Running Head : MOTIVATION IN HIGH SCHOOL 1 A Person - Centered Investigation of Academic Motivation , Performance , and Engagement in a High School Setting

This study used a person-centered approach to identify naturally occurring combinations of intrinsic and extrinsic academic motives and their correlates. 1061 high school students completed measures of academic motivation, performance, and school engagement. Cluster analysis revealed four motivational profiles characterized by high levels of both intrinsic and extrinsic motivations (high quantity), high intrinsic but low extrinsic motivation (good quality), low intrinsic but high extrinsic motivation (poor quality), and low levels of both intrinsic and extrinsic motivations (low quantity). Students in the high quantity and good quality profiles reported the strongest academic performance and extracurricular participation, implicating intrinsic motivation as the primary correlate of positive school outcomes in the high school environment. Students in the high quantity profile, however, reported the most emotional engagement with school. These findings suggest that extrinsic motivation may not be maladaptive at the high school level when coupled with high levels of intrinsic motivation. Running Head: MOTIVATION IN HIGH SCHOOL 3 A Person-Centered Investigation of Academic Motivation, Performance, and Engagement in a High School Setting High school is a low point in student engagement, despite being a critical time for developing the skills necessary to thrive in the adult world (Seidman & French, 1997). Both intrinsic (i.e., learning for enjoyment) and extrinsic (i.e., learning as a means to an end) motivations decrease across the high school transition (Otis, Grouzet, & Pelletier, 2005) and are lower than those of both elementary and college students (Martin, 2009). High school students also report being disconnected from school and teachers (Willms, 2003). This decreased engagement in school is particularly alarming considering adolescents’ newfound freedom; students who are not academically motivated may not pay attention, complete their school work, or even attend school (Yonezawa, Jones, & Joselowsky, 2009). It is vital, then, to investigate high school students’ academic motives and how they map on to meaningful school outcomes. Self-determination theory (SDT; Deci & Ryan, 1985) may be a useful framework for conceptualizing academic motivation. In SDT, motives exist along a spectrum ranging from behaviors originating within the self (autonomous, intrinsic) to those spurred by outside forces (controlled, extrinsic). Intrinsic motivation, one end of the motivational continuum, describes behaviors that are inherently interesting or enjoyable. Extrinsic motivation, by contrast, is an overarching term for behaviors governed to varying degrees by external forces; it encompasses identified regulation (behaviors that are not enjoyable but further a personally-held goal or belief), introjected regulation (behaviors spurred by external forces that have been internalized, i.e., guilt), and external regulation (behaviors initiated by external constraints, i.e., rewards or threats). These motives have important consequences in the academic realm. Intrinsic motivation and identified regulation are consistently associated with more positive outcomes than more Running Head: MOTIVATION IN HIGH SCHOOL 4 controlled motives, including a preference for challenging tasks (Pintrich & De Groot, 1990), enjoyment of material (Harter, 1981; Vallerand, 1997), creativity (Hennessey, 2000), attendance (Vallerand & Bissonnette, 1992), cognitive engagement (Miller, Behrens, & Green, 1993), conceptual learning (Benware & Deci, 1984; Ryan, Connell, & Deci, 1985), and academic performance (Boiché, Sarrazin, Grouzet, Pelletier, & Chanal, 2008; Burton, Lydon, D’Alessandro, & Koestner, 2006; Corpus, McClintic-Gilbert, & Hayenga, 2009; Lepper, Corpus, & Iyengar, 2005). Focusing on the Individual: A Person-Centered Approach What happens, though, when students simultaneously possess both intrinsic and extrinsic motivation? How might such students fare relative to those who possess what SDT would consider to be the ideal combination of intrinsic without extrinsic motivation? Because few studies have used a person-centered approach to examine academic motives in concert with one another, it is unclear precisely how intrinsic and extrinsic motives most frequently combine within individual students and to what effect. It is especially important to examine such combinations of academic motives during the high school years when external constraints are particularly salient (Ratelle, Guay, Vallerand, Larose, & Senécal, 2007). A small number of person-centered studies on intrinsic and extrinsic motivation have been conducted to date, but they have reached somewhat different conclusions about the prevalence and adaptiveness of different combinations of motivation (Hayenga & Corpus, 2010; Ratelle et al. 2007; Vansteenkiste, Soenens, Sierens, Luyckx, & Lens, 2009). Studies with 6through 8-grade students (Hayenga & Corpus, 2010) and 7–grade through college-aged students (Vansteenkiste et al., 2009) have revealed four profiles: one with high levels of both intrinsic and extrinsic motivations (high quantity), one with high intrinsic but low extrinsic Running Head: MOTIVATION IN HIGH SCHOOL 5 motivation (good quality), one with low intrinsic but high extrinsic motivation (poor quality), and one with low levels of both constructs (low quantity). In these studies, students with good quality motivation fared better than their peers from other profiles, thus supporting SDT (Vansteenkiste, Lens, & Deci, 2006). A third study identified the same four motivational groups in a college sample, but the good quality profile was absent from two large samples of high school students (Ratelle et al., 2007). Instead, high school students were characterized by profiles of high quantity, poor quality, and moderate levels of intrinsic and extrinsic motivation. Students with poor quality motivation exhibited the lowest academic performance and were most likely to drop out of school. To explain the absence of a good quality profile, Ratelle et al. (2007) argued that the high school environment may be too controlling to effectively foster pure intrinsic motivation (cf. Otis et al., 2005). Vansteenkiste et al. (2009) proposed a more statistically driven explanation, suggesting that it is more difficult for clusters with opposing scores on the relevant dimensions (e.g., high intrinsic, low extrinsic) to emerge when the dimensions of interest are highly correlated with one another. Indeed, intrinsic and extrinsic motivations were moderately positively correlated in Ratelle et al.’s high school samples but not in their college sample or the samples from Vansteenkiste et al. (2009) and Hayenga and Corpus (2010). We suggest two additional explanations for the discrepant findings. First, although both Ratelle et al. (2007) and Vansteenkiste et al. (2009) were situated within the SDT tradition, they used slightly different scales to measure motivation. In particular, the extrinsic items from the Academic Motivation Scale (Vallerand, Pelletier, Blais, Briére, Senécal, & Vallieres, 1993) used by Ratelle et al. represent arguably more internalized motivations than those from the Academic Self-Regulation Scale (Ryan & Connell, 1989) used by Vansteenkiste et al. This may account for Running Head: MOTIVATION IN HIGH SCHOOL 6 the stronger correlations among constructs in Ratelle et al.’s study and perhaps the different clusters that emerged. Second, Ratelle et al. studied a sample comprised exclusively of high school students; Vansteenkiste