The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

This study examines whether the classification and regression tree (CART) model improves the early identification of students at risk for reading comprehension difficulties compared with the more difficult to interpret logistic regression model. CART is a type of predictive modeling that relies on nonparametric techniques. It presents results in…

This study evaluates a large urban district's standards-based promotion policy decisions against a model-driven classification. Hierarchical logistic regression was used to explore factors related to grade retention at both the student and school level. Statistical results indicate that using students' next year achievement test scores as…