Application of CART, Neural Networks, and Generalized Additive Models: A Case Study

Abstract

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of pre-injury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has found conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The current study had two goals: 1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but which have not been frequently explored in the social sciences and 2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Prediction of Stanford Binet 5 FSIQ scores for preschool aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees (CART) provided more accurate prediction of FSIQ
scores than the more traditional regression approach. Implications of these results are discussed.