Hostname: page-component-5f745c7db-8qdnt Total loading time: 0 Render date: 2025-01-06T07:52:07.779Z Has data issue: true hasContentIssue false

A Comparison of Three Simple Test Theory Models

Published online by Cambridge University Press:  01 January 2025

J. O. Ramsay*
Affiliation:
McGill University
*
Requests for reprints should be sent to J. O, Ramsay, Department of Psychology, Stewart Biological Sciences Building, 1205 Dr. Penfield Avenue, Montreal QC, H3A 1B1 CANADA.

Abstract

In very simple test theory models such as the Rasch model, a single parameter is used to represent the ability of any examinee or the difficulty of any item. Simple models such as these provide very important points of departure for more detailed modeling when a substantial amount of data are available, and are themselves of real practical value for small or even medium samples. They can also serve a normative role in test design.

As an alternative to the Rasch model, or the Rasch model with a correction for guessing, a simple model is introduced which characterizes strength of response in terms of the ratio of ability and difficulty parameters rather than their difference. This model provides a natural account of guessing, and has other useful things to contribute as well. It also offers an alternative to the Rasch model with the usual correction for guessing. The three models are compared in terms of statistical properties and fits to actual data. The goal of the paper is to widen the range of “minimal” models available to test analysts.

Type
Original Paper
Copyright
Copyright © 1989 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This research was supported by grant AP320 from the Natural Sciences and Engineering Research Council of Canada. The author is grateful for discussions with M. Abrahamowicz, I. Molenaar, D. Thissen, and H. Wainer.

References

Andersen, E. (1973). Conditional inference and multiple choice questionnaires. British Journal of Mathematical and Psychology, 26, 3144.Google Scholar
Andersen, E. (1973). A goodness-of-fit test for the Rasch model. Psychometrika, 38, 123140.CrossRefGoogle Scholar
Andersen, E. (1980). Discrete statistical models with social science applications, New York: North-Holland.Google Scholar
Andersen, E., & Madsen, M. (1977). Estimating the parameters of the latent population distribution. Psychometrika, 42, 357374.CrossRefGoogle Scholar
Bock, R. D., & Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46, 443459.CrossRefGoogle Scholar
Bock, R. D., & Lieberman, M. (1970). Fitting a response model for n dichotomously scores items. Psychometrika, 35, 179197.CrossRefGoogle Scholar
Drasgow, F., Levine, M. V., & Williams, E. A. (1985). Appropriateness measurement with polychotomous item response models and standardized indices. British Journal of Mathematical and Statistical Psychology, 38, 6780.CrossRefGoogle Scholar
Fischer, G. (1987). Applying principles of specific objectivity and of generalizability to the measure of change. Psychometrika, 52, 565588.CrossRefGoogle Scholar
Mislevy, R. J. (1984). Estimating latent distributions. Psychometrika, 49, 359381.CrossRefGoogle Scholar
Mislevy, R. J. (1986). Bayes modal estimation in item response models. Psychometrika, 51, 177195.CrossRefGoogle Scholar
Molenaar, I. W., & Hoijtink, H. (in press). The many null distributions of person fit indices. Psychometrika.Google Scholar
Ramsay, J. O. (1975). Solving implicit equations in psychometric data analysis. Psychometrika, 40, 337360.CrossRefGoogle Scholar
Ramsay, J. O., & Abrahamowicz, M. (in press). Binomial regression with monotone splines: A psychometric application. Journal of the American Statistical Association.Google Scholar
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests, Copenhagen: Danmarks Paedagogiske Institut.Google Scholar
Rasch, G. (1966). An individualistic approach to item analysi. In Lasarsfeld, P. F., Henry, N. W. (Eds.), Readings in mathematical social science (pp. 89108). Chicago: Science Research Associates.Google Scholar
Stoer, J., Bulirsch, R. (1980). Introduction to numerical analysis, New York: Springer-Verlag.CrossRefGoogle Scholar
Thissen, D. (1982). Marginal maximum likelihood estimation for the one-parameter logistic model. Psychometrika, 47, 175186.CrossRefGoogle Scholar
Thissen, D., & Wainer, H. (1982). Some standard errors in item response theory. Psychometrika, 47, 397412.CrossRefGoogle Scholar
Wilson, E. B., & Hilferty, M. M. (1931). The distribution of chi square. Proceedings of the National Academy of Science, 17, 684688.CrossRefGoogle ScholarPubMed