et al.’s high school sample included mostly 7and 8-grade students (56%), who were overrepresented in the good quality profile. It is possible, then, that their good quality cluster was driven largely by middle school students and that students characterized by good quality motivation may be rare in high school settings. The present study addressed this ambiguity by focusing on a sample comprised exclusively of high school students. Using SDT as the theoretical groundwork, we preliminarily expected to find four motivational profiles with varying combinations of intrinsic and extrinsic motivation similar to those found in existing person-centered studies. The presence or absence of a good quality profile was of particular interest, given its absence in Ratelle et al.’s high school sample. Academic Performance and School Engagement Existing person-centered studies have focused on academic performance (i.e., GPA) and other learning outcomes (e.g., attitudes about cheating, distraction in class) as correlates of interest. To replicate findings from prior studies, and because grades are critical for students’ success, we measured the relationship between profile membership and performance. A profile characterized by good quality motivation, if found, was expected to possess the highest academic performance based on the tenets of SDT. Otherwise, we generally expected the best performance among students who exhibited the most intrinsic motivation (Burton, et al., 2006; Corpus et al., 2009; Lepper et al., 2005). We also examined school engagement because it is a major factor affecting academic achievement that is more tractable than many other predictive factors (e.g., race, SES) and is particularly low in the United States (Finn, 1993; Lee & Shute, 2010; Martin, 2008; YazzieRunning Head: MOTIVATION IN HIGH SCHOOL 7 Mintz, 2006). Early work on school engagement focused primarily on behavioral aspects such as attendance, participation, and help-seeking behavior (Finn, 1989, 1993; Nelson-LeGall & Jones, 1991; Rumberger, 1987; Willms, 2003). Recently, the definition has expanded to encompass cognitive (e.g., self-regulation) and emotional (e.g., teacher support) aspects of involvement (Appleton, Christenson, Kim, & Reschly, 2006; Fredricks, Blumenfeld, & Paris, 2004; Jimerson, Campos, & Greif, 2003). While many studies consider one aspect of engagement, they rarely consider two or all three types of engagement in concert with one another. Researchers have increasingly called for the treatment of engagement as a multidimensional construct, stressing the importance of considering how types of engagement interact (Fredricks, Blumenfeld, Friedel, & Paris, 2005). Accordingly, we considered several types of school engagement in the current study, including two forms of what is commonly referred to as emotional engagement (teacher support and school relatedness) and one form of behavioral, nonacademic engagement (participation in extracurricular activities). Emotional Engagement. Even though it is hypothesized to be one of the strongest factors affecting achievement, emotional engagement is the least frequently studied form of engagement in motivation research (Martin & Dowson, 2009). Teacher support and school relatedness were considered in the present study to represent emotional engagement. Understanding students’ relationship to the school community is crucial to understanding school success, as “school motivation cannot be understood apart from the social fabric in which it is embedded” (Weiner, 1990, p. 621; see also Patrick & Ryan, 2005). Teachers are particularly strong influences during the tumultuous transition to high school, serving to enhance academic motivation and model positive adult values (Goodenow & Grady, 1993; Lynch & Cicchetti, 1997; Reddy, Rhodes, & Mulhall, 2003; Rhodes, Grossman, & Resch, 2000; Wentzel, 1997). Running Head: MOTIVATION IN HIGH SCHOOL 8 Feeling connected to the school community is also crucial for adolescents due to the increasingly strong influence of peers at this stage of life (Johnson, 2008; Shin, Daly, & Vera, 2007). As such, the extent to which students perceive teacher support and school relatedness is very likely to relate to their academic motivation and subsequent achievement (Ryan & Patrick, 2001). We focused on student perceptions of engagement—rather than objective indicators—because these and other school-related perceptions (e.g., academic identification) are key predictors of motivation and achievement (Roeser, Midgley, & Urdan, 1996; Ryan & Grolnick, 1986; Walker, Greene, & Mansell, 2006) and because emotional engagement is most often measured subjectively (Fredricks et al., 2003). Emotional engagement was expected to be associated with high levels of intrinsic motivation because teacher and peer support encourage students’ investment in school and influence students’ perceptions of autonomy and competence (Connell & Wellborn, 1991; Patrick & Ryan, 2006; Vallerand and Bissonnette, 1992; Wentzel, 1997). Predictions were less clear regarding accompanying levels of extrinsic motivation. On the one hand, students with high extrinsic motivation may view school solely as a means of attaining future success, which would detract from emotional engagement. On the other hand, students with high extrinsic motivation may view interacting with teachers as a means of meeting their goals without having to necessarily engage with the academic material and accordingly might experience high levels of emotional engagement. It seemed most plausible, then, that students with either high quantity or good quality motivation would report the most emotional engagement. Extracurricular Activities. A more exploratory goal of this study was to investigate the relationship between motivation and participation in extracurricular activities; this was done in an attempt to understand how students might engage with school outside of the academic realm. Running Head: MOTIVATION IN HIGH SCHOOL 9 Participating in school-sponsored activities is arguably one of the highest levels of behavioral engagement (Finn, 1989, 1993; Fredricks & Eccles, 2006) and may be a useful indicator of school engagement in certain highly engaged academic settings where classic measures of engagement are not useful. While not explicitly linked to academic functioning, extracurricular participation might benefit students academically because it fosters skills (e.g., goal-setting, concentration, problem solving) that may transfer to the academic realm and help prepare students for adult life (Irvin, Farmer, Leung, Thompson, & Hutchins, 2010; Larson, 2010). In addition, involvement in extracurricular activities becomes increasingly important across the school years as the socializing effect of peer groups becomes stronger (Irvin et al., 2010). According to the participation-identification model, students who are involved in school-relevant activities, including extracurricular activities, are most likely to succeed in school (Finn, 1989). One might expect a positive association between extracurricular participation and intrinsic motivation because students generally choose their own activities or clubs and can relate to other students with similar interests in those contexts; this fulfills two of the fundamental psychological needs that, according to SDT, foster intrinsic motivation—i.e., autonomy and relatedness (Deci & Ryan, 2000; Jordan & Nettles, 2000; Larson, 2000; Mahoney, Larson, Eccles, & Lord, 2005). Such activities may also provide some students an opportunity to foster competence that they might not otherwise experience at school, thus addressing the third fundamental need underlying intrinsic motivation. There are