Hostname: page-component-745bb68f8f-b95js Total loading time: 0 Render date: 2025-01-08T12:13:21.375Z Has data issue: false hasContentIssue false

Identifiability of Nonlinear Logistic Test Models

Published online by Cambridge University Press:  01 January 2025

Timo M. Bechger*
Affiliation:
National Institute for Educational Measurement (Cito)
Norman D. Verhelst
Affiliation:
National Institute for Educational Measurement (Cito)
Huub H. F. M. Verstralen
Affiliation:
National Institute for Educational Measurement (Cito)
*
Requests for reprints should be sent to Timo M. Bechger, National Institute of Educational Measurement (Cito), P.O. Box 1034, 6801 MG Arnhem, THE NETHERLANDS. E-Mail: [email protected]

Abstract

The linear logistic test model (LLTM) specifies the item parameters as a weighted sum of basic parameters. The LLTM is a special case of a more general nonlinear logistic test model (NLTM) where the weights are partially unknown. This paper is about the identifiability of the NLTM. Sufficient and necessary conditions for global identifiability are presented for a NLTM where the weights are linear functions, while conditions for local identifiability are shown to require a model with less restrictions. It is also discussed how these conditions are checked using an algorithm due to Bekker, Merckens, and Wansbeek (1994). Several illustrations are given.

Type
Articles
Copyright
Copyright © 2001 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 article was written while the first author was a post doctoral fellow at the university of Twente. He gratefully acknowledges the university's hospitality and the financial support by NWO (project nr. 30002).

