The Sport Psychologist, 2011, 25, 190-211
© 2011 Human Kinetics, Inc.
Relationship Between Collegiate Athletes’
Psychological Characteristics
and Their Preferences for Different Types
of Coaching Behavior
Thelma S. Horn
Miami University
Patrick Bloom
University of Wisconsin—Stevens Point
Katie M. Berglund and Stacie Packard
Miami University
This study was based on Chelladurai’s (1978, 2001, 2007) Multidimensional
Model of Leadership and was designed to determine whether athletes’ preferred
coaching behavior would vary as a function of their psychological characteristics. Study participants (N = 195 collegiate athletes) completed questionnaires
to assess their sport anxiety (SAS), motivational orientation (SMS), as well as
their preferred coaching styles (LSS) and feedback patterns (CFQ). Canonical
correlation procedures revealed that athletes who were high in self-determined
forms of motivation and in somatic trait anxiety preferred coaches who exhibited
a democratic leadership style and who provided high amounts of training, social
support, and positive and informational feedback while athletes who were high in
amotivation indicated a preference for coaches who exhibited an autocratic style
and who provided high amounts of punishment-oriented feedback. In addition,
high cognitive sport anxiety was linked to greater preference for high frequencies
of positive and informational feedback and lower preference for punishmentoriented feedback.
Over the past couple of decades, a large number of sport psychology-based
studies have been conducted to examine the correlates of coaching effectiveness.
Recent reviews of this research (e.g., Amorose, 2007; Chelladurai, 2007; Duda
& Balaguer, 2007; Horn, 2008; Riemer, 2007) have provided support for the
hypothesized link between coaches’ behavior and athletes’ positive outcomes.
Horn, Berglund, and Packard are with the Dept. of Kinesiology and Health, Miami University, Oxford,
OH. Bloom is with the School of Health, Exercise Science, and Athletics, University of Wisconsin—
Stevens Point, Stevens Point, WI.
190
Athletes’ Preferred Coaching Behavior 191
Specifically, the types of behaviors and/or leadership styles exhibited by coaches
can either facilitate or undermine the psychosocial growth and development of
their athletes.
Another body of research, although somewhat less prolific in nature, has examined the types of coaching behaviors and leadership styles that athletes prefer their
coaches to exhibit. Most of this research has been based on the Multidimensional
Model of Leadership (MML), developed and subsequently refined by Chelladurai
(1978, 2001, 2007). The central thesis of Chelladurai’s model is that the positive
outcomes of successful performance and athlete satisfaction can be achieved in
contexts where there is congruence between three components of coaches’ behavior:
preferred, actual, and required. In other words, when the behaviors that the athletes
prefer their coach to exhibit are congruent or consistent with both the coaching
behaviors that the coach actually exhibits as well as the coaching behaviors that
are required/desirable in that particular sport context, then maximum performance
and athlete satisfaction can be achieved.
Since the advent and refinement of the MML (Chelladurai, 1978, 2001, 2007),
a number of studies have been conducted to test its applicability to the sport context
(see reviews by Chelladurai, 2007; Riemer, 2007). Because the current study focuses
only on the behaviors that athletes prefer their coaches to exhibit, this particular
component of the research base is explored more fully in the following section.
Athletes’ Preferred Coaching Behavior
According to the MML (Chelladurai, 1978, 2001, 2007), athletes’ preferences for
different types of coaching behaviors and leadership styles will vary as a function
of their own individual characteristics (e.g., abilities, traits, age) and the characteristics of the situation (e.g., sport level, sport type). In examining the effects of sport
type on athletes’ preferred coaching behavior, researchers have compared athletes
from sports or sport positions that vary in level of task dependency (e.g., individual
versus team; independent versus interdependent) and/or task variability (Chelladurai
& Saleh, 1978; Riemer & Chelladurai, 1995; Terry, 1984; Terry & Howe, 1984).
Although somewhat mixed results from these studies have been obtained, there
is general support for the notion that task type (however it is defined) has some
effect on the type of coaching behaviors and leadership styles that athletes prefer.
Some research evidence has also been accumulated to show that athletes’
preferred coaching behaviors vary as a function of characteristics specific to the
athletes (e.g., age, sport experience, competitive level; e.g., Chelladurai & Carron,
1981; 1983; Erle, 1981; Riemer & Toon, 2001; Terry, 1984). Other researchers
have tested the possibility that preferred coaching behavior would vary as a function of athletes’ gender (operationalized as biological sex; e.g., Beam, Serwatka,
& Wilson, 2004; Riemer & Toon, 2001; Terry & Howe, 1984; Sherman, Fuller, &
Speed, 2000). Quite disparate results have been found, leading some researchers
and scholars (e.g., Riemer, 2007; Riemer & Toon, 2001; Sherman et al., 2000)
to suggest that any observed differences between male and female athletes may
actually be due to other factors in the sport or social environment (e.g., gender of
coach, type or level of sport, sport culture) and/or that any observed differences
are overshadowed by similarities in male and female preferences.
192 Horn et al.
Relatively little research has been conducted to examine the possibility that
athletes’ psychological characteristics may impact their preferences for different
types of coaching behavior. Chelladurai and Carron (1981) did find that athletes
who evidenced a high need for environmental structure and information tended to
prefer more training and instructional behavior and less autocratic behavior from
their coaches than did those athletes who were lower in those characteristics. In
addition, athletes who were high in impulsivity indicated greater preference for
coaches’ social support than did athletes who were lower in impulsivity.
Erle (1981) also examined links between athletes’ motivational orientation
and preferred coaching behavior. He found that athletes who were task motivated
(focused on personal improvement) preferred coaches to provide high frequencies
of training and instructional behavior while athletes who were affiliation-oriented
(focused on group relationships) and extrinsically motivated exhibited greater
preference for social support behavior from coaches.
In general, then, the results of the research studies in this area suggest that athletes’ psychological characteristic may indeed be linked to the coaching behaviors
and styles that they prefer their coaches to exhibit. However the two studies cited
in the previous paragraphs are somewhat dated in regard to both the theoretical
and empirical tenants on which they were based. Thus, a more current research
perspective is needed. In particular, there may be two aspects of athletes’ psychological profiles that can be connected to their preferred coaching behavior. These
include motivational orientation and trait anxiety. These two specific psychological constructs were selected because there is a relatively large body of research
to suggest that these variables are very relevant in the sport context. Specifically,
recent reviews of the sport anxiety research literature (e.g., Beilock & Gray, 2007
and Landers & Arent, 2010) point to the negative impact that high trait and state
anxiety can have on the performance, behavior, and affective reactions of athletes.
Similarly, athletes’ motivational orientation profiles have also been clearly linked to
multiple aspects of their performance and behavior (see, for example, recent chapters
in text edited by Hagger & Chatzisarantis, 2007). Furthermore, researchers in the
coaching effectiveness literature have established connections between coaches’
leadership styles and behaviors and their athletes’ levels of competitive anxiety and
motivational orientation (see reviews of this literature by Amorose, 2007; Duda
& Balaguer, 2007; Horn, 2008). Thus, for the current study, these two constructs
were selected as possible predictors of athletes’ preferences for different types of
coaching leadership styles and behaviors. The research and theory corresponding
to these two constructs are reviewed in the following sections.
Motivational Orientation
The study of motivational orientation in athletes has been examined primarily from
the perspective of Self-Determination Theory (SDT; Deci & Ryan, 2000; Ryan &
Deci, 2007). In applying this theory to the sport context, Vallerand (2001, 2007)
posited that individual athletes vary in the degree to which their sport motivation is
self-determined. Such interindividual variability is construed to lie on a continuum
ranging from the least self-determined (amotivation and external regulation) to
the most self-determined forms of motivation (intrinsic motivation). Athletes who
exhibit the most self-determined forms of motivation engage in their sport and work
Athletes’ Preferred Coaching Behavior 193
hard at it for the pleasure and satisfaction they derive from it. In contrast, athletes
who exhibit less self-determined forms of motivation (e.g., external regulation) may
exhibit motivated behavior in sport contexts but are persuaded to do so by external
factors (e.g., parents, coaches, desire for awards, scholarships, or to avoid punishment). Finally, there are also athletes who exhibit amotivation. This term refers to
a relative lack of motivation (either intrinsic or extrinsic) and is thus considered to
be at the extreme end of the non-self-determined continuum.
