A Markov chain Monte Carlo approach to confirmatory item factor analysis

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43 Scopus citations

Abstract

Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal of this paper is to demonstrate a novel combination of various Markov chain Monte Carlo (MCMC) estimation routines to estimate parameters of a wide variety of confirmatory item factor analysis models. Further, I show that these methods can be implemented in a flexible way which requires minimal technical sophistication on the part of the end user. After providing an overview of item factor analysis and MCMC, results from several examples (simulated and real) will be discussed. The bulk of these examples focus on models that are problematic for current "gold-standard" estimators. The results demonstrate that it is possible to obtain accurate parameter estimates using MCMC in a relatively user-friendly package.

Original languageEnglish (US)
Pages (from-to)474-497
Number of pages24
JournalPsychometrika
Volume75
Issue number3
DOIs
StatePublished - Apr 1 2010
Externally publishedYes

Keywords

  • Markov chain Monte Carlo
  • item factor analysis
  • multidimensional item response theory

ASJC Scopus subject areas

  • Psychology(all)
  • Applied Mathematics

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