title: Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach.
abstract: Although prediction of class membership from observed variables in latent class analysis is well understood, predicting an observed distal outcome from latent class membership is more complicated. A flexible model-based approach is proposed to empirically derive and summarize the class-dependent density functions of distal outcomes with categorical, continuous, or count distributions. A Monte Carlo simulation study is conducted to compare the performance of the new technique to two commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudo-class draws. Simulation results show that the model-based approach produces substantially less biased estimates of the effect compared to either classify-analyze technique, particularly when the association between the latent class variable and the distal outcome is strong. In addition, we show that only the model-based approach is consistent. The approach is demonstrated empirically: latent classes of adolescent depression are used to predict smoking, grades, and delinquency. SAS syntax for implementing this approach using PROC LCA and a corresponding macro are provided.
authors.affiliation: The Methodology Center, The Pennsylvania State University ; College of Health and Human Development, The Pennsylvania State University.
authors.forename: Stephanie T
authors.initials: ST
authors.lastname: Lanza
authors.affiliation: The Methodology Center, The Pennsylvania State University.
authors.forename: Xianming
authors.initials: X
authors.lastname: Tan
authors.affiliation: Department of Psychology, Virginia Polytechnic Institute and State University.
authors.forename: Bethany C
authors.initials: BC
authors.lastname: Bray
keywords: distal outcome; finite mixture model; latent class analysis; pseudo-class draws