Coaching Behavior and Effectiveness in Sport and Exercise Psychology
Coaching Behavior and Effectiveness in Sport and Exercise Psychology
- Ronald E. SmithRonald E. SmithDepartment of Psychology, University of Washington
- and Frank L. SmollFrank L. SmollDepartment of Psychology, University of Washington
Coaches occupy a central role in sport, fulfilling instructional, organizational, strategic, and social relationship functions, and their relationships with athletes influence both skill development and psychosocial outcomes of sport participation. This review presents the major theoretical models and empirical results derived from coaching research, focusing on the measurement and correlates of coaching behaviors and on intervention programs designed to enhance coaching effectiveness.
A strong empirical literature on motor skill development has addressed the development of technical sport skills, guided in part by a model that divides the skill acquisition process into cognitive, associative, and autonomous phases, each requiring specific coaching knowledge and instructional techniques. Social-cognitive theory’s mediational model, the multidimensional model of sport leadership, achievement goal theory, and self-determination theory have been highly influential in research on the psychosocial aspects of the sport environment. These conceptual models have inspired basic research on the antecedents and consequences of defined coaching behaviors as well as applied research on coach training programs designed to enhance athletes’ sport outcomes. Of the few programs that have been systematically evaluated, outcomes such as enjoyment, liking for coach and teammates, team cohesion, self-esteem, performance anxiety, athletes’ motivational orientation, and sport attrition can be influenced in a salutary fashion by a brief intervention with specific empirically derived behavioral guidelines that focus on creating a mastery motivational climate and positive coach-athlete interactions. However, other existing programs have yet to demonstrate efficacy in controlled outcome research.
- Methods and Approaches in Psychology
- Sports Psychology
Coaches occupy a central role in sport, fulfilling instructional, organizational, strategic, and social relationship functions. Athletes’ skill acquisition, success, enjoyment, continued participation, and physical and psychological well-being are all strongly influenced by coaching behaviors. Not surprisingly, therefore, research on coaching behaviors and their consequences have been a strong focus of research in sport and exercise psychology. This body of scientific literature illustrates important reciprocal linkages between theory, research, and practice. This review focuses on three central facets of this research literature: measurement of coaching behaviors; relations between coaching behaviors and other variables, and interventions designed to enhance coaching practices.
Measurement and Correlates of Coaching Behaviors
Theory and measurement are intimately related. Theoretical models cannot be tested without measures that provide operational definitions of the model’s constructs, and the constructs provide the basis for the content of the measures, whether the measurement model involves questionnaire items completed by coaches and athletes or systematic observation and coding of actual coaching behaviors. Within sport and exercise psychology, several theoretical models have guided research on coaching behaviors. They are considered in their historical order of appearance.
Social-Cognitive Learning Theory: The Mediational Model
Direct observation of behavior is a hallmark of behavioral approaches, including social cognitive learning theory (Mischel, 1973; Bandura, 1986). The fact that coaching behaviors occur in a public context where they can be directly observed, categorized, and quantified inspired the development of behavioral coding systems beginning in the 1970s. An early example was the use of a ten-category system to assess the coaching behaviors of legendary University of California, Los Angeles basketball coach John Wooden (Tharp & Gallimore, 1976). Based on more than 30 hours of observation during team practices, the data showed that Wooden spent approximately 50% of his time giving verbal instruction, 12.7% in admonitions to hustle, and about 7% giving either encouragement and compliments or scolds, respectively. They also described stylistic aspects of his coaching, such as giving very brief and specific instructions and demonstrations that seldom lasted more than 5 seconds.
At about the same time, the Coaching Behavior Assessment System (CBAS) was developed as a research tool to permit the direct observation and coding of coaches’ actions during practices and games (Smith, Smoll, & Hunt, 1977). The CBAS contained 12 categories divided into two major classes of behaviors. Reactive (elicited) behaviors are responses to immediately preceding athlete or team behaviors, while spontaneous (emitted) behaviors are initiated by the coach and are not a response to a discernible preceding event. Reactive behaviors are responses to either desirable performance or effort (i.e., reinforcement, nonreinforcement), mistakes and errors (i.e., mistake-contingent encouragement, mistake-contingent technical instruction, punishment, punitive technical instruction, ignoring mistakes), or misbehaviors on the part of athletes (i.e., keeping control). The spontaneous class includes general technical instruction, general encouragement, organization, and general communication (unrelated to the current situation). The system thus involves basic interactions between the situation and the coach’s behavior. Use of the CBAS in observing and coding coaching behaviors in a variety of sports has shown that (a) the scoring system is sufficiently comprehensive to incorporate the vast majority of overt leader behaviors, (b) high interrater reliability can be obtained, and (c) individual differences in behavioral patterns can be discerned (Smith, Smoll, & Christensen, 1996).
The CBAS was developed within a cognitive-behavioral mediational model that involved interactions between the situation, coaching behaviors, the athlete’s perceptions of the behaviors, and the athlete’s reactions to the behavior (Smoll & Smith, 1989). The athlete’s reactions are assumed to be mediated by the athlete’s encoding and perception of the coach’s behavior. This assumption led to the development of a questionnaire (CBAS Perceived Behaviors sScale) for athletes, asking them how frequently their coach engaged in each of the CBAS categories. The latter consists of definitional items that provide examples of prototypic categorical behaviors. For example, the preface to a question on mistake-contingent encouragement may include the following: “A coach may say: ‘Sometimes players goof and make mistakes.’ Some coaches give their players support and encouragement after they make a mistake. For example, they may say, ‘That’s OK. Don’t worry about it; you’ll get ‘em next time.’ Other coaches don’t give much encouragement after mistakes.” Then the survey follows with “How often did your coach encourage you after you made mistakes?” In a study involving 51 youth baseball coaches, 542 athletes, and 57,213 coded behaviors, team-level bivariate correlations between observed and perceived behaviors were variable, with the highest levels of agreement occurring for the categories involving the coaches’ responses to mistakes (+.54 for punishment, +.37 for punitive technical instruction, and +.31 for mistake-contingent technical instruction). Canonical correlation analyses of the observed and perceived behaviors revealed dimensions that correlated +.89 with one another and were both related to attitudes toward the coach, assessed at the end of the season. Both behavioral and perceived dimensions had their highest loadings on the supportive (i.e., positive reinforcement and mistake-contingent encouragement) and the punitive behavioral categories. Notably, however, although the level of agreement reflects as much as 30% common variance, the level of agreement allows for substantial lack of correspondence between observed behaviors and athlete perceptions, and for variation in athletes’ perceptions of a particular coach. Also in accord with the mediational model, athlete-perceived coaching behaviors were more highly and consistently related to their attitudes toward the coach than were observed behaviors. Five behaviors (i.e., mistake-contingent encouragement, general encouragement, punishment, punitive technical instruction, and general technical instruction) were correlated with positive evaluations of the coach at values between .34 and .43, with the punitive categories being negatively correlated with attitudes toward the coach (Smith, Smoll, & Curtis, 1978).
