Scenario 3: Youth Sports
You are coaching your child’s soccer team and want psychologically proven ways to motivate your players. Consider what research says about motivation and sports. What are some specific coaching strategies that can increase enthusiasm and motivation?
Possible theories:
Self-determination theory.
Possible sources:
Step 2: Write Your Thesis StatementAs part of your discussion this week, you will you will create a thesis statement, which states your stance on the scenario, also known as the topic, you’ve chosen.
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Sport, Exercise, and Performance Psychology
2015, Vol. 4, No. 1, 50 – 61
© 2014 American Psychological Association
2157-3905/15/$12.00 http://dx.doi.org/10.1037/spy0000027
Changes in Perceived Autonomy Support, Need Satisfaction,
Motivation, and Well-Being in Young Elite Athletes
Andreas Stenling
Magnus Lindwall
Umeå University
University of Gothenburg
Peter Hassmén
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Umeå University and University of Canberra
A 4-stage motivational sequence was investigated, in line with self-determination
theory (perceived autonomy support from the coach ¡ need satisfaction ¡ motivation
¡ psychological well-being). More specifically, we examined level– change associations and relations between intraindividual changes in these variables over the course
of an athletic season. Young elite skiers (109 females, 138 males) enrolled at sport high
schools in Sweden responded to questionnaires assessing perceived autonomy support
from the coach, need satisfaction, motivation, and psychological well-being at 2 time
points separated by approximately 5 months. A latent difference score model were used
to analyze the data. Initial level of need satisfaction at Time 1 negatively predicted
change in perceived autonomy support, motivation, and well-being, and initial level of
motivation at Time 1 positively predicted change in perceived autonomy support and
change in well-being. Correlations between intraindividual changes in the study variables were estimated and the variables were all positively correlated. These results
indicate that the relations between these variables are complex, dynamic, and that more
attention should be given to potential reciprocal effects between the variables in this
motivational sequence.
Keywords: intraindividual change, interpersonal environment, basic psychological needs,
motivation, health
Being a young elite athlete can be a rewarding experience that facilitates a positive development, but also a stressful and pressuring
experience that is detrimental to motivation
and well-being. The quality of the interpersonal environment is often highlighted as a
key determinant (Mageau & Vallerand, 2003)
with autonomy-supportive environments positively affecting athletes’ motivation and
well-being (Gagné, Ryan, & Bargmann,
2003). Previous research has indicated that
the well-being of athletes in the population
under study—skiers enrolled at sport high
schools in Sweden—is at risk due to high
demands of their sport and schoolwork
(Gustafsson, Hassmén, & Podlog, 2010;
Isoard-Gautheur, Guillet-Descas, & Lemyre,
2012). Hence, it is highly relevant to identify
facilitating factors for these young athletes’
well-being.
Within self-determination theory (SDT,
Deci & Ryan, 2000), a four-stage motivational sequence (see Figure 1) is proposed to
explain how autonomy-supportive environments influence people’s health and wellbeing (cf. Ng et al., 2012). However, few
studies have longitudinally examined this
four-stage motivational sequence in sport settings (Gagné & Blanchard, 2007). Most pre-
This article was published Online First August 18, 2014.
Andreas Stenling, Department of Psychology, Umeå University; Magnus Lindwall, Department of Food and Nutrition, and Sport Science and Department of Psychology,
University of Gothenburg; Peter Hassmén, Department of
Psychology, Umeå University, and Faculty of Health, University of Canberra.
The authors express their sincere appreciation to Anna
Yttergård for helping with the data collection. A grant from
the Swedish National Centre for Research in Sports (CIF)
supported this research (Grant P2011-0177).
Correspondence concerning this article should be addressed to Andreas Stenling, Department of Psychology,
Umeå University, SE-901 87 Umeå, Sweden. E-mail:
andreas.stenling@psy.umu.se
50
YOUNG ATHLETES’ MOTIVATION AND WELL-BEING
Autonomy
support
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Figure 1.
Need
satisfaction
Motivation
51
Well-being
The proposed four-stage motivational sequence according to SDT.
vious studies in sport settings have been
cross-sectional (Blanchard, Amiot, Perreault,
Vallerand, & Provencher, 2009) or investigations predicting for example burnout at Time
2 with motivational variables at Time 1
(Isoard-Gautheur et al., 2012).
Following the propositions within SDT, the
primary purpose of this study was to longitudinally examine the four-stage motivational sequence (perceived autonomy support from the
coach ¡ need satisfaction ¡ motivation ¡
psychological well-being). Specifically, we examined level– change associations and relations
between intraindividual changes in the variables
over the course of the competitive season.
Motivation
Motivation research concerns what moves
people to act and the conditions and processes
that facilitate persistence, performance, and
healthy development (Deci & Ryan, 2008). According to SDT, people’s motivation regarding
an activity can be classified along a selfdetermination continuum varying in the degree
of internalization of the behavioral regulation
(Deci & Ryan, 2000). The most self-determined
type of motivation is intrinsic motivation, defined as an engagement driven by the inherent
joy in the activity itself. At the other end of the
continuum is amotivation, characterized by a
lack of engagement and motivation. Between
these two end-points are different types of extrinsic motivation, ranging from no or low degree of internalization (external regulation and
introjected regulation) to high degree of internalization (identified regulation and integrated
regulation).
In sports, more self-determined types of motivation have been related to better performance
(Gillet, Berjot, & Gobancé, 2009), persistence
(Sarrazin, Vallerand, Guillet, Pelletier, & Cury,
2002), and well-being (Gagné et al., 2003).
Hence, an important goal for people who lead
others, for example coaches, is to promote the
development of self-determined types of motivation.
Social-Contextual Factors
One central aspect of SDT is the influence of
social-contextual factors on people’s motivation
(Deci & Ryan, 2000). In sport settings, the
interpersonal style of the coach is regarded as
one of the most important social-contextual influences (Mageau & Vallerand, 2003). Of particular importance for athletes’ motivation are
coaches’ autonomy-supportive and/or controlling interpersonal styles (Deci & Ryan, 1987;
Mageau & Vallerand, 2003). Autonomysupportive coaches are able to take the athlete’s
perspective, provide a rationale, acknowledge
the athlete’s feelings, and provide relevant feedback and opportunities for choice. In contrast, a
controlling interpersonal coaching style is defined as pressuring athletes to think, feel, and
behave in certain ways, thereby ignoring their
basic psychological needs and feelings (Deci &
Ryan, 1987). Coaches with a controlling interpersonal style frequently inflict punishment, use
controlling statements, impose pressure and demands, promote ego involvement, and provide
reward without competence information (Mageau & Vallerand, 2003). It is proposed that
coaches indirectly promote more self-determined types of motivation when they interact
with athletes in an autonomy-supportive way
(Deci & Ryan, 2000; Mageau & Vallerand,
2003). The process underlying this indirect effect goes through the need-satisfying features of
an autonomy-supportive coaching style, leading
to an experience of need satisfaction for the
athlete.
The Basic Psychological Needs
Within SDT it is proposed that humans
have three innate basic psychological needs:
competence, autonomy, and relatedness (Deci
& Ryan, 2000). The need for competence
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52
STENLING, LINDWALL, AND HASSMÉN
refers to the person’s need to interact with his
or her environment effectively and experience
a sense of adequate ability. The need for
autonomy implies the experience of being the
origin of one’s actions, integration and freedom, an absence of pressure, and a sense of
engaging in voluntary actions. The need for
relatedness represents the desire to feel connected to significant others, be cared for, and
care for others in a secure communion. Satisfaction of one’s psychological needs is the
energizing force underlying and promoting
self-determined types of motivation (Deci &
Ryan, 2000). Therefore, an important function of the basic psychological needs is that
they allow for a prediction of which factors in
the social context will have positive (e.g.,
autonomy support) or negative (e.g., controlling) effects on peoples’ motivation (Gagné &
Blanchard, 2007).
The Four-Stage Motivational Sequence
Mageau and Vallerand (2003) argued that
autonomy support has the potential to satisfy all
three psychological needs, an argument supported in more recent research (Adie, Duda, &
Ntoumanis, 2012). More specifically, autonomy-supportive coaches who offer choices (autonomy support), take the athletes’ perspective
and acknowledge their feelings (relatedness
support), and convey trust in their abilities and
use noncontrolling feedback (competence support) will facilitate the athletes’ need satisfaction and the organismic process of intrinsic motivation, as well as their internalization of
extrinsic motivation (Gagné & Blanchard,
2007). In turn, when athletes’ motivation is
characterized by volition, choice, and autonomy
rather than demand, pressure, and control, the
results will be higher quality behavior and
greater psychological well-being (Deci & Ryan,
2000).
The link between autonomy-supportive environments, psychological need satisfaction,
motivation, and psychological well-being is
well established and has been found in different domains, such as health care, school, and
work settings (Deci & Ryan, 2008; Ng et al.,
2012). In sport settings, however, few studies
have longitudinally examined this four-stage
motivational sequence (Gagné & Blanchard,
2007). Two exceptions to this can be found in
previous research. First, Gagné et al. (2003)
conducted a diary study on young female
gymnasts aged 7 to 18 (M ⫽ 13, SD ⫽ 2.4)
and found support for the entire motivational
sequence. More specifically, they found that
perceptions of coach autonomy support and
involvement were positively related to need
satisfaction and autonomous motivation at the
between-person level. They also found that
incoming motivation was related to wellbeing before practice, whereas perceived need
satisfaction during practice had an overriding
effect on change in well-being from before to
after the practice. This diary study illustrated
how results may differ depending on the level
of analysis, that is, if we examine at the
interindividual or intraindividual level. However, their study did not assess change in
perceived autonomy support from the coach,
only included young female gymnasts with a
wide age range (7–18 years), and the authors
concluded that more studies on the motivational sequence are needed in more mature
samples, with male athletes, and with larger
samples. We did, to some extent, address all
these limitations in the present study. The
second study, by Isoard-Gautheur et al.
