Coaching Climates and the Destructive Effects of Mastery- Avoidance Achievement Goals on Situational Motivation

JOURNAL OF SPORT & EXERCISE PSYCHOLOGY, 2006, 28, 69-92 2006 Human Kinetics, Inc. Coaching Climates and the Destructive Effects of Mastery- Avoidance Achievement Goals on Situational Motivation David E. Conroy 1, Miranda P. Kaye 1, and J. Douglas Coatsworth 2 The Pennsylvania State University The present research tested a model of social-cognitive influences on situational motivation (i.e., youths reasons for participating in sport at a given moment in time) via youths 2 2 achievement goals. Boys and girls (N N = 165) participating in a summer swim league completed measures of their achievement goals and situational motivation on multiple occasions during a 6-week period; they also rated the coaching climate at the end of the season. All Situational Motivation Scale responses exhibited acceptable levels of longitudinal factorial invariance. Latent growth curve analyses revealed that intrinsic motivation and identified regulation did not appear to change over the course of the season; however, external regulation and amotivation increased significantly during that period. Youths perceptions of an avoidance-oriented coaching climate predicted corresponding residualized change in their own achievement goals over the season. Additionally, residualized change in youths mastery-avoidance goals (i.e., focus on avoiding self-referenced incompetence) was positively linked to the rate at which external regulation and amotivation scores changed. Key Words: competence, approach-avoidance, extrinsic motivation Coaches fill important roles in children s sport experiences as instructors, role models, or mentors. In addition to guiding skill development, coaches can have a pronounced impact on the self-concept, motivation, and affective experience of young athletes. Motivation is a particularly important youth outcome that coaches can influence because it can affect the quality of youths experience in an activity as well as their likelihood of participating in that activity in the future. Surprisingly little is known about the mechanisms by which coaches influence youth motivation as the course of their interactions unfolds. Research on youth sport motivation consistently finds that fun and competence related themes, such as skill development and challenge, are among the top reasons cited by youths for participating in sports (Weiss & Petlichkoff, 1989). Indeed, fun and competence motivation appear to be somewhat interdependent, as indicated by nonexperimental studies linking youths cognitive focus for demonstrating competence (i.e., achievement goals) with their intrinsic motivation (Cury, Elliot, 1 Dept. of Kinesiology and 2 Dept. of Human Development & Family Studies, The Pennsylvania State University, University Park, PA 16802. 69

70 / Conroy, Kaye, and Coatsworth Sarrazin, Da Fonseca, & Rufo, 2002). Both fun and competence motivation for young athletes can be influenced by youths perceptions of social factors in their sport environment; however, relatively little is known about the dynamic interplay between social factors, competence motivation, and the reasons why youth engage in organized sports. The present research tested a short-term longitudinal model of social influences on situational motivation (i.e., youths reasons for participating in sport at a given time). Youth achievement goals are posited as links between their perceptions of the motivational climate created by their coaches (i.e., how they think the coach is evaluating their competence) and their situational motivation. This model frames important questions concerning associations between youths perceptions of the coaching climate, changes in youth achievement goals, and changes in situational motivation. Specifically, it addresses the following questions: Do youth perceptions of how their coaches are evaluating their competence influence changes in either (a) how youth evaluate their competence over the season, or (b) the reasons that youth give for participating in sport? Are changes in youths own achievement goals associated with changes in their reasons for participating in sport? Do those changes in youths achievement goals mediate any links between youth perceptions of the coaching climate and changes in their reasons for participating in sport? Situational Motivation in Sport Motivation in Youth Sport Motivation has been conceptualized at varying levels of specificity (e.g., global, contextual, situational), and the present research focuses on situational motivation, the most temporally and contextually specific level of motivation. Situational motivation represents the here-and-now of motivation and reflects the reasons an individual has for engaging in an activity (Vallerand, 2001). Three major forms of situational motivation have been described along a continuum of self-determination: intrinsic motivation, extrinsic motivation, and amotivation (Deci & Ryan, 1991; Ryan & Deci, 2000). Intrinsic motivation represents behaviors that are performed for rewards provided by the activity itself such as accomplishment, knowledge, and stimulation (Deci & Ryan, 1985; Vallerand et al., 1992). Extrinsic motivation describes behaviors performed to attain a separable outcome (Deci & Ryan, 1985, 1991). Identified regulation and external regulation are two specific forms of extrinsic motivation. Identifi ed regulation characterizes extrinsically motivated behavior that is self-initiated, identified as valuable, and identified as important to the self despite not being intrinsically rewarding (Deci & Ryan, 1985). Similar to intrinsic motivation, it has been positively linked to concentration and enjoyment, albeit with slightly weaker effect sizes (Vallerand, 2001). External regulation reflects the classic definition of extrinsic motivation whereby individuals act to obtain external rewards or avoid external punishments (Deci & Ryan, 1985, 1991). Amotivation describes relatively aimless behavior (Ryan & Deci, 2000). These four degrees of self-determined situational motivation encompass some of the most common reasons for participating in youth sports (e.g., having fun [intrinsic motivation]; it s good for me to be outside and active part of the day [identified regulation]; my parents are making me [external regulation]; I really don t know why I m doing this [amotivation]). These four forms of motivation also

