Motivation and Emotion

, Volume 33, Issue 2, pp 173–183

Motivational climate and changes in young athletes’ achievement goal orientations


    • Department of PsychologyUniversity of Washington
  • Frank L. Smoll
    • Department of PsychologyUniversity of Washington
  • Sean P. Cumming
    • Department of PsychologyUniversity of Washington
    • School for HealthUniversity of Bath
Original Paper

DOI: 10.1007/s11031-009-9126-4

Cite this article as:
Smith, R.E., Smoll, F.L. & Cumming, S.P. Motiv Emot (2009) 33: 173. doi:10.1007/s11031-009-9126-4


Development of achievement-related motives in young athletes is believed to be influenced by the motivational climate created by coaches. In a longitudinal multilevel design utilizing 47 youth basketball teams, coach-initiated motivational climate was used to predict changes in 9–13 year old athletes’ achievement goal orientations over the course of a season. Mastery climate scores on the Motivational Climate Scale for Youth Sports were associated with significant increases in mastery goal orientation and decreases in ego orientation scores on the Achievement Goal Scale for Youth Sports. Ego motivational climate scores were significantly related to increases in ego goal orientation scores. These relations were not influenced by athletes’ age or gender. Intraclass correlations indicated low within-team consensus in athletes’ motivational climate scores, suggesting an individual- rather than team-level perceptual construct. These and other findings indicate that achievement goal orientation research can be extended downward to children below the age of 11.


Motivational climateAchievement goal orientationsMotivational change


In the United States, approximately 41 million youngsters are actively involved in agency-sponsored youth sport programs (e.g., Little League Baseball) and another 6–7 million participate in interscholastic athletics (Ewing and Seefeldt 2002). The sport environment is developmentally significant because it provides important socialization opportunities and places adaptive demands on participants that parallel those in other important life settings (Larson 2000; Ryan and Deci 2000; Scanlan 2002). Sport participation offers opportunities to perform under pressure and develop emotional self-regulation skills, to set goals and test goal-attainment strategies, to work within a team context, and to subordinate individual interests to the greater good (Danish et al. 1990). Moreover, the sport setting is highly involving for many youngsters, and mean ratings of intrinsic motivation and focused attention in sports exceed those obtained for classroom activities and interactions with friends (Csikszentmihalyi and Larson 1984; Larson and Kleiber 1993).

Because the sport environment is inherently a competence and achievement context, motivational factors play an important role in the ultimate effects of participation on psychosocial development. Not surprisingly, therefore, sport has been a useful setting in which to study achievement-related motives, as well as outcomes such as performance, enjoyment, and intrinsic motivation.

Achievement goal orientations

Building upon a strong empirical base in the educational domain (Ames 1992; Dweck 1999; Nicholls 1989), achievement goal theory has been applied to the sport domain as well (Duda and Hall 2001; Roberts et al. 1997). Although there are several variants of achievement goal theory (e.g., Elliot and McGregor 2001; Harackiewicz et al. 2002; Kaplan and Maehr 2007), all of them posit two orthogonal goal perspectives in achievement settings, namely task (mastery) and ego (performance) involvement. When task-involved, success is defined in a self-referenced manner as individuals focus on mastery of the task, skill development, exerting effort, and self-improvement. In contrast, when individuals are ego-involved, social comparison plays a major role in self-perceived success, and the emphasis is on outperforming others (preferably with minimal effort) in order to attain recognition and status. Nicholls (1989) proposed that goal orientation states come to have dispositional characteristics, so that athletes manifest individual differences in their tendencies to experience mastery- and/or ego-involved goal states in achievement contexts. This assumption has formed the basis for achievement goal orientation as a dispositional individual difference variable in sport research. In achievement goal theory, goals are directional determinants of motivated behavior.

In both academic and sport settings, approach goal orientations have exhibited a theoretically consistent pattern of relations with other developmentally significant variables. Mastery-oriented individuals tend to attribute success to effort, cooperation, and intrinsic interest, whereas ego-oriented individuals believe that success is due to superior ability and outsmarting others (Solomon and Boone 1993; Walling and Duda 1995). Compared with ego-oriented students and athletes, those high in mastery orientation report higher feelings of competence, greater enjoyment of the activity, and higher intrinsic motivation and effort (Kavussanu and Roberts 1996; Duda 2005). A mastery orientation (particularly in combination with a low ego orientation) is also related to lower levels of cognitive trait anxiety and pre-event state anxiety (Ommundsen and Pedersen 1999; Vealey and Campbell 1988; White and Zellner 1996). Finally, a mastery goal orientation is related to a variety of adaptive achievement behaviors, such as exerting consistent effort, persistence in the face of setbacks, and sustained and improved performance (Ames 1992; Duda 2005; Dweck 1999). Although an ego orientation has at times been linked to high levels of achievement, it also has a number of less desirable correlates, such as inconsistent effort, higher levels of performance anxiety, reduced persistence or withdrawal in the face of failure, decreased intrinsic motivation for sport involvement, and a willingness to use deception and illegal methods in order to win (Duda 2005; Roberts et al. 2007).

