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On Fair Person Classification Based on Efficient Factor Score Estimates in the Multidimensional Factor Analysis Model

Published online by Cambridge University Press:  01 January 2025

Pascal Jordan  and
Martin Spiess
Pascal Jordan*
Affiliation:
University of Hamburg
Martin Spiess
Affiliation:
University of Hamburg
*
Correspondence should be made to Pascal Jordan, University of Hamburg, Von-Melle-Park 5, 20146 Hamburg,Germany. Email: pascal.jordan@uni-hamburg.de
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Abstract

Since Hooker, Finkelman and Schwartzman (Psychometrika 74(3): 419–442, 2009) it is known that person parameter estimates from multidimensional latent variable models can induce unfair classifications via paradoxical scoring effects. The open question as to whether there is a fair and at the same time multidimensional scoring scheme with adequate statistical properties is addressed in this paper. We develop a theorem on the existence of a fair, multidimensional classification scheme in the context of the classical linear factor analysis model and show how the computation of the scoring scheme can be embedded in the context of linear programming. The procedure is illustrated in the framework of scoring the Wechsler Adult Intelligence Scale (WAIS-IV).

Keywords

factor scoresparadoxical resultsfairnessconvex programsbifactor model
Type
Original Paper
Information
Psychometrika , Volume 83 , Issue 3 , September 2018 , pp. 563 - 585
Copyright
Copyright © The Psychometric Society 2018

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11336-018-9613-1) contains supplementary material, which is available to authorized users.

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