1. ABSTRACT
Data randomly collected from Division I collegiate softball athletes (n = 148) and head softball coaches (n = 20) during the fall of 2012 were used to examine perceived differences in frequency of coaches’ positive and negative coaching behaviors towards athletes placed in high, low, or average expectation of performance groups. Coaches rated each athlete using an electronic version of The Modified Expectancy Rating Scale (MERS; Solomon, 2008) to capture coaches’ expectations about athletes’ performance. The Coaching Behavior Assessment System – Perceived Behavior Scale (CBAS-PBS; Cumming, Smith, & Smoll, 2006) was electronically issued to athletes to capture the perceived frequency coaches displayed both positive and negative behaviors. Overall, coaches were perceived as favorable, exhibiting positive behaviors “quite often” (M = 5.09, SD = .72) and negative behaviors “seldom” (M = 2.81, SD = .87). Cluster analysis produced three expectancy groups based on MERS scores: High expectancy (n = 64, M = 4.59), average expectancy (n = 66, M = 3.73), and low expectancy (n = 18, M = 2.60). One-way MANOVA followed by discriminant function analysis revealed significant differences in frequencies of positive and negative behaviors towards expectancy groups. Discriminant function analysis resulted in two functions characterized by behaviors reflecting “uninvolved” coaching behaviors and “engaged” behaviors. Low and average expectancy athletes perceived more “uninvolved” behaviors than high expectancy athletes, and average expectancy athletes perceived more “engaging” behaviors than low and high expectancy athletes. Despite coaches overall use of more positive behaviors, athletes with different coach-assigned performance expectation scores perceived behaviors differently.
Division I Softball Athletes: Perceptions of Coaching Behavior Differ by Performance Expectations
Megan M. Buning, PhD
Department of Advanced Studies and Innovation
Augusta University, Augusta, Georgia
INTRODUCTION
Horn, Lox, and Labrador (1998) introduced the expectation-performance process (EPP) detailing a self-fulfilling prophecy within sports. Coaches can and do form expectations about athletes’ performance ability based on different types of cues (Solomon, 2008; 2010), and do change behaviors based on these expectations (Amorose & Wiess, 1998; Horn et al., 1998; Solomon & Rhea, 2008). Differential feedback and behavior patterns, if perceived negatively, can be harmful to the athlete’s performance, enjoyment level, persistence, and overall motivation level to continuing playing (Amorose, 2003; Horn et al., 1998; Lyle, 1999; Smith, Ntoumanis, & Duda, 2010). A startling finding is many coaches are largely unaware or unrealistic about the behavior they display toward their athletes (Smith et al., 2010; Smith & Smoll, 2002). The truly exceptional coaches learn how to approach each athlete as an individual in terms of motivation and feedback, and successful coaches communicate consistently expectations of athletes both individually and as a team (Becker & Wrisberg, 2008; Bloom, Crumpton, & Anderson, 1999; Kahan, 1999). The focus around coaching behavior has shifted from the influence of behaviors to the elements of a healthy coach-athlete relationship (Jowett & Cramer, 2010) with an underlying assumption that differential treatment has somehow vanished in modern sport. The purpose of this study was to examine if softball athletes with different coach-assigned playing expectations differed in perception of coaching behaviors. This study was guided by the following research question: How do athletes’ perceptions of coaching behavior differ among athletes of different performance expectations? It was hypothesized athletes with higher performance expectations would perceive more positive coaching behaviors.
RESULTS
Cluster analysis using MERS scores resulted in three expectancy groups: low (LEx), high (HEx), and average (AEx) expectancy athletes. One-way MANOVA followed by discriminant function analysis examined group differences in perceptions of coaching behavior. Pillai’s trace revealed a significant group effect on perceptions of behavior, V = .30, F(24, 266) = 1.98, p = .01. Follow-up discriminant analysis revealed two functions that, in combination, significantly differentiated groups, λ = .72, χ2(24) = 45.69, p = .01. The first explained 70.3% of the variance, canonical R2 = .21, whereas the second explained 29.7%, canonical R2 = .10. Function one was correlated with more frequent non-reward (r = .53), infrequent general communication (r = -.43), and infrequent reward (r = -.39) resulting in a function label of “Uninvolved.” LEx and AEx athletes perceived more “uninvolved” behaviors than HEx athletes. Function two correlated most strongly with organization (r = .59), instructions (r = .49), corrective instruction (r = .37), and encouragement (r = .35) resulting in a label of “Engaged.” AEx athletes perceived more “engaging” behavior than LEx and HEx athletes.
CONCLUSIONS
Coaches formed expectations that significantly differentiated athletes, and athletes differed in perceived frequencies of behaviors (Solomon 2008, 2010). HEx athletes experienced less “uninvolved” behavior as consistent with literature (Solomon, 2008). Most studies group athletes into two expectancy groups (low, high): however, three groups were identified in this study which are noteworthy considering average athletes are the majority of most teams. Regardless, an individual’s interpretation and experience can be vital in determining achievement behavior and strategies (Treasure, 1997). This study suggests that athletes still perceive differential treatment even among coaches with predominantly frequent positive behaviors scores, and indicates a need for continued coach education and training around behavior awareness.
METHODS
Participants and procedures. The data used for this study was part of a mixed-methods dissertation. IRB approval was received and all participants completed electronic consent. Random cluster sampling was used to select Division I softball coaches and teams from the population (N = 292) during the fall of Head coaches (n = 20) completed an electronic version of the Modified Expectancy Rating Scale (MERS, Solomon, 2008) for each athlete (n = 147) on the active roster. The MERS provided coaches with eight items asking coaches to indicate a degree of how true each statement was for each athlete (Likert scale ranging from 1 to 5). Cluster analysis using the average MERS score for athletes was conducted to establish expectancy groups. Coaches then forwarded a link to electronic version of the Coaching Behavior Assessment System Perceived Behavior Scale (CBAS-PBS; Cumming, Smith, & Smoll, 2006). The CBAS-PBS is a 12-item measure that provides athletes with a definition of 12 different coaching behaviors and requires athletes to respond along a 7-point Likert scale of how often athletes experienced each behavior from her coach. Expectations and behaviors were recorded pre- and post-fall season. To obtain expectancy and behavior scores for this study, the average of the pre-post score was used for analysis.
Figure 1: Expectancy Group Centroid Position by Function
Selected References
Amorose, A. J. (2003). Reflected appraisals and perceived importance of significant others’ appraisals as predictors of college athletes’ self-perceptions of competence. Research Quarterly for Exercise and Sport, 74,
Cumming, S. P., Smith, R. E., & Smoll, F. L. (2006). Athlete-perceived coaching behaviors: Relating two measurement traditions. Journal of Sport and Exercise Psychology, 28,
Jowett, S., & Cramer, D. (2010). The prediction of young athletes’ physical self from perceptions of relationships with parents and coaches. Psychology of Sport & Exercise Science, 11(2),
Smith, A., Ntoumanis, N., & Duda, J. (2010). An investigation of coach behaviors, goal motives, and implementation intentions as predictors of well-being in sport. Journal of Applied Sport Psychology, 22,
Solomon, G. B. (2008). The assessment of athletic ability in intercollegiate sport: Instrument construction and validation. International Journal of Sports Science, 3(4),
Solomon, G. B. (2010). The assessment of athletic ability at the junior college level. International Journal of Sports Science & Coaching, 5(1),