Motivational climate

Achievement behaviors are influenced by interacting personal and situational factors. Situational factors can predispose individuals to enter particular goal states and, over time, to acquire a disposition toward experiencing mastery or ego goal states. In like manner, they can evoke fear of failure and contribute to the development of dispositional trait anxiety. These motivational effects are influenced in part by the way in which the situation is structured and success defined by relevant adults. An important situational construct in achievement goal theory is the motivational climate established by significant others who implicitly or explicitly endorse particular criteria for what constitutes success.

Like achievement goal states, motivational climate also is described in mastery (task) or ego (performance) terms. Ames (1992) described a mastery climate as one in which teachers, coaches, or parents define success in terms of self-improvement, task mastery, and exhibiting maximum effort and dedication. In such a climate, students 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. When coaches create an ego climate, they tend to give differential attention and positive reinforcement to athletes who are most competent and instrumental to winning, and skill development is deemed more important to winning than to personal improvement and self-realization (McArdle and Duda 2002). They are also more likely to respond to mistakes and poor performance with punitive responses. Several studies conducted in physical education classes have shown that motivational climate is a stronger predictor of such outcomes as intrinsic motivation and voluntary activity participation than are students’ achievement goal orientations (Cury et al. 1996; Dorobantu and Biddle 1997; Spray 2000).

Achievement goal theory leads to the prediction that the extant motivational climate will encourage the experiencing of corresponding goal orientation states and, with time, the development of dispositional mastery or ego goal orientations (Ames 1992; Nicholls 1989). This theoretical prediction has received a measure of support. In cross-sectional correlational studies of adolescents performed in physical education and sport contexts, motivational climate has exhibited significant relations with both achievement goal orientation measures obtained at the same time (e.g., Carr 2006). In accordance with theoretical expectations, a mastery climate is associated with stronger mastery goal orientations in athletes and an ego climate with stronger ego orientations (Duda 2005; Roberts et al. 2007). Although these cross-sectional studies suggest that the coach-initiated motivational climate may influence achievement-related motivational variables, a stronger test of this proposition would be afforded by studies that assess change over the course of the season as a function of the motivational climate. To date, only a few studies have used a longitudinal approach of this kind. Papaioannou et al. (2004) studied achievement goal changes in 200 elementary, middle, and high school physical education classes taught by 67 teachers over the course of an academic year. Using a motivational climate questionnaire designed for physical education classes, they found that students’ mastery climate ratings were associated with increases in students’ mastery goal orientations, and ego climate ratings were positively related to changes in students’ ego goal orientations. A particular strength of this study was the use of a multilevel analytic approach to take into account the fact that students were nested within different classes. No analyses of gender or age effects were reported.

For more than two decades, achievement goal research in sports has been largely restricted to participants above the age of 11 years, largely because of a conclusion drawn by Nicholls (1978). On the basis of experiments asking children to differentiate between effort and ability on academic tasks, Nicholls concluded that children below the age of 11 or 12 do not make the effort-ability distinctions that were thought by Nicholls to underlie task/mastery and ego achievement orientations. Understandably, therefore, achievement goal theory has focused on older populations, and the instruments used to measure goal orientations have been developed using adolescent and adult populations. However, we suggest that Nicholls’s conclusions may not apply to the sport domain because the physical and competitive nature of sport may make it easier for children to form judgments regarding effort and ability than occurs while observing academic performance. Through competition, children have the opportunity to compare their ability relative to others. Differences in ability are often made more salient through score keeping, performance statistics, league standings, and the awarding of trophies, medals, or most valuable player awards. In sports, effort is also associated with numerous physical and behavioral cues such as sweating, grimacing, increases in muscle tension and rate of breathing, muscle fatigue, and loss of technical form. Many of these cues are readily observable, making it easier to determine who is working hard and who is not. Such physical exertion cues are not normally present in academic settings. Sport participation is also an activity that many children value and consider inherently enjoyable. In short, young children may be more capable of and may have more motivation to compare effort and abilities in sport relative to other domains. If so, they might be capable of differentiating between effort- and ability-related success conceptions and to develop achievement goal orientations at an earlier age than previously believed.

