Assessing Academic Motivation among Elementary School Children: The Elementary School Motivation Scale (ESMS)

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1 GUA05378 Assessing Academic Motivation among Elementary School Children: The Elementary School Motivation Scale (ESMS) Frédéric Guay, Herbert W. Marsh, Martin Dowson, SELF Research Centre, University of Western Sydney, Australia, Simon Larose Université Laval, Canada The purpose of the present research was to develop and validate a measure of academic motivation that could be used with young school children, the Elementary School Motivation Scale (ESMS). The ESMS is designed to assess the constructs of intrinsic motivation, identified regulation, and external regulation in relation to reading, writing, and mathematics. A cross-sectional study of 426 students in Grades1, 2 and 3 was used to develop and validate the ESMS. Overall, CFA analyses show that a) the ESMS is composed of 9 internally consistent factors (3 types of motivation for the three school subjects, and b) that the construct validity of the scale is supported by a quasi-simplex pattern of correlations, and correlations with academic self-concept and school achievement. Results also provided some support for invariance of the factor solution over gender and age. Results from the CFA-MIMIC model reveal neither gender and age effects nor interaction effects between age and sex. In sum, the ESMS represents a valid self-report measure of intrinsic motivation, identified regulation, and external regulation in relation to reading, writing, and mathematics among elementary school children. Childrens motivation at school is recognized as an important factor that contributes to indices of school adjustment (Pintrinch, 2003). The construct of motivation has been operationalized from the stance of various theoretical approaches over the past decades. For example, some researchers conceptualize motivation in terms of intrapsychic mechanisms, incentives, self-efficacy beliefs, attribution, locus of control, achievement, and goals (Reeve, 1996). However, few of these theoretical approaches have shed light on how young children integrate school activities into their own set of values and how this integration facilitates learning and achievement. A useful theoretical framework for understanding the process of integration of school related activities is Self-Determination Theory (SDT; Deci & Ryan, 1985, 1991). SDT is an approach to human motivation that highlights the importance of the psychological need for autonomy. Autonomy implies that individuals experience choice in the initiation, maintenance, and regulation of their behaviors. Perceived autonomy has been typically operationalized via motivational processes (Ryan & Connell, 1989). Deci and Ryan (1985) thus propose that there are different types of motivation which can be situated along a self-determination continuum. Intrinsic motivation reflects the highest degree of self-determination or autonomy. It refers to engaging in an activity for its own sake and for the pleasure and satisfaction derived from participation. Extrinsic motivation refers to engaging in an activity as a means to an end rather than for its intrinsic qualities. However, in contrast to some theoretical perspectives, SDT posits that extrinsic motivation can vary in terms of self-determination. Indeed, this theory proposes that different types of extrinsic motivation exist, some of which may represent relatively high levels of self-determination. From low to high levels of autonomy, the different types of extrinsic motivation are: external regulation and identified regulation. External regulation refers to behaviours that are regulated through external means such as rewards and constraints. Identified regulation refers to behaviours that are performed by choice because the individual judges them to be important. According to SDT, individuals who act for intrinsic motivation and identified regulation have high levels of perceived autonomy. In contrast, individuals who perform activities for external regulation have low levels of autonomy. Numerous scales have been developed to assess perceived autonomy (or motivation) toward school related activities from the stance of SDT. Indeed, Vallerand and colleagues (1989, 1992, 1993) have developed the Academic Motivation Scale (AMS) to assess this construct. Although this scale has good psychometric properties, it is designed to assess autonomy at the high school level. More importantly, this scale does not focus on specific school subjects. In light of the above, the present study comprised three goals. The first goal was to develop items assessing intrinsic motivation, identified regulation, and external regulation toward three school subjects,