A companion self-report CBAS questionnaire modeled on the athlete perception form was also created for coaches. Research showed that, consistent with the mediational model, athlete-perceived coaching behaviors were more strongly related to outcome variables than were the observed behaviors. Furthermore, athletes’ reports were more strongly related to the observed behaviors than were the coaches’ self-reports, indicating that except with regard to punitive behaviors, coaches have limited awareness of how they behave (Smith et al., 1978).
Factor analyses of the CBAS revealed three major factors that account for approximately 75% of the behavioral variance: (a) supportiveness (comprised of reinforcement and mistake-contingent encouragement), (b) instructiveness (general technical instruction and mistake-contingent technical instruction versus general communication and general encouragement), and (c) punitiveness (punishment and punitive technical instruction). Relations between coaches’ scores on these behavioral dimensions and athletes’ postseason attitude measures indicated that players responded most favorably to coaches who engaged in higher percentages of supportive and instructional behaviors (Smith et al., 1978). Athletes on teams whose coaches created a supportive environment also liked their teammates more. A somewhat surprising finding was that the team’s win-loss record was essentially unrelated to how well the players liked the coach and how much they wanted to play for the coach in the future. This finding that coaching behaviors were far more important predictors of liking for the coach than was win-loss record was replicated in another study involving youth basketball (Cumming, Smoll, Smith, & Grossbard, 2007). Notably, however, winning assumed greater importance beyond age 12, although it continued to be a less important attitudinal determinant than coaching behaviors.
As the mediational model predicts, athlete’s reactions to coaching behaviors are influenced by both athlete and situational characteristics. For example, athletes with low in self-esteem are especially responsive to variations in supportive and instructional behaviors in terms of their liking for coaches, preferring coaches who are high on both dimensions, whereas children with high self-esteem are less influenced by how supportive or instructive the coach is (Smith & Smoll, 1990). Situational characteristics also matter. In one study in which score of the baseball games were assessed each half inning, factor scores on the supportiveness, punitiveness, and instructiveness dimensions revealed that the rate of supportive behaviors that coaches delivered while their team was winning correlated highly with athlete’s postseason liking, whereas supportive behaviors that occurred while the team was losing bore no relation to liking for the coach. The opposite occurred for punitive behaviors, which were strongly and negatively related to liking when delivered in losing situations, but were only weakly related when given during winning situations. Instructiveness was not differentially affected by the score at the time it occurred (Smith, Shoda, Cumming, & Smoll, 2009).
The CBAS has been used in many studies, particularly within youth sports, to develop behavioral profiles of coaches, to assess relations between coaching behaviors and other variables, such as evaluative reactions to the coach, team cohesion, and sport attrition, as well as athletes’ anxiety and self-esteem. It has also been used to measure behavioral changes that occur as a result of coach training (e.g., Smith, Smoll, & Curtis, 1979; Conroy & Coatsworth, 2004; Lewis, Groom, & Roberts, 2014). The CBAS has given impetus to the development of other behavioral coding systems containing similar or related behavioral categories (Morgan, Muir, & Abraham, 2014). One recent example, the Coach Analysis and Intervention System (Cushion, Harvey, Muir, & Nelson, 2012) uses computer technology to code a wide variety of verbal and nonverbal behaviors and the circumstances under which they occur, to whom they are directed, and how they are combined when a coach exhibits several behaviors simultaneously. Another valuable tool allows for the coding of both coach and athlete behaviors, permitting an analysis of coach-athlete interaction patterns (Erickson, Cộté, Hollenstein, & Deakin, 2011). These recent developments promise to build upon the research base derived from the CBAS over the past four decades.
Multidimensional Model of Sport Leadership
The study of leadership has a long history in mainstream psychology, spanning social psychology, industrial-organizational psychology, and military psychology (VanVactor, 2013). Drawing upon the many theories of leadership, Chelladurai (1993, 2012) advanced a multidimensional model of leadership that includes situational characteristics, leader characteristics, and member characteristics. To measure leader characteristics, Chelladurai focused on five dimensions of coaching behavior: (a) training and instruction; (b) democratic behavior (allowing athletes a voice in team decisions); (c) autocratic behaviors (decisions restricted to the coach); (d) social support (expressing personal concern for individual athletes); and (e) positive feedback for good performance. These dimensions are measured by a 40-item leadership scale for sports (LSS), which assesses athletes’ preferences for specific behaviors, their perceptions of their coach’s behaviors, and coaches’ perceptions of their own behavior. The scale has acceptable psychometric properties and has been used in many studies of coaches.
The multidimensional model predicts that athlete performance and satisfaction will be greatest when required (situationally elicited) behaviors, preferred leader behaviors, and actual leader behaviors are aligned. Although support has been found for this hypothesis (Chelladurai, 1984, 2012), results have been inconsistent, with congruent findings for some subscales and not for others, and with inconsistent patterns across studies. In general, however, low discrepancies between training and instruction, social support, and positive feedback tend to be more often related to satisfaction, while autocratic behaviors that exceed preferences are aversive and related to dissatisfaction.
Clearly, other variables interact with the congruence measure in ways as yet undetermined. Of particular interest in this regard is the fact that preferred leader behaviors can vary among athletes. For example, athletes with high anxiety prefer more social support and positive feedback behaviors than do athletes with low anxiety, and athletes with low levels of motivation prefer autocratic behaviors that apparently substitute for internal motivation (Horn, Bloom, Berglund, & Packard, 2011). Older and more accomplished athletes prefer coaches who are both autocratic and socially supportive. Males prefer training and instructional and an autocratic style more than women do, whereas women tend to prefer a more democratic style. Studies have also shown marked differences across different nations and cultures (Chelladurai & Reimer, 1998). Thus, within this model, there is no “one size fits all” preferred coaching pattern. Rather, coaches who are flexible and can adapt their coaching behaviors to the situation and to the preferences of individual athletes are likely to be most successful.
