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A Comparison of Latent Trait and Latent Class Analyses of Likert-Type Data

Published online by Cambridge University Press:  01 January 2025

Geofferey N. Masters
Geofferey N. Masters*
Affiliation:
University of Melbourne
*
Requests for reprints should be sent to Geofferey N. Masters, Centre for the Study of Higher Education, University of Melbourne, Parkville, Victoria, AUSTRALIA, 3052.
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Abstract

This paper brings together and compares two developments in the analysis of Likert attitude scales. The first is the generalization of latent class models to ordered response categories. The second is the introduction of latent trait models with multiplicative parameter structures for the analysis of rating scales. Key similarities and differences between these two methods are described and illustrated by applying a latent trait model and a latent class model to the analysis of a set of “life satisfaction” data. The way in which the latent trait model defines a unit of measurement, takes into account the order of the response categories, and scales the latent classes, is discussed. While the latent class model provides better fit to these data, this is achieved at the cost of a logically inconsistent assignment of individuals to latent classes.

Keywords

latent traitlatent classLikert scalesattitude measurement

Information

Type
Original Paper
Information
Psychometrika , Volume 50 , Issue 1 , March 1985 , pp. 69 - 82
Copyright
Copyright © 1985 The Psychometric Society

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Footnotes

The author wishes to thank Clifford C. Clogg, Otis Dudley Duncan and Benjamin D. Wright for their helpful comments on an earlier version of this paper.

References

Andersen, E. B. (1982). Latent trait models and ability parameter estimation. Applied Psychological Measurement, 6, 445–461.CrossRefGoogle Scholar
Andrich, D. (1979). A rating formulation for ordered response categories. Psychometrika, 43, 561–573.CrossRefGoogle Scholar
Andrich, D. (in press). An elaboration of Guttman scaling with Rasch models for measurement. In Brandon Tuma, N., Sociological Methodology. San Francisco: Jossey-Bass.Google Scholar
Bergman, J. R. (1983). Latent class models in educational research. Annual Review of Research in Education, 10, 305–360.Google Scholar
Bock, R. D. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika, 37, 29–51.CrossRefGoogle Scholar
Clogg, C. C. (1979). Some latent structure models for the analysis of Likert-type data. Social Science Research, 8, 287–301.CrossRefGoogle Scholar
Dawes, R. M. (1972). Fundamentals of attitude measurement, New York: Wiley.Google Scholar
Duncan, O. D. (1983). Rasch measurement in survey research: Further examples and discussion. In Turner, C. F. and Martin, E. (Eds.), Surveying subjective phenomenona, New York: Russell Sage Foundation.Google Scholar
Goodman, L. A. (1974). The analysis of systems of qualitative variables when some of the variables are unobservable: Part I. A modified latent structure approach. American Journal of Sociology, 79, 1179–1259.CrossRefGoogle Scholar
Hendrix, P. E., & Leonard, E. W. (1984). Nonlinear measurement models for discrete data: an explication and comparison of latent trait and latent class models. Unpublished manuscript.Google Scholar
Lazersfeld, P. F., & Henry, N. W. (1968). Latent structure analysis, Boston: Houghton-Mifflin.Google Scholar
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 5–55.Google Scholar
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149–174.CrossRefGoogle Scholar
Masters, G. N. (1984). Constructing an item bank using partial credit scoring. Journal of Educational Measurement, 21, 19–32.CrossRefGoogle Scholar
Masters, G. N., & Wright, B. D. (1984). The essential process in a family of measurement models. Psychometrika, 49, 529–544.CrossRefGoogle Scholar
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests, Copenhagen: Denmarks Paedogogiske Institut.Google Scholar
Rost, J. (1985). A latent class model for rating data. Psychometrika, 50, 37–49.CrossRefGoogle Scholar
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika, 34, (Suppl. 17).CrossRefGoogle Scholar
Wang, M. W., & Stanley, J. C. (1970). Differential weighting: A review of methods and empirical studies. Review of Educational Research, 40, 663–705.CrossRefGoogle Scholar
Wright, B. D., & Masters, G. N. (1982). Rating scale analysis, Chicago: MESA Press.Google Scholar