Situational Motivation / 71 have been associated with unique profiles of affective, behavioral, and cognitive consequences (Vallerand, 2001; Vallerand & Rousseau, 2001). Self-determination theory proposes that social factors influence the selfdetermination of behavior and that those effects are mediated by the satisfaction of three basic psychological needs: autonomy, relatedness, competence (Deci & Ryan, 1985, 1991). Given the prominence of competence related reasons for participating in youth sport (Weiss & Petlichkoff, 1989), we focused our attention on a competence related mechanism for social influences on the self-determination of situational motivation, namely achievement goals. Achievement Goal Theory Achievement goals refer to the dynamic cognitive focus of an individual s achievement behavior (Dweck & Leggett, 1988; Nicholls, 1984). An individual s goals create a framework for how he or she interprets, evaluates, experiences, and acts in achievement settings (Dweck, 1986; Nicholls, 1984). Historically these goals have been distinguished by their definition of competence, but recent research has demonstrated the value of distinguishing goals both by the definition of competence and the valence of the outcome (for a review, see Elliot, 2005). Defi nitions of Competence. Competence may be defined in terms of absolute (e.g., performing a task as well as possible), intrapersonal (e.g., learning and improvement), and interpersonal (e.g., normative comparisons) criteria (Elliot & McGregor, 2001). Most research has combined absolute and intrapersonal definitions of competence into a single mastery (or task) goal definition. In the sport domain, mastery goal involvement has been linked positively to competence, intrinsic motivation, effort, enjoyment, interest, and positive affect (Duda, 2005). Interpersonal definitions of competence have been identified as performance (or ego) goals and linked to a number of undesirable outcomes in sport such as reduced enjoyment and interest, elevated state anxiety, or decreased intrinsic motivation (Duda, 2005). Goal Valence. To resolve somewhat equivocal findings about relations between performance achievement goals and intrinsic motivation, Elliot (Elliot & Harackiewicz, 1996; Rawsthorne & Elliot, 1999) proposed distinguishing performance goals focused on outperforming others (i.e., performance-approach goals) from performance goals focused on not being outperformed by others (i.e., performance-avoidance goals). Experimental studies in the laboratory and nonexperimental studies in naturalistic settings have linked intrinsic motivation positively to performance-approach goals (when mastery-approach goals are simultaneously high) and negatively to performance-avoidance goals (Elliot & Harackiewicz, 1996; Rawsthorne & Elliot, 1999). Broadening the universe of achievement goals in sport research to consider both definitions of competence and goal valences may be similarly beneficial (Elliot & Conroy, 2005). Thus, a 2 2 framework comprising mastery-approach, mastery-avoidance, performance-approach, and performanceavoidance goals can be conceived for describing achievement goals (Elliot, 1999; Pintrich, 2000). The 2 2 Achievement Goal Framework. Mastery-approach (MAp) goals represent strivings to improve on a previous performance or to perform a task as well as possible. In sport, MAp goals have been linked with high levels of task absorption and low levels of state anxiety (Cury et al., 2002). Mastery-avoidance (MAv) goals are the most recent addition to the achievement goal literature and

72 / Conroy, Kaye, and Coatsworth refer to strivings to avoid performing worse than one previously performed (i.e., losing ability). Although no empirical evidence is available concerning associations between MAv goals and intrinsic motivation, they are likely to be inversely related to intrinsic motivation because of their focus on avoiding an unpleasant possibility. Performance-approach (PAp) goals describe strivings to outperform others (i.e., normative demonstrations of competence). In sport, individuals adopting PAp and mastery goals have been shown to display the same level of intrinsic motivation (Cury et al., 2002) and equivalent amounts of practice times (Cury, Da Fonséca, Rufo, Peres, & Sarrazin 2003). Performance-avoidance (PAv) goals represent strivings to avoid appearing incompetent in relation to others. Findings in sport and physical education linked PAv goals with reduced intrinsic motivation (Cury et al., 2002) and reduced preparation (Cury et al., 2003). 1 Relations Between 2 2 Goals and Situational Motivation Extant findings suggest the desirability of mastery relative to performance definitions of competence, and approach relative to avoidance goal valences when intrinsic motivation is the outcome of interest. Accordingly, intrinsic motivation in the present study was expected to be positively associated with MAp goals, negatively associated with PAv and MAv goals, and unassociated with PAp goals. The simplex-like structure of situational motivation suggests that relations with 2 2 goals will be opposite for amotivation than they were for intrinsic motivation (i.e., positive associations with PAv and MAv goals, negative associations with MAp goals, and nonsignificant associations with PAp goals). Identified regulations are more similar to intrinsic motivation than to amotivation, so the pattern of associations for goals was expected to resemble associations between goals and intrinsic motivation (but possibly to be weaker because identified regulations are less self-determined than intrinsic motivation). Likewise, external regulations are more similar to amotivation than to intrinsic motivation, so the pattern of associations for goals was expected to resemble associations between goals and amotivation, albeit weaker. Beyond static links between goals and situational motivation, this research attempted to capture dynamic aspects of motivation in youth sport. We propose that such motivation may change as a function of (a) changes in youth achievement goals over the course of the season, or (b) properties of the social ecology that catalyze changes in youths achievement goals. Social-Ecological Factors Influencing Youth Achievement Goals. Coaches, parents, and peers may all contribute to youths achievement motivation in organized sports, but coaches are thought to play an especially important role in this process because of their role as proximal adult models for young athletes. Coaching climates can influence youth states of goal involvement (Solmon, 1996; Theebom, De Knop, & Weiss, 1995; Treasure & Roberts, 2001). Changes in achievement goals also may stimulate changes in more distal processes such as intrinsic motivation (Cury et al., 2002; Elliot & Harackiewicz, 1996). For example, Theebom et al. (1995) found that mastery-oriented coaching climates were linked to youth having more fun (a construct similar to intrinsic situational motivation) during martial arts training. Thus, young people s perceptions of their coaches goals were expected to lead to corresponding changes in their own achievement goals (e.g., perceptions of coaches PAv goals predict changes in athletes PAv goals) and to changes in situational