Although there is a large body of research to suggest that athletes who are intrinsically motivated toward their sport tend to experience more positive psychological
and behavioral outcomes as compared with their less self-determined peers (see
recent reviews by Vallerand, 2007; Weiss & Amorose, 2008), the existing research
also shows that there are athletes at the collegiate and/or elite levels whose motivational orientation reflects a more extrinsic, or non-self-determined, orientation (e.g.,
Amorose & Horn, 2000; Gillet, Vallerand, & Rosnet, 2009; Mallett & Hanrahan,
2004; Treasure, Lemyre, Kuczka, & Standage, 2007). Given such interindividual
variability in athletes at this level, it may be important to determine if collegiate
athletes who vary in their motivational orientation would exhibit differences in
their preferences for coaching behaviors and leadership styles.
Based on the research and theory in motivational orientation and its relationship
to coaching behavior (e.g., Amorose, 2007; Bartholomew, Ntoumanis, & ThogersenNtoumani, 2009; Vallerand, 2007), it would seem likely that athletes who are at the
less self-determined end of the continuum (e.g., external regulation and/or amotivation) might prefer (or perceive that they need) coaches who are more controlling
or dictatorial in their leadership styles and in the type of feedback they provide.
This hypothesis is based on the fact that such athletes may be most dependent on
external factors (e.g., such as the coaches’ behavior) to regulate their practice and
competitive behaviors. Thus, they may prefer coaches who are autocratic in their
leadership style and who provide high frequencies of punishment-oriented feedback
to help motivate them to improve their performance. In contrast, athletes who are
at the self-determined end of the motivational orientation continuum (i.e., those
whose sport behavior is motivated by their own internal desire to learn, achieve, or
experience stimulation) might prefer coaches who are less controlling (more democratic) in leadership style, and who will provide them with the optimal conditions
needed for them to learn and achieve (i.e., coaches who provide high frequencies
of training, instruction, and informational feedback). Thus, the first aspect of the
current study was to examine the hypothesized links between collegiate athletes’
motivational orientation and their preferred coaching behaviors.
Competitive Trait Anxiety
A second study dimension was to explore athletes’ competitive trait anxiety levels
as a potential correlate of their preferred coaching behavior. Competitive trait
anxiety has been defined as a relatively stable individual difference characteristic
that affects the degree to which athletes perceive threat in competitive contexts,
which, in turn, determines the direction, level, and intensity of their anxiety and
arousal responses in specific sport situations (Smith, Smoll & Shultz, 1990; Smith,
Smoll, & Wiechman, 1998). The conceptual model of sport performance anxiety
proffered by Smith et al. (1998) suggests two primary causes or sources of threat
194 Horn et al.
in the sport context: (a) the possibility of performance failure (e.g., outcome loss
and/or poor individual performance), and (b) the possibility of receiving negative
evaluation from significant others (e.g., coaches, parents, teammates). Smith and
his colleagues (Smith, Smoll, Cumming, & Grossbard, 2006; Smith et al., 1990)
have also provided evidence that the construct of competitive trait anxiety is multidimensional in nature, with subdimensions consisting of somatic anxiety, worry,
and concentration-disruption.
Based on Smith et al.’s (1998) conceptual model of sport anxiety, it might be
hypothesized that collegiate athletes who are high in competitive trait anxiety may
prefer coaches who are socially supportive and who respond to athlete performances
with positive and encouraging, rather than critical or punishment-oriented, feedback.
In support of this hypothesis, studies conducted with collegiate or college-aged
athletes (e.g., Amorose & Horn, 2000; Baker, Côté & Hawes, 2000) have indicated
that the degree to which athletes perceive that their coaches exhibit an autocratic
leadership style and/or use punitive behaviors (e.g., yelling, intimidating) are lower
in perceived competence and higher in trait anxiety levels. In contrast, athletes
who reported that their coaches were strong in training, instructional, and strategyoriented behaviors (e.g., maintaining a consistent routine and showing confidence in
athletes) and who provided high frequencies of positive and informationally-based
feedback exhibited lower levels of perceived tension and sport anxiety.
In summary, then, the primary purpose of the current study was to determine
if athletes’ psychological characteristics would be correlated with their preferences for different types of coaching behaviors and leadership styles. Based on
previous research and theory, it was hypothesized that athletes who exhibited
more self-determined forms of motivation would show greater preference for
democratic coaches who provide high frequencies of training and instruction and
informationally-based feedback while athletes who exhibited non-self-determined
forms of motivation would be more apt to prefer autocratic coaches who provide
high frequencies of punishment-oriented feedback. In regard to athletes’ sport
anxiety levels, it was generally hypothesized that athletes with high levels of trait
sport anxiety would prefer coaches who provide high frequencies of training and
instruction and socially supportive, positive, and informationally-based feedback
behaviors while exhibiting low preference for a more punishment-oriented coaching
feedback pattern. In their study with adolescent and young adult athletes, Baker
et al. (2000) found that perceived coaching behaviors were linked to all of the
subscales from the SAS. Thus, no specific hypotheses were forwarded for the current study regarding the subdimensions of sport anxiety. However, it was deemed
an important component of this study to examine whether athletes’ preferences
for different types of coaching behavior would be differentially linked to the three
dimensions of sport anxiety.
Method
Participants
The sample recruited for this study included 207 athletes from National Collegiate
Athletic Association (NCAA) Division III teams. However, once data were screened
for missing values, 12 of these athletes were deleted from further analysis. Thus, the
Athletes’ Preferred Coaching Behavior 195
actual study sample included 195 athletes (109 males and 86 females) who ranged
in age from 18 to 26 years (M = 19.82; SD = 1.35) and were currently either in their
first (33.8%), second (25.6%), third (22.6%), fourth (15.4%) or fifth year (2.6%)
of college. Study participants were from team sports (volleyball, soccer, football,
baseball, softball, field hockey, lacrosse, and basketball) and from a number of different colleges or universities located in the eastern and midwestern United States.
The decision to conduct this study with collegiate athletes was made for a
couple of reasons. First, many of the previous studies in the coaching behavior
literature that have examined the link between coaching behavior and athletes’
trait anxiety have been conducted with youth sport participants (see, for example,
Conroy & Coatsworth, 2004; Smith, Smoll, & Barnett, 1995; Smith, Smoll, & Cumming, 2007). Second, recent interest in the research literature has been expressed
in relation to the range exhibited by athletes at more elite levels of play as to where
they lie on the self-determination continuum (e.g., Gillet et al., 2009; Treasure et
al., 2007) and how this range might be related to coaches’ behavior. Thus, it was
decided to examine the hypothesized links between athletes’ levels of sport anxiety
and motivational orientation and their preferences for different types of coaching
behaviors and leadership styles at the collegiate sport level.
In addition, the specific sample of athletes recruited for participation in this
study included NCAA Division III athletes only. This decision was made because
previous research (Amorose & Horn, 2000) has indicated that collegiate athletes’
levels of motivational orientation and tension/anxiety may vary as a function of their
athletic scholarship status (e.g., full, partial, or none). Because NCAA Division III
athletes do not receive athletic scholarships, the sample was limited to this group.
Furthermore, because previous research studies (e.g., Terry, 1984) have indicated
that athletes from individual and team sports may differ in their preferences for
different types of coaching behavior, the current study sample was limited to only
team sport athletes.
Measures
To assess the relationship between athletes’ psychological characteristics and
their preferences for different types of coaching behaviors, a series of self-report
questionnaires were administered to all study participants. These questionnaires
were specifically selected to measure the variables of interest and are described in
the following sections.
Preferred Coaching Behavior. To measure athletes’ preferences for coaches’
behavior and leadership style, two different self-report questionnaires were used.
The first was the Leadership Scale for Sports (LSS; Chelladurai & Saleh, 1980).
This scale was developed to measure five dimensions of leader behavior. Two of
the dimensions (the Democratic and Autocratic Behavior subscales) assess the
coach’s style of decision-making (i.e., the degree to which coaches allow their
athletes to participate in sport-related decisions). A third subscale (Training and
Instructional Behavior) assesses coaches’ focus or emphasis on hard and strenuous training, skill instruction, and sport tactics. The fourth and fifth subscales
(Social Support and Positive Feedback) assess coaches’ tendencies to create a
positive and supportive team atmosphere and to provide positive and encouraging
performance feedback.
