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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
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
6
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
7
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.
39
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
61
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
63
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
References Adie, J. W., Duda, J. L., & Ntoumanis, N. (2008). Autonomy support, basic
need satisfaction and the optimal functioning of adult male and female
sport participants: A test of basic needs theory. Motivation and
Emotion, 32, 189–199. doi:10.1007/s11031-008-9095-z
Adie, J. W., Duda, J. L., & Ntoumanis, N. (2012). Perceived coach-autonomy
support, basic need satisfaction and the well- and ill-being of elite
youth soccer players: A longitudinal investigation. Psychology of
Sport and Exercise, 13, 51–59. doi:10.1016/j.psychsport.2011.07.008
Ahlberg, M., Mallett, C. J., & Tinning, R. (2008). Developing autonomy
supportive coaching behaviors: An action research approach to coach
development. International Journal of Coaching Science, 2, 1–20.
Ames, C. (1992a). Achievement goals, motivational climate, and motivational
processes. In G. C. Roberts (Ed.), Motivation in sport and exercise
(pp. 161–176). Champaign, IL: Human Kinetics.
Ames, C. (1992b). Classrooms: Goals, structures, and student motivation.
Journal of Educational Psychology, 84, 261–271.
doi:10.1037/0022-0663.84.3.261
Ames, C., & Ames, R. (1984). Goal structures and motivation. The
Elementary School Journal, 85, 39–52. doi:10.1086/461390
Amorose, A. J., & Anderson-Butcher, D. (2007). Autonomy-supportive
coaching and self-determined motivation in high school and college
athletes: A test of self-determination theory. Psychology of Sport and
Exercise, 8, 654–670. doi:10.1016/j.psychsport.2006.11.003
Amorose, A. J., Anderson-Butcher, D., Newman, T. J., Fraina, M., & Iachini,
A. (2016). High school athletes’ self-determined motivation: The
independent and interactive effects of coach, father, and mother
autonomy support. Psychology of Sport and Exercise, 26, 1–8.
doi:10.1016/j.psychsport.2016.05.005
Andersen, M. B., McCullagh, P., & Wilson, G. J. (2007). But what do the
numbers really tell us?: Arbitrary metrics and effect size reporting in
sport psychology research. Journal of Sport & Exercise Psychology,
29, 664–672. doi:10.1123/jsep.29.5.664
Angyal, A. (1941). Foundations for a science of personality. Oxford, United
Kingdom: Commonwealth Fund.
Antonakis, J., Avolio, B. J., & Sivasubramaniam, N. (2003). Context and
leadership: An examination of the nine-factor full-range leadership
theory using the Multifactor Leadership Questionnaire. The
Leadership Quarterly, 14, 261–295. doi:10.1016/s1048-
9843(03)00030-4
72
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making
causal claims: A review and recommendations. The Leadership
Quarterly, 21, 1086–1120. doi:10.1016/j.leaqua.2010.10.010
Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2014). Causality and
endogeneity: Problems and solutions. In D. V. Day (Ed.), The Oxford
handbook of leadership and organizations (pp. 93–117). New York,
NY: Oxford University Press.
Appleton, P. R., Ntoumanis, N., Quested, E., Viladrich, C., & Duda, J. L.
(2016). Initial validation of the coach-created empowering and
disempowering motivational climate questionnaire (EDMCQ-C).
Psychology of Sport and Exercise, 22, 53–65.
doi:10.1016/j.psychsport.2015.05.008
Arthur, C. A., & Tomsett, P. (2015). Transformational leadership behaviour
in sport. In S. D. Mellalieu & S. Hanton (Eds.), Contemporary
advances in sport psychology: A review (pp. 175–201). New York,
NY: Routledge.
Arthur, C. A., Woodman, T., Ong, C. W., Hardy, L., & Ntoumanis, N. (2011).
The role of athlete narcissism in moderating the relationship between
coaches’ transformational leader behaviors and athlete motivation.
Journal of Sport & Exercise Psychology, 33, 3–19.
doi:10.1123/jsep.33.1.3
Asparouhov, T., & Muthén, B. O. (2009). Exploratory structural equation
modeling. Structural Equation Modeling: A Multidisciplinary
Journal, 16, 397–438. doi:10.1080/10705510903008204
Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture
modeling: Three-step approaches using Mplus. Structural Equation
Modeling: A Multidisciplinary Journal, 21, 329–341.
doi:10.1080/10705511.2014.915181
Assor, A., Roth, G., & Deci, E. L. (2004). The emotional costs of parents’
conditional regard: A self-determination theory analysis. Journal of
Personality, 72, 47–88. doi:10.1111/j.0022-3506.2004.00256.x
Avolio, B. J. (2007). Promoting more integrative strategies for leadership
theory-building. American Psychologist, 62, 25–33.
doi:10.1037/0003-066x.62.1.25
Balaguer, I., González, L., Fabra, P., Castillo, I., Mercé, J., & Duda, J. L.
(2012). Coaches’ interpersonal style, basic psychological needs and the
well- and ill-being of young soccer players: A longitudinal analysis.
Journal of Sports Sciences, 30, 1619–1629.
doi:10.1080/02640414.2012.731517
Baldwin, T. T., & Ford, J. K. (1988). Transfer of training: A review and
directions for future research. Personnel Psychology, 41, 63–105.
doi:10.1111/j.1744-6570.1988.tb00632.x
73
Bandura, A., & B, A. (1986). Social foundations of thought and action: A
social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable
distinction in social psychological research: Conceptual, strategic, and
statistical considerations. Journal of Personality and Social
Psychology, 51, 1173–1182. doi:10.1037/0022-3514.51.6.1173
Bartholomew, K. J., Ntoumanis, N., Ryan, R. M., Bosch, J. A., & Thøgersen-
Ntoumani, C. (2011). Self-determination theory and diminished
functioning: The role of interpersonal control and psychological need
thwarting. Personality and Social Psychology Bulletin, 37, 1459–1473.
doi:10.1177/0146167211413125
Bartholomew, K. J., Ntoumanis, N., & Thøgersen-Ntoumani, C. (2009). A
review of controlling motivational strategies from a self-determination
theory perspective: Implications for sports coaches. International
Review of Sport and Exercise Psychology, 2, 215–233.
doi:10.1080/17509840903235330
Bartholomew, K., Ntoumanis, N., & Thøgersen-Ntoumani, C. (2010). The
controlling interpersonal style in a coaching context: Development
and initial validation of a psychometric scale. Journal of Sport &
Exercise Psychology, 32, 193–216. doi:10.1123/jsep.32.2.193
Bass, B. (1985). Leadership and performance beyond expectations. New
York, NY: Free Press.
Bass, B. M. (1999). Two decades of research and development in
transformational leadership. European Journal of Work and
Organizational Psychology, 8, 9–32.
doi:10.1080/135943299398410
Bass, B. M., & Riggio, R. E. (2006). Transformational leadership (2nd ed.).
New York, NY: Psychology Press.
Baumeister, R. F., & Leary, M. R. (1995). The need to belong: Desire for
interpersonal attachments as a fundamental human motivation.
Psychological Bulletin, 117, 497–529.
doi:10.1037//0033-2909.117.3.497
Baumeister, R. F., Vohs, K. D., & Funder, D. C. (2007). Psychology as the
science of self-reports and finger movements: Whatever happened to
actual behavior? Perspectives on Psychological Science, 2, 396–403.
doi:10.1111/j.1745-6916.2007.00051.x
Beauchamp, M. R., Barling, J., Li, Z., Morton, K. L., Keith, S. E., & Zumbo, B.
D. (2010). Development and psychometric properties of the
Transformational Teaching Questionnaire. Journal of Health
Psychology, 15, 1123–1134. doi:10.1177/1359105310364175
Berlew, D. E. (1974). Leadership and organizational excitement. California
Management Review, 17(2), 21–30. doi:10.2307/41164557
74
Biesanz, J. C., Deeb-Sossa, N., Papadakis, A. A., Bollen, K. A., & Curran, P. J.
(2004). The role of coding time in estimating and interpreting growth
curve models. Psychological Methods, 9, 30-52. doi:10.1037/1082-
989X.9.1.30
Blanchard, C. M., Amiot, C. E., Perreault, S., Vallerand, R. J., & Provencher,
P. (2009). Cohesiveness, coach’s interpersonal style and psychological
needs: Their effects on self-determination and athletes’ subjective
well-being. Psychology of Sport and Exercise, 10, 545–551.
doi:10.1016/j.psychsport.2009.02.005
Bolger, N., & Laurenceau, J.-P. (2013). Intensive longitudinal methods: An
introduction to diary and experience sampling research. New York,
NY: Guilford Press.
Bollen, K. A. (1989). Structural equations with latent variables. New York,
NY: Wiley.
Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural
equation perspective. Hoboken, NJ: Wiley.
Bollen, K. A., & Pearl, J. (2013). Eight myths about causality and structural
equation models. In S. L. Morgan (Ed.), Handbook of causal analysis
for social research (pp. 301–328). Dordrecht, Netherlands: Springer.
Bono, J. E., Hooper, A. C., & Yoon, D. J. (2012). Impact of rater personality
on transformational and transactional leadership ratings. The
Leadership Quarterly, 23(1), 132–145.
doi:10.1016/j.leaqua.2011.11.011
Bormann, K. C., & Rowold, J. (2016). Transformational leadership and
followers’ objective performance over time: Insights from German
basketball. Journal of Applied Sport Psychology, 28, 367–373.
doi:10.1080/10413200.2015.1133725
Borsboom, D. (2008). Latent variable theory. Measurement:
Interdisciplinary Research and Perspectives, 6(1 & 2), 25–53.
doi:10.1080/15366360802035497
Box, G. E., & Draper, N. R. (1987). Empirical model-building and response
surfaces. New York, NY: Wiley.
Browne, M. W. (2001). An overview of analytic rotation in exploratory factor
analysis. Multivariate Behavioral Research, 36, 111–150.
doi:10.1207/S15327906MBR3601_05
Buunk, B. P., & Nauta, A. (2000). Commentaries on “The ‘what’ and ‘why’ of
goal pursuits: Human needs and the self-determination of behavior.”
Psychological Inquiry: An International Journal for the
Advancement of Psychological Theory, 11, 269–318.
doi:10.1207/S15327965PLI1104_02
Burns, J. (1978). Leadership. New York, NY: Harper & Row.
Callow, N., Smith, M. J., Hardy, L., Arthur, C. A., & Hardy, J. (2009).
