Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-08T09:42:40.610Z Has data issue: false hasContentIssue false

Loglinear Rasch Model Tests

Published online by Cambridge University Press:  01 January 2025

Hendrikus Kelderman*
Affiliation:
Twente University of Technology, Enschede, The Netherlands
*
Requests for reprints should be sent to Hendrikus Kelderman, Toegepaste Onderwijskunde, Technische Hogeschool Twente, Postbus 217, 7500 AE Enschede, The Netherlands.

Abstract

Existing statistical tests for the fit of the Rasch model have been criticized, because they are only sensitive to specific violations of its assumptions. Contingency table methods using loglinear models have been used to test various psychometric models. In this paper, the assumptions of the Rasch model are discussed and the Rasch model is reformulated as a quasi-independence model. The model is a quasi-loglinear model for the incomplete subgroup × score × item 1 × item 2 × ... × item k contingency table. Using ordinary contingency table methods the Rasch model can be tested generally or against less restrictive quasi-loglinear models to investigate specific violations of its assumptions.

Type
Article
Copyright
Copyright © 1984 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

The author thanks Gideon J. Mellenbergh, Pieter Vijn, Wim J. van der Linden, Ivo W. Molenaar, and Sebie J. Oosterloo for comments and Carolien Schamhardt and Anita Burchartz for typing the manuscript.