Given the substantial amount of research involving the LSS, it is puzzling that although many positive findings have occurred in terms of differences between groups of athletes and support has been found for the importance of alignment between preferred and actual coach behaviors, relations between hypotheses derived from the multidimensional model and objective measures of performance have proven to be weaker than expected, and at times inconsistent with expectations (Chelladurai & Reimer, 2012). Objective performance is an understandably challenging target variable, as it is affected by many factors beyond leadership style, including athletic talent, unforeseen injuries, strength of opponents, and an array of psychological factors that are largely beyond the coach’s influence. Also, quantitative measures of broad classes of behavior, whether coded with the CBAS or reported, do not necessarily reflect important qualities of the behavior (e.g., instructional adequacy or encouragement delivered in a sarcastic fashion), a fact that can reduce relations to performance. Moreover, there is evidence that coaches are perceived as responding differentially to more and less successful athletes. In a study of collegiate football players, for example, higher-performing athletes (starters) rated their coaches as engaging in significantly higher levels of training and instruction, as having a more democratic and a less autocratic decision-making style, as being more socially supportive, and as offering more positive feedback than did lower-status athletes labeled “survivors” by their coaches. The latter perceived their coaches as more autocratic and as low on the other four behavioral dimensions. Additionally, longitudinal evidence exists that LSS behaviors are not stable over the course of a season, with instructional, democratic, and positive feedback showing the largest changes (Fletcher & Roberts, 2013). Temporal invariance could therefore affect perceived behavior scores on the LSS and cloud relationships of the LSS with other variables across studies.
Finally, the multidimensional model is complex, with many “moving parts.” It is possible that complex and as yet undiscovered interactions among mediating factors remain hidden, as in the mediation model, where nonsignificant overall relations between CBAS observed behaviors and attitudes toward the coach when behaviors were aggregated across game situations suddenly became highly significant when the game situation variable was taken into account.
Given the degree of conceptual overlap between the mediational and multidimensional models of coaching behavior, it is interesting to assess relations between the CBAS and the LSS. A study of high school athletes that related LSS scores to scores on the CBAS athlete form revealed strong relations between scores on the two scales, and both LSS and CBAS scores accounted for substantial and similar amount of variance in positive attitudes toward the coach (Cumming, Smith, & Smoll, 2006). For example, the CBAS categories accounted for 39% of the variance, and the LSS scales accounted for 37% of the variance in the amount of enjoyment experienced while playing for the coach. In accord with predictions made by Chelladurai (1993), the LSS positive feedback scale was highly correlated with the CBAS categories of reinforcement and general encouragement and negatively related to nonreinforcement. However, the same pattern was shown for the LSS social support scale and, in general, all of the positively toned CBAS behaviors correlated well with all of the LSS scales, except autocratic, the only scale that correlated positively with the punitive CBAS categories. In general, therefore, convergent validity greatly exceeded discriminant validity in the LSS-CBAS relations. High positive correlations among the training, democratic, positive feedback, and social support scales of the LSS add to the discriminant validity issue.
Achievement Goal Theory
No theory has had a greater impact on sport psychology over the past two decades than achievement goal theory (AGT). Originally developed to study motivation within the educational domain (Nicholls, 1989; Ames, 1992), the relevance of the theory to motivational issues in sport soon became apparent, inspiring a substantial amount of sport psychology research.
Achievement goal theory focuses on the function and the meaning of goal-directed actions, based on how participants define success and how they judge whether or not they have demonstrated competence. The two central constructs in the theory are individual goal orientations that guide achievement perceptions and behavior, and the motivational climate created within achievement settings. The theory posits two separate conceptions of success represented in mastery (task) and ego achievement goal orientations. In mastery orientation, success is self-referenced, defined in terms of personal improvement, enjoyment, effort, and learning from mistakes. In ego orientation, success is other-referenced, achieved through besting others or equaling their level of performance using minimal effort (Ames, 1992; Roberts, 2001).
According to AGT, how an individual defines success and competence is influenced by interacting dispositional and environmental factors. Environmental conditions that emphasize and reinforce mastery or ego success criteria comprise the motivational climate. Achievement goal theory posits two types of motivational climates that promote either mastery or ego conceptions of success. A mastery climate emphasizes enjoyment, giving maximum effort, and personal improvement as indicators of success, stresses the importance of each team member and promotes mutual support and cooperative learning. Mistakes are viewed not as something to be dreaded but as a natural consequence of learning and as providing the feedback needed to improve performance; coaches provide encouragement and corrective instruction when they occur.
In an ego climate, there is a strong emphasis on outcome. Success is defined as winning out over others; differential attention is focused on the best athletes; intrateam rivalry is promoted by comparing athletes favorably or unfavorably with one another; and mistakes are negatively evaluated and often punished (Ames, 1992; Roberts, 2001).
Achievement goal theory has inspired the development of sport-specific measures designed to assess differences in both achievement goal orientations and in motivational climates created by coaches, parents, and peers. The most widely employed coach-initiated motivational climate scale is the Perceived Motivational Climate in Sport Questionnaire-2 (PIMCSQ-2; Newton, Duda, & Yin, 2000), which is appropriate by its reading level for adolescents and adult populations. An adaptation designed for children down to ages 8 or 9 is the Motivational Climate Scale for Youth Sport (MCSYS; Smith, Cumming, & Smoll, 2008). Both scales have separate mastery (task) and ego-climate subscales, but the PIMCSQ-2 also measures underlying facets of the task climate (i.e., cooperative learning, effort-improvement emphasis, and an important role for all participants) and ego climate (i.e., intrateam rivalry, unequal recognition, and punishment for mistakes). Most studies use the superordinate task and ego scales. The MCSYS mastery and ego scales correlate −.38, indicating that coaches engage in both classes of behavior. Sample mastery scale items are (a) “The coach told players to help each other get better,” (b) “The coach made players feel good when they improved a skill,” and (c) “Coach said that all of us were important to the team’s success.” Sample ego-scale items are (a) “Winning games was the most important thing for the coach,” (b) “Players were taken out of games if they made a mistake,” and (c) “The coach paid most attention to the best players.”
