to go about conducting research to gain knowledge

about the world. Following this socialization into communities, some of the

activities become habits, that is, “a way of being that is fluently undertaken,

going unquestioned unless problems arise” (Zyphur & Pierides, 2015, p. 13).

Many work practices become habitual, such as thinking, use of language, or

working with material objects such as surveys or statistical software. These

habits are consequences of one’s collective experiences and are more or less

implicit agreements and in many ways guide one’s thoughts, language, and

actions. Furthermore, an external reality is not viewed as a cause of

experience, which those adhering to the idea of a singular external reality

would argue, but rather, objects or notions of foundations exist as

experiences during activities. An external stimulus is the occasion for

experience, not its cause (Mead, 1903). “The point is that to organize work by

socializing or training researchers in habits of thought, language, and action

is to collectively organize the experiences that are associated with the activity

that defines a research community” (Zyphur & Pierides, 2015, p. 18). The

final theme relates to questions about truth and knowledge, on which

pragmatists note that after 2,500 years of debate, there is no agreement on

what truth and knowledge is, and the attempts so far, such as theories of

representation and correspondence, create various seemingly unsolvable

problems. Pragmatists are trying to move beyond unrealistic ideals about

5

truth and knowledge and instead focus on whatever works in the situation,

guiding action to reach practical ends (James, 1907). As Kuhn (2012)

pointed out, the standards for what is known as right or real vary in different

communities, and disagreements are resolved in relation to what is known

and how to know about it. The criteria for what is right or real are

institutionalized to standardize the thoughts, practices, and discourses that

are considered reasonable (Slager, Gond, & Moon, 2012). To put it simply,

instead of simplified ideas about correspondence and representation with an

external reality, “a pragmatist approach to research can focus on finding

whatever will work for specific people, at a specific time, doing specific

things, with problems and interests that are local and more or less practical”

(Zyphur & Pierides, 2015, pp. 22–23). I position myself as a pragmatist, as

this approach to research with an ends-in-view focus (i.e., finding practical

solutions to practical problems) corresponds well to my ideas about what

research is about. The present thesis is mostly concerned with what Dewey

(1938) referred to as theoretical (i.e., scientific) problems, and of particular

interest are the notions of reciprocal or reversed associations and

multidimensionality.

Leadership Research in Sport Psychology Leadership is one of the major research areas in sport psychology (Lindahl,

Stenling, Lindwall, & Colliander, 2015) and is referred to as a reemerging

theme in the sport psychology literature (Weiss & Gill, 2005). Leadership

research in psychology has a long history, going back to the early studies by

Lewin, Lippet, and White (1939), who examined the role of leadership styles

on children’s aggressive behaviors. The specific interest in leadership in

sports, however, really took off during the 1970s. Besides autonomy support

and controlling behaviors, as outlined in SDT (Deci & Ryan, 1985, 1987,

2000), two of the most influential approaches to leadership in sports since

the 1970s have been the mediational model of leadership (Smoll & Smith,

1989; Smoll, Smith, Curtis, & Hunt, 1978) and the multidimensional model

of leadership (Chelladurai, 1978, 1990, 2007). There has also been a

widespread interest in the coach-created motivational climate, following the

initial work by Nicholls (1984, 1989) and Ames (1992a, 1992b), as well as an

increased interest in transformational leadership in sports (Arthur &

Tomsett, 2015). I will provide a brief overview of each of these frameworks,

after which they will be contrasted with SDT.

Mediational Model of Leadership With the mediational model of leadership, Smoll, Smith, and colleagues (e.g.,

Smoll & Smith, 1989; Smoll et al., 1978) suggested that athletes’ evaluative

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reactions toward coaches’ actual behaviors (observed behaviors) are

mediated by athletes’ perceptions and recall of coaches’ behaviors. Within

the mediational model of leadership, a number of individual difference

variables and situational factors are suggested to have a strong influence on

the effect of coaches’ behaviors on athletes’ sports experiences. The primary

tool for assessing coaches’ behaviors has been the Coaching Behavior

Assessment System (CBAS; Smith & Smoll, 2007), which comprises coaches’

responses to desirable performances (e.g., reinforcement), responses to

mistakes (e.g., mistake-contingent encouragement/instruction and

punishment), responses to misbehaviors (e.g., keeping control), game-

related behaviors (e.g., general technical instructions), and game-irrelevant

behaviors (e.g., general communication). Coaches’ behaviors are influenced

by personal characteristics, such as motives, goals, behavioral intentions,

and sex, and situational factors, such as the nature of the sport, situation,

and level of play. Athletes are affected by coaches’ behaviors based on how

they interpret and evaluate coaches’ behaviors and personal characteristics,

such as age, sex, competitive anxiety, and achievement motives, as well as

situational factors (e.g., Smith & Smoll, 2007; Smith, Smoll, & Christensen,

1996).

Early research guided by the mediational model often combined

observations of coaches’ behaviors with coaches’ perceptions of their own

behaviors and youth athletes’ perceptions, recall, and evaluative reactions of

coaches’ behaviors. From this research, a cognitive-behavioral training

program for youth sports coaches was created, Coach Effectiveness Training

(CETr), consisting of behavioral guidelines with the intention to increase

reinforcing, encouraging, and supportive behaviors and decrease punitive

behaviors (Smith, Smoll, & Curtis, 1979). Evaluations of the CETr using the

CBAS (Smith, Smoll, & Hunt, 1977) have shown that CETr-trained coaches

engage in more reinforcing, encouraging, and supportive behaviors than

nontrained coaches (e.g., Smith et al., 1979). As a consequence of this

behavior change, young athletes coached by CETr-trained coaches have

reported reduced sports performance anxiety (Smith, Smoll, & Barnett,

1995); increased self-esteem, particularly among those with low self-esteem

(Smith & Smoll, 1990; Smoll, Smith, Barnett, & Everett, 1993); a more

positive evaluation of the coach; a better team atmosphere; and having more

fun, and teams have reported lower levels of attrition (Smoll & Smith, 2002).

Additionally, an important finding from Smith, Smoll, and colleagues is the

low correlation shown between coaches’ perceptions of their own behavior

and their actual (observed) behavior (e.g., Curtis, Smith, & Smoll, 1979). The

CETr laid the groundwork for the current Mastery Approach to Coaching

(MAC; Smith, Smoll, & Cumming, 2007; Smoll, Smith, & Cumming, 2007),

which has integrated the CETr principles with achievement goal theory

(Ames, 1992a, 1992b; Roberts, 2012). The research program focuses on how

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coaches can create an athletic environment that promotes fun, effort, and

commitment to get better, where skill development is defined as success and

pressure to win is minimized (Smoll & Smith, 2002).

Multidimensional Model of Leadership The multidimensional model of leadership (Chelladurai, 1978, 1990, 1993,

2007) provides a framework for effective coaching behaviors in sports and

proposes that member satisfaction and performance outcomes can be used

as measures of coaching effectiveness. Coach behaviors that produce the

desired outcome in terms of group performance and satisfaction are seen as

an interaction between three aspects of leader behavior, namely the leader’s

actual behavior, athletes’ preferred leader behavior, and leader behaviors

required in the specific situation. Specific antecedents or circumstances in

turn affect each of these aspects of leader behavior. Required leader behavior

is influenced by situational characteristics (e.g., goals of the group, type of

task, competitive level) and athletes’ characteristics (e.g., age, gender, skill

level, psychological characteristics). Preferred leader behavior is determined

by athletes’ characteristics and characteristics that are specific to the sport

situation (e.g., type of, sport social norms, organizational expectations).

Actual leader behavior is influenced by the leader’s personal characteristics

(e.g., age, gender, expertise, psychological characteristics) and also by

required and preferred leader behaviors. Consequently, actual leader

behavior is also influenced by situational characteristics through the

influence of required and preferred leader behaviors as well as the athletes’

level of satisfaction and performance. A fundamental hypothesis of the

model is that the degree of congruence between the three aspects of leader

behaviors will influence the outcome variables of performance and athlete

satisfaction. If the coach engages in behaviors that meet the situational

demands and are in line with the athletes’ preferences, then optimal

performance and member satisfaction will be achieved.

The majority of research with the multidimensional model of leadership

has operationalized leadership preferences with the Leadership Scale for

Sports (LSS; Chelladurai & Saleh, 1980). This instrument comprises five

dimensions of leadership behaviors: training and instruction, democratic

behavior, autocratic behavior, social support, and positive feedback. In an

extensive summary of research on the multidimensional model of leadership,

Reimer (2007) concluded that positive athlete outcomes (e.g., athlete

satisfaction) seem to be related to training and instruction and positive

feedback behaviors, which also are the most preferred leadership behaviors,

whereas autocratic behaviors are the least preferred (cf. Chelladurai, 1993).

There are inconclusive results regarding the congruence hypothesis, with a

few studies examining congruence between preferred and actual leadership

behaviors and almost no studies on the congruence between required, actual,

8

and preferred leadership (Reimer, 2007). The multidimensional model

provides a comprehensive framework, but research with the model has so far

been simplistic, correlational, and has not examined the proposed causal

links in the model. Segments of the model have been tested, including only a

limited number of situational, leader, and athlete characteristics (Reimer,

2007).

Motivational Climate Within achievement goal theory, the social environment is emphasized as

having motivational significance (Ames, 1992a, 1992b; Ames & Ames, 1984;

Elliot, 2005; Nicholls, 1989). Ames (1992b) accentuated how structures

established by the coach can facilitate athletes’ adoption of a task or ego

orientation. Several structures influencing the motivational climate have

been identified in achievement settings. Epstein (1988, 1989) originally

proposed a multidimensional model of the motivational climate in

educational settings under the acronym TARGET, representing task,

authority, recognition, grouping, evaluation, and time structures of

achievement situations. Later adopted by Ames (1992a, 1992b), TARGET

was identified as six important structures possible to manipulate by coaches

in order to create a mastery motivational climate in sports settings. When

referring to the motivational climate according to Ames’s (1992b) definition,

focus is often on the learning environment that is affecting athletes’

thoughts, feelings, and behaviors in achievement situations. A climate that is

coach created reflects what coaches recognize, value, and evaluate regarding

their athletes’ performance and learning, and this has consequences for the

athletes’ psychosocial and behavioral responses (Newton, Duda, & Yin,

2000; Roberts, 2012). These achievement cues in the motivational climate

have broadly been defined as consisting of mastery criteria (i.e., task-

involving aspects) and performance criteria (i.e., ego-involving aspects;

Ames, 1992a). In a mastery-oriented climate, athletes are given tasks that

involve variety and diversity and are involved in the decision-making

process. Coaches encourage effort, improvement, learning, and skill

development, and success and competence is based on self-referenced

criteria. From a learning perspective, mistakes are seen as part of the

developmental process, and coaches give feedback to athletes with the aim of

helping them in subsequent attempts toward task mastery. Athletes’ practice

in heterogeneous and cooperative groups and time are flexible and

maximized for improvement. In contrast, performance-climate tasks have a

unidimensional design, and the coach is the sole decision maker. Normative

standards regarding success and competence are promoted, outperforming

others is praised, and social comparisons are frequently used. Grouping is

made homogenously, and mistakes are followed by punitive feedback due to

task failure and lack of ability. How athletes experience and interpret these

9

situational achievement cues influences the degree to which a mastery or

performance climate is perceived as salient (Roberts, 2012).

Meta-analytic evidence supports the notion that a mastery climate is

associated with adaptive motivational outcomes, such as perceived

competence, self-esteem, objective performance, intrinsic forms of

motivational regulation, affective states, practice and competitive strategies,

moral attitudes, and the experience of flow. Perceiving a performance

climate, on the other hand, was associated with maladaptive motivational

outcomes, such as extrinsic regulation and amotivation, negative affect,

maladaptive strategy use, antisocial moral attitudes, and perfectionism and

was negatively associated with positive affect and feelings of autonomy and

relatedness (Harwood, Keegan, Smith, & Raine, 2015; Ntoumanis & Biddle,

1999). Harwood et al. (2015) noted that the vast majority of research in

sports has focused on the coach-created motivational climate. The

importance of coaches as creators of the motivational climate has also been

found in in-depth interviews with elite athletes. A coach-created mastery

climate was strongly preferred and perceived as conducive in regard to both

performance and well-being. Coaches’ feedback, what they said and when

they said it, and how they behaved toward the athletes emerged as important

aspects related to the perceived motivational climate (Pensgaard & Roberts,

2002). Similar results were found among adolescent athletes. Through

semistructured interviews in focus groups, a strong motivational antecedent

emerged in coaches’ feedback behavior, particularly through their verbal

feedback and behavioral reinforcement. When coaches engaged in positive

feedback, it was generally seen as a positive influence on motivation,

whereas negative feedback was seen as a negative influence. Rewards for

desirable behaviors (e.g., effort) were generally seen as positive for the

athletes’ experience, and oppositely, punitive behaviors were related to

negative affect and avoidance behaviors (Keegan, Spray, Harwood, &

Lavallee, 2010).

Transformational Leadership Despite being the dominant leadership theory in the organizational domain

during the last decades (Gardner, Lowe, Moss, Mahoney, & Cogliser, 2010;

Lowe & Gardner, 2000), transformational leadership has gained surprisingly

little attention in the sport psychology literature. In a recent review, only 14

empirical research papers were identified that examined transformational

leadership in a sports setting (Arthur & Tomsett, 2015). Given the recent

gain in interest for transformational leadership, I find it appropriate to

include it in this brief review of leadership theories in sport psychology

research.

Transformational leadership is based on a strong identification with the

leader and the social unit where the leadership takes place. In this process,

10

the leader raises follower awareness, understanding of moral values and

inspiring visions, and encourages followers to transcend their own personal

goals and interests for the collective good (Bass, 1999; Bass & Riggio, 2006).

Transformational leadership comprises four dimensions (Bass, 1985):

idealized influence (the leader acts as a role model who earns the admiration

of followers and articulates high expectations about the group’s goals and

mission), inspirational motivation (the leader provides meaning and a clear

and attractive vision while demonstrating confidence that goals can be

achieved), intellectual stimulation (the leader encourages followers to make

their own decisions, both creative and innovative), and individualized

consideration (the leader acts as a coach and mentor, considering followers’

individual needs, strengths, and aspirations). Transformational leadership is

part of the full-range model of leadership, which also encompasses several

components of transactional leadership behavior and laissez-faire leadership

behavior (Bass & Riggio, 2006). Transactional leadership can be exerted by

rewarding or disciplining the follower based on the follower’s performance.

When leaders are transactional, they use contingent reinforcement that is

either positive (contingent reward) or negative (active or passive

management by exception). Contingent rewards are viewed as a constructive

transaction that is effective for motivating others and are considered

transactional when the reward is a material one (e.g., bonus) and

transformational when the reward is psychological (e.g., praise; Antonakis,

Avolio, & Sivasubramaniam, 2003). Management-by-exception leaders

either actively monitor followers’ deviations from standards, mistakes, and

errors and take corrective action as necessary or passively wait for deviances

from standards, mistakes, and errors and take corrective action after the fact

(Bass & Riggio, 2006). Laissez-faire leadership is an avoidance or absence of

leadership and is by definition inactive. Laissez-faire leaders ignore their

responsibilities and avoid getting involved when important issues arise.

Research in the organizational domain largely supports the effectiveness of

transformational leadership and has shown positive associations with

outcomes such as subordinates’ satisfaction, commitment, performance, and

well-being (e.g., Gilbert & Kelloway, 2014). There is also meta-analytic

evidence suggesting that transactional leadership predicts positive follower

outcomes and that laissez-faire leadership is a negative predictor of various

follower outcomes (e.g., Judge & Piccolo, 2004).

Although the positive effects of transformational leadership have been

established in other contexts, studies in sports settings are scarce (Arthur &

Tomsett, 2015). The few studies conducted nevertheless reveal that coaches’

transformational leadership is related to athlete motivation and performance

(Arthur, Woodman, Ong, Hardy, & Ntoumanis, 2011; Bormann & Rowold,

2016; Charbonneau, Barling, & Kelloway, 2001), player aggression (Tucker,

Turner, Barling, & McEvoy, 2010), team/task cohesion (Callow, Smith,

11

Hardy, Arthur, & Hardy, 2009; Smith, Arthur, Hardy, Callow, & Williams,

2013), and athletes’ basic psychological need satisfaction and well-being

(Stenling & Tafvelin, 2014).

Self-Determination Theory The theoretical framework guiding the present thesis is SDT (Deci & Ryan,

1985, 2000). SDT can broadly be described as a macrotheory developed to

explain human motivation and personality. SDT is guided by the assumption

that people are active organisms, with evolved tendencies toward growth,

mastery, and integrating new experiences into a coherent sense of self. These

evolved tendencies toward development require ongoing social support and

nutriments; they do not operate automatically, and the social context can

either support or thwart these natural tendencies toward growth, active

engagement, and coherence (Vansteenkiste & Ryan, 2013). Deci and Ryan

(2000) described SDT as an organismic dialectical approach, where the

dialectic between the active organism and the social context is the basis for

predictions about behavior, experience, and development. The primary

nutriments for healthy development and effective functioning are specified

within SDT using the concept of basic psychological needs. The needs are

viewed as innate, rather than acquired, and they are defined at the

psychological level, rather than the physiological level (Deci & Ryan, 2000).

In SDT, needs specify “innate psychological nutriments that are essential for

ongoing psychological growth, integrity, and well-being” (Deci & Ryan,

2000, p. 229). Three specific needs have been identified within SDT, namely

the needs for autonomy, competence, and relatedness (Ryan & Deci, 2000a).

The need for autonomy is defined as an endorsement of one’s actions,

flexibility, an absence of pressure, and a sense that one is engaging in the

action voluntarily (de Charms, 1968; Deci & Ryan, 1985). The need for

competence implies that a person wants to interact effectively with the

environment and experience a sense of adequate ability (Harter, 1978;

White, 1959). The need for relatedness represents a desire to feel connected

to significant others, to be cared for, and to care for others in a safe

environment (Baumeister & Leary, 1995). From a functional perspective,

optimal functioning and well-being are expected to occur under

circumstances that continuously support need satisfaction, whereas under

conditions where the needs are thwarted, nonoptimal functioning and ill-

being are expected (Deci & Ryan, 2000). The assumption that ongoing

support for need satisfaction facilitates optimal functioning, performance,

and well-being has been supported by meta-analytic evidence in various

domains, ranging from exercise (Teixeira, Carraca, Markland, Silva, & Ryan,

2012) to sports (Li, Wang, Pyun, & Kee, 2013) to health contexts (Ng et al.,

2012) to performance settings (Cerasoli, Nicklin, & Ford, 2014; Deci,

12

Koestner, & Ryan, 1999) to work contexts (Van den Broeck, Ferris, Chang, &

Rosen, 2016).

SDT is a macrotheory that formally consists of six subtheories each

developed to address various facets of motivation, personality, and processes

related to motivationally based phenomena (see Ryan & Deci, 2002; Taylor,

2015; Vansteenkiste, Niemiec, & Soenens, 2010; Weinstein, 2014, for reviews

of SDT and its subtheories). A comprehensive review of all six subtheories is

beyond the scope of this thesis, but I will, in the following section, briefly

describe the subtheories in SDT relevant to what is presented herein.

Cognitive evaluation theory (CET) was the first subtheory developed

within SDT (Deci, 1975; Deci & Ryan, 1980). It was specifically developed to

identify and understand various external events (and later, internal events;

Ryan, 1982) that influence people’s intrinsic motivation. Intrinsic motivation

is displayed when people are fully engaged in an activity out of interest,

curiosity, or a sense of volition and functions without external rewards or

constraints (Deci & Ryan, 2000). In other words, when people engage in

activities for their own sake because such behaviors are rewarding in and of

themselves, they are intrinsically motivated. With the development of CET,

the goal was to examine how factors such as rewards, feedback, surveillance,

evaluations, and ways of communication enhance or diminish people’s

intrinsic motivation, as displayed by interest, enjoyment, and persistence in

activities (Ryan & Deci, 2002).

