by Ofer Harel and Diana Miglioretti

Journal of Data Science, v.5, no.2, 269-288

Abstract

Latent class analysis (LCA) is a popular method for analyzing multiple categorical outcomes. Given the potential for LCA model assumptions to influence inference, model diagnostics are a particulary important part of LCA. We suggest using the rate of missing information as an additional diagnostic tool. The rate of missing information gives an indication of the amount of information missing as a result of observing multiple surrogates in place of the underlying latent variable of interest and provides a measure of how confident one can be in the model results. Simulation studies and real data examples are presented to explore the usefulness of the proposed measure.

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