2 namely, reading, writing, and math among elementary school children. These school subjects were selected because they are the most important subjects during the elementary school years. The second goal was to verify the construct validity of the scale. To this end, we performed four sets of CFA analyses: 1- We tested the factorial structure of the ESMS, using confirmatory factor analyses. The factorial structure of the ESMS would be supported if the CFA analysis reveals the presence of a 9-factor solution (3 types of motivation for each of the three school subjects). 2- We performed a 15-factor CFA structure that includes the 9 motivational factors as well as academic selfconcept (3 factors) and achievement (3 factors) in the three school subjects. This 15-factor CFA analysis will be used to perform two construct validity tests. The first test is to verify a quasi-simplex pattern of correlations among the three subscales. This pattern is based on the self-determination continuum described previously. More precisely, the interrelations among subscales were expected to form an ordered pattern in which the adjacent subscales along the self-determination continuum would correlate more positively than those that are more distant from each other. For example, we expected that identified regulation and intrinsic motivation would be positively correlated, whereas intrinsic motivation and external regulation would be negatively correlated. The second test is to assess construct validity through correlations between the ESMS subscales and academic self-concept and academic achievement. Indeed, because self-determination is associated with enhanced psychological functioning, we expected that intrinsic motivation and identified regulation would be positively correlated with achievement and academic self-concept. On the other hand, we posited that external regulation would be negatively correlated with achievement and academic self-concept. In addition we posited that these relations would be specific for each school subject. More specifically, we proposed two guidelines to assess the construct validity in the present study. First, convergent validity would occur when the correlation between two constructs for the same school subject was high and significant. For example, convergent validity would be supported in the present study if there were a high and positive correlation between intrinsic motivation for reading and reading self-concept. Second, divergent validity would occur when correlations between two constructs for two different school subjects were lower than convergent correlations. For example, divergent validity would be supported in the present study if there were a lower correlation between intrinsic motivation for reading and math self-concept than the one between intrinsic motivation for reading and reading self-concept. In sum, construct validity would be supported when convergent correlations were high and divergent correlations were lower than convergent correlations. 3- We conducted tests of invariance of the 15 factor structure over gender and age cohorts as well as we tested the invariance of measured variable intercepts (Byrne, 1998; Byrne, Shavelson, & Muthén, 1989; Kaplan, 2000; Marsh & Grayson, 1994) to verify in a more stringent way if the factor solution applies to the different grouping variable. Indeed, unless there is complete or at least partial invariance of both the slopes and intercepts parameters across the multiple groups, the comparison of mean differences across the groups may be unwarranted. Based on previous research we expected that the factor solution would be invariant across sex. However, in line with self-concept research, we expected that some uniquenesses would not be invariant as well as some factor correlations for the different age groups (Guay, Marsh, and Boivin, 2003). Indeed, Guay et al. (2003) show that younger children are less accurate in their self-evaluation than older children leading to lower reliability and weaker relations with external indicators such as achievement. 4- We explored possible age and gender effects on the different 15 factors. Indeed, numerous studies have found that girls have higher levels of intrinsic and identified regulation toward school than boys (Vallerand, 1997). In addition, self-concept research typically found that younger students have higher academic self-concept than older ones (Harter, 1999). To explore these effects we use the Multiple-Indicator-Multiple-Indicator-Cause (MIMIC) model. This approach is clearly stronger than a traditional MANOVA approach because it is based on latent scores. We will discuss more extensively the MIMIC approach below. Method Scale Development The scale was developed by a committee of experts (i.e., professors). First, they were asked to generate items in line with the conceptual definition of intrinsic motivation, identified regulation, and external regulation,