Several lines of evidence suggest that achievement goal research in sports may be extended downward at least to 9 years of age. First, the results of a study by Fry and Duda (1997), who studied effort-ability discrimination using both cognitive and physical tasks and concluded that their data supported Nicholls’s age restriction, suggests that children can indeed make the effort-ability distinction at an earlier age in the physical domain than in the cognitive task domain studied by Nicholls. On the cognitive task, children below the age of 11 exhibited little discriminative ability, supporting Nicholls’s position. In contrast, however, when we applied Fisher’s exact test to Fry and Duda’s data on level of effort-ability discrimination in the physical domain (Table 1, p. 339), we found that 9 and 11 year old children did not differ significantly (p > .50). Moreover, the majority of children at both age levels indicated the ability to differentiate between effort and ability in the physical domain. Fry and Duda’s results thus suggest that achievement goal research might profitably be extended downward to at least age 9 using Nicholls’s criterion. However, the viability of such a downward extension would rest on evidence that (1) children discriminate between and exhibit individual differences in mastery and ego goals, and (2) that measures of children’s goal orientations relate to other theoretically relevant variables as they do in older groups studied in achievement goal research.

In addition to the reanalysis of the Fry and Duda (1997) data, Nicholls’ age-related conclusions have also been challenged by the results of other developmental research showing that children as young as age four can make adult-like inferences and differentiation about effort, ability, and outcome when information is presented in a concrete manner that minimizes information processing demands (e.g., Wimmer et al. 1982; Heyman et al. 2003). Heyman et al. (2003) suggested that Nicholls’ age-related results may be a product of younger children’s difficulties in processing the informational demands imposed in the rather complex experimental procedures he used in his studies of effort, ability, and task difficulty (Nicholls 1978; Nicholls and Miller 1983). More recently, suggestive evidence has emerged in the development of measures of mastery and ego achievement goal orientations and motivational climate designed to have reading comprehension levels appropriate for youth sport participants (Cumming et al. 2008; Smith et al. 2008). Validity assessments provided strong evidence that these age-appropriate measures were related to each other and to other variables, such as anxiety, self-esteem, and intrinsic-extrinsic motivation, in the same pattern of relations found in older populations. In light of these considerations, we conclude that a downward extension of achievement goal research is worth pursuing with regard to both achievement goal orientations and motivational climate.

The Motivational Climate Scale for Youth Sports (MCSYS; Smith et al. 2008) utilized in this study was developed because the most widely used measure of motivational climate, the Perceived Motivational Climate in Sport Questionnaire (PMCSQ-2; Newton et al. 2000), was designed for older populations and some of the items have reading-level scores at the high school level. The new scale’s items reflected the content of the PMCSQ-2, but all items have readability scores at or below grade four (approximately 8–9 years of age). Validity research on the new scale, including confirmatory factor analyses, provided strong evidence not only for the ability of young children to distinguish between ego and mastery climates, but also for the convergent and discriminant validity of the children’s measure (Smith et al. 2008). In line with theoretical predictions and previous research with older populations, mastery climate scores were related to higher mastery (task) and lower ego achievement goal orientation, whereas ego climate scores exhibited contrasting relations to these variables. Mastery climate scores were positively related to intrinsic motivation and negatively related to extrinsic motivational variables and to amotivation, whereas ego climate scores were negatively correlated with intrinsic motivation and positively correlated with amotivation. Our findings thus indicate that the motivational climate-intrinsic motivation relations reported in older adolescent populations (e.g., Seifriz et al. 1992; Treasure et al. 1999) extend downward to younger athlete populations. In accordance with expectations and previous findings, we also found mastery scores positively correlated and ego scores negatively correlated with self-esteem. The opposite relations were found with a measure of performance anxiety; mastery scale scores were negatively related to anxiety and ego scores were positively related. In terms of discriminant validity, the MCSYS scales were minimally related to a measure of social desirability, indicating that children’s scores are not affected by a tendency to give socially desirability responses. Finally, scores on MCSYS scales were sensitive to an experimental intervention designed to increase mastery-initiating behaviors and to decrease ego-initiating behaviors in youth sport coaches (Smith et al. 2007). Taken together, these diverse results support the construct validity of the MCSYS and its use with younger athlete populations. More importantly, they provide evidence that within the sport domain, research on motivational climate may be extended downward from the age level posited by Nicholls and other achievement goal theorists. Although several studies have, in fact, included athletes below the age of 11 years (see Duda and Whitehead 1998), these studies either have not had the statistical power necessary to examine children below the age of 11 years, or investigators have not tested factor models in the younger age groups.

Although motivational climate has been linked to changes in goal orientations in educational settings (e.g., Anderman and Anderman 1999; Pintrich 2000), including physical education classes (Carr 2006), there is as yet no evidence that exposure to a shorter-term (typically, about 2–3 months) youth sport setting influences such change. Conroy et al. (2006) found no relations between mastery and ego approach climates and changes in corresponding athletes’ goal orientations over a 6-week swimming season. The authors speculated that limited variability in motivational climate among the teams they studied may have limited the opportunity to detect climate effects. The 6-week participation period was also considerably shorter than was the 9-month period in the Papaioannou et al. (2004) study, allowing for less exposure to the coach-initiated motivational climate. Moreover, athletes ranging from 7 to 18 years of age were included in the sample, and it is possible that achievement goals are less malleable in older athletes than in younger ones, particularly over brief periods of time. Because of sample size limitations, Conroy et al. (2006) were unable to assess the role of athletes’ gender as a potential moderator of relations between motivational climate-goal orientation change.

