NDLI: Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis

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Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis

Content Provider	Scilit
Author	Bray, Bethany C. Lanza, Stephanie T. Tan, Xianming
Copyright Year	2014
Description	Journal: Structural Equation Modeling: A Multidisciplinary Journal 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.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299667/pdf
ISSN	10705511
e-ISSN	15328007
DOI	10.1080/10705511.2014.935265
Journal	Structural Equation Modeling: A Multidisciplinary Journal
Issue Number	1
Volume Number	22
Language	English
Publisher	Informa UK Limited
Publisher Date	2015-01-02
Access Restriction	Open
Subject Keyword	Journal: Structural Equation Modeling: A Multidisciplinary Journal Mathematical Social Sciences Classify-analyze Latent Class Analysis Maximum-probability Assignment Posterior Probabilities Pseudo-class Draws
Content Type	Text
Subject	Sociology and Political Science Economics, Econometrics and Finance Decision Sciences Modeling and Simulation

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