3 and to word them so as to indicate the underlying reasons for participating in the three following school subjects- -reading, writing, and mathematics (Deci & Ryan, 1985). A total of 27 items were developed to assess intrinsic motivation, identified regulation, and external regulation toward the three school subjects. Specifically, in relation to reading, three items were generated to assess intrinsic motivation, three for identified regulation, and three for external regulation toward. The same number of items was generated to assess these three motivational constructs in relation to the two other school subjects. Finally, children had to indicate whether or not the item applied to them according to the following scale (see Marsh et al., 1998): (1) no always (2) no sometimes (3) do not know (4) yes sometimes (5) yes always. The full 27-item version of the scale is presented in Appendix 1 while the means, standard deviation, Cronbach alpha for each factor are presented in Appendix 2. Participants Participants were three cohorts of 426 French Canadian children (226 girls, 200 boys; mean age = 7 years, range 6 10 years) from three elementary schools in Quebec City, Canada. Children in Cohorts 1 (n = 121), 2 (n = 126), and 3 (n = 178) were in Grades 1, 2, and 3, respectively. Participation required parental consent, and the parental participation rate was over 84% (only 16 % of the parents refused to have their children participate to this study). Procedure Questionnaires were administered in the classroom by a team of three well-trained research assistants. The following instructions were given to all children: This is a chance to help me to find out how you feel. It is not a test. There are no right or wrong answers and everyone will have different answers. I will ask you a question and then ask you to write how you feel on the questionnaire by saying yes if the sentence corresponds to how you feel and no if it doesn t. Be sure your answers show how you feel about yourself. We will not show your answers to anyone. If you do not understand a sentence or a word in a sentence, please tell us. For each answer the child had to indicate whether he/she meant sometimes yes or always yes or if he or she meant sometimes no or always no. In addition, teachers completed a short questionnaire assessing children s academic achievement in each of the three areas--writing, reading, and mathematics. Academic self-concept Measures Nine items were selected from the Academic Self-description Questionnaire (Marsh, 1990a). These items assess academic self-concept in math (3 items), reading (3 items) and writing (3 items). The wording of the three items was the same for each school subject (e.g., I have always done well in reading (writing, math), reading (writing, math) is easy for me, I learn things quickly in reading (writing, math)). Children had to rate each item according to the following scale: (1) no always (2) no sometimes (3) do not know (4) yes sometimes (5) yes always. Cronbach alpha values were.81 for the math subscale,.77 for the reading subscale, and.73 for the writing subscale. Academic Achievement The measure used to assess academic achievement was a three-item teacher rating scale. Each of the three items was designed to assess academic achievement in reading, writing, and mathematics. Teachers rated a child s academic performance in these three subjects relative to other classmates using the following scale: 1 (far under the mean), 2 (slightly under the mean), 3 (at the mean), 4 (slightly above the mean), and 5 (far above the mean; for a similar methodology, see also Frentz, Greshman, & Elliot, 1991; Hay, Ashman, & van Kraayenoord, 1997; Skaalvik & Hagtvet, 1990). A score of 5 represents the best academic performance, whereas a score of 1 represents the worst level of performance.