(2012), examined how sport-related burnout
at the end of the competitive season was
predicted by perceived autonomy support and
controlling behaviors from the coach, need
satisfaction, and motivation measured approximately five months earlier, in the beginning of the season. Intrinsic motivation to
know was negatively related to reduced sense
of accomplishment, and amotivation was positively related to sport devaluation. Although
they included autoregressive effects on the
dependent variables, the three burnout subscales, they did not assess intraindividual
change over time.
The Present Study
The primary purpose of this study was to
longitudinally examine a four-stage motivational sequence (perceived autonomy support
from the coach ¡ need satisfaction ¡ motivation ¡ psychological well-being). We examined two aspects of this motivational sequence: (a) level– change associations (e.g.,
initial level of perceived autonomy support at
T1 ¡ ⌬need satisfaction); and (b) associa-
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YOUNG ATHLETES’ MOTIVATION AND WELL-BEING
tions of intraindividual changes between the
variables. The participants in this study—
young elite athletes at sport high schools in
Sweden—were highly engaged in their sports,
and previous research has found that the wellbeing of athletes in this population may be at
risk due to the high demands of schoolwork
and their sport (Gustafsson et al., 2010;
Isoard-Gautheur et al., 2012). However, these
previous studies focused on sport-related
burnout, while the present study focused on
intraindividual change in these athletes’ general psychological well-being and functioning. Based on the findings in these previous
studies (Gustafsson et al., 2010; IsoardGautheur et al., 2012), it is important to identify factors that facilitate these athletes’
health and well-being.
First, we examined level– change associations where initial level of each variable at
Time 1 (T1) predicted change over the course
of the season in the other variables. We hypothesized that the temporality proposed by
SDT would be supported (i.e., initial level of
perceived autonomy support T1 ¡ ⌬need satisfaction; initial level of need satisfaction
T1 ¡ ⌬motivation; initial level of motivation
T1 ¡ ⌬well-being).
In addition, much previous research on the
four-stage motivational sequence (IsoardGautheur et al., 2012; Pope & Wilson, 2012)
has used static, stability-oriented approaches
with a focus on how interindividual differences at baseline explain interindividual differences at follow-up (Nesselroade & Ghisletta, 2000). Such analyses cannot, however,
make inferences about intraindividual
changes over time. An approach that captures
intraindividual changes and change– change
associations can further examine the dynamic
features of the proposed relations within SDT.
Therefore, in this study relations between intraindividual changes in these motivational
variables were examined. To accomplish this,
we estimated a latent difference score model
(LDS) in which the difference between T1
and T2 is an explicit part of the structural
equation model as a latent difference variable
including some degree of correction for measurement error (Selig & Preacher, 2009; Wu,
Selig, & Little, 2013). It was expected that the
latent difference variables would be positively correlated and that variables closer in
53
the motivational sequence (e.g., autonomy
support and need satisfaction) would display
stronger correlations than variables further
apart in the sequence (e.g., autonomy support
and motivation).
Method
Participants
We invited 18 sport high schools in Sweden
to participate in this study. These 18 sport
high schools provide opportunities for young
elite skiers to engage in high-level training
and competition, and the athletes’ academic
schedule is arranged around their sporting
activities. A total of 490 questionnaires were
sent out via post at T1 to the young elite
athletes (alpine skiers, biathletes, crosscountry skiers) enrolled at these sport high
schools. Of these, 247 (109 females, 138
males) were returned. At T2, approximately
five months later, 164 of the respondents at
T1 once again responded to the questionnaires. There were a variety of reasons for
dropping-out, but lack of time and bad timing
of the second measurement point were mentioned frequently. The athletes’ age ranged
from 16 to 20 years (M ⫽ 17.8; SD ⫽ 0.9),
and their competitive levels ranged from regional to international, with most competing
at national and international levels. On average, the participants practiced 12.5 hours
(SD ⫽ 3.6) per week and had been competing
in their sport for 9.7 years (SD ⫽ 3.1).
Measures
Autonomy support. A short version of the
Sport Climate Questionnaire (SCQ, Smith,
Ntoumanis, & Duda, 2007) was used to measure
athletes’ perceptions of their coach’s autonomy
support (e.g., “My coach provides me with
choices and options”). This seven-item instrument has been used in previous research in sport
contexts, and its psychometric properties have
been supported (Adie, Duda, & Ntoumanis,
2008). Responses were given on a seven-point
Likert scale from 1 (strongly disagree) to 7
(strongly agree). Composite reliability () for
this scale was .90T1/.91T2.
Need satisfaction. Athletes’ perceptions
of the basic needs competence, autonomy,
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54
STENLING, LINDWALL, AND HASSMÉN
and relatedness were measured using a Swedish version of the Basic Needs Satisfaction in
Sport Scale (BNSSS, Ng, Lonsdale, & Hodge,
2011). The need for competence was measured with five items (e.g., “I am skilled at my
sport”). The autonomy construct was measured with three qualities: choice (four items,
e.g., “In my sport, I have a say in how things
are done”), internal perceived locus of causality (IPLOC; three items, e.g., “In my sport,
I feel I am pursuing goals that are my own”),
and volition (three items, e.g., “I feel I participate in my sport willingly”). Relatedness
was measured with five items (e.g., “In my
sport, I feel close to other people”). Responses were given on a seven-point Likert
scale ranging from 1 (not true at all) to 7
(very true). Composite reliability () of the
BNSSS subscales were competence ⫽
.84T1/.86T2, choice ⫽ .74T1/.89T2, IPLOC ⫽
.76T1/.80T2, volition ⫽ .57T1/.65T2, and relatedness ⫽ .75T1/.73T2.
Motivation. Athletes’ motivation were
measured using a Swedish version of the Behavioral Regulation in Sport Questionnaire
(BRSQ, Lonsdale, Hodge, & Rose, 2008).
Participants were asked to indicate how well
the items corresponded to their reasons for
participating in sports, responding on a sevenpoint Likert scale from 1 (not true at all) to 7
(very true). The item stem was “I participate
in my sport . . ..” The version of BRSQ used
in this study included five 4-item subscales
designed to measure amotivation (e.g., “but I
question why I continue”), external regulation
(e.g., “in order to satisfy people who want me
to play”), introjected regulation (e.g., “because I would feel like a failure if I quit”),
identified regulation (e.g., “because I value
the benefits of my sport”), and intrinsic motivation (e.g., “because I enjoy it”). Composite reliability () of the subscales were amotivation ⫽ .83 T 1 /.86 T 2 , external ⫽
.86T1/.88T2, introjected ⫽ .87T1/.88T2, identified ⫽ .72 T 1 /.79 T 2 , and intrinsic ⫽
.84T1/.90T2.
The latent motivation variable used in this
study was a self-determination index (SDI), calculated by using weighted item scores based on
their respective placement on the self-determination continuum. The BRSQ item scores were
weighted and four SDI indicators were calculated using the formula [(2 ⫻ intrinsic ⫹ iden-
tified) ⫺ (introjected ⫹ 2 ⫻ external)]. The
result was four SDI observed score indicators
(cf. Gagné et al., 2003).
Psychological well-being. Psychological
well-being was assessed with the General
Health Questionnaire (GHQ-12, Goldberg et
al., 1997). The GHQ-12 consists of six negatively phrased and six positively phrased
items assessing an individual’s general psychological well-being and functioning during
the past couple of weeks. Hu, Stewart-Brown,
Twigg, and Weich (2007) found that the
GHQ-12 seems to consist of two dimensions,
symptoms of mental disorder (negatively
phrased items) and positive mental health
(positively phrased items), and that these two
factors showed different patterns of associations with demographic, socioeconomic, and
health-related variables. Hu et al. concluded
that the two dimensions have shown meaningful independence and that considering the
GHQ-12 as a two-dimensional instrument is
useful when studying determinants and consequences of well-being. In this study, only
the six positively phrased items were included
as a measure of psychological well-being.
Responses were given on a four-point Likert
scale ranging from 0 (disagree very much) to
3 (agree very much). The Swedish version of
the GHQ-12 has been validated in previous
research (Sconfienza, 1998). Composite reliability () for this scale was .80T1/.80T2.
Procedure
Initially, the school principal and the head
coach at each sport high school were contacted and informed about the purpose of the
study. When permission to approach the athletes was granted, the questionnaires were
sent via post to an administrator at each
school, who distributed them to the athletes.
The athletes were given written information
about the study and signed an informed consent before responding to the questionnaires,
and returned the questionnaires in sealed envelopes. The first questionnaire was administered approximately two months into the competitive season (November), and the second at
the end of the competitive season (April). The
time lag chosen in this study was based on
previous SDT research in sports in which
relations between levels in different variables
YOUNG ATHLETES’ MOTIVATION AND WELL-BEING
have been found over similar time lags
(Isoard-Gautheur et al., 2012; Reinboth &
Duda, 2006). Ethical approval was obtained
from the Regional Ethical Review Board at
the University prior to data collection.
