A longitudinal study of achievement goals for college in general: Predicting cumulative GPA and diversity in course selection

Contemporary Educational Psychology 34 (2009) 113–119

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A longitudinal study of achievement goals for college in general: Predicting cumulative GPA and diversity in course selection Amanda M. Durik *, Chelsea M. Lovejoy, Sara J. Johnson Department of Psychology, Northern Illinois University, DeKalb, IL 60115, USA

a r t i c l e

i n f o

Article history: Available online 4 January 2009 Keywords: Achievement goals Achievement motivation College students

a b s t r a c t This correlational longitudinal study examined how college students’ achievement goals for college in general predicted overall grade point average and diversity in course selection. During their first semester of college, students (N = 214) reported their performance-approach, performance-avoidance, and mastery-approach goals for college and completed a measure of achievement motivation. Two years later, students’ transcripts were obtained to determine each participant’s cumulative GPA and the diversity in their course selections. Regression analyses showed that, controlling for high school ability and achievement motivation, performance-approach goals positively predicted overall college performance, p < .01, performance-avoidance goals negatively predicted performance, p < .05, and mastery goals did not predict performance. Moreover, participants’ scores on workmastery negatively predicted diversity in course selections, p < .01, whereas mastery goals did not uniquely predict course diversity. The results are discussed in terms of the generalizability of goal effects across learning contexts at the college level. Ó 2008 Elsevier Inc. All rights reserved.

1. Introduction Achievement goals define the desired level of competence that is sought in a given context and guide subsequent behavior (Ames, 1992; Dweck & Leggett, 1988; Nicholls, 1984). Because competence can be conceptualized in various ways, achievement goals have been classified based on whether the goal defines competence in relation to other individuals’ performances (performance goals) or self-referentially (mastery goals). Research and theory suggest that achievement goals are rooted in individuals’ beliefs about the origins of competence (Dweck & Leggett, 1988) and their evaluations of personal competence (Cury, Elliot, Da Fonseca, & Moller, 2006). Performance goals, focused on being competent relative to other people, have been linked to individuals’ beliefs that competence is fixed—one either has the ability or not (Dweck & Leggett, 1988). In contrast, mastery goals, focused on personal improvement and skill development, have been linked to beliefs that competence is malleable and not fixed (Dweck & Leggett, 1988). A further division distinguishes between goals focused on attaining competence (approach goals) versus avoiding incompetence (avoidance goals, Elliot & Church, 1997; Elliot & Harackiewicz, 1996). Individuals who adopt approach goals have been shown to have higher personal evaluations of competence than those who adopt avoidance goals (Cury et al., 2006). Although a 2  2 achieve* Corresponding author. Fax: +1 815 753 8088. E-mail address: [email protected] (A.M. Durik). 0361-476X/$ - see front matter Ó 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.cedpsych.2008.11.002

ment goal framework emerges from these two dimensions defining achievement goals (Elliot & McGregor, 2001), research has largely focused on three of these goals. Performance-approach goals are focused on performing better than other individuals, performance-avoidance goals are focused on not performing worse than others, and mastery-approach goals are focused on self-referenced skill development and personal improvement. A body of longitudinal research exists examining the relationships between achievement goals and college outcomes. Often researchers have conducted this research within particular achievement contexts, such as in a college course. For example, course-specific goals measured at the beginning of a semester have been used to predict course performance and motivation measured later in the semester (e.g., Archer, 1994; Chalupa, Chen, & Charles, 2001; Elliot & Church, 1997; Garcia & Pintrich, 1996; Harackiewicz, Barron, Carter, Lehto, & Elliot, 1997). This research has shown how achievement goals operate within specific classroom environments. Performance-approach goals set in college courses positively predict course performance, whereas performanceavoidance goals negatively predict performance (see Harackiewicz, Barron, Pintrich, Elliot, & Thrash, 2002, for a review). Although there is some controversy surrounding the positive effects of performance-approach goals on performance (see Midgley, Kaplan, & Middleton, 2001), it appears that the mechanism through which performance-approach goals positively predict performance is through heightened persistence and effort (Elliot, McGregor, & Gable, 1999). In contrast, performance-avoidance goals have been found to lead to lower performance. Evidence suggests that this