References

Andersen, E. B. (1971). Asymptotic properties of conditional likelihood ratio tests. Journal of the American Statistical Association, 66, 630633.CrossRefGoogle Scholar
Andersen, E. B. (1972). The numerical solution of a set of conditional estimation equations. Journal of the Royal Statistical Society, Series B, 34, 4254.CrossRefGoogle Scholar
Andersen, E. B. (1973). Conditional inference models for measuring, Copenhagen: Mentalhygiejnisk Forlag.Google Scholar
Andersen, E. B. (1973). A goodness of fit test for the Rasch model. Psychometrika, 38, 123140.CrossRefGoogle Scholar
Andersen, E. B. (1980). Latent structure Analysis. Invited paper to the European Meeting of the Psychometric Society, Groningen.Google Scholar
Andersen, E. B. (1980). Discrete statistical models with social science applications, Amsterdam: North Holland.Google Scholar
Andersen, E. B. (1980). Comparing latent distributions. Psychometrika, 45, 121134.CrossRefGoogle Scholar
Ayres, F. (1974). Matrices, New York: McGraw Hill.Google Scholar
Baker, F. B. & Subkoviak, M. J. (1981). Analysis of test results via loglinear models. Applied Psychological Measurement, 5, 503515.CrossRefGoogle Scholar
Baker, R. J. & Nelder, J. A. (1978). The GLIM system: Generalized linear interactive modelling, Oxford: The Numerical Algorithms Group.Google Scholar
Bishop, Y. M. M., Fienberg, S. E. & Holland, P. W. (1975). Discrete multivariate analysis, Cambridge, Mass.: MIT Press.Google Scholar
Bock, R. D. (1975). Multivariate Statistical Methods in behavioral research, New York: McGraw Hill.Google Scholar
Clogg, C. C. & Sawyer, D. A. (1981). A comparison of alternative models for analyzing the scalability of response patterns. In Leinhardt, Samuel (Eds.), Sociological Methodology, San Francisco: Jossey-Bass.Google Scholar
Coombs, C. H. (1964). A theory of data, New York: Wiley.Google Scholar
Cressie, N. & Holland, P. W. (1983). Characterizing the manifest probabilities of latent trait models. Psychometrika, 48, 129142.CrossRefGoogle Scholar
Davison, M. L. (1979). Testing a unidimensional qualitative unfolding model for attitudinal or developmental data. Psychometrika, 44, 179194.CrossRefGoogle Scholar
Davison, M. L. (1980). A psychological scaling model for testing order hypotheses. British Journal of Mathematical and Statistical Psychology, 33, 123141.CrossRefGoogle Scholar
Dayton, C. M. & McReady, G. B. (1980). A scaling model with response errors and intrinsically unscalable respondents. Psychometrika, 45, 343356.CrossRefGoogle Scholar
Evers, M. & Namboodiri, N. K. (1979). On the design matrix strategy in the analysis of categorical data. In Schuessler, Karl F. (Eds.), Sociological Methodology, San Francisco: Jossey-Bass.Google Scholar
Fienberg, S. E. (1972). The analysis of incomplete multi-way contingency tables. Biometrics, 28, 177202.CrossRefGoogle Scholar
Fienberg, S. E. (1980). The analysis of cross-classified categorical data, Cambridge Mass.: MIT Press.Google Scholar
Fischer, G. H. (1974). Einführung in die Theorie Psychologischer Tests (Introduction to the theory of psychological tests), Bern: Huber (In German)Google Scholar
Fischer, G. H. (1981). On the existence and uniqueness of maximum likelihood estimates in the Rasch model. Psychometrika, 46, 5977.CrossRefGoogle Scholar
Fischer, G. H. (1978). Probabilistic test models and their applications. The German Journal of Psychology, 2, 298319.Google Scholar
Fischer, G. H. (1983). Logistic latent trait models with linear constraints. Psychometrika, 48, 326.CrossRefGoogle Scholar
Fischer, G. H. & Scheiblechner, H. (1970). Algorithmen und Programma für das probabilistische Testmodell von Rasch. Psychologische Beiträge, 12, 2351.Google Scholar
Goldstein, H. (1980). Dimensionality, bias, independence and measurement scale problems in latent trait test score models. British Journal of Mathematical and Statistical Psychology, 33, 234246.CrossRefGoogle Scholar
Goodman, L. A. (1959). Simple statistical methods for scalogram analysis. Psychometrika, 24, 2943.CrossRefGoogle Scholar
Goodman, L. A. (1964). A short computer program for the analysis of transaction flows. Behavioral Science, 9, 176186.CrossRefGoogle Scholar
Goodman, L. A. (1968). The analysis of cross-classified data: Independence, quasi-independence, and interactions in contingency tables with or without missing entries. Journal of the American Statistical Association, 63, 10911131.Google Scholar
Goodman, L. A. (1974). Exploratory latent structure analysis. Biometrika, 61, 215231.CrossRefGoogle Scholar
Goodman, L. A. (1975). A new model for scaling response patterns: An application of the quasi-independence concept. Journal of the American Statistical Association, 70, 755768.CrossRefGoogle Scholar
Goodman, L. A. (1978). Analyzing qualitative/categorical data: Loglinear models and latent structure analysis, London: Addison Wesley.Google Scholar
Goodman, L. A. & Fay, R. (1974). ECTA program, description for users, Chicago: Department of Statistics University of Chicago.Google Scholar
Gustafsson, J. E. (1977). The Rasch model for dichotomous items: Theory, applications and a computer program. Reports from the Institute of Education, University of Göteborg, no. 63, ED154018.Google Scholar
Gustafsson, J. E. (1980). Testing and obtaining the fit of data to the Rasch model. British Journal of Mathematical and Statistical Psychology, 33, 205233.CrossRefGoogle Scholar
Guttman, L.et al. (1950). The basis for scalogram analysis. In Stouffer, S. A.et al. (Eds.), Measurement and prediction: Studies in social psychology in World War II. Vol. 4, Princeton: Princeton University Press.Google Scholar
