Item factor analysis: Current approaches and future directions

R. J. Wirth, Michael C. Edwards

Research output: Contribution to journalArticle

416 Scopus citations

Abstract

The rationale underlying factor analysis applies to continuous and categorical variables alike; however, the models and estimation methods for continuous (i.e., interval or ratio scale) data are not appropriate for item-level data that are categorical in nature. The authors provide a targeted review and synthesis of the item factor analysis (IFA) estimation literature for ordered-categorical data (e.g., Likert-type response scales) with specific attention paid to the problems of estimating models with many items and many factors. Popular IFA models and estimation methods found in the structural equation modeling and item response theory literatures are presented. Following this presentation, recent developments in the estimation of IFA parameters (e.g., Markov chain Monte Carlo) are discussed. The authors conclude with considerations for future research on IFA, simulated examples, and advice for applied researchers.

Original languageEnglish (US)
Pages (from-to)58-79
Number of pages22
JournalPsychological Methods
Volume12
Issue number1
DOIs
StatePublished - Mar 1 2007
Externally publishedYes

Keywords

  • Categorical data
  • Confirmatory factor analysis
  • Item factor analysis
  • Item response theory
  • Markov chain Monte Carlo
  • Parameter estimation

ASJC Scopus subject areas

  • Psychology (miscellaneous)

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