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Coaching efficacy in intercollegiate coaches: sources, coaching behavior, and team variables
Psychology of Sport & Exercise 6 (2005) 129–143 www.elsevier.com/locate/psychsport
Coaching efficacy in intercollegiate coaches: sources, coaching behavior, and team variables Nicholas D. Myers, Tiffanye M. Vargas-Tonsing, Deborah L. Feltz Department of Kinesiology, Michigan State University, East Lansing, MI, 48824, USA Received 27 January 2003; received in revised form 6 June 2003; accepted 23 October 2003
Abstract Objectives: To examine the influence of (a) proposed sources of efficacy information on dimensions of coaching efficacy and (b) coaching efficacy on coaching behavior and team variables. Design: A field correlational design tested relationships at two time points: near the beginning and at three-fourths of the way through a season of competition. Method: At Time 1, head coaches (n=135) completed a questionnaire containing the Coaching Efficacy Scale, measures of sources, and demographic items. At Time 2, participants were a subset of coaches from Time 1 (n=101) and 1618 athletes. Coaches completed questionnaires on their perceived frequency of their efficacy-enhancing behaviors with their athletes. Athletes provided information on their satisfaction with their head coach. Results: For female coaches, social support was a stronger source of efficacy information compared to male coaches. Total coaching efficacy predicted coaching behavior, team satisfaction, and winning percentage for men’s teams. Total coaching efficacy predicted only coaching behavior across women’s teams. Within women’s teams, gender of the coach moderated the relationship between character building efficacy and team satisfaction. Character building efficacy was negatively related to team satisfaction in women’s teams with male coaches. Motivation efficacy was positively related to team satisfaction in women’s teams with female coaches. Conclusion: Findings provide novel corroborations to the coaching efficacy model proposed by Feltz, Chase, Moritz and Sullivan (1999: Journal of Educational Psychology, 91, 765–776) and offer some support to broader models of coaching effectiveness. # 2003 Elsevier Ltd. All rights reserved. Keywords: Coaching efficacy; Character building; Motivation; Social support; Coaching education; Coaching effectiveness
Corresponding author. Tel.: +1-517-355-4732; fax: +1-517-353-2944. E-mail address: [email protected] (D.L. Feltz).
1469-0292/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.psychsport.2003.10.007
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Introduction Over the past few decades, the majority of research in sport leadership has been directed toward identifying particular coaching styles that are most effective for successful performance and/or positive psychological responses from athletes (Horn, 2002). The two most prominent models of leadership effectiveness in sport, the Multidimensional Model of Leadership (Chelladurai, 1978) and the Mediational Model of Leadership (Smoll & Smith, 1989), have served as frameworks for much of the related research. Recently, elements of both models have been combined to form a working model of coaching effectiveness (Horn, 2002). Horn’s (2002) working model is founded on three assumptions. First, antecedent factors (i.e. sociocultural context, organizational climate, and personal characteristics of the coach) and athletes’ personal characteristics (e.g. age, gender, etc.) exert influence on coaches’ behavior indirectly through coaches’ expectancies, beliefs, and goals. Second, coaches’ behavior affects athletes’ evaluation of their coaches’ behavior and team performance. Third, the effectiveness of various coaching interventions is influenced by situational factors and individual differences. Much work remains in clarifying the specific relationships that exist within these broad assumptions. Coaching efficacy is an important variable within a constellation of personal characteristics (i.e. gender, motivational style, etc.) that affect coaching behavior (Horn, 2002). Coaching efficacy is a sport-specific construct that Feltz, Chase, Moritz, and Sullivan (1999) defined as the extent to which coaches believe they have the capacity to affect the learning and performance of their athletes. Feltz and her colleagues posit that coaching efficacy comprises four first-order factors: motivation, character building, game strategy, and technique efficacies. Motivation efficacy (ME) is the confidence coaches have in their ability to affect the psychological mood and skills of their athletes. Character building efficacy (CBE) is the confidence coaches have in their ability to influence the personal development and positive attitude toward sport in their athletes. Game strategy efficacy (GSE) is the confidence coaches have in their ability to lead during competition. Technique efficacy (TE) is the belief coaches have in their instructional and diagnostic skills. The complete hierarchical model also posits that the four first-order factors converge to define a more general second-order factor: total coaching efficacy (TCE). Feltz et al. (1999) designed the Coaching Efficacy Scale (CES) to measure the construct, and offered a model of coaching efficacy to serve as a starting point for related research. The CES and the coaching efficacy model are based on self-efficacy theory (Bandura, 1986) and a model of teacher efficacy (Denham & Michael, 1981). Initial data from a heterogeneous sample of high school coaches provided support for the structure of the CES, and for the coaching efficacy model. Specifically, Feltz et al. found that a set of sources (coach’s past success, coaching experience, perceived team ability, and social support) had a relationship with the set of coaching efficacy dimensions in a heterogeneous sample of high school coaches. In turn, TCE predicted observed coaching behavior, team satisfaction, and winning percentage for high school boys’ basketball teams. Recognizing the limits of a single investigation, Feltz and her colleagues called for further research to provide evidence for novel corroborations to the model of coaching efficacy in particular and, the coaching education literature at large1. 1
The hypothesized relationship between coaching efficacy and player/team efficacy that is proposed in the coaching efficacy model (Feltz et al., 1999) was not explored in this study to minimize time commitment for athletes, and because similar work is reported elsewhere (Vargas-Tonsing, Warners, & Feltz, 2004).
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Since then, related investigations have provided support for the structure of the CES, and the ability of a coaching education program to increase coaching efficacy. With a sample of inexperienced coaches, Malete and Feltz (2000) found increases in coaching efficacy (particularly for GSE and TE) following participation in a coaching education program. Also, participants’ efficacy judgments were higher than a similar group of coaches who did not participate in the program. With a heterogeneous sample of Singapore coaches, Lee, Malete, and Feltz (2002) reported support for the factor structure of the CES, higher GSE and TE for certified coaches compared to uncertified coaches, and higher GSE for male coaches compared to female coaches. None of the mentioned studies provided a comprehensive test for the proposed model in nonhigh school settings, nor has any research assessed the influence of coaching efficacy on coaching behavior and team variables in women’s teams. Thus, the two primary purposes of this investigation were to examine the influence of proposed sources of efficacy information on dimensions of coaching efficacy for intercollegiate coaches and, to determine the influence of coaching efficacy on self-reported coaching behavior and team variables in men and women’s intercollegiate teams.
Method Participants and design Data were collected at two time points within several seasons of competition. At Time 1, potential participants were 179 head coaches from several midwestern conferences within Division II and III intercollegiate athletics. A questionnaire containing the CES, measures of proposed sources, and demographic items was mailed to each coach at the one-quarter mark of the season with a requested return date of 2 weeks from delivery. Follow-up phone calls and electronic mail from investigators and league coordinators resulted in a 75% return rate (N=135). Respondents were predominantly Caucasian (88%) and male (67%), and represented the sports of softball (27%), baseball (19%), women’s soccer (20%), men’s soccer (14%) women’s basketball (13%), and men’s basketball (7%). Males (n=36) and females (n=45) coached women’s teams. Coaches ranged in age from 24 to 67 years (M=38.6, SD=10.1) and in experience as a collegiate head coach from 1 to 33 years (M=10.4, SD=7.4). At Time 2, participants were a subset of head coaches from time 1 (n=101) and 1618 athletes nested within participating coaches from Time 2. Most of the coaches from Time 1 submitted data for Time 2 (return rate of 75%). Time 2 packets were sent to head coaches at the threequarter mark of the season with a requested return date of 2 weeks from delivery. Coaches completed questionnaires on their perceived frequency of their efficacy-enhancing behaviors with their athletes. Athletes provided their perceptions of satisfaction with the head coach. Participating athletes were predominantly Caucasian (90%) and female (60%), and represented the sports of softball (25%), baseball (16%), women’s soccer (25%), men’s soccer (20%) women’s basketball (10%), and men’s basketball (4%). Athletes ranged in age from 17 to 26 years (M=19.8, SD=1.3).
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Measures Sources of coaching efficacy Proposed sources of coaching efficacy included perceived team ability, social support, career winning percentage, and years as a collegiate coach. The team ability item, ‘from my perspective, my current team’s overall ability level is’ was the same as the one used by Feltz et al. (1999) and assessed the coach’s perception on a 10-point Likert scale ranging from 0 (very poor) to 9 (excellent)2. The social support questionnaire also used by Feltz et al. contained five items with a similar framework, ‘from my perspective, this season the ______ has/ have been’, that assessed the coach’s perceptions of support for his/her team from the athletic director, faculty, student body, athletes’ parents, and the greater community. Each question was scored on a 10-point Likert scale ranging from 0 (not at all supportive) to 9 (extremely supportive). Social support items were examined individually for relationships with coaching efficacy. Social support from the athletes’ parents and the greater community had the greatest associations with dimensions of coaching efficacy. Data for career winning percentage and years as a collegiate head coach were obtained from coaches and verified on university websites when available. Coaching efficacy The CES (Feltz et al., 1999) is a 24-item instrument in which coaches are asked to assess the degree of confidence they have in their ability to affect the learning and performance of their athletes. A hierarchical model defined by a first-order four-factor structure (ME, CBE, GSE and TE) converging to a second-order general factor (TCE) is hypothesized. Items are scored on a 10-point Likert scale ranging from 0 (not at all confident) to 9 (extremely confident) and include the stem: ‘How confident are you in your ability to. . .’ The scale contains items such as ‘motivate your athletes’, identified by ME; ‘promote good sportsmanship’, identified by CBE; ‘recognize opposing team’s strengths during competition’, identified by GSE; and ‘detect skill errors’, identified by TE. Internal consistency analyses revealed standardized Cronbach a’s of 0.90 (ME), 0.87 (CBE), 0.92 (GSE), 0.84 (TE), and 0.94 (TCE). Reported coaching behavior and team variables Reported coaching behavior and team variable measures included coaches’ perceptions of their efficacy-enhancing behaviors with athletes, athlete satisfaction with their head coach, and winning percentage for the current season. Data for winning percentage at the conclusion of the current season were obtained from coaches and verified on league websites. The efficacy-enhancing behaviors scale was developed by Gould, Hodge, Peterson, and Giannini (1989) and assessed a coach’s perceptions of the frequency of his/her confidence-enhancing behaviors with athletes. The instrument contained 13 commonly advanced strategies for increasing confidence in athletes (i.e. ‘verbally persuade athletes that she can do it’). All items were 2 The measurement of perception of team ability was consistent with the literature, but future efforts should consider adding items to more fully represent the construct. A fuller representation of team ability would allow for psychometric evaluation of the measures produced, and would decrease the possibility of encountering problems associated with one-item measures (e.g. inadequately represent the construct which can result in measures that fail to accurately depict the variable of interest).