Measurement of transformational leadership and its relationship with
75
team cohesion and performance level. Journal of Applied Sport
Psychology, 21, 395–412. doi:10.1080/10413200903204754
Campbell, J. P. (1977). The cutting edge of leadership: An overview. In J. G.
Hunt & L. L. Larson (Eds.), Leadership: The cutting edge (pp. 221–
235). Carbondale, IL: Southern Illinois University Press.
Carpentier, J., & Mageau, G. A. (2013). When change-oriented feedback
enhances motivation, well-being and performance: A look at
autonomy-supportive feedback in sport. Psychology of Sport and
Exercise, 14, 423–435. doi:10.1016/j.psychsport.2013.01.003
Carver, C. S., & Scheier, M. F. (2000). Commentaries on “The ‘what’ and
‘why’ of goal pursuits: Human needs and the self-determination of
behavior.” Psychological Inquiry: An International Journal for the
Advancement of Psychological Theory, 11, 269–318.
doi:10.1207/S15327965PLI1104_02
Castillo, I., Tomás, I., Ntoumanis, N., Bartholomew, K. J., Duda, J. L., &
Balaguer, I. (2014). Psychometric properties of the Spanish version of
the Controlling Coach Behaviors Scale in the sport context.
Psicothema, 26, 409–414. doi:10.7334/psicothema2014.76
Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and
extrinsic incentives jointly predict performance: A 40-year meta-
analysis. Psychological Bulletin, 140, 980–1008.
doi:10.1037/a0035661
Chan, D. K., Ivarsson, A., Stenling, A., Yang, S. X., Chatzisarantis, N. L., &
Hagger, M. S. (2015). Response-order effects in survey methods: A
randomized controlled crossover study in the context of sport injury
prevention. Journal of Sport & Exercise Psychology, 37, 666–673.
doi:10.1123/jsep.2015-0045
Charbonneau, D., Barling, J., & Kelloway, E. K. (2001). Transformational
leadership and sports performance: The mediating role of intrinsic
motivation. Journal of Applied Social Psychology, 31, 1521–1534.
doi:10.1111/j.1559-1816.2001.tb02686.x
Chelladurai, P. (1978). A contingency model of leadership in athletics
(Unpublished doctoral dissertation). University of Waterloo, Ontario,
Canada.
Chelladurai, P. (1990). Leadership in sports: A review. International Journal
of Sport Psychology, 21, 328–354.
Chelladurai, P. (1993). Leadership. In R. N. Singer, M. Murphy, & K. L.
Tenant (Eds.), Handbook of research on sport psychology (pp. 647–
671). New York, NY: Macmillan Publishing Company.
Chelladurai, P. (2007). Leadership in sports. In G. Tenenbaum & R. C.
Eklund (Eds.), Handbook of sport psychology (3rd ed., pp. 3–30).
New York, NY: Wiley.
76
Chelladurai, P., & Saleh, S. D. (1980). Dimensions of leader behavior in
sports: Development of a leadership scale. Journal of Sport
Psychology, 2, 34–45.
Cheon, S. H., & Reeve, J. (2013). Do the benefits from autonomy-supportive
PE teacher training programs endure? A one-year follow-up
investigation. Psychology of Sport and Exercise, 14, 508–518.
doi:10.1016/j.psychsport.2013.02.002
Cheon, S. H., Reeve, J., & Moon, I. S. (2012). Experimentally based,
longitudinally designed, teacher-focused intervention to help physical
education teachers be more autonomy supportive toward their
students. Journal of Sport & Exercise Psychology, 34, 365–396.
doi:10.1123/jsep.34.3.365
Cheon, S. H., Reeve, J., Lee, J., & Lee, Y. (2015). Giving and receiving
autonomy support in a high-stakes sport context: A field-based
experiment during the 2012 London Paralympic Games. Psychology
of Sport and Exercise, 19, 59–69.
doi:10.1016/j.psychsport.2015.02.007
Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with
longitudinal data: Questions and tips in the use of structural equation
modeling. Journal of Abnormal Psychology, 112, 558–577.
doi:10.1037/0021-843x.112.4.558
Collins, L. M. (2006). Analysis of longitudinal data: The integration of
theoretical model, temporal design, and statistical model. Annual
Review of Psychology, 57, 505–528.
doi:10.1146/annurev.psych.57.102904.190146
Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy, and
relatedness: A motivational analysis of self-system processes. In M. R.
Gunnar & L. A. Sroufe (Eds.), Self processes and development. The
Minnesota symposia on child psychology (Vol. 23, pp. 43–77).
Hillsdale, NJ: Erlbaum.
Conroy, D. E., & Coatsworth, D. J. (2007). Assessing autonomy-supportive
coaching strategies in youth sport. Psychology of Sport and Exercise,
8, 671–684. doi:10.1016/j.psychsport.2006.12.001
Côté, J., Bruner, M., Erickson, K., Strachan, L., & Fraser-Thomas, J. (2010).
Athlete development and coaching. In J. Lyle & C. Cushion (Eds.),
Sports coaching: Professionalisation and practice (pp. 63–83).
Oxford, United Kingdom: Elsevier.
Cronbach, L. J., & Furby, L. (1970). How we should measure “change”: Or
should we? Psychological Bulletin, 74, 68–80.
doi:10.1037/h0029382
Cruickshank, A., & Collins, D. (2016). Advancing leadership in sport: Time to
take off the blinkers? Sports Medicine, 46, 1199–1204.
doi:10.1007/s40279-016-0513-1
77
Culver, D. M., Gilbert, W., & Sparkes, A. (2012). Qualitative research in sport
psychology journals: The next decade 2000-2009 and beyond. The
Sport Psychologist, 26, 261-281. doi:10.1123/tsp.26.2.261
Curran, T., Hill, A. P., Ntoumanis, N., Hall, H. K., & Jowett, G. E. (2016). A
three-wave longitudinal test of self-determination theory’s mediation
model of engagement and disaffection in youth sport. Journal of Sport
& Exercise Psychology, 38, 15–29. doi:10.1123/jsep.2015-0016
Curran, P. J., Obeidat, K., & Losardo, D. (2010). Twelve frequently asked
questions about growth curve modeling. Journal of Cognition and
Development, 11, 121–136. doi:10.1080/15248371003699969
Curtis, B., Smith, R. E., & Smoll, F. L. (1979). Scrutinizing the skipper: A
study of leadership behaviors in the dugout. Journal of Applied
Psychology, 64, 391–400. doi:10.1037/0021-9010.64.4.391
Davidson, K. W., Peacock, J., Kronish, I. M., & Edmondson, D. (2014).
Personalizing behavioral interventions through single-patient (n-of-1)
trials. Social and Personality Psychology Compass, 8(8), 408–421.
doi:10.1111/spc3.12121
de Charms, R. (1968). Personal causation: The internal affective
determinants of behavior. New York, NY: Academic Press.
De Meyer, J., Soenens, B., Vansteenkiste, M., Aelterman, N., Van Petegem,
S., & Haerens, L. (2016). Do students with different motives for
physical education respond differently to autonomy-supportive and
controlling teaching? Psychology of Sport and Exercise, 22, 72–82.
doi:10.1016/j.psychsport.2015.06.001
Deci, E. L. (1975). Intrinsic motivation. New York, NY: Plenum Press.
Deci, E. L., & Ryan, R. M. (1980). The empirical exploration of intrinsic
motivational processes. In L. Berkowitz (Ed.), Advances in
experimental social psychology (Vol. 13, pp. 39–80). New York, NY:
Academic Press.
Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-
determination in human behavior. New York, NY: Plenum Press.
Deci, E. L., & Ryan, R. M. (1987). The support of autonomy and the control
of behavior. Journal of Personality and Social Psychology, 53, 1024–
1037. doi:10.1037//0022-3514.53.6.1024
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits:
Human needs and the self-determination of behavior. Psychological
Inquiry: An International Journal for the Advancement of
Psychological Theory, 11, 227–268.
doi:10.1207/s15327965pli1104_01
Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and
psychological well-being across life’s domains. Canadian
Psychology/Psychologie Canadienne, 49, 14–23.
doi:10.1037/0708-5591.49.1.14
78
Deci, E. L., & Ryan, R. M. (2014). The importance of universal psychological
needs for understanding motivation in the workplace. In M. Gagné
(Ed.), The Oxford handbook of work engagement, motivation, and
self-determination theory (pp. 13–32). New York, NY: Oxford
University Press.
Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of
experiments examining the effects of extrinsic rewards on intrinsic
motivation. Psychological Bulletin, 125, 627–668.
doi:10.1037/0033-2909.125.6.627
Dewey, J. (1908). What does pragmatism mean by practical? Journal of
Philosophy, Psychology and Scientific Methods, 5, 85–99.
doi:10.2307/2011894
Dewey, J. (1938). Logic: The theory of inquiry. New York, NY: Henry Holt &
Company.
Downtown, J. V. (1973). Rebel leadership. New York, NY: Free Press.
Duncan, T. E., & Duncan, S. C. (2004). An introduction to latent growth
curve modeling. Behavior Therapy, 35, 333–363.
doi:10.1016/S0005-7894(04)80042-X
Dvir, T., & Shamir, B. (2003). Follower developmental characteristics as
predicting transformational leadership: A longitudinal field study. The
Leadership Quarterly, 14, 327–344.
doi:10.1016/s1048-9843(03)00018-3
Dweck, C. S. (1986). Motivational processes affecting learning. American
Psychologist, 41, 1040–1048. doi:10.1037/0003-066x.41.10.1040
Dweck, C. S., & Leggett, E. L. (1988). A social-cognitive approach to
motivation and personality. Psychological Review, 95, 256–273.
doi:10.1037/0033-295x.95.2.256
Dysik, A. & Kuvaas, B. (2014). Self-determination theory and workplace
training. In M. Gagné (Ed.), The Oxford handbook of work
engagement, motivation, and self-determination theory (pp. 218–
230). New York, NY: Oxford University Press.
Eccles, J. S., Barber, B. L., Stone, M., & Hunt, J. (2003). Extracurricular
activities and adolescent development. Journal of Social Issues, 59,
865–889. doi:10.1046/j.0022-4537.2003.00095.x
Edmunds, J., Ntoumanis, N., & Duda, J. L. (2008). Testing a self-
determination theory-based teaching style intervention in the exercise
domain. European Journal of Social Psychology, 38, 375–388.
doi:10.1002/ejsp.463
Ehrhart, M. G., & Klein, K. J. (2001). Predicting followers’ preferences for
charismatic leadership: The influence of follower values and
personality. The Leadership Quarterly, 12, 153–179.
doi:10.1016/s1048-9843(01)00074-1
79
Elliot, A. J. (1999). Approach and avoidance motivation and achievement
goals. Educational Psychologist, 34, 169–189.
doi:10.1207/s15326985ep3403_3
Elliot, A. J. (2005). A conceptual history of the achievement goal construct.