The early work on CET focused on intrinsic motivation and was based on

a dualistic distinction between intrinsic and extrinsic motivation. This

distinction, however, did not provide a nuanced picture of human motivation

that could explain the variety of activities that people engage in. With the

second subtheory, organismic integration theory (OIT), Deci and Ryan

(1985) provided a broader picture of various types of extrinsic motivation

able to explain behavior that is characterized by the goal of obtaining

outcomes separable from the behavior. Instead of viewing intrinsic and

extrinsic motivation as antithetical, four different types of extrinsic

regulations were identified as being more or less internalized and integrated

into one’s self. Intentional behavior can be classified according to the extent

it is self-regulated versus regulated by forces outside the self, which indexes

the relative integration of action. Increased internalization and integration of

behavioral regulation represents a transition from an external perceived

locus of causality to an internal (Ryan, 1995). Hence, within OIT, extrinsic

motivation is assumed to vary in the degree to which it has been internalized

and integrated into the person (Deci & Ryan, 2000). Within SDT, there is a

broad distinction between autonomous and controlled types of motivation

(Deci & Ryan, 2000, 2008). Besides intrinsic motivation, autonomous

motivation includes extrinsic types of motivation (integrated and identified)

toward activities that the person has integrated and identified as important

13

and in line with his or her values. Autonomous motivation has been

integrated into the person’s sense of self, and autonomously motivated

people experience volition and self-endorsement. Controlled motivation, on

the other hand, consists of external regulation, when one’s behavior is driven

by external rewards or punishments, or introjected regulation, when the

behavior is a function of avoidance behaviors, contingent self-esteem, or ego

involvements. Controlled motivation leads people to experience pressure to

think, feel, and behave in certain ways. In contrast to autonomous and

controlled motivation, which energize and direct behavior, stands

amotivation, which refers to a lack of motivation and intention. Within the

appropriate social conditions, people take in and accept values and norms

that regulate and guide behavior, by the process of internalization. Behaviors

that previously were considered as external prompts can over time become

increasingly integrated into the person and self-regulated (i.e., motives for

tasks become more autonomous; Ryan, 1995).

The organismic dialectical component embraced within SDT suggests

that people interact with various internal and external forces in the social

context that support or hinder individuals’ active engagement, growth, and

development (Ryan & Deci, 2000a). It is proposed that growth,

development, and well-being are most likely to occur in social contexts that

provide support for the basic psychological needs of autonomy, competence,

and relatedness (Deci & Ryan, 2000; Vansteenkiste & Ryan, 2013). In a third

subtheory, basic psychological needs theory (BPNT), the basic psychological

needs are, as previously described, argued to be the essential nutriments for

growth, development, and well-being. The BPNT is integrated with the CET

and OIT, as it provides a framework that specifies the explanatory

mechanisms through which the social context influences people’s intrinsic

motivation, as proposed within CET, and the internalization process

proposed within OIT.

Why SDT as the Theoretical Framework? Given the availability of different frameworks that could be applied to

understand the leadership process in sports, one might wonder how the

guiding framework of the present thesis—SDT—differs from these other

frameworks. In an attempt to identify how SDT differs from the four

abovementioned frameworks, I highlight three main aspects: (1) process

clarity, (2) empirical support, and (3) origin and school of thought. SDT has

a clearly defined and empirically supported process model that proposes the

basic psychological needs of autonomy, competence, and relatedness as

explanatory mechanisms in the process through which social-contextual

factors, such as coaches’ autonomy support and control, influence people’s

motivation, well-being, and subsequent behaviors (Deci & Ryan, 2000, 2014;

Ng et al., 2012). In the mediational model of leadership, athletes’ evaluative

14

reactions are hypothesized to mediate the relationship between coaches’

actual behavior and athlete outcomes (Smoll et al., 1978), a mediating effect

that has been found for punitive behaviors, but the results were more

ambiguous for supportive behaviors (Smoll & Smith, 2007). Moderators,

such as athlete or coach individual difference variables (e.g., sex,

achievement motives, anxiety, self-esteem) or situational factors (practices

versus games), have received far more attention than mediators in research

with the mediational model of leadership (cf. Smoll & Smith, 2007). In the

multidimensional model of leadership, several mediational paths are

specified, for example, from leader characteristics to athlete and team

performance through leaders’ actual behaviors (Chelladurai, 2007). This

process, however, has not been empirically examined. Although several

mediators have been examined in the transformational leadership literature

(cf. Tafvelin, 2013; Turnnidge & Côté, 2016), a major critique of

transformational leadership theory is that the mechanisms through which

transformational leaders influence followers are still poorly understood and

not explicitly identified (e.g., van Knippenberg & Sitkon, 2013; Yukl, 1999).

Within achievement goal theory (e.g., Ames, 1992a, 1992b; Nicholls, 1989),

which motivational climate research rests upon, various process models have

been specified (e.g., Dweck, 1986; Dweck & Legget, 1988; Elliot, 1999). One

key variable in these process models is athletes’ perceptions of competence,

which has been specified as a mediator or moderator of the relationship

between the coach-created motivational climate and athlete outcomes (e.g.,

Elliot, 1999; Nicholls, 1989; Roberts & Kristiansen, 2012). Others have

suggested that there are interactive effects between athletes’ goal

orientations and the motivational climate that affect subsequent behavior.

There is, however, limited research on this interaction and its influence on

outcomes (Roberts, 2012). Furthermore, although most motivational climate

research in sports has focused on the coach-created climate, it is debated

whether the items in measurements used (e.g., the Perceived Motivational

Climate in Sport Questionnaire-2 [PMCSQ-2]; Newton et al., 2000) truly

refer to the coach or the more general climate of the team (Harwood et al.,

2015).

While reviewing the empirical support for these frameworks, I have

noticed that the multidimensional model of leadership and transformational

leadership theory have received far less attention in the sport psychology

literature compared to SDT and research on the motivational climate. As

Reimer (2007) pointed out, only fractions of the propositions within the

multidimensional model of leadership have been investigated, and Arthur

and Tomsett (2015) noted that only 14 studies were published on

transformational leadership in sports settings. SDT and motivational climate

research has received considerable attention in the sport psychology

literature, illustrated by, for example, an entire volume dedicated to SDT

15

research in sport and exercise psychology (Hagger & Chatsizarantis, 2007)

and a meta-analysis including 104 published studies on intrapersonal

correlates of the motivational climate in sports and physical activity

(Harwood et al., 2015). The mediational model of leadership received quite a

lot of attention during the late 1970s to mid-1990s (see Smoll & Smith, 2002,

for an overview) but has more recently been integrated into research on the

motivational climate (e.g., Smith et al., 2007; Smoll et al., 2007). The

integration of the mediational model with motivational climate research is

logical, given that both are social-cognitive approaches to leadership (Smoll

& Smith, 2007).

Finally, these theoretical frameworks originate from and reside within

different schools of thought. Transformational leadership theory synthesizes

work by several scholars (e.g., Berlew, 1974; Burns, 1978; Downtown, 1973;

House, 1977; Weber, 1924/1971, 1947) sharing the common notion that

leadership involves inspiring followers via charismatic or emotional appeals,

which often includes some sort of vision component (Arthur & Tomsett,

2015). Similarly, Chelladurai (1978, 1990) tried to synthesize the major ideas

and findings from leadership research in organizational psychology (e.g.,

Bass, 1985; House, 1971; Osborne & Hunt, 1975; Yukl, 1971) into the

multidimensional model of leadership. The mediational model of leadership

(Smith & Smoll, 2007) and motivational climate research (Ames, 1992b;

Roberts, Treasure, & Conroy, 2007) are both described as social-cognitive

approaches (e.g., Bandura, 1986), in which beliefs, thoughts, and perceptions

are viewed as the basis of understanding motivation and behavior (Roberts,

2012). SDT is an organismic theory, acknowledging that people have innate

psychological needs that are the energizing forces underlying motivation and

also recognizing the dialectic occurring between the organism and the social

context (Deci & Ryan, 1985, 2000). SDT is built upon the early work by

humanistic psychologists (e.g., Angyal, 1941; Maslow, 1968; Rogers, 1961)

and the notion that “fully functioning humans are those who are acting in

accordance with an ‘organismic valuing process’ occurring within

themselves” (Sheldon & Kasser, 2001, p. 34). Such innate tendencies toward

authenticity, growth, and meaning are not acknowledged in social-cognitive

models, which view behavior as largely determined by roles and situations

(Deci & Ryan, 1985; Sheldon & Kasser, 2001). Criticism toward SDT is,

nevertheless, evident in the literature, and much of this critique relates to the

notion of basic psychological needs. In commentaries on the seminal paper

by Deci and Ryan (2000), several scholars questioned the notion of basic

psychological needs, asking questions about what constitutes a basic human

need; questioned the evidence to support the three specific needs proposed

within SDT; argued that the three needs are not structurally equivalent; and

also questioned the assumption that humans have a natural tendency toward

growth (e.g., Buunk & Nauta, 2000; Carver & Scheier, 2000; Pyszczynski,

16

Greenberg, & Solomon, 2000; see also Hardy, 2015). Regarding SDT-based

research in sports, Hardy (2015) argued that there is a need to challenge

some of the basic tenets of SDT and explore how robust the subtheories

really are and what the boundary conditions of the subtheories are, instead

of merely accepting the basic tenets of SDT, and to find ways to apply them

to sports. Researchers could, for example, specify two or more competing

hypotheses based on different theories to explain a certain phenomenon

(e.g., well-being or sports performance) and design studies to tease out the

explanatory value of each theory. Much of the criticism highlights important

questions in need of further investigation. Despite this criticism, from a

functional perspective, the explanatory mechanisms (i.e., the innate

psychological needs) through which the social context influences people’s

motivation, well-being, and subsequent behavior make SDT particularly

useful for explaining the leadership process in sports.

A Self-Determination Theory-Based Model of the Coach–Athlete Relationship Guided by the assumption that ongoing support for need satisfaction is

essential for optimal functioning, integration, and well-being, this thesis

focuses on one central aspect shaping the sport environment and thus

influencing athletes’ need satisfaction, namely coaches’ interpersonal styles

(interpersonal styles and behaviors will be used interchangeably). Although

many factors can influence young athletes’ ongoing development,

motivation, performance, and well-being, coaches are considered to be one

of the most influential motivational factors in sports settings (e.g., Gaudreau

et al., 2016; Mageau & Vallerand, 2003; Reimer, 2007; Smith & Smoll,

2007). There are, as previously described, several theories and models

explaining coaches’ interpersonal styles and how these various interpersonal

styles influence athletes’ motivation, performance, and well-being. The work

in the present thesis has been guided by an overarching motivational model

of the coach–athlete relationship developed by Mageau and Vallerand

(2003). The motivational model (see Figure 1) is, according to Mageau and

Vallerand, grounded in CET (Deci & Ryan, 1985), which is one of the sub-

theories within SDT, and the hierarchical model of intrinsic and extrinsic

motivation (HMIEM; Vallerand, 1997; Vallerand & Losier, 1999). The

motivational model, however, also includes the OIT and BPNT (Deci & Ryan,

1985, 2000). With the HMIEM, Vallerand (1997, 2007) proposed a four-

stage motivational sequence (social factors → psychological mediators →

types of motivation → consequences) building on the elements of SDT. The

motivational sequence is specified on three distinct but interrelated levels

(i.e., global, contextual, and situational; see also Ryan, 1995).

17

Coach’s

personal

orientation

Coach’s

involvement

Structure

provided by

the coach

Coach’s

autonomy-

supportive

behaviors

Perceived

relatedness

Perceived

autonomy

Perceived

competence

Autonomous

motivation,

controlled

motivation,

amotivation

Athletes’

behavior

and

motivation

Coaching

context

Affective,

behavioral,

cognitive

outcomesCoach’s

controlling

behaviors

Figure 1. A modified version of Mageau and Vallerand’s (2003) motivational model of the coach–athlete relationship.

18

The global level represents personality traits that predispose individuals to

be more or less autonomously motivated in their interactions with the

environment. The contextual level represents a person’s general motivational

orientation toward a specific context (e.g., leisure, sport, or work). The

situational level represents a motivational state in a given situation. The

work presented in the present thesis focuses solely on the contextual level.

Furthermore, given that I have not examined the specific corollaries

specified within the HMIEM, such as the interrelations between the

hierarchical levels or intrinsic motivation as a multidimensional construct, a

complete description of the model will not be provided. Interested readers

are referred to Vallerand (1997, 2007) for an overview of the HMIEM.

Briefly summarized, in the motivational model, Mageau and Vallerand

(2003) proposed that coaches’ interpersonal motivating styles are influenced

by their personal orientation toward coaching, the coaching context, and the

athletes’ behavior and motivation. In turn, coaches’ interpersonal styles

influence athletes’ satisfaction of the needs for autonomy, competence, and

relatedness, which have beneficial impact on athletes’ autonomous

motivation. Finally, autonomous motivation is related to adaptive outcomes,

such as improved performance and well-being, and controlled motivation is

related to maladaptive outcomes, such as ill-health (e.g., Cerasoli et al., 2014;

Gagné, Ryan, & Bargmann, 2003; Ng et al., 2012).

Autonomy-Supportive and Controlling Interpersonal Styles Deci and Ryan (1987) argued that contextual factors play a crucial role when

people initiate and regulate behavior, but they also argued that these

contextual factors do not determine behavior in a direct sense (Deci & Ryan,

1985). People ascribe psychological meaning—referred to as functional

significance—to the contextual factors, and the meaning is the crucial

element in determining behavior. When considering social-contextual

factors, the primary interest lies in whether they have a functional

significance of being either autonomy-supportive or controlling, and how

each type of functional significance is related to peoples’ experience and

behavior (Deci & Ryan, 1987). Research on coaches’ interpersonal styles

indicates that autonomy-supportive and controlling interpersonal styles play

a major role in shaping athletes’ performance and psychological experiences

(Mageau & Vallerand, 2003; Ntoumanis, 2012). When coaches are

autonomy-supportive, they take the athletes’ perspective, provide

explanatory rationales for tasks, limits, and rules, acknowledge the athletes’

feelings, and provide relevant information and opportunities for choice

within specific limits and rules, while at the same time minimizing pressure

and demands (Mageau & Vallerand, 2003). Autonomy support also involves

encouraging self-initiation, curiosity, allowing time for self-paced learning

and development, and relying on a noncontrolling language (Grolnick &

19

Ryan, 1989; Reeve, 2009). In line with de Charms’s (1968) concept of

personal causation, by providing autonomy support, coaches regard athletes

as individuals deserving autonomous self-regulation and allow them to be

the origin of their own behavior.

A controlling interpersonal style is characterized by pressuring athletes to

think, feel, or behave in specific ways, which undermines psychological need

satisfaction (Ntoumanis, 2012; Reeve, 2009). Coaches with a controlling

interpersonal style place value on control and power-assertive techniques

that pressure athletes to comply (Mageau & Vallerand, 2003). In contrast to

autonomy support, controlling coaches’ prioritize their own perspective and

let it overrun the athletes’ perspective via intrusion and pressure

(Bartholomew, Ntoumanis, & Thøgersen-Ntoumani, 2009, 2010; Reeve,

2009). Coaches with a controlling interpersonal style are likely to (implicitly

or explicitly) view athletes as pawns to be controlled and governed to obtain

a certain outcome (de Charms, 1968).

In hierarchical relationships, such as teacher–student, manager–

employee, parent–child, and coach–athlete, but also in mutual relationships,

such as peer relationships and partners, autonomy support has been linked

to a range of positive outcomes, whereas controlling behaviors have been

linked to negative outcomes (Deci & Ryan, 2014). Autonomy support has

been related to enhanced depth of information processing, motor learning,

test performance, and persistence in cognitive and motor tasks (Hooyman,

Wulf, & Lewthwaite, 2014; Vansteenkiste, Simon, Lens, Sheldon, & Deci,

2004). Two recent longitudinal studies showed that receiving autonomy

support from romantic partners was linked to lower blood pressure in

general and during conflict (Weinstein, Legate, Kumashiro, & Ryan, 2016).

Another study on the association between biological stress (i.e., salivary

cortisol) and interpersonal styles during a learning activity showed that an

autonomy-supportive teaching style lowered cortisol, whereas a controlled

style increased cortisol level compared to a neutral style (Reeve & Tseng,

2011). Continuing the biological track, neuroscience studies suggest that

when people engage in activities for autonomous reasons, there is an

increased insular cortex activity, which is involved in emotional processing,

whereas engaging in activities for controlled reasons increases posterior

cingulate cortex activity, which is generally interpreted as weighing the

learned value of external stimuli (Lee, Reeve, Xue, & Xiong, 2012).

Autonomously engaging in behavior has also been related to increased

anterior insular cortex activity, which is known to be active when people feel

agentic, whereas engaging for controlled reasons increased angular gyrux

activity, known to be active when people experience a loss of agency (e.g.,

experience pressure; Lee & Reeve, 2013). Finally, a recent meta-analysis

included effect sizes from 184 independent data sets and examined the SDT-

based four-stage motivational sequence by combining meta-analytic

20

techniques with path analysis (Ng et al., 2012). Autonomy support was

directly and indirectly related to need satisfaction, autonomous motivation,

and mental and physical health outcomes. Taken together, these findings

across various domains display a quite consistent pattern with regard to the

correlates of autonomy-supportive and controlling interpersonal styles.

Sports Coaches’ Interpersonal Styles Coaches’ autonomy-supportive and controlling interpersonal styles have also

received considerable attention in the sport psychology literature. It should

be noted that autonomy support is, despite the label, theorized to (Mageau &

Vallerand, 2003; Ryan & Deci, 2000b), and has often been empirically

shown to, satisfy all three basic psychological needs, not just the need for

autonomy (e.g., Adie, Duda, & Ntoumanis, 2008, 2012; Amorose &

Anderson-Butcher, 2005; Ng et al., 2012; Quested & Duda, 2011). In the

following section I will provide a brief overview of sport psychology research

on the correlates of coaches’ autonomy-supportive and controlling

interpersonal styles. This review will be divided into three parts according to

three broadly defined study designs: cross-sectional, longitudinal, and

intervention studies.

