Hostname: page-component-74656f9884-j7cmm Total loading time: 0 Render date: 2026-11-09T10:12:20.350Z Has data issue: false hasContentIssue false

Nonparametric Goodness-of-Fit Tests for the Rasch Model

Published online by Cambridge University Press:  01 January 2025

Ivo Ponocny*
Affiliation:
University of Vienna
*
Requests for reprints should be sent to Ivo Ponocny, Institut für Psychologie, Universität Wien, Liebiggasse 5, A-1010 Wien, AUSTRIA. E-Mail: ivo.ponocny@univie.ac.at

Abstract

A Monte Carlo algorithm realizing a family of nonparametric tests for the Rasch model is introduced which are conditional on the item and subject marginals. The algorithm is based on random changes of elements of data matrices without changing the marginals; most powerful tests against all alternative hypotheses are given for which a monotone characteristic may be computed from the data matrix; alternatives may also be composed. Computation times are long, but exactp-values are approximated with the quality of approximation only depending on calculation time, but not on the number of persons. The power and the flexibility of the procedure is demonstrated by means of an empirical example where, among others, indicators for increased item similarities, the existence of subscales, violations of sufficiency of the raw score as well as learning processes were found. Many of the features described are implemented in the program T-Rasch 1.0 by Ponocny and Ponocny-Seliger (1999).

Information

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.)

Article purchase

Temporarily unavailable