Dichotomous Factor Analysis of Symptom Data

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Abstract
This article discusses how a factor model with continuous latent variables can be used to analyze a set of strongly skewed dichotomous items and how such a model can be used for classification of subjects. The suitability of the specification of normally distributed latent variables, as is assumed with the use of tetrachoric correlations, is investigated. Both exploratory and confirmatory analyses, including multiple groups with mean structures, are illustrated. Substantive findings include support for unidimensionality of the items used in the DSM-III diagnosis of depression and a large degree of invariance in factor structure for the Baltimore and Durham sites.