A Bootstrap Procedure for Mixture Models
A bootstrap procedure useful in latent class models has been developed to determine the sufficient number of latent classes required to account for systematic group differences in the data. The procedure is illustrated in the context of a multidimensional scaling latent class model, CLASCAL. Real and artficial data are presented. The bootstrap procedure for selecting a sufficient number of classes seems to correctly select the correct number of latent classes at both low and high error levels. At higher error levels it outperforms Hope’s (1968) procedure.
KeywordsLatent Classis Bootstrap Sample Bootstrap Procedure Latent Class Model Artificial Data
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