Cross-sectional studies

Cross-sectional studies have shown that perceived autonomy support from

coaches is related to athletes’ basic psychological need satisfaction and self-

determined motivation (Amorose & Anderson-Butcher, 2005), need

satisfaction, vitality, negative affect, skill and performance self-concept (Adie

et al., 2008; Felton & Jowett, 2013), autonomy need satisfaction,

psychological and physical well-being (Reinboth, Duda, & Ntoumanis,

2004), and need satisfaction, enjoyment, and intentions to drop-out of

sports (Quested et al., 2013). Perceived coach autonomy support has further

been associated with autonomous motivation, prosocial behaviors (Hodge &

Lonsdale, 2011; Hodge & Gucciardi, 2015; Ntoumanis & Standage, 2009), as

well as sport-based (Fenton, Duda, & Barrett, 2016) and leisure-time

physical activity assessed with ActiGraph accelerometers among young

football players (Fenton, Duda, Quested, & Barrett, 2014). Conversely, in

cross-sectional studies, perceptions of a controlling interpersonal style have

been negatively related to athletes’ autonomy need satisfaction, self-

determined motivation, and subjective well-being (Blanchard, Amiot,

Perreauly, Vallerand, & Provencher, 2009). Coaches’ controlling behaviors

have also been negatively associated with autonomy and competence need

satisfaction and positively related to negative affect and skill and

performance self-concept (Felton & Jowett, 2013), controlled motivation,

antisocial behaviors, moral disengagement, attitudes toward doping, and

doping susceptibility (Hodge & Gucciardi, 2015; Hodge, Hargreaves,

21

Gerrard, & Lonsdale, 2013; Hodge & Lonsdale, 2011; Ntoumanis & Standage,

2009). Interpersonal control from coaches has further been related to a

variety of ill-being indicators, such as athlete need thwarting, depression,

disordered eating, negative affect, and burnout (Bartholomew, Ntoumanis,

Ryan, Bosch, & Thøgersen-Ntoumani, 2011). Taken together, these findings

indicate a pattern of positive outcomes associated with perceived autonomy

support and negative outcomes associated with perceiving coaches as

controlling. Although cross-sectional studies provide us with estimates of

how variables are related based on theory, it is well known that the direct

associations are most often upwardly biased (Podsakoff, MacKenzie, &

Podsakoff, 2012) and that the indirect associations, even under ideal

conditions, are often highly misleading (Cole & Maxwell, 2003; Maxwell &

Cole, 2007). In the following section, I review research on coaches’

autonomy-supportive and controlling interpersonal styles with stronger

study designs—longitudinal and intervention studies—that allow us to

examine temporal associations and approach causal inferences.

Longitudinal studies

In some longitudinal studies researchers have focused on prospective

predictions, and these studies have often included both cross-sectional

associations and prospective predictions. In a study of persistence in youth

swimmers, perceived coach autonomy support was cross-sectionally

associated with autonomous types of motivation that, in turn, were positively

associated with long-term persistence (over two seasons). Coaches’

controlling behaviors were cross-sectionally associated with controlled types

of motivation and amotivation that, in turn, were negatively associated with

long-term persistence (Pelletier, Fortier, Vallerand, & Brière, 2001). Positive

cross-sectional associations have also been found between perceived

autonomy support from the coach and contextual and situational self-

determined motivation, and situational motivation was in turn positively

related to objectively measured tournament performance among judokas

(Gillet, Vallerand, Amoura, & Baldes, 2010). Perceived autonomy support

and controlling behaviors from the coach have cross-sectionally been related

to cross-country runners’ need satisfaction and thwarting and mental

toughness that, in turn, were related to positive and negative affect and end-

season race time (Mahoney, Gucciardi, Ntoumanis, & Mallett, 2014). Positive

associations have also been found between perceived autonomy support

from the coach and autonomous goal motives at the start of the athletic

season and relative psychological well-being at mid-season, whereas

perceptions of coaches’ controlling behaviors were positively related to

controlled goal motives at the start of the season that, in turn, were

negatively related to relative psychological well-being at mid-season (Smith,

Ntoumanis, & Duda, 2010). The four-stage motivational sequence, in line

22

with SDT, has also been examined in young athletes in elite training centers.

Cross-sectional associations were found between perceptions of coaches’

autonomy-supportive and controlling behaviors and athletes’ need

satisfaction, motivation, and longitudinal associations with burnout (Isoard-

Gautheur, Guillet-Descas, & Lemyre, 2012). Some studies have also

examined cross-sectional associations while controlling for levels at previous

measurement points. Grounded in BPNT, perceived autonomy support has

been directly related to all three basic psychological needs that, in turn, were

related to burnout and subjective vitality (Balaguer et al., 2012; Quested &

Duda, 2011). Perceptions of a controlling coach have also been linked to need

thwarting and burnout (Balaguer et al., 2012). In a three-wave longitudinal

study, perceived autonomy support and interpersonal control from the coach

at the start of the season in opposite directions and indirectly predicted end-

season engagement through mid-season need satisfaction (Curran, Hill,

Ntoumanis, Hall, & Jowett, 2016). Autonomy support also negatively

predicted mid-season disaffection, and interpersonal control negatively

predicted mid-season engagement. Although the aforementioned studies are

regarded as longitudinal studies, they did not take advantage of many of the

strengths that come with having a longitudinal design. Some of the primary

advantages are the repeated measurements of all study variables to assess

temporal ordering of the variables and the possibility to not only assess

groups’ mean score changes or between-person differences but also within-

person changes (Stenling, Ivarsson, & Lindwall, 2016a, 2016b).

There have been a few studies examining longitudinal associations

between coaches’ interpersonal styles and athlete outcomes, which included

repeated measurements of the study variables and, in some cases, also

examined within-person change processes. In a sample of young gymnasts’,

perceptions of an autonomy-supportive/mastery-oriented coach

longitudinally predicted the athletes’ competence need satisfaction and was

also indirectly related to higher self-esteem through competence need

satisfaction (Kipp & Weiss, 2015). This study used a half-longitudinal design

(Cole & Maxwell, 2003) where all study variables were measured at two time

points (7 months apart). The predictions were estimated from the

independent variables at time point 1 to the mediators at time point 2, as

well as from the mediators at time point 1 to the dependent variables at time

point 2. An advantage with the half-longitudinal design is the possibility to

control for prior levels of the dependent variables or mediators, which is

important because most often the strongest predictor of a dependent

variable is the same variable measured at previous time points (Gollob &

Reichardt, 1987). The underlying assumption in this model is that of

stationarity, meaning the causal structure does not change. This assumption

may or may not hold, but it cannot be tested with only two waves of data.

Although this two-wave half-longitudinal design is stronger than cross-

23

sectional designs and prospective predictions, Kipp and Weiss (2015) only

examined the process from coaches to athletes, that is, as something the

coach does to the athletes and not as a relationship between the coach and

the athlete (cf. Ehrhart & Klein, 2001; Shamir, House, & Arthur, 1993).

Hence, Kipp and Weiss (2015) did not examine the temporal ordering of

variables (e.g., reciprocal relations) and did not examine within-person

changes.

Researchers have also examined the longitudinal associations between

young athletes’ perceived autonomy support from the coach, peer-created

task-involving motivational climate, and intrinsic motivation toward sports

(Jõessar, Hein, & Hagger, 2012). The longitudinal associations between

autonomy support and the motivational climate at two time points (one year

apart) were examined using a cross-lagged panel model (CLPM; Stenling et

al., 2016a), whereas intrinsic motivation was measured only at time point 2.

Perceived autonomy support from the coach and the peer-created task

involving climate longitudinally predicted higher levels of intrinsic

motivation. A closer look at the reciprocal relations between autonomy

support and task-involving climate showed that perceived autonomy support

positively predicted a peer-created task-involving climate one year later, but

not vice versa. These findings suggest that autonomy-supportive coaching

over time might be beneficial for creating a peer-climate characterized by

social support, a focus on effort, and individual improvement. Although the

CLPM takes into account prior levels of the dependent variables and allows

for the possibility to examine reciprocal relations, in a two-wave model, it is

difficult to assess temporal causality, reciprocal causation, or stationary

effects (Cole & Maxwell, 2003; Jang, Kim, & Reeve, 2012), given that only

one time lag is available. With more measurement points, researchers can

examine in more detail what drives what over time (temporal causality),

reciprocal effects and feedback loops (reciprocal causation), and stationary

effects, that is, are the effects stable over time. The CLPM has also received

critique due to its failure to adequately separate the within-person and

between-person level in the presence of time-invariant, trait-like individual

differences. As a consequence, the lagged parameter estimates are

confounded by the relationship that exists at the between-person level and

will not not reflect the actual within-person mechanism (Hamaker, Kuiper, &

Grasman, 2015).

Another way to longitudinally examine the dynamic coach–athlete

relationship is to employ a diary study (Bolger & Laurenceau, 2013) and use

longitudinal multilevel modeling (MLM; Raudenbush & Bryk, 2002) to

separate between-person and within-person effects. In one such diary study

the four-stage motivational sequence was examined at the situational level

by having young gymnasts (7 to 18 years) complete short questionnaires

before and after 15 practices spanning a 4-week period (Gagné et al., 2003).

24

Perceived autonomy support and involvement from coaches (and parents)

were assessed only at baseline, whereas athletes’ daily autonomous and

controlled motivation were assessed before each practice, and daily need

satisfaction and well-being were assessed before and after each practice. The

between-person analyses showed that perceptions of coach autonomy

support were positively related to need satisfaction and autonomous

motivation, whereas aggregate levels of incoming motivation before practice

and need satisfaction during practice did not have a statistically significant

influence on changes in well-being. Perceived autonomy support from the

coach was also related to higher levels of autonomous motivation at the

within-person level, and coaches’ involvement was positively related to

higher and stable self-esteem during practice. Furthermore, incoming

autonomous motivation positively predicted well-being before practice but

not changes in well-being during practice. Need satisfaction during practice,

however, had an overriding effect on change in well-being during practice. In

line with the classic Simpson’s paradox (Simpson, 1951; see also Kievit,

Frankenhuis, Waldorp, & Borsboom, 2013), Gagné et al. (2003) showed how

results may differ depending on the level of analysis, that is, whether the

measure is at the between-person or within-person level. It also provided

evidence of the influence of situational variability in need satisfaction on

athletes’ well-being. This study, however, did not assess changes in coaches’

interpersonal styles, did not assess the temporal ordering of the variables,

only included female athletes in a wide age range (7 to 18 years), and the

authors suggested that more studies on the four-stage motivational sequence

are needed in more mature samples, including male athletes, and with larger

samples. The shortcomings highlighted by Gagné et al. (2003) will, at least to

some extent, be addressed in the present thesis.

In a sample of male youth soccer players, between-person and within-

person effects of perceived coach autonomy support on athletes’ need

satisfaction and well- and ill-being were examined across two competitive

seasons (Adie et al., 2012). The study variables were assessed in the

beginning, middle, and end of both seasons, and MLM (Singer & Willet,

2003) was used to separate between-person differences and within-person

changes. As predicted by BPNT (Ryan & Deci, 2002), perceived coach

autonomy support positively predicted between-person differences and

within-person changes in the needs for autonomy, competence, and

relatedness over the two seasons. Coach autonomy support positively

predicted between-person differences and within-person changes in athletes’

well-being and negatively predicted between-person-differences in

exhaustion. Finally, an indirect effect from perceived coach autonomy

support to within-person changes in well-being through the needs for

competence and relatedness was also found. Overall, perceived coach

autonomy support was more consistently related to well-being, as measured

25

by subjective vitality, than exhaustion. Similar to previous studies (e.g.,

Gagné et al., 2003), Adie et al. (2012) included a rather narrow sample (male

youth soccer players), did not assess the temporal ordering of the variables

(e.g., reciprocal associations), and used the heavily criticized causal steps

approach (Baron & Kenny, 1986) to examine indirect effects, which is no

longer a recommended method to assess mediation (cf. Preacher, 2015;

Rucker, Preacher, Tormala, & Petty, 2011).

Two recent studies also used MLM to examine the between-person

differences and within-person changes in athletes’ perceptions of coaches’

interpersonal styles and how coaches’ interpersonal styles were related

athletes’ ill- and well-being (Bartholomew et al., 2011; Taylor, Turner,

Gleeson, & Hough, 2015). Across 8 training days spanning a period of 2

weeks, athletes’ (15 to 25 years) perceptions of an autonomy-supportive

interpersonal style positively predicted need satisfaction and negatively

predicted need thwarting at the between- and within-person levels.

Perceptions of a controlling interpersonal style negatively predicted need

satisfaction at the between-person level, but not at the within-person level,

and positively predicted need thwarting at both the between-person and

within-person level. Need satisfaction and thwarting was in turn

differentially related to self-reported ill- and well-being (Bartholomew et al.,

2011). In another study, researchers focused on the association between

perceptions of coaches’ interpersonal style and mucosal immunity measured

by salivary secretory immunoglobulin A (SIgA; Taylor et al., 2015). Elevated

levels of SIgA are indicative of immune disturbances and an increased risk

for infection, for example, caused by exposure to stressful events. The study

spanned over a period of 2 months and three measurement points, and it

was found that within-person changes in field hockey players’ perceptions of

interpersonal control from the coach positively predicted higher levels of

SIgA, but this association was not found at the between-person level. Hence,

this study provided novel evidence that coaches’ controlling interpersonal

style might elevate athletes’ risk for infections. This study did not establish

causal effects but did, however, provide evidence for the link between

coaches’ interpersonal styles and ill-being beyond the commonly used self-

report questionnaires. Although assessing changes in coaches’ interpersonal

styles over a short period of time (2 weeks to 2 months) and separating

between- and within-person effects, these two studies did not examine the

temporal ordering of the variables. Furthermore, in the study of Taylor and

colleagues (2015), the rather narrow sample of field hockey players

competed at the regional level (i.e., were not elite athletes), which might

explain the lack of association at the between-person level. A sample of elite

athletes who are more invested in their sport might be more appropriate to

examine such between-person differences (Taylor et al., 2015).

26

The results from these longitudinal studies display a fairly consistent

pattern of positive outcomes associated with perceived autonomy support

and negative outcomes associated with coaches’ controlling interpersonal

styles. Furthermore, the separation of between-person and within-person

associations provides a more nuanced picture of how variables are related

compared to single-level analyses not separating between-person and

within-person effects. These studies also show that the theoretical

predictions within SDT must be further specified (e.g., separate predictions

for the between-person and within-person level) as the evidence accumulates

from multi-level studies. Also, none of these longitudinal studies examined

the temporal ordering of the SDT variables (e.g., reciprocal or reversed

associations between coaches’ interpersonal styles, basic psychological

needs, and motivation), and none of them examined changes in the entire

four-stage motivational sequence. In the present thesis, I address some of

the aforementioned shortcomings of previous longitudinal research (e.g.,

temporal ordering and within-person changes).

Intervention studies

Research on the effectiveness of interpersonal coach education interventions

on athlete outcomes is scarce in the sport psychology literature. In a recent

systematic review, only four intervention studies were found that met the

inclusion criteria, and none of the studies were grounded in SDT (Langan,

Blake, & Lonsdale, 2013). SDT-based coach interventions were, however,

identified as a promising area for future research due to their usefulness to

explain the influence that coaches can have on athletes as well as the

mechanisms involved in this process. SDT-based interventions have shown

promising results in a variety of domains, such as educational settings

(Cheon, Reeve, & Moon, 2012; Cheon & Reeve, 2013), exercise promotion

(Teixeira et al., 2012), obesity interventions (Teixeira et al., 2015),

physiotherapists’ need-supportive behaviors (Murray et al., 2015), and for

reducing tobacco dependence (Pesis-Katz, Williams, Niemiec, & Fiscella,

2011).

There has also been a recent increased interest in SDT-based

interventions in sports settings focusing on autonomy-supportive training

for sports coaches that show somewhat mixed results. Two autonomy-

supportive interventions found no statistically significant effects on youth

sports coaches’ behaviors or on athletes’ perceptions of coaches’

interpersonal styles, need satisfaction and need thwarting, motivation, or

mental toughness (Langdon, Schlote, Harris, Burdette, & Rothberger, 2015;

Mahoney, Ntoumanis, Gucciardi, Mallett, & Stebbings, 2016). The lack of

effects in these two interventions can be attributable to several factors, such

as the focus on in-game situations, lack of opportunities to practice new

behaviors, and nonadherence to the online modules (Langdon et al., 2015),

27

and misinterpretation of the workshop content, relevance to the sport, time

demands, and other contextual constraints (Mahoney et al., 2016). Another

factor that probably influenced these interventions is the few and relatively

short group-based workshops that the coaches attended. Although both

interventions followed the recommendations provided by Su and Reeve

(2011), only providing one 1-hour workshop (Langdon et al., 2015) or two 2-

hour workshops within a week (Mahoney et al., 2016) is probably not enough

for sustained behavior change, regardless of the quality of additional

educational material provided to the participants.

Two other intervention studies can be considered more successful, as

changes in target behaviors and more distal athlete outcomes were observed.

In youth Gaelic football coaches, an autonomy-supportive intervention

increased coaches’ need supportive interpersonal style and decreased their

controlling interpersonal style (Langan, Blake, Toner, & Lonsdale 2015).

While burnout symptoms and amotivation increased among athletes in the

control group, no such increase was observed among the athletes in the

experimental group. Instead of group-based workshops, the intervention

spanned over 12 weeks and consisted of six one-on-one meetings where

previous coaching sessions were reviewed, coaches reflected upon their

experiences implementing the new strategies, and new goals were set

(Langan et al., 2015). These findings suggest that coaches’ autonomy support

can prevent increases in athlete burnout over the course of a season, which

might have long-term consequences for athletes’ performance and well-

being. Finally, following previous successful autonomy-supportive

intervention programs in the educational domain, one SDT-based

intervention has been performed in a high-stakes competitive sport context

with participants in the 2012 London Paralympic Games (Cheon, Reeve, Lee,

& Lee, 2015). The intervention showed consistent effects across athletes’ and

coaches’ self-reports, rater-scores, and objective dependent measures. A

longitudinal deterioration was observed for athletes and coaches in the

control group on all measures of coaches’ interpersonal styles, need

frustration and satisfaction, and engagement. Athletes and coaches in the

experimental group maintained or increased their levels on all measures of

coaches’ interpersonal styles, need frustration and satisfaction, and

engagement. Athletes in the experimental group also won more medals

compared to athletes in the control group. The authors concluded that an

autonomy-supportive coaching style can function as an antidote to coaches’

otherwise situationally-induced controlling style.

Similar to the conclusions drawn from reviewing the cross-sectional and

longitudinal studies, evidence from the intervention studies provides initial

support for the effectiveness of coach interventions based on SDT. But the

results in these studies display mixed results and also display potential

barriers that researchers must take into consideration when designing

28

autonomy-supportive training programs. Challenges and opportunities of an

autonomy-supportive approach to sports coaching have been highlighted in

the literature as an important avenue for future research (Occhino, Mallett,

Rynne, & Carlisle, 2014). This call has also gained some attention, for

example, by Langan et al. (2015) and Mahoney et al. (2016), who examined

the feasibility and acceptability of their interventions using follow-up

interviews with the participants. Some have also interviewed coaches to gain

more insight into the positive aspects and the challenges of autonomy-

supportive training programs (Langdon, Harris, Burdette, & Rothberger,

2015). Non-traditional study designs can probably be useful to further

uncover the potentials and barriers of SDT-based coach education programs,

such as action research methodology (Ahlberg, Mallett, & Tinning, 2008)

and single-case behavioral interventions (Davidson, Peacock, Kronish, &

Edmonson, 2014; Sousa, Smith, & Cruz, 2008). Another area with potential

to uncover facilitating and hindering factors in SDT-based interventions is

the transfer of training literature, which has been theoretically suggested

(Dysvik & Kuvaas, 2014) and empirically examined in a recent study

(Stenling & Tafvelin, 2016). With their transfer of the training model,

Baldwin and Ford (1988) proposed a conceptual framework focusing on

individual, training design, and work environment factors that influence

peoples’ ability to acquire new knowledge and skills as well as the

maintenance of these over time. SDT provides a theoretical framework that

can specify important individual (e.g., need satisfaction, motivation),

training design (e.g., need-supportive delivery, goal content), and work

environment (e.g., need-supportive work environment) factors hindering or

facilitating transfer of training.

Antecedents of Coaches’ Interpersonal Styles Whereas most of the SDT-based research in sports settings has focused on

the impact of coaches’ autonomy-supportive and controlling interpersonal

styles on athlete outcomes, there is a dearth of research on antecedents to

coaches’ interpersonal styles (Ntoumanis, 2012; Occhino et al., 2014).

