Item factor analysis: Current approaches and future directions.

TLDR

Factor analysis is applicable to both continuous and categorical data, but conventional models for continuous data are unsuitable for item‑level categorical data. The authors review and synthesize estimation methods for item factor analysis of ordered categorical data, focusing on challenges with many items and factors. They present popular IFA models and estimation techniques from SEM and IRT, and discuss recent advances such as Markov chain Monte Carlo for parameter estimation. The paper concludes with future research directions, simulated examples, and practical guidance for applied researchers.

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.