Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis

Bethany C. Bray, Stephanie T. Lanza, Xianming Tan

Research output: Contribution to journalArticle

96 Scopus citations

Abstract

Despite recent methodological advances in latent class analysis (LCA) and a rapid increase in its application in behavioral research, complex research questions that include latent class variables often must be addressed by classifying individuals into latent classes and treating class membership as known in a subsequent analysis. Traditional approaches to classifying individuals based on posterior probabilities are known to produce attenuated estimates in the analytic model. We propose the use of a more inclusive LCA to generate posterior probabilities; this LCA includes additional variables present in the analytic model. A motivating empirical demonstration is presented, followed by a simulation study to assess the performance of the proposed strategy. Results show that with sufficient measurement quality or sample size, the proposed strategy reduces or eliminates bias.

Original languageEnglish (US)
Pages (from-to)1-11
Number of pages11
JournalStructural Equation Modeling
Volume22
Issue number1
DOIs
StatePublished - Jan 2 2015

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)

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