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Statistical Analyses
The statistical analyses were carried out using
Mplus version 7.11 (Muthén & Muthén, 1998 –
2012). This study employed the full information
maximum likelihood (FIML) estimation of
missing data. FIML is unbiased and a more
efficient method under the missing at random
(MAR) assumption compared with, for example, listwise deletion, which yields biased parameters under the MAR assumption (Enders,
2010).1
The omega coefficient () was used as indicator of composite reliability (McDonald, 1999). A
longitudinal confirmatory factor analysis (CFA)
was estimated on the measurement model including all indicators and latent variables at the two
time points. Autonomy support was represented
by the seven items as indicators. For need satisfaction, the five subscales competence, choice,
IPLOC, volition, and relatedness were used as
indictors. The four SDI indicators were used as
indicators to the latent SDI variable, and finally,
the six well-being items were used as indicators to
the latent well-being variable. Configural, weak
(factor loadings), and strong (factor loadings and
intercepts) invariance was tested in the measurement model (Little, 2013). The increasingly restricted models imposing equality constraints
were compared with the previous model with less
constraints to evaluate longitudinal invariance.
Change in the comparative fit index (⌬CFI) and
root mean square error of approximation
(⌬RMSEA) was used as goodness of fit index
when comparing the three models. A change
of ⱖ⫺.01 in CFI and a change of ⱖ.015 in
RMSEA is regarded as an indication of noninvariance (Chen, 2007).
To examine level– change and change– change
associations, a latent difference score (LDS)
model was specified (Selig & Preacher, 2009; Wu
et al., 2013). In a two-wave LDS model, a latent
difference variable represents the difference between T1 and T2 corrected for measurement error.
A latent difference variable was specified for each
of the four constructs, that is autonomy support,
need satisfaction, SDI, and well-being. First, we
55
examined level– change associations by allowing
initial level of each variable at T1 in the four-stage
motivational sequence to predict change in the
other variables (e.g., perceived autonomy support
at T1 ¡ ⌬need satisfaction). Second, correlations
between change in autonomy support, change in
need satisfaction, change in SDI, and change in
well-being were estimated. All variables measured at T1 were allowed to covary, and initial
level at T1 and the latent difference variable were
allowed to covary within each construct. We also
compared correlations between the different latent
difference variables (e.g., comparing the correlation between change in perceived autonomy support and change in need satisfaction with the correlation between change in perceived autonomy
support and change in SDI) following the procedures outlined by Steiger (1980). Indicators were
allowed to covary over time in all these models to
account for indicator-specific effects over time
(Little, 2013).
Conventional fit indices were used to evaluate
the model fit in the SEM models, such as the
comparative fit index (CFI), the Tucker–Lewis
Index (TLI), the standardized root mean residual
(SRMR), and the root mean square error of approximation (RMSEA). Traditional cutoff criteria
(CFI and TLI ⬎ .90, SRMR and RMSEA ⬍ .08)
were used to indicate acceptable fit (Marsh, 2007).
Robust maximum likelihood estimation was employed in the SEM analysis.
Results
Table 1 displays correlations between the
study variables and descriptive statistics. The
1
To rule out potential selection bias, we examined differences between respondents answering only at T1 and
those responding at T1 and T2. A MANOVA on background variables (age, years competing in their sport, average hours of practice per week) displayed no significant
differences between the two samples, ⌳ ⫽ 0.98, F(3,
239) ⫽ 1.69, p ⫽ .17, 2p ⫽ .02; nor did they differ on the
study variables at T1 (perceived autonomy support, need
satisfaction, SDI, well-being), ⌳ ⫽ 0.98, F(4, 242) ⫽ 1.03,
p ⫽ .39, 2p ⫽ .02. There was also no significant relationship
between sex and group (only T1 or T1 and T2), 2(1) ⫽
0.14, p ⫽ .79, indicating that there were no significant
differences in distribution of males and females between the
two samples. These analyses indicated that there were no
significant differences between the two samples related to
background variables, study variables, or sex distribution.
Therefore, all 247 respondents at T1 were included in the
remaining analyses.
56
STENLING, LINDWALL, AND HASSMÉN
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Table 1
Correlations, Means, and Standard Deviations for the Study Variables
1. AS T1
2. AS T2
3. NS T1
4. NS T2
5. SDI T1
6. SDI T2
7. WB T1
8. WB T2
M
SD
1
2
3
4
5
6
7
5.91
5.92
6.12
6.14
13.07
13.05
2.14
2.21
0.87
0.91
0.54
0.67
4.18
4.50
0.50
0.52
.49ⴱⴱⴱ
.45ⴱⴱⴱ
.20ⴱⴱ
.24ⴱⴱⴱ
.10
.39ⴱⴱⴱ
.20ⴱ
.29ⴱⴱⴱ
.51ⴱⴱⴱ
.27ⴱⴱⴱ
.29ⴱⴱⴱ
.35ⴱⴱⴱ
.44ⴱⴱⴱ
.42ⴱⴱⴱ
.56ⴱⴱⴱ
.32ⴱⴱⴱ
.58ⴱⴱⴱ
.27ⴱⴱⴱ
.49ⴱⴱⴱ
.70ⴱⴱⴱ
.36ⴱⴱⴱ
.47ⴱⴱⴱ
.68ⴱⴱⴱ
.39ⴱⴱⴱ
.24ⴱⴱ
.27ⴱⴱⴱ
.41ⴱⴱⴱ
.43ⴱⴱⴱ
Note. Correlations involving only T1 variables N ⫽ 247. Correlations involving T2 variables N ⫽ 164. AS ⫽ autonomy
support; NS ⫽ need satisfaction; SDI ⫽ self-determination index; WB ⫽ well-being.
ⴱ
p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
variables were positively correlated and the athletes reported high and stable levels of perceived autonomy support, need satisfaction,
SDI, and well-being over the course of the
competitive season. Longitudinal invariance in
the measurement model was estimated using a
longitudinal CFA model including 44 indicators
of the eight latent variables. The covariances
between the latent variables were freely estimated. The longitudinal measurement model
displayed an acceptable fit: 2(852) ⫽ 1,188.21,
p ⬍ .001, CFI ⫽ .93, TLI ⫽ .92, SRMR ⫽ .06,
RMSEA ⫽ .04, 90% CI [.04, .05]. The increasing restrictions on the model did not result in
worse model fit (⌬CFI ⱕ ⫺.01, ⌬RMSEA ⱕ
.015); hence, strong longitudinal invariance was
supported. The remaining models were estimated while keeping strong invariance constraints in place.
Level–Change Associations
The LDS model displayed an acceptable fit,
(888) ⫽ 1,240.55, p ⬍ .001, CFI ⫽ .93,
TLI ⫽ .92, SRMR ⫽ .07, RMSEA ⫽ .04, 90%
CI [.04, .05]. To examine level– change associations, initial level of each variable at T1 was
allowed to predict change in the other variables
(see Table 2). Need satisfaction at T1 negatively
predicted change in perceived autonomy support ( ⫽ ⫺.46, p ⫽ .045), change in SDI ( ⫽
⫺.42, p ⫽ .005), and change in well-being ( ⫽
⫺.58, p ⫽ .001). SDI at T1 positively predicted
change in perceived autonomy support ( ⫽
.33, p ⫽ .024) and change in well-being ( ⫽
.35, p ⫽ .028).
2
Associations of Change
The general pattern for the correlations between the latent difference variables was that
variables closer in the motivational sequence
were more strongly correlated than variables
further apart; however, one deviation from this
pattern was detected. Change in perceived autonomy support was positively correlated with
change in need satisfaction (⌽ ⫽ .25, p ⫽ .005),
change in SDI (⌽ ⫽ .16, p ⫽ .086), and change
in well-being (⌽ ⫽ .29, p ⫽ .034). Change in
need satisfaction was positively correlated with
change in SDI (⌽ ⫽ .63, p ⬍ .001) and change
in well-being (⌽ ⫽ .25, p ⫽ .019), and finally
change in SDI was positively correlated with
Table 2
Standardized Regression Coefficients From the LDS
Model (N ⫽ 247)
LDS model

SE
p
AS T1 ¡ ⌬NS
AS T1 ¡ ⌬SDI
AS T1 ¡ ⌬WB
NS T1 ¡ ⌬AS
NS T1 ¡ ⌬SDI
NS T1 ¡ ⌬WB
SDI T1 ¡ ⌬AS
SDI T1 ¡ ⌬NS
SDI T1 ¡ ⌬WB
WB T1 ¡ ⌬AS
WB T1 ¡ ⌬NS
WB T1 ¡ ⌬SDI
⫺.11
⫺.05
⫺.03
⫺.46
⫺.42
⫺.58
.33
.11
.35
.20
⫺.10
.25
.11
.11
.10
.23
.15
.17
.15
.14
.16
.19
.17
.20
.290
.660
.738
.045
.005
.001
.024
.418
.028
.279
.547
.195
Note. AS ⫽ autonomy support; NS ⫽ need satisfaction;
SDI ⫽ self-determination index; WB ⫽ well-being; LDS ⫽
latent difference score.
YOUNG ATHLETES’ MOTIVATION AND WELL-BEING
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change in well-being (⌽ ⫽ .38, p ⬍ .001). Tests
for the difference between the correlations
showed that the correlations between adjacent
variables in the motivational sequence were
stronger than correlations between more distal
variables, except that perceived autonomy support was more strongly correlated with wellbeing than with SDI (see Table 3).
Discussion
The primary purpose of this study was to
examine a four-stage motivational sequence (cf.
Ng et al., 2012) among young elite athletes, in
line with SDT (perceived autonomy support
from the coach ¡ need satisfaction ¡ motivation ¡ psychological well-being). More specifically, we were interested in level– change and
change– change associations between the variables in the motivational sequence. Few studies
have longitudinally examined the four-stage
motivational sequence in sports (Gagné & Blanchard, 2007), and these studies (Gagné et al.,
2003; Isoard-Gautheur et al., 2012) did not examine level– change nor change– change associations between the variables.