also more extrinsically motivated reasons to participate in extracurricular activities, such as strengthening college applications and accruing social prestige. It is possible, then, that high extrinsic motivation may also be associated with extracurricular participation. Accordingly, we expected that students in the high quantity Running Head: MOTIVATION IN HIGH SCHOOL 10 and good quality motivation groups would pursue extracurricular activities more than their peers characterized by other patterns of motivation. We also considered how different activities might be associated with motivational profiles in light of research suggesting that particular activities are associated with distinct academic outcomes (Broh, 2002; Camp, 1990; Fredricks & Eccles, 2005, 2006, 2008; Hanks & Eckland, 1976). The Current Study The current study investigated how intrinsic and extrinsic motives may or may not complement one another among high school students. We identified naturally occurring motivational profiles using cluster analysis and examined each profile’s level of academic performance and school engagement. We hoped to shed light on the disparate findings of prior person-centered studies of motivation, as well as provide a richer understanding of how students with different motivational profiles engage with school. Method Participants and Procedure Participants were 1066 students (49% female; 83.9% white) enrolled in a Catholic high school in the Pacific Northwest. The school is known for its academic rigor, high attendance rate, and community service requirement. According to school statistics, 76% of the student body identifies as Catholic and 25% receive financial aid annually. There were fairly equal numbers of freshmen (n = 310, 29%), sophomores (n = 269, 25%), juniors (n = 249, 23%), and seniors (n = 236, 22%); two students did not specify their grade level. The entire student body was invited to complete an online survey that focused largely on alcohol and drug use; because of the sensitive nature of this material, responses were anonymous and objective data (e.g., GPA) could not be collected. Parental consent was received from 90.9% Running Head: MOTIVATION IN HIGH SCHOOL 11 of the students. Fifteen participants were dropped from analyses due to inconsistent responding on questions unrelated to the present study, leaving the sample described above. One of three proctors provided standardized instructions to groups of 20 to 60 students seated in the school’s computer laboratories during school hours. Students had 45 minutes to complete the survey, which included the measures described below along with questions unrelated to this study. Measures Academic Motivation. A subset of questions from the reliable and valid Academic Motivation Scale (AMS; Vallerand et al., 1993) was used to measure academic motivation. Participants indicated their reasons for going to school using a 7-point scale (1 = not at all true of me, 7 = exactly true of me). Three items each were used to measure intrinsic motivation (! = .88; e.g., “I go to school for the pleasure I experience when I discover new things never seen before”), introjected regulation (! = .84; e.g., “I go to school because of the fact that when I succeed in school I feel important”), and external regulation (! = .87; e.g., “I go to school in order to obtain a more prestigious job later on”). The full AMS used by Ratelle et al. (2007) included one additional item for each dimension of motivation, which we did not include due to school-imposed constraints limiting the number of questions we could ask. Academic Performance. Academic performance was measured using participants’ selfreported grade point average, as in Vansteenkiste et al. (2009). Self-report measures of grades have been found to correlate highly with actual grades (Dornbusch, Ritter, Leiderman, Roberts, & Fraleigh, 1987; Gray & Watson, 2002; Kuncel, Credé, & Thomas, 2005; Noftle & Robins, 2007). School personnel also indicated that students were keenly aware of their precise grade point averages. Running Head: MOTIVATION IN HIGH SCHOOL 12 School Engagement. School relatedness and teacher support were used to measure emotional engagement. Items for both constructs were drawn from the Resilience and Youth Development Module (RYDM) of the California Healthy Kids Survey (CHKS) 2002-2009 high school version. Supported by the California Department of Education, it has been used extensively in public California high schools and research (Davis, Kreutzer, Lipsett, King, & Shaikh, 2006; Rhee, Furlong, Turner, & Harari, 2001) and has been found to be reliable (Hanson & Austin, 2003). For school relatedness, participants responded to four questions from the RYDM School Connectedness section (! = .86; e.g., “I feel close to people at this school”) on a 5-point scale (1 = strongly disagree; 5 = strongly agree). Teacher support was measured by three questions from the Caring Relationships section (! = .90; e.g., “There is a teacher or other adult at school who really cares about me”) on a 4-point scale (1 = not at all true; 4 = very true). Extracurricular Activities. Participation in extracurricular activities was also used to indicate school engagement and was measured by an item created in conjunction with school administrators. Participants indicated whether they participated in any of six categories of activities—school athletics, community athletics, theater/dance, music/voice, arts/crafts, and community/Christian service—with a yes/no response. Such a dichotomous frequency count is common among research on extracurricular activities (e.g., Fredricks & Eccles, 2005, 2008; Guest & McRee, 2009; Mahoney, 2000; Mahoney & Stattin, 2000). A dichotomous measure was also considered appropriate because school policy restricted students from simultaneously participating in multiple activities within particular categories (e.g., school athletics). Statistical Analysis Strategy Cluster analysis was used to generate motivational profiles based on students’ responses to the items assessing intrinsic, introjected, and extrinsic motivation. Cluster analysis aims to Running Head: MOTIVATION IN HIGH SCHOOL 13 maximize both between-cluster heterogeneity and within-cluster homogeneity. In line with recommendations by Hair, Anderson, Tatham, and Black (1998), an agglomerative hierarchical clustering method (Ward’s linkage) followed by an iterative, nonhierarchical (k-means) clustering technique was used (Vansteenkiste et al., 2009; Hayenga & Corpus, 2010). An agglomerative cluster analysis starts with each observation in a data set as its own cluster. Similar data points are combined in subsequent steps until all observations are assigned to one large cluster. Specifically, Ward’s linkage is based on squared Euclidian distances and ensures that the within-cluster sum of squares is minimized over variables. The most favorable cluster solution is determined by considering percent of variance explained, a priori theorizing, and distinctiveness of groups. Because hierarchical clustering methods are particularly sensitive to outliers, all multivariate outliers (> 3 SDs from the mean), identified using criteria outlined by Hadi (1992; 1994), were excluded from further analyses. K-means clustering, which allows for reassignment of observations, was then used to fine-tune clusters. In k-means clustering, the number of clusters is selected prior to performing analyses and several observations are chosen as nonrandom starting points. In the present study, centroids from the Ward’s clusters served as these starting points. Any observation within a certain distance of the value of each starting point then becomes assigned to that cluster and the