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pattern emerges because individuals who adopt performanceavoidance goals tend to worry about poor performance and engage in disorganized studying (Elliot & McGregor, 1999; Elliot et al., 1999). Mastery-approach goals, on the other hand, consistently predict interest in the course material but do not predict performance (see Urdan, 1997, for a review). It is believed that mastery goals orient students to what they are learning so that they become absorbed in the material and come to value it (Hulleman, Durik, Schweigert, & Harackiewicz, 2008). The purpose of the current research was to test whether goals set for college in general predict global college outcomes. The outcomes focused on in particular were cumulative grade point average (GPA) and variety in students’ course selections (the number of different departments from which students took courses). It is important to test the predictive validity of goals set for college courses in general because doing so will help determine how goals predict outcomes over an entire college career. Notably, prior research has shown that performance-approach goals set in a particular course predict long-term overall grade point average (Harackiewicz, Barron, Tauer, Carter, & Elliot, 2000; Harackiewicz, Durik, Barron, Linnenbrink-Garcia, & Tauer, 2008). However, a global outcome variable to capture the broad-reaching influence of general mastery-approach goals has not been examined. We argue that diversity in course selections can provide a window into the curricular decisions that college students make, and may illuminate how mastery-approach goals operate at a more general level. Students take courses in a wide array of disciplines, but it is unclear at present what predicts whether students funnel their attention into a particular discipline or take courses in a smattering of areas before they settle into a field of study. Although there might be benefits of either of these course-taking approaches (focus in on a career path versus take full advantage of the many opportunities offered at college), it is worth considering how achievement goals predict these choices. Research on general goals and college outcomes can also provide insight into whether the effects of goals generalize across classroom contexts and content areas. This is highlighted by researchers who have suggested that the positive effects of performance-approach goals on performance might be due to aspects of the particular achievement environment such as the course grading structure and means of evaluation (e.g., Elliot et al., 1999; McGregor & Elliot, 2002). Moreover, a considerable amount of research has been conducted in introductory psychology, education, and business courses, leaving the question open of whether similar relationships would be found across the curriculum. By assessing goals for college courses in general and testing whether they predict global college outcomes, it will be possible to test the generalizability of goal effects across classroom contexts and academic domains. An issue that emerges alongside the purpose of the current research is the extent to which achievement goals measured at a general level are separable from achievement motivation. Whereas achievement goals are set within particular contexts, achievement motivation represents an individual’s general need to perform well, master his or her environment, work hard, and seek challenge (Atkinson, 1974; McClelland, Atkinson, Clark, & Lowell, 1953; Murray, 1938; Spence & Helmreich, 1983). A long history of research has shown that individual differences in achievement motivation predict goal adoption in particular situations (e.g., Tanaka & Yamauchi, 2001; VandeWalle, 1997; VanYperen, 2006; Zusho, Pintrich, & Cortina, 2005). Moreover, achievement motivation has been used to predict outcomes such as performance and achievement choices (see review by Stangler, 1992), which relate to the focal outcomes of the current study. Taken together, it is possible that achievement goals set in a broad context (i.e., college in general) and achievement motivation may be indistinguishable and reveal sim-