The present study, like Conroy et al.’s (2006), was conducted in a youth sport setting. The number of teams (47) allowed for more variability in motivational climate, and the time interval was longer (12 weeks). The sample also had a more limited age range (9–13 years), allowing a test of achievement goal hypotheses at earlier levels of athletic participation, when coaching behaviors might be more influential. All of the measures were explicitly designed to have reading levels appropriate to a youth sport population. Like Papaioannou et al. (2004), we used a hierarchical multilevel analytical approach that took into account the nested nature of athletes-within-teams to avoid violation of the assumption of independence that occurs when the scores of individual athletes are used as the unit of analysis (Bryk and Raudenbush 1992; Singer and Willett 2003). Finally, our sample contained enough boys and girls teams to permit an examination of gender differences, and their interactions with motivational climate, as predictors and possible moderators of changes in achievement goals over the course of the sport season.



Initially, the sample consisted of 290 male and female basketball players drawn from two community youth sport programs, one sponsored by the local Parks and Recreation Department (19 teams) and the other by the Catholic Youth Organization (31 teams). With cooperation from program directors, they participated in a study of “sport experiences and outcomes in children”.

In both programs, teams had two hour-long practices and one game per week, and both seasons extended over 12 weeks, providing equal exposure to coaches. To reduce demographic variability, we used US Census Tract data to select neighborhoods that were similar in terms of educational level (60% of the homes had a college graduate) and median annual family income. Based on US Census measures, the mean annual household income in the geographical areas in which the programs were conducted was $71,476. Two girls teams and one boys team were later dropped from the study because they did not provide data on at least five athletes at the two time points. The study is therefore based on 47 teams.

Because of irregular attendance at practices and athletes joining teams after the beginning of practices, we were able to collect both pre-season and late-season data from 145 boys and 98 girls (M = 5.17 athletes per team). An additional 47 athletes completed either pre-season or late-season measures, but not both, or they played on the three teams that did not provide data on at least five athletes. These 47 athletes, comprising 16% of the teams’ participants, did not differ significantly in their achievement goal or motivational climate scores from the athletes who completed all measures at the two time points.

The athletes in our sample ranged in age from 9 to 13 years (M = 11.28, SD = 3.07), with no significant gender differences in age. The sample consisted of 79.9% white athletes, with the remainder divided among African American (3.7%), Asian American/Pacific Islander (6.3%), Hispanic (4.8%), and mixed ethnicities (5.3%). The unpaid volunteer coaches in the study had a mean age of 40.07 years (SD = 6.77) and a mean of 7.07 years of coaching experience (SD = 4.64). All of the boys teams and 14 of the 18 girls teams had male coaches.


Motivational climate

The Motivational Climate Scale for Youth Sports (Smith et al. 2008) was used to measure athlete perceptions of coach-initiated motivational climate. Based on the subfactors of mastery and ego motivational climates identified by Newton et al. (2000), the MCSYS consist of 6-item mastery and ego subscales. The mastery climate subscale contains items reflecting the Newton et al. facets of Cooperative Learning, Effort/Improvement, and Important Role, and the ego climate subscale contained prototypic items indexing Intra-Team Rivalry, Unequal Recognition, and Punishment for Mistakes, as well as an emphasis on winning. Designed for youth sport samples, the scales’ items range from a Flesch-Kincaid reading grade level of 1.8–4.0, with an average elementary school grade level of 3.30. Sample mastery scale items are: “The coach told players to help each other get better,” “The coach made players feel good when they improved a skill.” and “Coach said that all of us were important to the team’s success.” Sample ego scale items are: “Winning games was the most important thing for the coach,” “Players were taken out of games if they made a mistake,” and “The coach paid most attention to the best players.” The athletes indicated their level of agreement with each item on a 5-point scale (1 = not at all true; 5 = very true). As is traditional in achievement goal research, mean item scores were used in our analyses.

Confirmatory factor analysis of the scale has provided strong support for a two-factor structure representing mastery and ego climates (GFI and CFI = .97; RMSEA = .04), and the scales correlate weakly with a children’s social desirability measure (Smith et al. 2008). In the present sample, both subscales exhibited acceptable internal consistency as measured by Cronbach’s alpha (mastery = .78; ego = .78). The scales correlated −.36 with one another.

