title: Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis.
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.
authors.affiliation: The Methodology Center, Penn State.
authors.forename: Bethany C
authors.initials: BC
authors.lastname: Bray
authors.affiliation: The Methodology Center, Penn State ; College of Health and Human Development, Penn State.
authors.forename: Stephanie T
authors.initials: ST
authors.lastname: Lanza
authors.affiliation: Biostatistics Core Facility, McGill University Health Centre.
authors.forename: Xianming
authors.initials: X
authors.lastname: Tan
keywords: classify-analyze; latent class analysis; maximum probability assignment; posterior probabilities; pseudo-class draws