In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and
motivation (pp. 52–72). New York, NY: Guildford Press.
Enders, C. K. (2010). Applied missing data analysis. New York, NY: Guilford
Press.
Enders, C. K. (2011). Analyzing longitudinal data with missing values.
Rehabilitation Psychology, 56, 267–288. doi:10.1037/a0025579
Epstein, J. (1988). Effective schools or effective students? Dealing with
diversity. In R. Haskins & B. MacRae (Eds.), Policies for America’s
public schools (pp. 89–126). Norwood, NJ: Ablex.
Epstein, J. (1989). Family structures and student motivation: A
developmental perspective. In C. Ames & R. Ames (Eds.), Research on
motivation in education (Vol. 3, pp. 259–295). New York, NY:
Academic Press.
Erickson, K., & Côté, J. (2016). A season-long examination of the
intervention tone of coach–athlete interactions and athlete
development in youth sport. Psychology of Sport and Exercise, 22,
264–272. doi:10.1016/j.psychsport.2015.08.006
Evans, M. B., McGuckin, M., Gainforth, H. L., Bruner, M. W., & Cote, J.
(2015). Coach development programmes to improve interpersonal
coach behaviours: A systematic review using the re-aim framework.
British Journal of Sports Medicine, 49, 871–877.
doi:10.1136/bjsports-2015-094634
Felfe, J., & Schyns, B. (2010). Followers’ personality and the perception of
transformational leadership: Further evidence for the similarity
hypothesis. British Journal of Management, 21, 393–410.
doi:10.1111/j.1467-8551.2009.00649.x
Felton, L., & Jowett, S. (2013). “What do coaches do” and “how do they
relate”: Their effects on athletes’ psychological needs and functioning.
Scandinavian Journal of Medicine & Science in Sports, 23, e130–
e139. doi:10.1111/sms.12029
Fenton, S. A. M., Duda, J. L., & Barrett, T. (2016). Optimising physical
activity engagement during youth sport: A self-determination theory
approach. Journal of Sports Sciences, 34, 1874–1884.
doi:10.1080/02640414.2016.1142104
Fenton, S. A. M., Duda, J. L., Quested, E., & Barrett, T. (2014). Coach
autonomy support predicts autonomous motivation and daily
moderate-to-vigorous physical activity and sedentary time in youth
sport participants. Psychology of Sport and Exercise, 15, 453–463.
doi:10.1016/j.psychsport.2014.04.005
80
Gagné, M., & Blanchard, C. (2007). Self-determination theory and well-being
in athletes. In M. S. Hagger & N. L. D. Chatzisarantis (Eds.), Intrinsic
motivation and self-determination in exercise and sport (pp. 243–
254). Champaign, IL: Human Kinetics.
Gagné, M., Ryan, R., & Bargmann, K. (2003). Autonomy support and need
satisfaction in the motivation and well-being of gymnasts. Journal of
Applied Sport Psychology, 15, 372–390. doi:10.1080/714044203
Gardner, W. L., Lowe, K. B., Moss, T. W., Mahoney, K. T., & Cogliser, C. C.
(2010). Scholarly leadership of the study of leadership: A review of the
leadership quarterly’s second decade, 2000–2009. The Leadership
Quarterly, 21, 922–958. doi:10.1016/j.leaqua.2010.10.003
Gaudreau, P., Morinville, A., Gareau, A., Verner-Filion, J., Green-Demers, I.,
& Franche, V. (2016). Autonomy support from parents and coaches:
Synergistic or compensatory effects on sport-related outcomes of
adolescent-athletes? Psychology of Sport and Exercise, 25, 89–99.
doi:10.1016/j.psychsport.2016.04.006
George, J. M., & Jones, G. R. (2000). The role of time in theory and theory
building. Journal of Management, 26, 657–684.
doi:10.1177/014920630002600404
Giacobbi, P. R., Poczwardowski, A., & Hager, P. (2005). A pragmatic
research philosophy for applied sport psychology. The Sport
Psychologist, 19, 18–31.
Gilbert, S. L., & Kelloway, K. E. (2014). Leadership. In M. Gagné (Ed.), The
Oxford handbook of work engagement, motivation, and self-
determination theory (pp. 181–198). New York, NY: Oxford
University Press.
Gillet, N., Vallerand, R. J., Amoura, S., & Baldes, B. (2010). Influence of
coaches’ autonomy support on athletes’ motivation and sport
performance: A test of the hierarchical model of intrinsic and extrinsic
motivation. Psychology of Sport and Exercise, 11, 155–161.
doi:10.1016/j.psychsport.2009.10.004
Gillison, F. B., Standage, M., & Skevington, S. M. (2013). The effects of
manipulating goal content and autonomy support climate on
outcomes of a PE fitness class. Psychology of Sport and Exercise, 14,
342–352. doi:10.1016/j.psychsport.2012.11.011
Goldberg, D. P., Gater, R., Sartorius, N., Ustun, T. B., Piccinelli, M., Gureje,
O., & Rutter, C. (1997). The validity of two versions of the GHQ in the
WHO study of mental illness in general health care. Psychological
Medicine, 27, 191–197. doi:10.1017/s0033291796004242
Goldberger, A. S., & Duncan, D. (Eds.). (1973). Structural equation models
in the social sciences. New York, NY: Academic Press.
Gollob, H. F., & Reichardt, C. S. (1987). Taking account of time lags in causal
models. Child Development, 58, 80-92. doi:10.2307/1130293
81
Grolnick, W. S. (2003). The psychology of parental control: How well-
meant parenting backfires. Mahwah, NJ: Lawrence Erlbaum
Associates.
Grolnick, W. S., & Ryan, R. M. (1989). Parent styles associated with
children’s self-regulation and competence in school. Journal of
Educational Psychology, 81(2), 143–154. doi:10.1037/0022-
0663.81.2.143
Haerens, L., Aelterman, N., Van den Berghe, L., De Meyer, J., Soenens, B., &
Vansteenkiste, M. (2013). Observing physical education teachers’
need-supportive interactions in classroom settings. Journal of Sports
& Exercise Psychology, 35, 3–17.
Hagger, M. S., & Chatzisarantis, N. L. (Eds.). (2007). Intrinsic motivation
and self-determination in exercise and sport. Champaigne, IL:
Human Kinetics.
Hall, R. J., & Lord, R. G. (1995). Multi-level information-processing
explanations of followers’ leadership perceptions. The Leadership
Quarterly, 6, 265–287. doi:10.1016/1048-9843(95)90010-1
Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of
the cross-lagged panel model. Psychological Methods, 20(1), 102–116.
doi:10.1037/a0038889
Hardy, L. (2015). Epilogue. In S. D. Mellalieu & S. Hanton (Eds.),
Contemporary advances in sport psychology: A review (pp. 258–
269). New York, NY: Routledge.
Harter, S. (1978). Effectance motivation reconsidered: Toward a
developmental model. Human Development, 21, 34–64.
doi:10.1159/000271574
Harwood, C. G., Keegan, R. J., Smith, J. M. J., & Raine, A. S. (2015). A
systematic review of the intrapersonal correlates of motivational
climate perceptions in sport and physical activity. Psychology of Sport
and Exercise, 18, 9–25. doi:10.1016/j.psychsport.2014.11.005
Hauser, R. M., & Goldberger, A. S. (1971). The treatment of unobservable
variables in path analysis. Sociological Methodology, 3, 81–117.
doi:10.2307/270819
Henderlong, J., & Lepper, M. R. (2002). The effects of praise on children’s
intrinsic motivation: A review and synthesis. Psychological Bulletin,
128(5), 774–795. doi:10.1037//0033-2909.128.5.774
Hershberger, S. L. (2003). The growth of structural equation modeling:
1994-2001. Structural Equation Modeling: A Multidisciplinary
Journal, 10(1), 35–46. doi:10.1207/s15328007sem1001_2
Hill, A. B. (1965). The environment and disease: Association or causation?
Proceedings of the Royal Society of Medicine, 58, 295–300.
Hodge, K., & Gucciardi, D. F. (2015). Antisocial and prosocial behavior in
sport: The role of motivational climate, basic psychological needs, and
82
moral disengagement. Journal of Sport & Exercise Psychology, 37,
257–273. doi:10.1123/jsep.2014-0225
Hodge, K., Hargreaves, E. A., Gerrard, D., & Lonsdale, C. (2013).
Psychological mechanisms underlying doping attitudes in sport:
Motivation and moral disengagement. Journal of Sport & Exercise
Psychology, 35, 419–432. doi:10.1123/jsep.35.4.419
Hodge, K., & Lonsdale, C. (2011). Prosocial and antisocial behavior in sport:
The role of coaching style, autonomous vs. controlled motivation, and
moral disengagement. Journal of Sport & Exercise Psychology, 33,
527–547. doi:10.1123/jsep.33.4.527
Hoffman, L., & Stawski, R. S. (2009). Persons as contexts: Evaluating
between-person and within-person effects in longitudinal analysis.
Research in Human Development, 6, 97–120.
doi:10.1080/15427600902911189
Hollander, E. P. (1992). Leadership, followership, self, and others.
Leadership Quarterly, 3, 43–54. doi:10.1016/1048-9843(92)90005-Z
Hollander, E. P. (1993). Legitimacy, power and influence: A perspective on
relational features of leadership. In M. M. Chemers & R. Ayman
(Eds.), Leadership theory and research: Perspectives and directions
(pp. 29–48). San Diego, CA: Academic Press.
Holzinger, K. J., & Swineford, S. (1937). The bifactor method.
Psychometrika, 2, 41–54. doi:10.1007/BF02287965
Hooyman, A., Wulf, G., & Lewthwaite, R. (2014). Impacts of autonomy-
supportive versus controlling instructional language on motor
learning. Human Movement Science, 36, 190–198.
doi:10.1016/j.humov.2014.04.005
Horn, T. S., & Butt, J. (2014). Developmental perspectives on sport amd
physical activity participation. In A. G. Papaioannou & D. Hackfort
(Eds.), Routledge companion to sport and exercise psychology:
Global perspectives and fundamental concepts (pp. 3–21). Cornwall,
United Kingdom: Routledge.
House, R. J. (1971). A path-goal theory of leader effectiveness.