Achievement goal orientations

Several measures developed with older athletic populations have been successfully used to assess dispositional achievement goal orientations, the most prominent being the Task and Ego Orientation in Sport Questionnaire (TEOSQ; Duda 1989) and the Perceptions of Success Questionnaire (POSQ; Roberts et al. 1998). We used the Achievement Goal Scale for Youth Sports (AGSYS; Cumming et al. 2008) because it was developed for younger populations. The AGSYS has separate factorially derived subscales for measuring mastery and ego goal orientations. Each subscale contains six items, answered on a 5-point scale (1 = not at all true; 5 = very true). Sample mastery scale items include “I feel successful when I do my best” and “The most important thing is to improve my skills.” Sample ego scale items are “I want to do better than others at my sport” and “I want to show that I am better than others.” Confirmatory factor analysis supported the underlying 2-factor structure (GFI and CFI = .95; RMSEA = .056). Like the motivational climate measure described above, all items have readability levels below grade 4.0 (mean reading grade = 2.85), making the scale suitable for participants in the present study. In this study’s sample, the goal orientation subscales yielded Cronbach’s alpha coefficients of .76 for mastery scores and .85 for ego scores at the pre-season administration and .78 and .88 at the late-season administration. Consistent with theoretical assumptions of goal orientation orthogonality, the mastery and ego orientation scores were uncorrelated with one another (r = .00) at pre-season and weakly correlated (r = −.11) at the late-season administration.


With written parental consent and athlete assent, the AGSYS was administered to athletes as part of a larger battery of measures during team practice sessions on two occasions separated by 12 weeks. The first session (Time 1) occurred during the first week of practice following formation of the teams, and the second (Time 2) took place during the final week of the competitive season. The MCSYS measure of coach-initiated motivational climate was administered only at the late-season assessment, as athletes would have had little basis for validly evaluating motivational climate during the first week of practice.

Trained research assistants made arrangements with the coaches to conduct the two data collection sessions. Coaches were told that the purpose of the research was to assess factors related to athletes’ attitudes and outcomes from youth sport participation. Athletes were told that the purpose of the study was to learn more about their experiences in sport. For the MCSYS administration, the stated goal was to see “how well athletes could remember how they were coached” during the season. To increase the likelihood of obtaining valid and complete data, athletes were told before the season that if they answered the questionnaire items carefully and accurately at both questionnaire sessions, they would be given a $4 Baskin-Robbins ice cream gift certificate redeemable at local franchise stores. The certificates were given to the athletes after they completed the late-season questionnaires.

Data analysis

In team sports, athletes are nested within different teams and play for different coaches. Because such clustering tends to promote within-team homogeneity, the assumption that athletes’ data constitute independent observations is violated. Multilevel modeling (also referred to as hierarchical linear modeling) is designed for the analysis of nested data like those of the present study (Bryk and Raudenbush 1992; Singer and Willett 2003). We utilized SPSS Version 11.5 and a procedure for testing Linear Mixed Growth Models as described by Singer and Willett (2003), with responses-within-athletes and athletes-within-teams treated as random effects and motivational climate, time, and gender as fixed effects.


Preliminary analyses revealed no significant pre-season differences between children who completed the late-season measures and those who were absent from the practice session during which the late-season measures were administered. Table 1 presents pre-season and late-season descriptive statistics for the motivational climate and achievement goal orientation measures. Analyses of pre-season scores revealed a significant gender difference in achievement goal orientations, with females having higher scores on the AGSYS mastery scale, t(241) = 2.36, p < .02, d = .25), and lower scores on the ego orientation scale, t(241) = 7.73, p < .001, d = .41. Similar differences were found at the end of the season for both mastery scores, t(241) = 2.97, p < .01, d = .29, and ego scores, t = 8.91, p < .001, d = .92. Finally, although scores on the MCSYS mastery and ego scales exhibited similar variability, a test for differences between nonindependent means revealed significantly higher mastery than ego scores, t(241) = 49.06, p < .001, d = 2.49.
Table 1

Means and standard deviations of pre-season and late-season measures








Mastery climate




Ego climate




Mastery goal orientation





Ego goal orientation





Motivational climate scores are summed across component items. Achievement goal orientation scores are mean item scores. n = 243

The change data were analyzed in two stages. A multilevel model with Time, Motivational Climate, Gender (dummy coded), and the Climate × Gender interaction product scores as predictors of athletes’ achievement goal orientations was first tested, primarily to determine if athletes’ gender moderated relations between motivational climate and changes in achievement goals.

The initial unconditional growth models revealed significant time effects for ego, but not for mastery orientation. For the athletes as a group, ego orientation decreased from pre-season to late-season, t(240) = 4.82, p < .001, whereas mastery orientation remained unchanged. The intercepts of both growth models did, however, demonstrate significant variance, suggesting that some of the variance in both mastery and ego orientation scores might be explained by including predictor variables.

The first series of conditional linear growth models included time, gender and the interaction between time and gender as fixed effects, with the purpose of determining the effects of gender on changes in ego and mastery orientations across the season. The growth model predicting change in ego orientation demonstrated a significant effect for gender on initial goal status t(240) = −4.90, p < .001, with boys reporting higher levels of ego orientation. However, gender was not related to changes in ego orientation across the season. The model predicting changes in mastery orientation failed to demonstrate a significant effect for gender or an interaction between gender and time. As gender was unrelated to changes in mastery or ego orientation across the season, we combined boys and girls in the final growth models.

