Model selection for complex multilevel latent class model
Date
2014
Authors
Zhang, Jieting
Zhang, Minqiang
Zhang, Wen
Jiao, Can
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Taylor & Francis Group
Abstract
Multilevel latent class analysis is conducive to providing more effective information on both individual and group typologies. However, model selection issues still need further investigation. Current study probed into issue of high-level class numeration for a more complex model using AIC, AIC3, BIC, and BIC*. Data simulation was conducted and its result was verified by empirical data. The result demonstrated that these criteria have a certain inclination relative to sample sizes. Sample size per group plays an evident role in improving accuracy of AIC3 and BIC. The complex model requires more sample size per group to ensure accurate class numeration.
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Communications in Statistics - Simulation and Computation
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Journal article
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2037-12-31
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