The means of the study variables remained at
a fairly high and stable level over the course of
the competitive season, a result that supports
some previous longitudinal findings (Adie et al.,
2012) and is contrary to others (Reinboth &
Duda, 2006). This suggests that the young elite
athletes in this study were active in a relatively
positive and adaptive environment where their
motivation was nurtured. These mixed findings
regarding stability in these motivational constructs may be due to the different samples
used, as this study and Adie et al. (2012) used
more homogenous samples compared with Reinboth and Duda (2006).
57
Level–Change Associations
We examined level– change associations in
the LDS model where initial level of each variable at T1 was allowed to predict change in the
other variables. Few of the hypothesized associations were supported and some unexpected
results were found. Level of perceived autonomy support at T1 did not predict change in
need satisfaction. One reason could be that the
athletes’ interindividual standing of perceived
autonomy support early in the season may not
be that important for intraindividual change in
need satisfaction over the competitive season.
As displayed in the LDS model, intraindividual
change in perceived autonomy support was positively associated with intraindividual change in
need satisfaction over the competitive season,
indicating that level of analysis (i.e., interindividual vs. intraindividual) influences the way
results can be interpreted. Different types of
associations between SDT variables have been
found depending on the level of analysis in
other studies (Gillison, Standage, & Skevington, 2013), where relations in the opposite direction were found at the between- and withinperson level in multilevel models. Further
exploration of associations at different levels of
analysis (e.g., interindividual vs. intraindividual) is warranted in future research.
We also found that level of need satisfaction
at T1 negatively predicted change in perceived
autonomy support, SDI, and well-being over the
competitive season. These unexpected results
may be a result of the high initial scores that
these athletes reported. Regressing change on
initial level often suffers from regression toward
the mean and a high initial value is often related
to a greater decrease, with ceiling effects as a
potential explanation (Tu & Gilthorpe, 2007).
Table 3
Differences Between the Correlations of Latent Difference Variables in the
Motivational Sequence (N ⫽ 247)
⌬AS
⌬AS
⌬NS
⌬SDI
↔
↔
↔
↔
⌬NS
⌬SDI
⌬SDI
⌬WB
(⌽⫽.25)
(⌽⫽.16)
(⌽⫽.63)
(⌽⫽.38)
vs.
vs.
vs.
vs.
⌬AS
⌬AS
⌬NS
⌬NS
↔
↔
↔
↔
⌬SDI
⌬WB
⌬WB
⌬WB
(⌽⫽.16)
(⌽⫽.29)
(⌽⫽.25)
(⌽⫽.25)
z-score
pa
1.71
⫺1.85
6.30
2.49
.044
.032
.000
.006
Note. AS ⫽ autonomy support; NS ⫽ need satisfaction; SDI ⫽ self-determination index;
WB ⫽ well-being.
a
One-tailed p-value.
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58
STENLING, LINDWALL, AND HASSMÉN
This is a common phenomenon when the scale
of measurement is closed-ended Likert-type
scales, such as those used in this study, and
the respondents report high initial values on the
variables (Little, 2013). It may also be that those
with high initial levels of need satisfaction over
the course of the season were exposed to other
factors that influenced their perception of autonomy support, motivation, and well-being, for
example an increasingly controlling environment and need thwarting. These results should
be replicated in other samples and with various
time lags to explore the generalizability of these
findings beyond the sample in this study.
Furthermore, initial level of SDI at T1 positively predicted change in well-being, as expected, but also predicted change in perceived
autonomy support from the coach. Jang, Kim,
and Reeve (2012) found similar level–level effects in their study on middle-school students
where midsemester engagement positively predicted late-semester perceived autonomy support from the teacher. Jang et al. suggested that
this indicated that the students can take action to
satisfy their psychological needs by influencing
their environment. A similar suggestion can be
made based on the results from the present
study. These results indicate that more autonomously motivated athletes will seek out need
satisfying environments to further support their
autonomous motivation and well-being (Weinstein & Ryan, 2011).
Because most previous SDT studies in sport
have examined level–level associations (IsoardGautheur et al., 2012), less is known about
level– change associations among the variables
in the four-stage motivational sequence. Future
studies examining level– change associations
among these motivational variables are warranted.
Associations of Change
With one minor exception, the LDS model
supported our hypothesis that variables closer in
the motivational sequence would display stronger relations than variables that were further
apart. The only deviation from this pattern was
that change in perceived autonomy support was
more strongly related to change in well-being
than with change in SDI. However, there have
been similar findings in cross-sectional studies
in which positive feedback from the coach was
more strongly correlated with athletes’ welland ill-being than autonomous motivation
(Mouratidis, Vansteenkiste, Lens, & Sideridis,
2008). Also, research grounded in the basic
psychological needs theory has shown that perceived autonomy support from the coach was
positively related to athletes’ well-being and
negatively related to burnout (Adie et al., 2012).
It may be so that athletes experiencing higher
levels of well-being are more receptive to the
positive effects of autonomy-supportive behaviors.2 Such effects have been found in, for example, work contexts in which employees’ selfreported well-being influenced their perceptions
of managers’ transformational leadership behaviors (Nielsen, Randall, Yarker, & Brenner,
2008). Reciprocal effects have also been found
within the SDT literature. Teachers’ needsupportive behaviors in the classroom facilitated students’ autonomous motivation and
agentic engagement and students’ agentic engagement in turn positively influenced their perceptions of teachers’ autonomy support (Reeve,
2013). In the sports domain, some studies have
found reciprocal relations between motivation
and burnout (Lonsdale & Hodge, 2011; Martinent, Decret, Guillet-Descas, & Isoard-Gautheur, 2014), highlighting the reciprocal nature
and complex relationships between these motivational variables. Apparently, not only may
athletes benefit from an autonomy-supportive
coaching environment; athletes receiving autonomy support may also in turn positively influence the coaches’ provision of autonomy support through increased engagement and better
psychological functioning (cf. Jang et al.,
2012).
In contrast to many previous SDT studies in
sport (Adie et al., 2012; Isoard-Gautheur et al.,
2012), this study focused on general well-being
and psychological functioning in life instead of
sport-related well- and ill-being (e.g., vitality
and burnout). The young elite athletes at these
sport high schools are highly engaged in their
sport, and most of them are pursuing a career as
2
An autoregressive cross-lagged (ARCL) model was also
estimated where each variable at T1 was allowed to predict
themselves and the other variables at T2. The results from
the ARCL model displayed that well-being at T1 positively
predicted perceived autonomy support from the coach at T2,
which is in line with this suggestion. The results from the
ARCL model is available upon request from the first author.
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YOUNG ATHLETES’ MOTIVATION AND WELL-BEING
an elite athlete. Due to the integral role of sport
participation in their lives and the vast amount
of time they spend in the sport environment, it is
very likely that the sporting environment has a
substantial impact on their everyday psychological well-being and functioning. The results
from this study support this idea, and suggest
that positive motivational features in young
elite athletes’ sporting environment are related
to their general psychological well-being and
functioning.
The results from this study and previous studies indicate a complex pattern of the relations
between the variables in this motivational sequence over time. One important issue for future research is to explicitly incorporate theories
of time and theories of change (cf. Ployhart &
Vandenberg, 2010) in the SDT framework,
which can advance our understanding of the
complex, dynamic, and potentially reciprocal
nature of these relationships over time.
Limitations, Directions for Future
Research, and Conclusions
Although this study had a longitudinal design
and examined change, it only included two
measurement points. Hence, the shape of the
change cannot be estimated. With more time
points, preferably more than three, it would be
possible to examine the shape of the trajectory
(Ployhart & Vandenberg, 2010). Second, this
study relied on self-report data; future research
can be advanced by including, for example,
observational data on coaches’ (Tessier et al.,
2013) and/or athletes’ behaviors or physiological measures of well- and/or ill-being (cf. Bartholomew, Ntoumanis, Ryan, Bosch, &
Thøgersen-Ntoumani, 2011). Third, this study
employed a longitudinal design and correlational data. Future research can benefit from
interventions and experimental designs in order
to come closer to causal conclusions. Fourth,
because subscales were used as indicators of the
latent need satisfaction variable only partial
control for measurement error was achieved for
this variable. In addition, the latent motivation
variables consisted of four manifest SDI indicators that were weighted according to their
placement on the self-determination continuum.
It should be acknowledged that this weighting
procedure with more weight given to the extremes on the continuum (external and intrinsic)
59
also gives more weight to measurement error in
these subscales. Fifth, the use of SDI as motivation measure can be useful when examining
complex models such as those estimated in this
study. We did not, however, investigate the
influence of the various types of behavioral
regulations. Future research should investigate
relations between the various types of regulations and the other variables in the motivational
sequence to further our understanding of the
complex nature of human motivation.
The level– change and change– change associations examined in this study provide an important extension of previous SDT research in
sports on the four-stage motivational sequence
at the contextual level, which predominantly
has relied on cross-sectional investigations
(Blanchard et al., 2009) or longitudinal studies
(Isoard-Gautheur et al., 2012; Pope & Wilson,
2012) focusing on how interindividual differences in one variable predict interindividual differences in another variable. Longitudinal research that explicitly examines intraindividual
change can benefit SDT research on the fourstage motivational sequence in sport and provide important information as to how individuals change over time in relation to themselves.
Recent advances in statistical modeling of longitudinal data (McArdle, 2009) may aid researchers in their understanding of the dynamic
features of motivational processes and of how
these processes unfold over time. Finally, focusing on change– change associations can be
particularly useful in intervention research,
which often relies on an assumption that changing one variable will lead to change in another
(Scholz, Nagy, Göhner, Luszczynska, & Kliegel, 2009). But such an assumption cannot be
tested using a static, stability-based approach
solely focusing on interindividual differences at
baseline and follow-up.
