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Latent Trait Models and Dichotomization of Graded Responses

Published online by Cambridge University Press:  01 January 2025

Paul G. W. Jansen
Affiliation:
Industrial Psychology Branch, The Netherlands Postal and Telecommunications Services, the Hague
Edward E. Roskam*
Affiliation:
University of Nijmegen
*
Requests for reprints should be sent to Edward Roskam, Department of Psychology, Mathematical Psychology Group, University of Nijmegen, Montessorilaan 3, P.O. Box 9104, 6500 HE Nijmegen, THE NETHERLANDS.

Abstract

This paper discusses the compatibility of the polychotomous Rasch model with dichotomization of the response continuum. It is argued that in the case of graded responses, the response categories presented to the subject are essentially an arbitrary polychotomization of the response continuum, ranging for example from total rejection or disagreement to total acceptance or agreement of an item or statement. Because of this arbitrariness, the measurement outcome should be independent of the specific polychotomization applied, for example, presenting a specific multicategory response format should not affect the measurement outcome. When such is the case, the original polychotomous model is called “compatible” with dichotomization.

A distinction is made between polychotomization or dichotomization “before the fact,” that is, in constructing the response format, and polycho- or dichotomization “after the fact,” for example in dichotomizing existing graded response data.

It is shown that, at least in case of dichotomization after-the-fact, the polychotomous Rasch model is not compatible with dichotomization, unless a rather special condition of the model parameters is met. Insofar as it may be argued that dichotomization before the fact is not essentially different from dichotomization after the fact, the value of the unidimensional polychotomous Rasch model is consequently questionable. The impact of our conclusion on related models is also discussed.

Type
Original Paper
Copyright
Copyright © 1986 The Psychometric Society

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References

Aitchison, J., Silvey, S. D. (1957). The generalization of profit analysis to the case of multiple responses. Biometrika, 44, 131140.CrossRefGoogle Scholar
Andersen, E. B. (1973). Conditional inference for multiple choice questionnaires. British Journal of Mathematical and Statistical Psychology, 26, 3157.CrossRefGoogle Scholar
Andersen, E. B. (1977). Sufficient statistics and latent trait models. Psychometrika, 42, 6981.CrossRefGoogle Scholar
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561573.CrossRefGoogle Scholar
Andrich, D. (1979). A model for contingency tables having an ordered response classification. Biometrics, 35, 403415.CrossRefGoogle Scholar
Benzécri, J. P. (1973). Analyse des données. II: L'analyse des correspondances [Data analysis, II: Correspondence analysis], Paris-Bruxelles: Dunot.Google Scholar
Berg, I. A., Rapaport, G. M. (1954). Response bias in an unstructured questionnaire. The Journal of Psychology, 38, 475481.CrossRefGoogle Scholar
Cohen, J. (1983). The cost of dichotomization. Applied Psychological Measurement, 7, 249253.CrossRefGoogle Scholar
Coombs, C. H. (1964). Theory of Data, New York: Wiley.Google Scholar
Drasgow, F., Parsons, C. K. (1983). Application of unidimensional item response theory models to multidimensional data. Applied Psychological Measurement, 7, 189199.CrossRefGoogle Scholar
Fischer, G. H. (1974). Einführung in die Theorie psychologischer tests [Introduction to psychological test theory], Bern: Huber.Google Scholar
Guttman, L. (1967). The development of nonmetric space analysis; a letter to John Ross. Multivariate Behavioral Research, 2, 7182.CrossRefGoogle ScholarPubMed
Hoogstraten, J. (1979). De machteloze onderzoeker [The helpless researcher], Meppel: Boom.Google Scholar
Jansen, P. G. W. (1983). Rasch analysis of attitudinal data, Den Haag: University of Nijmegen, and Rijks Psychologische Dienst.Google Scholar
Koch, W. R. (1983). Likert scaling using the graded response latent trait model. Applied Psychological Measurement, 7, 1532.CrossRefGoogle Scholar
Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 159.Google Scholar
Lingoes, J. C. (1968). The rationale of the Guttman-Lingoes nometric series: A letter to Doctor Philip Runkel. Multivariate Behavioral Research, 3, 495508.CrossRefGoogle Scholar
Lord, F. M. (1980). Applications of item response theory to practical testing problems, Hillsdale: Lawrence Erlbaum.Google Scholar
Masters, G. N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149174.CrossRefGoogle Scholar
Masters, G. N. (1985, March). A latent trait analysis of computer-administered items with feedback. Paper presented at the annual meeting of the American Educational Research Association, Chicago.Google Scholar
Nishisato, S. (1980). Analysis of categorical data: Dual scaling and its applications, Toronto: University of Toronto Press.CrossRefGoogle Scholar
Rasch, G. (1961). On general laws and the meaning of measurement in psychology. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 321333). Berkeley and Los Angeles: University of California Press.Google Scholar
Roskam, E. E. (1983). Allgemeine Datentheorie [General Theory of Data]. In Feger, H., Bredenkamp, H. (Eds.), Messen und Testen, Band 3 der Serie Forschungsmethoden der Psychologie der Enzyklopadie der Psychologie [Measurement and testing, Vol. 3 of the series psychological research methods of the encyclopedia of psychology] (pp. 1135). Gottingen: Hogrefe.Google Scholar
Roskam, E. E., Jansen, P. G. W. (1984). A new derivation of the Rasch model. In Degreef, E., van Buggenhaut, J. (Eds.), Trends in mathematical psychology (pp. 293307). Amsterdam: Elsevier-North-Holland.CrossRefGoogle Scholar
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded responses [Monograph]. Psychometrika, 34(4, Pt. 2).CrossRefGoogle Scholar
Thissen, D., Steinberg, L. (1984). A response model for multiple choice items. Psychometrika, 49, 501519.CrossRefGoogle Scholar
Tzeng, O. C. S. (1983). A comparative evaluation of four response formats in personality ratings. Educational and Psychological Measurement, 43, 935950.CrossRefGoogle Scholar
Van den Wollenberg, A. L. (1982). Two new test statistics for the Rasch model. Psychometrika, 47, 123140.CrossRefGoogle Scholar
Van Heerden, J., Hoogstraten, J. (1979). Response tendency in a questionnaire without questions. Applied Psychological Measurement, 3, 117121.CrossRefGoogle Scholar
Wright, B. D., Stone, M. H. (1979). Best test design: Rasch measurement, Chicago: MESA Press.Google Scholar