4 Statistical Analyses Confirmatory Factor Analyses (CFA) The adequacy of the models tested in the present study was assessed by Structural Equation Modeling (SEM) with the EQS program (Version 6.1). Models were all tested with standardized coefficients obtained from the maximum likelihood method of estimation. To ascertain the model fit, we used the comparative fit index (CFI), the non-normed fit index (NNFI), the root-mean-square error of approximation (RMSEA) and the chisquare test statistic. Correlated errors or uniquenesses of parallel items Although in most applications of CFA analyses, a priori models assume that uniqueness associated with each measured variable is independent of uniqueness associated with other measured variables, we decided to freely estimate some correlated uniquenesses. We have done this because the ESMS and academic self-concept measures imply that the same items measuring intrinsic motivation, identified regulation, external regulation, and self-concept are completed for the three school subjects. Thus, it is likely that the uniquenesses associated with the matching items are correlated (a method halo effect). If there were substantial correlated uniquenesses that are not included in the model, then the model fit indices would be attenuated. Multiple group tests of invariance When there are parallel data from more than one group it is possible to test the invariance of the solution by requiring any one, any set, or all parameter estimates to be the same in two or more groups. The minimal condition of factorial invariance is the invariance of the factor loadings. Separate tests were conducted to test the invariance of the following groups of parameters: factor loadings, factor variances, factor correlations, uniquenesses, and correlated uniquenesses for gender and the three age groups. If the addition of invariance constraints results in little or no change in goodness of fit, then there is strong support for the invariance of the factor structure for sex and age groups. Within this framework, it is also possible to extend tests of invariance to include measured variable intercepts (Byrne, 1998; Byrne, Shavelson, & Muthén, 1989; Kaplan, 2000; Marsh & Grayson, 1994). Adapting terminology from item response theory (Grayson & Marsh, 1994), each measured variable (t) is related to the latent construct (T) by the equation t = a + bt where b is the slope (or discrimination) parameter that reflects how changes in the observed variable are related to changes in the latent construct and a is the intercept parameter that reflects the ease or difficulty in getting high manifest scores for a particular measured variable. Unless there is complete or at least partial invariance of both the a and b parameters across the multiple groups, the comparison of mean differences across the groups may be unjustifiable. Multiple-indicator-multiple-indicator-cause (MIMIC) models The multiple group approach is reasonable when there is one group variable. However, it is not practical for variables that are continuous or have many categories (e.g., age), or for studies that simultaneously evaluate interaction effects (age by gender in the present study). For this reason, we decided to pursue some analyses in the tradition of the MIMIC models to test in a more stringent way the construct validity of the scale. Kaplan (2000; Grayson, Mackinnon, Jorm, Creasey & Broe, 2000; Jöreskog & Sörbom, 1996) described an alternative MIMIC model approach, which is like a multivariate regression model in which latent variables are caused by grouping variables that are represented by a single indicator. This approach is clearly stronger than a traditional MANOVA approach that is based on measured variables (i.e., scale scores) that are assumed to be measured without error rather than latent variables arising from the CFA. The MIMIC approach is also much more flexible than the traditional multivariate analysis of variance (MANOVA) approach in allowing a mixture of continuous and discrete independent variables and their interactions. In the MIMIC models, the construct validity of ESMS factors was further evaluated by relating the ESMS as well as self-concept and achievement factors to participant gender and age and the interaction term of age by gender. We initially constructed three single-degree-offreedom contrast variables to represent the linear effects of age (1 = Grade 1, 2 = Grade 2, 3 = Grade 3), gender

