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Models for Measurement, Precision, and the Nondichotomization of Graded Responses

Published online by Cambridge University Press:  01 January 2025

David Andrich
David Andrich*
Affiliation:
School of Education, Murdoch University
*
Requests for reprints should be sent to David Andrich, School of Education, Murdoch University, Murdoch University, Murdoch WA 6150, AUSTRALIA.
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Abstract

It is common in educational, psychological, and social measurement in general, to collect data in the form of graded responses and then to combine adjacent categories. It has been argued that because the division of the continuum into categories is arbitrary, any model used for analyzing graded responses should accommodate such action. Specifically, Jansen and Roskam (1986) enunciate a joining assumption which specifies that if two categories j and k are combined to form category h, then the probability of a response in h should equal the sum of the probabilities of responses in j and k. As a result, they question the use of the Rasch model for graded responses which explicitly prohibits the combining of categories after the data are collected except in more or less degenerate cases. However, the Rasch model is derived from requirements of invariance of comparisons of entities with respect to different instruments, which might include different partitions of the continuum, and is consistent with fundamental measurement. Therefore, there is a strong case that the mathematical implication of the Rasch model should be studied further in order to understand how and why it conflicts with the joining assumption. This paper pursues the mathematics of the Rasch model and establishes, through a special case when the sizes of the categories are equal and when the model is expressed in the multiplicative metric, that its probability distribution reflects the precision with which the data are collected, and that if a pair of categories is collapsed after the data are collected, it no longer reflects the original precision. As a consequence, and not because of a qualitative change in the variable, the joining assumption is destroyed when categories are combined. Implications of the choice between a model which satisfies the joining assumption or one which reflects on the precision of the data collection considered are discussed.

Keywords

latent trait theorydichotomizationrating datagraded responsesRasch models
Type
Original Paper
Information
Psychometrika , Volume 60 , Issue 1 , March 1995 , pp. 7 - 26
Copyright
Copyright © 1995 The Psychometric Society

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