ilar predictive relationships. Given our focus in the current research on the extent to which achievement goals uniquely predict college outcomes, we included individual differences in achievement motivation in order to better isolate these relationships. Specifically, Spence and Helmreich (1983) identified two dimensions of achievement motivation, workmastery and competitiveness, that have been found to consistently predict the adoption of achievement goals (Harackiewicz, Barron, Tauer, & Elliot, 2002). Workmastery involves the desire to work hard and master skills, and positively predicts learners’ adoption of mastery-approach goals. In contrast, competitiveness reflects the enjoyment of competition and has been found to positively predict performance-approach goals. Although prior research in classrooms indicates that goals are better predictors of course-specific outcomes than achievement motivation (Harackiewicz, Barron, Tauer, & Elliot, 2002) this might not be the case if the goals and outcomes are measured at a more general level. Given the possibility that achievement goals at a more global level might start to merge with achievement motivation, we wanted to test whether achievement goals for college in general uniquely predicted college outcomes, controlling for workmastery and competitiveness. See Table 1 for definitions of achievement motivation and achievement goals. 1.1. Current research The current research sought to test achievement motivation and achievement goal relationships by examining whether general achievement goals measured during students’ first semester of college predicted subsequent cumulative GPA and diversity in course selection. Specifically, participants’ achievement motivation and performance-approach, performance-avoidance, and mastery-approach goals for college courses in general were assessed during students’ first semester of college. Two years later, students’ transcripts were obtained to determine their cumulative GPA and the amount of variety in students’ course selections. It is important to note that achievement goals for college in general may be a result of students’ prior scholastic achievement in addition to a precursor of subsequent achievement. Although prior research has shown that goal effects within a particular classroom context remained even after controlling for prior ability (Harackiewicz et al., 2000), we wanted to ensure that any predictive relationship between general goals and outcomes were not due to prior academic experiences. Therefore, we obtained two measures of students’ academic ability (high school percentiles and ACT scores) in order to account for this construct when using general goals to predict college outcomes. Consistent with prior research (Harackiewicz, Barron, Tauer, & Elliot, 2002; Harackiewicz et al., 2002; Harackiewicz et al., 2008), we hypothesized that workmastery would positively predict mastery-approach goals and that competitiveness would positively predict performance-approach goals. Moreover, we predicted that performance-approach goals would positively predict cumulative GPA and that performance-avoidance goals would negatively predict cumulative GPA. These hypotheses parallel the results cited in the literature examining the effects of performance goals within particular college courses and we believe that these goals are likely to function similarly at a general level. This is because, first, performance goals have been found to be more domain general than mastery goals, suggesting that effects within particular classrooms are likely to generalize to other courses (Bong, 2001). Second, the relationship between course-specific performance-approach goals and cumulative GPA has already been documented (Harackiewicz, Barron, Tauer, & Elliot, 2002; Harackiewicz et al., 2002; Harackiewicz et al., 2008). It is likely that similar relationships will emerge for performance-approach goals for college in general.

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Table 1 Definitions of achievement motivation and goal constructs in the present study. Construct

Definition

Achievement motivation Workmastery Competitiveness

Dispositional tendency to strive to do things as well as possible General tendency to want to work hard and master skills General tendency to approach tasks competitively and to enjoy competition

Achievement goal Mastery-approach goal Performance-approach goal Performance-avoidance goal

Representation of an individual’s desired competence in an achievement situation Goal to learn and improve skills based on self-referenced standards Goal to perform better than other individuals Goal to not perform worse than other individuals

Note: The achievement motivation definition is based on Murray’s (1938) conception of need for achievement. Workmastery and competitiveness are dimensions of achievement motivation identified by Spence and Helmreich (1983). The achievement goal definitions are based on theorizing by Ames (1992), Dweck and Leggett (1988), Elliot and Harackiewicz (1996), Nicholls (1984).

