Extended criteria and predictors in college admission: Exploring the structure of study success and investigating the validity of domain knowledge

The utility of aptitude tests and intelligence measures in the prediction of the success in college is one of the empirically best supported results in ability research. However, the structure of the criterion “study success” has not been appropriately investigated so far. Moreover, it remains unclear which aspect of intelligence – fluid intelligence or crystallized intelligence – has the major impact on the prediction. In three studies we have investigated the dimensionality of the criterion achievements as well as the relative contributions of competing ability predictors. In the first study, the dimensionality of college grades was explored in a sample of 629 alumni. A measurement model with two correlated latent factors distinguishing undergraduate college grades on the one hand from graduate college grades on the other hand had the best fit to the data. In the second study, a group of 179 graduate students completed a Psychology knowledge test and provided available college grades in undergraduate studies. A model separating a general latent factor for Psychology knowledge from a nested method factor for college grades, and a second nested factor for “experimental orientation” had the best fit to the data. In the third study the predictive power of domain specific knowledge tests in Mathematics, English, and Biology was investigated. A sample of 387 undergraduate students in this prospective study additionally completed a compilation of fluid intelligence tests. The results of this study indicate as expected that: a) ability measures are incrementally predictive over school grades in predicting exam grades; and b) that knowledge tests from relevant domains were incrementally predictive over fluid intelligence. The results of these studies suggest that criteria for college admission tests deserve and warrant more attention, and that domain specific ability indicators can contribute to the predictive validity of established admission tests.