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Dichotomous Factor Analysis of Symptom Data

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  • BENGT O. MUTHÉN

    (University of California, Los Angeles)

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.

Suggested Citation

  • Bengt O. Muthã‰N, 1989. "Dichotomous Factor Analysis of Symptom Data," Sociological Methods & Research, , vol. 18(1), pages 19-65, August.
    • Handle: RePEc:sae:somere:v:18:y:1989:i:1:p:19-65
    DOI: 10.1177/0049124189018001002
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    References listed on IDEAS

    as
    1. George Ferguson, 1941. "The factorial interpretation of test difficulty," Psychometrika, Springer;The Psychometric Society, vol. 6(5), pages 323-329, October.
    2. John Carroll, 1961. "The nature of the data, or how to choose a correlation coefficient," Psychometrika, Springer;The Psychometric Society, vol. 26(4), pages 347-372, December.
    3. Bengt Muthén & Charles Hofacker, 1988. "Testing the assumptions underlying tetrachoric correlations," Psychometrika, Springer;The Psychometric Society, vol. 53(4), pages 563-577, December.
    4. Morton Brown & Jacqueline Benedetti, 1977. "On the mean and variance of the tetrachoric correlation coefficient," Psychometrika, Springer;The Psychometric Society, vol. 42(3), pages 347-355, September.
    Full references (including those not matched with items on IDEAS)

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