Administrative Science Quarterly, 16, 321–339. doi:10.2307/2391905
House, R. J. (1977). A 1976 theory of charismatic leadership. In J. G. Hunt &
L. L. Larson (Eds.), Leadership: The cutting edge (pp. 189–207).
Carbondale, IL: Southern Illinois University Press.
Howell, J. M., & Shamir, B. (2005). The role of followers in the charismatic
leadership process: Relationships and their consequences. Academy
of Management Review, 30, 96–112.
doi:10.5465/amr.2005.15281435
Hoyle, R. H. (Ed.). (2012). Handbook of structural equation modeling. New
York, NY: Guilford Press.
83
Iachini, A. (2013). Development and empirical examination of a model of
factors influencing coaches provision of autonomy-support.
International Journal of Sports Science and Coaching, 8, 661–676.
doi:10.1260/1747-9541.8.4.661
Isoard-Gautheur, S., Guillet-Descas, E., & Lemyre, P.-N. (2012). A
prospective study of the influence of perceived coaching style on
burnout propensity in high level young athletes: Using a self-
determination theory perspective. The Sport Psychologist, 26, 282–
298. doi:10.1016/j.psychsport.2011.04.005
Ivarsson, A., Andersen, M. B., Stenling, A., Johnson, U., & Lindwall, M.
(2015). Things we still haven’t learned (so far). Journal of Sport &
Exercise Psychology, 37, 449–461. doi:10.1123/jsep.2015-0015
James, W. (1907). Pragmatism: A new name for some old ways of thinking.
New York, NY: Longmans, Green, and Co.
Jang, H., Kim, E. J., & Reeve, J. (2012). Longitudinal test of self-
determination theory’s motivation mediation model in a naturally
occurring classroom context. Journal of Educational Psychology, 104,
1175–1188. doi:10.1037/a0028089
Jang, H., Kim, E. J., & Reeve, J. (2016). Why students become more engaged
or more disengaged during the semester: A self-determination theory
dual-process model. Learning and Instruction, 43, 27–38.
doi:10.1016/j.learninstruc.2016.01.002
Jang, H., Reeve, J., & Deci, E. L. (2010). Engaging students in learning
activities: It is not autonomy support or structure but autonomy
support and structure. Journal of Educational Psychology, 102, 588–
600. doi:10.1037/a0019682
Jennrich, R. I., & Bentler, P. M. (2011). Exploratory bi-factor analysis.
Psychometrika, 76, 537–549.doi:10.1007/s11336-011-9218-4
Jennrich, R. I., & Bentler, P. M. (2012). Exploratory bi-factor analysis: The
oblique case. Psychometrika, 77, 442–454. doi:10.1007/s11336-012-
9269-1
Jõesaar, H., Hein, V., & Hagger, M. S. (2012). Youth athletes’ perception of
autonomy support from the coach, peer motivational climate and
intrinsic motivation in sport setting: One-year effects. Psychology of
Sport and Exercise, 13, 257–262.
doi:10.1016/j.psychsport.2011.12.001
Jöreskog, K. G. (1970). A general method for analysis of covariance
structures. Biometrika, 57, 239–251. doi:10.1093/biomet/57.2.239
Jöreskog, K. G. (1973). A general method for estimating a linear structural
equation system. In A. Goldberger & O. D. Duncan (Eds.), Structural
equation models in the social sciences (pp. 85–112). New York, NY:
Academic Press.
84
Jöreskog, K. G. (1978). Structural analysis of covariance and correlation
matrices. Psychometrika, 43, 443–477. doi:10.1007/BF02293808
Joussemet, M., Landry, R., & Koestner, R. (2008). A self-determination
theory perspective on parenting. Canadian Psychology/Psychologie
Canadienne, 49, 194–200. doi:10.1037/a0012754
Jowett, S., & Poczwardowski, A. (2007). Understanding the coach–athlete
relationship. In S. Jowett and D. Lavallee (Eds.), Social psychology in
sport (pp. 3–14). Champaign, IL: Human Kinetics.
Judge, T. A., & Piccolo, R. F. (2004). Transformational and transactional
leadership: A meta-analytic test of their relative validity. Journal of
Applied Psychology, 89, 755–768. doi:10.1037/0021-9010.89.5.755
Kaplan, D., & Depaoli, S. (2012). Bayesian structural equation modeling. In
R. Hoyle (Ed.), Handbook of structural equation modeling (pp. 650–
673). New York, NY: Guilford Press.
Keegan, R. J., Harwood, C. G., Spray, C. M., & Lavallee, D. E. (2011). From
motivational climate to motivational atmosphere: A review of research
examining the social and environmental influences on athlete
motivation in sport. In B. D. Geranto (Ed.), Sport psychology (pp. 1–
55). New York, NY: Nova Science.
Keegan, R., Spray, C., Harwood, C., & Lavallee, D. (2010). The motivational
atmosphere in youth sport: Coach, parent, and peer influences on
motivation in specializing sport participants. Journal of Applied Sport
Psychology, 22, 87–105. doi:10.1080/10413200903421267
Kievit, R. A., Frankenhuis, W. E., Waldorp, L. J., & Borsboom, D. (2013).
Simpson’s paradox in psychological science: A practical guide.
Frontiers in Psychology, 4:513. doi:10.3389/fpsyg.2013.00513
Kipp, L. E., & Weiss, M. R. (2015). Social predictors of psychological need
satisfaction and well-being among female adolescent gymnasts: A
longitudinal analysis. Sport, Exercise, and Performance Psychology,
4, 153–169. doi:10.1037/spy0000033
Kuhn, T. S. (2012). The structure of scientific revolutions (4th ed.). Chicago,
IL: University of Chicago Press.
Lafrenière, M.-A. K., Jowett, S., Vallerand, R. J., & Carbonneau, N. (2011).
Passion for coaching and the quality of the coach–athlete relationship:
The mediating role of coaching behaviors. Psychology of Sport and
Exercise, 12, 144–152. doi:10.1016/j.psychsport.2010.08.002
Landers, D.M. (1983). Whatever happened to theory testing in sport
psychology? Journal of Sport Psychology, 5, 135–151.
Langan, E., Blake, C., & Lonsdale, C. (2013). Systematic review of the
effectiveness of interpersonal coach education interventions on athlete
outcomes. Psychology of Sport and Exercise, 14, 37–49.
doi:10.1016/j.psychsport.2012.06.007
85
Langan, E., Blake, C., Toner, J., & Lonsdale, C. (2015). Testing the effects of a
self-determination theory-based intervention with youth Gaelic
football coaches on athlete motivation and burnout. The Sport
Psychologist, 29, 293–301. doi:10.1123/tsp.2013-0107
Langdon, J., Harris, B. S., Burdette, G. P., & Rothberger, S. (2015).
Development and implementation of an autonomy supportive training
program among youth sport coaches. International Sport Coaching
Journal, 2, 169–177. doi:10.1123/iscj.2014-0068
Langdon, J., Schlote, R., Harris, B., Burdette, G., & Rothberger, S. (2015).
Effects of a training program to enhance autonomy supportive
behaviors among youth soccer coaches. Journal of Human Sport and
Exercise, 10, 1-14. doi:10.14198/jhse.2015.101.01
Larson, R. W. (2000). Toward a psychology of positive youth development.
American Psychologist, 55, 170–183. doi:10.1037/0003-066x.55.1.170
Lee, W., & Reeve, J. (2013). Self-determined, but not non-self-determined,
motivation predicts activations in the anterior insular cortex: An fMRI
study of personal agency. Social Cognitive and Affective
Neuroscience, 8, 538–545. doi:10.1093/scan/nss029
Lee, W., Reeve, J., Xue, Y., & Xiong, J. (2012). Neural differences between
intrinsic reasons for doing versus extrinsic reasons for doing: An fMRI
study. Neuroscience Research, 73, 68–72.
doi:10.1016/j.neures.2012.02.010
Lewin, K., Lippitt, R., & White, R. K. (1939). Patterns of aggressive behavior
in experimentally created “social climates.” The Journal of Social
Psychology, 10, 269–299. doi:10.1080/00224545.1939.9713366
Li, C., Wang, C. K. J., Pyun, D. Y., & Kee, Y. H. (2013). Burnout and its
relations with basic psychological needs and motivation among
athletes: A systematic review and meta-analysis. Psychology of Sport
and Exercise, 14, 692–700. doi:10.1016/j.psychsport.2013.04.009
Lindahl, J., Stenling, A., Lindwall, M., & Colliander, C. (2015). Trends and
knowledge base in sport and exercise psychology research: A
bibliometric review study. International Review of Sport and Exercise
Psychology, 8, 71–94. doi:10.1080/1750984x.2015.1019540
Little, T. D. (2013). Longitudinal structural equation modeling. New York,
NY: Guilford Press.
Lonsdale, C., & Hodge, K. (2011). Temporal ordering of motivational quality
and athlete burnout in elite sport. Medicine & Science in Sports &
Exercise, 43, 913–921. doi:10.1249/mss.0b013e3181ff56c6
Lonsdale, C., Hodge, K., & Rose, E. A. (2008). The behavioral regulation in
sport questionnaire (BRSQ): Instrument development and initial
validity evidence. Journal of Sport & Exercise Psychology, 30, 323–
355. doi:10.1123/jsep.30.3.323
86
Lord, R. G., Brown, D. J., & Freiberg, S. J. (1999). Understanding the
dynamics of leadership: The role of follower self-concepts in the
leader/follower relationship. Organizational Behavior and Human
Decision Processes, 78, 167–203. doi:10.1006/obhd.1999.2832
Lord, R. G., & Emrich, C. G. (2000). Thinking outside the box by looking
inside the box. The Leadership Quarterly, 11, 551–579.
doi:10.1016/s1048-9843(00)00060-6
Lord, R.G., & Maher, K.J. (1991). Cognitive theory in industrial and
organizational psychology. In M. D. Dunnette & L. M. Hough (Eds.),
Handbook of industrial and organizational psychology (Vol. 2, pp.
2–62). Palo Alto, CA: Consulting Psychologists Press.
Lowe, K. B., & Gardner, W. L. (2000). Ten years of the leadership quarterly.
