Building an identifiable latent class model with covariate effects on underlying and measured variables

Guan Hua Huang, Karen Bandeen-Roche

Research output: Contribution to journalArticle

Abstract

In recent years, latent class models have proven useful for analyzing relationships between measured multiple indicators and covariates of interest. Such models summarize shared features of the multiple indicators as an underlying categorical variable, and the indicators' substantive associations with predictors are built directly and indirectly in unique model parameters. In this paper, we provide a detailed study on the theory and application of building models that allow mediated relationships between primary predictors and latent class membership, but that also allow direct effects of secondary covariates on the indicators themselves. Theory for model identification is developed. We detail an Expectation-Maximization algorithm for parameter estimation, standard error calculation, and convergent properties. Comparison of the proposed model with models underlying existing latent class modeling software is provided. A detailed analysis of how visual impairments affect older persons' functioning requiring distance vision is used for illustration.

Original languageEnglish (US)
Pages (from-to)5-32
Number of pages28
JournalPsychometrika
Volume69
Issue number1
DOIs
StatePublished - Mar 2004

Keywords

  • EM algorithm
  • Finite mixture model
  • Identifiability
  • Multiple discrete indicators
  • Visual functioning

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Building an identifiable latent class model with covariate effects on underlying and measured variables'. Together they form a unique fingerprint.

  • Cite this