Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis

Jenn-Yun Tein, Stefany Coxe, Heining Cham

Research output: Contribution to journalArticlepeer-review

892 Scopus citations


Little research has examined factors influencing statistical power to detect the correct number of latent classes using latent profile analysis (LPA). This simulation study examined power related to interclass distance between latent classes given true number of classes, sample size, and number of indicators. Seven model selection methods were evaluated. None had adequate power to select the correct number of classes with a small (Cohen's d =.2) or medium (d =.5) degree of separation. With a very large degree of separation (d = 1.5), the Lo-Mendell-Rubin test (LMR), adjusted LMR, bootstrap likelihood ratio test, Bayesian Information Criterion (BIC), and sample-size-adjusted BIC were good at selecting the correct number of classes. However, with a large degree of separation (d =.8), power depended on number of indicators and sample size. Akaike's Information Criterion and entropy poorly selected the correct number of classes, regardless of degree of separation, number of indicators, or sample size.

Original languageEnglish (US)
Pages (from-to)640-657
Number of pages18
JournalStructural Equation Modeling
Issue number4
StatePublished - Oct 2013


  • interclass distance
  • latent profile analysis
  • mixture models
  • model section methods
  • power

ASJC Scopus subject areas

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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