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Additive Conjoint Isotonic Probabilistic Models (ADISOP)

Published online by Cambridge University Press:  01 January 2025

Hartman Scheiblechner*
Affiliation:
Philipps Universität
*
Requests for reprints should be sent to Hartmann Scheiblechner, FB 04 Universität Marburg, Gutenbergstraße 18, D-35032 Marburg GERMANY.

Abstract

The ISOP-model or model of twodimensional or bi-isotonicity (Scheiblechner, 1995) postulates that the probabilities of ordered response categories increase isotonically in the order of subject “ability” and item ”easiness”. Adding a conventional cancellation axiom for the factors of subjects and items gives the ADISOP model where the c.d.f.s of response categories are functions of an additive item and subject parameter and an ordinal category parameter. Extending cancellation to the interactions of subjects and categories as well as of items and categories (independence axiom of the category factor from the subject and item factor) gives the CADISOP model (completely additive model) in which the parallel c.d.f.s are functions of the sum of subject, item and category parameters. The CADISOP model is very close to the unidimensional version of the polytomous Rasch model with the logistic item/category characteristic(s) replaced by nonparametric axioms and statistics. The axioms, representation theorems and algorithms for model fitting of the additive models are presented.

Type
Original Paper
Copyright
Copyright © 1999 The Psychometric Society

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References

Abrahamowicz, M., & Ramsay, J.O. (1992). Multicategorial spline model for item response theory. Psychometrika, 57, 527.CrossRefGoogle Scholar
Andersen, E.B. (1995). Polytomous Rasch models and their estimation. In Fischer, G. H., & Molenaar, I. W. (Eds.), Rasch models: Foundations, recent developments, and applications (pp. 271292). New York: Springer.CrossRefGoogle Scholar
Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561573.CrossRefGoogle Scholar
Bartholomew, D.J. (1983). Isotonic inference. Encyclopedia of Statistical Sciences, 4, 260265.Google Scholar
Cliff, N. (1994). Predicting ordinal relations. British Journal of Mathematical and Statistical Psychology, 47, 127150.CrossRefGoogle Scholar
Cliff, N., & Donoghue, J.R. (1992). Ordinal test fidelity estimated by an item sampling model. Psychometrika, 57, 217236.CrossRefGoogle Scholar
Dykstra, R.L. (1983). An algorithm for restricted least squares regression. Journal of American Statistical Association, 78(384), 837842.CrossRefGoogle Scholar
Ellis, J.L., & van den Wollenberg, A.L. (1993). Local homogeneity in latent trait models. A characterization of the homogeneous monotone IRT model. Psychometrika, 58, 417429.CrossRefGoogle Scholar
Hemker, B.T. (1996). Unidimensional IRT models for polytomous items, with results for Mokken scale analysis. Unpublished Doctorial dissertation, Utrecht University.Google Scholar
Hemker, B.T., Sijtsma, K., Molenaar, I.W., & Junker, B.W. (1996). Polytomous IRT models and monotone likelihood ratio of the total score. Psychometrika, 61, 679693.CrossRefGoogle Scholar
Hemker, B.T., Sijtsma, K., Molenar, I.W., & Junker, B.W. (1997). Stochastic ordering using the latent trait and the sum score in polytomous IRT models. Psychometrika, 62, 331347.CrossRefGoogle Scholar
Holland, P.W., & Rosenbaum, P.R. (1986). Conditional association and unidimensionality in monotone latent variable models. The Annals of Statistics, 14, 15231543.CrossRefGoogle Scholar
Irtel, H. (1994). The uniqueness structure of simple latent trait models. In Fischer, G. H., & Laming, D. (Eds.), Contributions to mathematical psychology, psychometrics, and methodology (pp. 265276). New York: Springer.CrossRefGoogle Scholar
Irtel, H., & Schmalhofer, F. (1982). Psychodiagnostik auf Ordinalskalenniveau: Meßtheoretische Grundlagen, Modelltests und Parameterschätzung. Archiev für Psychologie, 134, 197218 [Psychodiagnostics on ordinal scale level: Measurement theoretic foundations, model test and parameter estimation]Google Scholar