Mageau and Vallerand (2003) proposed three broad factors of antecedents

to coaches’ interpersonal styles—coach’s personal orientation, coaching

context, and perceptions of athletes’ behaviors and motivation—and the

research so far in sports settings has almost exclusively focused on the

coaches’ perspective (i.e., coaches’ self-reports). Cross-sectional associations

have been found between coaches’ self-reported autonomy-supportive and

controlling interpersonal styles and harmonious and obsessive passion,

respectively (Lafrenière, Jowett, Vallerand, & Carbonneau, 2011). Perceived

pressure in the coaching environment has been negatively linked to

autonomy support (Iachini, 2013), whereas perceptions of athletes’ self-

determined motivation were positively associated with autonomy support

29

(Rocchi, Pelletier, & Couture, 2013). Perceived unity among athletes’ and

coaches’ self-determined motivation have been positively linked to coaches’

self-reported autonomy support (Solstad, van Hoye, & Ommundsen, 2015).

Narcissism has been positively related to coaches’ controlling behaviors, and

empathic concerns negatively related to controlling behaviors and positively

related to autonomy support (Matosic et al., 2015). Self-reported autonomy

support has further been positively associated with perceptions of resources

in the coaching context, need satisfaction, and self-reported well-being,

whereas controlling behaviors have been positively associated with

perceptions of demands in context, need thwarting, and ill-being (Stebbings,

Taylor, & Spray, 2011; Stebbings, Taylor, Spray, & Ntoumanis, 2012). These

latter findings have also been replicated in a three-wave longitudinal study

showing that between-person differences and within-person changes in

positive affect and integration of coaching into oneself positively predicted

coaches’ self-reported autonomy support. Self-reported interpersonal control

was positively associated with between-person differences and within-person

changes in negative affect (Stebbings, Taylor, & Spray, 2015).

Most SDT-based research on coaches’ interpersonal styles has focused on

athletes’ perceptions; therefore, it is also important to examine antecedents

to athletes’ perceptions of coaches’ interpersonal styles, which to a large

extent have been neglected in the SDT literature. Given the focus within SDT

on the psychological meaning that people ascribe to contextual factors (Deci

& Ryan, 1987; Soenens, Vansteenkiste, & Van Petegem, 2014), such as

coaches’ interpersonal styles, factors that influence peoples’ perceptions

become an important source of information for understanding the coach–

athlete relationship. Although this has not been given much attention in

sport psychology research, research in the educational domain suggests that

student characteristics are important determinants of teachers’ interpersonal

styles. Skinner and Belmont (1993) found that teachers’ autonomy support

and provision of structure longitudinally predicted students’ engagement

across the school year. Reciprocal effects of students’ engagement on

teachers’ need-supportive interpersonal styles were also found. These

findings have more recently been replicated, showing that students’ need

satisfaction (Jang et al., 2012) and agentic engagement (Reeve, 2013)

longitudinally predicted their perceptions of teachers’ autonomy support. It

was proposed that teachers likely adjust their classroom motivating styles to

students’ motivation and engagement. Students are likely to pick up on

teachers’ movement towards greater autonomy support when student

autonomy is high and also on teachers’ movement toward lesser autonomy

support (and more teacher control) when student autonomy is low (Jang et

al., 2012; Skinner & Belmont, 1993). Reeve (2013) suggested that these

findings show the importance of interpersonal synchrony (in this case,

student-teacher synchrony), which is a defining characteristic of most high-

30

quality relationships. In a further attempt to understand these complex and

reciprocal relationships over time, Jang, Kim, and Reeve (2016) examined a

dual-process model, including adaptive (perceived teacher autonomy

support, student need satisfaction, and engagement) and maladaptive

(perceived teacher control, student need thwarting, and disengagement)

processes. They found general support for the adaptive and maladaptive

processes from perceived teacher interpersonal styles to

engagement/disengagement. Reciprocal associations, however, were only

found for the maladaptive process where student disengagement negatively

predicted perceived teacher autonomy support and positively predicted

teacher control.

Research findings from work and organizational psychology corroborate

the findings from the SDT-based research in the educational domain and

suggest that follower characteristics are important factors influencing

leadership perceptions (e.g., Bono, Hopper, & Yoon, 2012). Empirical studies

have shown that follower values and personality predicted leadership

preferences for charismatic leadership (Ehrhart & Klein, 2001) and that

follower developmental characteristics—a composite variable of self-

actualization needs, internalization of moral values, collectivistic orientation,

critical-independent approach, active task engagement, and self-efficacy—

negatively predicted perceptions of transformational leadership over time

among direct followers (Dvir & Shamir, 2003). Follower personality (Felfe &

Schyns, 2010) and well-being (Nielsen, Randall, Yarker, & Brenner, 2008)

have also been found to predict perceptions of transformational leadership.

Although these findings support the notion that follower characteristics

influence leadership perceptions, the dominating line of research in the field

of leadership in general has been focused on the effectiveness of leaders’

behaviors with regard to different outcome criteria (e.g., follower outcomes;

Avolio, 2007), which have been labeled a leader-centric approach (Felfe &

Schyns, 2010). This leader-centric approach has also dominated leadership

research in sport psychology (Ntoumanis, 2012; Occhino et al., 2014). Many

scholars (e.g., Hall & Lord, 1995; Hollander, 1993; Howell & Shamir, 2005;

Yukl & Van Fleet, 1992), however, agree that leadership is a dynamic process

jointly produced by leaders and followers. Several scholars (e.g., Meindl,

1990, 1995) have also criticized the leader-centric approach and concluded

that few have attempted to empirically assess and theoretically specify the

role of followers in the leadership process (Howell & Shamir, 2005). As

stated by Yukl and Van Fleet (1992), “Most of the prevailing leadership

theories have been simple, unidirectional models of what a leaders does to

subordinates” (p. 186). Later Lord, Brown, and Freiberg (1999) added, “the

follower remains an under-explored source of variance in understanding

leadership processes” (p. 167). Although these quotes date back two decades

and primarily refer to the work and organizational literature, they are

31

equally valid today in sport psychology research in general and in SDT-based

sport psychology research in particular.

There are several potential explanations as to why athlete characteristics

will influence leadership perceptions. In line with the propositions in the

mediational model of leadership (Smoll et al., 1978), researchers have

emphasized the role of individuals’ perception and attribution of meaning to

others’ interpersonal styles (Deci & Ryan, 1987; Soenens et al., 2015). How

other peoples’ behaviors are appraised is in turn influenced by individual

characteristics, and within the SDT literature, it has been proposed that

peoples’ sensitivity to different interpersonal styles might vary depending on

their motivational profiles (De Meyer et al., 2016; Soenens et al., 2015). One

potential mechanism related to this sensitivity could be a process of

sensitization and desensitization (Moller, Deci, & Elliot, 2010). More

specifically, people with a history of need satisfaction are more sensitive and

more receptive to new opportunities for need satisfaction. Conversely, people

with a history of need frustration/thwarting would be less sensitive to

opportunities for need satisfaction and might even be more sensitive to need

frustrating/thwarting events, which might increase the deleterious effects of

such need frustrating/thwarting events and lead to maladaptive outcomes.

Following this line of reasoning, athletes higher in autonomous motivation,

which likely have experienced more need satisfaction in the past, would

benefit more from coaches’ autonomy support. On the contrary, athletes

higher in controlled motivation and amotivation, who likely have

experienced more need frustration/thwarting in the past, would benefit less

from coaches’ autonomy support and might even be more sensitive and

receptive to coaches’ controlling behaviors, leading to more negative

consequences (De Meyer et al., 2016).

Another potential explanation is centered on the idea of interpersonal

synchrony between for example coaches and athletes. Based on research in

the educational domain, the student-teacher relationship was suggested to

be characterized by reciprocal causation (Jang et al., 2012; Reeve, 2013,

2015). That is, what one person says and does transforms what the other says

and does, and vice versa (Sameroff, 2009). Following this line of reasoning,

coaches’ autonomy support will lead to greater need satisfaction and

autonomously regulated behaviors in athletes that, in turn, will increase

coaches’ autonomy support. Such a positive upward spiral can produce

benefits for both partners, and they become motivational and environmental

assets for each other. If coaches instead rely on interpersonal control,

athletes are likely to experience need thwarting and maladaptive behaviors

driven by controlled motivation that, in turn, will increase coaches’

controlling behaviors. The two partners thus become motivational and

environmental liabilities to each other. This latter negative downward spiral

shifts relationships toward unidirectional causality instead of a mutually

32

supportive relationship because the relationship becomes characterized by

coaches shutting down the athletes’ autonomy and agency (Reeve, 2015).

A recent study showed that physical education teachers believed that

students with high controlled motivation would benefit most from a more

controlling interpersonal style and that autonomy support would be most

effective for students with high autonomous motivation. On the other hand,

students reported the belief that teacher autonomy support would be most

beneficial regardless of the students’ motivation (De Meyer et al., 2016).

Similar findings have been reported among trainee sport and exercise

students expressing the belief that obese individuals with high controlled

motivation would benefit less from autonomy-supportive strategies (Ng,

Thøgersen-Ntoumani, & Ntoumanis, 2012). These findings can also be

linked to research showing that teachers interacted with students in a more

controlling way when students were perceived as being less motivated

(Sarrazin, Tessier, Pelletier, Trouilloud, & Chanal, 2006). It is likely that

similar processes occur in the sport domain in that coaches’ expectations on

and perceptions of athletes’ motivation as more autonomous or controlled

will influence coaches’ levels of autonomy support and interpersonal control

toward the athletes.

Lord and Maher (1991) suggested that leadership can be defined as a

perceptual process within subordinates. It has also been suggested that

cognitive categories used by followers will influence the impact that leaders

have on subordinates and that followers’ self-identities serve as important

cognitive structures in leadership perceptions (Lord et al., 1999). Several

scholars have proposed that followers’ self-concepts play a central role for

the impact that leaders have on followers (e.g., Howell & Shamir, 2005; Lord

et al., 1999; Hall & Lord, 1995). Leaders’ effect on followers has been argued

to depend upon their ability to appeal to existing elements of followers’ self-

concepts, more specifically followers’ values and identities (Shamir et al.,

1993). Hall and Lord (1995) proposed a multi-level information-processing

framework to explain followers’ leadership perceptions and suggested that

affective and cognitive information-processing mechanisms work at a variety

of levels (person, dyad, and group) to determine followers’ perceptions of

leaders. These affective (e.g., depression/anxiety) and cognitive (e.g., goals

and motives) influences at the person level, which are of primary interest in

the present thesis, can be characteristics that vary between persons (i.e.,

person-wholes) and/or vary within persons (i.e., person-parts). Between-

person differences can, for example, be influenced by self-schemas that make

people more likely to notice attributes and behaviors of others when those

factors are included in their self-schema. Similar processes are suggested to

operate at the within-person level, in that when followers’ cognitive

characteristics, such as goals and motives, are congruent with situational

cues, leader characteristics are subjected to more comprehensive processing.

33

The propositions within this information-processing framework (Hall &

Lord, 1995) are compatible with Reeve’s (2013, 2015) and Jang et al.’s (2012)

suggestions of interpersonal synchrony and upward and downward spirals,

as well as the sensitization-desensitization process proposed by Moller et al.

(2010) and De Meyer et al. (2016).

Multidimensional Nature of Coaches’ Interpersonal Styles SDT-based research in competitive sports contexts has primarily focused on

autonomy support (Ntoumanis, 2012). However, researchers in the

educational psychology and parental literatures (e.g., Grolnick & Ryan, 1989;

Skinner & Belmont, 1993) have suggested that it is also important to

acknowledge the provision of structure and involvement in addition to

autonomy-supportive and controlling interpersonal styles. Mageau and

Vallerand (2003) also included coaches’ structure and involvement as

important determinants of the satisfaction of athletes’ psychological needs

for competence and relatedness in their motivational model of the coach–

athlete relationship. A need-supportive interpersonal style, including

autonomy support, structure, and involvement, have been examined in

physical education settings (e.g., Haerens et al., 2013; Standage, Duda, &

Ntoumanis, 2005; Taylor & Ntoumanis, 2007; Tessier, Sarrazin, &

Ntoumanis, 2010) and exercise settings (e.g., Edmunds, Ntoumanis, & Duda,

2008; Markland & Tobin, 2010), but the potential role of perceived structure

and involvement from coaches in sports is largely unexplored (Mageau &

Vallerand, 2003; Ntoumanis, 2012; Wilson, Gregson, & Mack, 2009).

Structure involves providing clear and understandable guidelines and

expectations, instilling a sense of competence in the athletes, and providing

relevant feedback to the athletes (Reeve, 2002). Involvement is displayed

when coaches show a genuine interest in their athletes and their well-being

and spend a considerable amount of time, energy, and resources on them

(Grolnick & Ryan, 1989). Ryan (1991) and others (e.g., Markland & Tobin,

2010; Niemiec et al., 2006) have argued that the three dimensions are highly

interrelated and are therefore often combined into a broader category

labeled “need support”. Research from the educational domain and parental

literature, however, suggests that they are complementary interpersonal

styles fostering motivation (Grolnick & Ryan, 1989; Jang, Reeve, & Deci,

2010; Niemiec et al., 2006; Reeve & Su, 2014).

Few studies have examined the role of structure and involvement from

coaches in sports settings. One exception is Reinboth et al.’s (2004) cross-

sectional study that showed positive associations between athletes’

perceptions of coach autonomy support, coach-created task-involving

climate, and social support and autonomy, competence, and relatedness

need satisfaction, respectively. Autonomy and competence need satisfaction

was in turn related to indices of ill- and well-being. Reinboth and colleagues

34

did not, however, directly assess structure and involvement in line with SDT

but instead relied on other theoretical frameworks (e.g., achievement goal

theory).

A more recent study on university rugby players showed that a composite

of need-supportive coaching, including autonomy support, provision of

structure, and involvement, was positively associated with athletes’ need

satisfaction, autonomous motivation, and effort (Pope & Wilson, 2012). The

instrument assessing the three dimensions of need support—the

Interpersonal Supportiveness Scale-Coach (ISS-C; Wilson et al., 2009)—was

based on two previous instruments: the Health Care Climate Questionnaire

(HCCQ; Williams, Grow, Freedman, Ryan, & Deci, 1996) measuring

perceived autonomy support, and Tobin’s (2003) work on the provision of

structure and involvement in the exercise domain. The lack of instruments to

assess structure and involvement from coaches could be a potential

explanation for the lack of research in sports settings. The ISS-C could

therefore be a contribution to the SDT-based sport psychology research by

providing a self-report instrument of athletes’ perceptions of coaches’

autonomy support, structure, and involvement. Although Wilson and

colleagues (2009) provided some initial evidence for the factorial validity of

the ISS-C, the authors called for additional studies on the congeneric nature

of the three dimensions due to relatively strong correlations between the

factors. Given the multidimensional nature of a need-supportive

interpersonal style (e.g., Mageau & Vallerand, 2003; Ntoumanis, 2012) and

the recent calls for more research on these various interpersonal styles in

physical activity settings (Ntoumanis, 2012; Pope & Wilson, 2012; Standage,

2012), a comprehensive investigation of the multidimensionality in

measures capturing coaches’ need support in sports contexts seems

warranted.

A controlling interpersonal style has also recently been extended beyond

the autonomy support versus control SDT-based work and has been

conceptualized as a multidimensional construct. Following an extensive

literature review, a number of controlling motivational strategies that

coaches use were identified (Bartholomew et al., 2009, 2010). The most

prominent strategy was controlling use of rewards, characterized by coaches

using extrinsic rewards and praise to ensure athlete compliance,

engagement, and persistence in certain behaviors. This controlling strategy

has been closely linked to the undermining effect of rewards on intrinsic

motivation (Deci et al., 1999; Vansteenkiste & Deci 2003), defined as a

negative effect of tangible rewards on intrinsic motivation, particularly when

the reward is expected. Verbal rewards and praise can also produce similar

effects (Henderlong & Lepper 2002). Coaches can display negative

conditional regard by withholding attention and affection when athletes do

not display the desired attributes or behaviors (Assor, Roth, & Deci, 2004).

35

When coaches’ affection and attention are highly contingent upon athletes

showing appropriate behaviors, coaches exhibit conditional regard. This

often forces athletes to give up their autonomy to maintain the relationship

with the coach (Bartholomew et al., 2010). Intimidation is an abusive and

power-based controlling motivational strategy. It is used to belittle and

humiliate through verbal abuse, to pose threats, and to control athletes’

behaviors through yelling (Bartholomew et al., 2010). Intimidation promotes

external regulation and creates pressure from the outside, which makes

athletes engage in certain behaviors to avoid external punishment (Deci &

Ryan, 1987). A fourth strategy is excessive personal control, displayed when

coaches engage in intrusive monitoring of athletes’ free time and impose

strict limits (Bartholomew et al., 2010). When coaches restrict athletes’ free

time (e.g., by setting curfews) or attempt to hinder them from engaging in

other sports, coaches demonstrate excessive personal control. Such

controlling interpersonal behavior promotes a sense of pressure from the

coach to prioritize one’s sports involvement over other important aspects of

the athlete’s life.

Bartholomew et al. (2010) developed the Controlling Coach Behaviors

Scale (CCBS) to assess athletes’ perceptions of coaches’ controlling

interpersonal styles and concluded “that a controlling interpersonal style is a

multidimensional construct represented by a number of separate, but

related, controlling coaching strategies” (p. 205). In a series of studies, the

content and factorial validity, internal consistency, and measurement

invariance across gender and sports were examined. More specifically, first-

and second-order independent clusters model confirmatory factor analyses

(ICM-CFA) were used to examine the factorial structure of the CCBS. The

psychometric properties of the CCBS have also been examined in other

studies (e.g., Castillo et al., 2014), but a comprehensive examination of the

multidimensional nature of the CCBS has not yet been undertaken.

The interpersonal styles are multidimensional constructs, consisting of

theoretically separable subdimensions, but they also reflect a global

construct alongside these subdimensions. Such constructs contain two

sources of psychometric multidimensionality—a global factor and specific

subdimensions—and a comprehensive examination of the multidimensional

structure needs to involve both sources (Morin, Arens, & Marsh, 2015).

Many theory-based multidimensional scales correspond to a bifactor

structure consisting of a global construct (e.g., need support) as well as

specific subdimensions (e.g., autonomy support, structure, and

involvement), which means that there are two sources of construct-relevant

variance (Myers, Martin, Ntoumanis, Celimli, & Bartholomew, 2014).

Although multidimensional scales often correspond to a bifactor structure,

they are rarely examined with bifactor measurement models in the sport and

exercise psychology literature. This is unfortunate because bifactor models

36

can provide researchers with an opportunity to match the theory behind an

instrument with the model imposed on the data when evaluating

multidimensional scales. This theory-model match is lacking when the

commonly used first- or second-order ICM-CFA is applied, because no direct

influence from the global construct to the indicators is estimated (Myers et

al., 2014).

Purpose and Research Questions The aim of this thesis is to increase our knowledge of the leadership process

in sports from an SDT perspective, particularly how coaches’ autonomy-

supportive and controlling interpersonal styles are longitudinally associated

with young athletes’ motivation and ill- and well-being. By complementing

the dominating leader-centric perspective (i.e., that leadership is something

that leaders impart on their followers), with a follower-centered perspective

(i.e., in which leadership is defined as a process jointly produced by both the

leader and the follower; Deci & Ryan, 1985, 1987; Hollander, 1992), it is

possible to gain increased insight into the role of followers in the dynamic

and interactive leadership process (Howell & Shamir, 2005; Lord & Emrich,

2000; Ntoumanis, 2012; Occhino et al., 2014). Grounded in SDT (Deci &

Ryan, 1985, 2000), Study 1 and Study 2 examined the longitudinal

associations between athletes’ perceptions of coaches’ autonomy-supportive

and controlling interpersonal styles and athletes’ need satisfaction,

motivation, and ill- and well-being. In the first study, an adaptive

motivational process was examined (i.e., longitudinal associations between

autonomy support, need satisfaction, self-determined motivation, and well-

being), whereas the second study focused on a maladaptive process (i.e.,

longitudinal associations between perceptions of coaches’ controlling

behaviors, controlled motivation, and ill-being). Study 3 moved beyond the

unidimensional view on autonomy support versus controlling behaviors and

examined multidimensionality in sport-specific measures of athletes’

perceptions of coaches’ interpersonal styles (i.e., need-supportive and

controlling behaviors). Four overarching research questions are addressed in

the three studies:

I. Are there longitudinal associations between levels of, and within-

person changes in, perceived autonomy support, need satisfaction,

self-determined motivation, and well-being over the course of an

athletic season? (Study 1).