196 Horn et al.
Three versions of the LSS were developed (Chelladurai & Saleh, 1980) to
assess coaches’ perceptions of their own behavior, athletes’ perceptions of their
coaches’ behavior, and athletes’ preferences for their coaches’ behavior. All three
versions include 40 items divided into the five subscales. For the preferred version,
the item stem is: “I would prefer my coach to” and the response format for each
item includes five choices (ranging from never to always).
In their original work on the LSS, Chelladurai and Saleh (1980) provided
evidence for the reliability and consistency of all three versions of the instrument,
and other researchers have subsequently verified the subscale structure of the LSS,
with samples that included collegiate and college-aged athletes (see summary by
Chelladurai, 2007). Furthermore, Chelladurai and Riemer (1998) used confirmatory
factor analysis to provide support for the construct validity of both the preferred
and the perceived versions with collegiate athletes. Nevertheless, some concerns
about the internal consistency of the autocratic subscale remain (see critiques by
Chelladurai, 2007; Riemer, 2007). Therefore, based on recommendations by Price
and Weiss (2000), three additional items, (each with the stem “I would prefer my
coach to”) were added to the Autocratic Behavior subscale for the current study.
These three items include: (1) not take into account athletes’ suggestions when
making decisions; (2) control what athletes can do and cannot do; (3) make decisions regardless of what athletes think.
Five subscale scores were calculated by taking the mean average of the items
comprising each subscale. Assessment of the internal consistency of the five subscales was conducted using Cronbach’s alpha. Obtained coefficients (presented at
the bottom of Table 1) indicate acceptable levels for all five subscales (i.e., at or
above .70; Nunnally & Bernstein, 1994).
The second questionnaire used to assess athletes’ preferences for their coaches’
behavior was a preferred version of the Coaching Feedback Questionnaire (CFQ).
The CFQ was developed by Amorose and Horn (2000) as a questionnaire version
of the Coaching Behavior Assessment System (Smith, Smoll, & Hunt, 1977) and
was intended to assess collegiate athletes’ perceptions regarding the type of feedback their coaches give in response to their performance successes and failures.
The CFQ includes 16 items representing eight different types of preferred
feedback responses. These eight categories included three that can be given by
coaches in response to players’ performance successes (praise/reinforcement,
nonreinforcement, reinforcement combined with technical instruction) and five
that are given in response to players’ performance errors (mistake-contingent
encouragement, ignoring mistakes, corrective instruction, punishment, and corrective instruction combined with punishment). For each of the 16 items, athletes are
asked to indicate on a 5-point scale (not at all preferred to very much preferred)
how much they would prefer to receive that type of feedback from their coaches
after a successful or unsuccessful performance.
Although the perceived version of the CFQ has been used in previous studies
with collegiate athletes (Amorose & Horn, 2000) and with high school female
basketball athletes (Smith, Fry, Ethington, & Li, 2005), the preferred version
used in the current study has not previously been used. Thus, athletes’ responses
to the 16 items from the preferred CFQ were subjected to a principal axis factor
analysis to determine the structure underlying athletes’ preferred feedback patterns. Initial factors were extracted using a minimum eigenvalue of 1.0, and both
Athletes’ Preferred Coaching Behavior 197
Table 1 Factor Loadings for Exploratory Factor Analysis with Varimax
Rotation of Preferred Coaching Feedback Questionnaire (CFQ)
Scale Item
Factor 1
Factor 2
Factor 3
Non-Reinforcement
.60
-.09
.18
Non-Reinforcement
.68
-.11
.26
Ignoring Mistake
.82
-.11
.10
Ignoring Mistake
.60
-.15
.17
Punishment
.58
-.05
.58
Reinforcement
-.21
.50
-.01
Reinforcement + Technical Instruction
-.03
.83
.00
Reinforcement + Technical Instruction
-.07
.81
.04
Reinforcement
-.27
.56
-.16
Mistake-Contingent Technical Instruction
-.01
.42
.13
Mistake-Contingent Technical Instruction
-.05
.48
.30
Punishment + Technical Instruction
.31
.07
.60
Punishment + Technical Instruction
.14
-.08
.76
Punishment
.34
-.12
.70
Mistake-Contingent Encouragement
-.07
.26
-.01
Mistake-Contingent Encouragement
-.02
.23
.08
Eigenvalues
4.64
3.13
1.36
% of variance
29.00
19.55
8.49
orthogonal and oblique rotation solutions were examined. Examination of the
scree plots and the factor loadings from both solutions revealed the presence of
three conceptually distinct factors that explained a total of 57% of the variance
in the data. To interpret the factors, loadings (see Table 1) were examined using
a criterion value of .40. Thirteen of the 16 total items in the scale loaded on only
one factor. Two of the items (both mistake-contingent encouragement feedback
items) did not load on any of the three factors, and one of the punishment items
cross-loaded on two of the three factors. Thus, these three items were not used in
the interpretation of the factors. The four items loading highly on Factor 1 reflected
a coaching style in which athletes’ performance successes and failures are ignored
(i.e., coach gives no reinforcement in response to players’ successes and ignores
athletes’ performance errors). Thus, this factor was labeled nonreinforcement/
ignoring mistakes. The six items loading high on Factor 2 described a coaching
feedback style characterized by high frequencies of positive and informationallybased feedback as responses given to athletes following performance successes
and errors. Example statements in this category include (a) “Great play. Now
you’re keeping your eyes on the ball”; and (b) “You dropped your elbow. Next
time keep it up”. Given these loadings, Factor 2 was labeled positive and informational feedback. Finally, examination of the three items loading highly on
198 Horn et al.
Factor 3 suggest a coaching feedback style characterized by high frequencies of
punishment-oriented feedback given in response to players’ performance errors.
Example statements include (a) “Your technique looks lousy! Keep your head
up” and (b) “How many times have I told you to extend your elbow?” Thus, this
factor was labeled punishment-oriented feedback.
Factor scores for each study participant were computed and used in subsequent
analyses as a measure of athletes’ preferred coaching feedback. Cronbach’s alpha
coefficients, calculated using the items loading highly on each subscale, ranged
from .77 to .80 (see bottom of Table 2), indicating acceptable internal consistency
for the three subscales.
Motivational Orientation. To measure participants’ motivational orientation
toward their sport, the Sport Motivation Scale (SMS; Pelletier, Fortier, Vallerand, Tuson, & Blais, 1995) was used. The SMS was designed to operationalize
motivation in the sport context in regard to athletes’ perceived reasons for their
participation (i.e., the “why” of sport behavior). The 28 items that comprise the
scale identify different sport participation reasons, and athletes respond to each
item using a 7-point Likert-type format (does not correspond at all to corresponds
exactly). The stem question asks athletes why they practice their sport. The 28 items
were selected to assess seven stages of motivation: intrinsic motivation to know
(i.e., “For the pleasure it gives me to know more about the sport that I practice”),
intrinsic motivation to accomplish things (i.e., “Because I feel a lot of personal
Table 2 Correlations, Internal Consistency, and Descriptive Data
for All Preferred Coaching Behavior Subscales
Variable
1
1. LSS: T&I
-
2
2. LSS: Auto
-.10
.08
3
4
5
3. LSS: Demo
.47**
4. LSS: SocSupp
.46** .34**
.54**
-
5. LSS: PosFB
.70** -.22**
.47**
.39**
-
-.02
.07
-.28**
6. CFQ: NonRF/IM -.24** .50**
6
7
8
-
7. CFQ: PosInfo
.41**
-.05
.27**
8. CFQ: Punish
.07
.46**
.01
-
.25** .45**
-.04
-
.21**
-.10
.12
.02
Cronbach’s α
.86
.86
.77
.74
.83
.80
.77
.77
M
3.77
2.55
3.42
3.18
3.98
.00
.00
.00
SD
.59
.81
.59
.63
.72
.91
.91
.89
1–5
1–5
Possible Range
1–5
Obtained Range
1–5
1–4.5 1.56–4.78
1–5
1–5
1–5
1–5
-1.41–2.58 -3.15–1.39 -2.15–2.94
Note. LSS = Leadership Scale for Sports; T&I = training and instruction; Auto = autocratic behavioral style; Demo
= democratic behavioral style; SocSupp = socially supportive style; PosFB = positive feedback behaviors; CFQ =
Coaching Feedback Questionnaire; NonRF/IM = Factor 2 (nonreinforcement and ignoring mistakes); PosInfo = Factor
2 (positive and informational feedback pattern); Punish = Factor 3 (punishment-oriented feedback)
*p <.05. **p<.01.