process continues until all scores are assigned. A double-split cross-validation procedure was performed to ensure the resulting cluster solution was stable and replicable (Breckenridge, 2000). In this procedure, the data set is split into two random halves and subjected to hierarchical and nonhierarchical clustering techniques. Each half then undergoes nearest neighbor analysis, during which each observation in one half is reassigned to the cluster to which its nearest neighbor in the other half belongs. The resulting Running Head: MOTIVATION IN HIGH SCHOOL 14 solution for each half is then compared to the other half’s original cluster solution using Cohen’s kappa. A kappa greater than .60 is considered acceptable. As a final step, we ensured that clusters were distinct in terms of input and outcome variables. A multivariate analysis of variance (MANOVA) and follow-up analysis of variance (ANOVA) tests determined if motivational profiles differed in motivation levels. Subsequent ANOVAs and chi-square analyses assessed whether profiles were associated with the outcome variables—academic performance and school engagement—in distinct ways. Results Due to the sample size, we used a significance criterion of p < .01 to avoid Type 1 errors. In the very few cases (< .01%) where data from one item of a measure was missing, composite variables were calculated by averaging the values of the completed items for that measure. Correlations Correlations among all variables are presented in Table 1. Patterns of relationships were largely consistent with prior research. One exception was that correlations among the different types of motivation did not follow the simplex pattern described by SDT (Ryan & Connell, 1989). Introjected regulation correlated more strongly with intrinsic motivation than with external regulation. Additionally, intrinsic motivation and external regulation were significantly positively correlated. Other person-centered motivation studies have found similar correlations between intrinsic motivation and introjected regulation (Boiché et al., 2008; Ratelle et al., 2007). Cluster Analysis Five multivariate outliers were excluded from analyses, leaving a final sample of 1061 students. Ward’s linkage followed by k-means clustering identified a four-cluster solution as best fitting the data. The four-cluster solution explained a substantial percent of variance in intrinsic Running Head: MOTIVATION IN HIGH SCHOOL 15 (80.2%), introjected (70.7%), and extrinsic (76.4%) motivations, well above the 50% threshold used in related studies (Vansteenkiste et al., 2009). A three-cluster solution explained an adequate percent of variance but combined the theoretically distinct good and poor quality profiles. A five-cluster solution explained additional variance, but split the high quantity profile into two groups with similarly high levels of motivation. A double-split cross-validation confirmed that the cluster solution was replicable and stable (" = 0.92). Z scores for the resulting clusters are presented in Figure 1. There are a number of advantages to using z scores as opposed to raw scores for cluster analysis. Z scores are standardized values, and thus allow for direct comparison of the groups in terms of their levels of motivation. They also provide information on the distinctiveness of clusters; the extent to which scores deviate from the mean is analogous to effect size (Vansteenkiste et al., 2009). Finally, z scores allow for an easier comparison between the groups found in the present study with those found in other studies using z scores (Hayenga & Corpus, 2010; Vansteenkiste et al., 2009). Borrowing terminology from Vansteenkiste et al. (2009), motivational profiles were labeled in terms of either motivation quality (ratio of intrinsic to extrinsic motivation) or quantity (sum of intrinsic and extrinsic motivations). Students were characterized by high quantity (n = 445; 42.83%), good quality (n = 203; 19.54%), poor quality (n = 275; 26.47%), or low quantity (n = 116; 11.16%) motivation relative to the sample. A MANOVA confirmed that the clusters differed in motivation levels, F(9, 2568) = 426.93, p < .0001. Univariate ANOVAs revealed differences between clusters in terms of intrinsic motivation, F(3, 1057) = 837.92, p < .0001, #p = .70; introjected regulation, F(3, 1057) = 796.62, p < .0001, #p = .69; and external regulation, F(3, 1057) = 260.44, p < .0001, #p = .43. The high quantity and poor quality profiles, however, possessed similar levels of external regulation. Running Head: MOTIVATION IN HIGH SCHOOL 16 Recall that Vansteenkiste and colleagues (2009) proposed that the absence of a good quality profile in Ratelle et al.’s (2007) sample was due to a moderately strong correlation between types of motivation. Results from the current study work against this hypothesis; even though intrinsic and extrinsic motives were more positively correlated in the current sample (r = .21) than in that of Ratelle et al. (r = .19), a good quality profile emerged. Fisher’s r-to-z transformation confirmed that types of motivation were more strongly positively correlated in the present study than in Ratelle et al.’s (2007) sample, p < .0001. Grade and gender differences among profiles were also examined. Chi-square tests indicated no grade differences between clusters, $(9, N = 1061) = 19.84, ns, but did detect a significant difference in gender makeup, $(3, N = 1061) = 21.79, p < .0001. Females were overrepresented in the high quantity and good quality profiles, consistent with previous personcentered studies (Ratelle et al., 2007; Vansteenkiste et al., 2009). School-Related Outcomes Academic Performance. A one-way ANOVA revealed a significant difference among the profiles in grade point average, F(3, 1050) = 24.68, p < .0001, #p = .06. Follow up Tukey HSD tests indicated that students in the high quantity (M = 3.65, SD = .31) and good quality (M = 3.65, SD = .30) profiles reported higher grades than students in the poor quality profile (M = 3.53, SD = .32), who in turn reported higher grades than students in the low quantity profile (M = 3.38, SD = .45). Findings are displayed in Figure 2. Emotional Engagement. Teacher support and school relatedness were standardized and averaged to form a single measure of emotional engagement in order to simplify results. The two constructs were fairly highly correlated (r = .40) and displayed similar patterns. A one-way ANOVA indicated that motivational profiles differed significantly in emotional engagement, Running Head: MOTIVATION IN HIGH SCHOOL 17 F(3, 1053) = 39.74, p < .0001, #p = .15. Students in the high quantity profile (Z = .29) reported higher levels of emotional engagement than students in the good quality (Z = -.04) and poor quality (Z = -.14) profiles, who in turn indicated higher engagement than students in the low quantity profile (Z = -.72). Extracurricular Activities. There was a significant difference among the profiles in total number of extracurricular activities, F(3, 1057) = 5.72, p < .0001, #p = .02. Students with high quantity (M = 2.46, SD = 1.11) and good quality (M = 2.42, SD = 1.20) motivation participated in more types of extracurricular activities than students with poor quality (M = 2.21, SD = .97) and low quantity (M = 2.08, SD = 1.07) motivation. The results of chi-square tests for participation in individual extracurricular activities are displayed in Table 2. Adjusted standardized residuals indicated that students in the good quality cluster were overrepresented in theater/dance and music/voice, while students in the high quantity profile were overrepresented in community/Christian service. Students with low quantity motivation were underrepresented