II. Are athletes’ perceptions of controlling coach behaviors

longitudinally associated with controlled motivation and ill-being at

the between-person level? (Study 2).

37

III. Do athlete characteristics, in terms of controlled motivation and ill-

being, predict athletes’ perceptions of controlling coach behaviors at

the within-person level? (Study 2).

IV. Are there distinct sources of construct-relevant psychometric

multidimensionality in measures of coaches’ need-supportive and

controlling behaviors? (Study 3).

Materials and Methods The studies contain data collected in three different populations of young

athletes, including both individual and team sport athletes. An overview of

the study designs, participants, instruments, and statistical analyses used in

three studies are provided in Table 1. The project was approved by the

Regional Ethical Review Board at Umeå University prior to the data

collections.

Samples and Procedures The samples in Study 1 and Study 2 initially comprised 247 (109 females, 138

males) young elite athletes (alpine skiers, biathletes, cross-country skiers)

enrolled at one of 18 sport high schools in Sweden. These sport high schools

provide young athletes the opportunity to combine high-level training with

an academic schedule arranged around their sporting activities. The age

range in the sample was 16 to 20 years (M = 17.8, SD = 0.9). They practiced

on average 12.5 hours (SD = 3.6) per week and competed in their sport for an

average of 9.7 years (SD = 3.1). Most athletes (96%) competed at the national

or international level. Data were collected via post at three time points

during a competitive season, in November (T1), January (T2), and April

(T3), with approximately 2.5 months between each data collection. At T2,

178 athletes responded to the multi-section questionnaire, and 164 athletes

responded at T3.

The two samples in Study 3 included young team sport athletes (floorball,

ice hockey). The first sample (Study 3a) comprised 277 (142 female, 135

male) floorball players ranging in age from 15 to 22 years (M = 16.8, SD =

1.1). They had on average been competing in floorball for 8.4 years (SD =

2.8) and practiced on average 6.8 hours (SD = 3.4) per week. The second

sample (Study 3b) consisted of 233 young male ice hockey players aged 15 to

20 years (M = 17.1, SD = 1.4) who on average had been competing in their

sport for 10.6 years (SD = 2.1). Data were collected by a research assistant

who visited each team and provided the athletes with verbal and written

information about the study purpose before commencing the data collection.

38

Measures We relied on previously used and validated self-report instruments to assess

the study variables. A summary of the instruments used in the three studies

and references to the original sources are provided in Table 1.

Data Analysis Structural equation modeling (SEM; see Bollen, 1989; Hoyle, 2012) was used

to analyze the data in the three studies. Excellent reviews of the strengths

and limitations of SEM can be found elsewhere (MacCullum & Austin, 2000;

Marsh & Hau, 2007; Tomarken & Waller, 2005); I will nevertheless briefly

summarize the history of SEM and some of its key strengths and limitations.

SEM has become the most used multivariate technique among psychologists

(Hershberger, 2003), and its increased popularity can, for example, be

illustrated by the establishment of a journal solely devoted to it (Structural

Equation Modeling: A Multidisciplinary Journal). There is also a

widespread use of SEM in contemporary sport and exercise psychology

research (Lindahl et al., 2015). In psychology, Pearson (1901) and Spearman

(1904) published early papers on factor analysis, work later continued and

expanded by Thurstone’s (1935, 1947) correlated factor-model and Holzinger

and Swinford’s (1937) bifactor measurement model. Important work that

laid the foundation for contemporary SEM was conducted in various fields,

such as sociology, statistics, and econometrics during the 20th century, but

1970 is often referred to as a key year in SEM history. During 1970, seminal

work on SEM was published and publicly presented, such as Jöreskog’s

(1970) general method of analyzing covariance structures, Hauser and

Goldberger’s (1971) work on unobservable variables in path analysis, and

Zellner’s (1970) generalized least squares (GLS) results on unobservable

independent variables. The Conference on Structural Equation Models was

also held for the first time this year, providing an interdisciplinary forum for

SEM researchers, which resulted in the volume Structural Equation Models

in the Social Sciences (Goldberger & Duncan, 1973). Two particularly

influential papers were published during this time. The first was Jöreskog’s

(1973) paper on his maximum likelihood (ML) framework for estimating

SEM, accompanied by a computer software (LISREL) for empirical

applications, and various substantive extensions of SEM. The second paper

was Hauser and Goldberger’s (1971) integrative approach, bringing together

work from econometrics, psychometrics, sociology, and statistics to examine

unobservable variables in path analysis. Continuing this line of work,

Jöreskog (1970, 1973, 1978) outlined a general approach to covariance

analysis in a model (i.e., the LISREL model) that incorporates factor

analysis, simultaneous equation models, and path analysis into a general

covariance structure model.

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Table 1

Overview of the Three Studies

Study 1 Study 2 Study 3

Purpose To examine longitudinal associations between levels of, and within-person changes in, perceived coach autonomy support, athletes’ need satisfaction, self-determined motivation, and well-being.

To examine longitudinal associations between athletes’ controlled motivation, ill-being, and perceptions of coaches’ controlling behaviors at the between- and within-person levels.

To examine psychometric multidimensionality in self-report measures of athletes’ perceptions of coaches’ need-supportive and controlling behaviors.

Design

Longitudinal (two time points) Longitudinal (three time points) Cross-sectional

Participants

Young elite athletes NT1 = 247; NT2 = 164

Young elite athletes NT1 = 247; NT2 = 178; NT3 = 164

Young elite athletes Study 1, N = 277; Study 2, N = 233

Variable Instrument (source)

Coaches’ autonomy support Sport Climate Questionnaire (SCQ; Smith, Ntoumanis, & Duda, 2007)

Coaches’ controlling interpersonal style Coaches Controlling Behaviors (Smith et al., 2010)

Coaches’ need support Interpersonal Supportiveness Scale-Coach (ISS-C; Wilson et al., 2009)

Need satisfaction Basic Need Satisfaction in Sport Scale (BNSSS; Ng, Lonsdale, & Hodge, 2011 )

Controlled motivation Behavioral Regulation in Sport Questionnaire (BRSQ; Lonsdale, Hodge, & Rose, 2008)

Coaches’ controlling interpersonal style Controlling Coach Behaviors Scale (CCBS; Bartholomew et al., 2010)

Self-determined motivation Behavioral Regulation in Sport Questionnaire (BRSQ; Lonsdale, Hodge, & Rose, 2008)

Ill-being General Health Questionnaire (GHQ-12; Goldberg et al., 1997)

Well-being General Health Questionnaire (GHQ-12; Goldberg et al., 1997)

Statistical analysis

Cross-Lagged Panel Model (CLPM) Latent Difference Score (LDS) Model

Bayesian Latent Growth Curve Modeling (LGCM)

Confirmatory Factor Analysis (CFA) Exploratory Structural Equation Modeling (ESEM) Bifactor Exploratory Structural Equation Modeling (B-ESEM)

40

There has been a rapid development since the 1970s, and now the SEM

framework includes models with discrete, limited, and ordinal dependent

variables (Muthén, 1984), latent growth curve models (LGCM; Bollen &

Curran, 2006; Meredith & Tisak, 1990), latent class analysis (e.g., Nagin,

2005), growth mixture models (Muthén, 2004), Bayesian approaches (e.g.,

Muthén & Asparouhov, 2012; Scheines, Hoijtink, & Boomsma, 1999; Zellner,

1971), and multilevel SEM (Muthén & Asparouhov, 2011; Rabe-Hesketh,

Skrondal, & Pickles, 2004).

One reason for the increasing popularity of SEM is its many advantages

compared to other data-analytic techniques. In latent variable models, it is

possible to obtain separate estimates for the latent constructs and their

observed manifest indicators (i.e., measurement model) and the relations

between the latent constructs (i.e., structural model). This approach allows

researchers to partial out the “true score” of an estimate and the

measurement error, implying that specified relations in a SEM model are

corrected for biases attributable to random error and construct-irrelevant

variance (Tomarken & Waller, 2005). It is also possible to obtain a global

assessment of fit for complex models containing several linear equations and

to make model comparisons between nested models of varying complexity

using a chi-square test or other fit indices (West, Taylor, & Wu, 2012).

Another commonly used pro-argument for SEM is that it allows for

estimating complex models in a single analysis, in contrast to other

multivariate techniques (e.g., multivariate regression), which makes it

possible to estimate direct, indirect, and/or moderated effects in one

analysis, rather than testing mini-parts of the model in separate analyses.

Furthermore, SEM can handle nominal, binary, categorical, count,

continuous variables, and various combinations of different types of

variables, as well as variable-centered and person-centered approaches

(Muthén, 2002). Missing data are easily handled in most SEM software,

where full information maximum likelihood (FIML) estimation is often the

default for continuous variables, but multiple imputation and the inclusion

of auxiliary variables are also easily implemented in SEM (Enders, 2010).

Recent developments also include techniques to handle data that are missing

not at random (MNAR) by incorporating the missingness patterns directly

into the latent variable model (e.g., Enders, 2011; Muthén, Asparouhov,

Hunter, & Leuchter, 2011). Finally, the flexibility in SEM makes it useful for

various types of designs, including, for example, psychometric testing,

longitudinal studies, cohort studies, intensive diary studies, intervention

studies, and multilevel data.

As with all statistical analyses, there are limitations with SEM, which

often has more to do with researchers applying SEM than the data-analytic

technique itself. There has been an overreliance on the use of rules of thumb

when researchers decide whether the model fits the data (Marsh & Hau,

41

2007; Marsh, Hau, & Wen, 2004; Tomarken & Waller, 2005). Rules of

thumb regarding sample size, indicators per factor, and particularly the use

of goodness of fit indexes have turned into “golden rules,” much like the

(mis)use of the p value in psychological research (see Ivarsson, Andersen,

Stenling, Johnson, & Lindwall, 2015). Many of these commonly applied rules

of thumb have little empirical support, and several scholars (e.g., Marsh et

al., 2004) have emphasized that “data interpretations and their defense is a

subjective undertaking that requires researchers to immerse themselves in

their data, methodological issues and substantive concerns” (Marsh & Hau,

2007, pp. 159–160).

Tomarken and Waller (2005) argued that using statistical analyses or

other means to prove that a model is correct is impossible. Alternative

models may be available that fit the data equally well or better. This is

unfortunately often ignored by researchers, and there seems to be an

overstated certainty and strength related to the conclusions drawn from SEM

analyses. Although nested model comparisons are sometimes conducted,

these are often conducted on a small subset of possible comparisons. Others

have put forward similar critiques arguing that there seems to be an

overreliance on conclusions drawn from single studies and that more

attention should be given to the generalizability of findings from studies

applying SEM (MacCallum & Austin, 2000). A related critique is the use of

data-driven model modifications, which may lack validity (MacCallum,

1986) and are highly susceptible to capitalization on chance (MacCallum,

Roznowski, & Necowitz, 1992). Data-driven modifications to the model

should be explicitly acknowledged as exploratory and data-driven, the

modifications should be substantively meaningful, and the modified model

should be evaluated in an independent sample. Although models subjected

to data-driven modifications are sometimes acknowledged as exploratory

and can be meaningful, they are seldom evaluated in an independent sample

(MacCallum & Austin, 2000).

Although SEM is highly flexible, it cannot compensate for limitations in

design and method (Tomarken & Waller, 2005). As noted by Box and Draper

(1987), “Essentially, all models are wrong, but some are useful” (p. 424), or

stated differently, all models are approximations and some can be useful

approximations. This quote fits nicely within the SEM context, where it is

possible to specify highly complex causal structures and obtain global

assessment of fit and parameter estimates that researchers interpret based

on the a priori determined model. Just because researchers can specify a

causal structure does not, however, mean that a causal relation can be

inferred (Bollen & Pearl, 2013). Causal inference does not reside in the

statistical model; it is something that is inferred by designing studies that

allow researchers to draw causal inferences (Shadish, Cook, & Campbell,

2002). Another common design flaw when applying SEM is the use of

42

mediational models on cross-sectional data (Tomarken & Waller, 2005). It is

well known that cross-sectional mediation typically yields substantively

biased estimates, even under ideal conditions (Cole & Maxwell, 2003;

Maxwell & Cole, 2007). Researchers interested in mediational processes

should collect multiple waves of data and pay close attention to the

appropriate time lag for the phenomena under study (Maxwell & Cole,

2007).

To summarize, SEM is a useful technique that can incorporate latent

variables (i.e., unobserved variables), which are common in psychological

research. Some scholars even argue that “a psychological variable is latent

until proven observed” (Boorsboom, 2008, p. 49). This feature, in

combination with the flexibility of SEM, makes it an attractive data-analytic

approach for researchers in the social sciences. SEM is not, however, a

statistical magical bullet, and researchers applying SEM must be aware of its

strengths, limitations, and pitfalls, which have been known to

methodologists for a long time (Tomarken & Waller, 2005). As previously

emphasized, it is important to acknowledge and accept the subjective nature

of data analysis and not rely on “golden rules” that lack empirical support

(Marsh & Hau, 2007).

Study 1: Latent difference score modeling

In Study 1, a latent difference score (LDS) model (McArdle & Nesselroade,

1994; Selig & Preacher, 2009; Wu, Selig, & Little, 2013) was specified to

estimate associations of within-person changes in the study variables. In the

LDS model, change is explicitly specified as a latent variable (Y) and is

defined by a linear function between two consecutive time points (Yt ─ Yt ─ 1).

Hence, difference scores are not computed prior to analysis, but a structural

equation model is instead specified with strategically fixed parameters to

model the difference as a latent variable (Wu et al., 2013). Difference scores

have historically been used in psychology by calculating the absolute

difference between a variable score at two time points. The difference score

can then be used as an outcome variable regressed on various predictors.

Difference scores have also been severely criticized for being unreliable (e.g.,

Cronbach & Fury, 1970), but more recent research has found that when

sufficient individual differences in change over time exist, change scores can

show reasonable levels of reliability (Rogosa, 1995). Unreliability is,

however, dealt with when using LDS modeling because latent variable

modeling accounts for measurement error (i.e., unreliability). Various

parameters can be estimated using LDS modeling, such as dynamic change

or proportional change, and the latent difference variable can be used as any

other latent variable in SEM (Wu et al., 2013). The LDS model focuses on

within-person change and between-person differences in change, and with

two measurement points, the LDS can be viewed as a linear two-wave

43

LGCM. Hence, specifying an LDS model was deemed appropriate given that

the aim of Study 1 was to examine longitudinal associations of within-person

changes in a two-wave model.

Study 2: Bayesian latent growth curve modeling

In Study 2, we used LGCM (Bollen & Curran, 2006; Meredith & Tisak, 1990)

to examine between-person differences in within-person change as well as

within-person associations over time in a three-wave model. LGCM can be

used to examine individuals’ growth trajectories as latent factors, and of the

primary interest are the latent intercept and slope factors. The intercept and

slope factors each have a mean and a variance, which represents different

levels of analysis. The mean is a group-level parameter (i.e., fixed effect), and

the variance corresponds to individual differences around the mean (i.e.,

random effect). The intercept mean often represents the initial status of the

outcome variable, which is specified by the location of the zero in the

specification of the slope factor (Little, 2013). The intercept, however, can be

placed at any measurement point and should be placed on a meaningful

occasion of measurement based on substantive meaning (Biesanz, Deeb-

Sossa, Papadakis, Bollen, & Curran, 2004). The intercept variance represents

individual differences around the group’s intercept mean. The slope mean is

the average rate of change per unit of time in the outcome variable, and the

slope variance represents individual differences in change around the

group’s mean (i.e., heterogeneity in the growth trajectories; Stenling et al.,

2016a). In the LGCM, it is possible to define different types of slope

trajectories. The most straightforward are some of the polynomial functions:

linear slope, quadratic slope (i.e., accelerating/decelerating change), and

cubic slope (i.e., two bends in the growth curve). More complex functional

forms are also possible, but introducing nonlinear functions makes the

model substantially more difficult to estimate (Curran, Obeidat, & Losardo,

2010). The intercept and slope factors in the LGCM can be used as any other

latent variable to predict or be predicted by other variables (Duncan &

Duncan, 2004). Also, the time-specific indicators of the outcome variable,

which is used to define the intercept and slope factors, can be predicted or

predict other variables, for example, by including time-varying covariates in

the model (Bollen & Curran, 2006).

Furthermore, instead of relying on the most commonly applied estimator,

the maximum likelihood (ML) estimator (Marsh, 2007) within a frequentist

framework, we choose to estimate the LGCM within a Bayesian framework

(e.g., Stenling, Ivarsson, Johnson, & Lindwall, 2015). Frequentist and

Bayesian statistics stem from different theories of probability, the frequentist

paradigm and the subjective probability paradigm (van de Schoot et al.,

2014). Hypothesis testing within a frequentist framework corresponds to the

probability of the data given the model p(y | ). Hypothesis testing within a

44

Bayesian framework corresponds to the reversed question: What is the

probability of the model given the data p( | y) (Kaplan & Depaoli, 2012)?

This latter Bayesian interpretation is probably what most researchers often

want to know, that is, what is the probability of the model or hypothesis

given the data at hand (Stenling, Ivarsson, Johnson, & Lindwall, 2015)?

Another difference between the frequentist framework and the Bayesian

framework is the nature of unknown parameters in the model (Kaplan &

Depaoli, 2012). When applying frequentist statistics, researchers assume

that there is only one true population parameter that is fixed but unknown.

Within Bayesian statistics, all unknown parameters incorporate uncertainty

that can be defined by a probability distribution and an interval that captures

this uncertainty that contains the parameter of interest (van de Schoot &

Depaoli, 2014). Although not applied in our study, it is also possible with

Bayesian estimation to incorporate previous knowledge about a parameter

directly into the analyses. This previous knowledge is incorporated into the

model estimation as a prior probability distribution of the parameters of

interest, and researchers can specify a narrow or wide variance of the

parameter depending on the level of (un)certainty about the parameter value

of interest (Zyphur & Oswald, 2015). Hence, with Bayesian estimation,

researchers can continuously update their knowledge and directly

incorporate that new knowledge into their analyses instead of testing the

same null hypothesis over and over again (van de Schoot et al., 2014).

Furthermore, by including prior information, it is possible to estimate

models that would be underidentified when employing ML estimation

(Zyphur & Oswald, 2015) and to estimate new types of models, for example,

by including cross-loadings and correlated residuals that can fix common

problems associated with unrealistic model constraints (Muthén &

Asparouhov, 2012). Bayesian estimation has better small-sample

performance because asymptotic inferences are not required, and therefore,

no normal approximations are required on the posterior. Because Bayesian

estimation does not rely on numerical integration as ML estimation does,

complex models that are computationally cumbersome or are impossible to

estimate with ML can be estimated with Bayesian estimation (e.g., Muthén &

Asparouhov, 2012; Scheines et al., 1999). In contrast to the frequentist

confidence interval, the credibility intervals that can be generated for any

parameter of interest indicate the probability (e.g., 95%) that the parameter

of interest lies within the two values given the observed data (Stenling,

Ivarsson, Johnson, & Lindwall, 2015).