Situational Motivation / 73 motivation. In turn, changes in youths achievement goals were expected to influence changes in youth situational motivation over the course of the season. Purpose This research addressed three objectives. First, the study evaluated the longitudinal factorial invariance of four types of situational motivation that span the continuum of self-determination: intrinsic motivation, extrinsic motivation with identified regulation, extrinsic motivation with external regulation, and amotivation. Second, the pattern of latent growth in each form of situational motivation over the course of a youth sport season was characterized. Finally, conditional latent growth curve models were tested to determine the extent to which initial levels of situational motivation were associated with youth 2 2 achievement goals at the beginning of the season, and whether subsequent changes in situational motivation were associated with (a) changes in youth 2 2 achievement goals over the season, or (b) youth perceptions of the 2 2 achievement goals that coaches have for them. With regard to predicting situational motivation, youth with more optimal achievement goals (i.e., mastery-defined or approach-valenced) at the beginning of the season were expected to have greater intrinsic motivation and identified regulation and less external regulation and amotivation than youth with less optimal achievement goals (i.e., performance-defined or avoidance-valenced). Youth who perceived that their coaches espoused more optimal achievement goals were expected to exhibit the greatest increases in intrinsic motivation and identified regulation, and the greatest decreases in external regulation and amotivation. This coaching climate effect was hypothesized to occur through a process whereby youth achievement goals would become more similar to their perceptions of their coaches goals over the course of the season. Youth whose achievement goals became more optimal over the season were expected to increase their levels of intrinsic motivation and identified regulation, and to decrease their levels of external regulation and amotivation over the season. Participants Methods Participants were recruited from a swim league sponsored by the department of parks and recreation (participation rate = 54%). This convenience sample included 66 boys and 99 girls (N = 165). The mean age of participants was 11.2 years (SD = 2.2; range = 7 18 years). Consistent with the demographic composition of the area, participants were almost exclusively white (79.4%; African American 3%; Asian American 1.8%; Hispanic 0.6%; other 7.3%; did not respond 7.9%). The majority of participants swam on a team only in this league (60%), and participants had been swimming on their team an average of 3.01 years (SD = 2.33; range = 0 13 years). Instruments The Situational Motivation Scale (SMS; Guay, Vallerand, & Blanchard, 2000) is a 16-item measure of one s self-determination of his or her choice to participate in an activity, but two items were dropped from the present study because of previous research indicating a lack of factorial invariance for those items (Standage,

74 / Conroy, Kaye, and Coatsworth Treasure, Duda, & Prusak, 2003). The resulting 14-item SMS provided separate scores for intrinsic motivation (4 items), identified regulation (3 items), external regulation (3 items), and amotivation (4 items). Participants rated different reasons for participating in an activity on a scale ranging from corresponds not at all (1) to corresponds exactly (7). The SMS has displayed satisfactory factorial validity, internal consistency, test-retest reliability, and external validity with both psychological and behavioral indices of motivation. Guay et al. (2000) additionally demonstrated that the SMS is sensitive enough to detect changes within individuals in motivation as a function of changes in autonomy, relatedness, and competence. The 12-item 2 2 Achievement Goals Questionnaire for Sport (AGQ-S; Conroy, Elliot, & Hofer, 2003) was used to measure youth achievement goals. The AGQ-S was developed by modifying items from the original Achievement Goals Questionnaire (Elliot & McGregor, 2001) to make them more applicable to sport and increase the salience of normative comparisons in the PAv goal items. The AGQ-S provided scores for MAp ( It is important to me to perform as well as I possibly can ), MAv ( I worry that I may not perform as well as I possibly can ), PAp ( It is important to me to do well compared to others ), and PAv ( My goal is to avoid performing worse than everyone else ) achievement goals in sport. Participants were asked to consider their present goals for swimming and to rate each item on a scale ranging from not at all true of me ( 3) to very true of me (+3). In an unpublished study from a separate sample of young swimmers, responses to the AGQ-S scales demonstrated acceptable internal consistency, factorial validity, strict longitudinal factorial invariance, and acceptable temporal stability for latent mean scores (Conroy, Coatsworth, & Cassidy, 2005). Youth perceptions of the goals that their coaches used to evaluate their competence (i.e., coaching climate) were assessed by modifying stems for items on the AGQ-S. The Perceptions of Coaches Achievement Goals Questionnaire for Sport (PCAGQ-S) provided scores for current perceptions of coaches MAp ( My coaches believe that it is important for me to master all aspects of my performance ), MAv ( My coaches worry that I may not perform as well as I possibly can ), PAp ( My coaches believe that it is important for me to perform as well as I possibly can ), and PAv ( My coaches just want me to avoid performing worse than others ) achievement goals in sport. Participants were asked to rate each item on a scale ranging from not at all true ( 3) to very true (+3). Procedures Permission to conduct this study was received from the town council s parks and recreation board, the director of the department of parks and recreation, the league supervisor, and the university institutional review board. Participants were recruited during the first week of practice. Letters and parental consent forms were sent home informing parents of the nature of the study and requesting permission for their child s participation. After receiving parental consent, research assistants described the study to the youth and requested their informed assent (for participants age 13 or younger) or informed consent (for participants age 14 or older). During the first week of the season, participants completed the SMS and AGQ-S. During Week 3 they completed the SMS. During Week 6 (the final week that culminated in the league championship meet), participants completed the SMS, AGQ-S, and the PCAGQ-S. Research assistants were available at all times to assist any who were having difficulty understanding or completing the questionnaires.

