Data from the National Survey of Student Engagement (NSSE) collected across seven years were used to predict final, cumulative grade point averages (GPA). Cross-product regression was used to explore the predictive abilities of the NSSE benchmark scores for freshmen (n = 2578) and seniors (n = 2293) collected in cross-sectional cohorts. Hierarchical regression was also used with 127 longitudinal responses in students' first and senior years of college. In the cross-sectional analyses, Level of Academic Challenge emerged as a significant predictor of GPA for freshmen, whereas the Active and Collaborative Learning benchmark was a significant predictor for seniors; both effects were modest. The cross-sectional data explained 22.6% of the variance with 18.2% of this variance accounted for by pre-college control factors (American College Test score and high school GPA). For the analysis of longitudinal data, 31.3% of the variance was explained and 27.8% was attributed to the pre-college indicators. No benchmark scores were significant predictors of GPA in the longitudinal data. Results suggest that cross-sectional analyses can adequately detect modest effects on final GPA. In contrast, longitudinal models explain more variance, though they lack the power to reveal modest effects. This study suggests approaches for the responsible use of cross-sectional and longitudinal data in educational research. (Contains 4 tables and 2 figures.)