Athletes’ Preferred Coaching Behavior 199
satisfaction when mastering certain training techniques), intrinsic motivation to
experience stimulation (i.e. “For the pleasure I feel living exciting experiences”),
identified regulation (i.e., “Because, in my opinion, it is one of the best ways to
meet people”), introjected regulation (i.e., “Because it is absolutely necessary
to do sport if one wants to be in shape”), external regulation (i.e., “Because it
allows me to be well regarded by people that I know”), and amotivation (i.e.,
“I used to have good reasons for doing sports, but now I am asking myself if I
should continue doing it”).
The SMS was initially developed and tested for reliability and validity by Pelletier et al. (1995), and subsequent other studies have reaffirmed its psychometric
qualities for a range of athletes, including those at the collegiate level (see recent
review by Pelletier & Sarrazin, 2007). For the current study, seven subscale scores
were calculated by averaging the four items assigned to each subscale. Obtained
alpha coefficients for the seven SMS subscales were all above .70 (see bottom of
Table 2).
Competitive Trait Anxiety. To assess athletes’ level of competitive trait anxiety, the
Sport Anxiety Scale (SAS: Smith et al., 1990) was used. The SAS was developed
as a multidimensional measure of trait anxiety as it is exhibited in the competitive
sport context. The SAS is comprised of 21 items that are divided into three subscales, one of which measures somatic anxiety (e.g., “My body feels tense”). The
other two subscales measure two dimensions of cognitive anxiety: worry (e.g., “I
have self-doubts”) and concentration-disruption (e.g., “During competition, I find
myself thinking about unrelated things”). A four-point Likert-type response format
is used (not at all to very much so).
The reliability and validity of the SAS has been demonstrated across a number
of studies (Smith et al., 1990; Smith et al., 1998) that have included collegiate or
college-aged athletes. However, a recent reexamination of its factorial integrity
(Smith, Cumming, & Smoll, 2006) revealed some problems with three of the 21
items. Based on these issues, Smith et al. (2006) recommend the deletion of three of
the items from the original SAS. These recommendations were used in the current
study, and three subscale scores were created by averaging across the remaining
items comprising each of the three subscales. Obtained alpha coefficients for the
current study (see bottom of Table 2) were all above .70.
Procedure
To recruit collegiate athletes for participation in this study, coaches and/or athletic
administrators from a variety of Division III college and university teams were
contacted. These individuals were provided with a general description of the study
and were asked to schedule a time during which a member of the research team
could meet with athletes at their university or college to request their participation
in the study and to complete the self-report questionnaires. All individuals who
were contacted agreed to participate, but three of the athlete groups that had initially agreed to participate were not actually surveyed due to inability to schedule
a data collection session.
Athletes completed self-report questionnaires either before or after a sport
practice or during a study table session. At this data collection session, a member
of the research team provided both a written and oral explanation of the research
200 Horn et al.
project assuring athletes that their responses would be collected in an anonymous
format (i.e., no identifying information was collected). To standardize time of
season, all athletes completed the study questionnaires during the last one-third of
their collegiate competitive season. The study protocol was reviewed and approved
by an Institutional Review Board.
Statistical Analyses
Descriptive statistics for all relevant study variables were computed and screened
for linearity and normality. Univariate correlational analyses were used to assess
the strength of the relationship between the variables in each of the two data sets
(preferred coaching behavior and athletes’ psychological profiles) and to determine whether any multicollinearity within the two sets existed. For the main study
analysis, canonical correlational procedures were used to determine if there was a
multivariate relationship between athletes’ psychological characteristics and their
preferences for different types of coaching behavior and leadership styles.
Results
Descriptive Results
Means, standard deviations, and range scores for all study variables are presented
in the bottom four rows of Tables 2 (preferred coaching behavior) and 3 (motivational orientations and competitive trait anxiety). Examination of the preferred
coaching behavior means indicates that this sample of athletes scored below the
midpoint (3.0 on a 5.0 scale) on preference for autocratic coaching behavior but
above the midpoint on the rest of the LSS subscales. However, the obtained range
and standard deviation results suggest considerable interindividual variability for
both the LSS subscale scores and the CFQ factor scores.
The descriptive statistics for the psychological characteristics (see last four rows
in Table 3) reveal mean scores that were below the midpoint on the SAS subscales
(2.5 on a 4-point scale). In regard to the SMS, high mean scores (above the midpoint
of 4.0) were seen for the more self-determined subscales (e.g., intrinsic motivation,
and identified regulation) with below the midpoint mean values for the less selfdetermined (introjected and external regulation) and amotivation subscales. Again,
however, the range values as well as the standard deviations indicate considerable
interindividual variability in all of the SAS and SMS subscales.
Correlational Data: Preferred Coaching Behavior Subscales
Univariate correlational analyses were conducted to determine the degree of
association between the five subscales from the LSS and the three factor scores
from the CFQ. These results (Table 2) indicate some correlation between the eight
subscales. However, the absolute size of all but one of the statistically significant
coefficients (significant r-values range from .21 to .54) reveal only low to moderate
relationships, thus supporting the notion that the LSS and CFQ subscales provide
relatively distinct dimensions of preferred coaching behavior. The one coefficient
that suggests a higher correlation is the one obtained for the relationship between
two LSS subscales: Training and Instruction and Positive Feedback (r = .70).
Athletes’ Preferred Coaching Behavior 201
Table 3 Correlations, Internal Consistency, and Descriptive Data for all
Psychological Subscales
1
1. SAS: Worry
2
3
4
5
6
7
8
9
10
-
2. SAS: SomAnx
.61**
3. SAS: ConDis
.30** .32**
-
4. SMS: IMKnow
.09
.21**
-.01
-
5. SMS: IMStim
.18*
.21**
-.08
.70**
6. SMS: IMAcc
.17*
.25**
-.10
.75** .78**
7. SMS: Ident
.15*
.24**
.04
.65** .63** .64**
8. SMS: Introj
.16*
.26**
.10
.37** .35** .39** .53**
-
9. SMS: ExtReg
.09
.17*
.16*
.42** .36** .38** .63**
.71**
-
10. SMS: Amot
.13
.05
.21**
.28**
.23**
-.13
-
-.26** -.21**
-
-.03
-
Cronbach’s α
.89
.91
.76
.85
.80
.85
.73
.73
.75
.84
M
2.26
1.94
1.68
4.61
4.97
5.24
4.43
3.83
3.83
3.00
SD
.77
.74
.68
1.28
1.31
1.18
1.27
1.35
1.32
1.61
Possible Range
1–4
1–4
1–4
1–7
1–7
1–7
1–7
1–7
1–7
1–7
Obtained Range
1–4
1–4
1–4
1–7
1–7
1–7
1–7
1–6.75
1–7
1–6.75
Note. SAS = Sport Anxiety Scale; Worry = worry; SomAnx = somatic anxiety; ConDis = Concentration-Disruption;
SMS = Sport Motivation Scale; IMKnow = intrinsic motivation to know; IMStim = intrinsic motivation to experience
stimulation; IMAcc = intrinsic motivation to accomplish; Ident = extrinsic motivation identified; Introj = extrinsic
motivation introjected; ExtReg = extrinsic motivation external regulation; Amot = amotivation.
*p < .05. **p < .01.
Correlational Data: Trait Anxiety and Motivational Orientation
The univariate correlational results presented in Table 3 reveal some association between the trait anxiety subscale scores and the sport motivation subscale
scores. In particular, athletes’ level of trait worry is positively but weakly correlated with two of the intrinsic motivation subscales and with both identified and
introjected regulation. Athletes’ level of somatic anxiety is positively, and more
moderately, correlated with all of the motivation subscales except amotivation.
Finally, athletes’ concentration-disruption scores are positively correlated with
two of the most non-self-determined forms of motivation (external regulation
and amotivation).