The Leadership Quarterly, 11, 459–514. doi:10.1016/s1048-
9843(00)00059-x
MacCallum, R. (1986). Specification searches in covariance structure
modeling. Psychological Bulletin, 100, 107–120. doi:10.1037/0033-
2909.100.1.107
MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation
modeling in psychological research. Annual Review of Psychology, 51,
201–226. doi:10.1146/annurev.psych.51.1.201
MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model
modifications in covariance structure analysis: The problem of
capitalization on chance. Psychological Bulletin, 111, 490–504.
doi:10.1037/0033-2909.111.3.490
McArdle, J. J., & Nesselroade, J. R. (1994). Using multivariate data to
structure developmental change. In S. H. Cohen & H. W. Reese (Eds.),
Life-span developmental psychology: Methodological contributions
(pp. 223–267). Hillsdale, NJ: Erlbaum.
Mageau, G. A., & Vallerand, R. J. (2003). The coach–athlete relationship: A
motivational model. Journal of Sports Sciences, 21, 883–904.
doi:10.1080/0264041031000140374
Mahoney, J. W., Gucciardi, D. F., Ntoumanis, N., & Mallett, C. J. (2014).
Mental toughness in sport: Motivational antecedents and associations
with performance and psychological health. Journal of Sport &
Exercise Psychology, 36, 281–292. doi:10.1123/jsep.2013-0260
Mahoney, J. W., Ntoumanis, N., Gucciardi, D. F., Mallett, C. J., & Stebbings,
J. (2015). Implementing an autonomy-supportive intervention to
develop mental toughness in adolescent rowers. Journal of Applied
Sport Psychology, 28, 199–215. doi:10.1080/10413200.2015.1101030
Marcio, J. J. (2001). Abductive inference, design science, and Dewey’s theory
of inquiry. Transactions of the Charles S. Peirce Society, 37, 97–121.
Markland, D., & Tobin, V. J. (2010). Need support and behavioural
regulations for exercise among exercise referral scheme clients: The
87
mediating role of psychological need satisfaction. Psychology of Sport
and Exercise, 11, 91–99. doi:10.1016/j.psychsport.2009.07.001
Marsh, H. W. (2007). Application of confirmatory factor analysis and
structural equation modeling in sport and exercise psychology. In G.
Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology
(3rd ed., pp. 774–798). Hoboken, NJ: Wiley.
Marsh, H. W., & Hau, K.-T. (2007). Applications of latent-variable models in
educational psychology: The need for methodological-substantive
synergies. Contemporary Educational Psychology, 32, 151–170.
doi:10.1016/j.cedpsych.2006.10.008
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules:
Comment on hypothesis-testing approaches to setting cutoff values for
fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999)
findings. Structural Equation Modeling: A Multidisciplinary Journal,
11, 320–341. doi:10.1207/s15328007sem1103_2
Marsh, H.W., Morin, A. J. S., Parker, P. D., & Kaur, G. (2014). Exploratory
structural equation modelling: An integration of the best features of
exploratory and confirmatory factor analyses. Annual Review of
Clinical Psychology, 10, 85–110.
doi:10.1146/annurev-clinpsy-032813-153700
Martela, F. (2015). Fallible inquiry with ethical ends-in-view: A pragmatist
philosophy of science for organizational research. Organization
Studies, 36, 537–563. doi:10.1177/0170840614559257
Martinent, G., Decret, J.-C., Guillet-Descas, E., & Isoard-Gautheur, S.
(2014). A reciprocal effects model of the temporal ordering of
motivation and burnout among youth table tennis players in intensive
training settings. Journal of Sports Sciences, 32, 1648–1658.
doi:10.1080/02640414.2014.912757
Maslow, A. (1968). Toward a psychology of being (2nd ed.). New York, NY:
Van Nostrand.
Matosic, D., Ntoumanis, N., Boardley, I. D., Sedikides, C., Stewart, B. D., &
Chatzisarantis, N. (2015). Narcissism and coach interpersonal style: A
self‐determination theory perspective. Scandinavian Journal of
Medicine & Science in Sports. Advance online publication.
doi:10.1111/sms.12635
Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of
longitudinal mediation. Psychological Methods, 12, 23–44.
doi:10.1037/1082-989x.12.1.23
Mead, G. H. (1903). The definition of the psychical. The decennial
publications (Series I, Vol. III, pp. 77–112). Chicago, IL: Chicago
University Press.
Mead, H. A. (1929). A pragmatic theory of truth. University of California
Publications in Philosophy, 11, 65–88.
88
Meindl, J. R. (1990). On leadership: An alternative to the conventional
wisdom. Research in Organizational Behavior, 12, 159–203.
Meindl, J. R. (1995). The romance of leadership as a follower-centric theory:
A social constructionist approach. Leadership Quarterly, 6, 329–341.
doi:10.1016/1048-9843(95)90012-8
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55,
107–122. doi:10.1007/bf02294746
Moller, A. C., Deci, E. L., & Elliot, A. J. (2010). Person-level relatedness and
the incremental value of relating. Personality and Social Psychology
Bulletin, 36, 754–767. doi:10.1177/0146167210371622
Morin, A. J. S., Arens, A. K., & Marsh, H. W. (2015). A bifactor exploratory
structural equation modeling framework for the identification of
distinct sources of construct-relevant psychometric
multidimensionality. Structural Equation Modeling: A
Multidisciplinary Journal, 23, 116–139.
doi:10.1080/10705511.2014.961800
Morin, A. J. S., & Wang, J. C. K. (2016). A gentle introduction to mixture
modeling using physical fitness performance data. In N. Ntoumanis &
N. D. Myers (Eds.), An introduction to intermediate and advanced
statistical analyses for sport and exercise scientists (pp. 195–241).
Chichester, United Kingdom: Wiley.
Murray, A., Lonsdale, C., Hall, A, Williams, G. C., McDonough, S. M.,
Ntoumanis, N., Taylor, I. M., . . . Hurley, D. A. (2015). Effect of a self-
determination theory–based communication skills training program
on physiotherapists’ psychological support for their patients with
chronic low back pain: A randomized controlled trial. Archives of
Physical Medicine and Rehabilitation, 5, 809–816.
doi:10.1016/j.apmr.2014.11.007
Muthén, B. (1984). A general structural equation model with dichotomous,
ordered categorical, and continuous latent variable indicators.
Psychometrika, 49, 115–132. doi:10.1007/BF02294210
Muthén, B. (2002). Beyond SEM: General latent variables modeling.
Behaviormetrika, 29, 81–117. doi:10.2333/bhmk.29.81
Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and
related techniques for longitudinal data. In D. Kaplan (Ed.),
Handbook of quantitative methodology for the social sciences (pp.
345–368). Newbury Park, CA: Sage Publications.
Muthén, B. & Asparouhov, T. (2011). Beyond multilevel regression modeling:
Multilevel analysis in a general latent variable framework. In J. Hox &
J. K. Roberts (Eds.), Handbook of advanced multilevel analysis (pp.
15–40). New York, NY: Taylor and Francis.
89
Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation
modeling: A more flexible representation of substantive theory.
Psychological Methods, 17, 313–335. doi:10.1037/a0026802
Muthén, B., Asparouhov, T., Hunter, A. M., & Leuchter, A. F. (2011). Growth
modeling with nonignorable dropout: Alternative analyses of the
STAR*D antidepressant trial. Psychological Methods, 16, 17–33.
doi:10.1037/a0022634
Myers, N. D., Martin, J. J., Ntoumanis, N., Celimli, S., & Bartholomew, K. J.
(2014). Exploratory bifactor analysis in sport, exercise, and
performance psychology: A substantive-methodological synergy.
Sport, Exercise, and Performance Psychology, 3, 258–272.
doi:10.1037/spy0000015
Nagin, D. S. (2005). Group-based modeling of development. Cambridge,
MA: Harvard University Press.
Newton, M., Duda, J. L., & Yin, Z. (2000). Examination of the psychometric
properties of the perceived motivational climate in sport questionnaire
- 2 in a sample of female athletes. Journal of Sports Sciences, 18, 275–
290. doi:10.1080/026404100365018
Ng, J. Y. Y., Lonsdale, C., & Hodge, K. (2011). The basic needs satisfaction in
sport scale (BNSSS): Instrument development and initial validity
evidence. Psychology of Sport and Exercise, 12, 257–264.
doi:10.1016/j.psychsport.2010.10.006
Ng, J. Y. Y., Ntoumanis, N., Thøgersen-Ntoumani, C., Deci, E. L., Ryan, R.
M., Duda, J. L., & Williams, G. C. (2012). Self-determination theory
applied to health contexts: A meta-analysis. Perspectives on
Psychological Science, 7, 325–340. doi:10.1177/1745691612447309
Ng, J. Y. Y., Thøgersen-Ntoumani, C., & Ntoumanis, N. (2012). Motivation
contagion when instructing obese individuals: A test in exercise
settings. Journal of Sport & Exercise Psychology, 34, 525–538.
doi:10.1123/jsep.34.4.525
Nicholls, J. G. (1984). Achievement motivation: Conceptions of ability,
subjective experience, task choice, and performance. Psychological
Review, 91, 328–346. doi:10.1037/0033-295x.91.3.328
Nicholls, J. G. (1989). The competitive ethos and democratic education.
Cambridge, MA: Harvard University Press.
Nielsen, K., Randall, R., Yarker, J., & Brenner, S. O. (2008). The effects of
transformational leadership on followers’ perceived work
characteristics and psychological well-being. A longitudinal study.
Work & Stress, 22, 16–32. doi:10.1080/02678370801979430
Niemiec, C. P., Lynch, M. F., Vansteenkiste, M., Bernstein, J., Deci, E. L., &
Ryan, R. M. (2006). The antecedents and consequences of
autonomous self-regulation for college: A self-determination theory
90
perspective on socialization. Journal of Adolescence, 29, 761–775.
doi:10.1016/j.adolescence.2005.11.009
Northouse, P. G. (2016). Leadership: Theory and practice (7th ed.).
Thousand Oaks, CA: Sage.
Ntoumanis, N. (2012). A self-determination theory perspective on
motivation in sport and physical education: Current trends and
possible future directions. In G. C. Roberts & D. C. Treasure (Eds.),
Advances in motivation in sport and exercise (pp. 91–128).
Champaign, IL: Human Kinetics.
Ntoumanis, N., & Biddle, S. J. H. (1999). A review of motivational climate in
physical activity. Journal of Sports Sciences, 17, 643–665.
doi:10.1080/026404199365678
Ntoumanis, N., & Mallett, C. J. (2014). Motivation in sport: A self-
determination theory perspective. In A. G. Papaioannou & D. Hackfort
(Eds.), Routledge companion to sport and exercise psychology:
Global perspectives and fundamental concepts (pp. 67–82). Cornwall,
United Kingdom: Routledge.