The motivational climate created by coaches has been shown to be related to a wide array of sport outcomes (Duda & Treasure, 2015; McArdle & Duda, 2002). As in educational settings, a strong body of empirical evidence shows that a mastery climate is linked to a wide array of positive outcomes, including enhanced enjoyment and satisfaction, higher levels of perceived competence and performance, lower performance anxiety, higher levels of self-esteem, and higher levels of intrinsic motivation for sport participation. A mastery environment fosters the belief that effort, which is controllable, is the key to sport success, whereas athletes in an ego climate place greater emphasis on ability. A mastery climate promotes greater goal persistence and sustained effort, and athletes tend to adopt adaptive achievement strategies such as selecting challenging tasks, giving maximum effort, persisting in the face of setbacks, and taking pride in personal improvement. In contrast, an ego-involving climate promotes social comparison as a basis for success judgments, whereas an ego environment yields discouragement when a positive outcome is not achieved. In a mastery climate, athletes show more positive and prosocial moral attitudes, whereas an ego climate is associated with greater willingness to cheat or do whatever is necessary to win. Finally, a mastery climate fosters greater team cohesion, attraction among team members, positive evaluations of the coach, and lower rates of sport attrition compared with an ego climate. Consistent with AGT, a large body of research shows that mastery and ego climates promote and strengthen corresponding goal orientations (Duda & Treasure, 2015). Over the course of a sport season, youth athletes exposed to a mastery climate exhibit increases in mastery goal orientation scores and decreases in ego goal orientation, whereas those in an ego climate show increases in ego goal orientation (Smith, Smoll, & Cumming, 2009).
Notably, behaviors associated with mastery and ego climates are not mutually exclusive; rather, they are a matter of emphasis. Most coaches engage in a mixture of mastery- and ego-oriented behaviors, particularly during competition, when the orientation is likely to shift the outcome. The same is true of athletes’ goal orientations. Highly successful athletes often have an overall mastery orientation but shift into an ego-oriented state during competition, when the focus is on winning.
One indicator of the influence of the motivational climate comes from studies comparing its effects on athletes’ reactions to their sport experience with team success (win-loss record). In a study of 10- to 15-year-old athletes, their team’s winning percentage was positively related to athletes’ judgments of their coaches’ perceived knowledge and teaching ability, but motivational climate accounted for far more variance than did winning percentage in terms of how much they liked playing for the coach and wished to do so in the future (Cumming et al., 2007). In a later study of adolescent basketball players, motivational climate exhibited stronger and more pervasive relations to the athletes’ attitudes toward the coach, teammates, and the sport experience than did winning (Breiger, Cumming, Smith, & Smoll, 2015). For both boys and girls, winning percentage was related to enjoyment derived from playing the sport and intention to continue participation the following season. Likewise, for both boys and girls, mastery climate scores were positively and significantly related to enjoyment playing on the team, liking for the coach, and perceived liking by the coach. However, the results also showed that gender influences athletes’ responses to both winning and to the motivational climate. An ego climate clearly had a more negative impact on girls, with ego climate scores being negatively related to how much girls liked the sport, how much fun they had playing on their teams, and how much they believed the coach liked them.
An ego climate also affected the importance of win-loss record in ways a mastery climate did not. For both boys and girls, significant relations were found between winning percentage and liking for the sport, personal importance of winning, and intention to return the following year. Nonetheless, gender differences also occurred. In an ego climate, liking for and desire to again play for the coach, liking for teammates and enjoyment playing on the team were positively related to winning record for boys, but not for girls. Enjoyment playing on the team and desire to play for the coach again were positively and significantly related to winning record for boys, but not for girls. It thus appears that winning within an ego climate is more important than it is in a mastery climate, but that winning may affect different attitudes and aspects of the experience for boys than for girls.
Motivational climate research has focused attention on the coach-athlete relationship. Building upon this foundation, several new conceptual models have appeared that focus on the quality of the relationship that is to be found particularly within a mastery climate. Relational coaching (Jowett, 2009) focuses on four important aspects of the coach-athlete relationship: (a) mutual closeness, (b) commitment to the relationship, (c) complementarity (ability to work cooperatively), and (d) co-orientation (the ability to view the relationship from both one’s own and the other’s perspective). The Coach-Athlete Relationship Questionnaire is used to measure these aspects of the relationship, and research using this measure shows that relationships that are high on these factors produce the most enjoyable and productive coach-athlete climate.
Another derivative conception, again related to the mastery climate but not identical with it, is the caring environment, where individuals are made to feel a sense of belonging and in which participants treat one another with kindness and mutual respect. Research on the caring environment has shown that the positive emotions produced by such an environment mediate positive well-being in athletes (Fry, Guivernau, Kim, Newton, Gano-Overway, & Magyar, 2012).
All of the AGT results cited so far are based on athlete perceptions of the motivational climate, using either the PMCSQ or the MCSYS instruments. This is an entirely defensible approach, for as the mediational model described earlier emphasizes, it is the athlete’s perceptions of the climate that mediate the effects of coach behaviors on outcome variables. Nonetheless, the need to assess the actual climate-relevant behaviors of coaches from both methodological and theoretical perspectives has repeatedly been cited (N. Smith et al., 2015). A new theoretical advance integrating AGT and self-determination theory, described in the following section, has inspired the development of a new observation system tied to the expanded model.
A recent theoretical advance integrates AGT with another prominent motivational theory that has special relevance to sport-related motivation (Duda, 2013). Self-determination theory (SDT; Deci & Ryan, 2000) focuses on factors that influence the development of motivation, particularly intrinsic and extrinsic motivation. The relative strength of intrinsic and extrinsic motivation determines an individual’s sense of autonomy, the extent to which behavior is viewed as self-governed. Together with competence (the perceived mastery over behavior) and relatedness (the perceived sense of belonging), autonomy is considered a basic need that facilitates psychological well-being (Deci & Ryan, 2000). SDT proposes that the social environment influences the extent to which these basic needs are satisfied.
SDT holds that internal and external behavioral goals are distributed on a continuum of self-determination. On the self-determined end lies intrinsic motivation, where actions are performed in the service of inherent enjoyment of the activity. The continuum also contains three different variants of extrinsic motivation. From higher to lower self-determination, these are termed (a) identified regulation (in which behavior is related to other goals, such as engaging in the sport to lose weight or improve conditioning), (b) introjected regulation (in which behavior functions to avoid a negative emotion or for ego enhancement), and (c) external regulation (in which the behavior is performed for external reasons, such as tangible rewards or the avoidance of punishment). SDT also retains the concept of amotivation, in which behavior loses all reinforcement value and occurs largely out of habit (e.g., “I’m not sure why I swim any more.”). Generally, because behavior is guided more by external incentives or becomes amotivated, positive qualities of human nature are hindered, whereas greater self-determination or autonomy allows positive qualities to flourish (Deci & Ryan, 2000). An imposing literature both within and outside of sport supports this contention and demonstrates superior well-being (i.e., high feelings of autonomy, competence and relatedness) under conditions that foster high intrinsic motivation (e.g., Deci & Ryan, 2000; Duda, 2013; Amorose & Anderson-Butcher, 2015).