Our major interest was in how motivational climate was related to changes in achievement goal orientations from early- to late-season. To test these relations, we constructed a 3-level hierchical model,1 where observations at each time point (Level 1) were nested within athletes (Level 2) who were nested within teams (Level 3). We modeled the level (intercept) and time-related changes (slope) of achievement goal orientation at level 1 and tested the variation in the time-related slopes of achievement goal orientations in relation to mastery and ego motivational climate scores at level 2. We allowed the intercept and slope to be random at level 3, which allows teams to differ in their average level and changes in achievement goal orientations, but we treated the effects of achievement goal orientations and motivational climate as fixed effects.

The random effects Chi-square analyses revealed that for mastery goal orientation, athletes differed significantly in both their intercepts at Time 1, χ2 (196) = 911.06, p < .001, and in their Time 1-Time2 slopes, χ2 (194) = 449.54, p < .001). Teams likewise differed in both their intercepts, χ2 (46) = 75.21, p > 004, and their slopes, χ2 (46) = 66.83, p < .03. For ego goal orientation, athletes differed in both intercepts, χ2 (196) = 165,776.91, p < .001, and slopes, χ2 (194) = 82,403.37, p < .001. Teams differed significantly in intercepts, χ2 (46) = 91.84, p < .001, but not in ego orientation change slopes, χ2 (46) = 50.02. The random effects tests thus indicated considerable variability in both Time 1 scores and in change slopes at both the individual and team level.

The multilevel fixed effects statistical tests for the MCSYS mastery and ego climate scores in relation to changes in the goal orientation criterion variables, taking into account the nesting of athletes within teams, are shown in Table 2. Mastery climate scores were significantly associated with increases in athletes’ AGSYS mastery scores (p < .001) and to decreases in ego orientation scores (p < .05) over the course of the season. Ego climate scores were related to increases in athletes’ ego orientation scores (p < .001), but the decrease in mastery orientation scores associated with ego climate was not significant.
Table 2

Fixed effects results of multilevel analyses of coach-initiated motivational climate in relation to changes in athletes’ achievement goal orientations






Mastery climate

    Change in mastery orientation





    Change in ego orientation





Ego climate

    Change in mastery orientation





    Change in ego orientation





A positive fixed-effect coefficient signifies an increase in goal orientation scores associated with motivational climate; a negative coefficient indicates a decrease


The results of this study support the proposition that coach-initiated motivational climate can play an important role in the experiences and psychosocial development of young athletes. Previous cross-sectional research has revealed positive relations between a mastery climate and mastery goal orientations in athletes, and negative relations between a mastery climate and an ego orientation (Duda and Hall 2001; Smith et al. 2008). Although our multilevel regression-based results are inherently correlational in nature, our longitudinal data demonstrate a similar pattern, this time in relation to actual changes in goal orientations over the course of a sport season. Combined with the findings of Papaioannou et al. (2004) and intervention studies (e.g., Smith et al. 2007), our results contribute to an increasingly convincing picture of mastery climate-goal orientation relations that is consistent with expectations derived from achievement goal theory.

The hypothesized impact of an ego motivational climate on achievement goals was also largely supported. The promotion and reinforcement of ego-oriented values by a respected adult might be expected to foster an ego goal orientation in young athletes, and our results, though unable to establish causality, are consistent with that prediction in that MCSYS ego climate scores were strongly associated with increases in ego goal orientation over the season. In contrast to Papaioannou et al. (2004), however, we did not find that an ego orientation was associated with a decrease in athletes’ mastery goal orientation. Although ego and mastery climate scores are negatively correlated because some of the behaviors (e.g., support in response to mistakes versus punishment for mistakes) are incompatible, achievement goal orientations are basically orthogonal, so that changes in one goal orientation need not be associated with changes in the other.

Gender differences and potential moderator effects have been largely ignored in previous studies of motivational climate and changes in sport-related achievement variables. We found no evidence that gender moderates the relations between motivational climate and changes in achievement goal orientations described above. There were, however, some notable gender differences that are consistent with findings in older populations (Duda and Whitehead 1998). On the AGSYS goal orientation measure, girls had significantly higher mastery scores than boys, whereas boys had higher ego orientation scores than did girls. This result is consistent with other AGSYS findings involving athletes across age levels ranging from 10 to 16 years (Cumming et al. 2008). On the late-season measure of motivational climate, male athletes reported a stronger ego climate on their teams than did girls, but boys and girls did not differ in mastery climate scores. The ego climate difference is also consistent with normative data on the MCSYS (Smith et al. 2008). Finally, comparison of mean MCSYS mastery and ego scores indicated that both boys and girls perceived their coaches as creating a climate that was far more mastery-initiating than ego-initiating, an encouraging finding at the youth sport level where we believe that the emphasis should be on skill development and enjoyment of the activity. Nonetheless, there was sufficient variability among coaches to detect strong relations between both sets of climate-relevant behaviors and changes in achievement goals over time.