Data Analysis Situational Motivation / 75 Data from this study were analyzed with AMOS 4.01 software (Arbuckle & Wothke, 1999), and independence models were estimated separately to provide a basis for calculating the correct relative fit indices. Missing data appeared to be a combination of an arbitrary pattern and wave nonresponse. Logistic regression analyses indicated that the odds of missing data at each occasion were not significantly associated with participants age, sex, or initial achievement goal or situational motivation scores. Based on this evidence, we concluded that data was missing at random and tested our models using full-information maximum likelihood estimation to capitalize fully on the available data (Enders & Bandalos, 2001). Longitudinal Factorial Invariance (LFI) Analyses. A series of nested models with increasingly restrictive constraints on model parameters were tested and compared (Meredith, 1993; Meredith & Horn, 2001). The configural invariance model was used as a baseline for subsequent comparisons. This model imposed the same factor structure at each measurement occasion but did not impose equality constraints on any parameters in the model. A weak factorial invariance model was estimated next by constraining corresponding item-factor regression coefficients to be equal across waves of measurement (all other specifications were identical to the configural invariance model). A strong factorial invariance model was tested by adding constraints to corresponding item intercepts across measurement occasions (in addition to the specifications for the weak factorial invariance model). The final and most restrictive model was the strict factorial invariance model, which was estimated by imposing equality constraints on corresponding uniquenesses across measurement occasions (in addition to all of the constraints on the strong factorial invariance model). For identification purposes, the item-factor regression coefficient for the first item on each factor was fixed to 1.0 (each factor variance was freely estimated in these models). Additionally, corresponding manifest variable uniquenesses (i.e., uniquenesses from a single item at different occasions) were permitted to correlate across waves of measurement. Latent Growth Curve (LGC) Analyses. A series of LGC analyses were conducted using the best fitting and most constrained longitudinal factorial invariance models identified in the analyses described above. Second-order intercept and slope factors were then added to these models (Sayer & Cumsille, 2001). Regression coefficients between the second-order intercept factor and the first-order goal factors for each occasion of measurement were fixed at 1.0. To identify the second-order factor means in this model, the intercepts for manifest variables and first-order factors were fixed to zero. A progression of unconditional LGC models was then tested for each of the four motivational profiles to ascertain the nature of changes in the latent means (Meredith & Tisak, 1990). The first two models assumed no changes in situational motivation scores over time. The first no-growth model assumed that youth started the season with equivalent scores for each situational motivation scale (fixed intercepts) whereas the second no-growth model allowed for interindividual variability in initial scores for each situational motivation scale (random intercepts). Next, a pair of lineartrajectory growth models were specified by adding a second-order slope factor to the random coefficients no-growth model (Sayer & Cumsille, 2001). Parameters between the slope factor and the three first-order motivation factors were fixed at 0, 2, and 5 to indicate the rate of weekly change relative to starting values (the

76 / Conroy, Kaye, and Coatsworth availability of three measurement occasions limited us to testing linear growth trajectories). The two linear-trajectory growth models differed such that one assumed no interindividual variability in slopes (fixed slopes) whereas the other allowed for interindividual variability in slopes, thus the intercept represented each individual s latent mean at the start of the season and slope represented the subsequent weekly rate of change in motivation. Evaluating Model Fit. A combination of absolute (i.e., χ 2, RMSEA) and relative (i.e., NFI, NNFI, CFI) fit indices were used to evaluate model fit. Values above.90 for the latter indices typically indicate acceptable model fit whereas values above.95 indicate good model fit. RMSEA values of.08,.05, and.00 were interpreted as indicating acceptable, good, and exact fit. To evaluate the fit of nested invariance models, we examined changes in absolute and relative fit indices. A significant reduction in model fit (via chi-square difference tests or changes greater than.01 in relative fit indices) indicated that the null hypothesis of invariance should be rejected and that the less constrained model is more appropriate (Cheung & Rensvold, 2002). Results Descriptive statistics and internal consistency estimates for the SMS items and scales at each time point are listed in Table 1. Table 2 presents descriptive statistics and internal consistency estimates for the achievement goals and coaching climate scales. Each scale exhibited acceptable internal consistency. At the beginning of the season, older participants endorsed higher levels of PAp goals than younger participants (r r =.30, p <.05), and boys endorsed higher levels of PAv goals than girls (r r =.22, p <.05). Neither age nor sex was associated with any of the other situational motivation or achievement goal scores at the beginning of the study (p >.05), so subsequent analyses employed data from the entire sample. Three main questions derived directly from the study s three objectives were examined: (1) Did the measure of situational motivation tap the same construct over time (longitudinal factorial invariance)? (2) How should patterns of latent growth in situational motivation scores be modeled (unconditional latent growth)? (3) How do the coaching climate and youth achievement goals relate to levels of and influence changes in situational motivation (conditional latent growth)? 1. Did the Measure of Situational Motivation Tap the Same Construct Over Time? The first step in testing the proposed social-cognitive model of situational motivation dynamics involved evaluating the longitudinal factorial invariance of situational motivation responses over a 6-week interval. As seen in Table 3, responses to the intrinsic motivation scale exhibited strict factorial invariance, responses to the two extrinsic motivation scales exhibited weak factorial invariance, and responses to the amotivation scale exhibited configural factorial invariance. Further examination revealed that only two parameters in the amotivation model varied noticeably (i.e., Item 8 at Time 2 and Item 12 at Time 3). When those parameters were left unconstrained, the amotivation model achieved partial weak factorial invariance ( χ 2 [4] = 0.47, p <.05) and even partial strong factorial invariance ( χ 2 [8] = 17.10, p <.05).

