Power computation for likelihood ratio tests for the transition parameters in latent Markov models

D.W. Gudicha, V.D. Schmittmann, J.K. Vermunt

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

Latent Markov (LM) models are increasingly used in a wide range of research areas including psychological, sociological, educational, and medical sciences. Methods to perform power computations are lacking, however. This article presents methods for preforming power analysis in LM models. Two cases of tests of hypotheses on the transition parameters of LM models are considered. The first case concerns the situation where the likelihood ratio test statistic follows a chi-square distribution, implying that the power computation can also be based on this theoretical distribution. In the second case, power needs to be computed based on empirical distributions constructed via Monte Carlo methods. Numerical studies are conducted to illustrate the proposed power computation methods and to investigate design factors affecting the power of this test.
Keywords: likelihood ratio, Monte Carlo methods, transition probabilities, design factors, power computation
Original languageEnglish
Pages (from-to)234-245
JournalStructural Equation Modeling
Volume23
Issue number2
DOIs
Publication statusPublished - 2016

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