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Item Screening in Graphical Loglinear Rasch Models

Published online by Cambridge University Press:  01 January 2025

Svend Kreiner  and
Karl Bang Christensen
Svend Kreiner*
Affiliation:
University of Copenhagen
Karl Bang Christensen
Affiliation:
University of Copenhagen
*
Requests for reprints should be sent to Svend Kreiner, Department of Biostatistics, University of Copenhagen, Oster Farimagsgade 5, B, POB 2029, 1014 Copenhagen K, Denmark. E-mail: s.kreiner@biostat.ku.dk
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Abstract

In behavioural sciences, local dependence and DIF are common, and purification procedures that eliminate items with these weaknesses often result in short scales with poor reliability. Graphical loglinear Rasch models (Kreiner & Christensen, in Statistical Methods for Quality of Life Studies, ed. by M. Mesbah, F.C. Cole & M.T. Lee, Kluwer Academic, pp. 187–203, 2002) where uniform DIF and uniform local dependence are permitted solve this dilemma by modelling the local dependence and DIF. Identifying loglinear Rasch models by a stepwise model search is often very time consuming, since the initial item analysis may disclose a great deal of spurious and misleading evidence of DIF and local dependence that has to disposed of during the modelling procedure.

Like graphical models, graphical loglinear Rasch models possess Markov properties that are useful during the statistical analysis if they are used methodically. This paper describes how. It contains a systematic study of the Markov properties and the way they can be used to distinguish spurious from genuine evidence of DIF and local dependence and proposes a strategy for initial item screening that will reduce the time needed to identify a graphical loglinear Rasch model that fits the item responses. The last part of the paper illustrates the item screening procedure on simulated data and on data on the PF subscale measuring physical functioning in the SF36 Health Survey inventory.

Keywords

chain graph modelsgraphical Rasch modelsloglinear Rasch modelsglobal Markov propertiesdifferential item functioninglocal dependenceMantel–Haenszel analysispartial gamma coefficient

Information

Type
Original Paper
Information
Psychometrika , Volume 76 , Issue 2 , April 2011 , pp. 228 - 256
Copyright
Copyright © 2011 The Psychometric Society

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