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The Rasch model, additive conjoint measurement, and new probabilistic measurement theory

By George Karabatsos


This research describes some of the similarities and differences between additive conjoint measurement (a type of fundamental measurement) and the Rasch model. It seems that there are many similarities between the two frameworks, however, their differences are nontrivial. For instance, while conjoint measurement specifies measurement scales using a data-free, non-numerical axiomatic frame of reference, the Rasch model specifies measurement scales using a numerical frame of reference that is, by definition, data dependent. In order to circumvent difficulties that can be realistically imposed by this data dependence, this research formalizes new non-parametric item response models. These models are probabilistic measurement theory models in the sense that they explicitly integrate the axiomatic ideas of measurement theory with the statistical ideas of orderrestricted inference and Markov Chain Monte Carlo. The specifications of these models are rather flexible, as they can represent any one of several models used in psychometrics, such as Mokken’s (1971) monotone homogeneity model, Scheiblechner’s (1995) isotonic ordinal probabilistic model, or the Rasch (1960) model. The proposed non-parametric item response models are applied to analyze both real and simulated data sets. Requests for reprints should be sent to George Karabatsos, Assistant Professor of Biostatistics

Publisher: JAM Press
Year: 2011
OAI identifier: oai:CiteSeerX.psu:
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