Citation
Lanza, Stephanie T.; Tan, Xianming; & Bray, Bethany C. (2013). Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach. Structural Equation Modeling: A Multidisciplinary Journal. vol. 20 (1) pp. 1-26Abstract
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 2 commonly used classify-analyze techniques: maximum-probability assignment and multiple pseudoclass 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.URL
http://dx.doi.org/10.1080/10705511.2013.742377Reference Type
Journal ArticleJournal Title
Structural Equation Modeling: A Multidisciplinary JournalAuthor(s)
Lanza, Stephanie T.Tan, Xianming
Bray, Bethany C.