Ntoumanis, N., & Standage, M. (2009). Morality in sport: A self-
determination theory perspective. Journal of Applied Sport
Psychology, 21, 365–380. doi:10.1080/10413200903036040
Occhino, J., Mallett, C. J., Rynne, S., & Carlisle, K. (2014). Autonomy-
supportive pedagogical approach to sports coaching: Research,
challenges and opportunities. International Journal of Sports Science
and Coaching, 9, 401–416. doi:10.1260/1747-9541.9.2.401
Osborne, R. N., & Hunt, J. G. (1975). An adaptive–reactive theory of
leadership: The role of macro variables in leadership research. In J. G.
Hunt & L. L. Larson (Eds.), Leadership frontiers (pp. 27–44). Kent,
OH: Kent State University Press.
Parmar, B. L., Phillips, R., & Freeman, E. R. (2016). Pragmatism and
organization studies. In R. Mir, H. Willmott, & M. Greenwood (Eds.),
The Routledge companion to philosophy in organization studies (pp.
199–211). New York, NY: Routledge.
Pearson, K. (1901). On lines and planes of closest fit to systems of points in
space. The London, Edinburgh, and Dublin Philosophical Magazine
and Journal of Science, 2, 559–572.
doi:10.1080/14786440109462720
Peirce, C. S. (1931). Collected papers (Vols. 1–6). Cambridge, MA: Harvard
University Press.
Pelletier, L. G., Fortier, M. S., Vallerand, R. J., & Brière, N. M. (2001).
Associations among perceived autonomy support, forms of self-
regulation, and persistence: A prospective study. Motivation and
Emotion, 25, 279–306. doi:10.1023/A:1014805132406
91
Pensgaard, A. M., & Roberts, G. C. (2002). Elite athletes’ experiences of the
motivational climate: The coach matters. Scandinavian Journal of
Medicine & Science in Sports, 12, 54–59. doi:10.1034/j.1600-
0838.2002.120110.x
Pesis-Katz, I., Williams, G. C., Niemiec, C. P., & Fiscella, K. (2011). Cost-
effectiveness of intensive tobacco dependence intervention based on
self-determination theory. The American Journal of Managed Care,
17, e393-e398.
Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research: The
theory, design, and analysis of change. Journal of Management, 36,
94–120. doi:10.1177/0149206309352110
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of
method bias in social science research and recommendations on how
to control it. Annual Review of Psychology, 63, 539–569.
doi:10.1146/annurev-psych-120710-100452
Pope, J., & Wilson, P. (2012). Understanding motivational processes in
university rugby players: A preliminary test of the hierarchical model
of intrinsic and extrinsic motivation at the contextual level.
International Journal of Sports Science and Coaching, 7, 89–108.
doi:10.1260/1747-9541.7.1.89
Preacher, K. J. (2015). Advances in mediation analysis: A survey and
synthesis of new developments. Annual Review of Psychology, 66,
825–852. doi:10.1146/annurev-psych-010814-015258
Pyszczynski, T., Greenberg, J., & Solomon, S. (2000). Commentaries on “The
‘what’ and ‘why’ of goal pursuits: Human needs and the self-
determination of behavior.” Psychological Inquiry: An International
Journal for the Advancement of Psychological Theory, 11, 269–318,
doi:10.1207/S15327965PLI1104_02
Quested, E., & Duda, J. L. (2011). Antecedents of burnout among elite
dancers: A longitudinal test of basic needs theory. Psychology of Sport
and Exercise, 12, 159–167. doi:10.1016/j.psychsport.2010.09.003
Quested, E., Ntoumanis, N., Viladrich, C., Haug, E., Ommundsen, Y., Van
Hoye, A., . . . Duda, J. L. (2013). Intentions to drop-out of youth
soccer: A test of the basic needs theory among European youth from
five countries. International Journal of Sport and Exercise
Psychology, 11, 395–407. doi:10.1080/1612197x.2013.830431
Rabe-Hesketh, S., Skrondal, A., & Pickles, A. (2004). Generalized multilevel
structural equation modeling. Psychometrika, 69, 167–190.
doi:10.1007/bf02295939
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models:
Applications and data analysis methods (2nd ed.). Thousand Oaks,
CA: Sage Publications.
92
Reeve, J. (2002). Self-determination theory applied to educational settings.
In E. L. Deci & R. M. Ryan (Eds.), Handbook of self-determination
research (pp. 183–202). Rochester, NY: Rochester University Press.
Reeve, J. (2009). Why teachers adopt a controlling motivating style toward
students and how they can become more autonomy supportive.
Educational Psychologist, 44, 159–175.
doi:10.1080/00461520903028990
Reeve, J. (2013). How students create motivationally supportive learning
environments for themselves: The concept of agentic engagement.
Journal of Educational Psychology, 105, 579–595.
doi:10.1037/a0032690
Reeve, J. (2015). Giving and summoning autonomy support in hierarchical
relationships. Social and Personality Psychology Compass, 9(8),
406–418. doi:10.1111/spc3.12189
Reeve, J., & Cheon, S. H. (2016). Teachers become more autonomy
supportive after they believe it is easy to do. Psychology of Sport and
Exercise, 22, 178–189. doi:10.1016/j.psychsport.2015.08.001
Reeve, J., & Su, Y.-L. (2014). Teacher motivation. In M. Gagné (Ed.), The
Oxford handbook of work engagement, motivation, and self-
determination theory (pp. 349–362). New York, NY: Oxford
University Press.
Reeve, J., & Tseng, C.-M. (2011). Cortisol reactivity to a teacher’s motivating
style: The biology of being controlled versus supporting autonomy.
Motivation and Emotion, 35, 63–74. doi:10.1007/s11031-011-9204-2
Reimer, H. A. (2007). Multidimensional model of coach leadership. In S.
Jowett & D. Lavallee (Eds.), Social psychology in sport (pp. 15–27).
Champaign, IL: Human Kinetics.
Reinboth, M., Duda, J. L., & Ntoumanis, N. (2004). Dimensions of coaching
behavior, need satisfaction, and the psychological and physical welfare
of young athletes. Motivation and Emotion, 28, 297–313.
doi:10.1023/b:moem.0000040156.81924.b8
Reis, H. T., Sheldon, K. M., Gable, S. L., Roscoe, J., & Ryan, R. M. (2000).
Daily well-being: The role of autonomy, competence, and relatedness.
Personality and Social Psychology Bulletin, 26, 419–435.
doi:10.1177/0146167200266002
Reise, S. P. (2012). The rediscovery of bifactor measurement models.
Multivariate Behavior Research, 47, 667–696.
doi:10.1080/00273171.2012.715555
Roberts, G. C. (2012). Motivation in sport and exercise from an achievement
goal theory perspective: After 30 years, where are we? In G. C. Roberts
& D. C. Treasure (Eds.), Advances in motivation in sport and exercise
(pp. 5–58). Champaign, IL: Human Kinetics.
93
Roberts, G. C. & Kristiansen, E. (2012). Goal-setting to enhance motivation
in sport. In G. C. Roberts & D. C. Treasure (Eds.), Advances in
motivation in sport and exercise (pp. 207–228). Champaign, IL:
Human Kinetics.
Roberts, G. C., Treasure, D. C., & Conroy, D. E. (2007). Understanding the
dynamics of motivation in sport and physical activity. In G.
Tenenbaum & R. C. Eklund (Eds.), Handbook of sport psychology
(3rd ed., pp. 3–30). New York, NY: Wiley.
Rocchi, M. A., Pelletier, L. G., & Couture, A. (2013). Determinants of coach
motivation and autonomy supportive coaching behaviours.
Psychology of Sport and Exercise, 14, 852–859.
doi:10.1016/j.psychsport.2013.07.002
Rogers, C. (1961). On becoming a person. Boston, MA: Houghton Mifflin.
Rogosa, D. (1995). Myths and methods: “Myths about longitudinal research”
plus supplemental questions. In J. M. Gottman (Ed.), The analysis of
change (pp. 3–66). Mahwah, NJ: Erlbaum.
Rorty, R. (1982). Consequences of pragmatism. Minneapolis, MN:
University of Minnesota Press.
Rucker, D. D., Preacher, K. J., Tormala, Z. L., & Petty, R. E. (2011).
Mediation analysis in social psychology: Current practices and new
recommendations. Social and Personality Psychology Compass, 5,
359–371. doi:10.1111/j.1751-9004.2011.00355.x
Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An
extension of cognitive evaluation theory. Journal of Personality and
Social Psychology, 43, 450–461. doi:10.1037/0022-3514.43.3.450
Ryan, R. M. (1991). The nature of the self in autonomy and relatedness. In J.
Strauss & G. R. Goethals (Eds.), Multidisciplinary perspectives on the
self (pp. 208–238). New York, NY: Springer-Verlag.
Ryan, R. M. (1995). Psychological needs and the facilitation of integrative
processes. Journal of Personality, 63, 397–427. doi:10.1111/j.1467-
6494.1995.tb00501.x
Ryan, R. M., & Deci, E. L. (2000a). The darker and brighter sides of human
existence: Basic psychological needs as a unifying concept.
Psychological Inquiry: An International Journal for the
Advancement of Psychological Theory, 11, 319–338.
doi:10.1207/S15327965PLI1104_03
Ryan, R. M., & Deci, E. L. (2000b). Self-determination theory and the
facilitation of intrinsic motivation, social development, and well-
being. American Psychologist, 55, 68–78.
doi:10.1037//0003-066x.55.1.68
Ryan, R. M., & Deci, E. L. (2002). An overview of self-determination theory:
An organismic-dialectical perspective. In E. L. Deci & R. M. Ryan
94
(Eds.), Handbook of self-determination research (pp. 3–36).
Rochester, NY: University of Rochester Press.
Sameroff, A. (Ed.). (2009). The transactional model of development: How
children and contexts shape each other. Washington, DC: American
Psychological Association.
Sánchez-Oliva, D., Kinnafick, F., Smith, N., Stenling, A., & García-Calvo, T.
(2016). Perceived need support and need satisfaction in physical
education. Manuscript in preparation.
Sarrazin, P. G., Tessier, D. P., Pelletier, L. G., Trouilloud, D. O., & Chanal, J.
P. (2006). The effects of teachers’ expectations about students’
motivation on teachers’ autonomy-supportive and controlling
behaviors. International Journal of Sport & Exercise Psychology, 4,
283–301.