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scored on a five-point Likert scale ranging from 1 (never use) to 5 (always use). Previous psychometric data for the scale was not available. Thus, a principal components analysis was performed and 10 of the 13 items had a reasonable loading (>0.30) on one dominant component which we labeled, ‘efficacy-enhancing behavior’. This component was the only component above the lower asymptote, had an eigenvalue of 2.77, and accounted for 28% of the total variance across the items. Internal consistency analysis for the 10-item component revealed a standardized Cronbach a of 0.70. Athlete satisfaction with the coach consisted of selected items from a scale that was intended to measure, in part, attitudes toward the coach (Smith, Smoll, & Curtis, 1978). The four-items that were utilized for this study included ‘how much do you like playing for your coach’; ‘if you were able to play next year, how much would you like to have the same coach again’; ‘how much does your coach like you’; and, ‘how much does your coach know about basketball’. The items were scored on a seven-point Likert scale ranging from 1 (very little) to 7 (a lot). A principal components analysis was performed and all four items had high loadings (>0.70) on a dominant component that we labeled, ‘athlete satisfaction with the coach’. This component had an eigenvalue of 2.8, was the only component above the lower asymptote, and accounted for 70% of the total variance across the items. Internal consistency analysis for the four-item component revealed a standardized Cronbach a of 0.85. As recommended by Feltz and Chase (1998) and Moritz and Watson (1998), prior to aggregating data to the team-level, perceptual consensus of clustered individuals should be assessed. When the individual-level perception (in this case, athlete satisfaction with the coach) is relatively homogenous within identified clusters (in this case, teams), the group mean can be used to represent the collective perception for each team. Thus, in this study, consensus was demonstrated prior to aggregating athlete-level data to the team-level (N=101). Interrater agreement ranged from 0.65 to 0.99 (M=0.84, SD=0.08), which indicated that team members held fairly homogenous beliefs regarding satisfaction with the head coach (James, Demaree, & Wolf, 1984). Procedure Permission was obtained from the institutional review board for human subjects, league coordinators, and the head coaches prior to data collection. Head coaches were asked to appoint someone not directly affiliated with the team (i.e. athletic trainer) to verbally explain the study to the athletes. The same person was responsible for administering the questionnaires to the athletes. Minors were informed to not participate. Informed consent was obtained from all of the participating athletes. Athletes were guaranteed confidentiality for their responses. Requested return dates for all questionnaires were 2 weeks from date of delivery. Reminders from investigators and league coordinators along with a lottery drawing for US$100 for fully participating coaches were employed to encourage returns.
Results Seven coaches submitting complete data at Time 1 were dropped because they were in their first year as an intercollegiate head coach making both years as a collegiate head coach and
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career record questionable sources of efficacy information. Two additional cases were dropped as they were identified as multivariate outliers. Five coaches were missing data for reported efficacy-enhancing behavior with athletes but had complete data otherwise and were retained for subsequent analyses (N=126). Fifty-seven athletes (<4% of the total sample) were dropped due to missing data (N=1561). Descriptive statistics Years as a collegiate head coach, team ability, and CBE were significantly skewed (Z>3.29) and were transformed prior to performing inferential statistics. Years as a collegiate coach was positively skewed because a majority of participants had <9 years experience as a collegiate head coach. A base 10 logarithm was applied to years as a collegiate coach. Perceived team ability was negatively skewed because a majority of coaches perceived their teams to be quite able at the one-quarter mark of the season. CBE was negatively skewed because most coaches were quite confident in their ability to influence the personal development and positive attitude toward sport in their athletes. Athlete satisfaction was also negatively skewed because a majority of athletes were quite satisfied with their head coach at the three-quarter mark of the season. All of the negatively skewed variables were reflected to create positive skewness and then transformed with base 10 logarithms (Tabachnick & Fidell, 2001). Descriptive statistics for the proposed sources of coaching efficacy, coaching efficacies, reported coaching behavior, and team variables are presented in Table 1. As can be viewed in the table, non-transformed descriptive values for all of the variables were reasonable. Of note, there was a good deal of disparity among the values for the social support variables, with the
Table 1 Descriptive statistics for the proposed sources of coaching efficacy, coaching efficacies, reported coaching behavior, and team variables
Perceived team ability Support from the athletic director Support from the faculty Support from the student body Support from the athletes’ parents Support from the community Career winning percentage Experience as a collegiate coach Total coaching efficacy Motivation efficacy Character building efficacy Game strategy efficacy Technique efficacy Efficacy-enhancing behaviors Athlete satisfaction with coach Team satisfaction with coach Winning percentage
N
M
SD
Minimum
Maximum
126 126 126 126 126 126 125 126 126 126 126 126 126 99 1561 94 124
5.97 6.38 5.23 4.69 7.08 4.59 0.54 10.80 7.54 7.10 7.94 7.61 7.71 3.48 5.44 5.43 0.52
1.35 2.17 1.98 1.93 1.57 2.20 0.16 7.38 0.65 0.85 0.84 0.81 0.75 0.51 1.26 0.80 0.21
1.00 0.00 0.00 0.00 2.00 0.00 0.15 1.00 5.29 4.29 4.50 5.43 5.50 2.50 1.00 2.61 0.11
9.00 9.00 9.00 9.00 9.00 9.00 0.81 33.00 9.00 9.00 9.00 9.00 9.00 4.70 7.00 6.64 0.92
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Table 2 Multivariate analysis of variance for coaching efficacy dimensions on various demographics Demographic
Efficacy
Wilks’ k
df
F
p
Division coached
– Motivation Character building Game strategy Technique – Motivation Character building Game strategy Technique – Motivation Character building Game strategy Technique –
0.93 – – – – 0.83 – – – – 0.96 – – – – –
4106 1 1 1 1 20352 5 5 5 5 4106 1 1 1 1 109
1.96 2.28 1.09 0.22 0.01 1.03 0.36 1.22 0.74 1.60 1.13 0.16 1.43 0.53 0.87 –
0.11 0.13 0.30 0.64 0.94 0.42 0.87 0.30 0.60 0.17 0.35 0.69 0.23 0.47 0.35 –
Sport coached
Gender of coach
Error
perception of support from the athletes’ parents emerging as the source of social support with the highest mean and least dispersion. Table 2 provides evidence that the means of the dimensions of coaching efficacy did not vary based on division coached, sport coached, or gender of the coach. Table 3 contains the correlations between dimensions of coaching efficacy and TCE. Correlations among dimensions of coaching efficacy ranged from 0.39 to 0.75, and correlations of TCE with dimensions of coaching efficacy ranged from 0.68 to 0.86. These relationships are congruent with the hierarchical structure suggested by Feltz et al. (1999). The moderately high correlation between TE and GSE (r=0.75) is consistent with previous research (Feltz et al., 1999; Lee et al., 2002) and presents potential problems with multicollinearity if both variables are used simultaneously as predictors within a regression equation.
Table 3 Pearson correlations between dimensions of coaching efficacy and total coaching efficacy
Motivation efficacy (ME) Character building efficacy (CBE) Game strategy efficacy (GSE) Technique efficacy (TE) Total coaching efficacy (TCE) Note. For all correlations, p<0.001.
ME
CBE
GSE
TE
TCE
— — — — —
0.52 — — — —
0.52 0.45 — — —
0.45 0.39 0.75 — —
0.81 0.68 0.86 0.81 —
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Table 4 Correlations of sources with total coaching efficacy and dimensions of coaching efficacy TCE
ME
CBE
GSE
TE
All coaches (N=126) Team ability Athletes’ parents support Community support Career winning percentage Years as collegiate head coach
0.39 0.29 0.19 0.26 0.18
0.44 0.32 0.29 0.20 0.16
0.29 0.25 0.22 0.09 0.15
0.28 0.18 0.09 0.33 0.18
0.21 0.20 0.03 0.16 0.06
Male coaches (n=84) Team ability Athletes’ parents support Community support Career winning percentage Years as collegiate head coach
0.34 0.18 0.09 0.24 0.21
0.39 0.25 0.25 0.22 0.21
0.20 0.12 0.01 0.04 0.20
0.26 0.08 0.02 0.31 0.17
0.20 0.12 0.05 0.13 0.06
Female coaches (n=42) Team ability Athletes’ parents support Community support Career winning percentage Years as collegiate head coach
0.45 0.50 0.37 0.27 0.09
0.50 0.44 0.35 0.14 0.04
0.45 0.38 0.50 0.29 0.17
0.29 0.42 0.23 0.30 0.10
0.20 0.37 0.18 0.19 0.03
Note. TCE=Total Coaching Efficacy, ME=Motivation Efficacy, CBE=Character Building Efficacy, GSE=Game Strategy Efficacy, and TE=Technique Efficacy. 0 p<0.05. p<0.01. p<0.001.