Relevance of the 2 × 2 achievement goal model

A significant conceptual development in recent years is an expansion of achievement goal theory beyond the traditional mastery and ego approach goals to a 2 × 2 framework that also incorporates mastery and ego (performance) avoidance goals (Conroy et al. 2003; Elliot and McGregor 2001). Conroy et al. (2006) have also introduced a 2 × 2 motivational climate scale. The scale differs from traditional climate scales in asking athletes to infer their coaches’ goals for their teams rather than to report on specific climate-initiating coaching behaviors. At this point, it is not known how these two measurement approaches relate to one another and if they are measuring the same constructs. As noted earlier, the Conroy et al. (2006) approach scales were unrelated to changes in corresponding approach goal orientations. However, their mastery-avoidance and performance-avoidance climate scales did significantly predict increases in corresponding avoidance goal orientations assessed by a 2 × 2 achievement goal scale, as well as decreases in intrinsic motivation. In traditional achievement goal theory with dichotomous mastery- and ego-approach constructs, ego climate and goal orientation have been linked to such negative outcomes as anxiety, low intrinsic motivation, lowered enjoyment, and sport attrition. Moreover, there seems to be considerable overlap in the correlates of the performance (ego) approach and avoidance goal orientations in the 2 × 2 measurement model, and the performance-approach, performance-avoidance, and mastery-avoidance goals all tend to correlate positively with anxiety/fear of failure measures (Conroy et al. 2003; Elliot and McGregor 2001). Moreover, there is some question about whether the 2 × 2 goal distinctions, established with college students, apply to children. Although factor analytic results indicate that children in this study’s age group clearly can discriminate between mastery and ego approach goals, Cumming et al. (2008) reported an apparent inability of 10–14 year old children to discriminate between the two avoidance goal orientations, even with mastery- and ego-avoidance items having high face validity and appropriate readability levels. They concluded that pending further measurement developments in the 2 × 2 model, measures of performance anxiety, such as the age-appropriate Sport Anxiety Scale-2 (Smith et al. 2006) remains a better-established marker for fear of failure in child athletes than are current avoidance goal orientation scales. These points notwithstanding, the 2 × 2 model is a welcome conceptual development that has and will continue to provide a framework for empirical and conceptual advances concerning achievement-related motivational variables.

Motivational climate: a team- or individual-level variable?

Motivational climate has traditionally been construed as a situational variable that influences the experiences of team members in a relatively uniform manner (Chi 2004). To the extent that coaching behaviors are directed to the team as a whole and do not vary widely in their application to different members of a team, motivational climate can be regarded conceptually as a group-level situational variable that reflects the “shared environment” of a team. On the other hand, to the extent that motivational climate scores are influenced by individualized experiences or personalized perceptions of coaching behaviors, interathlete variability would be expected due to the “unshared” aspects of the coach-athlete relationship patterns within a team. Recent examinations in motivational climate studies of intraclass correlations, which reflect the proportion of response variance that can be attributed to common motivational climate perceptions among members of a class, team, or other group, call the uniformity assumption into question. Intraclass correlations among sport team and physical education class members are relatively modest in magnitude, indicating some degree of within-team homogeneity that requires a multilevel analytical approach, but failing to reach .20 in the sample of 47 sport teams in this study (.13 for ego climate and .17 for mastery climate) and in the physical education classes studied by Papaioannou et al. (2004).

It appears, therefore, that scores on the MCSYS and other motivational climate scales may be more a product of individual athletes’ experiences and perceptions than they are of a team’s uniformly experienced athletic situation. It may be that personally experienced interactions are more salient, meaningful, and easier to recollect than are behaviors directed at teammates. Variation in how the coach interacts with different team members may further explain the limited concordance of motivational climate reports. Given the modest intraclass correlations reported thus far in athlete and physical education samples, an increased use of observational assessment of coaching behaviors, using a motivational climate coding system such as that developed by Morgan et al. (2005), seems highly desirable. It is worth noting that these investigators found only moderate correlations between their behavioral measures of motivational climate in physical educational classes and student-reported motivational climate, perhaps reflecting the individual variability in students’ perceptions suggested above. Multimodal assessments would allow for comparisons of observed and athlete-perceived behaviors, which have been found to be only moderately correlated in previous studies using other coding systems (e.g., Smith et al. 1978). Finally, behavioral assessment would allow a determination of how consistently climate-initiating coaching behaviors are applied across different situations and to different athletes. Clearly, much remains to be learned about motivational climate, its measurement, and its effects on sport outcomes.