Study 3: Bifactor exploratory structural equation modeling

Recently, Morin et al. (2015) proposed a bifactor exploratory structural

equation modeling (ESEM) framework to identify distinct sources of

construct-relevant psychometric multidimensionality. Given that the aim of

45

Study 3 was to examine multidimensionality in measures of coaches’ need-

supportive and controlling behaviors, the recently proposed framework of

Morin et al. (2015) provides a statistical model that corresponds well to the

study aim. Multidimensional scales often correspond to a bifactor structure,

that is, they consist of a general latent construct alongside several latent

subdimensions that are more narrowly defined (Myers et al., 2014). Hence,

both sources of construct-relevant variance need to be examined when

evaluating multidimensional scales (Stenling, Ivarsson, Hassmén, &

Lindwall, 2015). The bifactor measurement model has a long history,

originating from the early work of Holzinger and Swineford (1937), but it has

for a long time been overshadowed by Thurstone’s (1947) correlated-factor

model, also known as the independent clusters model (ICM). The bifactor

model, however, has been rediscovered in psychology (Reise, 2012), and it

has also been extended into an ESEM framework (e.g., Jennrich & Bentler,

2011, 2012; Morin et al., 2015).

ESEM is a relatively recent development within the SEM literature, and

applications of ESEM are increasing due to its ability to overcome some of

the inherent problems with the ICM-CFA (e.g., Asparouhov & Muthén,

2009; Marsh, Morin, Parker, & Kaur, 2014). Indicators incorporate a part of

random measurement error, but they also tend to have some degree of

systematic association with other constructs, a problem referred to as the

fallible nature of indicators (Morin et al., 2015). Such associations are

constrained to be zero on the ICM-CFA but are dealt with by including cross-

loadings in exploratory factor analysis. When not accounting for these

systematic associations in multidimensional scales, factor correlations are

extensively biased, and poor model fit is displayed (Asparouhov & Muthén,

2009; Marsh et al., 2014). It is very reasonable to assume that items are

imperfect to some degree and have systematic associations with other

constructs based on substantive theory or merely item content (Asparouhov

& Muthén, 2009; Morin et al., 2015). By combining a bifactor measurement

model with ESEM, researchers have the opportunity to investigate the co-

existence of global and specific components within the same measurement

model and account for the fallible nature of indicators (Morin et al., 2015).

Several different rotations can be specified when estimating an ESEM model.

In our study, we used target rotation (Asparouhov & Muthén, 2009; Browne,

2001), by which cross-loadings can be specified to be close to zero but not

exactly zero. Hence, with target rotation, ESEM can be used for confirmatory

purposes by specifying an a priori factor structure, freely estimating all

target loadings, and specifying all non-target loadings close to zero

(Asparouhov & Muthén, 2009).

46

The Empirical Studies

Study 1: Changes in Perceived Autonomy Support, Need Satisfaction, Motivation, and Well-Being in Young Elite Athletes

Background and aim

A four-stage motivational sequence outlined within SDT has been suggested

to explain a process through which coaches’ interpersonal styles influence

athletes’ need satisfaction, self-determined motivation, and well-being (e.g.,

Deci & Ryan, 2000; Mageau & Vallerand, 2003; Ng et al., 2012). Although

support for this motivational process has been found in different domains,

such as health care, work, and school settings (Deci & Ryan, 2008; Ng et al.,

2012), few studies have longitudinally examined the entire motivational

sequence. The few studies conducted in sport settings on the four-stage

motivational sequence (Gagné et al., 2003; Isoard-Gauther et al., 2012) were

limited with regard to sample diversity. They did not asses temporal order

among the variables and did not examine within-person changes in all

variables in the motivational sequence. The primary purpose of Study 1 was

to address some of these limitations and to examine the associations of

within-person changes in the four-stage motivational sequence (perceived

coach autonomy support → need satisfaction → self-determined motivation

→ well-being) in a sample of young elite athletes. In addition, level-change

associations between the study variables as well as reciprocal associations

between the variables in the motivational sequence were examined. We

hypothesized that changes in variables closer in the motivational sequence

would be more strongly associated than those in variables further apart in

the sequence and that the temporality proposed in the four-stage

motivational sequence would be supported.

Method

Data were collected from a sample of 247 young elite skiers at two time

points, in the beginning (T1) and at the end (T2) of the competitive season.

The instruments are summarized in Table 1, and the primary statistical

model employed was an LDS model (Figure 2; Selig & Preacher, 2009; Wu et

al., 2013). As a secondary analysis we also examined reciprocal associations

in a CLPM (Figure 3; Stenling et al., 2016a).

47

Y1

a1 a2 a3

∆Y

Y2

b1 b2 b3

1

1

Figure 2. Latent difference score model.

Results and discussion

With one exception, variables closer in the motivational sequence displayed

stronger correlations compared to variables further apart. The exception was

that change in perceived autonomy support was more strongly related to

change in well-being than change in self-determined motivation. Few of the

hypotheses for the level-change association (i.e., temporality proposed

within SDT) were supported, and the results showed that higher levels of

self-determined motivation at T1 positively predicted changes in perceived

autonomy support and changes in well-being. Higher need satisfaction at T1

negatively predicted changes in perceived autonomy support, self-

determined motivation, and well-being.

In the CLPM, higher self-determined motivation at T1 predicted higher

need satisfaction and well-being at T2, whereas higher levels of well-being at

T1 positively predicted perceived autonomy support at T2.

Taken together, these results indicate a complex and to some extent

reciprocal or reversed pattern of associations between the variables in the

four-stage motivational sequence over time. Reciprocal associations have

been found in previous studies within the sport domain, for example,

between motivation and burnout (Lonsdale & Hodge, 2011; Martinent,

Decret, Guillet-Descas, & Isoard-Gautheur, 2014) and between need

satisfaction and engagement (Curran et al., 2016). Recent research in the

educational domain has also found reciprocal or reversed associations

between students’ engagement and disengagement and perceptions of

teachers’ autonomy support and control (Jang et al., 2012, 2016; Reeve,

48

2013). These latter findings suggest that individual characteristics might be

important determinants of people’s perceptions of others’ interpersonal

behaviors. The results from Study 1 indicate that athletes with higher

autonomous motivation and well-being might be more likely to seek out

need supportive environments and might also be more receptive to the

positive effects of coaches’ autonomy support.

Coach’s

autonomy

support

Need

satisfactionWell-being

Self-

determined

motivation

Coach’s

autonomy

support

Need

satisfactionWell-being

Self-

determined

motivationT1

T2

Figure 3. Cross-lagged panel model. Only the structural part of the model is

shown.

Study 2: Longitudinal Associations Between Athletes’ Controlled Motivation, Ill-Being, and Perceptions of Controlling Coach Behaviors: A Bayesian Latent Growth Curve Approach

Background and aim

Individual characteristics have been highlighted as important determinants

of how athletes perceive coaches’ interpersonal styles (Deci & Ryan, 1987;

Soenens et al., 2015; Stenling, Lindwall, & Hassmén, 2015). This is also

supported by findings in the educational domain showing that students’

engagement and disengagement longitudinally predicted perceptions of

teachers’ autonomy support and control (Jang et al., 2012, 2016; Reeve,

2013) and findings from work and organizational psychology showing

similar results (Howell & Shamir, 2005). The findings in Study 1 showed that

athletes’ self-determined motivation and well-being longitudinally predicted

perceptions of coaches’ autonomy support. In Study 2, we explored whether

similar associations were present when focusing on a maladaptive

motivational process and examining the longitudinal associations between

athletes’ controlled motivation, ill-being, and perceptions of coaches’

49

controlling behaviors. The primary purpose of Study 2 was to examine

athletes’ controlled motivation and ill-being as predictors of their

perceptions of coaches’ controlling behaviors, at the between- and within-

person levels. A secondary purpose was to examine changes in the

perceptions of coaches’ controlling behaviors as a predictor of athletes’

controlled motivation and ill-being, as well as reciprocal associations

between athletes’ controlled motivation and ill-being over time.

Method

The same sample as in Study 1 was used in Study 2, with the exception that

we included data from all three time points: the beginning of the competitive

season (T1), midseason (T2), and the end of the season (T3). We used

Bayesian LGCM (e.g., Song & Lee, 2012; Zhang, Hamagami, Wang,

Nesselroade, & Grimm, 2007) with two different specifications. In the first

model (Figure 4), we focused on the between-person level and examined

athletes’ controlled motivation and ill-being as predictors of the slope factor

of coaches’ controlling behaviors. Furthermore, the intercept and slope

factors of coaches’ controlling behaviors predicted athletes’ controlled

motivation and ill-being at T3, and reciprocal associations were estimated

between athletes’ controlled motivation and ill-being at T1 and T3. In the

second LGCM (Figure 5), we estimated effects at the within-person level and

examined athletes’ controlled motivation and ill-being as predictors of their

perceptions of coaches’ controlling behaviors.

Results and discussion

In the first model, athletes’ controlled motivation and ill-being were not

credible predictors of the slope factor (i.e., change) of coaches’ controlling

behaviors over the competitive season. The slope factor of coaches’

controlling behaviors was a credible predictor of athletes’ controlled

motivation at T3, but it was not a credible predictor of athletes’ ill-being at

T3. Athletes’ controlled motivation at T1 was a credible predictor of ill-being

at T3 but not vice versa. In the second LGCM, athletes’ controlled motivation

was a positive and credible predictor of athletes’ perceptions of coaches’

controlling behaviors at the within-person level. The results from the

conditional LGCM at the between-person level were in line with the tenets

and the temporal order proposed within the SDT process model (e.g., Ng et

al., 2012; social-contextual factors → the basic psychological needs →

motivation → health). The associations between athletes’ controlled

motivation and ill-being are in line with previous studies (Lonsdale & Hodge,

2011) with similar study designs regarding the sample, spacing between

measurement points, and instrument for assessing motivation.

50

CB T1 CB T3CB T2

I S

1 1 1

0 1 2

IB T1

CM T1 CM T3

IB T3

Figure 4. Conditional latent growth curve model. CB = controlling coach

behaviors, CM = controlled motivation, IB = ill-being, I = intercept, S =

slope. The covariance between the intercept and slope factors was set to zero

in the conditional latent growth curve model.

CB T1 CB T3CB T2

S

I

1

0

2

1

1

1

CM T1IB T1 CM T2IB T2 CM T3IB T3

Figure 5. Unconditional latent growth curve model with time-varying

covariates. CB = controlling coach behaviors, CM = controlled motivation, IB

= ill-being, I = intercept, S = slope.

51

However, they differ from previous studies that involved younger athletes,

had shorter intervals between measurements, and used other instruments

for assessing motivation (Martinent et al., 2014). These results suggest that

these design issues are important to consider when examining reciprocal

associations between athletes’ motivation and ill- and well-being. The

within-person results indicate that individual characteristics, such as

motivation and ill-being, predict social perceptions to the extent that they

are attributed to the social stimuli. Controlled motivation was a positive

predictor of coach perception, which might be due to the contextual

similarity of the measures, whereas athletes might not attribute their general

ill-being to the coach (Hall & Lord, 1995). The results are in line with

suggestions that individual characteristics make people more or less

sensitive to environmental stimuli (Deci & Ryan, 1987; De Meyer et al., 2016;

Hall & Lord, 1995; Moller et al., 2010; Soenens et al., 2015) and might

indicate that athletes with higher controlled motivation are more likely to

notice controlling coach behaviors because it matches their motivational

profile.

Study 3: Using Bifactor Exploratory Structural Equation Modeling to Examine Global and Specific Factors in Measures of Sports Coaches’ Interpersonal Styles

Background and aim

In line with most previous research in sports, coaches’ autonomy-supportive

and controlling interpersonal styles were in Study 1 and Study 2

conceptualized as unidimensional constructs (Ntoumanis, 2012). Several

scholars (Ntoumanis, 2012; Pope & Wilson, 2012; Standage, 2012), however,

have highlighted that more attention should be given to the

multidimensional conceptualizations of these interpersonal styles. In

addition to autonomy support, coaches’ provision of structure and

involvement are also important determinants of athletes’ need satisfaction,

motivation, and well-being (Mageau & Vallerand, 2003). Coaches’

controlling behaviors also consist of several subdomains, controlling use of

rewards, negative conditional regard, intimidation, and excessive personal

control (Bartholomew et al., 2009, 2010). Both of these constructs (need-

supportive and controlling coach behaviors) contain a general construct and

several specific subdimensions; hence, they correspond to a bifactor

structure (Holzinger & Swineford, 1937; Reise, 2012). The purpose of Study 3

was to examine distinct sources of construct-relevant psychometric

multidimensionality in two sport-specific measures of coaches’ need-

supportive and controlling interpersonal styles.

52

Method

Study 3 consists of two studies: 3a and 3b. The sample in Study 3a was 277

young male and female floorball players who responded to the ISS-C (Wilson

et al., 2009). The sample in Study 3b was 233 young male ice-hockey players

who responded to the CCBS (Bartholomew et al., 2010). Data were analyzed

following the framework proposed by Morin et al. (2015). In the first step, we

compared ICM-CFA with ESEM models, and then we estimated bifactor

ESEM models to examine two sources of psychometric multidimensionality

in the ISS-C and CCBS. The models are graphically depicted in Figure 6a-6c.

Results and discussion

For both instruments, the ESEM model provided a better fit to the data

compared to the ICM-CFA, indicating that items had systematic associations

with non-target constructs that needed to be accounted for in the analyses.

The ISS-C did not display a bifactor pattern. All items had statistically

significant loadings onto the general factor, and most items had low and not

statistically significant loadings onto their specific subdimensions. Two of

the four subdimensions (negative conditional regard and excessive personal

control) of the CCBS displayed a bifactor pattern with relatively strong factor

loadings on both the general and specific factors. Hence, these two factors

seem to consist of two sources of construct-relevant psychometric

multidimensionality (Morin et al., 2015). The controlling-use-of-rewards

factor displayed weak loadings on the general factor and strong loadings on

the specific factor. These results indicate that it might represent a slightly

different aspect of controlling coach behaviors compared to the other three

dimensions that appear to have a common core. Finally, the intimidation

factor displayed weak loadings onto the specific factor and strong loadings

onto the general factor, which shows that this factor is mostly explained by

the general factor. These results suggest that the ISS-C in its present form is

a unidimensional scale rather than a multidimensional scale. A lack of

multidimensionality is a common observation when applying assumed-to-be

multidimensional self-report instruments of leadership, such as perceptions

of transformational leadership (e.g., van Knippenberg & Sitkin, 2013). It is

important that measures can discriminate between different subdimensions;

collecting other types of data could be useful for that purpose. The

differential patterns displayed for the CCBS subdimensions cause one to

question the multidimensional nature of this instrument in its present form.

Future research should replicate these findings and more closely examine

the antecedents and consequences of the specific subdimensions to enhance

our knowledge of the nature of controlling coach behaviors.

53

F1

F2

F3

X1

X2

X6

X3

X4

X5

Z1

Z2

Z6

Z3

Z4

Z5

Y1

Y2

Y6

Y3

Y4

Y5

F1

F2

F3

X1

X2

X6

X3

X4

X5

Z1

Z2

Z6

Z3

Z4

Z5

Y1

Y2

Y6

Y3

Y4

Y5

F1

GF2

F3

X1

X2

X6

X3

X4

X5

Z1

Z2

Z6

Z3

Z4

Z5

Y1

Y2

Y6

Y3

Y4

Y5

Figure 6a-6c. Independent clusters model confirmatory factor analysis (top

left), exploratory structural equation model (top right), and bifactor

exploratory structural equation model (bottom left).

54

General Discussion The primary aim of this thesis was to explore and increase our knowledge of

the leadership process in sports from an SDT perspective. Specifically, I

examined how coaches’ autonomy-supportive and controlling interpersonal

styles longitudinally are associated with young athletes’ motivation and ill-

and well-being. The psychometric multidimensionality in the self-report

measures of athletes’ perceptions of need-supportive and controlling coach

behaviors was also investigated. The discussion will initially be organized

according to these two broad themes: (I) longitudinal associations and

temporal ordering (reciprocal or reversed associations) of coaches’

interpersonal styles, athletes’ motivation, and ill- and well-being (Study 1

and Study 2); and (II) the multidimensional nature of measures of coaches’

interpersonal styles (Study 3). Following the discussion of the findings

related to these themes, the main limitations of the studies will be discussed,

followed by a discussion of challenges associated with an autonomy- or need-

supportive interpersonal style. Finally, implications and suggestions for

future research and some final remarks are provided.

Coaches’ Interpersonal Styles and Athletes’ Motivation and Ill- and Well-being The focus of Study 1 and Study 2 were longitudinal associations between

athletes’ perceptions of coaches’ autonomy-supportive and controlling

interpersonal styles, motivation, and ill- and well-being. Using the SDT-

based motivational sequence as a starting point (i.e., social factors →

psychological mediators → types of motivation → consequences), previous

research was extended by focusing on within-person change processes and

further exploring the temporal ordering of the variables in the sequence. The

initial hypothesis was that temporal ordering in the motivational sequence

would be supported, a hypothesis only partially supported by the empirical

data. In Study 1, it was observed that within-person changes in perceived

autonomy support, need satisfaction, self-determined motivation, and well-

being were all positively correlated, a result in line with the tenets of SDT

(Deci & Ryan, 2000) and previous research (e.g., Gagné et al., 2003). Results

also in line with SDT and previous research were that self-determined

motivation over time positively predicted level and change in well-being

(Study 1), that changes in the perceptions of coaches’ controlling behaviors

positively predicted late-season controlled motivation, and that controlled

motivation early in the season positively predicted late-season ill-being

(Study 2). These latter results follow the theoretical propositions within SDT

(Deci & Ryan, 1985, 2000, 2014) and previous empirical findings (Lonsdale

& Hodge, 2011; Ng et al., 2012; Pelletier et al., 2001). The results indicate

that when athletes’ experience that their coach is more autonomy supportive,

55

they also experience higher degrees of need satisfaction, autonomous

motivation, and well-being compared to other athletes’ experiencing their

coach as less autonomy supportive. Conversely, athletes’ experiencing their

coach as more controlling also seem to experience more controlled

motivation and ill-being compared to athletes’ experiencing their coach as

less controlling. Hence, the adaptive and maladaptive process models

proposed within SDT were (at least partially) supported in Study 1 and Study

2.

Several paths in Study 1 and Study 2 also suggested reciprocal or reversed

associations among the variables in the motivational sequence. Most SDT-

based research has followed a unidirectional temporal order of the

motivational sequence and has examined associations between coaches’

autonomy-supportive and controlling interpersonal styles and athlete

outcomes (Ntoumanis, 2012; Occhino et al., 2014). Little attention has been

given to potential reciprocal or reversed associations despite several

scholars’ acknowledging them as salient for understanding motivational

processes (Deci & Ryan, 1987; Ntoumanis, 2012; Occhino et al., 2014; Reeve,

2015; Skinner & Belmont, 1993; Smoll & Smith, 1989). The results from

Study 1 showed that higher self-determined motivation early in the season

predicted higher levels of need satisfaction late in the season. Higher self-

determined motivation early in the season also predicted within-person

changes in perceived autonomy support from the coach. Higher well-being

early in the season was a positive predictor of perceived autonomy support

from the coach at the end of the season. Furthermore, the results from Study

2 indicated that controlled motivation, but not ill-being, positively predicted

perceptions of coaches’ controlling behaviors at the within-person level. The

fact that well-being but not ill-being predicted athletes’ perceptions of

coaches’ interpersonal styles might indicate that the athletes attribute their

well-being to factors associated with the coach, whereas they might not

attribute their ill-being to the coach (cf. Hall & Lord, 1995). This explanation

is, however, highly speculative, and more research is needed on this topic.