Influence of sources of coaching efficacy on dimensions of coaching efficacy To determine the relationships between the sources and dimensions of coaching efficacy collected at Time 1, we calculated Pearson product-moment correlations (Table 4). Across coaches, the proposed sources had mostly significant relationships with the dimensions of coaching efficacy. The strongest source of efficacy information was perception of team ability, while the weakest source was years as a collegiate coach. Total years in coaching (not listed) exhibited even weaker relationships with dimensions of coaching efficacy than did years as a collegiate coach. The influence of years as a collegiate coach, career-winning percentage, and perception of team ability on dimensions of coaching efficacy did not differ for male (n=84) and female (n=42) coaches. The correlations between perceived social support from the community and CBE were different for male (r=0.01) and female (r=0.50) coaches, Z=2.76. However, due to the modest number of female coaches, the complexity of the proposed model, and mostly similar patterns of influence of the sources on dimensions coaching efficacy, subsequent analyses were conducted across male and female coaches. The relationship between the two perceptual sets (i.e. sources and dimensions of coaching efficacy) was tested via canonical correlation analysis (see Table 5). Canonical correlation was
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Table 5 Summary of the canonical correlation analysis from Time 1 data Correlations Independent variate Perceived team ability Support from athletes’ parents Support from community Career winning percentage Experience as a collegiate head coach Variance extracted Redundancy index Dependent variate Motivation efficacy Character building efficacy Game strategy efficacy Technique efficacy Variance extracted Redundancy index Canonical correlation
Coefficients
0.89 0.66 0.60 0.38 0.36
0.78 0.47 0.02 0.18 0.21 36.93% 9.48
0.97 0.70 0.56 0.48
0.83 0.27 0.04 0.03 49.63% 12.74 0.51
appropriate because the coaching efficacy model specifies that a set of sources are related to a set of coaching efficacies (first-order dimensions), and the said analysis is specifically designed ‘to determine if and how two sets of variables are related to each other’ (Tabachnick & Fidell, 2001, p.177)3. One reliable relationship was observed between the two perceptual sets, Wilks’ k=0.63, F(20, 389)=2.90, p<0.01. The correlation between the two variates was moderate, Rc=0.51, Rc2=0.26, p<0.01. The redundancy index of interest revealed that 12.7% of the variance in the dimensions of coaching efficacy was accounted for by the set of sources. The canonical variate pair linked low scores on the proposed sources with low scores on the dimensions of coaching efficacy. Correlations between the variables and the canonical variates suggested that all of the sources were important contributors to the independent variate, while all of the dimensions of coaching efficacy were important contributors to the dependent variate (i.e. correlations with absolute values >0.30). To more specifically explain the observed relationship between the two perceptual sets, a multivariate multiple regression analysis was conducted to determine the predictive strength of each of the proposed sources on dimensions of coaching efficacy. Perception of team ability predicted ME, F(1, 124)=14.63, p>0.01, and CBE, F(1, 124)=5.39, p=0.02. Perception of social support from athletes’ parents predicted ME, F(5, 120)=6.31, p=0.01, CBE, F(5, 120)=4.06, p<0.05, and TE, F(5, 120)=4.60, p=0.03. Career winning percentage predicted GSE, F(5, 120)=4.26, p=0.04. 3
Although canonical correlation was an appropriate statistical framework given the generality of the current coaching efficacy model (i.e. sets of variables) and the research questions posed, future researchers are encouraged to utilize the information from this and previous studies to specify variable-level relationships that can be tested with powerful inferential techniques, such as structural equation modeling.
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Univariate equations with the set of sources predicting each dimension of coaching efficacy separately, were all significant: ME, F(5, 120)=7.73, p<0.01, CBE, F(5, 120)=3.64, p<0.01, GSE, F(5, 120)=4.21, p<0.01, and TE, F(5, 120)=2.39, p<0.05. The significance of the CBE and TE equations is unmatched in previous literature. The relevance of CBE may be explained by the overriding mission of the private, often religiously affiliated, institutions where many of these coaches were employed. The importance of TE may be explained by the relatively young, and presumably physically able coaches included in this sample. Influence of total coaching efficacy on coaching behavior and team variables Horn’s (2002) model of coaching effectiveness suggests that gender of the coach and athletes exert indirect influences on coaches’ behavior through coaches’ beliefs. Thus, we examined the influence of TCE on reported coaching behavior for male and female coaches, separately. Horn’s model also suggests that gender of the athlete directly influences athletes’ evaluative reactions of their coaches’ behavior. Thus, we examined the influence of TCE on satisfaction with the coach within men and women’s teams, separately. Due to small samples for some subgroups of interest, potential multicollinearity between TE and GSE, and dependency of the satisfaction data, differing regression analyses were selected when modeling each dependant variable. However, because dimensions of coaching efficacy define TCE, bivariate associations of dimensions of coaching efficacy with coaching behavior and team variables were also reported to more specifically explain the influences of TCE (Table 6). TCE predicted the reported frequency of efficacy-enhancing behaviors with athletes (coaching behavior) for female, F(1,34)=12.99, p<0.01, and male coaches, F(1,58)=10.39, p<0.01. Within women’s teams, the relationship between coaching efficacies and coaching behavior was somewhat different for male and female coaches. For female coaches, TCE and all of the dimensions of coaching efficacy were related to coaching behavior. For male coaches of women’s teams, neither TCE nor any of the dimensions of coaching efficacy were related to coaching behavior. However, it is important to note that across women’s teams, coaching behavior did not vary based on coach’s gender, F(1, 58)=0.44, p=0.51. For male coaches of men’s teams, TCE and all of the dimensions of coaching efficacy were related to coaching behavior. Thus, only when gender of the coach matched gender of the team, did a coach’s belief in his/her abilities to affect the learning and performance of athletes predict coaching behavior. However, it is important to note that across all teams, coaching behavior did not vary based on matched gender between the team and the coach, F(1, 94)=2.00, p=.16. The influence of TCE on winning percentage in the current year was determined through simple regression analyses for men and women’s teams. TCE predicted winning percentage for men’s teams, F(1,34)=5.75, p=0.02, but not for women’s teams, F(1, 63)=0.88, p=0.35. Within women’s teams, neither TCE nor any of the dimensions of coaching efficacy were associated with winning percentage in the current year for male or female coaches. Within men’s teams, CBE (r=0.42) and ME (r=0.38) were significantly associated with winning percentage in the current year. Satisfaction with the coach was measured at the athlete level and aggregated to the team level to calculate the correlations that are illustrated in Table 6. Aggregating the satisfaction data to the team level (L-2), however, ignores variability within teams (L-1). The intraclass correlation
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Table 6 Correlations of team variables and coaching behavior with total coaching efficacy and dimensions of coaching efficacy TCE
ME
CBE
GSE
TE
Efficacy-enhancing behaviors (N=96) Female coaches (n=36) Male coaches (n=60) Male coaches of men’s teams (n=36) Male coaches of women’s teams (n=24)
0.53 0.39 0.54 0.15
0.41 0.34 0.50 0.09
0.35 0.28 0.42 0.13
0.44 0.28 0.35 0.11
0.44 0.39 0.48 0.23
Team satisfaction (N=101) Female coaches (n=38) Male coaches (n=63) Male coaches of men’s teams (n=36) Male coaches of women’s teams (n=27)
0.20 0.16 0.33 0.18
0.35 0.15 0.28 0.02
0.13 0.15 0.03 0.42
0.05 0.13 0.27 0.14
0.02 0.30 0.40 0.04
Winning percentage in current year (N=101) Female coaches (n=38) Male coaches (n=63) Male coaches of men’s teams (n=36) Male coaches of women’s teams (n=27)
0.15 0.24 0.38 0.03
0.20 0.21 0.38 0.02
0.25 0.11 0.42 0.22
0.02 0.29 0.32 0.25
0.05 0.11 0.16 0.02
Note. TCE=Total Coaching Efficacy, ME=Motivation Efficacy, CBE=Character Building Efficacy, GSE=Game Strategy Efficacy, and TE=Technique Efficacy. 0 p<0.05. p<0.01. p<0.001.
coefficient for satisfaction with the coach was 0.32, which suggested that only 32% of the variance in athlete satisfaction was due to between teams differences. Hierarchical linear modeling examines multiple levels of variance simultaneously. Specifying predictors at L-1 increases precision at L-2 (Raudenbusch & Bryk, 1992). Hierarchical linear modeling (HLM) For men’s teams, playing time dichotomized as ‘played in less than 75% of games’ (n=246) and ‘played in at least 75% of games’ (n=364) accounted for 4% of the within teams variance and 5% of the between teams variance, t(35)=4.01, p<0.01. The playing time effect was similar across teams, v2(34)=38.89, p=0.26. TCE was added as a L-2 predictor and accounted for an additional 26% of the between teams variance, t(34)=2.60, p=0.01. However, it should be noted that TE was actually more strongly associated with team satisfaction (r=0.40) than was TCE (r=0.33). For women’s teams, playing time was not selected as a L-1 variable because 70% of the athletes played in at least 75% of games. Instead, ‘starting’ was dichotomized as ‘started in less than 75% of games’ (n=450) and started at least 75% of games (n=494) and accounted for 4% of the within teams variance and 16.5% of the between teams variance, t(64)=3.48, p<0.01. However, the starting effect varied somewhat across teams, v2(64)=84.93, p=0.04. Winning percentage in the current year, coaching experience, and coach’s gender did not explain the varia-
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bility in the starting effect across women’s teams. TCE did not significantly contribute as a L-2 predictor, t(63)=0.09, p=0.93. Further, none of the dimensions of coaching efficacy were associated with team satisfaction across women’s teams. Within women’s teams, ME was related to team satisfaction for female coaches of women’s teams (r=0.35), but not for male coaches (r=–0.06). Conversely, CBE was negatively related to team satisfaction for male coaches of women’s teams (r= 0.42), but not for female coaches (r=0.13). Thus, gender of the coach moderated the relationship between CBE and team satisfaction (Z=2.21) within women’s teams. However, it is important to note that team satisfaction did not vary based on gender of the coach within women’s teams, F(1,63)=0.02, p=0.90, or matched gender between the team and the coach across all teams F(1, 99)=0.02, p=0.89. That is, although coaches’ gender moderated the relationship between CBE and team satisfaction in women’s teams, team satisfaction means did not differ for male and female coaches of women’s teams, or for teams with a head coach of the same sex versus teams with a head coach of the opposite sex.