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Received January 28, 2014
Revision received June 27, 2014
Accepted June 30, 2014 䡲
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Correction to Stenling, Lindwall, and Hassmén (2015)
In the article, “Changes in Perceived Autonomy Support, Need Satisfaction, Motivation, and Well-Being in Young Elite Athletes,” by Andreas Stenling, Magnus
Lindwall, and Peter Hassmén (Sport, Exercise, and Performance Psychology, 2015,
Vol. 4, No. 1, pp. 50 – 61. http://dx.doi.org/10.1037/spy0000027), the following
sentences require corrections (changes are in bold).
Page 50, abstract, line 8: “A latent difference score model was used to analyze
the data.”
Page 55, left column, paragraph 3, line 4 from bottom: “A change of >ⴚ.005
in CFI and a change of >.010 in RMSEA was regarded as an indication of
noninvariance (Chen, 2007).”
Page 56, left column, paragraph 1, line 7 from bottom: “The increasing restrictions on the model did not result in worse model fit (⌬CFI ⱕ ⴚ.005,
⌬RMSEA ⱕ .010); hence, strong longitudinal invariance was supported. The
LDS model was estimated while keeping strong invariance constraints in
place.”
http://dx.doi.org/10.1037/spy0000034
Sport, Exercise, and Performance Psychology
2015, Vol. 4, No. 3, 206 –218
© 2015 American Psychological Association
2157-3905/15/$12.00 http://dx.doi.org/10.1037/spy0000038
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Exploring the Independent and Interactive Effects of
Autonomy-Supportive and Controlling Coaching Behaviors on
Adolescent Athletes’ Motivation for Sport
Anthony J. Amorose
Dawn Anderson-Butcher
Illinois State University
The Ohio State University
Theory and research support the conclusion that an autonomy-supportive interpersonal
style is an effective motivational technique for coaches, whereas a controlling style is
ineffective. However, the behaviors associated with each of these styles have been
found to be relatively independent. The purpose of this study was to test the independent and interactive effects of perceived autonomy-supportive and controlling coaching
behaviors on the motivational response of adolescent athletes. Athletes (n ⫽ 301, M
age ⫽ 15.68 years; 63% female) completed surveys assessing their perceptions of their
coaches’ behaviors, their need satisfaction, motives for sport, and level of burnout.
Hierarchical regression analyses showed that the independent effects of autonomysupportive and controlling behaviors together significantly (p ⬍ .01) predicted each of
the motivational variables (R2 range .07 – .40). In 5 of the 10 analyses, however, the
main effects were superseded by a significant (p ⬍ .05) interaction (⌬R2 ⫽ .01–.02).
The interaction plots reveal that (a) positive motivational responses increased as
perceptions of autonomy support increased—particularly when the athletes also perceived a relatively lower level of controlling behaviors, and (b) the most positive
motivational outcomes were associated with the perceptions of relatively higher autonomy support and relatively lower controlling behaviors. In summary, autonomysupportive and controlling coaching behaviors are each related to athletes’ motivational
responses, and in some cases the interaction of these behaviors adds, at least minimally,
to our understanding.
Keywords: burnout, coaching effectiveness, need satisfaction, self-determined motivation
The coach has been identified as a powerful
socializing agent in the physical domain
(Horn, 2008; Smoll & Smith, 2002). At all
competitive levels—from youth to professional sport—the way in which coaches structure practice and game situations, the processes they use to make decisions, the quality
and quantity of feedback they provide, the
relationships they establish with athletes, the
techniques they use to motivate their players,
and so on, can all impact athletes’ behaviors,
This article was published Online First April 20, 2015.
Anthony J. Amorose, School of Kinesiology and Recreation, Illinois State University; Dawn Anderson-Butcher,
College of Social Work, The Ohio State University.
Correspondence concerning this article should be addressed to Anthony J. Amorose, School of Kinesiology and
Recreation, Illinois State University, Normal, IL 617905120. E-mail: ajamoro@ilstu.edu
206
cognitions, and affective responses (see Amorose, 2007; Horn, 2008; Mageau & Vallerand,
2003). Understanding which coaching behaviors promote positive behaviors, experiences,
and psychological functioning on the part of
the athletes, as well as those that minimize
maladaptive outcomes, is a critical area of
inquiry for researchers and practitioners alike.
The general goal of this study is to explore
how autonomy-supportive and controlling
coaching behaviors relate to athletes’ motivational responses. Although not a comprehensive list, the targeted aspects of athlete motivation include both adaptive (e.g., basic
psychological need satisfaction, self-determined motives) and maladaptive responses
(e.g., non-self-determined motives, burnout),
each of which has been identified in the literature as a critical dimension of athlete motivation (see Weiss & Amorose, 2008).
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EXPLORING THE INDEPENDENT AND INTERACTIVE EFFECTS
One of the more frequently studied aspects of
coaching effectiveness uses self-determination
theory (SDT; Ryan & Deci, 2002) to explore the
degree to which coaches exhibit autonomysupportive or controlling behaviors in their interactions with athletes (Amorose, 2007; Bartholomew, Ntoumanis, & Thøgersen-Ntoumani,
2009; Mageau & Vallerand, 2003). Autonomysupportive coaches engage in behaviors that
acknowledge athletes’ thoughts and feelings;
encourage choice, self-initiation, and selfregulation of behavior; and minimize the use of
pressure and demands to control others. According to Mageau and Vallerand (2003), examples of specific behaviors that make up an
autonomy-supportive coaching style include the
following: (a) providing choice to athletes
within specific limits and rules, (b) providing
athletes with a meaningful rationale for activities, limits, and rules, (c) asking about and acknowledging athletes’ feelings, (d) providing
opportunity for athletes to take initiative and act
independently, (e), providing noncontrolling
performance feedback, (f) avoiding overt control, guilt-induced criticism and controlling
statements, and (g) minimizing behaviors that
promote ego involvement. Coaches using a controlling interpersonal style, on the other hand,
engage in behaviors that pressure athletes to
think, feel, and act in a way consistent with the
needs and wants of the coaches. A controlling
interpersonal coaching style, according to Bartholomew and colleagues (2009), includes behaviors such as the following: (a) using rewards
to manipulate athletes’ behavior, (b) using
overly critical feedback in an attempt to motivate athletes to perform better, (c) attempting to
influence athletes’ behaviors and lives outside
the sport setting, (d) using power assertive techniques to force athlete compliance, (e) using
social comparison as the reference for evaluating athletes, and (f) recognizing athletes when
they are performing well and withdrawing attention when athletes are struggling.
Recent research has shown that the degree to
which coaches have interpersonal styles that are
autonomy-supportive and/or controlling is related to a wide range of psychological and behavioral outcomes in athletes (see Amorose,
2007; Bartholomew et al., 2009; Mageau &
Vallerand, 2003). More specifically, studies
have consistently shown that autonomysupportive behaviors are effective in promoting
207
adaptive forms of motivation and well-being in
athletes (e.g., Adie, Duda, & Ntoumanis, 2012;
Amorose & Anderson-Butcher, 2007; Carpentier & Mageau, 2013; Gillet, Vallerand,
Amoura, & Baldes, 2010; Isoard-Gautheur,
Guillet-Descas, & Lemyre, 2012; Kipp &
Weiss, 2013; Pope & Wilson, 2012; Smith,
Ntoumanis, & Duda, 2010). The effects of controlling behaviors, on the other hand, have been
shown to negatively relate to athletes’ motivation and well-being (e.g., Bartholomew, Ntoumanis, Ryan, Bosch, & Thøgersen-Ntoumani,
2011; Blanchard et al., 2009; Isoard-Gautheur
et al., 2012; Matosic, Cox, & Amorose, 2014;
Pelletier, Fortier, Vallerand, & Briére, 2001;
Smith, Ntoumanis, & Duda, 2010).
Whereas many of the studies exploring the
motivational implications of coaches’ interpersonal styles have looked at either autonomysupportive or controlling behaviors separately,
increasingly researchers have begun to include
indicators of both coaching styles simultaneously (e.g., Bartholomew et al., 2011; IsoardGautheur et al., 2012; Pelletier et al., 2001;
Smith et al., 2010). For example, Pelletier and
colleagues found positive associations between
perceived autonomy support and self-determined forms of motivation in a sample of 13- to
22-year-old swimmers, whereas controlling
coaching behaviors related to less self-determined forms of motivation. They also reported
that swimmers who dropped out over the course
of the 2-year study reported lower autonomy
support and greater controlling behaviors from
their coaches relative to swimmers who maintained their participation.
There are a number of interesting conclusions
that can be reached when summarizing the overall pattern of the research on the interpersonal
styles of coaches. First, researchers have found
repeatedly that athletes’ perceptions of behaviors exhibited by their coaches are predictive a
host of motivational outcomes. Further, it is
clear that an autonomy-supportive interpersonal
coaching style is an effective motivational technique, whereas a controlling interpersonal style
is less effective. That is, autonomy-supportive
behaviors positively relate to adaptive motivational outcomes, such as the satisfaction of the
basic psychological needs of competence, autonomy, and relatedness. They also are positively related to more self-determined participation motives (i.e., intrinsic motivation,
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208
AMOROSE AND ANDERSON-BUTCHER
integrated regulation, identified regulation) described in self-determination theory (see Ryan
& Deci, 2002). Additionally, autonomysupportive behaviors negatively relate to maladaptive outcomes, such as the thwarting of the
basic psychological needs, more non-selfdetermined participation motives (i.e., introjected regulation, external regulation, amotivaiton) described in self-determination theory (see
Ryan & Deci, 2002), as well as burnout
(Raedeke & Smith, 2001). Controlling behaviors, on the other hand, appear to positively
relate to more maladaptive outcomes (see Bartholomew et al., 2011).