The integration of AGT and SDT was inspired by clear conceptual overlap between a mastery motivational climate and situational factors identified in SDT research that promoted the satisfaction of autonomy, relatedness, and competency needs, as well as strong evidence that a mastery motivational climate had salutary effects on the need variables within SDT. Moreover, in the prediction of meaningful sport outcomes, concurrently applying AGT and SDT measures as predictor variables accounted for overlapping but also independent sources of variance (Quested & Duda, 2010), and they also related differentially to important aspects of athlete well-being and quality of functioning. Duda (2013) advanced a hierarchical multidimensional model of empowering and disempowering motivational climates. An empowering environment is mastery-oriented, socially supportive, and autonomy supportive of internal self-regulation. A disempowering climate is ego oriented, punitive, nonsupportive, and controlling.
Although applications of the model are in their relative infancy, both perceived and behavioral-observation measures of the integrated motivational climate have been developed. A 30-item Empowering and Disempowering Motivational Climate Questionnaire-Coach (EDMCQ-C; Appleton, Ntoumanis, Quested, Viladrich, & Duda, 2016) allows athletes to rate their coach’s climate relevant behavior on an agree-disagree scale and is used to measure five dimensions of the perceived motivational climate: (a) task involved, (b) ego involved, (c) controlling, (d) autonomy supportive, and (e) socially supportive.
A behavioral observational system, the Multidimensional Motivational Climate Observation System (MMCOS; N. Smith et al., 2015) provides a measure used to rate the measure the coach’s observed behaviors in relation to the theoretical model. It is a complex system, consisting of two superordinate dimensions (empowering and disempowering) seven environmental dimensions (autonomy support, controlling, task-involving, ego-involving, relatedness support, relatedness thwarting, and structure), and 32 lower-order coaching strategies that are checked off and used to rate the higher-order dimensions. The system is used to code temporally or event-defined segments (e.g., 5-minute segments in a soccer match). The MMCOS is clearly more complex than the CBAS and provides a more nuanced behavioral profile. However, unlike the CBAS and other behavioral coding systems, it is not a quantitative measure of the frequency with which observed behaviors actually occur; rather, it is a set of observer ratings.
Initial assessment of the construct validity of the perceived and observed behavior measures have been conducted with large multinational samples of athletes representing a variety of sports. One study that assessed relationships between athlete-perceived (EDMCQ-C) and observed (MMCOS) behaviors as well as the relations of both measures to athlete indices of autonomous (internal), externally controlled motivation, and amotivation (N. Smith, Tessier, Tzioumakis, Fabra, Quested, Appleton et al., 2016). None of the correlations between athlete-perceived and observed behaviors providing indices of the five empowering and disempowering dimensions exceeded .09, reflecting less than 1% common variance, a figure far lower than that obtained with the CBAS categories derived from social learning theory in a youth sport sample similar in age. Likewise, relationships between the MCCOS behavior measures and the theoretically related athlete motivation measures were quite low, ranging from −.01 to .09 and accounting for less than 1% of the motivational outcome variance. Correlations of the athlete-perceived EDMCQ-C measures with the athlete motivation variables were more favorable, with correlations exceeding .30 found between coach controlling and ego-oriented behaviors and athletes’ externally controlled motivation and amotivation. These results lend stronger evidence for the construct validity of the athlete-perceived measure than those reported for the observational measure. In another study, empowering climate scores on the EDMCQ-C were positively related to enjoyment and self-esteem and negatively related to reduced accomplishment, devaluation, and physical health symptoms, whereas a disempowering climate was negatively related to enjoyment and self-esteem, and positively related to athlete burnout and negative health symptoms (Appleton & Duda, 2016). However, a more sophisticated analysis that simultaneously assessed the interactive effects of empowering and disempowering motivational climates accounted for only about 1% of the variance in these targeted outcome variables. Buffering the effects of disempowering coach behaviors required a very high level of empowering behaviors. This result is consistent with CBAS findings that although punitive behavior categories occur with far less frequency than do the positive behaviors (also shown in the observational data of N. Smith et al., 2016), they have a disproportionate negative impact on athletes by creating an aversive sport environment.
Enhancing Coaching Effectiveness
There has never been any question that coaches occupy a central role in sports, exerting key influence on sport outcomes through their roles as teachers and strategic planners, and in the relationships they form with athletes and parents. Understandably, therefore, enhancing their pedagogical, strategic, and interpersonal capabilities has long been a focus within sport and exercise psychology. Two lines of emphasis are evident that, historically, have occurred along relatively independent tracks. The first involves instruction in motor learning principles and strategic techniques designed to develop athletes’ physical skills and optimal strategic decision making by coaches. The second emphasis, of more recent origin, is focused on helping coaches to create a psychosocial sport environment that enhances outcomes for athletes. As empirical evidence, such as that reviewed in the previous section, has accumulated showing consistent relationships between coaching behaviors and their impact on athletes and team functioning, coach interventions addressing this domain have been developed.
Enhancing Strategic and Instructional Capabilities
Motor skill learning is highly sport specific, but a strong science base has emerged on general principles involved in learning, maintaining, and improving such skills (Coker, 2013; Magill, 2013). The dominant model divides the motor learning process into three phases: the cognitive, associative, and autonomous phases. Each of these requires different coaching techniques. In the cognitive phase, explanations and demonstrations by the coach allow athletes to develop a motor program, a set of internal representations, and self-instructions to guide the movement. With practice and feedback, both from the athlete’s sensory systems and from the coach, the motor program is revised, corrected, and refined so that the skill is executed in an increasingly synchronized fashion. The coach designs exercises and practice routines, adjusts instruction depending on the progress being shown, gives corrective feedback, and provides encouragement to facilitate the process.