The hypotheses concerning diversity in students’ selection of courses were more uncertain. Recall that in prior research, mastery-approach goals have predicted interest in the course material, whereas performance goals have not (e.g., Harackiewicz et al., 2000; Lee, Sheldon, & Turban, 2003; Rawthorne & Elliot, 1999). However, in contrast to a particular course where one can assess interest in the course material as an outcome, it is less clear how to assess students’ interest in their college courses more generally. We opted to assess the variety of students’ course selections by examining the number of departments in which students had taken courses (corrected for the total number of courses taken). We developed two competing hypotheses for the relationship between mastery-approach goals and variety in students’ course selection. Specifically, we reasoned that interest stemming from mastery goals could take two forms: it could lead individuals to either funnel their interests into a few specific disciplines or expand their interests into many disciplines. Given that mastery goals have been found to predict domain-specific interest, we hypothesized that mastery goals would lead students to develop deep interests in particular domains and therefore pursue a reduced number of disciplines. In other words, these students would identify a course of study and pursue it with vigor. However, we also reasoned that mastery-approach goals for college courses in general could reflect a broad orientation toward learning. If this is the case, then mastery-approach goals might positively predict variety in students’ course selections. 2. Method 2.1. Participants Participants in this study were 240 college students who began college during the Fall of 2005 at a university in the Midwestern United States. Of these 240 students, 11 (5%) were omitted from the analyses because they did not continue at the university on a full-time basis between the Fall of 2005 and the Summer of 2007 (when transcripts were obtained). An additional 15 (5%) students were omitted from the analyses because they were dismissed from the university due to low academic performance during the Spring semester of 2006. The primary analyses included 214 participants (82% female and 18% male) who were European-American (58%), African American (7%), Hispanic (5%), Asian American (7%), and Native American (<1%). Nineteen percent of participants reported that their ethnicity was something other than the options provided and 4% did not report their ethnicity. The university from which the sample was obtained has 25,000 undergraduate and graduate students and is located 65 miles from a major metropolitan area. The university largely attracts undergraduate students from the nearby region, and 91% of the undergraduate population is from within the state. The general

education requirements, including both core competency and distributive studies requirements are the same for all undergraduate students, regardless of the college from which a given student obtains his or her degree. The university is divided into seven colleges and has 40 departments. The average class size is 28 students. Given that one of the aims of this study was to test the predictive validity of achievement goals across multiple academic domains, we examined the colleges of the university in which students in this sample had selected to major. Most of the sample had declared a major in one of the following four colleges: business (14%), education (22%), health and human sciences (34%), or liberal arts and sciences (26%). The remaining portion of the sample (4%) was in either engineering or visual and performing arts, or had not yet declared a major. These data suggest that the participants in this sample were pursuing degrees in a wide array of academic disciplines. 2.2. Measures We used Spence and Helmreich’s (1983) two-dimensional measure of achievement motivation to assess workmastery and competitiveness. Items to assess workmastery (14 items, e.g., ‘‘I prefer to work in situations that require a high level of skill,” a = .81) and competitiveness (5 items, e.g., ‘‘I enjoy working in situations involving competition with others,” a = .71) were rated on a scale from 1 (strongly disagree) to 5 (strongly agree). A five-point scale was used, according to the description provided by the scale’s authors (Spence & Helmreich, 1983). The goal items were patterned after those used by Harackiewicz et al. (2002) and Harackiewicz, Barron, Tauer, and Elliot (2002). At least two items were used to assess each of the three goal types, performance-approach goals (e.g., ‘‘I want to do better than other students at this university” a = .78), performance-avoidance goals (e.g., ‘‘I just want to avoid doing poorly in college.” a = .57), and mastery-approach goals (e.g., ‘‘My goal in college is to learn as much as possible.” a = .81). Participants rated each item on a scale from 1 (strongly disagree) to 7 (strongly agree). The reliability for performance-avoidance items was lower than desired. It is unclear why this reduced reliability occurred; however, reduced reliability could attenuate the observed effects (Cohen, Cohen, West, & Aiken, 2003) and therefore should be kept in mind when interpreting effects. Transcripts were obtained after students had completed 2 years in college. The transcripts were coded for academic performance and diversity in students’ course selections. Academic performance was measured with cumulative GPA. The measure of diversity in students’ course selections was assessed by counting the number of different departments in which students had taken courses and dividing it by the total number of courses students had completed at the university. This correction was necessary because students who had taken more courses would have had more opportunities

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Table 2 Zero-order correlations and descriptive statistics for all variables.

1. 2. 3. 4. 5. 6. 7. 8. 9.

