The statistical classification of "N" individuals into "G" mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis,…

Differential item functioning (DIF) continues to receive attention both in applied and methodological studies. Because DIF can be an indicator of irrelevant variance that can influence test scores, continuing to evaluate and improve the accuracy of detection methods is an essential step in gathering score validity evidence. Methods for detecting…

This study compares the classification accuracy of linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR), and classification and regression trees (CART) under a variety of data conditions. Past research has generally found comparable performance of LDA and LR, with relatively less research on QDA and…

The power of the classical Linear Discriminant Function (LDF) is compared, using Monte Carlo techniques with five other procedures for classifying observations from certain non-normal distributions. The alternative procedures considered are the Quadratic Discriminant Function, a Nearest Neighbor Procedure with Probability Blocks, and three density…

This paper comments on the impact of computers on statistical analysis and presents a concise, nontechnical overview of five statistical methods now being applied in educational research. Appropriate uses of these techniques are pointed out, along with dangers concerning misapplications. (PP)

Classification studies are important for practitioners who need to identify individuals for specialized treatment or intervention. When interventions are irreversible or misclassifications are costly, information about the proficiency of different classification procedures becomes invaluable. This study furnishes information about the relative…