Once the athlete can execute the skill in the way it was demonstrated, the associative phase begins. In this intermediate phase of learning, the learner has moved from having a general idea of how to perform the skill to being able to perform it accurately and consistently. Speed, accuracy, coordination, and consistency improves even further and the athlete develops an implicit “feel” for the activity based on a more autonomous motor program and the ability to self-correct when errors occur. The coach’s role is now to utilize the skill in actual sport situations and to plan strategy, largely by designing effective practices that allow the athlete to apply the skill to simulated or real competitive situations. Error correction requires the ability to detect increasingly subtle errors and to provide demonstrations and feedback in a manner that can be used for further refinement and skill application. In closed skills (e.g., bowling or free throw shooting), the environment is fairly constant, and consistency of movement is the primary focus. In open skills, where the environment is diverse and unpredictable (e.g., in golf), the coach must help the athlete diversify the movement to meet environmental demands and teach the athlete which environmental cues are key to planning and making adjustments. The provision of effective feedback continues to be an important function of the coach during this phase.
In the autonomous or advanced phase of skill learning, the motor program is run off with little conscious thought or attention to the movement. Indeed, conscious attention to the movement can degrade performance by disrupting the automaticity of the highly developed skill sequence. As Baseball Hall of Famer Yogi Berra once said, “You can’t think and hit at the same time.” The phrase “paralysis by analysis,” popular among coaches and athletes, captures the phenomenon. Performance slumps are perpetuated by cognitive interference with the normal flow of skill execution. During this phase, the major demands on the coach involve highly refined practice routines, subtle error correction, and encouragement and motivational overtures where needed. At this level, exquisite understanding of the skill and ability to communicate effectively are prime requisites for effective coaching. Excellent resources are available to help coaches at all levels of sport refine their sport-specific teaching and strategic skills (e.g., Coker, 2013; Martens, 2012). Among the newer additions to the coach’s performance enhancement tool kit is instruction in utilizing and teaching athletes not only motor and strategic skills, but also empirically supported psychological skills such as systematic goal setting, attention control, stress management, self-talk, confidence, and mental rehearsal procedures (e.g., Burton & Raedeke, 2008).
Enhancing Psychosocial Outcomes in Athletes
Increased awareness of the manner in which the coach-athlete relationship can positively or adversely affect not only skill development but also a wide range of psychosocial outcomes in athletes of all ages is attributable to an enormous body of empirical research. Coaching behaviors have been shown to influence athletes’ self-esteem, motivation, performance anxiety, attitudes toward their sport experience, peer relationships, burnout, psychological skills development, physical well-being, and sport attrition. Concerns about athletes’ well-being, an alarming sport dropout rate exceeding 30% per year in young athletes (Gould, 1987) and, in some cases, formal legislation requiring training for youth sport coaches, has stimulated the development of many training programs for coaches over the past four decades. Unfortunately, development has far outstripped systematic evaluation of their effects.
Coach Effectiveness Training/Mastery Approach to Coaching
Widespread concerns about adult-created problems in youth sports prompted the Youth Enrichment in Sports program of research and application. The aims of the project, carried out in two phases were (a) to study relations between coaching behaviors and young athletes’ reactions to their youth sport experience and (b) to use the empirical results as the basis for an evidence-based intervention for coaches (Smith et al., 1978). Cognitive social learning theory (Bandura, 1986; Mischel, 1973) formed the basis for instrument development (e.g., the CBAS) and the intervention procedures, which involved modeling and role playing of desirable behaviors and coach self-monitoring of their behaviors to enhance awareness. A more comprehensive discussion of cognitive-behavioral principles and techniques used in conducting psychologically oriented coach training programs appears elsewhere (Smoll & Smith, 2015). Essentially, however, the intervention is designed to influence observed and athlete-perceived coaching behaviors, and these changes, are thought to mediate other effects of the training on young athletes.
Data derived from two large-scale phase 1 studies provided clear links between the CBAS dimensions of supportiveness, instructiveness, and punitiveness and athletes’ reactions to their coach, their teammates, and other aspects of their experience. Phase 2 involved the development and evaluation of a brief and highly focused intervention for youth sport coaches based on the evidence-based phase 1 findings. The intervention initially was called Coach Effectiveness Training (CET). With the development of AGT a decade later, it became clear that the CET guidelines (particularly its conception of success) were entirely consistent with the mastery motivational climate described by AGT, and a later version of the intervention formally introduced motivational climate content. The 75-minute intervention was therefore renamed the Mastery Approach to Coaching (MAC).
The MAC program incorporates two major themes. First, it strongly emphasizes the distinction between positive versus aversive control of behavior (Smith, 2015). In a series of coaching “do’s and don’ts” derived from the foundational phase 1 research on coaching behaviors and their effects, coaches are encouraged to increase four specific behaviors: (a) positive reinforcement, (b) mistake-contingent encouragement, (c) corrective instruction given in a positive and encouraging fashion, and (d) sound technical instruction. Coaches are urged to avoid nonreinforcement of positive behaviors, punishment for mistakes, and punitive technical instruction following mistakes. They are also instructed how to establish team rules and reinforce compliance with them to avoid discipline problems, and to reinforce socially supportive behaviors among team members. These guidelines, which are summarized in Table 1, are designed to increase positive coach-athlete interactions, enhance team solidarity, reduce fear of failure, and promote a positive atmosphere for skill development.
The second important MAC theme is a conception of success as giving maximum effort and becoming the best one can be, rather than an emphasis on winning or outperforming others. Derived from Coach John Wooden’s definition of success as “the sense of self-satisfaction from knowing that you did your best to become the best that you are capable of becoming,” (Wooden & Carty, 2005, p. 12), MAC-trained coaches are thus encouraged to adopt a four-part philosophy of winning (Smith & Smoll, 2012, pp. 27–28):
Winning isn’t everything, nor is it the only thing. Young athletes cannot get the most out of sports if they think that the only objective is to beat their opponents. Although winning is an important goal, it is not the most important objective.
Failure is not the same thing as losing. It is important that athletes do not view losing as a sign of failure or as a threat to their personal value.
Success is not equivalent to winning. Neither success nor failure need depend on the outcome of a contest or on a win-loss record. Winning and losing pertain to the outcome of a contest, whereas success and failure do not.
Athletes should be taught that success is found in striving for victory (i.e., success is related to commitment and effort). Athletes should be taught that they are never “losers” if they give maximum effort.
This philosophy, which is highly congruent with a mastery motivational climate, is designed to maximize young athletes’ enjoyment of sport and their chances of deriving the benefits of participation, partly as a result of combating competitive anxiety. Although seeking victory is encouraged as inherent to competitive sports, the ultimate importance of winning is reduced relative to other participation motives. In recognition of the inverse relation between enjoyment and postcompetition stress (Smith, Smoll, & Passer, 2002), fun is highlighted as the paramount objective. The philosophy also promotes separation of the athlete’s feelings of self-worth from the game’s outcome, which serves to help overcome fear of failure. The mastery-oriented coaching guidelines and philosophy of winning are thus consistent with the procedures successfully designed by Ames (1992) and Epstein (1988) to create a mastery learning climate in the classroom. The behavioral guidelines that form the core of the MAC intervention are shown in Table 1. The MAC workshop, together with supporting materials, is now available online at www.y-e-sports.org.