Discussion The two primary purposes of this study were to examine the influence of proposed sources of coaching efficacy on dimensions of coaching efficacy and, to determine the influence of TCE on self-reported coaching behavior, satisfaction with the coach, and winning percentage for men’s and women’s intercollegiate teams. Findings support previous research by demonstrating a relationship between the sources and dimensions of coaching efficacy, and the influence of TCE on self-reported coaching behavior and team variables in men’s teams. Findings extend previous research by identifying the influences that the sources exert on specific dimensions of coaching efficacy for intercollegiate coaches, a moderating role for coach’s gender on the influence of perceived social support from the community on CBE, and a moderating role for coach’s gender on the influence of CBE on team satisfaction in women’s teams. Perceived team ability, social support from the athletes’ parents and the community, career winning percentage and years as a collegiate head coach were important sources of coaching efficacy information. Thus, coaches who had higher perceptions of their team’s ability, perceived social support from the athletes’ parents and the community, had a higher career winning percentage, and were more experienced as a collegiate head coach were more confident in their motivating, character building, game strategizing, and technique teaching abilities. The strength of specific sources of efficacy information for collegiate coaches was somewhat different compared to findings with high school coaches (Feltz et al., 1999). For high school coaches, community support exerted the strongest influence across most dimensions of coaching efficacy. For Division II and III collegiate coaches, perception of team ability was the most dominant source of coaching efficacy information. Perhaps the major influence of perceived team ability has to do with the more competitive nature of collegiate sports and, the greater control collegiate coaches have over whom they coach. Additionally, the influence of years as a collegiate coach was noticeably weaker than the experience effect in high school coaches. The weakness of the experience indicator in this sample can be partially explained by a restriction of range due to the fact that most of the coaches had <9 years of experience as a collegiate head
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coach. Additionally, examination of a scatterplot identified a couple of coaches in their second season with relatively robust estimates of their coaching abilities. Bandura (1997) suggests that some individuals with limited previous experience may inaccurately estimate their proximal capabilities. Across coaches, perception of support from the athletes’ parents predicted CBE, TE, and ME. However, the significance of these relationships was mostly attributable to moderate associations for female coaches. Similarly, the influence of perceived social support from the community on CBE was significantly greater for female coaches. That some sources of coaching efficacy information may be unique to female coaches parallels previous findings (Barber, 1998). If social support is an important source of efficacy information for female coaches, building social support networks may be a useful strategy in combating the declining number of female coaches in intercollegiate sports (Acousta & Carpenter, 1995). In terms of its consequences, TCE exerted somewhat differing influences for men and women’s teams. In men’s teams, TCE predicted self-reported efficacy-enhancing behaviors, winning percentage, and team satisfaction with the coach. Playing time (as described under HLM in the results section), in addition to the TCE effect, was a significant predictor of team satisfaction with the coach. As more team members had an opportunity to play, team satisfaction increased. Thus, coaches who provided playing time for increasing numbers of their athletes in most games, and who reported higher confidence in their ability to affect the learning and performance of their athletes, had teams that were more satisfied. The relationship between TCE and team satisfaction appeared to be driven by one’s confidence in his instructional and diagnostic abilities (TE). Across women’s teams, TCE predicted only coaches’ reported efficacy-enhancing behaviors with athletes (coaching behavior). However, the relationship between TCE and coaching behavior was only significant for female coaches. Thus, across men’s and women’s teams, only when gender of the coach matched gender of the team, did a coach’s belief in his/her abilities to affect the learning and performance of athletes predict coaching behavior. That gender of the coach (a personal characteristic of the coach) and gender of the team (a team characteristic) may exert indirect influences on coaches’ behavior through coaches’ beliefs is congruent with relationships posited in models of coaching effectiveness (Horn, 2002). From an applied perspective, if the relationship between coaching efficacy and coaching behavior is dependent on matched gender between the coach and team (an assertion that cannot be supported from this study alone), then coaching education program administrators may want to consider this matching when implementing cognitive-behavioral interventions that are designed to help coaches improve their effectiveness in relating to their teams (Smith & Smoll, 2001). Across women’s teams, TCE did not predict team satisfaction. However, the influence that specific dimensions of coaching efficacy exerted on team satisfaction was somewhat different for male and female coaches of women’s teams. ME was positively related to team satisfaction for female coaches, but unrelated to team satisfaction for male coaches. CBE was negatively related to team satisfaction for male coaches, but unrelated to team satisfaction for female coaches. Further, gender of the coach moderated the relationship between CBE and team satisfaction. Thus, as male coaches reported higher confidence in their ability to influence the personal development and positive attitude toward sport in their athletes, team satisfaction decreased in women’s teams. The negative relationship between CBE and team satisfaction for male coaches of
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women’s teams was unexpected, so previous research was considered to inform speculations as to its meaning. The effect of gender bias on athletes’ evaluations of coaches was one line of research that was considered in interpreting the moderating role of coach’s gender on the relationship between CBE and team satisfaction (Frankl & Babbitt, 1998; Medwechuk & Crossman, 1994; Parkhouse & Williams, 1986). Because most of this research has explored high school athletes’ evaluations of hypothetical coaches, parallels with a finding between character building efficacy and team satisfaction in collegiate athletics were limited. Still, that athletes have exhibited a preference for a same sex coach even when male and female coaches were rated as equally able in areas such as ability to motivate (Medwechuk & Crossman, 1994), suggests that athletes’ gender bias may have contributed to the mediating role of coach’s gender in our study. However, because the possibility for athletes’ gender bias may not fully explain why coaches’ gender would moderate a relationship between a coach’s belief and team satisfaction, behavioral explorations were also sought. Some research suggests that female athletes prefer more participation in decision-making when compared to male athletes (Chelladurai & Arnott, 1985). Perhaps male coaches, who led female teams and were highly confident in their ability to influence the personal development of athletes, preferred a mostly autocratic style of decision-making. Because we did not measure athletes’ perceptions of their coach’s decision-style in this study, further research is necessary to test this hypothesis. A limitation of the research was the temporal disparity between measurements of coaching efficacy, coaching behavior, and team variables. Although efficacy beliefs are likely most predictive when measured in close temporal proximity to the dependent variable of interest, efficacy beliefs have been shown to be predictive over long time periods as well (Bandura, 1997). Further, Feltz et al. (1999) provided evidence for test-retest reliability of the CES (1-week between measures), but future research is necessary to determine the stability of coaching efficacy across a season of competition. Another limitation of the research was the reliance on a psychometrically questionable instrument to assess coaches’ reported frequency of efficacy-enhancing behaviors with athletes. The borderline reliability of the measure coupled with previous research suggesting discordance between coaches’ reported behavior and observed coaching behavior (Smith et al., 1978), warrants caution in the observed relationship between coaching efficacy and coaches’ reported efficacy-enhancing behaviors. Still, our findings were similar to an observational field study that reported greater praise and encouragement behaviors in high efficacy coaches compared to low efficacy coaches (Feltz et al., 1999). Future fieldwork could increase certainty that coaching efficacy is related to coaches’ efficacy-enhancing strategies with athletes.
Acknowledgements We would like to acknowledge Dana Munk for her work on data collection.