Cumulative GPA Course diversity High school % ACT score Workmastery Competitiveness Mastery-approach goal Performance-approach goal Performance-avoidance goal

Mean Standard deviation N

1

2

3

4

5

6

7

8

.25** .36** .31** .14* .01 .17* .30** .20**

.17* .08 .24** .08 .18* .07 .02

.22** .21** .02 .21** .15* .05

.16* .09 .01 .13 .21**

.18** .53** .26** .07

.06 .41** .07

.31** .05

.04

3.05 0.51 214

0.57 0.13 214

69.28 17.05 208

22.49 2.58 212

3.63 0.51 214

3.42 0.74 214

5.74 0.92 214

4.81 1.39 214

9

5.23 1.52 214

Note: Higher means reflect greater amounts of a given construct. * p < .05. p < .01.

**

to explore different departments. High school ability measures (ACT score and high school percentile) were also coded for use as covariates in predicting college outcomes. 2.3. Design and procedure This was a longitudinal, correlational study that was conducted in two phases. In the first phase (during students’ first semester in college) students completed measures of achievement motivation and their general achievement goals for college. We obtained students’ transcripts 2 years later.

mance. These groups did not differ on ACT score, workmastery, competitiveness, mastery-approach goals, performance-approach goals, or performance-avoidance goals. The only variable revealing a group difference was high school percentile, F(2, 231) = 6.21, p < .01.1 A Tukey post hoc test revealed that participants dismissed from the university for academic reasons (M = 52.86, SD = 15.54) had lower high school percentile scores than those in the sample (M = 69.28, SD = 17.05) and those omitted because they chose to leave the university (M = 66.33, SD = 17.39). These latter two groups did not differ from one another. 3.3. Primary analyses

3. Results 3.1. Data screening The data were examined to identify oddities that could compromise the interpretation of the results. Examination of skewness and kurtosis indicators showed that the variables were fairly normally distributed. Moreover, multicolinearity did not emerge as a problem (see correlations in Table 2). The relationship between mastery-approach goals and workmastery and that between performance-approach goals and competitiveness were strong, consistent with what one would expect. Related to this, the reason to include achievement motivation was to test the effects of goals, controlling for achievement motivation, knowing that these constructs were correlated. This was addressed further in the ancillary analyses. The assumptions of multiple regression were also tested by evaluating the normality of the residuals and examining scatterplots of the residuals as a function of each predictor. The residuals appeared to be both normal and homoscedastic. Finally, predictors were screened for having quadratic relationships with each outcome variable. First, a significant quadratic relationship emerged using competitiveness to predict mastery goals and several variables (high school percentile, workmastery, competitiveness, and performance-approach goals) revealed significant quadratic relationships with cumulative GPA. These quadratic terms were retained in the relevant analyses and are detailed below. 3.2. Attrition analyses A set of one-way, three-group ANOVAs was conducted to test differences between individuals included in the final sample, those omitted because they did not have sufficient data to warrant longitudinal analysis, and those omitted due to low academic perfor-

Multiple regression was used to test the hypotheses in this research. First, a series of regressions were conducted to predict students’ achievement goals. Next, two additional regressions were conducted to predict cumulative GPA and diversity in course choices. Preliminary analyses indicated that gender did not predict any outcome and therefore was excluded from analyses. Moreover, all two-way interactions among variables entered on a given step were tested and none were significant. Therefore, no interaction terms were included in the final analyses. An additional seven participants were excluded from the regression analyses because they were missing either ACT and/or high school percentile scores. Bivariate correlations and descriptive statistics for all variables in the study are presented in Table 2. 3.4. Predicting achievement goals Three regressions tested the predictors of general achievement goals for college courses. Each regression used high school percentile, ACT score, workmastery, and competitiveness as predictors. These predictors were entered simultaneously. When the variables were used to predict mastery-approach goals, the overall model was significant, F(4, 202) = 24.36, p < .01, R2 = 33. Two significant univariate effects emerged. As predicted, workmastery positively predicted the adoption of mastery goals, t(202) = 9.16, p < .01, b = .55. In contrast, competitiveness negatively predicted mastery goals, t(202) = 2.36, p < .05, b = .14. No other effects were significant. The quadratic term of competitiveness found previously to be related to mastery goals was not significant when entered alone on the final step. When the variables were used to predict students’ adoption of performance-approach goals, the model was significant, F(4, 202) = 1 The degrees of freedom for the error term are reduced because high school percentile scores were not available for everyone.

