Prediction of University Students' Academic Achievement by Linear and Logistic Models

Abstract

University students' academic achievement measured by means of academic progress is modeled through linear and logistic regression, employing prior achievement and demographic factors as predictors. The main aim of the present paper is to compare results yielded by both statistical procedures, in order to identify the most suitable approach in terms of goodness of fit and predictive power. Grades awarded in basic scientific courses and demographic variables were entered into the models at the first step. Two hypotheses are proposed: (a) Grades in basic courses as well as demographic factors are directly related to academic progress, and (b) Logistic regression is more appropriate than linear regression due to its higher predictive power. Results partially confirm the first prediction, as grades are positively related to progress. However, not all demographic factors considered proved to be good predictors. With regard to the second hypothesis, logistic regression was shown to be a better approach than linear regression, yielding more stable estimates with regard to the presence of ill-fitting patterns.