Modeling Change in Effort Across a Low-Stakes Testing Session: A Latent Growth Curve Modeling Approach

ABSTRACT

We examined change in test-taking effort over the course of a three-hour, five test, low-stakes testing session. Latent growth modeling results indicated that change in test-taking effort was well-represented by a piecewise growth form, wherein effort increased from test 1 to test 4 and then decreased from test 4 to test 5. There was significant variability in effort for each of the five tests, which could be predicted from examinees’ conscientiousness, agreeableness, mastery approach goal orientation, and whether the examinee “skipped” or attended the initial testing session. The degree to which examinees perceived a particular test as important was related to effort for the difficult, cognitive test but not for less difficult, noncognitive tests. There was significant variability in the rates of change in effort, which could be predicted from examinees’ agreeableness. Interestingly, change in test-taking effort was not related to change in perceived test importance. Implications of these results for assessment practice and directions for future research are discussed.

Notes

¹ Cognitive tests measure knowledge and contain items scored as right or wrong, whereas noncognitive tests measure attitudes or affect with no correct answer.

² A quadratic model would have resulted in similar substantive interpretations compared to the piecewise model. Specifically, for the quadratic model, the linear slope factor mean was positive and significant, and the quadratic factor mean was negative and significant, indicating that as the testing session progressed, change in effort became less positive. The correlation between linear and quad slope factors was negative (i.e., individuals with greater increase in effort between test 1 and 2 have more of a subsequent decrease in effort across remaining tests).

³ Given the number of statistical tests conducted, we adjusted the alpha level to assess statistical significance for all external variables. We decided against the Bonferroni adjustment given its conservative nature; we did not want to fail to uncover effects due to low power as a result of an alpha level of 0.0003. We felt comfortable with a value of 0.01 as a compromise.

⁴ The quadratic model provided similar interpretations in the conditional piecewise LGM. Similar to the piecewise model where agreeableness predicted the first linear slope (i.e., more agreeable students had more of an increase in effort from test 1 to test 4), in the quadratic model, agreeableness predicted the linear slope (i.e., more agreeable students had greater increases in effort from test 1 to test 2).