Individual factors, such as motivation and well-being, appear to function

as important determinants of how athletes perceive coaches’ interpersonal

styles. The importance of individual factors as influential for interpersonal

perceptions have been acknowledged in the SDT literature (e.g., Deci &

Ryan, 1987; De Meyer et al., 2016; Reeve, 2015; Soenens et al., 2015), is

included in the mediational model of leadership (Smoll & Smith, 1989,

2002), and has been highlighted in the organizational leadership literature

(Avolio, 2007; Hall & Lord, 1995; Howell & Shamir, 2005; Shamir et al.,

1993). The results suggest that athletes’ motivation is associated with how

they perceive their coach’s interpersonal styles at the within-person level, in

line with the propositions of interpersonal synchrony (Jang et al., 2012;

Reeve, 2013, 2015), the sensitization-desensitization process (De Meyer et

56

al., 2016; Moller et al., 2010), and motives as cognitive influences of

leadership perceptions at the person level (Hall & Lord, 1995). These

reversed associations have been observed in the educational domain (Jang et

al., 2012, 2016), and our results suggest that similar processes might occur in

the sport domain.

The next step is to understand the underlying mechanisms through which

these processes occur, which is an important issue to address in future

research. Several potential explanations for understanding this reciprocal or

reversed process have been put forward in the literature, and here, I provide

three plausible explanations that can guide future research. The first

explanation is related to athletes’ perceptions and how individual

characteristics influence perceptions of others’ interpersonal styles. Several

scholars (Deci & Ryan, 1987; De Meyer et al., 2016; Hall & Lord, 1995;

Moller et al., 2010; Soenens et al., 2015) have proposed that individual

characteristics make people more or less sensitive to environmental stimuli.

Thus, athletes with higher levels of autonomous or controlled motivation are

more likely to notice autonomy-supportive or controlling coach behaviors,

respectively, because such behaviors match their motivational profiles. This

reasoning is also supported by neuroscience research showing that

adolescents have an increased sensitivity to both positive and negative

environmental and motivational cues, such as heightened amygdala

responses to emotional facial expressions and increased ventral striatal

responses in incentive-related decision-tasks (Somerville & Casey, 2010;

Somerville, Jones, & Casey, 2010). These results can therefore be explained

as an intraindividual process occurring within athletes, making them more

or less prone to certain environmental and motivational cues that their coach

provides, such as body language, rewards, tone of voice, and type of

feedback.

A second explanation is that individual characteristics affect athletes’

behaviors that, in turn, will influence coaches’ behaviors. Reeve (2015)

proposed that students’ agentic engagement can orient teachers to provide

students with a more autonomy-supportive interpersonal style and that

students’ disengagement can orient teachers toward a more controlling

interpersonal style. This reasoning would suggest that athletes’ motivation

will influence athletes’ behaviors that, in turn, will influence coaches’

behaviors to be more in synchrony with the athletes’ behaviors. Thus, these

results could be viewed as an interindividual process between athletes and

coaches.

A third explanation is that coaches’ individual characteristics,

expectations, and perceptions of athletes will influence their behaviors.

Research in the educational domain indicates that teachers interacted in a

more controlling way when students were perceived as less motivated

(Sarrazin et al., 2006). Research also shows that teachers believe that

57

students with high autonomous motivation would benefit most from an

autonomy-supportive interpersonal style, whereas students with high

controlled motivation would benefit most from a controlling interpersonal

style (De Meyer et al., 2016). This suggests that how coaches perceive

athletes’ behaviors, motivation, and other characteristics (e.g., engagement),

and what they expect of their athletes will influence how they behave

towards them. This latter explanation is also in line with Mageau and

Vallerand’s (2003) motivational model of the coach–athlete relationship,

suggesting that coaches’ perceptions of athletes’ motivation and behaviors

will influence their interpersonal styles. Hence, the results can be explained

as an intraindividual process within coaches.

All three explanations are viable alone or in combination. They are also

possible to test with various study designs and would lend themselves

particularly well to experimental designs or intensive longitudinal designs

(e.g., diary studies) focusing on dyadic associations. Future research

examining these three explanations in sports settings would enhance our

knowledge of the dynamics of the coach–athlete relationship.

The Multidimensional Nature of Measures of Coaches’ Interpersonal Styles SDT-based research in sports has mainly been focused on autonomy support

versus control from a unidimensional perspective (Ntoumanis, 2012;

Stenling, Ivarsson, Hassmén, & Lindwall, 2015). Recently, however, scholars

(e.g., Mageau & Vallerand, 2003; Ntoumanis, 2012; Standage, 2012;

Stenling, Ivarsson, Hassmén, & Lindwall, 2015) have suggested that more

attention should be given to the multidimensional nature of need-supportive

and controlling interpersonal styles. Despite being somewhat neglected in

the sport psychology literature, the notion of several interacting

interpersonal styles is not new in the SDT literature. Deci and Ryan (1985)

and others (Connel & Wellborn, 1991; Grolnick & Ryan, 1989) argued that

structure and involvement are also essential motivational styles that function

as important determinants of people’s perceptions of competence and

relatedness. These interpersonal styles have been the focus of much

research, for example, in the educational domain (e.g., Jang et al., 2010;

Reeve & Su, 2014; Skinner & Belmont, 1993) and in research on parenting

(e.g., Grolnick, 2003; Joussemet, Landry, & Koestner, 2008), but the impact

of these various interpersonal styles in the sport domain is scarce.

It is argued that the need-supportive interpersonal styles—autonomy

support, structure, and involvement—are highly interrelated (e.g., Markland

& Tobin, 2010; Ryan, 1991), and they are therefore often combined into a

higher-order construct labeled “need support.” The results from Study 3a

were in line with such reasoning as indicated by the strong factor

correlations between the subdimensions and the strong factor loadings onto

58

the general need-support factor. Strong correlations between the

subdimensions are common in the SDT literature (Niemiec et al., 2006;

Wilson et al., 2009), and we have observed similar patterns in the work

domain for measures of managers’ need support (Tafvelin & Stenling, 2016)

and in the educational domain for measures of physical education teachers’

need support (Sánchez-Oliva, Kinnafick, Smith, Stenling, & García-Calvo,

2016). Similar patterns can also be found in the general leadership literature,

where subdimensions on multidimensional scales are often highly

interrelated (e.g., in transformational leadership research; Beauchamp et al.,

2010; van Knippenberg & Sitkin, 2013). Strong interrelationships between

need-support dimensions might indicate that coaches who are need

supportive with regard to one dimension (e.g., autonomy support) are also

need supportive with regard to other dimensions. This complementary

hypothesis is supported by research in the educational domain showing that

teachers’ autonomy support and structure are positively correlated (r = .60;

Jang et al., 2010), as well as observational research of soccer coaches

indicating positive associations among autonomy support, the provision of

structure, and involvement (rs ranged from .59 to .68; Smith, Quested,

Appleton, & Duda, 2016).

Another explanation is related to the use of self-report measures, such as

questionnaires for measuring perceived leadership. The respondents may

not be able to distinguish between items intended to capture these various

dimensions of need support. In general, there is an overreliance on self-

report measures in the SDT-based sport psychology literature, and

complementary operationalizations of need-supportive coaching (as well as

basic psychological needs and motivation) are warranted. Nearly 40 years

ago, Campbell (1977) argued that:

We are in very grave danger of transforming the study of

leadership to a study of self-report questionnaire behavior, if,

indeed, the transformation has not already occurred. The

method is too quick, too cheap, and too easy, and there are

now many such questionnaires that possess no construct

validity whatsoever. I submit that when both the independent

and dependent variables are based on self-reports by the same

person, we have learned absolutely nothing about leadership,

no matter what the results turn out to be. (p. 229)

If researchers are to rely on the self-report instruments of leadership

perceptions, potential problems related to common-method bias, such as

response-order effects (e.g., Chan et al., 2015), should a priori be addressed

by the research design. Several recommendations for remedying common-

method bias have been provided, such as collecting data on the independent

59

and dependent variables from different sources; temporal, proximal, or

psychological separation between independent and dependent variables;

eliminating common scale properties; and reducing item ambiguity and

social desirability (Podsakoff et al., 2012). The systematic use of these

recommendations and increased transparency in the reporting of the steps

that researchers take to remedy common-method bias would strengthen the

conclusions drawn and increase the possibility for other researchers to

determine the potential impact of common-method bias in research studies.

An increased transparency is strongly encouraged in future research.

The ability to empirically distinguish multidimensional constructs is

important, as it would allow researchers to explore whether these leadership

dimensions truly are additive (i.e., the more the better), which is assumed

when sum scores of need support are used, or if their relationships have

other forms (cf. van Knippenberg & Sitkin, 2013). For example, rather than

assuming an additive effect, it may be a matter of specifying a minimum

value on each of these dimensions that determines when someone can be

characterized as need supportive. Another argument could be that the need-

support dimensions are interactive, and engaging in one type of need

support would make the other types more effective (i.e., moderating effects).

Structure and involvement are often presented as “what” a person does,

whereas autonomy support refers to “how” the person does it (Deci & Ryan,

2000; Grolnick, 2003; Joussemet et al., 2008), which implies that there

should be interactive effects, such that structure and involvement will be

effective when provided in an autonomy-supportive way. This line of thought

has to some extent been supported in educational research, where Jang et al.

(2010) found that autonomy support and structure appear to be two

complementary engagement-fostering interpersonal styles among teachers

associated with students’ classroom engagement. A third argument could be

that one need-support dimension can compensate for the lack of another

(van Knippenberg & Sitkin, 2013). Most evidence so far suggests that the

three need-support dimensions are interactive, in that structure and

involvement will be enhanced when provided in an autonomy-supportive

way (Reeve & Su, 2014). However, research on these various hypotheses is

lacking in the sport psychology literature. Furthermore, to investigate these

hypotheses, it is imperative that the three need-support dimensions are

empirically distinguishable, regardless of how they are operationalized. This

is an important avenue for future research on the assessment of coaches’

need support.

The results from Study 3b call into question that the CCBS consists of a

coherent multidimensional structure. Three of the subdimensions displayed

relatively coherent factor-loading patterns onto the general factor, indicating

that they share a common core, whereas the subdimension of controlling use

of rewards did not. The controlling-use-of-rewards subdimension is

60

theoretically grounded in the undermining effect, which explains how and

when rewards undermine people’s intrinsic motivation (Deci et al., 1999).

The undermining effect have been found salient when tangible rewards are

provided as incentives for engaging in and completing a task (i.e., task-

contingent reward) or are given for reaching certain performance standards

(i.e., performance-contingent rewards), particularly when the reward is

expected. The undermining effect of rewards on intrinsic motivation has also

been found in sports contexts (e.g., Vansteenkiste & Deci, 2003). Most of the

undermining-effect studies, however, have been conducted at the situational

level in experimental settings where variables, such as different types of

rewards, are easy to manipulate. It is possible that the questions in the CCBS

do not capture an undermining effect because they are measured at the

contextual level, and due to the way in which they are formulated. It might

also be argued that the controlling-use-of-reward items are not necessarily

perceived as controlling by the respondents, depending on, for example,

individual difference factors, such as motivational profile, need satisfaction,

and personality. Also, depending on the context under study, it might be that

the other three subdimensions (negative conditional regard, intimidation,

and excessive personal control) are more frequently occurring and share a

common core because they all represent a clearer set of behaviors compared

to the subdimension of controlling use of rewards. All in all, the items do not

necessarily in their current form capture the known prerequisites for an

undermining effect (e.g., tangible rewards, known rewards), which might

explain the weak associations with the general factor.

Limitations The empirical studies herein contain several limitations. The specific

limitations of each study can be found in the papers. Here, I will summarize

some of the main limitations of the three studies. First, all three studies

relied on data collected via self-report questionnaires. Hence, the knowledge

generated in the present thesis refers to athletes’ perceptions of coaches’

interpersonal styles, as well as athletes’ self-reported need satisfaction,

motivation, and ill- and well-being. Relying on a single source of data

increases the risk for method bias (Podsakoff et al., 2012), and

questionnaire-generated data have been critiqued for often being arbitrary

metrics (Andersen, McCullagh, & Wilson, 2007; Baumeister, Vohs, &

Funder, 2007; Ivarsson et al., 2015). Common method bias is a well-

established phenomenon; one common observation is that associations

between variables from the same source (e.g., self-report questionnaires) are

stronger compared to associations based on data from different sources, and

these associations are inflated due to common method bias (Podsakoff et al.,

2012). The sources of the bias can be related to various factors, such as the

effects of response style, item wording, proximity and reversed items, item

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context, and person factors, such as mood and interpersonal context. The

longitudinal design in Study 1 and Study 2, as well as the use of latent

variable modeling, which accounts for measurement error, are remedies

against method bias, at least to some extent, but method bias is still an issue

in this type of study.

Andersen et al. (2007) stated that:

Sport and exercise psychology researchers use numbers

extensively. Some of those numbers are directly related to

overt real-world behaviors such as how high an athlete jumps

or how far some object is thrown. Many of those measures,

however, do not have such intimate connections to real-world

performance or behavior. They often involve self-reports on

inventories or surveys that are measuring (or attempting to

measure) some psychological, underlying, or latent variables

such as task and ego orientation or competitive state anxiety.

What those scores on self-report inventories mean may be

somewhat of a mystery if they are not related back to overt

behaviors. (p. 664)

Given that all of the studies relied on self-report measures (i.e., arbitrary

metrics), the results and implications of the results are not easily interpreted

in terms of real-world meaning. We did not assess coaches’ behaviors

directly (e.g., observed the coaches’ behaviors), we did not assess the

behavioral consequences of athletes’ motivation or ill- or well-being, and we

did not include data from other sources, such as physiological measures of

health (e.g., cortisol), which all would have been viable options for the

measures used in the three studies. Although the study designs we have used

are common in the sport psychology literature, the need for progression

exists with regard to operationalization and study designs to avoid common-

method bias and arbitrary metrics that make the translation into real-world

meaning difficult (Arthur & Tomsett, 2015; Ivarsson et al., 2015).

Second, all three studies focused on the contextual level, which refers to

athletes’ general experiences in relation to their sport participation.

Assessing at this level has some downsides, such as which timeframe the

reported experience captures, the athletes’ capacity to actually summarize

and provide an average experience, and the decontextualization of the

motivational process under study. All of the variables assessed in these

studies (coaches’ interpersonal styles, athletes’ need satisfaction, motivation,

and ill- and well-being) fluctuate and change over time, often over very short

periods of time (e.g., hours, days). The dynamic nature of these constructs

makes them highly suitable for study designs that can capture dynamic

processes in specific situational contexts (Gagné & Blanchard, 2007; Ryan,

62

1995). Diary studies have shown that daily variation in, for example, need

satisfaction is related to daily variation in well-being (e.g., Reis, Sheldon,

Gable, Roscoe, & Ryan, 2000), findings that have been replicated in sports

settings (e.g., Bartholomew et al., 2011; Gagné et al., 2003). Studies situated

in specific contexts would also make it easier to assess the real-world

implications of changes in these motivational variables.

Third, Study 1 and Study 2 relied on longitudinal data, which allowed us

to examine temporal associations among the study variables. Although we

followed a common measurement scheme in the sport psychology

literature—early in the season, middle of the season, and end of the season—

it might not accurately reflect the change process we are trying to capture

(Collins, 2006; Ployhart & Vandenberg, 2010). Furthermore, three

measurement points limit the possibility of modeling different shapes of the

growth trajectory, such as quadratic or cubic, which would have required

four or five measurement points, respectively (Bollen & Curran, 2006).

Although several longitudinal studies have focused on the SDT-based

motivational sequence, a theory of time has yet to be incorporated into SDT

(see Wasserkampf & Kleinert, 2016, for one of the few papers on change and

time in SDT-based research). George and Jones (2000) provided a useful

framework for the role of time in theory and theory building that could aid

the incorporation of time into SDT. They provided six time dimensions: (a)

past, future, and present and subjective experience of time; (b) time

aggregations; (c) durations of steady states and rates of change; (d)

incremental versus discontinuous change; (e) frequency, rhythm, and cycles;

and (f) spirals and intensity. Researchers can use these to theorize about the

change process under study. George and Jones (2000) further proposed that

these six time dimensions should be combined with the “what” (referring to

a single construct), “how” (referring to relationships among two or more

constructs), and “why” (referring to the underlying mechanisms) of theory

for a more complete understanding of processes over time. Integrating this

framework into SDT-based research would be useful for gaining a better

understanding of the dynamic nature of motivational processes.

Fourth, a common saying in statistics is that correlation does not imply

causation (Shadish et al., 2002), a saying also true for the work presented in

the present thesis. The data presented in all three studies are correlational in

nature, meaning that the conclusions drawn refer to associations and not

causal effects. Three necessary criteria often specified for causation are that:

(a) the cause preceded the effect, (b) the cause was related to the effect, and

(c) there was no other plausible alternative explanation for the effect other

than the cause (Shadish et al., 2002; see also Hill, 1965). The main problems

in observational studies are related to the first and last criteria, that is, to

untangle the temporal order and to rule out alternative explanations. As

reported in the introduction, there have been very few SDT-based

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intervention studies in sport settings using designs that allow researchers to

draw causal inferences (see Langan et al., 2013). Although experimental

designs, such as randomized controlled trials, often are the design of choice

for causal inferences, methods for inferring causality in observational studies

exist, but these methods are not widely known in the social sciences

(Antonakis, Bendahan, Jacquart, & Lalive, 2014). Experimental designs and

randomization may in many cases not be applicable in field-based research;

therefore, researchers need to apply other methods to be able to infer

causality (Antonakis, Bendahan, Jacquart, & Lalive, 2010). A problem in

non-experimental designs is endogeneity—which includes omitted variables,

omitted selection, simultaneity, common-method variance, and

measurement error—which can make estimates causally uninterpretable.

Increased attention in observational and non-experimental studies to

appropriate design conditions and statistical methods (e.g., propensity score

analysis, instrumental variables, regression discontinuity, and difference-in-

difference models) under which causal inferences can be drawn would be a

useful contribution to the SDT literature.

Challenges of an Autonomy- or Need-Supportive Interpersonal Style Plenty of evidence suggests that an autonomy-supportive interpersonal style

is associated with positive outcomes and a controlling interpersonal style is

associated with negative outcomes (Ntoumanis, 2012). Becoming an

autonomy-supportive coach, however, is not easy, and the related hurdles

and challenges require attention from both practitioners and researchers to

help coaches translate theory into practice (Ntoumanis & Mallett, 2014;

Occhino et al., 2014). Based on research in the educational domain, Reeve

(2009) proposed seven reasons for why teachers adopt a controlling

interpersonal style, which were classified into three broader categories: (a)

pressure from above; (b) pressure from below; and (c) pressure from within.

Although research on these factors in the sport domain is scarce, research

from other domains are useful for understanding why autonomy-supportive

coaching might be challenging. Pressure from above refers to interpersonal-

power differences in relationships and that coaches (who are in a one-up

position) have an inherent powerful social role, which may steer them

toward controlling behaviors. Potentially coaches’ take on double burdens:

accountability due to job conditions and responsibility for athletes’ behaviors

and outcomes, which can pressure them to become more controlling. Being

controlling can be culturally valued, and controlling instructional strategies

can be seen as an indicator of competence. Hence, social and cultural

expectations of what it means to be a coach can increase controlling

behaviors. Coaches sometimes equate control with structure and autonomy

support with a chaotic or laissez-faire interpersonal style. A structured

64

environment versus a chaotic environment is one aspect of coaches’

interpersonal styles, whereas autonomy support versus control is another,

separate aspect of coaches’ interpersonal styles. However, differentiating

aspects of coaches’ interpersonal styles in such a way may not be obvious for

coaches. Pressure from below refers to the people in a one-down position, in

this case, the athletes and how their behaviors and motivation influence

coaches’ interpersonal styles. For example, when athletes’ express disruptive

behaviors or are perceived as less motivated or extrinsically motivated,

coaches are likely to adopt a more controlling interpersonal style (Sarrazin et

al., 2006; Skinner & Belmont, 1993). Finally, pressure from within refers to

control-oriented personal dispositions. Coaches who themselves coach for

nonautonomous reasons are more likely to interact with and try to motivate

athletes in a controlling way (Occhino et al., 2014). This latter point is

further supported by meta-analytic evidence showing that trainees who were

more autonomy oriented were more likely to benefit from autonomy-

supportive training programs compared to trainees with a control

orientation (Su & Reeve, 2011).