Situational Motivation / 77 Table 1 Descriptive Statistics for Situational Motivation Items Across 3 Waves Wave 1 Wave 2 Wave 3 N M SD α N M SD α N M SD α Intrinsic Motivation 159 5.80 1.25.85 156 5.76 1.15.84 137 5.80 1.27.86 Item 1 160 5.54 1.62 158 5.61 1.37 140 5.74 1.41 Item 5 159 5.67 1.55 158 5.49 1.51 137 5.53 1.62 Item 9 160 6.19 1.30 156 6.18 1.28 138 6.16 1.42 Item 13 160 5.73 1.58 158 5.75 1.41 138 5.76 1.62 Identified Regulation 160 5.44 1.32.69 156 5.27 1.38.77 137 5.39 1.43.80 Item 2 160 4.89 1.95 157 4.71 1.88 138 4.92 1.92 Item 6 160 5.77 1.54 157 5.78 1.47 137 5.74 1.47 Item 14 160 5.64 1.53 158 5.32 1.59 138 5.38 1.68 External Regulation 158 2.17 1.43.81 156 2.29 1.53.84 138 2.60 1.79.90 Item 3 158 2.14 1.61 157 2.32 1.77 138 2.76 1.95 Item 7 160 2.17 1.69 158 2.21 1.75 138 2.56 1.99 Item 15 160 2.21 1.72 157 2.43 1.82 138 2.47 1.97 Amotivation 156 1.72 1.09.82 151 1.90 1.13.78 138 2.00 1.31.85 Item 4 159 1.92 1.63 157 2.05 1.60 138 2.08 1.66 Item 8 159 1.77 1.37 156 1.71 1.31 138 2.16 1.75 Item 12 159 1.52 1.15 157 1.78 1.33 138 1.80 1.41 Item 16 154 1.79 1.28 154 2.08 1.53 138 1.96 1.49

78 / Conroy, Kaye, and Coatsworth Table 2 Descriptive Statistics for Achievement Goal and Coaching Climate Scores Scale N M SD α Achievement Goals Questionnaire for Sport (Week 1) MAp 154 5.93 1.05.76 MAv 157 3.41 1.57.85 PAp 153 3.43 1.60.81 PAv 159 4.00 1.80.80 Achievement Goals Questionnaire for Sport (Week 6) MAp 136 6.03 1.08.84 MAv 138 3.50 1.79.90 PAp 136 3.84 1.78.87 PAv 139 4.10 1.92.92 Perceptions of Coaches Achievement Goals Questionnaire for Sport MAp 131 3.43 1.83.91 MAv 131 2.72 1.43.82 PAp 129 5.73 1.30.74 PAv 130 3.20 1.87.91 Despite changes in the magnitude of two item-factor regression coefficients, the unconstrained parameter estimates were in the expected direction and the latent factor accounted for at least 42% of the variance in responses to each of those two items (compared to 35 79% for the other items). We concluded that the differences in these two model parameters should be modeled by releasing them from the weak invariance constraints, but they were unlikely to jeopardize the validity of our conclusions regarding changes in amotivation scores. To enhance our confidence in conclusions about changes in amotivation scores, we applied the strong invariance constraints to all other item-factor regression coefficients in the amotivation model. 2. How Should Changes in Situational Motivation be Modeled? The second set of analyses addressed the question of how changes in situational motivation should be modeled. These latent growth curve models employed the longitudinal invariance constraints identified in our earlier analyses. Table 4 depicts the fit comparisons for the no-growth and linear-growth models. Intrinsic Motivation. The random-intercept, no-growth model fit the data significantly better than the fixed-intercept, no-growth model for intrinsic motivation. There was significant interindividual variability in participants initial intrinsic motivation and, on average, there was no significant change in youths intrinsic motivation over the course of the season. The random coefficients model had a negative variance estimate for the slope factor so that the parameter was fixed to zero and the remaining models were not tested.

Situational Motivation / 79 Table 3 Fit Indices for Longitudinal Factorial Invariance Tests of Situational Motivation df χ 2 NFI NNFI CFI RMSEA (90% CI) Intrinsic Independence 66 1243.72 Configural 39 104.33.94.94.96.10 (.08.13) Weak 45 110.09.94.95.96.09 (.07.12) Strong 53 116.76.94.95.96.09 (.07.11) Strict 61 131.41.93.96.96.08 (.06.10) Identified Independence 36 651.90 Configural 15 20.27.97.98.99.05 (.00.09) Weak 19 24.44.96.98.99.04 (.00.09) Strong 25 38.11.94.97.98.06 (.01.09) Strict 31 47.08.93.97.97.06 (.02.09) External Independence 36 899.52 Configural 15 16.07.98 1.00 1.00.02 (.00.08) Weak 19 17.95.98 1.00 1.00.00 (.00.06) Strong 25 36.02.96.98.99.05 (.00.09) Strict 31 40.53.96.99.99.04 (.00.08) Amotivation Independence 66 980.56 Configural 39 65.04.93.95.97.06 (.04.09) Weak 45 84.46.91.94.96.07 (.05.10) Strong 53 102.23.90.93.95.08 (.05.10) Strict 61 150.24.85.89.90.09 (.08.11) Model Comparisons df difference χ 2 difference Intrinsic Model Weak Configural 6 5.76 Strong Weak 8 6.67 Strict Strong 8 14.65 Identified Model Weak Configural 4 4.17 Strong Weak 6 13.67* Strict Strong 6 8.97 External Model Weak Configural 4 1.88 Strong Weak 6 18.07* Strict Strong 6 4.51 Amotivation Model Weak Configural 6 19.46* Strong Weak 8 17.77 Strict Strong 8 48.01 *p <.05, one-tailed.