in nearly all extracurricular activities, and those with poor quality motivation were underrepresented in music/voice and community/Christian service. Discussion In the present study, a person-centered approach was used to examine combinations of intrinsic and extrinsic motives in a U.S. high school sample, which were then considered in relation to different school outcomes. In addition, several explanations for discrepant findings in past research were examined. The findings contribute to understanding how motivation manifests in high school students and suggest paths for future research. Running Head: MOTIVATION IN HIGH SCHOOL 18 Emerging Motivational Profiles A four-cluster solution was selected as best fitting the data, largely replicating Vansteenkiste et al.’s (2009) cluster solutions and resembling Ratelle et al.’s (2007) findings with the addition of a good quality profile. These results speak against Ratelle et al.’s claim that the high school environment is too controlling to foster high intrinsic motivation without correspondingly high extrinsic motivation. Interestingly, if data in the present study had been considered in terms of raw scores—as in Ratelle et al.’s study —three rather than four clusters might have been retained. Instead, we adopted the approach used by Vansteenkiste et al. (2009) and converted motivational ratings into standardized z scores; as a result, a relatively good quality group of students as compared to the other motivational profiles was clearly present (see Figure 1). It is uncertain whether Ratelle et al. would have retained three clusters if they had analyzed their data in this manner. Because z scores facilitated the detection of a good quality profile in the present study, and generally allow for direct comparison between studies, their use in interpreting results of cluster analysis may be more favorable than using raw scores. Vansteenkiste et al. (2009) offered a more statistically driven explanation for the discrepancies between studies, claiming that stronger correlations between motivation types limit the possible combinations of said motivations. Results from the current study work against this hypothesis. Despite motives being more positively correlated in the present study than in Ratelle et al.’s sample, four clusters emerged in our sample. We proposed two additional hypotheses for why Vansteenkiste et al. and Ratelle et al. arrived at different cluster solutions: (1) the studies used different motivation scales and (2) Vansteenkiste et al.’s good quality profile may have been driven by middle school students. Even though we used the same motivation scale as Ratelle et al. with a sample of strictly high school students, we still identified a good quality Running Head: MOTIVATION IN HIGH SCHOOL 19 profile. Our findings suggest that it is possible to possess good quality motivation in high school, at least an academically rigorous Catholic high school in the United States. Performance: The Compensatory Relationship Between Quality and Quantity Once a cluster solution was chosen, we examined how academic performance and school engagement were associated with each motivational profile. For performance, students with high quantity and good quality motivation were equally successful. Because these two profiles reported relatively high, though significantly different, levels of intrinsic motivation, the most basic explanation is that intrinsic motivation is the primary correlate of school performance. This is much in line with findings from previous variable-centered studies (e.g., Boiché et al., 2008; Lepper et al., 2005; Soenens & Vansteenkiste, 2005). Simply noting a relationship between intrinsic motivation and good performance, however, would be an incomplete account of the findings. In reality, the results may implicate a compensatory relationship between the ratio of intrinsic to extrinsic motivation and the total amount of motivation present. As evidence, the good quality profile was just as adaptive in terms of performance as the high quantity profile despite possessing less intrinsic motivation; conversely, the high quantity profile performed just as well academically as the good quality profile, despite having a less favorable ratio of intrinsic to extrinsic motivation (see Figure 1). The poor quality profile also consistently outperformed the low quantity profile—though the two had comparable ratios of intrinsic to extrinsic motivation—arguably because it possessed more motivation in total. The hypothesis that a high ratio of intrinsic to extrinsic motivation can compensate for a relatively low total amount of motivation aligns with SDT’s claims about the benefits of autonomous motivation (Deci & Ryan, 1985). The claim that a large total amount of motivation Running Head: MOTIVATION IN HIGH SCHOOL 20 can mitigate unfavorable ratios of intrinsic to extrinsic motivation, however, suggests that extrinsic motivation may not be as maladaptive as SDT would posit. Of course, related research with middle school and college populations has shown that a profile of high intrinsic motivation coupled with low extrinsic motivation is far more adaptive than one with high levels of both motivation types (Hayenga & Corpus, 2010; Vansteenkiste et al., 2009). Perhaps the controlling nature of the high school environment makes extrinsic motivation more adaptive than it is in other educational settings. School Engagement Students in the high quantity profile reported the greatest perceived teacher support and school relatedness, followed by those in good and poor quality profiles, and finally, students in the low quantity profile. That profiles with high quantity and poor quality motivation both reported relatively high levels of emotional engagement implicates the significance of extrinsic motivation for perceived teacher support and school relatedness. Extrinsic motivation may imply a desire to please authority figures – those who are the gatekeepers to rewards in most high school settings. Teachers, in response, may reach out most to students who are receptive to such support. Students who also possess high levels of intrinsic motivation have the additional incentive of being genuinely interested in class material and thus appear to be the most emotionally engaged with the school community. Although students characterized by good quality motivation are similarly interested in learning, they may feel stifled by the controlling aspects of the high school environment that do not support their learning goals (e.g., tedious assignments; Otis et al., 2005). This could precipitate feeling unsupported by the school community and, consequently, result in less emotional engagement. Running Head: MOTIVATION IN HIGH SCHOOL 21 In summary, it appears that having high intrinsic or extrinsic motivation alone may be insufficient to foster the highest emotional engagement with teachers and the school community at the high school level. Instead, it may be only when students possess high amounts of both types of motivation that they will feel most supported by teachers and most affiliated with school. This is a conclusion that would not have been drawn without the use of a person-centered approach. An additional, more exploratory goal of this study was to examine students’ involvement in school-sponsored activities. Overall, students in the good quality and high quantity motivation profiles were the most frequently involved in extracurricular activities. Not surprisingly, students with low quantity motivation were underrepresented in each type of activity, suggesting that unmotivated students are unlikely to expend extra effort to engage with the school community outside of the