Table 1. Summary of Mastery Approach to Coaching Guidelines
I. Reacting to Athlete Behaviors and Game Situations
A. Good Plays
Do: Provide reinforcement! Do so immediately. Let the athletes know that you appreciate and value their efforts. Reinforce effort as much as you do results. Look for positive things, reinforce them, and you will see them increase. Remember, whether athletes show it or not, the positive things you say and do remain with them.
Don’t: Take their efforts for granted.
Do: Give encouragement immediately after mistakes. That’s when the youngster needs your support the most. If you are sure the athlete knows how to correct the mistake, then encouragement alone is sufficient. When appropriate, give corrective instruction, but always do so in an encouraging manner. Do this by emphasizing not the bad things that just happened but the good things that will happen if the athlete follows your instruction (the “why” of it). This will make the athlete positively self-motivated to correct the mistakes rather than negatively motivated to avoid failure and your disapproval.
Don’t: Punish when things are going wrong! Punishment isn’t just yelling. It can be tone of voice, action, or any indication of disapproval. Athletes respond much better to a positive approach. Fear of failure is reduced if you work to reduce fear of punishment. Indications of displeasure should be limited to clear cases of lack of effort; but, even here, criticize the lack of effort rather than the athlete as a person.
Don’t: Give corrective instruction in a hostile, demeaning, or harsh manner. That is, avoid punitive instruction. This is more likely to increase frustration and create resentment than to improve performance. Don’t let your good intentions in giving instruction be self-defeating.
C. Misbehaviors, Lack of Attention
Do: Maintain order by establishing clear expectations. Emphasize that during a game all members of the team are part of the activity, even those on the bench. Use reinforcement to strengthen team participation. In other words, try to prevent misbehaviors by using the positive approach to strengthen their opposites.
Don’t: Get into the position of having to constantly nag or threaten athletes to prevent chaos. Don’t be a drill sergeant. If an athlete refuses to cooperate, deprive him or her of something valued. Don’t use physical measures, such as running laps. The idea here is that if you establish clear behavioral guidelines early and work to build team spirit in achieving them, you can avoid having to repeatedly keep control. Youngsters want clear guidelines and expectations, but they don’t want to be regimented. Try to achieve a healthy balance.
II. Getting Positive Things to Happen and Creating a Good Learning Atmosphere
Do: Give technical instruction. Establish your role as a caring and competent teacher. Try to structure participation as a learning experience in which you are going to help the athletes become the best they can be. Always give instruction in a positive way. Satisfy your athletes’ desire to improve their skills. Give instruction in a clear, concise manner and, if possible, demonstrate how to do skills correctly.
Do: Give encouragement. Encourage effort; don’t demand results. Use encouragement selectively so that it is meaningful. Be supportive without acting like a cheerleader.
Do: Concentrate on the activity. Be “in the game” with the athletes. Set a good example for team unity.
Don’t: Give either instruction or encouragement in a sarcastic or degrading manner. Make a point, then leave it. Don’t let “encouragement” become irritating to the athletes.
Note. Excerpted from the manual given to MAC workshop participants (Smoll & Smith, 2009).
A notable finding from observational studies is that coaches have very limited awareness of how they behave, as indicated by low correlations between observed and coach-rated behaviors (N. Smith et al., 2016; R. Smith et al., 1978). Because behavior change does not occur without awareness of one’s behavior, MAC coaches are taught the use of two proven behavioral-change techniques, namely, behavioral feedback and self-monitoring. To obtain feedback, coaches are encouraged to work with their assistants as a team and share descriptions of each others’ behaviors. Another feedback procedure involves coaches soliciting input directly from their athletes. With respect to self-monitoring, the workshop manual includes a brief Coach Self-Report Form, containing nine items related to the behavioral guidelines that coaches complete after practices and games (Smoll & Smith, 2009, p. 25). On the form, coaches are asked how often they engaged in the recommended behaviors in relevant situations.
The CET/MAC intervention has been evaluated numerous times in experimental and quasi-experimental studies since its development (for more detailed reviews, see Smith & Smoll, 2011; Smoll & Smith, 2015). The outcomes supporting the efficacy of the coach-training program are summarized here:
Trained coaches were better liked and rated as better teachers; and their athletes reported more fun playing the sport, and a higher level of attraction among teammates. Increases in athletes’ perceptions of both task-related and social group cohesion have also been reported for youngsters who played for trained versus untrained coaches (Smith et al., 1979; McLaren, Eys, & Murray, 2015).
Athletes’ reports of their team’s coach-initiated motivational climate clearly supported the efficacy of the intervention. In this regard, trained coaches received significantly higher mastery-climate scores and lower ego-climate scores on the MCSYS climate measure compared with untrained coaches. Moreover, in accord with AGT, male and female athletes who played for trained coaches exhibited increases in mastery goal orientation scores and significant decreases in ego orientation scores. In contrast, athletes who played for control group coaches did not change in their goal orientations from preseason to late season. Paralleling the significant difference between intervention and control groups in sport-related mastery scores, a significant group difference was found on the mastery score of an academic achievement goal scale as well, suggesting generalization of achievement goals (Smoll et al., 2007).
Consistent with a self-esteem enhancement model, children with low self-esteem who played for trained coaches show significant increases in feelings of self-worth. Youngsters with low self-esteem in the control group did not change (Smith et al., 1979; Smoll, Smith, Barnett, & Everett, 1993; Coatsworth & Conroy, 2006).
Attrition in youth sports is a pervasive concern that has negative health and psychosocial implications. With the win-loss record controlled, children who played for untrained youth baseball coaches dropped out of all sports the following season at a rate of 26%, whereas those who played for trained coaches had only a 5% dropout rate (Barnett, Smoll, & Smith, 1992).
Traditionally, CET/MAC training has been offered in a workshop format. However, many sport psychologists work with individual coaches. A recent and promising adaptation is the Individualized Program for Counseling Coaches (Sousa, Smith, & Cruz, 2008; Cruz, Mora, Sousa, & Alcaraz, 2016). This individualized intervention combines MAC principles and behavioral guidelines with behavioral feedback and systematic goal setting to help coaches modify their behavior in accordance with their own behavioral objectives.