5 (1 = male, 2 = female), age x gender interaction. In order to minimize the potential confounding effects of multicollinearity, all variables were standardized (M = 0, SD = 1) before constructing the product terms, and the main effects were partialled from the interaction terms. Missing data handling In the present study, we observed nearly 5% of missing data for the overall data points. In order to have more accurate estimate of the different parameters, we decided to estimate missing values with the normal theory maximum likelihood. Jamshidian and Bentler (1994) concluded that the normal theory maximum likelihood estimates are preferred over other methods proposed to handle missing data (mean imputation, listwise, pairwise, regression imputation). Recently, Peugh and Enders (2004) reach the same conclusion empirical studies have, almost unequivocally, demonstrated the superiority of these methods (ML and Multiple Imputation) over traditional method such as pairwise and listwise deletion. (p. 552). A number of algorithms have been proposed to compute ML estimates for model with missing data. In the present study we use the EM algorithm (Jamshidina and Bentler, 1999) for mean and covariance structure analysis that is implanted in EQS. Briefly, this methodology rebuilds the covariance matrix and the sample means estimates with the EM algorithm. In this way, missing cell will not lose its observations while gaining more accurate results comparing to other traditional missing data imputation. Results and Discussion Total group analyses A confirmatory factor analysis was performed on the ESMS. CFA analyses were conducted in two steps. Firstly, we tested the adequacy of the 9 factors of the scale. Fit indices are presented in Table 1 and provided good support the factor structure of the ESMS. Factor loadings of this CFA analysis are presented in Appendix 2. All factor loadings are over.50. Because the fit indices of this first analysis are satisfactory, we decided to test the 15-factor CFA model that includes motivational and self-concept subscales as well as achievement. Fit indices of the 15-factor model are very good (see Table 1). Correlations among all of 15 factors are presented in Appendix 3. To facilitate the comprehension of the results we decided to subdivide this correlation matrix in 3 correlation matrices that are presented in Tables 2, 3 and 4. Correlations between each school subjects are presented in Table 2. Correlations among external regulation, and identified regulation for the various school subjects are quite high, thus raising doubts about children s ability to differentiate these types of motivation across school subjects. As we mentioned above, the construct validity of the scale was also assessed via two other correlation analyses. The first analysis revealed that most correlations among the three subscales for each school subject represented a quasi-simplex pattern of relations (see correlations in Table 3). That is, the overall pattern of interrelations among the subscales was made up of an ordered pattern in which the adjacent subscales along the self-determination continuum were more positively correlated than those that were more distant from each other. For reading, intrinsic motivation correlated positively with identified regulation (r =.50) but this correlation was lower with external regulation (r =.14). A similar pattern was found for writing but not for math. Indeed, the correlation was higher between intrinsic motivation and external regulation (.44) than the one between identified regulation and external regulation (.35). Furthermore, we expected that the correlation between intrinsic motivation and external regulation would be negative for each school subject but in fact this correlation is positive, though small. The second correlation analysis was performed between the ESMS subscales and the two following variables: academic self-concept and academic achievement in reading, writing and mathematics. Correlations are presented in Table 4. This correlation matrix is divided into two components: a) correlations in bold represent relations among different constructs assessing the same school subjects and b) correlations in italics represent relations among different constructs assessing different school subjects. First, convergent validities are relatively high. Most of the 9 correlations between types of motivation and selfconcept are significant and range from.05 to.74, thereby providing relatively good support for the first guideline. For example, the correlation between intrinsic motivation for reading and reading self-concept is

6 positive and high (r =.62). Interestingly, the relations between external regulation and self-concept are low but positive. This last finding does not support SDT because this theory will predict a negative correlation between external regulation and self-concept. Second, convergent correlations are mostly higher than divergent correlations, thereby providing good support for the second criterion. However, it is important to note that this pattern of relation was not supported between motivational constructs and achievement despite the fact that there are some negative correlations between external regulation and academic achievement. In sum, these last results provide mixed support for the construct validity of the scale. That is, we would have expected higher correlations between the motivational constructs and achievement in the various school subjects. Table 1 Fit Indices for the Various Models Tested Model 1-Total group CFA Models Chi-square df CFI NNFI RMSEA 1a. 9 factors b. 15 Factors Model 2-CFA Gender Group Invariance: 2a. No invariance b. FL fixed c. FL+FV d. FL+FV+FC e. FL+FV+FC+U f. FL+FV+FC+U+CU g. FL+FV+FC+U+CU+ item intercept Model 3-CFA Age Group Invariance: 3a. No invariance b. FL fixed c. FL+FV d. FL+FV+FC e. FL+FV+FC+U f. FL+FV+FC+U+CU g. FL+FV+FC+U+CU+ item intercept Model 4-SEM model MIMIC 4a. All paths estimated b. All main effects paths set to b. All main effects paths + interactions paths set to Note: FL= Factor loadings, FV = Factor variances, FC = Factor covariances, U = Uniquenesses, CU = Correlated Uniquenesses.

7 Table 2 CFA Correlations Between School Subjects Measure Intrinsic motivation 1. Reading - 2. Writing Mathematics Identified regulation 1. Reading - 2. Writing Mathematics External regulation 1. Reading - 2. Writing Mathematics Note: All Correlations are significant at the 0.05 level. Table 3 CFA Correlations for the Quasi-Simplex Pattern of Relations Measure Reading 1. Intrinsic motivation - 2. Identified regulation External regulation Writing 1. Intrinsic motivation - 2. Identified regulation External regulation Mathematics 1. Intrinsic motivation - 2. Identified regulation External regulation Note: **. Correlations are significant at the 0.05 level.