Second, as pointed out by Pelletier et al.
(2001) and Bartholomew and colleagues (2009,
2011), autonomy-supportive and controlling
coaching behaviors are not opposite ends of a
continuum. In other words, at least from the
athletes’ perspective, coaches can engage in
both autonomy-supportive and controlling
coaching behaviors when interacting with athletes. In support of this argument, studies simultaneously measuring autonomy-supportive and
controlling behaviors have typically found correlations (albeit small) between these coaching
behaviors. For example, the bivariate correlation between autonomy-supportive and controlling coaching behaviors reported in Smith et al.
(2010) was minimal (r ⫽ ⫺.08), whereas the
correlations reported in Bartholomew et al.
(2011) were more moderate in size (Study 1,
r ⫽ ⫺.43, Study 2, r ⫽ ⫺.37).
Although the growing body of research appears to be painting a relatively clear picture
about the motivational implications of these
coaching behaviors, questions still remain. The
focus of this study is on one issue that has yet to
be tested—namely the potential influence of the
interactive or combined effect of autonomysupportive and controlling coaching behaviors
on athletes’ motivation. As noted, a number of
studies have simultaneously included indicators
of both autonomy-supportive and controlling
behaviors (e.g., Bartholomew et al., 2011;
Isoard-Gautheur et al., 2012; Pelletier et al.,
2001; Smith et al., 2010). These studies, however, have treated these interpersonal styles separately when exploring their relationships with
motivational outcomes. A more complete understanding the influence of coaches on athletes’ motivation, however, requires an exploration of both the independent and combined or
interactive effects of these behaviors. For example, although we would expect positive motivational outcomes from coaches who provide high
degrees of autonomy-support and low levels of
control, we do not know what the motivational
implications of coaches who are perceived to
exhibit both types of behaviors simultaneously.
Thus, an important next step in the research is
testing the potential interactive effects of these
interpersonal styles.
Consequently, the specific purpose of the
study was to test whether the combined or interactive effects of autonomy-supportive and
controlling coaching behaviors adds to our understanding of the athletes’ motivational regulations, basic psychological need satisfaction,
and burnout above and beyond the independent
effect of these behaviors. We hypothesized that
autonomy-supportive coaching behaviors
would positively relate to more adaptive motivational responses (i.e., intrinsic regulation, integrated regulation, identified regulation, perceived competence, autonomy, and feelings of
relatedness), and negatively relate to more maladaptive responses (i.e., introjected regulation,
external regulation, amotivation, and burnout).
Controlling behaviors were predicted to show
an opposite pattern of relationships. Further, it
was predicted that the relative strength of the
relationships would be dependent on the nature
of the type of the outcomes. That is, autonomysupportive behaviors would more strongly relate to the adaptive motivational responses,
whereas perceived controlling behaviors would
demonstrate a relatively stronger effect on the
maladaptive outcomes. We hypothesized that
the interactions would add meaningfully to the
prediction of different motivational outcomes
above and beyond the independent effects of the
two coaching styles. In the case of a significant
interaction, we expected that the motivational
responses would generally be more positive under conditions where the athletes perceived relatively higher autonomy support and relatively
lower controlling behaviors exhibited by their
coaches.
Method
Participants
The participant sample (N ⫽ 301) was comprised of male (n ⫽ 111) and female (n ⫽ 190)
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EXPLORING THE INDEPENDENT AND INTERACTIVE EFFECTS
athletes from a variety of individual and team
sports from the Midwestern portion of the
United States. Participants, who ranged in age
from 14 to 18 years (M ⫽ 15.68, SD ⫽ 1.19;
freshman, n ⫽ 91; sophomores, n ⫽ 77; juniors,
n ⫽ 79; seniors, n ⫽ 54), were recruited from
school-based athletic teams (baseball, n ⫽ 29;
basketball, n ⫽ 87; football, n ⫽ 46; soccer,
n ⫽ 25; softball, n ⫽ 16; swimming, n ⫽ 12;
tennis, n ⫽ 21; track, n ⫽ 23; volleyball, n ⫽
31; wrestling, n ⫽ 11). The average number of
years of participation in the athletes’ respective
sport was 6.87 (SD ⫽ 3.42). The average number of seasons on their current team was 1.94
(SD ⫽ 1.12), and the average years playing with
their current coach was 1.74 (SD ⫽ 1.46). Most
athletes identified themselves as Caucasian
(85.0%), with the remaining identifying as African American (8.6%), Asian (1.3%), Hispanic
(1.3%), or other (3.3%).
Measures
Perceived autonomy-supportive coaching
behaviors. Athletes’ perception of the autonomy-supportive behaviors exhibited by their
coaches over the course of the season was assessed using the short version of the Sport Climate Questionnaire (“Perceived autonomy support,” n.d.). Response options, which range
from strongly disagree to strongly agree, for the
6 items are scored on a 7-point scale with higher
scores indicating a more autonomy-supportive
coaching style. Sample items include “I feel that
my coach provides me choices and options”;
“My coach conveys confidence in my ability to
do well at athletics”; and “I feel understood by
my coach”). Research by Amorose and Anderson-Butcher (2007) on a sample of high school
and college athletes have provided evidence
that the items reflecting perceived autonomysupportive coaching styles are internally consistent and possess factorial and construct validity.
Perceived controlling coaching behaviors.
Athletes’ perception of controlling behaviors
exhibited by their coaches over the course of the
season was assessed using the Controlling
Coach Behavior Scale (Bartholomew et al.,
2010). The measure includes 15 items reflecting
4 related dimensions of controlling behaviors,
including the controlling use of rewards (e.g.,
“My coach only rewards/praises me to make me
train harder”), negative conditional regard (e.g.,
209
“My coach is less accepting of me if I have
disappointed him/her”), intimidation (e.g., “My
coach shouts at me in front of others to make me
do certain things”), and excessive personal control (e.g., “My coach tries to control what I do
during my free time”). Response options, which
range from strongly disagree to strongly agree,
are scored on a 7-point scale with higher scores
indicating a more controlling coaching style.
Scores from the 4 subscales were averaged together into a single score to reflect an overall
degree of controlling behavior on the part of the
coach. Bartholomew and colleagues (2010)
have provided internal consistency and factorial
and construct validity evidence for the scale
with adolescent athletes and for the use of an
overall controlling behavior score.
Motivational regulations. The Behavioral
Regulation in Sport Questionnaire (BRSQ;
Lonsdale, Hodge, & Rose, 2008) was used to
assess athletes’ motivation for their sport. The
24-item BRSQ includes subscales measuring
intrinsic motivation (e.g., “because I find it
pleasurable”), integrated regulation (e.g., “because it’s an opportunity to just be who I am”),
identified regulation (e.g., “because I value the
benefits of my sport”), introjected regulation
(e.g., “because I would feel ashamed if I quit”),
external regulation (e.g., “because I feel pressure from other people to play”), and amotivation (e.g., “but I wonder what’s the point”).
Response options ranged from not true at all to
very true and are scored on a 7-point scale with
higher scores reflecting greater endorsement of
that motive for participating in sport. Each form
of motivational regulation was treated separately in the analyses. Evidence supporting the
psychometric properties of the BRSQ is reported by Lonsdale et al. (2008).
Need satisfaction. Athletes’ level of need
satisfaction was assessed using three distinct
measures. The players’ perception of sport competence was assessed using the corresponding
subscale of the Intrinsic Motivation Inventory
(McAuley, Duncan, & Tammen, 1989). Response options for the 5 items (e.g., “I am pretty
skilled at volleyball.”) range from strongly disagree to strongly agree and are scored on a
7-point scale with higher scores reflecting
higher or more positive perceptions of competence. Athletes’ satisfaction of their need for
autonomy was assessed using a scale developed
by Hollembeak and Amorose (2005). Each of
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210
AMOROSE AND ANDERSON-BUTCHER
the 6 items asks respondents to indicate the
amount of choice or control they have when it
comes to participating in their current sport
(e.g., “I have a say in what I do when participating in my sport.”). Response options range
from not at all true for me to completely true for
me, are scored on a 7-point scale with higher
scores reflecting greater autonomy. The satisfaction of the athletes’ need for relatedness was
assessed using the sport-oriented version of
Richer and Vallerand’s (1998) Feelings of Relatedness Scale. This scale asks respondents the
extent to which they agree with a series of 5
adjectives (e.g., “supported”) describing their
relationships with the members of their sport
team, (i.e., coaches, teammates). Response options, ranging from do not agree at all to very
strongly agree, are scored on a 7-point scale
with higher scores reflecting a greater sense of
relatedness. Each of these measures has demonstrated adequate psychometric properties, including internal consistency, factorial, and construct validity, with adolescent athletes in
similar studies exploring coaching behaviors
from a SDT lens (e.g., Amorose & AndersonButcher, 2007; Amorose, Anderson-Butcher, &
Cooper, 2009).
Burnout. Athletes’ level of burnout was
assessed using the Athlete Burnout Questionnaire (Raedeke & Smith, 2001). Each of the 15
items assesses one of three underlying dimensions of burnout, including emotional/physical
exhaustion (e.g., “I am exhausted by the mental
and physical demands of my sport.”), reduced
sense of accomplishment (e.g., “I am not
achieving much in my sport.”), and sport devaluation (e.g., “I have negative feelings toward
my sport.”). Response options, which range
from almost never to most of the time, are
scored on a 5-point scale with higher scores
indicating greater burnout. Raedeke and Smith
(2001) have reported psychometric support for
the measure. Consistent with previous research
(e.g., Amorose et al., 2009), we averaged the
scores from all items into a single global indicator of burnout.