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References Acousta, R.V., & Carpenter, L.J. (1995). Women in intercollegiate sport. A longitudinal study-twenty one year update1977–1998. Unpublished manuscript, Brooklyn College, New York. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hill. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman. Barber, H. (1998). Examining gender differences in sources and levels of perceived competence in interscholastic coaches. The Sport Psychologist, 12, 237–252. Chelladurai, P. (1978). A contingency model of leadership in athletics. Unpublished doctoral dissertation, University of Waterloo, Canada. Chelladurai, P., & Arnott, M. (1985). Decision styles in coaching: Preferences of basketball players. Research Quarterly for Exercise and Sport, 56, 15–24. Denham, C. H., & Michael, J. J. (1981). Teacher sense of efficacy: A definition and a model for further research. Educational Research Quarterly, 5, 39–63. Feltz, D. L., Chase, M. A., Moritz, S. E., & Sullivan, P. J. (1999). A conceptual model of coaching efficacy: Preliminary investigation and instrument development. Journal of Educational Psychology, 91, 765–776. Feltz, D. L., & Chase, M. A. (1998). The measurement of self-efficacy and confidence in sport. In J. L. Duda (Ed.), Advancements in sport and exercise psychology measurement (pp. 65–80). Morgantown, WV: Fitness Information Technology. Frankl, D., & Babbitt, D. G. (1998). Gender bias: A study of high school track & field athletes’ perceptions of hypothetical male and female head coaches. Journal of Sport Behavior, 21, 396–407. Gould, D., Hodge, K., Peterson, K., & Giannini, J. (1989). An exploratory examination of strategies used by elite coaches to enhance self-efficacy in athletes. Journal of Sport & Exercise Psychology, 11, 128–140. Horn, T. S. (2002). Coaching effectiveness in the sports domain. In T. S. Horn (Ed.), Advances in sport psychology (2nd ed.) (pp. 309–354). Champaign, IL: Human Kinetics. James, L. R., Demaree, R. G., & Wolf, G. (1984). Estimating within-group rater reliability with and without response bias. Journal of Applied Psychology, 69, 85–98. Lee, K. S., Malete, L., & Feltz, D. L. (2002). The effect of a coaching education program on coaching efficacy. International Journal of Applied Sport Sciences, 14, 55–67. Malete, L., & Feltz, D. L. (2000). The effect of a coaching education program on coaching efficacy. The Sport Psychologist, 14, 410–417. Medwechuk, N., & Crossman, J. (1994). Effects of gender bias on the evaluation of male and female swim coaches. Perceptual and Motor Skills, 78, 163–169. Moritz, S. E., & Watson, C. B. (1998). Levels of analysis issues in group psychology: Using efficacy as an example of a multilevel model. Group Dynamics, 2, 285–298. Parkhouse, B. L., & Williams, J. M. (1986). Differential effects of sex and status on evaluation of coaching ability. Research Quarterly for Exercise and Sport, 57, 53–59. Raudenbusch, S. W., & Bryk, A. S. (1992). Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage. Smith, R. E., & Smoll, F. L. (2001). Conducting sport psychology training programs for coaches: Cognitivebehavioral principles and techniques. In J. M. Williams (Ed.), Applied sport psychology (4th ed.) (pp. 378–400). Mount View, CA: Mayfield. Smith, R. E., Smoll, F. L., & Curtis, B. (1978). Coaching behaviors in little league baseball. In F. L. Smoll, & R. E. (Eds.), Psychological perspectives in youth sports (pp. 173–201). Washington, DC: Hemisphere. Smoll, F. L., & Smith, R. E. (1989). Leadership behaviors in sport: A theoretical model and research paradigm. Journal of Applied Social Psychology, 19, 1522–1551. Tabachnick, B. A., & Fidell, L. S. (2001). Using multivariate statistics. (4th ed.). Boston, MA: Allyn and Bacon. Vargas-Tonsing, T.M., Warners, A. L., & Feltz, D.L. (2004). The predictability of coaching efficacy on team efficacy and player efficacy in volleyball. Journal of Sport Behavior (in press).
Coaching efficacy in intercollegiate coaches: sources, coaching behavior, and team variables Nicholas D. Myers, Tiffanye M. Vargas-Tonsing, Deborah L. Feltz Department of Kinesiology, Michigan State University, East Lansing, MI, 48824, USA Received 27 January 2003; received in revised form 6 June 2003; accepted 23 October 2003
Abstract Objectives: To examine the influence of (a) proposed sources of efficacy information on dimensions of coaching efficacy and (b) coaching efficacy on coaching behavior and team variables. Design: A field correlational design tested relationships at two time points: near the beginning and at three-fourths of the way through a season of competition. Method: At Time 1, head coaches (n=135) completed a questionnaire containing the Coaching Efficacy Scale, measures of sources, and demographic items. At Time 2, participants were a subset of coaches from Time 1 (n=101) and 1618 athletes. Coaches completed questionnaires on their perceived frequency of their efficacy-enhancing behaviors with their athletes. Athletes provided information on their satisfaction with their head coach. Results: For female coaches, social support was a stronger source of efficacy information compared to male coaches. Total coaching efficacy predicted coaching behavior, team satisfaction, and winning percentage for men’s teams. Total coaching efficacy predicted only coaching behavior across women’s teams. Within women’s teams, gender of the coach moderated the relationship between character building efficacy and team satisfaction. Character building efficacy was negatively related to team satisfaction in women’s teams with male coaches. Motivation efficacy was positively related to team satisfaction in women’s teams with female coaches. Conclusion: Findings provide novel corroborations to the coaching efficacy model proposed by Feltz, Chase, Moritz and Sullivan (1999: Journal of Educational Psychology, 91, 765–776) and offer some support to broader models of coaching effectiveness. # 2003 Elsevier Ltd. All rights reserved. Keywords: Coaching efficacy; Character building; Motivation; Social support; Coaching education; Coaching effectiveness
Corresponding author. Tel.: +1-517-355-4732; fax: +1-517-353-2944. E-mail address: [email protected] (D.L. Feltz).
1469-0292/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.psychsport.2003.10.007
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Introduction Over the past few decades, the majority of research in sport leadership has been directed toward identifying particular coaching styles that are most effective for successful performance and/or positive psychological responses from athletes (Horn, 2002). The two most prominent models of leadership effectiveness in sport, the Multidimensional Model of Leadership (Chelladurai, 1978) and the Mediational Model of Leadership (Smoll & Smith, 1989), have served as frameworks for much of the related research. Recently, elements of both models have been combined to form a working model of coaching effectiveness (Horn, 2002). Horn’s (2002) working model is founded on three assumptions. First, antecedent factors (i.e. sociocultural context, organizational climate, and personal characteristics of the coach) and athletes’ personal characteristics (e.g. age, gender, etc.) exert influence on coaches’ behavior indirectly through coaches’ expectancies, beliefs, and goals. Second, coaches’ behavior affects athletes’ evaluation of their coaches’ behavior and team performance. Third, the effectiveness of various coaching interventions is influenced by situational factors and individual differences. Much work remains in clarifying the specific relationships that exist within these broad assumptions. Coaching efficacy is an important variable within a constellation of personal characteristics (i.e. gender, motivational style, etc.) that affect coaching behavior (Horn, 2002). Coaching efficacy is a sport-specific construct that Feltz, Chase, Moritz, and Sullivan (1999) defined as the extent to which coaches believe they have the capacity to affect the learning and performance of their athletes. Feltz and her colleagues posit that coaching efficacy comprises four first-order factors: motivation, character building, game strategy, and technique efficacies. Motivation efficacy (ME) is the confidence coaches have in their ability to affect the psychological mood and skills of their athletes. Character building efficacy (CBE) is the confidence coaches have in their ability to influence the personal development and positive attitude toward sport in their athletes. Game strategy efficacy (GSE) is the confidence coaches have in their ability to lead during competition. Technique efficacy (TE) is the belief coaches have in their instructional and diagnostic skills. The complete hierarchical model also posits that the four first-order factors converge to define a more general second-order factor: total coaching efficacy (TCE). Feltz et al. (1999) designed the Coaching Efficacy Scale (CES) to measure the construct, and offered a model of coaching efficacy to serve as a starting point for related research. The CES and the coaching efficacy model are based on self-efficacy theory (Bandura, 1986) and a model of teacher efficacy (Denham & Michael, 1981). Initial data from a heterogeneous sample of high school coaches provided support for the structure of the CES, and for the coaching efficacy model. Specifically, Feltz et al. found that a set of sources (coach’s past success, coaching experience, perceived team ability, and social support) had a relationship with the set of coaching efficacy dimensions in a heterogeneous sample of high school coaches. In turn, TCE predicted observed coaching behavior, team satisfaction, and winning percentage for high school boys’ basketball teams. Recognizing the limits of a single investigation, Feltz and her colleagues called for further research to provide evidence for novel corroborations to the model of coaching efficacy in particular and, the coaching education literature at large1. 1
The hypothesized relationship between coaching efficacy and player/team efficacy that is proposed in the coaching efficacy model (Feltz et al., 1999) was not explored in this study to minimize time commitment for athletes, and because similar work is reported elsewhere (Vargas-Tonsing, Warners, & Feltz, 2004).
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Since then, related investigations have provided support for the structure of the CES, and the ability of a coaching education program to increase coaching efficacy. With a sample of inexperienced coaches, Malete and Feltz (2000) found increases in coaching efficacy (particularly for GSE and TE) following participation in a coaching education program. Also, participants’ efficacy judgments were higher than a similar group of coaches who did not participate in the program. With a heterogeneous sample of Singapore coaches, Lee, Malete, and Feltz (2002) reported support for the factor structure of the CES, higher GSE and TE for certified coaches compared to uncertified coaches, and higher GSE for male coaches compared to female coaches. None of the mentioned studies provided a comprehensive test for the proposed model in nonhigh school settings, nor has any research assessed the influence of coaching efficacy on coaching behavior and team variables in women’s teams. Thus, the two primary purposes of this investigation were to examine the influence of proposed sources of efficacy information on dimensions of coaching efficacy for intercollegiate coaches and, to determine the influence of coaching efficacy on self-reported coaching behavior and team variables in men and women’s intercollegiate teams.
Method Participants and design Data were collected at two time points within several seasons of competition. At Time 1, potential participants were 179 head coaches from several midwestern conferences within Division II and III intercollegiate athletics. A questionnaire containing the CES, measures of proposed sources, and demographic items was mailed to each coach at the one-quarter mark of the season with a requested return date of 2 weeks from delivery. Follow-up phone calls and electronic mail from investigators and league coordinators resulted in a 75% return rate (N=135). Respondents were predominantly Caucasian (88%) and male (67%), and represented the sports of softball (27%), baseball (19%), women’s soccer (20%), men’s soccer (14%) women’s basketball (13%), and men’s basketball (7%). Males (n=36) and females (n=45) coached women’s teams. Coaches ranged in age from 24 to 67 years (M=38.6, SD=10.1) and in experience as a collegiate head coach from 1 to 33 years (M=10.4, SD=7.4). At Time 2, participants were a subset of head coaches from time 1 (n=101) and 1618 athletes nested within participating coaches from Time 2. Most of the coaches from Time 1 submitted data for Time 2 (return rate of 75%). Time 2 packets were sent to head coaches at the threequarter mark of the season with a requested return date of 2 weeks from delivery. Coaches completed questionnaires on their perceived frequency of their efficacy-enhancing behaviors with their athletes. Athletes provided their perceptions of satisfaction with the head coach. Participating athletes were predominantly Caucasian (90%) and female (60%), and represented the sports of softball (25%), baseball (16%), women’s soccer (25%), men’s soccer (20%) women’s basketball (10%), and men’s basketball (4%). Athletes ranged in age from 17 to 26 years (M=19.8, SD=1.3).