8 Table 4 CFA Correlations among Motivation, Self-concept, and Achievement Measure Reading Writing Mathematics Academic Academic Academic self-concept self-concept self-concept achievement Achievement Achievement in reading in writing in math Reading 1. Intrinsic motivation.62*.44*.25*.18*.15* Identified regulation.37*.45*.30* External regulation.05.21* * -.16* -.21* Writing 1. Intrinsic motivation.28*.68*.22* Identified regulation.21*.49*.26* External regulation * * -.15* -.18* Mathematics 1. Intrinsic motivation.10.23*.74* Identified regulation.09.21*.41* External regulation * -.12* * Academic Self-Concept 4. Reading Writing.61* Maths.24*.40* Note. **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

9 Multiple Group Analyses Gender We evaluated the invariance of the following sets of parameters in 6 different models: factor loadings, factor variance, factor correlations, uniqueness, correlated uniqueness, and item intercept. Results of these analyses are presented in Table 1. Seven models were performed to verify the invariance of the factor structure and item intercepts. In the first model, no parameters were constrained to equality (2a) whereas in the subsequent models we constrained various parameters to be equal across groups where in the final and more restrictive models all parameters were equal (2g). Support for the invariance of the factor structure and item intercepts would be obtained if the NNFI and RMSEA do not decrease substantially as stringent equality constraints were imposed. Indeed, if fit indices of the more parsimonious model 2g are not different from the less parsimonious one 2a than very good support will be found the invariance of the factor structure over gender. Results provided very good support for the invariance of the 15 factors structure across gender. Indeed, fit indices of the various models were nearly identical to the no invariance model. For example, the fit indices of the more restrictive model 2g (including item intercepts) were.90 (NNFI) and.05 (RMSEA) whereas for the no invariance model 2a these indices were.92 and.05. Age groups We tested exactly the same invariance models that we tested for gender. Results of these analyses are presented in Table 1. Overall, these results provided good support for the invariance of the 15 factors across age groups. However, the decrease in NNFI and RMSEA values for model 3d and 3e leads us to conclude that some uniqueness were not invariant across groups as well as some factor correlations. These results are consistent with previous research (Guay et al., 2003) showing that younger children are less accurate in their self-evaluation than older children leading to lower reliability and weaker relations with external indicators such as achievement. However, the item intercepts were found to be invariant. In sum, results based on the stringent multiple groups comparisons lead us to conclude that is possible to explore means differences as a function of age and gender with the MIMIC model. MIMIC Model of the effect of age and sex on the latent variables A MIMIC (structural equation path) model was constructed in which the main effects of age and gender and gender x age interactions were related to each of the 15 factors outlined above. Fit indices for the MIMIC model are presented in Table 1. Results of the MIMIC model reveal two main effects of age, two main effects of sex, and four interaction effects. However, to verify the extent to which these effects were important in the present model, we decided to constrain in model 4b main effects path to 0 and in model 4c main effects and interaction paths to 0. The very low decrease in NNFI and RMSEA indicates that these paths are not very important. For that reason we do interpret neither main effects nor interaction effects in the present study. In sum, the purpose of the present study was to develop and validate a measure of motivations toward elementary school subjects, namely the Elementary School Motivation Scale (ESMS). Our results provide good support for the psychometric properties of the ESMS. First, results from CFA analyses reveal that the ESMS has a 9-factor structure that reflects the theorized constructs of intrinsic motivation, identified regulation, and external regulation for each school subject. However, CFA analyses provided mixed support for the fact that identified regulation and external regulation could be differentiate across different school subjects. Third, the construct validity of the ESMS is supported through the results of multiple analyses. More precisely, correlation analyses reveal a quasi simplex-like pattern of relations among the ESMS subscales. In addition, results based on divergent and convergent correlations provide