Demographic information. Participants
were asked to indicate basic demographic information such as age, gender, race/ethnicity,
sport, the length of time they have been participating, and the length of time they have played
for their current coach.
Procedures
Approved Institutional Review Board procedures were followed in terms of securing assent/
consent from parents/guardians and athletes before the beginning of the study. The data
reported in this study were collected at a regularly scheduled practice session near the end of
the athletes’ competitive season (i.e., approximately 1–2 weeks before the last official competition). Participants were given as much time
as needed to voluntarily complete the survey
and were told that their answers would remain
confidential. Coaches were asked to leave the
area while the athletes completed the surveys.
These data were collected as part of a larger
study focused on youth sport participation;
however, none of the other data have been published to date.
Data Analyses
Following standard data screening procedures and basic descriptive analyses, we tested
the main research question using a hierarchical
regression analysis using procedures outlined
by Aiken and West (1991). The independent
effects of autonomy-supportive and controlling
coaching behaviors were entered on step one of
the hierarchical regression equation predicting
each of the motivational outcomes. The 2-way
interaction term (i.e., autonomy support x controlling behavior) was then entered on the second step to determine whether the combination
of the different behaviors added to the prediction of athletes’ motivation beyond the independent effects. Consistent with the recommendations of Aiken and West (1991), the predictor
variables were centered and the interaction
terms were formed as the cross-product of the
centered variables. Each of the motivational responses was analyzed in a separate regression.
Standardized regression coefficients and
squared semipartial correlations from the final
regression equations were used to determine the
pattern and relative strength of each coaching
variable to the prediction of the motivational
responses. To aide in the interpretation of any
significant interaction, predicted values were
computed by systematically substituting values
equal to ⫹1 SD and ⫺1 SD for the coaching
behavior variables into the final regression
equation. These values were then used to create
EXPLORING THE INDEPENDENT AND INTERACTIVE EFFECTS
separate regression lines representing individuals who perceived relatively higher and lower
levels of autonomy-supportive and controlling
behaviors (see Aiken & West, 1991).
Results
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Preliminary Analyses
All participants provided complete data. Basic descriptive statistics are presented in Table
1. The internal consistency estimates (␣) for all
the measures ranged from .75 to .95, indicating
acceptable reliability. The mean scores, relative
to the scale midpoints (potential range of scores
was 1–7), demonstrated that the sample of athletes perceived that their coaches were moderate
to high in autonomy-supportive behaviors (M ⫽
4.86) and relatively low in controlling behaviors
(M ⫽ 2.83). Mean scores also revealed that the
sample of athletes reported a relatively positive
motivational profile with scores on the selfdetermined forms of motivation (i.e., intrinsic,
integrated, identified) and level need satisfaction (i.e., perceived competence, autonomy,
relatedness) all above the midpoint of the respective scales. The scores on the non–selfdetermined forms of motivation (i.e., introjected, external, amotivation) and burnout were
below the midpoint of the scales.
211
Table 1 also presents the bivariate correlations between the two coaching behaviors and
the motivational responses of the athletes. Perceived autonomy-supportive coaching behaviors positively and significantly related the more
adaptive motivational responses and negatively
related to the more maladaptive responses,
whereas perceived controlling behaviors
showed the opposite pattern of relationships.
These relationships tended to be moderate to
weak in strength. The only exceptions involved
the lack of a significant relationship between
autonomy support and both introjected and external regulation. As seen in Table 1, the correlation between the 2 coaching behaviors was
⫺.50.
Main Analysis
First, we evaluated the key assumptions for
multiple regression analyses (e.g., normality,
linearity, and homoscedasticity of residuals; absences of multicollinearity and singularity, absences of multivariate outliers) as outlined by
Tabachnick and Fidell (2013). No violations
were noted, and therefore we proceeded to test
the primary research question with a series of
hierarchical regression analyses. A summary of
these analyses is presented in Table 2. The
results showed that the independent effects of
Table 1
Descriptive Statistics for Study Variables and Correlations Between Perceived Coaching Behaviors and
Motivational Variables (n ⫽ 301)
Bivariate correlations (r)
Variable
M
SD
␣
Autonomy-supportive
behaviors
1. Autonomy-supportive behaviors
2. Controlling behaviors
3. Intrinsic regulation
4. Integrated regulation
5. Identified regulation
6. Introjected regulation
7. External regulation
8. Amotivation
9. Perceived competence
10. Perceived autonomy
11. Perceived relatedness
12. Burnout
4.86
2.83
6.25
5.69
5.84
4.06
3.25
2.18
5.74
4.88
5.47
2.27
1.51
1.23
.93
1.17
1.04
1.41
1.33
1.27
.97
1.03
1.19
.71
.95
.92
.87
.85
.75
.77
.78
.84
.89
.76
.90
.92
—
⫺.50ⴱ
.41ⴱ
.37ⴱ
.41ⴱ
.02
⫺.02
⫺.38ⴱ
.30ⴱ
.53ⴱ
.62ⴱ
⫺.43ⴱ
Controlling behaviors
⫺.50ⴱ
—
⫺.37ⴱ
⫺.26ⴱ
⫺.24ⴱ
.22ⴱ
.34ⴱ
.49ⴱ
⫺.17ⴱ
⫺.41ⴱ
⫺.44ⴱ
.55ⴱ
Note. Potential range of score for all scales is 1–7, with the exception of Burnout, which is 1–5. A full correlation matrix
with all study variables is available on request from the first author.
ⴱ
Correlation significant at p ⬍ .05.
212
AMOROSE AND ANDERSON-BUTCHER
Table 2
Summary of Hierarchical Regression Analyses Predicting Athletes’ Motivational Responses
Standardized regression coefficients (sr2)
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Regression
Intrinsic regulation
Step 1
Step 2
Integrated regulation
Step 1
Step 2
Identified regulation
Step 1
Step 2
Introjected regulation
Step 1
Step 2
External regulation
Step 1
Step 2
Amotivation
Step 1
Step 2
Perceived competence
Step 1
Step 2
Perceived autonomy
Step 1
Step 2
Perceived relatedness
Step 1
Step 2
Burnout
Step 1
Step 2
F
R2
⌬R2
Autonomy-supportive
behaviors
Controlling
behaviors
Interaction of
coaching behaviors
38.15ⴱ
25.45ⴱ
.20
.20
—
.00
.30ⴱ (.07)
.30ⴱ (.07)
⫺.22ⴱ (.04)
⫺.23ⴱ (.04)
—
⫺.03 (.00)
25.79ⴱ
18.80ⴱ
.14
.15
—
.01ⴱ
.32ⴱ (.08)
.35ⴱ (.09)
⫺.10 (.01)
⫺.12 (.01)
—
⫺.12ⴱ (.01)
30.03ⴱ
21.57ⴱ
.16
.17
—
.01ⴱ
.38ⴱ (.11)
.41ⴱ (.12)
⫺.05 (.00)
⫺.06 (.00)
—
⫺.11ⴱ (.01)
11.62ⴱ
7.82ⴱ
.07
.07
—
.00
.17ⴱ (.02)
.17ⴱ (.02)
.31ⴱ (.07)
.32ⴱ (.07)
—
.03 (.00)
25.84ⴱ
17.33ⴱ
.14
.14
—
.00
.20ⴱ (.03)
.19ⴱ (.03)
.44ⴱ (.15)
.45ⴱ (.15)
—
.04 (.00)
54.22ⴱ
36.20ⴱ
.26
.26
—
.00
⫺.18ⴱ (02)
⫺.17ⴱ (.02)
.40ⴱ (.12)
.40ⴱ (.12)
—
⫺.03 (.00)
14.88ⴱ
11.49ⴱ
.09
.10
—
.01ⴱ
.28ⴱ (.06)
.31ⴱ (.07)
⫺.03 (.00)
⫺.05 (.00)
—
⫺.12ⴱ (.01)
65.60ⴱ
46.83ⴱ
.31
.32
—
.02ⴱ
.43ⴱ (.14)
.46ⴱ (.15)
⫺.20ⴱ (.03)
⫺.21ⴱ (.03)
—
⫺.13ⴱ (.02)
100.60ⴱ
67.57ⴱ
.40
.40
—
.00
.52ⴱ (.21)
.54ⴱ (.19)
⫺.18ⴱ (.03)
⫺.18ⴱ (.02)
—
.05 (.00)
74.64ⴱ
53.45ⴱ
.33
.35
—
.02ⴱ
⫺.21ⴱ (.03)
⫺.24ⴱ (.04)
.44ⴱ (.15)
.46ⴱ (.16)
—
.14ⴱ (.02)
Note. sr2 ⫽ squared semipartial correlation.
ⴱ
Significant at p ⬍ .05.
autonomy-supportive and controlling behaviors
entered together on step 1 of the equations significantly (p ⬍ .05) predicted each of the motivational variables. Based on the R2 values, the
amount of variance accounted for in the motivational responses was lowest for introjected
regulation (R2 ⫽ .07) and highest for perceived
relatedness (R2 ⫽ .40).
Examination of the standardized regression
coefficients revealed that the coaching behaviors, for the most part, predicted the motivational responses in the expected directions. For
example, autonomy-supportive behaviors were
significant positive predictors of the adaptive motivational responses and significant negative predictors of the maladaptive responses. The only
exception to the expected pattern was in the case
of the two non–self-determined forms of extrinsic motivation (i.e., introjected and external regulation), where autonomy support was a significant positive predictor. Controlling behaviors
also generally followed the expected pattern.
