# Judith M. Harackiewicz University of Wisconsin Madison. Kenneth E. Barron James Madison University. Andrew J. Elliot University of Rochester

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5 566 HARACKIEWICZ, BARRON, TAUER, AND ELLIOT Table 1 Means and Standard Deviations for Primary Variables Variable N Range M SD Mastery goals Performance goals Work avoidance goals Ability ACT SAT , , High school percentile Workmastery Competitiveness Interest in psychology Enjoyment of the class Final grade Semester GPA Courses taken Major Psychology GPA Subsequent GPA section differences in all variables. The only significant instructor effects were on enjoyment and final grade, indicating that students enjoyed sections taught by one instructor more and received higher grades than students in the sections taught by the other instructor. Therefore, we included a contrast code term to test and control for differences between the two instructors in all subsequent analyses (Cohen & Cohen, 1983). Regression Models Overview. We conducted one series of multiple regression analyses to examine the predictors of the achievement goals adopted in the class, a second series to examine the effects on outcome variables measured during the semester in which students took Introductory Psychology (the short term), and a final series of regressions to examine the effects on measures collected in the follow-up assessment (the long term). Within the long-term analyses, we tested different models for psychology-specific (courses taken, major, and psychology GPA) and general (subsequent GPA) measures. This data analytic strategy allowed us to retain the continuous nature of the variables and to test the independent effects of each predictor variable as well as the interactions between them. For the following analyses, all continuous main effect terms were standardized, and multiplicative two- and three-way interaction terms were created with these variables (Aiken & West, 1991). To interpret significant interaction effects from these analyses, we computed predicted values (Ŷs) for representative high and low groups (one standard deviation above and below the mean) from the regression equations using the unstandardized regression coefficients. Missing data. Because we did not have measures of high school performance for all students, we used missing data techniques recommended by Cohen and Cohen (1983) to retain all cases in our model. Specifically, we added a dichotomous missingdata code, in which participants who were missing high school performance data were coded 1, and participants with high school performance data were coded 0. On the actual high school performance variable, we substituted the sample mean for students who had missing values. These steps allowed us to avoid the risk of nonrepresentativeness that can occur by dropping participants with missing data (Cohen & Cohen, 1983) as well as test whether there were any significant differences on any measure between students with high school percentile scores and those students missing this score. Predictors of Goals Our first series of multiple regression analyses examined the effects of ability, high school performance, and achievement motivation on goals adopted in the classroom. Initial regression models included the main effect terms for ability, high school percentile, the dichotomous missing-data code, workmastery, competitiveness, gender (coded 1 for women and 1 for men), and Table 2 Zero-Order Correlations Variable Mastery goals 2. Performance goals Work avoidance goals.28*.22* 4. Ability High school percentile a * 6. Work mastery.32*.06.17*.01.14* 7. Competitiveness.06.40* * 8. Interest in psychology.40*.02.22* * Enjoyment of the class.23*.03.21* *.04.57* 10. Final grade.07.14*.15*.38*.46*.10*.05.31*.18* 11. Semester GPA *.51*.15*.06.14*.12*.79* 12. Courses taken.13* * Major b.13* * * 14. Psychology GPA c *.41* *.57* Subsequent GPA *.44*.13* *.64* * Note. N 471 unless otherwise noted. a N 422. b N 429. c N 225. * p.01.

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