Journal
PSYCHOLOGICAL METHODS
Volume 12, Issue 1, Pages 58-79Publisher
AMER PSYCHOLOGICAL ASSOC
DOI: 10.1037/1082-989X.12.1.58
Keywords
item factor analysis; parameter estimation; categorical data; confirmatory factor analysis; item response theory
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Funding
- NATIONAL INSTITUTE ON DRUG ABUSE [R01DA015398, F31DA017546] Funding Source: NIH RePORTER
- NIDA NIH HHS [R01 DA015398-06, R01DA015398, R01 DA015398, F31DA017546, F31 DA017546] Funding Source: Medline
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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.
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