80 / Conroy, Kaye, and Coatsworth Table 4 Fit Indices and Parameter Estimates for No-Growth and Longitudinal Growth Curve Models of Situational Motivation RMSEA Intercept Slope df χ 2 NFI NNFI CFI (90% CI) M (var) M (var) Intrinsic Motivation (strict invariance constraints) No-Growth Models Fixed effects 67 359.08.67.72.71.16 (.15.18) 5.61** (0) 0 (0) Random effects 66 154.47.86.91.91.09 (.07.11) 5.61** (0.89**) 0 (0) Linear Trajectory Models Fixed slope 65 154.20.86.91.91.09 (.73.11) 5.59** (0.89**) 0.01 (0) Identifi ed Regulation (weak invariance constraints) No-Growth Models Fixed effects 30 171.09.78.73.77.17 (.15.19) 4.90** (0) 0 (0) Random effects 29 42.37.94.97.98.05 (.00.09) 4.88** (0.83**) 0 (0) Linear Trajectory Models Fixed slope 28 42.22.94.97.98.06 (.01.09) 4.90** (0.83**) 0.01(0) Random slope 27 41.93.94.97.98.06 (.02.09) 4.90** (0.82**) 0.01 (0.01) External Regulation (weak invariance constraints) No-Growth Models Fixed effects 30 214.20.76.74.79.19 (.17.22) 2.37** (0) 0 (0) Random effects 29 43.69.95.98.98.06 (.01.09) 2.32** (1.59**) 0 (0) Linear Trajectory Models Fixed slope 28 33.00.96.99.99.03 (.00.07) 2.21** (1.59**) 0.08** (0) Random slope 27 30.72.97.99 1.00.03 (.00.07) 2.21** (1.57**) 0.08** (0.02)

Situational Motivation / 81 Amotivation (partial strong invariance constraints) No-Growth Models Fixed effects 58 336.94.66.65.70.17 (.15.19) 1.98** (0) 0 (0) Random effects 57 119.47.88.92.93.08 (.06.10) 2.01** (0.95**) 0 (0) Linear Trajectory Models Fixed slope 56 107.64.89.93.94.08 (.05.10) 1.91** (1.08**) 0.05* (0) Random slope 55 102.27.90.94.95.07 (.05.09) 1.89** (0.98**) 0.05* (0.02*) Model Comparisons df difference χ 2 difference Intrinsic No Growth (fixed) No Growth (random) 1 204.61 No Growth (random) Linear Trajectory (fixed) 1 0.27* Identified No Growth (fixed) No Growth (random) 1 128.72 No Growth (random) Linear Trajectory (fixed) 1 0.15* Linear Trajectory (fixed) Linear Trajectory (random) 1 0.29* External No Growth (fixed) No Growth (random) 1 107.51 No Growth (random) Linear Trajectory (fixed) 1 10.69 Linear Trajectory (fixed) Linear Trajectory (random) 1 2.28* Amotivation No Growth (fixed) No Growth (random) 1 217.47 No Growth (random) Linear Trajectory (fixed) 1 11.83 Linear Trajectory (fixed) Linear Trajectory (random) 1 5.37 *p <.05, **p * <.01

82 / Conroy, Kaye, and Coatsworth Identifi ed Regulation. The random-intercept, no-growth model fit the data significantly better than the fixed-intercept, no-growth model for identified regulation. Fit did not improve significantly when a linear growth trajectory was modeled. Thus there was significant interindividual variability in participants initial scores and, on average, there was no significant change in identified regulation over the course of the season. Extrinsic Regulation. The random-intercept, no-growth model fit the data significantly better than the fixed-coefficients, no-growth model, suggesting that there was significant interindividual variability in participants initial external regulation. Linear growth models fit the data better than the no-growth models; there was, on average, a significant increase in youths extrinsic regulation over the course of the season. Amotivation. The random-intercept, no-growth model fit the data significantly better than the fixed-coefficients, no-growth model for amotivation as well. There was significant interindividual variability in participants initial amotivated regulation scores. A linear growth model fit better than the no-growth models, with the random linear effect model fitting the best. There were, on average, slight but significant increases in youths amotivation over the course of the season, and significant variability characterized the rate at which youths amotivation scores changed. 3. How Did the Coaching Climate and Youth Achievement Goals Predict Initial Status or Subsequent Changes in Situational Motivation? The final set of analyses employed conditional LGC modeling to test the substantive model posited in the introduction. Separate models were estimated for each form of situational motivation. As reported earlier, intrinsic motivation and identified regulation scores neither changed over the course of the season (on average) nor exhibited interindividual variability in the rate of change. Accordingly, those models tested relationhips between achievement goals in Week 1 and those two forms of situational motivation at the same time and did not include any predictors of the second-order slope term. For external regulation and amotivation, both intercepts and slopes were predicted. Each analysis was conducted in two steps so that parameter estimates from the two models could be compared. The first step tested the direct effects of (a) youths achievement goals in Week 1 on external regulation or amotivation intercepts, and (b) the perceived coaching climate on external regulation or amotivation slopes. In the second step, residualized change in youth achievement goals were added as potential mediators or pathways for indirect effects of the perceived coaching climate on youth external regulation and amotivation. These residualized change scores were operationally defined as the disturbances resulting when a Week 1 goal score predicted a corresponding Week 6 goal score. Prior to testing this substantive model, age and gender were tested as potential predictors of situational motivation intercepts, situational motivation slopes, achievement goals in Week 1, and residualized change in achievement goals over the season. Neither age nor gender significantly predicted intercepts or slopes for any of the situational motivation scores (p >.01). Additionally, no age or gender differences were evident in either initial levels of or residualized changes in MAp or MAv goals (p >.01). Older youth started the season with higher PAp goals (β =