hours they are required to be at school. Of course, students undoubtedly have a host of non-academic motives governing their involvement in clubs or sports (e.g., work, family obligations, social prestige); it is notable that systematic links were found between motivational profiles and extracurricular participation despite such complexities. Students in the good quality profile participated the most in creative activities like theater/dance and music/voice. Although little research has examined the effects of participating in artistic programs (Shernoff & Vandell, 2007), Amabile’s (1996) body of work suggests that creativity flourishes in the presence of intrinsic motivation and the absence of extrinsic motivation – precisely the combination found in our good quality profile. Other studies have also found an association between intrinsic motivation and the creative arts (Shernoff & Vandell, 2007; Winner & Hetland, 2000). Somewhat surprisingly, students in the high quantity profile participated in community/Christian service to a greater extent than their peers. Recall that Running Head: MOTIVATION IN HIGH SCHOOL 22 students with high quantity motivation also reported the greatest emotional engagement with school; this may have encouraged volunteering given that the school values community service and even mandates such service at some point during each student’s high school career. Of course, any speculations as to why these patterns emerged must be tentative, as our measure of extracurricular participation was a simple frequency count and did not capture the depth of students’ engagement in the activities. Although this approach is often used in research on extracurricular activities (e.g., Barber, Eccles, & Stone, 2001; Eccles & Barber, 1999; Johnson, Beebe, Mortimer, & Snyder, 1998; Mahoney, 2000; Mahoney & Stattin, 2000; Mahoney, Schweder, & Stattin, 2002), studies should strive to include more sensitive measures of involvement that could better assess depth of engagement in relation to these hypotheses (Rose-Krasnor, Busseri, Willoughby, & Chalmers, 2006). Future research should also consider how eligibility requirements for extracurricular participation (e.g., minimum GPA) might influence students’ extrinsic motivation. This was unlikely a driving force in the present study as this school requires only passing grades to participate in clubs and sports – a goal that students at a high achieving institution would presumably already possess. Limitations and Future Directions This study had a number of limitations that merit discussion. Because the data were correlational in nature, no conclusions can be drawn about causality. In all likelihood, the relationships are bidirectional. Concerning academic performance, variable-centered research indicates that intrinsic motivation and grades predict one another over time, but that the relationship between extrinsic motivation and grades appears unidirectional such that poor performance predicts rises in extrinsic motivation but not vice versa (Corpus et al., 2009). In terms of engagement, forms of school engagement have been studied as both precursors to (e.g., Running Head: MOTIVATION IN HIGH SCHOOL 23 Goodenow & Grady, 1993; Grouzet, Vallerand, Thill, & Provencher, 2004; Murdock & Miller, 2003; Wentzel, Battle, Russell, & Looney, 2010) and consequences of (e.g., Fredricks et al., 2004) academic motivation. Additionally, any number of third variables—such as demographic factors, personality traits, or parenting style in the home—might have influenced both motivation and outcome variables. Future person-centered studies should employ cross-lagged longitudinal designs to address the directionality of these relationships. Another vital limitation is the use of self-reported measures. The sensitive nature of this survey, which concerned substance use, precluded obtaining objective measures of performance. Although participants were guaranteed anonymity, they may not have responded truthfully or accurately. Future studies should attempt to obtain objective reports of behaviors (e.g., academic transcripts, club rosters) when possible. Similarly, as mentioned previously, our measure of extracurricular participation was superficial in that it only captured categories of activities that students participated in, rather than the number of activities or level of involvement. Finally, our findings with an academically-rigorous Catholic high school sample may not be widely generalizable. Although the central purpose of this study was to compare profiles of students to one another, there is still a concern that similar motivation profiles would not be found in other school environments. This concern may be partially offset by the very high participation rate and percent of the student body on scholarship at this institution. Despite these weaknesses, the present study raises several points future research could fruitfully address. Most importantly, our results suggest that motivational profiles may be differentially adaptive for different age groups. Perhaps, as Ratelle et al. (2007) argued, the high school environment is one in which some degree of extrinsic motivation can be helpful. Studies with different age groups would help determine whether a compensatory relationship between Running Head: MOTIVATION IN HIGH SCHOOL 24 motivation quantity and quality is specific to the high school environment or applies more broadly. Such person-centered investigations may get to the root of how motivation types collectively operate within individual students at different levels of schooling. Running Head: MOTIVATION IN HIGH SCHOOL25 ReferencesAmabile, T. E. (1996). Creativity in Context. Boulder: Westview Press.Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring cognitive andpsychological engagement: Validation of the student engagement instrument. Journal ofSchool Psychology, 44, 427-445.Barber, B. L., Eccles, J. S., & Stone, M. R. (2001). Whatever happened to the jock, the brain, andthe princess? Young adult pathways linked to adolescent activity involvement and socialidentity. Journal of Adolescence Research, 16, 429-455.Benware, C. A., & Deci, E. L. (1984). Quality of learning with an active versus passivemotivational set. American Educational Research Journal, 21, 755-765.Boiché, J. C. S., Sarrazin, P. G., Grouzet, F. M. E., Pelletier, L. G., & Chanal, J. P. (2008).Students’ motivational profiles and achievement outcomes in physical education: A self-determination perspective. Journal of Educational Psychology, 100, 688-701.Breckenridge, J. N. (2000). Validating cluster analysis: Consistent replication and symmetry.Multivariate Behavioral Research, 35, 261-285.Broh, B. A. (2002). Linking extracurricular programming to academic achievement: Whobenefits and why? Sociology of Education, 75, 69-91.Burton, K. D., Lydon, J. E., D’ Alessandro, D. U., & Koestner, R. (2006). The differential effectsof intrinsic and identified motivation on well-being and performance: Prospective,experimental, and implicit approaches to self-determination theory. Journal ofPersonality and Social Psychology, 91, 750-762.Camp, W. G. (1990). Participation in student activities and achievement: A covariance structuralanalysis. Journal of Educational Research, 83, 272-278. Running Head: MOTIVATION IN HIGH SCHOOL26 Connell, J. P., & Wellborn, J. G. (1991). Competence, autonomy, and relatedness: Amotivational analysis of self-system processes. In M. R. Gunnar & L. A. Sroufe (Eds.),Minnesota Symposium on Child Psychology: Vol. 23. Self-processes in development (pp.43-77). Hillsdale, NJ: Erlbaum.Corpus, J. H., McClintic-Gilbert, M. S., & Hayenga, A. (2009). Within-year changes inchildren’s intrinsic and extrinsic motivational orientations: Contextual predictors andacademic outcomes. Contemporary Educational Psychology, 34, 154-166.Davis, A. M., Kreutzer, R., Libsett, M., King, G., & Shaikh, N. (2006). Asthma prevalence inHispanic and Asian Ethnic Subgroups: Results from the California Healthy Kids Survey.Pediatrics, 118, 363-370.Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Self-determinationin personality. Journal of Research in Personality, 19, 109-134.Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs andthe self-determination of behavior. Psychological Inquiry, 11, 227268.Dornbusch, S. M., Ritter, P. L., Leiderman, H., Roberts, D. F., & Fraleigh, M. J. (1987). Therelation of parenting style of adolescent school performance. Child Development, 58,1244-1257.Eccles, J. S., & Barber, B. L. (1999). Student council, volunteering, basketball, or marchingband: What kinds of extracurricular involvement matters? Journal of AdolescenceResearch, 14, 10-43.Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59, 117142.Finn, J. D. (1993). School engagement and students at risk. Washington, DC: National Center forEducation Statistics. Running Head: MOTIVATION IN HIGH SCHOOL27 Fredericks, J.A., Blumenfeld, P., Friedel, J., & Paris, A. (2005). School engagement. In K.A.Moore & L. Lippman (Eds.) What do children need to flourish?: Conceptualizing andmeasuring indicators of positive development. New York, NY: Springer Science andBusiness Media.Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School engagement: Potential of theconcept, state of the evidence. Review of Educational Research, 74, 29-58.Fredricks, J. A., & Eccles, J. S. (2005). Developmental benefits of extracurricular involvement:Do peer characteristics mediate the link between activities and youth outcomes? Journalof Youth and Adolescence, 34, 507-520.Fredricks, J. A., & Eccles, J. S. (2006). Is extracurricular participation associated with beneficialoutcomes? Concurrent and longitudinal relations. Developmental Psychology, 42, 698-713.Fredricks, J. A., & Eccles, J. S. (2008). Participation in extracurricular activities in the middleschool years: Are there developmental benefits for African American and EuropeanAmerican youth? Journal of Youth and Adolescence, 37, 1029-1043.Goodenow, C., & Grady, K. E. (1993). The relationship of school belonging and friends’ valuesto academic motivation among urban adolescent students. The Journal of ExperimentalEducation, 62, 60-71.Gray, E. K., & Watson, D. (2002). General and specific traits of personality and their relation tosleep and academic performance. Journal of Personality, 70, 177-206.Grouzet, F. M. E., Vallerand, R. J., Thill, E. E., & Provencher, P. J. (2004). From environmentalfactors to outcomes: A test of an integrated motivational sequence. Motivation andEmotion, 28, 331-346. Running Head: MOTIVATION IN HIGH SCHOOL28 Guest, A. M., & McRee, N. (2009). School-level analysis of adolescent extracurricular activity,delinquency, and depression: The importance of situational context. Journal of Youth andAdolescence, 38, 51-62.Hadi, A. S. (1992). Identifying multiple outliers in multivariate data. Journal of the RoyalStatistical Society, Series B (Methodological), 54, 761-771.Hadi, A. S. (1994). A modification of a method for the detection of outliers in multivariatesamples. Journal of the Royal Statistical Society, Series B (Methodological), 56, 393-396.Hair, J. R., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis.New York: Macmillan.Hanks, M. P., & Eckland, B. K. (1976). Athletics and social participation in the educationalattainment process. Sociology of Education, 49, 271-294.Hanson, T. L., & Austin, G. (2003). Student Health Risks, Resilience, and AcademicPerformance in California: Year 2 Report, Longitudinal Analyses. Los Alamitos, CA:WestEd.Harter, S. (1981). A new self-report scale of intrinsic versus extrinsic orientation in theclassroom: Motivational and informational components. Developmental Psychology, 17,300-312.Hayenga, A. O., & Corpus, J. H. (2010). Profiles of intrinsic and extrinsic motivation: A person-centered approach to motivation and achievement in middle school. Motivation andEmotion, 34, 371-383.Hennessey, B. A. (2000). Self-determination theory and the social psychology of creativity.Psychological Inquiry, 11, 293-298. Running Head: MOTIVATION IN HIGH SCHOOL29 Irvin, M. J., Farmer, T. W., Leung, M., Thompson, J. H., & Hutchins, B. C. (2010). School,community, and church activities: Relationship to academic achievement of low-incomeAfrican American early adolescents in the rural deep south. Journal of Research in RuralEducation, 25, 1-21.Jimerson, S. R., Campos, E., & Greif, J. L. (2003). Toward an understanding of definitions andmeasures of school engagement and related terms. The California School Psychologist, 8,7-28.Johnson, L. (2008). Relationship of instructional methods to student engagement in two publichigh schools. American Secondary Education, 36, 69-87.Johnson, M. K., Beebe, T., Mortimer, J. T., & Snyder, M. (1998). Volunteerism in adolescence:A process perspective. Journal of Research on Adolescence, 8, 309-332.Jordan, W., & Nettles, M. (2000). How students invest their time outside of school: Effects onschool-related outcomes. Sociological Psychological Education, 3, 217-243.Kuncel, N. R., Credé, M., & Thomas, L. L. (2005). The validity of self-reported grade pointaverages, class ranks, and test scores: A meta-analysis and review of the literature.Review of Educational Research, 75, 63-82.Larson, R. W. (2000). Toward a psychology of positive youth development. AmericanPsychologist, 55, 170-183.Lee, J., & Shute, V. J. (2010). Personal and social-contextual factors in K-12 academicperformance: An integrative perspective on student learning. Educational Psychologist,45, 1-19. Running Head: MOTIVATION IN HIGH SCHOOL30 Lepper, M. R., Corpus, J. H., & Iyengar, S. S. (2005). Intrinsic and extrinsic motivationalorientations in the classroom: Age differences and academic correlates. Journal ofEducational Psychology, 87, 184-196.Lynch, M., & Cicchetti, D. (1997). Children’s relationships with adults and peers: Anexamination of elementary and junior high school students. Journal of SchoolPsychology, 35, 81-99.Mahoney, J. L. (2000). School extracurricular activity participation as a moderator in thedevelopment of antisocial patterns. Child Development, 71, 502-516.Mahoney, J. L., Larson, R. W., Eccles, J. S., & Lord, H. (2005). Organized activities asdevelopmental contexts for children and adolescents. In J. L. Mahoney, R. W. Larson, &J. S. Eccles (Eds.), Organized activities as contexts of development: Extracurricularactivities, after-school and community programs (pp. 3-22). Mahwah, N.J: LawrenceErlbaum Associates.Mahoney, J. L., Schweder, A. E., & Stattin, H. (2002). Structured after-school activities as amoderator of depressed mood for adolescents with detached relations to their parents.Journal of Community Psychology, 30, 69-86.Mahoney, J. L., & Stattin, H. (2000). Leisure activities and adolescent antisocial behavior: Therole of structure and social context. Journal of Adolescence, 23, 113-127.Martin, A. J. (2008). Enhancing student motivation and engagement: The effects of amultidimensional intervention. Contemporary Educational Psychology, 33, 239-269.Martin, A. J. (2009). Motivation and engagement across the academic life span: Adevelopmental construct validity study of elementary school, high school, anduniversity/college students. Educational and Psychological Measurement, 69, 794-825. Running Head: MOTIVATION IN HIGH SCHOOL31 Martin, A., & Dowson, M. (2009). Interpersonal relationships, motivation, engagement, andachievement: Yields for theory, current