Specifically, the controlling coaching behaviors
were significant positive predictors of the maladaptive motivational responses (i.e., introjected regulation, external regulation, amotivation, and burnout). The perception of
controlling behavior also was found to be a
significant negative predictor of intrinsic regulation, perceived autonomy, and feelings of relatedness.
Based on the size of the standardized regression coefficients () and the squared-semipartial correlations (sr2), which indicates the
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EXPLORING THE INDEPENDENT AND INTERACTIVE EFFECTS
Discussion
The general goal of the study was to explore
the effects of autonomy-supportive and controlling coaching behaviors on athletes’ motiva-
7
Lower (-1SD) Controlling Behavior
Higher (+1SD) Controlling Behavior
6.5
Integrated Regulation
amount of unique variance accounted for by
each predictor variable, the relative strength of
the relationships between the coaching styles
and the dependent variables followed the expected pattern (see Table 2). In particular, the
autonomy-supportive behaviors were found to
more strongly related to the adaptive motivational responses, whereas perceived controlling
behaviors showed a relatively stronger effect on
the maladaptive outcomes. As an example, autonomy support had a relatively stronger independent effect on the athletes’ feelings of relatedness ( ⫽ .52; sr 2 ⫽ .21) relative to
controlling behaviors ( ⫽ ⫺.18; sr2 ⫽ .03).
Conversely, the unique effect of controlling
coaching behaviors was relatively larger in the
prediction of amotivation ( ⫽ .40; sr2 ⫽ .12)
relative to autonomy-supportive behaviors ( ⫽
⫺.18; sr2 ⫽ .02).
Interestingly, the hierarchal regression analyses also showed that the main effects of autonomy-supportive and controlling behaviors
were superseded by a significant (p ⬍ .05)
interaction in 5 of the 10 analyses (see Table 2).
Although the inclusion of the interaction effects
on Step 2 of these analyses added only a small
amount of explained variance (⌬R2 ranged from
.01–.02), these results indicate that the combination of the two coaching styles added to the
prediction of the certain motivational variables—namely, perceived autonomy, perceived
competence, integrated regulation, identified
regulation, and burnout.
Using the procedures recommended by Aiken and West (1991), separate regression lines
were plotted representing individuals with relatively high and low levels on the two coaching
behaviors. The general conclusions based on the
examination of the interaction plots (see Figures
1–5) are that (a) the positive motivational responses increased as perceptions of autonomy
support increased—particularly when the athletes also perceived a relatively lower level of
controlling behaviors, and (b) the most positive
motivational outcomes were associated with the
perceptions of relatively higher autonomy support and relatively lower controlling behaviors.
213
6
5.5
5
4.5
4
3.5
Lower (-1SD)
Higher (+1SD)
Autonomy Supportive Behaviors
Figure 1. Regression lines illustrating the significant interaction of autonomy-supportive and controlling coaching
behaviors on the prediction of athletes’ integrated regulation.
tional responses, with a particular interest in
exploring the potential interactive effects of
these two coaching behaviors. We forwarded
the hypothesis that the interactions would add
meaningfully to the prediction of different motivational outcomes above and beyond the independent effects of the two coaching styles. Further, we expected that the motivational
responses would generally be more positive under conditions where the athletes perceived relatively higher autonomy support and relatively
lower controlling behaviors in cases where significant interactions might emerge. The results
of the hierarchical regressions revealed some
support for the importance of considering the
interactions and for our expectations in terms of
the patterns of influence.
First, the interactions among the two coaching styles were predictive in 5 of the 10 analyses. There was no clear pattern in the types of
responses (e.g., adaptive or maladaptive responses, motives vs. need satisfaction) where
the interaction added explained variance. A significant interaction emerged across the range of
responses—two of the motivational regulations
(integrated and identified regulation), two of the
needs (perceived competence and perceived autonomy), and burnout. The fact, however, that
the effect was seen in a variety of the outcomes
provides some initial evidence in support of a
small interactive effect.
The potential importance of considering the
interactive effect of the two coaching behaviors
may be better supported by the fact that the
pattern of relationships found in each of these
analyses was very consistent across the motiva-
214
AMOROSE AND ANDERSON-BUTCHER
7
7
Lower (-1SD) Controlling Behavior
Higher (+1SD) Controlling Behavior
Perceived Autonomy
Identified Regulation
6
5.5
5
4.5
4
Higher (+1SD) Controlling Behavior
6
5.5
5
4.5
4
3.5
Lower (-1SD)
3
Higher (+1SD)
Autonomy Supportive Behaviors
Lower (-1SD)
Higher (+1SD)
Autonomy Supportive Behaviors
Figure 2. Regression lines illustrating the significant interaction of autonomy-supportive and controlling coaching
behaviors on the prediction of athletes’ identified regulation.
Figure 4. Regression lines illustrating the significant interaction of autonomy-supportive and controlling coaching
behaviors on the prediction of athletes’ perceived autonomy.
tional responses. This finding was consistent
with our expectations. The interaction plots
show that the motivational benefit of increasingly higher levels of autonomy-support was
greater when the athletes also reported lower
controlling behavior. The most positive motivational outcomes were associated with perceptions of higher autonomy support and lower
controlling behaviors.
The similarity in the pattern of results across
the variety of motivational responses leads us to
suggest that further research exploring the interactive effects of autonomy-supportive and
controlling coaching behaviors is warranted.
In this study the interactions added only a
small amount of variance to the explanation
of the outcomes. Although statistically significant, the maximum contribution was only 2%
of the overall variation explained. Thus, the
meaningfulness of the interactions in terms of
our understanding of athletes’ motivation may
be limited.
Perhaps, however, a small influence at one
point in time may become more meaningful
with repeated exposure. The effects of coaching
behaviors on athletes’ motivational responses
are likely cumulative in nature. Consider the
situation where an athlete is exposed to her/his
coach’s behaviors for 1 to 2 hours a day, 4 to 5
hours per week, over the course of an entire
season, and potentially over multiple seasons.
As noted by Abelson (1985), in cumulative processes such as this, “it is quite possible that
small variance contributions of independent
variables in single-shot studies grossly understate the variance contribution in the long run”
(p. 133). So, even though we feel it would be
imprudent to definitively claim that the 1% to
2% of the variance self-reported motivational
responses accounted for by the interaction
would translate into substantial differences, say,
Lower (-1SD) Controlling Behavior
7
Higher (+1SD) Controlling Behavior
5
6.5
Lower (-1SD) Controlling Behavior
Higher (+1SD) Controlling Behavior
4.5
6
4
5.5
3.5
Burnout
Perceived Competence
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
3.5
Lower (-1SD) Controlling Behavior
6.5
6.5
5
4.5
2
4
3.5
3
2.5
1.5
Lower (-1SD)
Higher (+1SD)
Autonomy Supportive Behaviors
1
Lower (-1SD)
Higher (+1SD)
Autonomy Supportive Behaviors
Figure 3. Regression lines illustrating the significant interaction of autonomy-supportive and controlling coaching
behaviors on the prediction of athletes’ perceived competence.
Figure 5. Regression lines illustrating the significant interaction of autonomy-supportive and controlling coaching
behaviors on the prediction of athletes’ burnout.
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
EXPLORING THE INDEPENDENT AND INTERACTIVE EFFECTS
in motivated behavior (e.g., dropping out due to
feeling burnt out), the nature of the processes at
work might signal that the effects could possibly grow over time. Therefore, the influence of
the interactions would be more significant longterm.1 We suggest that future studies adopt a
longitudinal design to determine if the interactions are truly meaningful over time.
Although the most unique contribution of our
study involved the testing of the interactions,
our results also contribute to the growing body
of research supporting the motivational implications of perceived autonomy-supportive and
controlling coaching behaviors (e.g., Bartholomew et al., 2011; Isoard-Gautheur et al.,
2012; Pelletier et al., 2001; Smith et al., 2010),
and support the idea that an autonomysupportive interpersonal style is an effective
motivational technique for coaches, whereas a
controlling interpersonal style is relatively ineffective. The only exception to the expected pattern was in the case of the two non–selfdetermined forms of extrinsic motivation (i.e.,
introjected and external regulation). In this case,
autonomy support was found to be a significant
positive predictor of each outcome. Interestingly, a closer examination of some existing
research assessing perceived autonomy-supportive coaching behaviors and the various
forms of behavioral regulation identified by
SDT (Ryan & Deci, 2002) suggests that this
finding may not be so unexpected. For example,
studies by Pelletier et al. (2001); Pope and Wilson (2012), and Isoard-Gautheur et al. (2012)
each reported small but positive relationships
among autonomy support and the two non–selfdetermined forms of extrinsic motivation. A
few studies in the physical education context
(Ntoumanis, 2005; Taylor & Ntoumanis, 2007)
also have reported weak but positive relationships between perceived autonomy support
from a teacher and students introjected regulation. The findings in physical education studies
overall, however, seem to be more consistent
with our initial prediction of a negative relationship between autonomy support and non-selfdetermined forms of motivation (e.g., Lim &
Wang, 2009). It is unclear why this may be the
case. Perhaps, in terms of introjected regulation,
athletes who perceive their coaches to be autonomy-supportive form a closer bond with their
coach (Lafrenière, Jowett, Vallerand, & Carbonneau, 2011) and subsequently are more
215
likely to engage in activities as a way to avoid
letting their coaches down. Alternatively, the
athletes may have begun the process of internalizing externally regulated aspects of sport
(e.g., training activities that are not inherently
enjoyable), given the coach is creating a situation where the athletes needs are being supported, yet the process has not continued to the
point where the behavior has become selfdetermined (Mageau & Vallerand, 2003; Ryan
& Deci, 2002). In future studies researchers
should continue to explore the nature and underlying mechanism of how non–self-determined for…
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