IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v83y2018i3d10.1007_s11336-018-9613-1.html
   My bibliography  Save this article

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

Author

Listed:
  • Pascal Jordan

    (University of Hamburg)

  • Martin Spiess

    (University of Hamburg)

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).

Suggested Citation

  • Pascal Jordan & Martin Spiess, 2018. "On Fair Person Classification Based on Efficient Factor Score Estimates in the Multidimensional Factor Analysis Model," Psychometrika, Springer;The Psychometric Society, vol. 83(3), pages 563-585, September.
  • Handle: RePEc:spr:psycho:v:83:y:2018:i:3:d:10.1007_s11336-018-9613-1
    DOI: 10.1007/s11336-018-9613-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11336-018-9613-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11336-018-9613-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Giles Hooker & Matthew Finkelman, 2010. "Paradoxical Results and Item Bundles," Psychometrika, Springer;The Psychometric Society, vol. 75(2), pages 249-271, June.
    2. Wim Krijnen & Theo Dijkstra & Richard Gill, 1998. "Conditions for factor (in)determinacy in factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 63(4), pages 359-367, December.
    3. Matthew D. Finkelman & Giles Hooker & Zhen Wang, 2010. "Prevalence and Magnitude of Paradoxical Results in Multidimensional Item Response Theory," Journal of Educational and Behavioral Statistics, , vol. 35(6), pages 744-761, December.
    4. Pascal Jordan & Martin Spiess, 2012. "Generalizations of Paradoxical Results in Multidimensional Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 127-152, January.
    5. Wim Linden, 2012. "On Compensation in Multidimensional Response Modeling," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 21-30, January.
    6. Giles Hooker, 2010. "On Separable Tests, Correlated Priors, and Paradoxical Results in Multidimensional Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 694-707, December.
    7. James Steiger, 1979. "Factor indeterminacy in the 1930's and the 1970's some interesting parallels," Psychometrika, Springer;The Psychometric Society, vol. 44(2), pages 157-167, June.
    8. Jules Ellis & Brian Junker, 1997. "Tail-measurability in monotone latent variable models," Psychometrika, Springer;The Psychometric Society, vol. 62(4), pages 495-523, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pascal Jordan & Martin Spiess, 2018. "A New Explanation and Proof of the Paradoxical Scoring Results in Multidimensional Item Response Models," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 831-846, December.
    2. Pascal Jordan, 2023. "On Reverse Shrinkage Effects and Shrinkage Overshoot," Psychometrika, Springer;The Psychometric Society, vol. 88(1), pages 274-301, March.
    3. Pascal Jordan & Martin Spiess, 2012. "Generalizations of Paradoxical Results in Multidimensional Item Response Theory," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 127-152, January.
    4. Roderick McDonald, 2011. "Measuring Latent Quantities," Psychometrika, Springer;The Psychometric Society, vol. 76(4), pages 511-536, October.
    5. Wim Linden, 2012. "On Compensation in Multidimensional Response Modeling," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 21-30, January.
    6. Sacha Epskamp & Mijke Rhemtulla & Denny Borsboom, 2017. "Generalized Network Psychometrics: Combining Network and Latent Variable Models," Psychometrika, Springer;The Psychometric Society, vol. 82(4), pages 904-927, December.
    7. Matthias Davier & Shelby Haberman, 2014. "Hierarchical Diagnostic Classification Models Morphing into Unidimensional ‘Diagnostic’ Classification Models—A Commentary," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 340-346, April.
    8. Ivo Molenaar, 1998. "Data, model, conclusion, doing it again," Psychometrika, Springer;The Psychometric Society, vol. 63(4), pages 315-340, December.
    9. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2014. "Dynamic factor models: A review of the literature," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 73-107.
    10. Jörg Henseler & Marko Sarstedt, 2013. "Goodness-of-fit indices for partial least squares path modeling," Computational Statistics, Springer, vol. 28(2), pages 565-580, April.
    11. William Stout, 2002. "Psychometrics: From practice to theory and back," Psychometrika, Springer;The Psychometric Society, vol. 67(4), pages 485-518, December.
    12. Wim Krijnen, 2002. "On the construction of all factors of the model for factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 161-172, March.
    13. Jules L. Ellis & Klaas Sijtsma, 2023. "A Test to Distinguish Monotone Homogeneity from Monotone Multifactor Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 387-412, June.
    14. Reinartz, Werner & Haenlein, Michael & Henseler, Jörg, 2009. "An empirical comparison of the efficacy of covariance-based and variance-based SEM," International Journal of Research in Marketing, Elsevier, vol. 26(4), pages 332-344.
    15. Dave Grayson, 2006. "Might “unique” factors be “common”? On the possibility of indeterminate common–unique covariances," Psychometrika, Springer;The Psychometric Society, vol. 71(3), pages 521-528, September.
    16. Noelia Cámara & David Tuesta, 2018. "Measuring financial inclusion: a multidimensional index," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The role of data in supporting financial inclusion policy, volume 47, Bank for International Settlements.
    17. Matthew D. Finkelman & Giles Hooker & Zhen Wang, 2010. "Prevalence and Magnitude of Paradoxical Results in Multidimensional Item Response Theory," Journal of Educational and Behavioral Statistics, , vol. 35(6), pages 744-761, December.
    18. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    19. Stegeman, Alwin, 2016. "A new method for simultaneous estimation of the factor model parameters, factor scores, and unique parts," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 189-203.
    20. Ayushi Raichoudhury, 2020. "Major Determinants of Financial Inclusion: State-Level Evidences from India," Vision, , vol. 24(2), pages 151-159, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:psycho:v:83:y:2018:i:3:d:10.1007_s11336-018-9613-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.