, Volume 83, Issue 3, pp 563–585 | Cite as

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

  • Pascal JordanEmail author
  • Martin Spiess


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).


factor scores paradoxical results fairness convex programs bifactor model 

Supplementary material

11336_2018_9613_MOESM1_ESM.r (3 kb)
Supplementary material 1 (R 2 KB)


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Copyright information

© The Psychometric Society 2018

Authors and Affiliations

  1. 1.University of HamburgHamburgGermany

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