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Measures Sources of coaching efficacy Proposed sources of coaching efficacy included perceived team ability, social support, career winning percentage, and years as a collegiate coach. The team ability item, ‘from my perspective, my current team’s overall ability level is’ was the same as the one used by Feltz et al. (1999) and assessed the coach’s perception on a 10-point Likert scale ranging from 0 (very poor) to 9 (excellent)2. The social support questionnaire also used by Feltz et al. contained five items with a similar framework, ‘from my perspective, this season the ______ has/ have been’, that assessed the coach’s perceptions of support for his/her team from the athletic director, faculty, student body, athletes’ parents, and the greater community. Each question was scored on a 10-point Likert scale ranging from 0 (not at all supportive) to 9 (extremely supportive). Social support items were examined individually for relationships with coaching efficacy. Social support from the athletes’ parents and the greater community had the greatest associations with dimensions of coaching efficacy. Data for career winning percentage and years as a collegiate head coach were obtained from coaches and verified on university websites when available. Coaching efficacy The CES (Feltz et al., 1999) is a 24-item instrument in which coaches are asked to assess the degree of confidence they have in their ability to affect the learning and performance of their athletes. A hierarchical model defined by a first-order four-factor structure (ME, CBE, GSE and TE) converging to a second-order general factor (TCE) is hypothesized. Items are scored on a 10-point Likert scale ranging from 0 (not at all confident) to 9 (extremely confident) and include the stem: ‘How confident are you in your ability to. . .’ The scale contains items such as ‘motivate your athletes’, identified by ME; ‘promote good sportsmanship’, identified by CBE; ‘recognize opposing team’s strengths during competition’, identified by GSE; and ‘detect skill errors’, identified by TE. Internal consistency analyses revealed standardized Cronbach a’s of 0.90 (ME), 0.87 (CBE), 0.92 (GSE), 0.84 (TE), and 0.94 (TCE). Reported coaching behavior and team variables Reported coaching behavior and team variable measures included coaches’ perceptions of their efficacy-enhancing behaviors with athletes, athlete satisfaction with their head coach, and winning percentage for the current season. Data for winning percentage at the conclusion of the current season were obtained from coaches and verified on league websites. The efficacy-enhancing behaviors scale was developed by Gould, Hodge, Peterson, and Giannini (1989) and assessed a coach’s perceptions of the frequency of his/her confidence-enhancing behaviors with athletes. The instrument contained 13 commonly advanced strategies for increasing confidence in athletes (i.e. ‘verbally persuade athletes that she can do it’). All items were 2 The measurement of perception of team ability was consistent with the literature, but future efforts should consider adding items to more fully represent the construct. A fuller representation of team ability would allow for psychometric evaluation of the measures produced, and would decrease the possibility of encountering problems associated with one-item measures (e.g. inadequately represent the construct which can result in measures that fail to accurately depict the variable of interest).
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scored on a five-point Likert scale ranging from 1 (never use) to 5 (always use). Previous psychometric data for the scale was not available. Thus, a principal components analysis was performed and 10 of the 13 items had a reasonable loading (>0.30) on one dominant component which we labeled, ‘efficacy-enhancing behavior’. This component was the only component above the lower asymptote, had an eigenvalue of 2.77, and accounted for 28% of the total variance across the items. Internal consistency analysis for the 10-item component revealed a standardized Cronbach a of 0.70. Athlete satisfaction with the coach consisted of selected items from a scale that was intended to measure, in part, attitudes toward the coach (Smith, Smoll, & Curtis, 1978). The four-items that were utilized for this study included ‘how much do you like playing for your coach’; ‘if you were able to play next year, how much would you like to have the same coach again’; ‘how much does your coach like you’; and, ‘how much does your coach know about basketball’. The items were scored on a seven-point Likert scale ranging from 1 (very little) to 7 (a lot). A principal components analysis was performed and all four items had high loadings (>0.70) on a dominant component that we labeled, ‘athlete satisfaction with the coach’. This component had an eigenvalue of 2.8, was the only component above the lower asymptote, and accounted for 70% of the total variance across the items. Internal consistency analysis for the four-item component revealed a standardized Cronbach a of 0.85. As recommended by Feltz and Chase (1998) and Moritz and Watson (1998), prior to aggregating data to the team-level, perceptual consensus of clustered individuals should be assessed. When the individual-level perception (in this case, athlete satisfaction with the coach) is relatively homogenous within identified clusters (in this case, teams), the group mean can be used to represent the collective perception for each team. Thus, in this study, consensus was demonstrated prior to aggregating athlete-level data to the team-level (N=101). Interrater agreement ranged from 0.65 to 0.99 (M=0.84, SD=0.08), which indicated that team members held fairly homogenous beliefs regarding satisfaction with the head coach (James, Demaree, & Wolf, 1984). Procedure Permission was obtained from the institutional review board for human subjects, league coordinators, and the head coaches prior to data collection. Head coaches were asked to appoint someone not directly affiliated with the team (i.e. athletic trainer) to verbally explain the study to the athletes. The same person was responsible for administering the questionnaires to the athletes. Minors were informed to not participate. Informed consent was obtained from all of the participating athletes. Athletes were guaranteed confidentiality for their responses. Requested return dates for all questionnaires were 2 weeks from date of delivery. Reminders from investigators and league coordinators along with a lottery drawing for US$100 for fully participating coaches were employed to encourage returns.
Results Seven coaches submitting complete data at Time 1 were dropped because they were in their first year as an intercollegiate head coach making both years as a collegiate head coach and
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career record questionable sources of efficacy information. Two additional cases were dropped as they were identified as multivariate outliers. Five coaches were missing data for reported efficacy-enhancing behavior with athletes but had complete data otherwise and were retained for subsequent analyses (N=126). Fifty-seven athletes (<4% of the total sample) were dropped due to missing data (N=1561). Descriptive statistics Years as a collegiate head coach, team ability, and CBE were significantly skewed (Z>3.29) and were transformed prior to performing inferential statistics. Years as a collegiate coach was positively skewed because a majority of participants had <9 years experience as a collegiate head coach. A base 10 logarithm was applied to years as a collegiate coach. Perceived team ability was negatively skewed because a majority of coaches perceived their teams to be quite able at the one-quarter mark of the season. CBE was negatively skewed because most coaches were quite confident in their ability to influence the personal development and positive attitude toward sport in their athletes. Athlete satisfaction was also negatively skewed because a majority of athletes were quite satisfied with their head coach at the three-quarter mark of the season. All of the negatively skewed variables were reflected to create positive skewness and then transformed with base 10 logarithms (Tabachnick & Fidell, 2001). Descriptive statistics for the proposed sources of coaching efficacy, coaching efficacies, reported coaching behavior, and team variables are presented in Table 1. As can be viewed in the table, non-transformed descriptive values for all of the variables were reasonable. Of note, there was a good deal of disparity among the values for the social support variables, with the
Table 1 Descriptive statistics for the proposed sources of coaching efficacy, coaching efficacies, reported coaching behavior, and team variables
Perceived team ability Support from the athletic director Support from the faculty Support from the student body Support from the athletes’ parents Support from the community Career winning percentage Experience as a collegiate coach Total coaching efficacy Motivation efficacy Character building efficacy Game strategy efficacy Technique efficacy Efficacy-enhancing behaviors Athlete satisfaction with coach Team satisfaction with coach Winning percentage
N
M
SD
Minimum
Maximum
126 126 126 126 126 126 125 126 126 126 126 126 126 99 1561 94 124
5.97 6.38 5.23 4.69 7.08 4.59 0.54 10.80 7.54 7.10 7.94 7.61 7.71 3.48 5.44 5.43 0.52
1.35 2.17 1.98 1.93 1.57 2.20 0.16 7.38 0.65 0.85 0.84 0.81 0.75 0.51 1.26 0.80 0.21
1.00 0.00 0.00 0.00 2.00 0.00 0.15 1.00 5.29 4.29 4.50 5.43 5.50 2.50 1.00 2.61 0.11
9.00 9.00 9.00 9.00 9.00 9.00 0.81 33.00 9.00 9.00 9.00 9.00 9.00 4.70 7.00 6.64 0.92
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Table 2 Multivariate analysis of variance for coaching efficacy dimensions on various demographics Demographic
Efficacy
Wilks’ k
df
F
p
Division coached
– Motivation Character building Game strategy Technique – Motivation Character building Game strategy Technique – Motivation Character building Game strategy Technique –
0.93 – – – – 0.83 – – – – 0.96 – – – – –
4106 1 1 1 1 20352 5 5 5 5 4106 1 1 1 1 109
1.96 2.28 1.09 0.22 0.01 1.03 0.36 1.22 0.74 1.60 1.13 0.16 1.43 0.53 0.87 –
0.11 0.13 0.30 0.64 0.94 0.42 0.87 0.30 0.60 0.17 0.35 0.69 0.23 0.47 0.35 –
Sport coached
Gender of coach
Error
perception of support from the athletes’ parents emerging as the source of social support with the highest mean and least dispersion. Table 2 provides evidence that the means of the dimensions of coaching efficacy did not vary based on division coached, sport coached, or gender of the coach. Table 3 contains the correlations between dimensions of coaching efficacy and TCE. Correlations among dimensions of coaching efficacy ranged from 0.39 to 0.75, and correlations of TCE with dimensions of coaching efficacy ranged from 0.68 to 0.86. These relationships are congruent with the hierarchical structure suggested by Feltz et al. (1999). The moderately high correlation between TE and GSE (r=0.75) is consistent with previous research (Feltz et al., 1999; Lee et al., 2002) and presents potential problems with multicollinearity if both variables are used simultaneously as predictors within a regression equation.