Junker, B.W. (1998). Some remarks on Scheiblechner's treatment of ISOP models. Psychometrika, 63, 7385.CrossRefGoogle Scholar
Krantz, D.H. (1974). Measurement theory and qualitative laws in psychophysics. In Krantz, D. H., Luce, R. D., Atkinson, R. C., & Suppes, P. (Eds.), Measurement, psychophysics, and neural information processing. Contemporary developments in mathematical psychology, Vol. 2 (pp. 160199). San Francisco: Freeman and Company.Google Scholar
Krantz, D.H., Luce, R.D., Suppes, P., & Tversky, A. (1971). Foundations of measurement, New York: Academic Press.Google Scholar
Lehmann, E.L. (1986). Testing statistical hypothesis 2nd ed.,, New York: J. Wiley.CrossRefGoogle Scholar
Luce, R.D., Krantz, D.H., Suppes, P., & Tversky, A. (1990). Foundations of measurement, Vol. 3, San Diego: Academic Press.Google Scholar
Masters, G.N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149174.CrossRefGoogle Scholar
Meredith, W. (1965). Some results based on a general stochastic model for mental tests. Psychometrika, 30, 419440.CrossRefGoogle ScholarPubMed
Mokken, R.J. (1971). A theory and procedure for scale analysis, Paris/Den Haag: Mouton.CrossRefGoogle Scholar
Mokken, R.J., & Lewis, C. (1982). A nonparametric approach to the analysis of dichotomous item responses. Applied Psychological Measurement, 6, 417430.CrossRefGoogle Scholar
Molenaar, I.W. (1991). A weighted Loevinger H-coefficient extending Mokken scaling to multicategory items. Kwantitatieve Methoden, 37, 97117.Google Scholar
Orth, B. (1974). Einführung in die Theorie des Messens, Stuttgart: Kohlhammer [Introduction into the theory of measurement]Google Scholar
Ramsay, J.O., & Abrahamowicz, M. (1989). Binomial regression with monotone splines: A psychometric application. Journal of the American Statistical Association, 84, 906915.CrossRefGoogle Scholar
Rasch, G. (1961). On general laws and the meaning of measurement in psychology. Proceedings of the IV. Berkeley Symposium on mathematical statistics and probability, 4, 321333.Google Scholar
Robertson, T., Wright, F.T., & Dykstra, R.L. (1988). Order restricted statistical inference, New York: Wiley.Google Scholar
Rosenbaum, P.R. (1988). Item bundles. Psychometrika, 53, 349359.CrossRefGoogle Scholar
Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph, No. 17.CrossRefGoogle Scholar
Scheiblechner, H. (1995). Isotonic ordinal probabilistic models (ISOP). Psychometrika, 60, 281304.CrossRefGoogle Scholar
Scheiblechner, H. (1998). Corrections of theorems in Scheiblechner's treatment of ISOP models and comments on Junker's remarks. Psychometrika, 63, 8791.CrossRefGoogle Scholar
Scheiblechner, H. (in press). Nonparametric IRT: Testing the bi-isotonicity of isotonic probabilistic models (ISOP). Psychometrika.Google Scholar
Scott, D. (1964). Measurement models and linear inequalities. Journal of Mathematical Psychology, 1, 233247.CrossRefGoogle Scholar
Schwarz, W. (1990). Experimental and theoretical results for some models of random dot pattern discrimination. Psychological Research, 52, 299305.CrossRefGoogle ScholarPubMed
Sijtsma, K., & Junker, B.W. (1996). A survey of theory and methods of invariant item ordering. British Journal of Mathematical and Statistical Psychology, 49, 79105.CrossRefGoogle ScholarPubMed
Sijtsma, K., & Meijer, R.R. (1992). A method for investigating the intersection of item response functions in Mokken's nonparametric IRT model. Applied Psychological Measurement, 16, 149157.CrossRefGoogle Scholar
Stout, W.F. (1987). A nonparametric approach for assessing latent trait unidimensionality. Psychometrika, 52, 589617.CrossRefGoogle Scholar
Stout, W.F. (1990). A new item response theory modeling approach with applications to unidimensionality assessment and ability estimation. Psychometrika, 55, 293325.CrossRefGoogle Scholar
Suppes, P., & Zinnes, J.L. (1963). Basic measurement theory. In Luce, R. D., Bush, R. R., & Galanter, E. (Eds.), Handbook of mathematical psychology, Vol. 1, New York: Wiley.Google Scholar