Scheines, R., Hoijtink, H., & Boomsma, A. (1999). Bayesian estimation and
testing of structural equation models. Psychometrika, 64, 37–52.
doi:10.1007/bf02294318
Schwarz, U., Lundmark, R., & Hasson, H. (2016). The dynamic integrated
evaluation model (DIEM): Achieving sustainability in organizational
intervention through a participatory evaluation approach. Stress &
Health. Advance online publication. doi:10.1002/smi.2701
Selig, J. P., & Preacher, K. J. (2009). Mediation models for longitudinal data
in developmental research. Research in Human Development, 6, 144–
164. doi:10.1080/15427600902911247
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and
quasi-experimental designs for generalized causal inference (2nd
ed.). Boston, MA: Houghton Mifflin.
Shamir, B., House, R. J., & Arthur, M. B. (1993). The motivational effects of
charismatic leadership: A self-concept based theory. Organization
Science, 4, 577–594. doi:10.1287/orsc.4.4.577
Sheldon, K. M., & Kasser, T. (2001). Goals, congruence, and positive well-
being: New empirical support for humanistic theories. Journal of
Humanistic Psychology, 41, 30–50.
doi:10.1177/0022167801411004
Simpson, E. H. (1951). The interpretation of interaction in contingency
tables. Journal of the Royal Statistical Society. Series B
(Methodological), 13, 238–241.
Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis:
Modeling change and event occurrence. New York, NY: Oxford
University Press.
Skinner, E. A., & Belmont, M. J. (1993). Motivation in the classroom:
Reciprocal effects of teacher behavior and student engagement across
the school year. Journal of Educational Psychology, 85, 571–581.
doi:10.1037//0022-0663.85.4.571
95
Slager, R., Gond, J.-P., & Moon, J. (2012). Standardization as institutional
work: The regulatory power of a responsible investment standard.
Organization Studies, 33, 763–790. doi:10.1177/0170840612443628
Smith, M. J., Arthur, C. A., Hardy, J., Callow, N., & Williams, D. (2013).
Transformational leadership and task cohesion in sport: The
mediating role of intrateam communication. Psychology of Sport and
Exercise, 14, 249–257. doi:10.1016/j.psychsport2012.10.002
Smith, A., Ntoumanis, N., & Duda, J. (2007). Goal striving, goal attainment,
and well-being: Adapting and testing the Self-Concordance model in
sport. Journal of Sport & Exercise Psychology, 29, 763–782.
doi:10.1123/jsep.29.6.763
Smith, A., Ntoumanis, N., & Duda, J. (2010). An investigation of coach
behaviors, goal motives, and implementation intentions as predictors
of well-being in sport. Journal of Applied Sport Psychology, 22, 17–
33. doi:10.1080/10413200903403190
Smith, N., Quested, E., Appleton, P. R., & Duda, J. L. (2016). Observing the
coach-created motivational environment across training and
competition in youth sport. Journal of Sports Sciences. Advance
online publication. doi:10.1080/02640414.2016.1159714
Smith, R. E., & Smoll, F. L. (1990). Self-esteem and children’s reactions to
youth sport coaching behaviors: A field study of self-enhancement
processes. Developmental Psychology, 26, 987–993.
doi:10.1037/0012-1649.26.6.987
Smith, R. E., & Smoll, F. L. (2007). Social-cognitive approach to coaching
behaviors. In S. Jowett & D. Lavallee (Eds.), Social psychology in
sport (pp. 75–90). Champaign, IL: Human Kinetics.
Smith, R. E., Smoll, F. L., & Barnett, N. P. (1995). Reduction of children’s
sport performance anxiety through social support and stress-
reduction training for coaches. Journal of Applied Developmental
Psychology, 16, 125–142. doi:10.1016/0193-3973(95)90020-9
Smith, R. E., Smoll, F. L., & Christensen, D. S. (1996). Behavioral assessment
and interventions in youth sports. Behavior Modification, 20, 3–44.
doi:10.1177/01454455960201001
Smith, R. E., Smoll, F. L., & Cumming, S. P. (2007). Effects of a motivational
climate intervention for coaches on young athletes’ sport performance
anxiety. Journal of Sport & Exercise Psychology, 29, 39–59.
doi:10.1123/jsep.29.1.39
Smith, R. E., Smoll, F. L., & Curtis, B. (1979). Coach effectiveness training: A
cognitive-behavioral approach to enhancing relationship skills in
youth sport coaches. Journal of Sport Psychology, 1, 59–75.
Smith, R. E., Smoll, F. L., & Hunt, E. B. (1977). A system for the behavioral
assessment of athletic coaches. Research Quarterly, 48, 401–407.
96
Smoll, F. L., & Smith, R. E. (1989). Leadership behaviors in sport: A
theoretical model and research paradigm. Journal of Applied Social
Psychology, 19, 1522–1551. doi:10.1111/j.1559-1816.1989.tb01462.x
Smoll, F. L., & Smith, R. E. (2002). Coaching behavior research and
intervention in youth sports. In F. L. Smoll & R. E. Smith (Eds.),
Children and youth in sport: A biopsychosocial perspective (2nd ed.,
pp. 211–234). Dubuque, IA: Kendall/Hunt.
Smoll, F. L., Smith, R. E., Barnett, N. P., & Everett, J. J. (1993).
Enhancement of children’s self-esteem through social support training
for youth sport coaches. Journal of Applied Psychology, 78, 602–610.
doi:10.1037/0021-9010.78.4.602
Smoll, F. L., Smith, R. E., & Cumming, S. P. (2007). Effects of a motivational
climate intervention for coaches on changes in young athletes’
achievement goal orientations. Journal of Clinical Sport Psychology,
1, 23–46.
Smoll, F. L., Smith, R. E., Curtis, B., & Hunt, E. (1978). Toward a
meditational model of coach–player relationships. Research
Quarterly, 49, 528–541.
Soenens, B., Vansteenkiste, M., & Van Petegem, S. (2015). Let us not throw
out the baby with the bathwater: Applying the principle of
universalism without uniformity to autonomy-supportive and
controlling parenting. Child Development Perspectives, 9, 44–49.
doi:10.1111/cdep.12103
Solstad, B. E., Stenling, A., Wold, B., Heuzé J.-P., Sarrazin, P., &
Ommundsen, Y. (2016). A Bayesian approach to the validation of the
Empowering and Disempowering Motivational Climate
Questionnaire-Coach (EDMCQ-C). Manuscript submitted for
publication.
Solstad, B. E., van Hoye, A., & Ommundsen, Y. (2015). Social-contextual and
intrapersonal antecedents of coaches’ basic need satisfaction: The
intervening variable effect of providing autonomy-supportive
coaching. Psychology of Sport and Exercise, 20, 84–93.
doi:10.1016/j.psychsport.2015.05.001
Somerville, L. H., & Casey, B. (2010). Developmental neurobiology of
cognitive control and motivational systems. Current Opinion in
Neurobiology, 20, 236–241. doi:10.1016/j.conb.2010.01.006
Somerville, L. H., Jones, R. M., & Casey, B. J. (2010). A time of change:
Behavioral and neural correlates of adolescent sensitivity to appetitive
and aversive environmental cues. Brain and Cognition, 72, 124–133.
doi:10.1016/j.bandc.2009.07.003
Song, X. Y., & Lee, S. Y. (2012). Basic and advanced Bayesian structural
equation modeling: With applications in the medical and behavioral
sciences. Hoboken, NJ: Wiley.
97
Sousa, C., Smith, R. E., & Cruz, J. (2008). An individualized behavioral goal-
setting program for coaches. Journal of Clinical Sport Psychology, 2,
258–277.
Spearman, C. (1904). General intelligence, objectively determined and
measured. The American Journal of Psychology, 15, 201–292.
doi:10.2307/1412107
Standage, M. (2012). Self-determination theory and performance in sport. In
S. Murphy (Ed.), The Oxford handbook of sport and performance
psychology (pp. 233–249). New York, NY: Oxford University Press.
Standage, M., Duda, J. L., & Ntoumanis, N. (2005). A test of self-
determination theory in school physical education. British Journal of
Educational Psychology, 75, 411–433.
doi:10.1348/000709904x22359
Statistics Sweden. (2009). Living conditions, Report no 116. Children’s
leisure time. Retrieved from
http://www.scb.se/statistik/_publikationer/LE0106_2006I07_BR_L
E116BR0901.pdf
Stebbings, J., Taylor, I. M., & Spray, C. M. (2011). Antecedents of perceived
coach autonomy supportive and controlling behaviors: Coach
psychological need satisfaction and well-being. Journal of Sport &
Exercise Psychology, 33, 255–272. doi:10.1123/jsep.33.2.255
Stebbings, J., Taylor, I. M., & Spray, C. M. (2015). The relationship between
psychological well- and ill-being, and perceived autonomy supportive
and controlling interpersonal styles: A longitudinal study of sport
coaches. Psychology of Sport and Exercise, 19, 42–49.
doi:10.1016/j.psychsport.2015.02.002
Stebbings, J., Taylor, I. M., Spray, C. M., & Ntoumanis, N. (2012).
Antecedents of perceived coach interpersonal behaviors: The coaching
environment and coach psychological well- and ill-being. Journal of
Sport & Exercise Psychology, 34, 481–502. doi:10.1123/jsep.34.4.481
Stenling, A., Ivarsson, A., & Lindwall, M. (2016a). Cross-lagged structural
equation modeling and latent growth modeling. In N. Ntoumanis & N.
D. Myers (Eds.), An introduction to intermediate and advanced
statistical analyses for sport and exercise scientists (pp. 150–171).
Chichester, United Kingdom: Wiley.
Stenling, A., Ivarsson, A., & Lindwall, M. (2016b). The only constant is
change: Analyzing and understanding change in sport and exercise
psychology research. International Review of Sport and Exercise
Psychology, 10, 230–251. doi:10.1080/1750984X.2016.1216150
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.
98
doi:10.3389/fpsyg.2015.01303
Stenling, A., Ivarsson, A., Johnson, U., & Lindwall, M. (2015). Bayesian
structural equation modeling in sport and exercise psychology.
Journal of Sport & Exercise Psychology, 37, 410–420.
doi:10.1123/jsep.2014-0330
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
Stenling, A., & Tafvelin, S. (2014). Transformational leadership and well-
being in sports: The mediating role of need satisfaction. Journal of
Applied Sport Psychology, 26, 182–196.
doi:10.1080/10413200.2013.819392
Stenling, A., & Tafvelin, S. (2016). Transfer of training after an
organizational intervention in Swedish sports clubs: A self-
determination theory perspective. Journal of Sport & Exercise
Psychology. Manuscript accepted for publication.