Haberman, S. J. (1978). Analysis of qualitative data, Vol. 1, New York: Academic Press.Google Scholar
Haberman, S. J. (1979). Analysis of qualitative data: New developments, Vol. 2, New York: Academic Press.Google Scholar
Hambleton, R. K., Swaminatan, H., Cook, L. L., Eignor, D. R. & Gifford, J. A. (1978). Developments in latent trait theory: Models technical uses and applications. Review of Educational Research, 48, 467510.CrossRefGoogle Scholar
Karlin, S. & Studden, W. J. (1966). Tchebycheff systems: with applications in analysis and statistics, New York: Interscience Publishers.Google Scholar
Kelderman, H., Mellenbergh, G. J. & Elshout, J. J. (1981). Guilford's facet theory of intelligence: An empirical comparison of models. Multivariate Behavioral Research, 16, 3762.CrossRefGoogle ScholarPubMed
Kempf, W. (1974). Dynamische Modelle zur Messung sozialer Verhaltensdispositionen (Dynamic models for attitude measurement.). In Kempf, W. (Eds.), Probabalistische Modelle in der Sozialpsychologie (Probabilistic models in social psychology) (pp. 1355). Bern: Huber.Google Scholar
Lancaster, H. O. (1961). Significance tests in discrete distributions. Journal of the American Statistical Association, 56, 223234.CrossRefGoogle Scholar
Lazersfeld, P. F. (1950). The interpretation and computation of some latent structures. In Stouffer, Samuel A. et al. (Eds.), Measurement and prediction in World War II, Vol. 4, Princeton: Princeton University Press.Google Scholar
Lazersfeld, P. F. & Henry, N. W. (1968). Latent structure analysis, Boston: Houghton-Miffin.Google Scholar
Lienert, G. A. & Raatz, U. (1981). Item homogeneity defined by multivariate axial symmetry. Applied Psychological Measurement, 5, 263269.CrossRefGoogle Scholar
Lord, F. M. & Novick, M. R. (1968). Statistical theories of mental test scores, Reading, Mass.: Addison-Wesley.Google Scholar
Loyd, B. H. & Hoover, H. D. (1980). Vertical equating using the Rasch model. Journal of Educational Measurement, 17, 179193.CrossRefGoogle Scholar
Lumsden, J. (1978). Tests are perfectly reliable. British Journal of Mathematical and Statistical Psychology, 31, 1926.CrossRefGoogle Scholar
McDonald, R. P. & Krane, W. R. (1979). A Monte Carlo study of local identifiability and degrees of freedom in the asymptotic likelihood ratio test. British Journal of Mathematical and Statistical Psychology, 32, 121132.CrossRefGoogle Scholar
McHugh, R. B. (1956). Efficient estimation and local identification in latent class analysis. Psychometrika, 21, 331347.CrossRefGoogle Scholar
Mellenbergh, G. J. (1972). Applicability of the Rasch model in two cultures. In Cronbach, L. J. & Drenth, P. J. D. (Eds.), Mental tests and cultural adaptation, The Hague: Mouton.Google Scholar
Mellenbergh, G. J. & Vijn, P. (1981). The Rasch model as a loglinear model. Applied Psychological Measurement, 5, 369376.CrossRefGoogle Scholar
Molenaar, I. W. (1983). Some improved diagnostics for failure of the Rasch model. Psychometrika, 48, 4973.CrossRefGoogle Scholar
Mood, A. M., Graybill, F. A. & Boes, D. C. (1974). Introduction to the theory of statistics, Tokyo: McGraw-Hill Kogakuska.Google Scholar
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests, Copenhagen: Paedagogiske Institut.Google Scholar
Rasch, G. (1961). On general laws and the meaning of measurement in psychology. In Neyman, J. (Eds.), Proceedings of the Fourth Symposium on Mathematical Statistics and Probability, Vol. 4 (pp. 321333). Berkeley: University of California Press.Google Scholar
Rasch, G. (1966). An item analysis that takes individual differences into account. British Journal of Mathematical and Statistical Psychology, 19, 4957.CrossRefGoogle ScholarPubMed
Rasch, G. (1966). An individualistic approach to item analysis. In Lazersfeld, P. F., Henry, N. W. (Eds.), Readings in Mathematical Social Science (pp. 89107). Cambridge, Mass.: MIT Press.Google Scholar
Renz, R. R. & Bashaw, W. L. (1977). The National reference scale for reading: An application of the Rasch model. Journal of Educational Measurement, 14, 161179.CrossRefGoogle Scholar
Rudner, L. M., Getson, P. R. & Knight, D. L. (1980). Biased item detection techniques. Journal of Educational Statistics, 5, 213233.CrossRefGoogle Scholar
Shoemaker, D. M. (1973). Principles and procedures of multiple matrix sampling, Cambridge, Mass.: Ballinger.Google Scholar
Stouffer, S. A. (1950). An overview of the contributions to scaling and scale theory. In Stouffer, S. A. et al. (Eds.), Measurement and prediction: Studies in Social Psychology in World War II, (Vol. 4), Princeton: Princeton University Press.Google Scholar
Tjur, T. (1982). A connection between Rasch's item analysis model and a multiplicative Poisson model. Scandinavian Journal of Statistics, 9, 2330.Google Scholar
Van den Wollenberg, A. L. (1979). The Rasch model and time-limit tests. Doctoral dissertation, Nijmegen.Google Scholar
Van den Wollenberg, A. L. (1982). Two new test statistics for the Rasch model. Psychometrika, 47, 123140.CrossRefGoogle Scholar
Vijn, P. & Mellenbergh, G. J. (1982). The Rasch model versus the loglinear model. Révész Berichten no. 42, Psychologisch Laboratorium, Universiteit van Amsterdam.Google Scholar
Wood, R. (1978). Fitting the Rasch model—A heady tale. British Journal of Mathematical and Statistical Psychology, 31, 2732.CrossRefGoogle Scholar
Wright, B. D. & Mead, R. J. (1977). BICAL: Calibrating items and scales with the Rasch model. Research Memorandum, 23, Statistical Laboratory, Department of Education, University of Chicago.Google Scholar
Wright, B. D. & Panchapakesan, N. (1969). A procedure for sample-free item analysis. Educational and Psychological Measurement, 29, 2348.CrossRefGoogle Scholar