The purpose of this study was to determine the efficiency of a system containing aptitude, achievement, and noncognitive data, and a measure of college performance (first-semester grade point average) in predicting the longrange educational-vocational decisions of engineering students. What are the relevant sets of predictors when academic status is defined according to (a) persisters in engineering, transfers from engineering, and university withdrawals; (b) engineering subfields; or (c) distinct major fields of study? Noncognitive data best predicts between intellectually homogeneous student groupings, whereas first-semester grade point average dominates the predictor set relevant to more intellectually heterogeneous groupings. Concerning the discriminant procedures, their results were similar except for a decision rule that used sample proportions as estimates of prior probabilities.

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