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The Order-Restricted Association Model: Two Estimation Algorithms and Issues in Testing

Published online by Cambridge University Press:  01 January 2025

Francisca Galindo-Garre  and
Jeroen K. Vermunt
Francisca Galindo-Garre*
Affiliation:
Academic Medical Center, University of Amsterdam
Jeroen K. Vermunt
Affiliation:
Tilburg University
*
Requests for reprints should be addressed to F. Galindo-Garre, Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, P.O. Box 226600, 1100 DE Amsterdam, The Netherlands. Email: F.GalindoGarre@amc.uva.nl
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Abstract

This paper presents a row-column (RC) association model in which the estimated row and column scores are forced to be in agreement with an a priori specified ordering. Two efficient algorithms for finding the order-restricted maximum likelihood (ML) estimates are proposed and their reliability under different degrees of association is investigated by a simulation study. We propose testing order-restricted RC models using a parametric bootstrap procedure, which turns out to yield reliable p values, except for situations in which the association between the two variables is very weak. The use of order-restricted RC models is illustrated by means of an empirical example.

Keywords

Row-column association modelsorder-constraintsML estimation algorithmsParametric bootstrap

Information

Type
Theory And Methods
Information
Psychometrika , Volume 69 , Issue 4 , December 2004 , pp. 641 - 654
Copyright
Copyright © 2004 The Psychometric Society

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Footnotes

Francisca Galindo performed this research as a part of her PhD. dissertation project at Tilburg University.

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