Examination of the correlations among the SAS subscales reveals low to
moderate positive association (.30 to .61) while the correlations between the SMS
subscales reveal higher relationships between adjoining subscales on the selfdetermination continuum and lower correlation between nonadjacent subscales.
These results are consistent with motivational theory and with previous research
using the SMS (Chatzisarantis, Haggar, Biddle, Smith, & Wang, 2003; Pelletier &
Sarrazin, 2007). Consistent, also, with this research base, the three intrinsic motivation subscales in the current study were highly correlated (.70 to .78).
202 Horn et al.
Relationships Between Psychological Characteristics
and Preferred Coaching Behavior
To determine if study participants’ psychological characteristics were significantly
related to their preferences for different types of coaching behavior, multivariate
canonical correlation procedures were used. Specifically, a group of variables representing participants’ preferred coaching behavior comprised one data set (Data Set
1), and a group of variables representing participants’ psychological characteristics
(motivational orientation and trait anxiety) served as the second data set (Data Set
2). Because the univariate correlational results (Table 2) revealed high correlation
(r = .70) between two of the LSS subscales (training and instruction and positive
feedback), the positive feedback subscale was deleted from the canonical analysis.
The decision to delete the positive feedback subscale rather than the training and
instruction subscale was based on the notion that the CFQ (the second instrument
that was used to measure preferred coaching behavior in this study) provides a
more specific measure of feedback than does the positive feedback subscale from
the LSS. In contrast, the CFQ does not provide a measure of coaches’ training and
instructional behavior. Thus, it was deemed important to retain the LSS training
and instructional subscale. Second, based on recommendations from Pelletier and
Sarrazin (2007), the seven subscales from the SMS were regrouped. Specifically,
the three intrinsic motivation subscales, along with the identified regulation subscale
were combined to form a single global score of autonomous motivation. Although
Pelletier and Sarrazin suggested that the remaining three subscales (introjected
regulation, external regulation, and amotivation) could also be regrouped to form a
global score of nonautonomous motivation, the univariate correlational results (see
Table 3) revealed relatively low correlation between amotivation and the other two
forms of non-self-determined motivation. Thus, for the current study, the amotivation subscale was retained, and the introjected and external regulation scores were
combined into one score. These collation procedures resulted in three SMS subscale
scores (autonomous motivation, externally-regulated motivation, and amotivation).
These three subscale scores were combined with the three anxiety subscales from
the SAS to form the second data set for the canonical correlation analysis.
The results of the canonical analysis revealed that a significant relationship
did exist between the two sets of data, Wilks’ Λ = .28, F (42, 857.11) = 6.44, p <
.00. Furthermore, this relationship was best captured by three canonical functions
(R1 = .72, R12 = .52; R2 = .57, R22 = .32; R3 = .34, R32 = .11). As a measure of the
effect size for this multivariate relationship, the cumulative redundancy index for
the three canonical functions was calculated to be 23.64%, indicating that 24% of
the variance in athletes’ preferred coaching behavior was explained by their motivational and trait anxiety levels. According to Pedhazur (1982), a redundancy index of
10% or higher suggests significant and meaningful relationships between data sets.
To determine which variables within each function contributed to the relationship between the two sets of data, the structure coefficients were examined (Courville & Thompson, 2001). These values, along with the squared structure coefficients
(rs2) and the communalities (h2) across the three functions for each variable, are
presented in Table 4. A criterion value of .35 was used to interpret the structure
coefficients (at least 12% or higher of shared variance, Tabatchnick & Fidell, 2007).
For the first function, high scores on the amotivation and externally-regulated
Athletes’ Preferred Coaching Behavior 203
Table 4 Canonical Results Showing Relationship between Athletes’
Psychological Characteristics and their Preferred Coaching Behavior
Function 1
Function 2
Function 3
Str Coef
(rs)
rs2
(%)
Str Coef
(rs)
rs2
(%)
Str Coef
(rs)
rs2
(%)
h2
(%)
-.19
3.61
.93
86.49
-.24
5.76
95.86
LSS: Auto
.89
79.21
.02
.04
-.03
.09
79.34
LSS: Demo
.24
5.76
.65
42.25
.38
14.44
62.45
Set 1 Variables
LSS: T&I
LSS: SocSupp
.32
10.24
.53
28.09
.12
1.44
39.77
CFQ: NonRF/IM
.64
40.96
-.16
2.56
.18
3.24
46.76
CFQ: PosInfo
-.08
.64
.62
38.44
.51
26.01
65.09
CFQ: Punish
.62
38.44
.18
3.24
-.52
27.04
68.72
SAS: Worry
-.17
2.89
.34
11.56
.44
19.36
33.81
SAS: SomAnx
.04
.16
.48
23.04
.25
6.25
29.45
Set 2 Variables
SAS: ConDis
.25
6.25
.04
.16
.90
81.00
87.41
SMS: AutonMot
-.16
2.56
.94
88.36
-.10
1.00
91.92
SMS: ExtReg
.56
31.36
.54
29.16
-.19
3.61
64.13
SMS: Amot
.84
70.56
.06
.36
.20
4.00
74.92
Note: str coef = canonical structure coefficient (canonical loading); rs2 = squared structure coefficient; h2 = communality coefficient; T&I = LSS training and instruction; Auto = LSS autocratic behavior; SocSupp = LSS social
support behavior; NonRF/IM = Factor 1 (nonreinforcement and ignoring mistakes); PosInfo = Factor 2 (positive and
informationally-based feedback); Punish = Factor 3 (punishment-oriented feedback); Worry = SAS worry; SomAnx
= SAS somatic anxiety; ConDis = SAS concentration-disruption; AutonMot = autonomous (self-determined)
motivation; ExtReg = externally-based (non-self-determined) motivation; Amot = amotivation
motivation subscales were positively correlated with preference for an autocratic
leadership style combined with high frequencies of nonreinforcement/ignoring
mistakes and punishment-oriented feedback. In particular, the relative size of the
loadings suggest that amotivation (.84) and preferred autocratic leadership style
(.89) are the variables from the two data sets that contribute the most to the relationship between the two sets.
For the second function, high scores on both the autonomous and externallyregulated motivation subscales, combined with high somatic trait anxiety scores,
were positively correlated with preference for high frequencies of training and
instructional behavior, social support, a democratic coaching style, and high frequencies of positive and informational feedback. Again, comparison of the size of
the loadings suggest that autonomous motivation (.94) and a coaching style high
in training and instruction (.93) contribute the most to the relationship between
the two sets of variables. On the third function, high scores on two of the trait
anxiety subscales (worry and concentration-disruption) were positively associated
204 Horn et al.
with greater preference for a democratic leadership style and high frequencies of
informationally-based feedback from coaches combined with lower preference for
punishment-oriented feedback.
The h2 values (shown in the last column in Table 4) indicate the proportion of
variance in each variable that is explained by the complete canonical solution. Thus,
this value provides an estimate of the contribution that each observed variable makes
to the overall analytic model depicting the relationship between the two data sets
(Sherry & Henson, 2005). Examination of these values indicates that all three of the
motivational orientation subscale scores from Data Set 2 (amotivation, externallyregulated, and autonomous motivation) make high contributions to the overall
model. In addition, one trait anxiety subscale (concentration/disruption) appears
especially important, with the other two anxiety subscales making less meaningful
contributions (i.e., > 45%). In regard to the preferred coaching behaviors (Data Set
1), three of the LSS subscales (training and instruction, autocratic, and democratic
behavior) and two of the CFQ factor scores (positive and informationally-based
feedback and punishment-oriented feedback) were the highest contributors with
the other two coaching behavior variables making less meaningful contributions
to the overall model.
Discussion
This study was conducted to examine the strength of the relationship between
collegiate athletes’ psychological characteristics and their preferred coaching
behavior. The results of the multivariate analyses revealed a strong link between
the two sets of data, thus providing additional support for Chelladurai’s (1978,
2001, 2007) MML. According to this model, athletes’ preferred coaching behavior
will vary as a function of both their own personal characteristics and factors in the
sport context. Previous research has demonstrated a connection between athletes’
coaching behavior preferences and aspects of the sport context (e.g., sport or task
type) and with selected athlete characteristics (e.g., age/competitive level, sport
ability; e.g., Chelladurai & Carron, 1983; Erle, 1981; Riemer & Toon, 2001; Terry,
1984). The results of the current study add to this research base by providing
evidence that athletes’ coaching behavior preferences can also be linked to their
psychological characteristics.