Table 3 Pearson correlations between dimensions of coaching efficacy and total coaching efficacy
Motivation efficacy (ME) Character building efficacy (CBE) Game strategy efficacy (GSE) Technique efficacy (TE) Total coaching efficacy (TCE) Note. For all correlations, p<0.001.
ME
CBE
GSE
TE
TCE
— — — — —
0.52 — — — —
0.52 0.45 — — —
0.45 0.39 0.75 — —
0.81 0.68 0.86 0.81 —
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Table 4 Correlations of sources with total coaching efficacy and dimensions of coaching efficacy TCE
ME
CBE
GSE
TE
All coaches (N=126) Team ability Athletes’ parents support Community support Career winning percentage Years as collegiate head coach
0.39 0.29 0.19 0.26 0.18
0.44 0.32 0.29 0.20 0.16
0.29 0.25 0.22 0.09 0.15
0.28 0.18 0.09 0.33 0.18
0.21 0.20 0.03 0.16 0.06
Male coaches (n=84) Team ability Athletes’ parents support Community support Career winning percentage Years as collegiate head coach
0.34 0.18 0.09 0.24 0.21
0.39 0.25 0.25 0.22 0.21
0.20 0.12 0.01 0.04 0.20
0.26 0.08 0.02 0.31 0.17
0.20 0.12 0.05 0.13 0.06
Female coaches (n=42) Team ability Athletes’ parents support Community support Career winning percentage Years as collegiate head coach
0.45 0.50 0.37 0.27 0.09
0.50 0.44 0.35 0.14 0.04
0.45 0.38 0.50 0.29 0.17
0.29 0.42 0.23 0.30 0.10
0.20 0.37 0.18 0.19 0.03
Note. TCE=Total Coaching Efficacy, ME=Motivation Efficacy, CBE=Character Building Efficacy, GSE=Game Strategy Efficacy, and TE=Technique Efficacy. 0 p<0.05. p<0.01. p<0.001.
Influence of sources of coaching efficacy on dimensions of coaching efficacy To determine the relationships between the sources and dimensions of coaching efficacy collected at Time 1, we calculated Pearson product-moment correlations (Table 4). Across coaches, the proposed sources had mostly significant relationships with the dimensions of coaching efficacy. The strongest source of efficacy information was perception of team ability, while the weakest source was years as a collegiate coach. Total years in coaching (not listed) exhibited even weaker relationships with dimensions of coaching efficacy than did years as a collegiate coach. The influence of years as a collegiate coach, career-winning percentage, and perception of team ability on dimensions of coaching efficacy did not differ for male (n=84) and female (n=42) coaches. The correlations between perceived social support from the community and CBE were different for male (r=0.01) and female (r=0.50) coaches, Z=2.76. However, due to the modest number of female coaches, the complexity of the proposed model, and mostly similar patterns of influence of the sources on dimensions coaching efficacy, subsequent analyses were conducted across male and female coaches. The relationship between the two perceptual sets (i.e. sources and dimensions of coaching efficacy) was tested via canonical correlation analysis (see Table 5). Canonical correlation was
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Table 5 Summary of the canonical correlation analysis from Time 1 data Correlations Independent variate Perceived team ability Support from athletes’ parents Support from community Career winning percentage Experience as a collegiate head coach Variance extracted Redundancy index Dependent variate Motivation efficacy Character building efficacy Game strategy efficacy Technique efficacy Variance extracted Redundancy index Canonical correlation
Coefficients
0.89 0.66 0.60 0.38 0.36
0.78 0.47 0.02 0.18 0.21 36.93% 9.48
0.97 0.70 0.56 0.48
0.83 0.27 0.04 0.03 49.63% 12.74 0.51
appropriate because the coaching efficacy model specifies that a set of sources are related to a set of coaching efficacies (first-order dimensions), and the said analysis is specifically designed ‘to determine if and how two sets of variables are related to each other’ (Tabachnick & Fidell, 2001, p.177)3. One reliable relationship was observed between the two perceptual sets, Wilks’ k=0.63, F(20, 389)=2.90, p<0.01. The correlation between the two variates was moderate, Rc=0.51, Rc2=0.26, p<0.01. The redundancy index of interest revealed that 12.7% of the variance in the dimensions of coaching efficacy was accounted for by the set of sources. The canonical variate pair linked low scores on the proposed sources with low scores on the dimensions of coaching efficacy. Correlations between the variables and the canonical variates suggested that all of the sources were important contributors to the independent variate, while all of the dimensions of coaching efficacy were important contributors to the dependent variate (i.e. correlations with absolute values >0.30). To more specifically explain the observed relationship between the two perceptual sets, a multivariate multiple regression analysis was conducted to determine the predictive strength of each of the proposed sources on dimensions of coaching efficacy. Perception of team ability predicted ME, F(1, 124)=14.63, p>0.01, and CBE, F(1, 124)=5.39, p=0.02. Perception of social support from athletes’ parents predicted ME, F(5, 120)=6.31, p=0.01, CBE, F(5, 120)=4.06, p<0.05, and TE, F(5, 120)=4.60, p=0.03. Career winning percentage predicted GSE, F(5, 120)=4.26, p=0.04. 3
Although canonical correlation was an appropriate statistical framework given the generality of the current coaching efficacy model (i.e. sets of variables) and the research questions posed, future researchers are encouraged to utilize the information from this and previous studies to specify variable-level relationships that can be tested with powerful inferential techniques, such as structural equation modeling.
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Univariate equations with the set of sources predicting each dimension of coaching efficacy separately, were all significant: ME, F(5, 120)=7.73, p<0.01, CBE, F(5, 120)=3.64, p<0.01, GSE, F(5, 120)=4.21, p<0.01, and TE, F(5, 120)=2.39, p<0.05. The significance of the CBE and TE equations is unmatched in previous literature. The relevance of CBE may be explained by the overriding mission of the private, often religiously affiliated, institutions where many of these coaches were employed. The importance of TE may be explained by the relatively young, and presumably physically able coaches included in this sample. Influence of total coaching efficacy on coaching behavior and team variables Horn’s (2002) model of coaching effectiveness suggests that gender of the coach and athletes exert indirect influences on coaches’ behavior through coaches’ beliefs. Thus, we examined the influence of TCE on reported coaching behavior for male and female coaches, separately. Horn’s model also suggests that gender of the athlete directly influences athletes’ evaluative reactions of their coaches’ behavior. Thus, we examined the influence of TCE on satisfaction with the coach within men and women’s teams, separately. Due to small samples for some subgroups of interest, potential multicollinearity between TE and GSE, and dependency of the satisfaction data, differing regression analyses were selected when modeling each dependant variable. However, because dimensions of coaching efficacy define TCE, bivariate associations of dimensions of coaching efficacy with coaching behavior and team variables were also reported to more specifically explain the influences of TCE (Table 6). TCE predicted the reported frequency of efficacy-enhancing behaviors with athletes (coaching behavior) for female, F(1,34)=12.99, p<0.01, and male coaches, F(1,58)=10.39, p<0.01. Within women’s teams, the relationship between coaching efficacies and coaching behavior was somewhat different for male and female coaches. For female coaches, TCE and all of the dimensions of coaching efficacy were related to coaching behavior. For male coaches of women’s teams, neither TCE nor any of the dimensions of coaching efficacy were related to coaching behavior. However, it is important to note that across women’s teams, coaching behavior did not vary based on coach’s gender, F(1, 58)=0.44, p=0.51. For male coaches of men’s teams, TCE and all of the dimensions of coaching efficacy were related to coaching behavior. Thus, only when gender of the coach matched gender of the team, did a coach’s belief in his/her abilities to affect the learning and performance of athletes predict coaching behavior. However, it is important to note that across all teams, coaching behavior did not vary based on matched gender between the team and the coach, F(1, 94)=2.00, p=.16. The influence of TCE on winning percentage in the current year was determined through simple regression analyses for men and women’s teams. TCE predicted winning percentage for men’s teams, F(1,34)=5.75, p=0.02, but not for women’s teams, F(1, 63)=0.88, p=0.35. Within women’s teams, neither TCE nor any of the dimensions of coaching efficacy were associated with winning percentage in the current year for male or female coaches. Within men’s teams, CBE (r=0.42) and ME (r=0.38) were significantly associated with winning percentage in the current year. Satisfaction with the coach was measured at the athlete level and aggregated to the team level to calculate the correlations that are illustrated in Table 6. Aggregating the satisfaction data to the team level (L-2), however, ignores variability within teams (L-1). The intraclass correlation
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Table 6 Correlations of team variables and coaching behavior with total coaching efficacy and dimensions of coaching efficacy TCE
ME
CBE
GSE
TE
Efficacy-enhancing behaviors (N=96) Female coaches (n=36) Male coaches (n=60) Male coaches of men’s teams (n=36) Male coaches of women’s teams (n=24)
0.53 0.39 0.54 0.15
0.41 0.34 0.50 0.09
0.35 0.28 0.42 0.13
0.44 0.28 0.35 0.11
0.44 0.39 0.48 0.23
Team satisfaction (N=101) Female coaches (n=38) Male coaches (n=63) Male coaches of men’s teams (n=36) Male coaches of women’s teams (n=27)
0.20 0.16 0.33 0.18
0.35 0.15 0.28 0.02
0.13 0.15 0.03 0.42
0.05 0.13 0.27 0.14
0.02 0.30 0.40 0.04
Winning percentage in current year (N=101) Female coaches (n=38) Male coaches (n=63) Male coaches of men’s teams (n=36) Male coaches of women’s teams (n=27)
0.15 0.24 0.38 0.03
0.20 0.21 0.38 0.02
0.25 0.11 0.42 0.22
0.02 0.29 0.32 0.25
0.05 0.11 0.16 0.02
Note. TCE=Total Coaching Efficacy, ME=Motivation Efficacy, CBE=Character Building Efficacy, GSE=Game Strategy Efficacy, and TE=Technique Efficacy. 0 p<0.05. p<0.01. p<0.001.