Situational Motivation / 83.22, p <.01), but age was not associated with residualized change in PAp goals (p >.01). There were no gender differences in either initial PAp goals or the residualized change in those goals (p >.01). There were no age differences in initial levels of or residualized change in PAv goals. Boys started the season with slightly higher levels of PAv goals (β =.80, p <.01) and increased those goals faster than girls (β =.77, p <.01). Intrinsic Motivation. Intrinsic motivation intercepts were positively associated with MAp goals (β = 0.58, p <.01), negatively associated with MAv (β = 0.17, p <.05) and PAv (β = 0.16, p <.05) goals, and unassociated with PAp goals (p >.05; R 2 =.39). Identified Regulation. Identified regulation intercepts were positively associated with Week 1 MAp goals (β = 0.50, p <.01), but not associated with Week 1 MAv, PAp, or PAv goals (p >.05; R 2 =.27). External Regulation. External regulation intercepts were positively associated with Week 1 MAv, PAp, and PAv goals, and negatively associated with Week 1 MAp goals (R 2 =.29). When the perceived coaching climate was modeled as the only predictor of external regulation slopes, youth perceptions of their coaches goals for them were unrelated to their external regulation slopes (p >.05). Figure 1 presents parameter estimates for the model after youth achievement goals were added as potential predictors of external regulation slopes and as potential pathways for indirect effects of the coaching climate. In this model, perceptions of an avoidance-oriented coaching climate positively predicted residualized changes in youth avoidance-oriented achievement goals (R 2 MAv =.36; R 2 PAv =.45), but perceptions of an approach-oriented coaching climate did not predict residualized changes in youth approach-oriented achievement goals (i.e., MAp, PAp; p >.05). 2 Changes in youth MAv goals were positively associated with external regulation slopes; however, changes in youth MAp, PAp, and PAv goals were not associated with external regulation slopes. In the full model with residualized changes in achievement goals, perceptions of coaches MAp, PAp, and PAv goals were unassociated with external regulation slopes, but perceptions of coaches MAv goals were negatively associated with external regulation slopes (β = 0.39, p <.05). Thus, perceptions of a MAv coaching climate had an inverse relation with variance in external regulation slopes after controlling for changes in youth achievement goals (R 2 =.39). Amotivation. Amotivation intercepts were positively associated with Week 1 PAp goals, negatively associated with Week 1 MAp goals, and not associated with Week 1 MAv and PAv goals (R 2 =.22). When the coaching climate was modeled as the only predictor of amotivation slopes, youth perceptions of their coaches MAp goals (β = 0.42, p <.01) predicted amotivation slopes, but youth perceptions of their coaches MAv, PAp, and PAv goals did not predict amotivation slopes (p >.05; R 2 =.25). Figure 2 presents parameter estimates for the model after youth achievement goals were added as potential mediators or pathways for indirect effects of the perceived coaching climate on amotivation slopes. Changes in youth MAv goals were positively associated with amotivation slopes; however, changes in youth MAp, PAp, and PAv goals were not associated with amotivation slopes. With youth achievement goals (and residualized changes therein) as a part of this model, the standardized path between youth perceptions of coaches MAp goals and amotivation slopes was reduced to 0.31 (p <.01); none of the remaining

84 / Conroy, Kaye, and Coatsworth Figure 1 Structural model of perceived coach achievement goals predicting residualized change in youth achievement goals, and the rate at which extrinsic motivation (external regulation) scores changed during a youth sport season. Unstandardized parameters between Week 6 achievement goal scores and corresponding residualized change scores were fixed at 1.0 to estimate the variance in the disturbance term representing residualized changes in youth achievement goals (values in the figure are the standardized coefficients). Solid and dashed lines represent relations that were and were not statistically significant, respectively. *p <.05, **p* <.01

Situational Motivation / 85 Figure 2 Structural model of perceived coach achievement goals predicting residualized change in youth achievement goals, and the rate at which amotivation scores changed during a youth sport season. Unstandardized parameters between Week 6 achievement goal scores and corresponding residualized change scores were fixed at 1.0 to estimate the variance in the disturbance term representing residualized changes in youth achievement goals (values in the figure are the standardized coefficients). Solid and dashed lines represent relations that were and were not statistically significant, respectively. *p <.05, **p* <.01