Furthermore, these study results also contribute to the broader literature on
coaching behavior by providing reinforcement or further support for the connection
between coaches’ leadership styles and behaviors and athletes’ psychosocial status.
Specifically, there is a relatively large body of research to show that the behaviors
exhibited by coaches in practice and competitive contexts do have a significant
impact on their athletes’ levels of anxiety and motivational orientation (see reviews
by Amorose, 2007; Duda & Balaguer, 2007; Gilbert & Trudel, 2004; Horn, 2008).
In addition, the theoretical models recently proffered by Côté and Gilbert (2009)
and Horn (2008) establish frameworks specifying how and why coaches’ behaviors
can affect their athletes’ psychosocial health and well-being. Côté and Gilbert, for
example, used the existing research on coaching behavior to develop an integrative definition of coaching effectiveness and expertise. Their identification and
categorization of the positive outcomes of effective coaching (competence, confidence, connection, and character) provide a structure for examining the correlates
Athletes’ Preferred Coaching Behavior 205
of effective coaching behavior. Horn’s model, as well, illustrates the processes by
which coaches’ behaviors, attitudes, values, and beliefs can affect their athletes’
performance and psychosocial growth. Furthermore, Horn specified in her model
that athletes’ personal characteristics (including their psychological profiles) can
affect the way in which they perceive, interpret, and evaluate their coaches’ behavior.
The results of the current study provide support for Horn’s model by showing that
athletes’ psychosocial characteristics (trait anxiety and motivational orientation)
are also linked to their preferences for different types of coaching behavior. In
addition to the general support for the MML (Chelladurai, 1978, 2001, 2007) and
the coaching effectiveness literature, the results of the current study can also be
interpreted relative to the theoretical and empirical literature in both trait anxiety
and motivational orientation.
Trait Anxiety and Preferred Coaching Behavior
Consistent with the study hypotheses, the multivariate results revealed that athletes’
scores on the sport anxiety subscales were positively linked to preference for training and instruction, a democratic leadership style, social support, and positive and
informationally-based feedback but were negatively associated with preference
for punishment-oriented feedback. Thus, it appears from this study, that high trait
anxious athletes, who may be distinguished by higher levels of fear of failure and
fear of negative evaluation, may (or perceive that they will) function most effectively in the competitive sport context when their coaches employ a supportive,
encouraging, and organized (training and instruction) but still democratic leadership
style and when such coaches also respond to athletes’ performance successes and
failures with positive and informational, rather than punishment-oriented, feedback.
It is also interesting to note that the somatic and cognitive subscales loaded on
different canonical functions and exhibited somewhat different relationships with
the preferred coaching behaviors. Somatic trait anxiety was primarily associated
with preferred leadership styles (LSS) while both of the cognitive subscale scores
(especially concentration-disruption) were primarily associated with preferred
coaching feedback patterns (CFQ). In particular, athletes with high trait tendencies to experience concentration disruption in competitive sport context indicated
a preference for positive and informational feedback but a dislike for punishmentoriented feedback. Perhaps athletes who are easily distracted by irrelevant cues in
the sport environment may be overloaded by coaches who provide high frequencies
of punishing feedback. Alternatively, punishment-oriented feedback from coaches
may induce disruptions in the concentration of athletes who are already prone to
this phenomenon. Whatever the interpretation, these results are consistent with the
existing research and theory in the sport anxiety literature (see recent reviews by
Beilock & Gray, 2007; Landers & Arent, 2010; Smith et al., 1998) that indicates
that high trait anxious athletes perceive greater threat than do low trait anxious
athletes in the same sport situation and that such high anxious individuals may
also interpret the situation differently, thus leading to higher levels of state anxiety
and arousal. Furthermore, the differential loadings for the somatic and cognitive
subscales found in this study exemplify the multidimensionality of the trait anxiety
construct and indicate that athletes who differ in these two types of anxiety may
also need (or certainly prefer) different treatment from their coaches.
206 Horn et al.
Motivational Orientation and Preferred Coaching Behavior
Based on SDT (Deci & Ryan, 2000; Ryan & Deci, 2007; Vallerand, 2001, 2007),
it had been hypothesized that athletes’ motivational orientation scores would be
correlated with their preferred coaching behavior scores. In general, the results
of this study did provide support for such a link. Specifically, in Function 1, high
amotivation scores combined with moderate externally-regulated motivation scores
were positively correlated with preference for an autocratic leadership style and high
frequencies of both nonreinforcement/ignoring mistakes and punishment-oriented
feedback. In Function 2, high scores on autonomous motivation, combined with
more moderate externally-regulated motivation scores, were positively linked to
preference for coaches who exhibit a democratic leadership style and who provide high frequencies of training and instruction, social support, and positive and
informationally-based feedback.
These results can be interpreted in light of SDT (Vallerand, 2001, 2007) in
that athletes who exhibit a self-determined or autonomous motivational profile may
perceive that they do not need their coaches to motivate them. Rather, because such
athletes have internal drives to achieve and to learn, they prefer coaches who provide
them with opportunities to learn (i.e., coaches who provide high frequencies of
training and instruction and informationally-based feedback) and with opportunities
to contribute to team decisions (i.e., coaches who exhibit a democratic leadership
style). In contrast, athletes who exhibit an amotivated profile and/or one of the more
non-self-determined forms of motivational orientation may perceive that they need
coaches who serve as the source of their motivation (i.e., coaches who are autocratic
in their leadership style and who punish athletes who do not achieve success).
Another interesting aspect of the canonical correlational results is that the
motivational subscales at the two extremes of the self-determined continuum
(autonomous motivation and amotivation) clearly loaded on two different functions and were associated with two very different preferred coaching behavior
profiles. In contrast, the externally-regulated motivation subscale score loaded at
a moderate level on both functions 1 and 2 and was therefore linked with two different preferred coaching profiles. This suggested that athletes who exhibit high
external and introjected regulation scores combined with high amotivation scores
may be substantially different in needs, wants, and affective reactions from those
athletes who exhibit high external and introjected regulation scores but combined
with high autonomous (intrinsic or identified regulation) scores. This perspective
would be consistent with arguments advanced by others (see, for example, Gillet
et al., 2009; Pelletier & Sarrazin, 2007; Treasure et al., 2007)
These results are also consistent with recent articles (e.g., Bartholomew et al.,
2009; Chelladurai, 2007; Treasure et al. 2007) that suggest that a coaching style that
includes both controlling and autonomy-supportive behaviors may be necessary and/
or more effective at the higher, or more elite, levels of play. That is, given that athletes
at the elite level have been found to range in where they lie on the self-determination
continuum (Gillet et al., 2009; Treasure et al., 2007), it is not surprising that an
autocratic and/or a controlling leadership style has been identified as more effective
for some athletes (see, also, Bartholomew et al., 2009). Of course, it is equally as
important to keep in mind that the coaching styles that athletes indicate that they
prefer may not necessarily be the coaching styles that are best for them or that will,
Athletes’ Preferred Coaching Behavior 207
in the end, be the most effective. Thus, the results of the current study only provide
evidence of a significant link between athletes’ psychological characteristics and
their preferred coaching behavior.
Study Limitations and Future Research Directions
In interpreting the results of this study, it is necessary to note that the sample was
limited to collegiate athletes from NCAA Division III schools. Furthermore, only
athletes from team sports were included. This somewhat limited range in regard to
the study sample was deemed necessary because previous research has revealed differences in preferred coaching behavior as a function of several different athlete and
situational variables (e.g., age/competitive level, type of sport, nationality). Thus, to
get a clearer test of the relationship between athletes’ psychological characteristics
and their preferred coaching behavior, it was decided a priori to limit the sample
by sport type (sports classified as team rather than individual), age (college athletes
only) and competitive level (Division III). Certainly, this limits the generalizability
of the results. In particular, it is possible that the links found in this study between
athletes’ motivational orientation and anxiety levels and their preferred coaching
behavior may not be found at the younger and/or less elite levels of play. As noted
by Chelladurai (2007), effective leader behaviors (and, by extension, preferred
leader behaviors) may differ as a function of the sport context (e.g., sport that is
conducted for the pursuit of pleasure versus sport that is conducted for the pursuit
of excellence). Other writers (e.g., Côté & Gilbert, 2009) have made similar points.
