Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models

F.B. Tekle, D.W. Gudicha, J.K. Vermunt

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)

Abstract

Latent class (LC) analysis is used to construct empirical evidence on the existence of latent subgroups based on the associations among a set of observed discrete variables. One of the tests used to infer about the number of underlying subgroups is the bootstrap likelihood ratio test (BLRT). Although power analysis is rarely conducted for this test, it is important to identify, clarify, and specify the design issues that influence the statistical inference on the number of latent classes based on the BLRT. This paper proposes a computationally efficient ‘short-cut’ method to evaluate the power of the BLRT, as well as presents a procedure to determine a required sample size to attain a specific power level. Results of our numerical study showed that this short-cut method yields reliable estimates of the power of the BLRT. The numerical study also showed that the sample size required to achieve a specified power level depends on various factors of which the class separation plays a dominant role. In some situations, a sample size of 200 may be enough, while in others 2000 or more subjects are required to achieve the required power.
Keywords: Bootstrap, Latent class models, Likelihood ratio test, Power Sample size
Original languageEnglish
Pages (from-to)209-224
JournalAdvances in Data Analysis and Classification
Volume10
Issue number2
DOIs
Publication statusPublished - 2016

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