10 support for the construct validity of the scale while using academic self-concept as the criterion variable, but weak support is obtained while using academic achievement. Fourth, the factor structure of the scale is invariant across gender and mostly across age groups. Finally, results from the MIMIC model indicate that most of the main and interactions effects were not significantly important. About the authors: Professor Frederic Guay is a professor of guidance counselling in the Faculty of education at Laval University. Since 2003, he is the Chairholder of the Canada Research Chair on Motivation and Academic Success. Frederic.Guay@fse.ulaval.ca Professor Herbert Marsh is Professor of Educational Psychology, founding Director of the SELF Research Centre. He is Australia s most widely cited researcher in both education and psychology, and the 11 th mostly widely cited researcher in the world across all disciplines of psychology. h.marsh@uws.edu.au Dr. Martin Dowson is Research Fellow at the SELF Research Centre, University of Western Sydney. His research interests centre on educational psychology (particularly self-concept, motivation, and metacognition), the psychology of religion (particularly spirituality, burnout, and religious identity/self-concept), and psychometrics (particularly Structural Equation Modeling). mdowson@unisurf.com.au Professor Simon Larose is a professor in Educational Psychology at Laval University. His current research program focuses on the understanding of the mechanisms of school persistence in late adolescence and in young adulthood. Simon.Larose@fse.ulaval.ca Contact Details Frédéric Guay, Ph.D. Chairholder of the Canada Research Chair on Motivation and Academic Success Département des fondements et pratiques en éducation Faculté des sciences de l'éducation Université Laval Ste-Foy (Québec) Canada G1K 7P4 Office 942 Phone: poste 2379 Fax.: Frederic.Guay@fse.ulaval.ca Web:

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13 Appendix 1 The complete 27-item version of the scale. Reading Intrinsic Motivation I like reading. Reading interests me a lot. I read even when I am not obliged to do so. Identified Regulation Reading will allow me to learn many useful things. I chose to read to learn many things. In life, it s important to learn how to read. External Regulation I read to get a nice reward. I read to please my parents or my teacher. I read to show others how good I am. Writing Intrinsic Motivation I like writing. Writing interests me a lot. I write even when I am not obliged to do so. Identified Regulation Writing will allow me to learn many useful things.) I chose to write to learn many things. In life it s important to learn how to write. External Regulation I write to get a nice reward. I write to please my parents or my teacher. I write to show others how good I am. Mathematics Intrinsic Motivation I like mathematics. Mathematics interests me a lot. I do mathematics even when I am not obliged to do so. Identified Regulation Mathematics will allow me to learn many useful things. I chose to do mathematics to learn many things. In life, it s important to learn how to do mathematics. External Regulation I do mathematics to get a nice reward. I do mathematics to please my parents or my teacher. I do mathematics to show others how good I am.

14 Appendix 2 Mean and Standard Deviations for Each Motivational Factor as a Function of the Three School Subjects Measure M SD Reading 1. Intrinsic motivation Identified regulation External regulation Writing 1. Intrinsic motivation Identified regulation External regulation Mathematics 1. Intrinsic motivation Identified regulation External regulation

15 Appendix 3 Loadings from the 9-factor CFA solution Measure Reading Writing Mathematics Intrinsic MI MI MI Identified IDEN IDEN IDEN External REG REG REG

16 Appendix 4 CFA Correlations from the 15-Factor Model for the Total Sample Factors F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F1-IM-Reading - F2-Identified-Reading.64 - F3-External-Reading F4-IM-Writing F5-Identified-Writing F6-External- Writing F7-IM-Maths F8-Identified-Maths F9-External-Maths F10-ASC-Reading F11-ASC-Writing F12-ASC-Maths F13-ACH-Reading F14-ACH-Writing F15-ACH-Maths Note: Correlations between.10 and -.10 are nonsignificant at the IM = Intrinsic motivation, ASC = Academic self-concept, ACH = Achievement.