Ntoumanis and Mallett (2014) and Occhino et al. (2014) further

elaborated on the challenges of an autonomy-supportive pedagogical

approach and translating theory into practice. A shift from a more

controlling to a more autonomy-supportive interpersonal style can be

challenging for many coaches, and research on the challenges and barriers of

translating SDT theory into practice is scarce (Ntoumanis & Mallett, 2014).

In the motivational model of the coach–athlete relationship (Mageau &

Vallerand, 2003), three broad factors are specified as antecedents to coaches’

interpersonal styles: coaches’ personal orientations, the coaching context,

and coaches’ perceptions of athletes’ behaviors and motivation. These three

factors correspond well to the categorization of influencing factors as

pressure from within, pressure from above, and pressure from below (Reeve,

2009). Occhino et al. (2014) highlighted the limited evidence of the

challenges of translating theory into practice, the limited understanding of

the individual and the interaction effects of coaches’ personal orientations

and the coaching context, and athletes’ perceptions of quality coaching as

additional factors that might hinder the translation from theory to practice.

Furthermore, teachers become more autonomy supportive after they believe

it is easy to do (referred to as easy-to-implement beliefs; Reeve & Cheon,

2016). Hence, future interventions could be structured to better support

changes in the participants’ easy-to-implement beliefs, which can aid the

shift toward becoming more autonomy supportive.

In a recent autonomy-supportive intervention, coaches were interviewed

after the intervention about their experiences with the intervention program

and the potential barriers to adopting an autonomy-supportive coaching

style (Mahoney et al., 2016). The identified barriers were restrictions on time

65

and dissonance between the workshop content and performance context (the

coaching context), relapse into previous coaching behaviors (coaches’

personal orientations), and limited understanding of the workshop

materials, which can be referred to as a training-design factor. These barriers

highlight the importance of contextualizing interventions to fit the needs of

the participants. Instead of a top-down approach, researchers can apply a

bottom-up approach and involve the participants already in the design phase

of the intervention to develop an intervention that matches the needs of the

participants and also to understand the context in which the participants

operate, as well as potential barriers and challenges within that context.

Useful frameworks in the organizational literature, such as the dynamic

integrated evaluation model (DIEM; Schwarz, Lundmark, & Hasson, 2016),

are available for researchers in sport psychology to adopt, as they detail the

steps necessary for contextualizing, implementing, and evaluating

interventions. Although mostly applied in organizational interventions,

frameworks such as the DIEM provide researchers with tools for

contextualizing, implementing, and evaluating interventions in a way that

captures the dynamic change processes occurring during the different phases

of an intervention, instead of relying on traditional pretest-posttest designs.

An integration of SDT with frameworks, such as the DIEM, when designing,

implementing, and evaluating interventions have the potential to increase

our knowledge of and overcome some barriers and challenges with

translating SDT theory into practice.

Implications for Research I set out to explore the leadership process in sports from an SDT perspective,

and the results have generated knowledge about temporal order (reciprocal

or reversed associations) and multidimensionality—two theoretical problems

in need of further inquiry. Going back to the underlying philosophy of

science guiding this work—pragmatism—with an ends-in-view approach, one

might wonder what practical knowledge has thus been gained? Guided by

abductive inference, also known as “inference to the best explanation”

(Marcio, 2001, p. 103), our interpretation of the results provides some

implications for research with regard to the leadership process and the

dynamic coach–athlete relationship, which also leads to opportunities for

future research. These results and their implications, however, should be

taken as provisional and functional, not as fixed and given (Dewey, 1938).

The first implication relates to the findings in Study 1 and Study 2,

referred to as reversed associations. Several scholars have acknowledged the

importance of individual factors’ influence on people’s perceptions of

contextual factors (Deci & Ryan, 1987; Soenens et al., 2015; Weinstein &

Ryan, 2011). Deci and Ryan (1987) stated that:

66

However, dispositional or person factors are also relevant to

the study of autonomy and control. There are evident

individual differences in the functional significance people give

to contextual factors. (p. 1025)

Soenens et al. (2015) similarly argued that:

Parents’ actual behaviors are distinct from children’s appraisal

of these behaviors, which involves perceiving and attributing

meaning to parents’ behavior. Although parents’ actual

behavior is associated with the way the behavior is appraised,

this link is not perfect; that is, different children perceive and

interpret the same parental behavior differently, and these

differential appraisals may be shaped by the factors

highlighted in relativistic accounts of parenting. (pp. 45–46)

It appears that athletes’ general well-being and autonomous and

controlled motivation may function as determinants of their sensitivity to

coaches’ autonomy support and control. In the present thesis, three plausible

mechanisms are proposed that can explain these reversed associations: (a)

an intraindividual process within athletes; (b) an interindividual process

between athletes and coaches; and (c) and intraindividual process within

coaches. These potential mechanisms provide opportunities for future

research to further untangle these reversed associations.

A second implication stems from the findings in Study 3a and Study 3b

about the multidimensional nature of measures of coaches’ interpersonal

styles and the potential mismatch between theories about coaches’

interpersonal styles and how they are operationalized. The theoretical notion

of various interrelated but distinct subdimensions of interpersonal styles

(Ryan, 1991) renders itself to empirical testing using various

operationalizations. If researchers are to adequately address the theoretical

notion of distinct subdimensions, it is important to be able to tease out

whether these high interrelations reflect a measurement bias or reflect that

coaches exhibiting high levels of one subdimension (e.g., structure) also

exhibit high levels of another subdimension (e.g., autonomy support). The

bifactor ESEM framework (Morin et al., 2015) allows researchers to match

the theory behind the measurement instrument with the model imposed on

the data when evaluating multidimensional constructs (Myers et al., 2014).

In both studies, the results indicated a mismatch between the theory behind

the measurement instrument and what the instrument captures. Finding

measures and statistical models that match the assumptions in the theory is

a crucial step when designing studies. When such a match exists, researchers

can adequately test the propositions specified within a theory. Hence, the

67

continued refinement of measures of coaches’ interpersonal styles is

warranted in future research.

From a methodological standpoint, the statistical analyses used are also

viewed as a contribution, as they provide examples of useful but

underutilized statistical tools that correspond well too many of the research

questions of interest for sport psychology researchers. Longitudinal studies

in sport psychology have mainly been analyzed with traditional analyses,

such as ANOVAs, regression, or SEM, such as CLPM, which are analyses

capturing associations at the between-person level (Stenling et al., 2016a;

2016b). The between-person level refers to one person’s level compared to

another person’s level. Within-person analyses, on the other hand, refer to

whether the association for one person is higher or lower compared to the

expected association for that particular person. The type of analyses used in

Study 1 and Study 2 can be used to tease apart associations at the between-

and within-person levels, and these associations do not always show the

same magnitude or even the same direction (Hoffman & Stawski, 2009;

Kievit et al., 2013). Kievit et al. (2013) showed an extreme but very

illustrative example where the association between alcohol intake and IQ

was positive at the between-person level, but for each person over time, the

same association was negative (cf. Simpson’s paradox; Simpson, 1951).

Different associations at the between- and within-person levels have also

been found in SDT-based physical education research (Gillison, Standage, &

Skevington, 2013). At the between level, the results suggested that students

perceived greater lesson value and formed stronger future intentions to

exercise in a controlling, extrinsic goal–focused condition. At the within

level, the results were the opposite, indicating that students perceived

greater lesson value, positive lesson-related outcomes (i.e., motivation,

effort, enjoyment, value, exercise-induced affect), and future intention to

exercise in the autonomy-supportive, intrinsic goal–focused condition.

Increased attention to associations at different levels (i.e., between- and

within-person levels) will force researchers to more thoroughly specify their

hypotheses and whether similar associations can be expected when athletes

are compared to other athletes and when they are compared to themselves

over time.

The bifactor ESEM framework (Morin et al., 2015) applied in Study 3 also

provides researchers with a statistical tool that corresponds well to research

questions of interest in sport psychology. The statistical analysis that

researchers choose do not always correspond to theory, such as theories

about time, change, associations between variables, or theory underlying the

measures used in a study. Morin et al. (2015) have provided a statistical

framework that corresponds well to research questions of interest in sport

psychology, referring to distinct sources of psychometric multi-

dimensionality.

68

Suggestions for Future Research Suggestions for future research have been provided in various sections, and I

will summarize some of these suggestions here and provide some new ones.

First, more research is warranted on the leadership process in sports as a

dynamic process that coaches and athletes jointly produce. Focusing on

dyadic patterns could be one way of addressing this issue, in which both

athletes’ and coaches’ behaviors and motivation could be assessed repeatedly

over time in intensive longitudinal designs (e.g., Bolger & Laurenceau, 2013).

A focus on dyadic patterns in intensive longitudinal designs could be one

way of further identifying reciprocal or reversed associations (e.g., feedback

loops) between coaches’ interpersonal styles and athletes’ motivation,

behaviors, and well-being.

Second, in Study 2, we observed a relatively small mean-level change in

athletes’ perceptions of controlling behaviors, but we also observed that

there was heterogeneity in how athletes’ perceptions changed over time.

Future research could try to further understand this heterogeneity and why

athletes’ perceptions differ. A useful statistical framework for this would be

to apply growth mixture modeling (e.g., Morin & Wang, 2016), in which

subgroups in the sample with different growth trajectories can be identified.

These subgroups can then be predicted to advance knowledge on why they

differ or can be used as predictors to examine what the consequences are of

having different growth trajectories (e.g., Asparouhov & Muthén, 2014).

Following this line of thought of interactive effects, future research should

also focus more on the interactive effects of different contextual factors.

Recent research indicates that there are interactive effects of coaches’ and

parents’ autonomy support on athletes’ motivation and performance

(Amorose, Anderson-Butcher, Newman, Fraina, & Iachini, 2016; Gaudreau

et al., 2016), but other social agents, such as peers and teachers, also

influence athletes’ experiences in sport. Keegan et al. (2010) coined the term

“motivational atmosphere” to reflect the apparent supercomplexity of the

social milieu in determining athletes’ motivation (see also Keegan, Harwood,

Spray, & Lavalle, 2011). Taking a more holistic approach and focusing on the

individual and interactive effects of various contextual factors

simultaneously would further advance our understanding of the motivational

atmosphere influencing athletes’ motivation.

Third, in addition to expanding the SDT-based leadership research to

include structure and involvement as important interpersonal styles,

variations of autonomy (or need) support and controlling interpersonal

styles should also be the focus of future research. Research has been

conducted on variations of these interpersonal styles, such as autonomy-

supportive change-oriented feedback (Carpentier & Mageau, 2013),

multidimensional conceptualizations of autonomy support (i.e., interest in

athletes’ input and praise for autonomous behavior; Conroy & Coatsworth,

69

2007), the integration of SDT and achievement goal theory into higher-order

constructs of empowering and disempowering motivational climates

(Appleton, Ntoumanis, Quested, Viladrich, & Duda, 2016; Solstad et al.,

2016), and coaches’ intervention tone, indicating the psychological meaning

that the particular expression of that content conveys (Erickson & Côté,

2016). Such developments have the potential of enhancing our

understanding of the active ingredients of coaches’ interpersonal styles that

influence athletes’ behaviors and motivation.

Fourth, there is a need for SDT-based coach interventions to improve

interpersonal coach behaviors (cf. Evans, McGuckin, Gainforth, Bruner, &

Côté, 2015; Landers, 1983; Langan et al., 2013). The field would benefit from

integrating SDT-based coach interventions with frameworks for intervention

implementation, such as the DIEM (Schwarz et al., 2016) or other models,

such as the transfer of training model (Baldwin & Ford, 1988; see also

Stenling & Tafvelin, 2016). Using such frameworks provides researchers with

tools for designing detailed interventions that are contextualized and match

the needs of the participants, as well as the possibility of gaining a deeper

understanding of individual factors, training-design factors, and work-

environment factors influencing the transfer of new skills and knowledge

from training to practice.

Some Final Remarks In the beginning, I outlined my epistemological and ontological positions,

stating that I adhere to a pragmatist philosophy of science (cf. Dewey, 1938;

James, 1907; Martela, 2015; Peirce, 1931; Zyphur & Pierides, 2015). But what

constitutes a pragmatic way of doing research? Martela (2015) identified

three essential elements of a pragmatic approach for conducting research:

First there is the connection to practice, doing research with

ends-in-view. Second, there is the fallible abductive process of

inference through which the researcher aims to gain insight

into the question at hand. And third, there is the collective

dimension of inquiry, the way the researcher interacts with

other members of the scientific community and in the end aims

to convince them about the soundness of one’s insights. (p. 553)

Dewey (1938) argued that the outcome of a successful inquiry is a

transformation of the situation, meaning that the most basic conception of

inquiry is the determination of the indeterminate situation. Hence, through

pragmatism, scholars are encouraged to show how theoretical differences

make a real difference to practice (Parmar, Phillips, & Freeman, 2016).

70

Given the topic of the present thesis, one might also wonder how this

pragmatic philosophy of science is related to contemporary leadership

research in sports. In a recent opinion paper, Cruickshank and Collins (2016)

argued that leadership research should aim to be more cognitively oriented,

behaviorally balanced, and practically meaningful. The latter point was

directly linked to a pragmatic approach to research philosophy for applied

sport psychology (cf. Giacobbi et al., 2005). To elaborate a bit on these

points, the authors argued that it is time to advance leadership research in

sports by leveling out the behavior-cognition imbalance, which currently is

skewed in favor of the former. Researchers have primarily focused on the

identification of effective behaviors, that is, what leaders overtly do, rather

than why and how they lead in a certain way at a certain time. They also

argued that there has been an overreliance on positivist/post-positivist

research on the bright side of leadership and that current knowledge has not

sufficiently accounted for the “it depends,” nested, or darker elements of

optimal applied leadership. Finally, Cruickshank and Collins (2016) argued

that closer attention is needed to the full spectrum of leadership intentions

and behaviors, professional judgments, and decision-making that underpins

successful practice. I agree with the calls that Cruickshank and Collins (2016)

put forward, and they are well-suited for a pragmatic approach to leadership

research. A pragmatic approach to leadership research could also aid seeing

the practical problems from the inside through the lived experiences of

leaders in sports, and it could show how theoretical differences make a real

difference to leadership practice (cf. Parmar et al., 2016).

71

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Acknowledgements

“A wizard is never late, Frodo Baggins. Nor is he early. He arrives precisely

when he means to.”

- Gandalf

I am not claiming to be a wizard (merely a time optimist), nevertheless, I do

feel that I have arrived at this moment just in time. Just as Frodo Baggins

journey to Mount Doom and back to the Shire, my journey has been long,

quite bumpy, and filled with many ups and downs. It is safe to say that Frodo

would not have completed his journey without his trustworthy friend Sam by

his side. I am glad that I have had more than one shoulder to lean on and I

am extremely grateful for the guidance, inspiration, and support from

colleagues, family, and friends that have been there along the way.

First and foremost I would like to express my sincere appreciation to all

of the young athletes for taking part in the studies, without you there

would be no thesis to defend.

I would probably not have begun this endeavor if it had not been for

Edmund Edholm’s persistent attempts to get me into university studies.

Edmund, I am deeply grateful for the efforts you made some 15 years ago,

who would have thought that it would lead to this.

Bo Molander, John Jansson, and Stefan Holmström. Thank you for

introducing me to the academic world and for sparking my interest by

challenging me and providing me with opportunities to develop my

scientific curiosity.

My supervisors Peter Hassmén and Magnus Lindwall. Peter, thank you

for believing in me, providing me with the opportunity to pursue a PhD,

and for letting me pursue my own ideas. I have felt your autonomy,

competence, and relatedness support along the way, which have been

much appreciated. Magnus, this quote might be familiar: “No man is an

island, entire of itself; every man is a piece of the continent, a part of the

main.” I really appreciate your generosity and ability to make people

(including me) feel as a part of the main. I also owe much of my interest

in latent variable modeling to you. I particularly remember when we

during a 20 minute drive to the airport ran latent difference score models

in the back seat of a car. After that I was hooked. You have provided

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invaluable support and intellectual sharpness during my doctoral studies

and for that I am deeply grateful.

I also want to thank colleagues at the Department of Psychology for

making it a pleasure going to work everyday. I particularly want to thank

my current and former PhD colleagues one and all (i.e., ingen nämnd,

ingen glömd) for encouragement, support, and fun times along the way. A

special thanks goes to the psych department ice hockey team for making

every Thursday something to long for.

Thank you Ian Taylor for providing valuable feedback at my midway

seminar and Clifford Mallett for acting as the external rewiever of my

thesis. Many thanks to Linus Andersson for rewieving my work not once,

but twice (midway seminar and final seminar). Your non-sport psych

research input have been very valuable. Also, thank you for being my PhD

mentor and for being a sane voice in a sometimes crazy environment.

The “hard data, softhearted scientists” crew (Andreas Ivarsson & Magnus

Lindwall). It all started with a discussion about the weaknesses of

coefficient alpha; what else should one talk about over a pot of moules

frites at a restaurant in Ghent?! Since the first official meeting in

Gothenburg and the “moment” in Halmstad, it’s been a true pleasure

collaborating (and drinking beer) with you guys. I’m eager to see what the

future holds for softhearted scientists like us.

Erik Lundkvist, it’s been great having you as colleague, GAFF teammate,

roomie, travel companion, and friend during the last six years. You have

provided invaluable support along the way, thank you for being there. I

think we deserve at least one Westvleteren now, don’t we?!

Susanne Tafvelin. Thank you for being curious, inspirational, and

supportive. Working with you have really made me widen my academic

horizon and I feel much better prepared for the post-PhD life because of

you. I very much enjoy collaborating with you and look forward to our

future endeavors. Hopefully we can see some transfer of training after

this!

Friends in the “real world” one and all. I will shamelessly steal the words

of my wife and say thank you for hardly caring about my thesis work and

for caring so much for me.

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The Stenlings, what a wonderful family you are, thank you for letting me

be a part of it. Your unconditional support means the world to us. From

the bottom of my heart, thank you for everything.

To my dear brother. I never thought that we would end up in the same

building at the same university, pursuing similar goals, going through the

same PhD struggles, and publish papers together. I think that’s kind of

cool. To you and your family, thank you for being just that, family.

Cissi. We did it! Hard work. Dedication (Dolvett Quince, personal

communication). Maybe Bengt Grensjö played an important part in all

this? I am extremely grateful that I get to spend this adventure we call life

with such a wonderful person like you. I don’t know what I would do

without you. You truly are the light of my life, I love you with all my heart.

Aldor and Bertil, A-dawg and B-dawg. It is wonderful to see how you have

grown into little human beings with a mind and will of your own during

this journey. Thank you for constantly reminding me of what’s truly

important in life.

Mom and dad. If I were to write all the things I am grateful for I would

probably need to write another thesis. So I’ll keep it to a simple thank you

for everything. You are the best, I love you.

Umeå, October, 2016

Andreas Stenling