Thus, future research examining these links in other populations is warranted.
Second, the study sample did include both male and female athletes. As noted
earlier in this paper, previous research studies have produced mixed results regarding
gender differences in preferred coaching behavior. In his discussion of these results,
Riemer (2007) suggests that biological sex may be an imprecise way to operationalize athletes’ gender. Thus, it may ultimately be more relevant to assess variables
within the sport and social environment (e.g., gender of coach, sport culture) to
better understand both male and female athletes’ coaching behavior preferences.
This argument is consistent with both Hyde’s (2005) gender similarities hypothesis
and with Gill’s (2007) notion regarding the need to examine the social context and
social processes rather than to continue looking at gender as an individual difference
factor. Thus, additional research is needed to incorporate these ideas.
Third, it is important to note that the links found in this study between athletes’
levels of trait anxiety and motivational orientation and their preferred coaching
behaviors are correlational only. Thus, no causality can be claimed. In particular,
there may be intervening variables (e.g., team climate) that may explain the connection between athletes’ psychological characteristics and their preferred coaching
behavior. Thus, further research studies, perhaps using more sophisticated research
designs, are needed.
Fourth, this study only investigated selected psychological characteristics of
athletes and selected coaching behaviors. There are certainly other dimensions that
could be investigated. For example, do athletes with an internal locus of control
prefer autonomy-supportive coaches while athletes with an external locus of control prefer controlling coaches? Do athletes who are high or low in self-efficacy/
self-confidence exhibit differences in the types of efficacy-enhancing strategies (cf
208 Horn et al.
Vargas-Tonsing, Myers, & Feltz, 2004) they prefer their coaches to use? Do high
and low competitive trait anxious athletes differ in the type of attributional feedback
they prefer their coaches to give in response to performances?
Fifth, it may also be important to conduct more research that examines the
link between athletes’ psychological characteristics and the effectiveness of different types of coaching behaviors. Initial research in this area (see, for example,
Briere, Vallerand, Blais, & Pelletier, 1995) has confirmed that athletes who differ
in selected psychological characteristics at the beginning of the season react over
the season to coaches’ behaviors in different ways. Certainly, more of this research
is needed in the sport context.
Sixth, the scale used in this study (the preferred CFQ) to measure athletes’
preferences for different types of feedback patterns from their coaches has not
previously been established to be reliable and valid as a measurement tool. The
CFQ has been used by other researchers (e.g., Amorose & Horn, 2000; Smith et al.,
2005) to measure athletes’ perceptions of their coaches’ feedback behavior. But,
a stable factor structure for the original perceived CFQ and also for the preferred
version used in this study has not yet been demonstrated. Thus, some caution with
regard to the results from this scale may be needed.
Practical Implications
Quite probably the simplest practical implication based on the results of this study
is that individual athletes want different things from their coach. To coaches, this
may not be new information. That is, coaches themselves have noted that to be
effective they need to get to know each of their athletes so as to provide him or her
with the kind of behavior (e.g., feedback, motivational techniques) that will be best
for that individual athlete. The results of this study do confirm that athletes with
different psychological characteristics want different things from their coaches.
In particular, coaches may be advised that they should provide their high anxious
athletes with very supportive, encouraging, positive, and informationally-based
feedback (e.g., “That was a good shot to take, but you didn’t bend your knees in
order to get enough power. That’s why you didn’t make the basket. We’ll work on
that in our next practice.”).
Similarly, the results of this study suggest that athletes who are in an amotivated
state (i.e., don’t really have either an internally or externally-based motivational
source) may want (and perhaps need) their coach to be more controlling or autocratic
in enforcing an appropriate level of motivated behavior in practices and competitive
events. Of course, the conundrum here is that the use of a controlling or autocratic
coaching style has also been associated with decreases in athletes’ levels of motivation and positive affect (see reviews by Amorose, 2007; Vallerand, 2007). Thus,
perhaps the best recommendation is that coaches employ a controlling/autocratic
style only as a short-term approach (i.e., to get the athlete to exhibit motivated
behavior in the immediate practice or competitive environment) with the ultimate
goal of working to change the amotivation or external regulation motivation to a
more self-determined form. Furthermore, it is possible that there may be certain
athletic situations (e.g., the intensive training component) when athletes may best
be motivated by the less self-determined forces (see discussion by Bartholomew
et al., 2009; Treasure et al., 2007). Thus, again, coaches of older and/or more elite
Athletes’ Preferred Coaching Behavior 209
athletes may and probably should “choose” when to employ a more autocratic or
controlling leadership style, recognizing again that such a coaching style should
not be the only, nor the primary, form of interaction with their athletes.
References
Amorose, A.J. (2007). Coaching effectiveness: Exploring the relationship between coaching behavior and self-determined motivation. In M.S. Hagger & N.L.D. Chatzisarantis
(Eds.), Intrinsic motivation and self-determination in exercise and sport (pp. 209–228).
Champaign, IL: Human Kinetics.
Amorose, A.J., & Horn, T.S. (2000). Intrinsic motivation: Relationships with collegiate
athletes’ gender, scholarship status, and perceptions of their coaches’ behavior. Journal
of Sport & Exercise Psychology, 22, 63–84.
Baker, J., Côté, J., & Hawes, R. (2000). The relationship between coaching behaviours and
sport anxiety in athletes. Journal of Science and Medicine in Sport, 3(2), 110–119.
Bartholomew, K.J., Ntoumanis, N., & Thogersen-Ntoumani, C. (2009). International Review
of Sport and Exercise Psychology, 2(2), 215–233.
Beam, J.W., Serwatka, T.S., & Wilson, W.J. (2004). Preferred leadership of NCAA Division
I and II intercollegiate student-athletes. Journal of Sport Behavior, 27, 3–17.
Beilock, S.L., & Gray, R. (2007). Why do athletes choke under pressure? In G. Tenenbaum
& R.C. Eklund (Eds.), Handbook of sport psychology (3rd ed., pp. 425–444). NY:
John Wiley.
Briere, N.M., Vallerand, R.J., Blais, M.R., & Pelletier, L.G. (1995). On the development and
validation of the French form of the Sport Motivation Scale. International Journal of
Sport Psychology, 26, 465–489.
Chatzisarantis, N.L.D., Hagger, M.S., Biddle, S.J.H., Smith, B., & Wang, J.C.K. (2003). A
meta-analysis of perceived locus of causality in exercise, sport, and physical education
contexts. Journal of Sport & Exercise Psychology, 25(3), 284–306.
Chelladurai, P. (1978). A contingency model of leadership in athletics (Unpublished doctoral
dissertation). University of Western Ontario, London, Canada.
Chelladurai, P. (2001). Managing organizations for sport and physical activity: A systems
perspective. Scottsdale, AZ: Holcomb Hathaway.
Chelladurai, P. (2007). In G. Tenenbaum & R.C. Eklund (Eds.), Handbook of sport psychology (3rd ed., pp. 113–135). NY: John Wiley.
Chelladurai, P., & Carron, A.V. (1981). Task characteristics and individual differences and
their relationship to preferred leadership in sports. In G.C. Roberts & D.M. Landers
(Eds.), Psychology of motor behavior and sport-1980 (pp. 87). Champaign, IL: Human
Kinetics.
Chelladurai, P., & Carron, A.V. (1983). Athletic maturity and preferred leadership. Journal
of Sport Psychology, 5, 371–380.
Chelladurai, P., & Riemer, H.A. (1998). Measurement of leadership in sport. In J.L. Duda
(Ed.), Advances in sport and exercise psychology measurement (pp. 227–256). Morgantown, WV: Fitness Information Technology.
Chelladurai, P., & Saleh, S.D. (1978). Preferred leadership in sports. Canadian Journal of
Applied Sport Sciences, 3, 85–92.
Chelladurai, P., & Saleh, S.D. (1980). Dimensions of leader behavior in sports: Development
of a leadership scale. Journal of Sport Psychology, 2, 34–45.