Limitations and conclusions

Several limitations and issues raised by the present study deserve comment. The focus of the study was on goal orientation dispositions rather than goal involvement states. Dispositional factors interact with relevant situational cues to produce a goal involvement state that is the proximal influence on achievement outcomes in a given situation. With a few exceptions (e.g., Harwood and Swain 2002), sport researchers have not attempted to assess goal involvement states during sporting events, or to relate them to motivational climate. Such assessment would be a major advance in achievement goal research in athletics. In studies like the present one, it would be of interest to know if the coach-initiated motivational climate would increase the frequency and intensity of athletes’ mastery or ego goal states before and during competition.

We measured motivational climate only at the end of the sport season because we reasoned that athletes would have little basis for reporting on relevant coaching behaviors during the first week of practice, when we assessed baseline achievement goals. However, the lack of earlier MCSYS scores required that we rely on athlete retrospective reports of coaching behaviors that occurred over the course of the entire 12-week season. Aside from potential retrospective distortions (e.g., primacy or recency effects in recall), this design prevented us from tracking potential changes in coaching behaviors over time. An obvious improvement over our design would involve periodic assessments of both motivational climate perceptions and achievement goals over the course of the season, as has occurred in several studies in educational settings (Anderman and Anderman 1999; Carr 2006; Pintrich 2000). This would allow for the examination of multi-point change trajectories in achievement goals as opposed to a single Time 1-Time 2 change. Finally, the inherently correlational nature of the data makes explicit causal statements impossible. Although interventions directed at fostering a mastery motivational climate have resulted in changes in achievement goals similar to those found in our study (see Dweck 1999), it is also quite possible that children’s achievement goals influence their perceptions of the coach-initiated motivational climate. From this perspective, our data could indicate that children who change in their achievement goal orientations perceive their team’s motivational climate in a change-congruent fashion at the end of the season. Indeed, it is quite conceivable that climate-goal orientation relations reflect bidirectional causal relations.

Conceptual and measurement issues notwithstanding, motivational climate research illustrates the influential role that coach-athlete relationships play in the youth sport environment, as in other achievement settings (Dweck 1999). Clearly, coaches can have a highly influential impact on child athletes, and the goal priorities and values they communicate seem to be a major channel for such influence. Indeed, climate-related coaching behaviors may be more important than other experiential factors in influencing athletes’ sport experiences. Cumming et al. (2007) found that mastery and ego MCSYS scores correlated .55 and −.29, respectively, with how positively athletes evaluated their coaches, whereas teams’ won-lost records were largely unrelated to attitudes toward the coach. Likewise, mastery scores correlated .40 with how much fun athletes said they had, whereas teams’ winning record correlated only .12 with fun. However, much remains to be learned about the strength, generality, and durability of motivational climate influences. Although our study revealed climate-related changes in motivational variables over the course of a sport season, more research is needed to determine how long these changes persist, whether they generalize to other achievement domains such as the classroom, and which specific aspects of the multifaceted motivational climate are the “active ingredients” that promote achievement goal changes.


The equations for the hierarchical linear model (in this case, for ego goal orientation) are as follows:

Level 1: \( {\text{Ego}}_{tij} = \pi_{0ij} + \pi_{1} \,{\text{Time}} + e_{tij} \)

Level 2: \( \pi_{0ij} = \beta_{00j} + r_{0} \)\( \pi_{1ij} = \beta_{10j} + \beta_{11} \,{\text{Mastery}}\,{\text{Climate}} + \beta_{12} \,{\text{Ego}}\,{\text{Climate}} + r_{1} \)

Level 3: \( \beta_{00} = \gamma_{000} + \mu_{00} ;\,\beta_{10} = \gamma_{100} + \mu_{10} ;\,\beta_{10j} = \gamma_{120} ;\,\beta_{12} = \gamma_{120} \)

Mixed Model: \( {\text{Ego}}_{tij} = \gamma_{000} + \gamma_{110} \, *\,{\text{Time}}\,+\,\gamma_{110} {\text{Mastery}}\,{\text{Climate}}\, *\,{\text{Time}}\)\( \,+\,\gamma_{120} {\text{Ego}}\,{\text{Climate}}\, *\,{\text{Time}}\,+\,\mu_{10} \, *\,{\text{Time}} + r_{1} \, *\,{\text{Time}} + \mu_{00} + r_{0} + e. \)



This research was partially supported by Grant 2297 from the William T. Grant Foundation. We express our appreciation to the following for their assistance in data collection: Erica Coppel, Polo DeCano, Kira Elste, Christopher Harris, Leslie Lombardo, Kim Matz, Cheree Monroe-Wilson, Olivia Morrow, Tori Nutsch, Dana Ryan, Jason Victor, and Nathalie Walker.We also thank Kevin King for his statistical consultation.

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© Springer Science+Business Media, LLC 2009