86 / Conroy, Kaye, and Coatsworth coaching climate variables were significantly associated with amotivation slopes. Thus, perceptions of MAp coaching climates and increases in youth MAv goals were positively linked to amotivation slopes (R 2 =.37). Discussion This study tested a model of social-ecological influence on young swimmers situational motivation, and achievement motivation theory was posited as a potential explanation for observed changes in situational motivation. Major findings from this investigation revolved around three research questions concerning measurement, the nature of observed change, and covariates of observed change. The first set of findings emerged from LFI analyses and helped to rule out changes in model parameters (e.g., item-factor regression coefficients) as plausible explanations for differences in latent factor scores. Most analyses of change data do not test LFI but rather assume equivalent factor structures and item-factor regression coefficients over time (i.e., weak invariance). To the extent that this assumption is incorrect, the statistical conclusion validity of findings will be threatened. In the present study, intrinsic motivation scores exhibited the highest level of LFI (i.e., strict invariance), the two extrinsic motivation scores exhibited moderate levels of LFI (i.e., weak invariance), and amotivation scores exhibited a slightly lower but nevertheless acceptable level of LFI (i.e., partial weak invariance). These results strengthened our confidence that differences in latent situational motivation scores were meaningful and not merely statistical artifacts attributable to large differences in model parameters. Previous research has found that the 14-item Situational Motivation Scale yields data with invariant item-factor regression coefficients, internal consistency, and the proposed simplex pattern of interrelationships between groups of male youth soccer players, middle school PE students, and participants in college physical activity classes (Standage et al., 2003). The present findings were the first to investigate the LFI of responses to the Situational Motivation Scale. In combination with the internal consistency estimates reported for each scale, these results suggest a high level of validity for situational motivation scores in the present study. The second major set of findings from this study concerned the nature of changes in situational motivation scores over the course of the season. Across individuals, uniform stability in intrinsic motivation and identified regulation over the course of the season was evident. In contrast, both external regulation and amotivation scores increased on average over the course of the season, with the biggest increases apparent in external regulation. These findings indicated a modest deterioration in self-determined motivation over the 6 weeks of data collection. This finding contrasted with the apparent stability of situational motivation scores during a 10-day physical education unit with middle school girls (Prusak, Treasure, Darst, & Pangrazi, 2004). 3 Previous research has not documented how situational motivation changes for youth sport participants, so these findings provide valuable insight into how young athletes situational motivation can deteriorate over the course of a youth sport season of moderate length. Further research is needed to examine whether these patterns of change are similar or different (e.g., curvilinear) during longer sport seasons. Achievement goal theory provides one potential explanation for the degradation in self-determined situational motivation during the youth sport season. Previous

Situational Motivation / 87 research linking achievement goals with situational motivation has focused exclusively on definitions of competence (e.g., mastery vs. performance) and implicitly delimited attention to approach-valenced goals (e.g., Parish & Treasure, 2003; Standage & Treasure, 2002). Consistent with previous findings, MAp goals at the beginning of the season were positively associated with the more self-determined forms of situational motivation (i.e., intrinsic motivation, identified regulation), and PAp goals at the beginning of the season were positively (albeit more weakly) associated with the less self-determined forms of motivation (i.e., external regulation, amotivation). These findings mirrored previous reports with middle school physical education students (Parish & Treasure, 2003; Standage & Treasure, 2002). These findings departed somewhat from previous studies that distinguished approach from avoidance goals because PAp goals were not associated with intrinsic motivation in the present study whereas PAp goals have been positively associated with intrinsic motivation in previous research (Cury et al., 2002). 4 This finding may reflect developmental differences between our sample of children and adolescents and previous samples of college students. With age, normative sources of information about competence become more salient (Horn, 2004), so it is possible that relations between PAp goals and intrinsic motivation are attenuated in younger children and increase with age. Previous reports in the physical domain (e.g., sport, physical education) have not examined relations between avoidance achievement goals and the different forms of situational motivation per se. In this study, both avoidance goals (i.e., MAv, PAv) exhibited negative relations with intrinsic motivation and positive relations with external regulation, and neither avoidance goal was associated with identified regulation or amotivation. It appears that focusing on undesirable possibilities (i.e., incompetence) reduces the self-determination of situational motivation. Whether that occurred because avoidance goal involvement inhibited competence-need satisfaction requires further research. Overall, these results clearly indicated that goal valence was at least as critical as definitions of competence in determining situational motivation. Achievement goals are commonly employed in the prospective prediction of motivational outcomes such as intrinsic motivation. The present study was unique because it examined how changes in these dynamic foci (i.e., goals) over a youth sport season might be linked to changes in situational motivation during the same period. Changes in MAv goals were positively associated with external regulation and amotivation slopes. These findings extend previous research reporting negative relations between avoidance goals and an individual s level of self-determination (Church, Elliot, & Gable, 2001; Cury et al., 2002; McGregor & Elliot, 2002). It was somewhat surprising that changes in PAv goals over the course of the season did not predict changes in external regulation and amotivation during that same period. Given that boys had greater residualized change scores for PAv goals than girls did, sex may moderate the dynamic relations between changes in PAv goals and changes in these forms of situational motivation. We did not plan on testing that hypothesis and did not recruit a large enough sample to conduct the necessary multigroup analyses. Future research may benefit from recruiting large groups of both sexes to test this moderation hypothesis. Changes in approach goals were not associated with external regulation or amotivation. Although the difference was not statistically significant, residualized change scores for MAp and PAp goals exhibited slightly less variance (0.74 and