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

Pascal Jordan () and Martin Spiess ()
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Pascal Jordan: University of Hamburg
Martin Spiess: University of Hamburg

Psychometrika, 2018, vol. 83, issue 3, No 3, 563-585

Abstract: 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 scores; paradoxical results; fairness; convex programs; bifactor model (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11336-018-9613-1

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