References

Aitchison, W., & Silvey, S.D. (1958). Maximum likelihood estimation of parameters subject to restraints. Annals of Mathematical Statistics, 28, 813828.CrossRefGoogle Scholar
Andersen, E.B. (1995). Polytomous Rasch models and their estimation. In Fisher, G.H., & Molenaar, I.W. (Eds.), Rasch models: Foundations, recent developments and applications. New York, NY: Springer Verlag.Google Scholar
Andres, J. (1990). Grundlagen Linearer Strukturgleichungsmodelle [Foundations of linear structural equation models], New York, NY: Peter Lang.Google Scholar
Baker, F.B. (1993). Sensitivity of the linear logistic test model to misspecification of the weight matrix. Applied Psychological Measurement, 17, 201211.CrossRefGoogle Scholar
Basilevsky, A. (1983). Applied matrix algebra in the statistical sciences. New York, NY: North-Holland.Google Scholar
Bechger, T.M., Verstralen, H.H.F.M., & Verhelst, N.D. (2000). Equivalent linear logistic test models. Arnhem, The Netherlands: Cito.Google Scholar
Bekker, P.A. (1989). Identification in restricted factor models. Journal of Econometrics, 41, 516.CrossRefGoogle Scholar
Bekker, P.A., Merckens, A., & Wansbeek, T.J. (1994). Identification, equivalent models, and computer algebra. Boston, MA: Academic Press.Google Scholar
Borden, R.S. (1998). A course in advanced calculus. New York, NY: Dover Publications.Google Scholar
Butter, R.P. (1994). Item response models with internal restrictions on item difficulty. Belgium: Catholic University of Leuven.Google Scholar
Butter, R.P., De Boeck, P., & Verhelst, N.D. (1998). An item response model with internal restrictions on item difficulty. Psychometrika, 63, 4763.CrossRefGoogle Scholar
Dieudonné, J. (1969). Foundations of modern analysis. New York, NY: Academic Press.Google Scholar
Fisher, F.M. (1966). The identification problem in Econometrics. New York, NY: McGraw-Hill.Google Scholar
Fischer, G.H. (1983). Logistic latent trait models with linear constraints. Psychometrika, 48, 326.CrossRefGoogle Scholar
Fischer, G.H. (1995). The linear logistic test model. In Fisher, G.H., Molenaar, I.W. (Eds.), Rasch models: Foundations, recent developments and applications. New York, NY: Springer Verlag.CrossRefGoogle Scholar
Fischer, G.H., & Ponocny, I. (1994). An extension of the partial credit model with an application to the measurent of change. Psychometrika, 59, 177192.CrossRefGoogle Scholar
Fischer, G.H., & Ponocny, I. (1995). Extended rating scale and partial credit models for assessing change. In Fischer, G.H., & Molenaar, I.W. (Eds.), Rasch models: Foundations, recent developments and applications (chap. 10). New York, NY: Springer Verlag.CrossRefGoogle Scholar
Fuller, S.A., Sinyakov, M.N., & Tischchenko, S.V. (2000). Linearity and the mathematics of several variables. Singapore: World Scientific.Google Scholar
Gabrielsen, A. (1978). Consistency and identifiability. Journal of Econometrics, 8, 261263.CrossRefGoogle Scholar
Gill, L., & Lewbel, A. (1992). Testing the rank and definiteness of estimated matrices with application to factor, statespace and ARMA models. Journal of the American Statistical Association, 87, 766776.CrossRefGoogle Scholar
Glas, C.A.W., & Verhelst, N.D. (1995). Testing the Rasch model. In Fischer, G.H., & Molenaar, I.W. (Eds.), Rasch models: Their foundations, recent developments and applications. New York, NY: Springer Verlag.Google Scholar
Henrion, D., & Sebel, M. (1998, July). Numerical methods for polynomial rank evaluation. Paper presented at the IFAC conference on systems structure and control, Nantes, France.CrossRefGoogle Scholar
Krantz, S.G., Parks, H.R. (1992). A primer on real analytic functions. Basel, Switzerland: Birkhauser Verlag.CrossRefGoogle Scholar
Lang, S. (1987). Linear algebra 3rd ed., New York, NY: Springer Verlag.CrossRefGoogle Scholar
Lang, S. (1996). Calculus of several variables 3rd ed., New York, NY: Springer Verlag.Google Scholar
Luijben, Th. C.W. (1991). Equivalent models in covariance structure analysis. Psychometrika, 56, 653665.CrossRefGoogle Scholar
Parthasarathy, T. (1983). On global univalence theorems. New York, NY: Springer Verlag.CrossRefGoogle Scholar
Ponocny, I., & Pononcny-Seliger, E. (1997). Applications of the program LpcM in the field of measuring change. In Wilson, M. (Eds.), Objective measurement: Theory into practice (pp. 231238). Norwood, NJ: Ablex.Google Scholar
Rao, C.R. (1947). Large sample tests of statistical hypothesis concerning several parameters with applications to the problems of estimation. Proceedings Cambridge Philosophical Society, 44, 5057.Google Scholar
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research.Google Scholar
Rasch, G. (1966). An individualistic approach to item analysis. In Lazersfeld, P.F., & Henry, N.W. (Eds.), Readings in mathematical social science. Cambridge, MA: The MIT Press.Google Scholar
Rothenberg, T.J. (1971). Identification in parametric models. Econometrica, 39, 577591.CrossRefGoogle Scholar
Shapiro, A. (1983). On local identifiability in structural models. Pretoria, South Africa: University of South Africa, Department of Mathematics and Applied Mathematics.Google Scholar
Shapiro, A. (1985). Identifiability of factor analysis: Some results and open problems. Linear Algebra and Its Applications, 70, 17.CrossRefGoogle Scholar
Shapiro, A. (1986). Asymptotic theory of overparameterized structural models. Journal of the American Statistical Association, 81, 142149.CrossRefGoogle Scholar
Shapiro, A., & Browne, M.W. (1983). On the investigation of local identifiability: A counterexample. Psychometrika, 48, 303304.CrossRefGoogle Scholar
Silvey, S.D. (1959). The Lagrange multiplier test. Annals of Mathematical Statistics, 30, 398407.CrossRefGoogle Scholar
Smits, D.J.M. (1999). A componential model for guilt feelings: An application of the LLTM and the MIRID. Leuven, Belgium: University of Leuven, Department of Psychological Science.Google Scholar
Wald, A. (1950). Note on the identifiability of economic relations. In Koopmans, T.C. (Eds.), Statistical inference in dynamic economic models. New York, NY: Wiley.Google Scholar
Wu, M.L., Adams, R.J., & Wilson, M. (1998). ConQuest: Generalised item reponse modelling. Melbourne: Australian Council for Educational Research (ACER) Press.Google Scholar