title: Using Classification and Regression Trees (CART) and random forests to analyze attrition: Results from two simulations.
abstract: In this article, we describe a recent development in the analysis of attrition: using classification and regression trees (CART) and random forest methods to generate inverse sampling weights. These flexible machine learning techniques have the potential to capture complex nonlinear, interactive selection models, yet to our knowledge, their performance in the missing data analysis context has never been evaluated. To assess the potential benefits of these methods, we compare their performance with commonly employed multiple imputation and complete case techniques in 2 simulations. These initial results suggest that weights computed from pruned CART analyses performed well in terms of both bias and efficiency when compared with other methods. We discuss the implications of these findings for applied researchers.
authors.affiliation: Department of Psychology, University of Southern California.
authors.forename: Timothy
authors.identifier: 0000-0003-1999-0940
authors.initials: T
authors.lastname: Hayes
authors.affiliation: Department of Psychology, University of Tsukuba.
authors.forename: Satoshi
authors.initials: S
authors.lastname: Usami
authors.affiliation: Department of Psychology, University of Southern California.
authors.forename: Ross
authors.initials: R
authors.lastname: Jacobucci
authors.affiliation: Department of Psychology, University of Southern California.
authors.forename: John J
authors.initials: JJ
authors.lastname: McArdle
mesh_terms: D015982:Bias; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D012044:Regression Analysis