Model selection for complex multilevel latent class model
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Communications in Statistics - Simulation and Computation|
|01_Zhang_Model_selection_for_complex_2014.pdf||626.91 kB||Adobe PDF||Request a copy|
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