A.M. Durik et al. / Contemporary Educational Psychology 34 (2009) 113–119

15.53, p < .05, R2 = 24. Consistent with prior research, competitiveness strongly predicted the adoption of performance-approach goals, t(202) = 6.26, p < .01, b = .39. Interestingly, workmastery also positively predicted performance-approach goals, t(202) = 2.78, p < .01, b = .18. No other predictors were significant. The model predicting students’ adoption of performance-avoidance goals was also significant, F(4, 202) = 2.95, p < 05, R2 = .06. The only significant univariate predictor was ACT score, t(202) = 2.94, p < .01, b = .21, indicating that individuals who scored lower on the ACT were more apt to adopt performance-avoidance goals. Overall, the analyses examining the extent to which achievement motivation and high school ability predicted general goal adoption were consistent with our hypotheses. Individuals who scored higher in workmastery were indeed more likely to adopt mastery-approach goals and individuals who scored higher in competitiveness were more likely to adopt performance-approach goals. Achievement motivation did not predict the adoption of performance-avoidance goals. 3.5. Predicting cumulative GPA and course diversity In the analyses predicting cumulative GPA and course diversity, achievement motivation, high school percentile, and ACT score were entered on the first step. The three goal variables were entered on the second step. The four variables that revealed quadratic relationships with cumulative GPA were entered simultaneously on the final step predicting this variable. When achievement motivation and high school ability were entered on the first step to predict cumulative GPA, the variables accounted for a significant portion of variance, F(4, 202) = 11.33.17, p < .01, R2 = .18. ACT score, t(202) = 3.61, p < .01, b = .24, and high school percentile, t(202) = 4.39, p < .01, b = .29, positively predicted college performance. Neither workmastery nor competitiveness uniquely predicted cumulative GPA. When the goals were entered on the second step, there was a significant increase in variability accounted for, F(3, 199) = 6.31, p < .01, DR2 = .07. The univariate effects revealed that performance-approach goals positively predicted overall college performance, t(199) = 3.60, p < .01, b = .26, whereas performance-avoidance goals negatively predicted performance, t(199) = 2.27, p < .01, b = .14. Mastery goals did not uniquely predict cumulative GPA, t(199) = 0.44, p = .66, b = .03. Not surprisingly, ACT score, t(199) = 2.98, p < .05, b = .20, and high school percentile, t(199) = 4.14, p < .01, b = .27, were still positive and significant predictors of cumulative GPA after goals were entered into the analysis. This analysis revealed two independent effects of achievement goals for college courses in general on cumulative GPA and parallels the results of research conducted within more specific classroom contexts. Recall that preliminary analyses indicated that four variables showed quadratic relationships with cumulative GPA. When these quadratic terms were entered on the last step, two showed statistically significant positive effects (high school percentile, t(198) = 1.99, p < .05, b = .13, and competitiveness, t(198) = 2.01, p < .05, b = .14), but the goal effects described above were still prominent, and even stronger. The pattern of the quadratic relationships showed that high school percentile had a more positive relationship with performance at the higher end of the continuum of high school percentile (where there were more data points) and almost no relationship at lower levels of high school percentile. The slope between competitiveness and GPA was slightly negative at the low end of competitiveness but the slope leveled off at higher levels of competitiveness. In the analysis predicting diversity in course selection, achievement motivation and high school ability variables were entered on the first step, F(4, 202) = 4.98, p < .01, R2 = .09. The univariate effects revealed a significant negative effect of workmastery, t(202) = 3.42, p < .01, b = .23, and a significant positive effect of compet-