The intervention occurs in six steps. First, the CBAS is used to code the coach’s behaviors during a series of practices and matches to provide an average of 250–400 coded behaviors, thereby providing baseline data to help coaches become more aware of their coaching pattern and to assess postintervention changes. Next, a 60-minute session is held to go over basic principles concerning the motivational climate and its effects on athletes. In a second 60-minute session, the behavioral guidelines shown in Table 1 are presented in an interactive fashion. In the following session, the coach is presented with his or her behavioral profile derived from the CBAS observations, summarized in terms of the three factorial dimensions of supportiveness, punitiveness, and instructiveness, together with feedback on which behaviors would best be increased or decreased to optimize the coach’s effectiveness. The coach is then asked to select three CBAS categories that they want to increase or decrease. Finally, role playing is used to help the coach rehearse the target behaviors with the guidance of the trainer. The coach is encouraged to self-monitor during subsequent practices and matches and are given guidelines and reminders. CBAS data as well as athlete and coach reports are then collected during two subsequent practices and two matches as at baseline.
The results of the intervention have been very encouraging. In separate single-subject studies involving a total of 5 coaches, the trained coaches have exhibited behavior changes in accordance with their goals in most instances, increasing desirable behaviors and reducing negative ones (Sousa et al., 2008; Cruz et al., 2016). Of additional interest, generalization effects have been shown in behavior categories that were not specifically targeted by the coach, yielding a more positive behavioral profile overall following the intervention. For example, coaches who chose to increase positive reinforcement and encouragement showed a concomitant drop in punitive behaviors. These behavior changes were in most cases consistent with athlete’s perceptions of the coach’s behaviors on the athlete perception CBAS questionnaire. The encouraging results obtained in these single-coach studies indicate that this adaptation is worthy of further investigation and that its use of feedback and individualized goal setting, both of which have strong empirical support, is a significant feature of the training program.
Despite the rapid proliferation of coach education programs since the early 1970s, almost all of the systematic outcome research on the efficacy of coach training has been done with the CET/MAC program (Langan, Blake, & Lonsdale, 2013). Evidence for the efficacy of the intervention has now been provided by five different research groups. Based on the outcome studies, it appears that the empirically derived behavioral principles can be readily applied by coaches and that their application has salutary effects on a range of psychosocial outcome variables in young male and female athletes. However, there is a need for further research, particularly follow-up studies to assess the longer-term impact of the intervention on both coaches and athletes.
The integration of AGT with SDT (Duda, 2013) is a major theoretical advance that has resulted in the concept of empowering and disempowering motivational climates. In an empowering climate, athletes strive for mastery goals, feel a sense of belonging, and believe they have a choice over how they behave. In a disempowering environment, the emphasis is on ego goals, punishment is applied, and athletes feel controlled by their coach.
Based on this model, an Empowering CoachingTM intervention was developed, applied, and evaluated in five European countries. The intervention is of 6 hours duration and educates coaches about the tenets of AGT and SDT relating to task and ego climates and intrinsic-extrinsic motivation and offers guidelines for increasing the empowering climate and reducing its disempowering aspects. Video clips and reflective exercises are designed to engage coaches in the content of the workshop (for a more detailed descriptions of the project and the intervention, see Duda, 2013; Project PAPA, 2016). The program’s emphasis differs somewhat from the CET/MAC empirically based behavioral guidelines approach:
Furthermore, this education programme is not about providing coaches with a “laundry list” of strategies or responses they can and should employ when interacting with their athletes . . . Rather, . . . the aim is to develop coaches’ conceptual understanding of motivation, motivation processes and their consequences. It is assumed that this enhanced “working knowledge” will make it more likely that a more empowering approach to coaching will be adopted, maintained, and generalized to different situations (Duda, 2013, p. 315).
The intervention was tested in the largest experimental trial undertaken to date, involving 175 clubs, 854 teams, and 7,769 children in five European countries. Outcome variables involved athletes’ perceptions of empowering and disempowering aspects of the motivational climate using the behavioral EDMCQ-C measure, as well as measures of self-esteem, enjoyment, anxiety, and intentions to drop out. Some children wore accelerometers to record activity level during the week, and a subset of coaches was filmed so that their behaviors could be coded using the MCCOS observational instrument.
Several positive results were obtained. Behavioral observations in a subset of trained coaches revealed a more empowering and less disempowering climate over the course of the season. Children who played for trained coaches viewed their motivational climate as less disempowering (but not more empowering) and rated themselves as less likely to drop out of their program. However, no statistically significant positive outcomes have been reported for other important athlete variables, including autonomy, competence, and relatedness need satisfaction, enjoyment, self-esteem, anxiety, athlete burnout, and increased physical activity (Project PAPA, 2016). Possibly, the Empowering CoachesTM programs’ heavy emphasis on theoretical and conceptual content interfered with the development of the kind of rule-governed behavior that has been shown to result from adherence to specific behavioral guidelines (Baldwin & Baldwin, 2001). A more focused approach with greater emphasis on clear and specific behavioral guidelines may prove more efficacious while at the same time resulting in a more time-limited intervention.
Undoubtedly, coaches play a vital role in the athletic environment, and their behaviors influence the technical, cognitive, strategic, and psychosocial development of athletes. There is a wealth of empirical support for methods of teaching technical skills. The same is not the case in the psychosocial domain. Despite the substantial number of coach intervention programs developed over the past 30 years designed to enhance psychosocial outcomes, it is rather astounding that only a few of these programs have undergone any evaluation of efficacy. Coach training, particularly in the area of youth sports, has become a large-scale commercial enterprise in the United States. The American Sport Education Program, the National Youth Sports Coaches Association, and the Positive Coaching Alliance are among the most visible. Unfortunately, however, although their content does not deviate from what has been established empirically as producing a positive athletic climate, virtually nothing is known about what effects these specific programs actually have on coaches and athletes and how well they achieve their objectives. This absence of empirical attention is understandable, as developers of existing programs have been focused primarily on development, marketing, and dissemination rather than evaluation, and they have not had the benefit of research grants to support evaluation research. However, evaluation research is not only desirable, but essential to providing coaches with the quality of evidence-based training that will have the most salutary impact on their athletes. In the words of Lipsey and Cordray (2000), “the overarching goal of the program evaluation enterprise is to contribute to the improvement of social conditions by providing scientifically credible information and balanced judgment to legitimate social agents about the effectiveness of interventions intended to produce social benefits” (p. 346).
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