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Latent Class Analysis of Incomplete Data via an Entropy-Based Criterion
Chantal Larose1, Ofer Harel2, Katarzyna Kordas3
1School of Business, State University of New York at New Paltz, New Paltz, NY, USA.
Abstract:
Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC.
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