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itiveness, t(202) = 1.95, p = .05, b = .13. These effects showed that individuals who scored higher in workmastery showed less variability in their course selections and that individuals who scored higher in competitiveness showed more variability in their course selections. Although these effects were not predicted to emerge for achievement motivation, the workmastery effect is consistent with the notion that a mastery orientation helps funnel interest into specific disciplines. It is inconsistent with the notion that a mastery approach helps to broaden interests. No other univariate effects were significant. When entered on the second step to predict course diversity, the goal variables did not account for a significant increase in variance accounted for, F(3, 199) = 0.45, p = .72, DR2 < .01. None of the goals were significant predictors of course diversity, mastery-approach goals in particular, t(199) = 0.26, p = .80, b = .02. The effects of workmastery, t(199) = 2.59, p = .01, b = .22, and competitiveness, t(199) = 2.04, p < .05, b = .16, were still present on the second step. Fig. 1 depicts a model of the significant paths from this series of analyses. The full picture shows that workmastery and competitiveness strongly predicted mastery and performance-approach goals, respectively, consistent with prior research. ACT only predicted performance-avoidance goals, such that higher ACT was associated with lower performance-avoidance goals. High school percentile did not predict goal adoption, but directly predicted cumulative GPA. Achievement motivation predicted course diversity more strongly than achievement goals, such that individuals who scored high on workmastery and those who scored low on competitiveness funneled their academic attention more narrowly. Finally, performance goals as well as ability measures predicted cumulative GPA. Taken together, the figure reveals that the factors of achievement motivation were stronger predictors of course choices and achievement goals were stronger predictors of college performance. 3.6. Ancillary analyses Given that, contrary to expectations, mastery-approach goals did not predict course diversity when achievement motivation was in the model, a final analysis was conducted to test the extent to which goals predicted course variety, excluding achievement motivation. This analysis included achievement goals, high school percentile, and ACT scores as predictors, which yielded a significant

Fig. 1. Model developed from multiple regression analyses. Only significant paths are shown although all paths were tested. The values represent standardized regression coefficients. The dashed path from mastery-approach goals to course diversity represents the relationship when achievement motivation was not in the analysis. This path was nonsignificant (b = .02) when achievement motivation was in the analysis.

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model, F(5, 201) = 2.31, p < .05, R2 < .05. The only significant univariate effect was a negative effect of mastery-approach goals on course diversity, t(201) = 2.21, p = .05, b = .16. This effect indicated that individuals who set mastery-approach goals were more likely to focus in on particular domains rather than take a more diverse set of college courses. The path between mastery-approach goals and course diversity is included as a dashed line in Fig. 1. 4. Discussion These data replicate and extend prior work on achievement goals and college outcomes. Consistent with prior research, achievement motivation predicted achievement goals in this study, such that workmastery was a strong and positive predictor of mastery-approach goals and competitiveness was a strong and positive predictor of performance-approach goals. Whereas prior research has documented similar relationships within particular classroom contexts, the current study showed that these relationships hold when goals are measured for college courses in general. It is also noteworthy that workmastery positively predicted performanceapproach goals. The analyses predicting cumulative GPA indicated that general achievement goals for college had unique effects beyond those of achievement motivation, suggesting that achievement goals measured at a general level are still empirically separable from individuals’ general approaches to achievement situations. Most interesting for the purpose of this study was that performance-approach goals positively predicted cumulative GPA and performance-avoidance goals negatively predicted cumulative GPA, while controlling for ability measures (which were also strong predictors of college performance). The positive effects of performance-approach goals and the negative effects of performanceavoidance goals are consistent with prior research. Extrapolating from the research cited earlier, the results of the current study suggest that individuals who set performance-approach goals for college (i.e., those who are more likely to believe that competence is a fixed quality and that they do indeed have the ability to perform well), persist longer and put more effort into studying (Elliot et al., 1999). In contrast, individuals who set performance-avoidance goals do not do as well in college. These individuals fear doing poorly in college, which might cause them to use less efficient studying strategies and to engage in self-handicapping (Midgley & Urdan, 2001; Urdan & Midgley, 2001). Specifically, prior research has shown that individuals who adopt performance-avoidance goals view exams as threatening, experience test anxiety, and use disorganized strategies while studying for exams (Elliot et al., 1999; McGregor & Elliot, 2002). Disorganized studying (e.g., reports of not knowing how to study effectively for a given course) has been shown to mediate the relationship between performance-avoidance goals and exam performance in a particular class (Elliot et al., 1999). The negative relationship between performance-avoidance goals and overall GPA in the current study suggests that these processes could play out repeatedly across many courses and semesters. These effects of performance goals were obtained with a sample of students studying in areas across the university curriculum and in a variety of class contexts, adding to the generalizability of these effects in a broad number of domains and beyond learning environments cultivated in particular classrooms. That said, the presence of performance goal effects on cumulative GPA were operating within a particular university context. A further question is whether the context that supported these relationships is unique to large public universities in the United States, unique to colleges and universities in the United States more generally, or truly general, operating across contexts and even across cultures.