Conroy, D.E., & Coatsworth, J.D. (2004). The effects of coach training on fear of failure in
youth swimmers: A latent growth curve analysis from a randomized, controlled trial.
Journal of Applied Developmental Psychology, 25, 193–214.
Côté, J., & Gilbert, W. (2009). An integrative definition of coaching effectiveness and expertise. International Journal of Sports Science & Coaching, 4, 307–323.
210 Horn et al.
Courville, T., & Thompson, B. (2001). Use of structure coefficients in published multiple
regression articles: Beta is not enough. Educational and Psychological Measurement,
61(2), 229–248.
Deci, E.L., & Ryan, R.J. (2000). The “what” and “why” of goal pursuits: Human needs and
the self-determination of behavior. Psychological Inquiry, 11, 227–268.
Duda, J.L., & Balaguer, I. (2007). Coach-created motivational climate. In S. Jowett & D.
Lavallee (Eds.), Social psychology in sport (pp. 117–130). Champaign, IL: HKP.
Erle, F.J. (1981). Leadership in competitive and recreational sport (Unpublished master’s
thesis). University of Western Ontario, London, Canada.
Gilbert, W.D., & Trudel, P. (2004). Analysis of coaching science research published from
1970-2001. Research Quarterly for Exercise and Sport, 75, 388–399.
Gill, D.C. (2007). Gender and cultural diversity. In G. Tenenbaum & R.C. Eklund (Eds.),
Handbook of sport psychology (3rd ed., pp. 823–844). NY: John Wiley.
Gillet, N., Vallerand, R.J., & Rosnet, E. (2009). Motivational clusters and performance in a
real-life setting. Motivation and Emotion, 33, 49–62.
Haggar, M.S., & Chatzisarantis, N.L.D. (Eds.). (2007). Intrinsic motivation and selfdetermination in exercise and sport. Champaign, IL: Human Kinetics.
Horn, T.S. (2008). Coaching effectiveness in the sport domain. In T.S. Horn (Ed.), Advances
in sport psychology (3rd ed.). Champaign, IL: Human Kinetics.
Hyde, J.S. (2005). The gender similarities hypothesis. The American Psychologist, 60,
581–592.
Landers, D.M., & Arent, S.M. (2010). Arousal-performance relationships. In J.M. Williams
(Ed.), Applied sport psychology: Personal growth to peak performance (6th ed., pp.
221–246). Boston: McGraw-Hill.
Mallett, C.J., & Hanrahan, S.J. (2004). Elite athletes: Why does the “fire” burn so brightly?
Psychology of Sport and Exercise, 5, 183–200.
Nunnally, J.C., & Bernstein, I.H. (1994). Psychometric theory (3rd ed.). NY: McGraw-Hill.
Pedhazur, E. (1982). Multiple regression in behavioral research. NY: Holt.
Pelletier, L., G., Fortier, J. S., Vallerand, R. J., Tuson, K. M., Briere, N. M., & Blais, M.
R. (1995). Toward a new measure of intrinsic motivation, extrinsic motivation, and
amotivation in sports: The Sport Motivation Scale (SMS). Journal of Sport & Exercise
Psychology, 17, 35–53.
Pelletier, L.G., & Sarrazin, P. (2007). Measurement issues in self-determination theory and
sport. In M.S. Hagger & N.L.D. Chatzisarantis (Eds.), Intrinsic motivation and selfdetermination in exercise and sport (pp. 143–152). Champaign, IL: Human Kinetics.
Price, M.S., & Weiss, M.R. (2000). Relationships among coach burnout, coach behaviors,
and athletes’ psychological responses. The Sport Psychologist, 14, 391–409.
Riemer, H.A. (2007). Multidimensional model of coach leadership. In S. Jowett & D. Lavallee (Eds.), Social psychology in sport (pp. 57–74). Champaign, IL: Human Kinetics.
Riemer, H., & Chelladurai, P. (1995). Leadership and satisfaction in sport. Journal of Sport
& Exercise Psychology, 17(3), 276–293.
Riemer, H., & Toon, K. (2001). Leadership and satisfaction in tennis: Examination of congruence, gender, and ability. Research Quarterly for Exercise and Sport, 72, 243–256.
Ryan, R.M., & Deci, E.L. (2007). Active human nature: Self-determination theory and the
promotion and maintenance of sport, exercise, and health. In M.S. Hagger & N.L.D.
Chatzisarantis (Eds.), Intrinsic motivation and self-determination in exercise and sport
(pp. 1–19). Champaign, IL: Human Kinetics.
Sherman, C.A., Fuller, R., & Speed, H.D. (2000). Gender comparisons of preferred coaching
behaviors in Australian sports. Journal of Sport Behavior, 23, 389–402.
Sherry, A., & Henson, R.K. (2005). Conducting and interpreting canonical correlation
analysis in personality research: A user-friendly primer. Journal of Personality Assessment, 84(1), 37–48.
Athletes’ Preferred Coaching Behavior 211
Smith, R.E., Cumming, S.P., & Smoll, F.L. (2006). Factorial integrity of the Sport Anxiety
Scale: A methodological note and revised scoring recommendations. Journal of Sport
& Exercise Psychology, 28, 109–112.
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.
Smith, R.E., Smoll, F.L., & Cumming, S.P. (2007). Effects of a motivational climate intervention for coaches on youth athletes’ sport performance anxiety. Journal of Sport &
Exercise Psychology, 29, 39–59.
Smith, R.E., Smoll, F.L., Cumming, S.P., & Grossbard, J.R. (2006). Measurement of multidimensional sport performance anxiety in children and adults: The Sport Anxiety
Scale-2. Journal of Sport & Exercise Psychology, 28, 479–501.
Smith, R.E., Smoll, F.L., & Hunt, E.B. (1977). A system for the behavioral assessment of
athletic coaches. Research Quarterly, 48, 401–407.
Smith, R.E., Smoll, F.L., & Wiechman, S.A. (1998). Measurement of trait anxiety in sport.
In J.L. Duda (Ed.), Advances in sport and exercise psychology measurement (pp.
105–128). Morgantown, WV: Fitness Information Technology.
Smith, R.E., Smoll, F.L., & Schutz, R.W. (1990). Measurement and correlates of sportspecific cognitive and somatic trait anxiety: The Sport Anxiety Scale. Anxiety Research,
2, 263–280.
Smith, S.L., Fry, M.D., Ethington, C.A., & Li, Y. (2005). The effect of female athletes’ perceptions of their coaches’ behaviors on their perceptions of the motivational climate.
Journal of Applied Sport Psychology, 17(2), 170–177.
Tabachnick, B.G., & Fidell, L.S. (2007). Using multivariate statistics (5th ed.). Boston:
Pearson.
Terry, P.C. (1984). The coaching preferences of elite athletes competing at Universiade ‘83.
Canadian Journal of Applied Sport Sciences, 9, 201–208.
Terry, P.C., & Howe, B.L. (1984). The coaching preferences of athletes. Canadian Journal
of Applied Sport Sciences, 9, 188–193.
Treasure, D.C., Lemyre, P., Kuczka, K.K., & Standage, M. (2007). In M.S. Hagger & N.L.D.
Chatzisarantis (Eds.), Intrinsic motivation and self-determination in exercise and sport
(pp. 153–166). Champaign, IL: Human Kinetics.
Vallerand, R.J. (2001). A hierarchical model of intrinsic and extrinsic motivation in sport
and exercise. In G. Roberts (Ed.), Advances in motivation in sport and exercise (2nd
ed., pp. 263–319). Champaign, IL: Human Kinetics.
Vallerand, R.J. (2007). Intrinsic and extrinsic motivation in sport and physical activity: A
review and a look at the future. In G. Tenenbaum & R.C. Eklund (Eds.), Handbook of
sport psychology (3rd ed., pp. 59–83). NY: John Wiley.
Vargas-Tonsing, T.M., Myers, N.D., & Feltz, D.L. (2004). Coaches’ and athletes’ perceptions
of efficacy-enhancing techniques. The Sport Psychologist, 18, 397–414.
Weiss, M.R., & Amorose, A.J. (2008). In T.S. Horn (Ed.), Advances in sport psychology
(3rd ed., pp. 115–156). Champaign, IL: Human Kinetics.