Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis

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...

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Bibliographic Details
Published in:Structural equation modeling 2015-01, Vol.22 (1), p.1-11
Main Authors: Bray, Bethany C., Lanza, Stephanie T., Tan, Xianming
Format: Article
Language:English
Subjects:
classify-analyze
Estimation bias
latent class analysis
Mathematical models
maximum-probability assignment
posterior probabilities
Probability
pseudo-class draws
Research methodology
Statistical analysis
Statistical methods
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Summary: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.
ISSN:1070-5511
1532-8007
DOI:10.1080/10705511.2014.935265