Su, Y.-L., & Reeve, J. (2011). A meta-analysis of the effectiveness of
intervention programs designed to support autonomy. Educational
Psychology Review, 23, 159–188. doi:10.1007/s10648-010-9142-7
Tafvelin, S. (2013). The transformational leadership process: Antecedents,
mechanisms, and outcomes in the social services (Doctoral
dissertation). Umeå University, Umeå. Retrieved from
http://umu.diva-
portal.org/smash/record.jsf?pid=diva2%3A640843&dswid=5670
Tafvelin, S., & Stenling, A. (2016). Development and initial validation of the
Need Satisfaction and Need Support at Work Scales: A validity-
focused approach. Manuscript submitted for publication.
Taylor, I. M. (2015). The five self-determination mini-theories applied to
sport. In S. D. Mellalieu & S. Hanton (Eds.), Contemporary advances
in sport psychology: A review (pp. 68–90). New York, NY: Routledge.
Taylor, I. M., & Ntoumanis, N. (2007). Teacher motivational strategies and
student self-determination in physical education. Journal of
Educational Psychology, 99, 747–760.
doi:10.1037/0022-0663.99.4.747
Taylor, I. M., Turner, J. E., Gleeson, M., & Hough, J. (2014). Negative
psychological experiences and saliva secretory immunoglobulin A in
field hockey players. Journal of Applied Sport Psychology, 27, 67–78.
doi:10.1080/10413200.2014.949907
Teixeira, P. J., Carraça, E. V., Markland, D., Silva, M. N., & Ryan, R. M.
(2012). Exercise, physical activity, and self-determination theory: A
systematic review. International Journal of Behavioral Nutrition and
Physical Activity, 9:78. doi:10.1186/1479-5868-9-78.
99
Teixeira, P. J., Carraça, E. V., Marques, M. M., Rutter, H., Oppert, J.-M.,
Bourdeaudhuij, I. D., . . . Brug, J. (2015). Successful behavior change
in obesity interventions in adults: A systematic review of self-
regulation mediators. BMC Medicine, 13:84. doi: 10.1186/s12916-015-
0323-6
Tessier, D., Sarrazin, P., & Ntoumanis, N. (2010). The effect of an
intervention to improve newly qualified teachers’ interpersonal style,
students’ motivation and psychological need satisfaction in sport-
based physical education. Contemporary Educational Psychology, 35,
242–253. doi:10.1016/j.cedpsych.2010.05.005
Thurstone, L. L. (1935). The vectors of mind. Chicago, IL: University of
Chicago Press.
Thurstone, L. L. (1947). Multiple factor analysis. Chicago, IL: University of
Chicago Press.
Tobin, V. (2003). Facilitating exercise behavior change: A self-
determination theory and motivational interviewing perspective
(Unpublished doctoral dissertation). University of Wales, Bangor.
Tomarken, A. J., & Waller, N. G. (2005). Structural equation modeling:
Strengths, limitations, and misconceptions. Annual Review of Clinical
Psychology, 1, 31–65. doi:10.1146/annurev.clinpsy.1.102803.144239
Tucker, S., Turner, N., Barling, J., & McEvoy, M. (2010). Transformational
leadership and children’s aggression in team settings: A short-term
longitudinal study. The Leadership Quarterly, 21, 389–399.
doi:10.1016/j.leaqua.2010.03.004
Turnnidge, J., & Côté, J. (2016). Applying transformational leadership
theory to coaching research in youth sport: A systematic literature
review. International Journal of Sport and Exercise Psychology.
Advance online publication. doi:10.1080/1612197X.2016.1189948
Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and
extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental
social psychology (pp. 271–360). San Diego, CA: Academic Press.
Vallerand, R. J. (2007). Intrinsic and extrinsic motivation in sport and
physical activity: A review and a look at the future. In G. Tennenbaum
& R. Eklund (Eds.), Handbook of sport psychology (3rd ed., pp. 59–
83). New York, NY: Wiley.
Vallerand, R. J., & Losier, G. F. (1999). An integrative analysis of intrinsic
and extrinsic motivation in sport. Journal of Applied Sport
Psychology 11, 142–169. doi:10.1080/10413209908402956
Van den Broeck, A., Ferris, D. L., Chang, C., & Rosen, C. C. (2016). A review
of self-determination theory’s basic psychological needs at work.
Journal of Management. Advance online publication.
doi:10.1177/0149206316632058
100
Van de Schoot, R., & Depaoli, S. (2014). Bayesian analyses: Where to start
and what to report. European Health Psychologist, 16, 75–84.
Van de Schoot, R., Kaplan, D., Denissen, J., Asendorpf, J. B., Neyer, F. J., &
Aken, M. A. (2014). A gentle introduction to Bayesian analysis:
Applications to developmental research. Child Development, 85, 842–
860. doi:10.1111/cdev.12169
van Knippenberg, D., & Sitkin, S. B. (2013). A critical assessment of
charismatic–transformational leadership research: Back to the
drawing board? The Academy of Management Annals, 7, 1–60.
doi:10.1080/19416520.2013.759433
Vansteenkiste, M., & Deci, E. L. (2003). Competitively contingent rewards
and intrinsic motivation: Can losers remain motivated? Motivation
and Emotion, 27, 273–299. doi:10.1023/A:1026259005264
Vansteenkiste, M., & Ryan, R. M. (2013). On psychological growth and
vulnerability: Basic psychological need satisfaction and need
frustration as a unifying principle. Journal of Psychotherapy
Integration, 23, 263–280. doi:10.1037/a0032359
Vansteenkiste, M., Niemiec, C. P., & Soenens, B. (2010). The development of
the five mini-theories of self-determination theory: An historical
overview, emerging trends, and future directions. In T. C. Urdan & S.
A. Karabenick (Eds.), The decade ahead: Theoretical perspectives on
motivation and achievement (Advances in motivation and
achievement, Volume 16 Part A) (pp. 105–165). London, United
Kingdom: Emerald Group Publishing Limited.
Vansteenkiste, M., Simons, J., Lens, W., Sheldon, K. M., & Deci, E. L.
(2004). Motivating learning, performance, and persistence: The
synergistic effects of intrinsic goal contents and autonomy-supportive
contexts. Journal of Personality and Social Psychology, 87, 246–260.
doi:10.1037/0022-3514.87.2.246
Vealey, R. S. (2006). Smocks and jocks outside the box: The paradigmatic
evolution of sport and exercise psychology. Quest, 58, 128–159.
doi:10.1080/00336297.2006.10491876
Wasserkampf, A., & Kleinert, J. (2015). Organismic integration as a dynamic
process: A systematic review of empirical studies on change in
behavioral regulations in exercise in adults. International Review of
Sport and Exercise Psychology, 9, 65–95.
doi:10.1080/1750984x.2015.1119873
Weber, M. (1924/1971). Legitimate authority and bureaucracy. In D. S.
Peugh (Ed.), Organization theory: Selected readings (pp. 3–15).
London, United Kingdom: Penguin.
Weber, M. (1947). The theory of economic and social organization. New
York, NY: Oxford University Press.
101
Weinstein, N. (2014). Human motivation and interpersonal relationships:
Theory, research and applications. New York, NY: Springer.
Weinstein, N., & Ryan, R. M. (2011). A self-determination theory approach to
understanding stress incursion and responses. Stress & Health, 27, 4–
17. doi:10.1002/smi.1368
Weinstein, N., Legate, N., Kumashiro, M., & Ryan, R. M. (2016). Autonomy
support and diastolic blood pressure: Long term effects and conflict
navigation in romantic relationships. Motivation and Emotion, 40,
212–225. doi:10.1007/s11031-015-9526-6
Weiss, M. R., & Gill, D. L. (2005). What goes around comes around.
Research Quarterly for Exercise and Sport, 76(sup2), S71–S87.
doi:10.1080/02701367.2005.10599291
West, S. G., Taylor, A. B., & Wu, W. (2012). Model fit and model selection in
structural equation modeling. In R. H. Hoyle (Ed.), Handbook of
structural equation modeling (pp. 209–231). New York, NY: Guilford
Press.
White, R. W. (1959). Motivation reconsidered: The concept of competence.
Psychological Review, 66, 297–333. doi:10.1037/h0040934
Williams, G. C., Grow, V. M., Freedman, Z. R., Ryan, R. M., & Deci, E. L.
(1996). Motivational predictors of weight loss and weight-loss
maintenance. Journal of Personality and Social Psychology, 70, 115–
126. doi:10.1037//0022-3514.70.1.115
Wilson, P. M., Gregson, J. P., & Mack, D. E. (2009). The importance of
interpersonal style in competitive sport: A self-determination theory
approach. In C. H. Chang (Ed.), Handbook of sport psychology (pp.
259–276). Hauppauge, NY: Nova Science.
Wittgenstein, L. (1973). Philosophical investigations (3rd ed.). Oxford:
Blackwell.
Wu, W., Selig, J. P., & Little, T. D. (2013). Longitudinal data analysis. In T.
D. Little (Ed.), The Oxford handbook of quantitative methods in
psychology (Vol. 2, pp. 387–410). New York, NY: Oxford University
Press.
Yukl, G. (1971). Toward a behavioral theory of leadership. Organizational
Behavior and Human Performance, 6, 414–440. doi:10.1016/0030-
5073(71)90026-2
Yukl, G. (1999). An evaluation of conceptual weaknesses in transformational
and charismatic leadership theories. The Leadership Quarterly, 10,
285–305. doi:10.1016/S1048-9843(99)00013-2
Yukl, G., & Van Fleet, D. D. (1992). Theory and research on leadership in
organizations. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of
industrial and organizational psychology (2nd ed., pp. 147–197).
Palo Alto, CA: Consulting Psychologists Press.
102
Zellner, A. (1970). Estimation of regression relationships containing
unobservable independent variables. International Economic Review,
11, 441–454. doi:10.2307/2525323
Zellner, A. (1971). Introduction to Bayesian inference in econometrics. New
York, NY: Wiley.
Zhang, Z., Hamagami, F., Wang, L., Nesselroade, J. R., & Grimm, K. J.
(2007). Bayesian analysis of longitudinal data using growth curve
models. International Journal of Behavioral Development, 31, 374–
383. doi:10.1177/0165025407077764
Zyphur, M. J., & Oswald, F. L. (2015). Bayesian estimation and inference: A
user’s guide. Journal of Management, 41, 390–420.
doi:10.1177/0149206313501200
Zyphur, M. J., & Pierides, D. C. (2015). Quantitative research as if it were
done by people at work: A pragmatist theory of quantitative
research. Manuscript submitted for publication.
103
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