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Psychological tests from a (fuzzy-)logical point of view

Author

Listed:
  • Matthias Buntins

    (University of Bamberg)

  • Katja Buntins

    (University of Bamberg)

  • Frank Eggert

    (University of Braunschweig - Institute of Technology)

Abstract

Psychometric theory relies on two basic assumptions: (a) psychological constructs refer to latent (unobservable) variables and (b) psychological tests serve as a way to measure these constructs. This view is complemented by an alternative interpretation of psychological constructs, which neither relies on latent variables nor on the concept of measurement. Using the formal apparatus of many-valued logic, psychological constructs are re-interpreted as linguistic concepts (rather than latent variables), which can be inferred by means of logical calculus (as opposed to measurement). Thus, test scores do not refer to the values of latent variables, but to the degree to which the necessary and sufficient conditions for the ascription of a construct are fulfilled. Following this rationale, a formal theory of psychological tests is developed, which models the process of testing as logical inference. Applying the derived procedures, a person’s testing behaviour yields the degree to which a construct describes her adequately.

Suggested Citation

  • Matthias Buntins & Katja Buntins & Frank Eggert, 2016. "Psychological tests from a (fuzzy-)logical point of view," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(6), pages 2395-2416, November.
    • Handle: RePEc:spr:qualqt:v:50:y:2016:i:6:d:10.1007_s11135-015-0268-z
    DOI: 10.1007/s11135-015-0268-z
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    References listed on IDEAS

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    1. Jan Toporowski & Jo Michell, 2012. "Introduction," Chapters, in: Jan Toporowski & Jo Michell (ed.), Handbook of Critical Issues in Finance, pages i-ii, Edward Elgar Publishing.
    2. Luana Micallef & Peter Rodgers, 2014. "eulerAPE: Drawing Area-Proportional 3-Venn Diagrams Using Ellipses," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-18, July.
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    Cited by:

    1. Jana Uher, 2019. "Data generation methods across the empirical sciences: differences in the study phenomena’s accessibility and the processes of data encoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 221-246, January.

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