coefficient for satisfaction with the coach was 0.32, which suggested that only 32% of the variance in athlete satisfaction was due to between teams differences. Hierarchical linear modeling examines multiple levels of variance simultaneously. Specifying predictors at L-1 increases precision at L-2 (Raudenbusch & Bryk, 1992). Hierarchical linear modeling (HLM) For men’s teams, playing time dichotomized as ‘played in less than 75% of games’ (n=246) and ‘played in at least 75% of games’ (n=364) accounted for 4% of the within teams variance and 5% of the between teams variance, t(35)=4.01, p<0.01. The playing time effect was similar across teams, v2(34)=38.89, p=0.26. TCE was added as a L-2 predictor and accounted for an additional 26% of the between teams variance, t(34)=2.60, p=0.01. However, it should be noted that TE was actually more strongly associated with team satisfaction (r=0.40) than was TCE (r=0.33). For women’s teams, playing time was not selected as a L-1 variable because 70% of the athletes played in at least 75% of games. Instead, ‘starting’ was dichotomized as ‘started in less than 75% of games’ (n=450) and started at least 75% of games (n=494) and accounted for 4% of the within teams variance and 16.5% of the between teams variance, t(64)=3.48, p<0.01. However, the starting effect varied somewhat across teams, v2(64)=84.93, p=0.04. Winning percentage in the current year, coaching experience, and coach’s gender did not explain the varia-
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bility in the starting effect across women’s teams. TCE did not significantly contribute as a L-2 predictor, t(63)=0.09, p=0.93. Further, none of the dimensions of coaching efficacy were associated with team satisfaction across women’s teams. Within women’s teams, ME was related to team satisfaction for female coaches of women’s teams (r=0.35), but not for male coaches (r=–0.06). Conversely, CBE was negatively related to team satisfaction for male coaches of women’s teams (r= 0.42), but not for female coaches (r=0.13). Thus, gender of the coach moderated the relationship between CBE and team satisfaction (Z=2.21) within women’s teams. However, it is important to note that team satisfaction did not vary based on gender of the coach within women’s teams, F(1,63)=0.02, p=0.90, or matched gender between the team and the coach across all teams F(1, 99)=0.02, p=0.89. That is, although coaches’ gender moderated the relationship between CBE and team satisfaction in women’s teams, team satisfaction means did not differ for male and female coaches of women’s teams, or for teams with a head coach of the same sex versus teams with a head coach of the opposite sex.
Discussion The two primary purposes of this study were to examine the influence of proposed sources of coaching efficacy on dimensions of coaching efficacy and, to determine the influence of TCE on self-reported coaching behavior, satisfaction with the coach, and winning percentage for men’s and women’s intercollegiate teams. Findings support previous research by demonstrating a relationship between the sources and dimensions of coaching efficacy, and the influence of TCE on self-reported coaching behavior and team variables in men’s teams. Findings extend previous research by identifying the influences that the sources exert on specific dimensions of coaching efficacy for intercollegiate coaches, a moderating role for coach’s gender on the influence of perceived social support from the community on CBE, and a moderating role for coach’s gender on the influence of CBE on team satisfaction in women’s teams. Perceived team ability, social support from the athletes’ parents and the community, career winning percentage and years as a collegiate head coach were important sources of coaching efficacy information. Thus, coaches who had higher perceptions of their team’s ability, perceived social support from the athletes’ parents and the community, had a higher career winning percentage, and were more experienced as a collegiate head coach were more confident in their motivating, character building, game strategizing, and technique teaching abilities. The strength of specific sources of efficacy information for collegiate coaches was somewhat different compared to findings with high school coaches (Feltz et al., 1999). For high school coaches, community support exerted the strongest influence across most dimensions of coaching efficacy. For Division II and III collegiate coaches, perception of team ability was the most dominant source of coaching efficacy information. Perhaps the major influence of perceived team ability has to do with the more competitive nature of collegiate sports and, the greater control collegiate coaches have over whom they coach. Additionally, the influence of years as a collegiate coach was noticeably weaker than the experience effect in high school coaches. The weakness of the experience indicator in this sample can be partially explained by a restriction of range due to the fact that most of the coaches had <9 years of experience as a collegiate head
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coach. Additionally, examination of a scatterplot identified a couple of coaches in their second season with relatively robust estimates of their coaching abilities. Bandura (1997) suggests that some individuals with limited previous experience may inaccurately estimate their proximal capabilities. Across coaches, perception of support from the athletes’ parents predicted CBE, TE, and ME. However, the significance of these relationships was mostly attributable to moderate associations for female coaches. Similarly, the influence of perceived social support from the community on CBE was significantly greater for female coaches. That some sources of coaching efficacy information may be unique to female coaches parallels previous findings (Barber, 1998). If social support is an important source of efficacy information for female coaches, building social support networks may be a useful strategy in combating the declining number of female coaches in intercollegiate sports (Acousta & Carpenter, 1995). In terms of its consequences, TCE exerted somewhat differing influences for men and women’s teams. In men’s teams, TCE predicted self-reported efficacy-enhancing behaviors, winning percentage, and team satisfaction with the coach. Playing time (as described under HLM in the results section), in addition to the TCE effect, was a significant predictor of team satisfaction with the coach. As more team members had an opportunity to play, team satisfaction increased. Thus, coaches who provided playing time for increasing numbers of their athletes in most games, and who reported higher confidence in their ability to affect the learning and performance of their athletes, had teams that were more satisfied. The relationship between TCE and team satisfaction appeared to be driven by one’s confidence in his instructional and diagnostic abilities (TE). Across women’s teams, TCE predicted only coaches’ reported efficacy-enhancing behaviors with athletes (coaching behavior). However, the relationship between TCE and coaching behavior was only significant for female coaches. Thus, across men’s and women’s teams, only when gender of the coach matched gender of the team, did a coach’s belief in his/her abilities to affect the learning and performance of athletes predict coaching behavior. That gender of the coach (a personal characteristic of the coach) and gender of the team (a team characteristic) may exert indirect influences on coaches’ behavior through coaches’ beliefs is congruent with relationships posited in models of coaching effectiveness (Horn, 2002). From an applied perspective, if the relationship between coaching efficacy and coaching behavior is dependent on matched gender between the coach and team (an assertion that cannot be supported from this study alone), then coaching education program administrators may want to consider this matching when implementing cognitive-behavioral interventions that are designed to help coaches improve their effectiveness in relating to their teams (Smith & Smoll, 2001). Across women’s teams, TCE did not predict team satisfaction. However, the influence that specific dimensions of coaching efficacy exerted on team satisfaction was somewhat different for male and female coaches of women’s teams. ME was positively related to team satisfaction for female coaches, but unrelated to team satisfaction for male coaches. CBE was negatively related to team satisfaction for male coaches, but unrelated to team satisfaction for female coaches. Further, gender of the coach moderated the relationship between CBE and team satisfaction. Thus, as male coaches reported higher confidence in their ability to influence the personal development and positive attitude toward sport in their athletes, team satisfaction decreased in women’s teams. The negative relationship between CBE and team satisfaction for male coaches of
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women’s teams was unexpected, so previous research was considered to inform speculations as to its meaning. The effect of gender bias on athletes’ evaluations of coaches was one line of research that was considered in interpreting the moderating role of coach’s gender on the relationship between CBE and team satisfaction (Frankl & Babbitt, 1998; Medwechuk & Crossman, 1994; Parkhouse & Williams, 1986). Because most of this research has explored high school athletes’ evaluations of hypothetical coaches, parallels with a finding between character building efficacy and team satisfaction in collegiate athletics were limited. Still, that athletes have exhibited a preference for a same sex coach even when male and female coaches were rated as equally able in areas such as ability to motivate (Medwechuk & Crossman, 1994), suggests that athletes’ gender bias may have contributed to the mediating role of coach’s gender in our study. However, because the possibility for athletes’ gender bias may not fully explain why coaches’ gender would moderate a relationship between a coach’s belief and team satisfaction, behavioral explorations were also sought. Some research suggests that female athletes prefer more participation in decision-making when compared to male athletes (Chelladurai & Arnott, 1985). Perhaps male coaches, who led female teams and were highly confident in their ability to influence the personal development of athletes, preferred a mostly autocratic style of decision-making. Because we did not measure athletes’ perceptions of their coach’s decision-style in this study, further research is necessary to test this hypothesis. A limitation of the research was the temporal disparity between measurements of coaching efficacy, coaching behavior, and team variables. Although efficacy beliefs are likely most predictive when measured in close temporal proximity to the dependent variable of interest, efficacy beliefs have been shown to be predictive over long time periods as well (Bandura, 1997). Further, Feltz et al. (1999) provided evidence for test-retest reliability of the CES (1-week between measures), but future research is necessary to determine the stability of coaching efficacy across a season of competition. Another limitation of the research was the reliance on a psychometrically questionable instrument to assess coaches’ reported frequency of efficacy-enhancing behaviors with athletes. The borderline reliability of the measure coupled with previous research suggesting discordance between coaches’ reported behavior and observed coaching behavior (Smith et al., 1978), warrants caution in the observed relationship between coaching efficacy and coaches’ reported efficacy-enhancing behaviors. Still, our findings were similar to an observational field study that reported greater praise and encouragement behaviors in high efficacy coaches compared to low efficacy coaches (Feltz et al., 1999). Future fieldwork could increase certainty that coaching efficacy is related to coaches’ efficacy-enhancing strategies with athletes.
Acknowledgements We would like to acknowledge Dana Munk for her work on data collection.
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