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Applied Psychological Measurement
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Full-Information Item Factor Analysis

R. Darrell Bock

University of Chicago

Robert Gibbons

University of Illinois

Eiji Muraki

National Opinion Research Center

A method of item factor analysis based on Thur stone's multiple-factor model and implemented by marginal maximum likelihood estimation and the EM algorithm is described. Statistical significance of suc cessive factors added to the model is tested by the likelihood ratio criterion. Provisions for effects of guessing on multiple-choice items, and for omitted and not-reached items, are included. Bayes constraints on the factor loadings are found to be necessary to suppress Heywood cases. Numerous applications to simulated and real data are presented to substantiate the accuracy and practical utility of the method. Index terms: Armed Services Vocational Aptitude Bat tery, Beta prior, EM algorithm, Item factor analysis, TESTFACT, Tetrachoric correlation.

Applied Psychological Measurement, Vol. 12, No. 3, 261-280 (1988)
DOI: 10.1177/014662168801200305
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