issues, and educational practice. Review ofEducational Research, 79, 327-365.Miller, R. B., Behrens, J. T., & Greene, B. A. (1993). Goals and perceived ability: Impact onstudent valuing, self-regulation, and persistence. Contemporary Educational Psychology,18, 2-14.Murdock, T. B., & Miller, A. (2003). Teachers as sources of middle school students’motivational identity: Variable-centered and person-centered analytic approaches. TheElementary School Journal, 103, 383-399.Nelson-LeGall, S., & Jones, E. (1991). Classroom help-seeking behaviors of African-Americanchildren. Education and Urban Society, 24, 27-40.Noftle, E. E., & Robins, R. W. (2007). Personality predictors of academic outcomes: Big fivecorrelates and GPA and SAT scores. Journal of Personality and Social Psychology, 93,116-130.Otis, N., Grouzet, F. M. E., & Pelletier, L. G. (2005). Latent motivational change in an academicsetting: A 3-year longitudinal study. Journal of Educational Psychology, 97, 170-183.Patrick, H., & Ryan, A. M. (2005). Identifying adaptive classrooms: Dimensions of theclassroom social environment. In K.A. Moore & L. Lippman (Eds.) What do children need to flourish?: Conceptualizing and measuring indicators of positive development. New York, NY: Springer Science and Business Media.Patrick, H., & Ryan, A. M. (2006). Teacher practices that communicate mastery goal structure:Students’ views. Manuscript submitted for publication.Pintrich, P. R., & DeGroot, E. V. (1990). Motivational and self-regulated learning components Running Head: MOTIVATION IN HIGH SCHOOL32 of classroom academic performance. Journal of Educational Psychology, 82, 33-40.Ratelle, C. F., Guay, F., Vallerand, R. J., Larose, S., & Senecal, C. (2007). Autonomous,controlled, and amotivated types of academic motivation: A person-oriented analysis.Journal of Educational Psychology, 99, 734-746.Reddy, R., Rhodes, J. A., & Mulhall, P. (2003). The influence of teacher support on studentadjustment in the middle school years: A latent growth curve study. Development andPsychopathology, 15, 119-138.Rhee, S., Furlong, M. J., Turner, J. A., & Harari, I. (2001). Integrating strength-basedperspectives in psychoeducational evaluations. The California School Psychologist, 6, 5-17.Rhodes, J. E., Grossman, J. B., & Resch, N. R. (2000). Agents of change: Pathways throughwhich mentoring relationships influence adolescents’ academic adjustment. ChildDevelopment, 71, 1662-1671.Roeser, R. W., Midgley, C., & Urdan, T. C. (2001). Perceptions of the school psychologicalenvironment and early adolescents’ psychological and behavioral functioning in school:The mediating role of goals and belonging. Journal of Educational Psychology, 88, 408-422.Rose-Krasnor, L., Busseri, M. A., Willoughby, T., & Chalmers, H. (2006). Breadth and intensityof youth activity involvement contexts for positive development. Journal of Youth andAdolescence, 35, 385-399.Rumberger, R. W. (1987). High school dropouts: A review of issues and evidence. Review ofEducational Research, 57, 101-121..Ryan, R. M., & Connell, J. P. (1989). Perceived locus of causality and internalization: Running Head: MOTIVATION IN HIGH SCHOOL33 Examining reasons for acting in two domains. Journal of Personality and SocialPsychology, 57, 749-761.Ryan, R. M., Connell, J. P., & Deci, E. L. (1985). A motivational analysis of self-determinationand self-regulation in education. In C. Ames & R. E. Ames (Eds.), Research onmotivation in education: The classroom milieu (pp. 1351). New York: Academic.Ryan, R. M., & Grolnick, W. S. (1986). Origins and pawns in the classroom: Self-report andprojective assessments of individual differences in children’s perceptions. Journal ofPersonality and Social Psychology, 50, 550-558.Ryan, A. M., & Patrick, H. (2001). The classroom social environment and changes inadolescents’ motivation and engagement during middle school. American EducationalResearch Journal, 38, 437-460.Seidman, E., & French, S. E. (1997). Normative school transitions among urban adolescents:When, where, and how to intervene. In H. J. Wahlberg & O. Reyes (Eds.), Children andyouth: Interdisciplinary perspectives (pp. 166189). Thousand Oaks: Ca: SagePublications.Shernoff, D. J., & Vandell, D. L. (2007). Engagement in after-school program activities: Qualityof experience from the perspective of participants. Journal of Youth and Adolescence, 36,891-903.Shin, R., Daly, B., & Vera, E. (2007). The relationships of peer norms, ethnic identity, and peersupport to school engagement in urban youth. Professional School Counseling, 10, 379-388.Soenens, B., & Vansteenkiste, M. (2005). Antecedents and outcomes of self-determination in 3life domains: The role of parents’ and teachers’ autonomy support. Journal of Youth and Running Head: MOTIVATION IN HIGH SCHOOL34 Adolescence, 34, 589-604.Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation.Advances in Experimental Social Psychology, 29, 271-360.Vallerand, R. J., & Bissonnette, R. (1992). Intrinsic, extrinsic, and amotivational styles aspredictors of behavior: A prospective study. Journal of Personality, 60, 599-620.Vallerand, R. J., Pelletier, L. G., Blais, M. R., Briére, N. M., Senécal, C., & Vallieres, E. F.(1993). On the assessment of intrinsic, extrinsic, and amotivation in education: Evidenceon the concurrent and construct validity of the Academic Motivation Scale. Educationaland Psychological Measurement, 53, 159-172.Vansteenkiste, M., Lens, W., & Deci, E. L. (2006). Intrinsic vs. extrinsic goal contents in self-determination theory: Another look at the quality of academic motivation. EducationalPsychologist, 41, 19-31.Vansteenkiste, M., Soenens, B., Sierens, E., Luyckx, K., & Lens, W. (2009). Motivationalprofiles from a self-determination perspective: The quality of motivation matters. Journalof Educational Psychology, 101, 671-688.Walker, C. O., Greene, B. A., & Mansell, R. A. (2006). Identification with academics,intrinsic/extrinsic motivation, and self-efficacy as predictors of cognitive engagement.Learning and Individual Differences, 16, 1-12.Weiner, B. (1990). The history of motivation research in education. Journal of EducationalPsychology, 82, 616-622.Wentzel, K. R. (1997). Student motivation in middle school: The role of perceived pedagogicalcaring. Journal of Educational Psychology, 89, 411– 419. Running Head: MOTIVATION IN HIGH SCHOOL35 Wentzel, K. R., Battle, A., Russell, S. L., & Looney, L. B. (2010). Social supports from teachersand peers as predictors of academic and social motivation. Contemporary EducationalPsychology, 35, 193-202.Willms, J. D. (2003). Student engagement at school: A sense of belonging and participation:Results from PISA 2000. Paris: OECD, Organisation for economic co-operation anddevelopment.Winner, E., & Hetland, L. (2000). The arts in education: Evaluating the evidence for a causallink. Journal of Aesthetic Education, 34, 3-10.Yazzie-Mintz, E. (2006). Voices of student engagement: A report on the 2006 high school surveyof student engagement. Retrieved October 10, 2009, http ://ceep.indiana.edu/hssse/pdf/HSSSE2006Report.pdf.Yonezawa, S., Jones, M., & Joselowsky, F. (2009). Youth engagement in high schools:Developing a multidimensional, critical approach to improving engagement for allstudents. Journal of Educational Change, 10, 191-209. Running Head: MOTIVATION IN HIGH SCHOOL36 FootnotesThis paper was based, in part, on the first author’s senior thesis at Reed College.Department of Psychology, Reed College, Portland, Oregon.Correspondence concerning this article should be addressed to Jennifer HenderlongCorpus, Department of Psychology, Reed College, 3203 SE Woodstock Blvd., Portland, Oregon97202. E-mail: [email protected]. Running Head: MOTIVATION IN HIGH SCHOOL37