In contrast to the effects on cumulative GPA, achievement goals for college in general were not related to diversity in course selection when achievement motivation was included in the model. Although mastery-approach goals predicted course diversity when only ability and goals were in the model, this relationship was overshadowed by the stronger relationship between workmastery and course diversity. The negative relationship observed between workmastery and course diversity indicates that, when making course selections, individuals who are oriented toward workmastery focus in on specific domains rather than out to new learning experiences. Although not hypothesized to take this form, the adoption of mastery-approach goals predicted course diversity only when achievement motivation was excluded from the model. In other words, individuals high in workmastery were more likely to adopt mastery-approach goals and these individuals were more likely to take courses in a fewer number of departments. Recall that prior research has shown that mastery-approach goals predict the development of course interest when controlling for interest at the beginning of the semester (Harackiewicz et al., 2008). This suggests that students who are workmastery oriented may adopt mastery-approach goals and consequently find most if not all of their college courses interesting. By adopting mastery goals, high workmastery individuals might end up studying disciplines they initially encounter in college. An alternative explanation for the positive correlation between workmastery and mastery goals and their negative relationships with course diversity is that individuals high in workmastery may enter college with a clearer idea of what they want to study. These individuals may be more likely to select courses early on in college in identified areas of interest. Given that interest is both an antecedent and a consequence of mastery-approach goals (Harackiewicz et al., 2008), it is possible that having identified academic interests may cause high workmastery individuals to adopt mastery goals for their courses overall. To our knowledge, there is no data about whether workmastery orientation is associated with domain interests prior to college. It would be useful in future research to determine whether domain interests prior to college can account for the effects found here predicting course diversity. The predictive relationships reported here between achievement motivation and achievement goals and college outcomes may have implications for educators trying to identify individuals who might flounder in college. Students begin college with goals and motivation that guide their approaches to courses in general. These data suggest that individuals who are low in performanceapproach goals and high in performance-avoidance goals might be vulnerable to academic difficulties. Moreover, individuals who score low in workmastery might find it especially difficult to focus in on a major of study. Achievement motivation and general achievement goals could serve as markers for students who might need assistance from college counselors and advisors in maintaining good grades and identifying a major field of study. References Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84(3), 261–271. Archer, J. A. (1994). Achievement goals as a measure of motivation in university students. Contemporary Educational Psychology, 19, 430–446. Atkinson, J. W. (1974). The mainspring of achievement oriented activity. In J. W. Atkinson & J. O. Raynor (Eds.), Motivation and achievement (pp. 13–41). Washington DC: Winston. Bong, M. (2001). Between- and within-domain relations of academic motivation among middle and high school students: Self-efficacy, task-value, and achievement goals. Journal of Educational Psychology, 93, 23–34. Chalupa, M., Chen, C., & Charles, T. (2001). An analysis of college students’ motivation and learning strategies in computer courses: A cognitive view. Delta Pi Epsilon Journal, 43, 185–199. Cohen, J., Cohen, P., West, S. G., & Aiken, L. A. (2003). Applied multiple regression/ correlation analysis for the behavioral sciences (3rd ed.). Hillsdale, NJ: Erlbaum.

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