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Item response theory requires logically unjustifiable assumptions

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  • Merton S. Krause

Abstract

If items have different levels of difficulty (or sensitivity) relative to some psychological attribute, passing (or endorsing) any one cannot mean the same about a person as passing any other, so percent of items passed regardless of which these are cannot indicate a person’s level on any attribute. If persons have different levels on a psychological attribute, an item’s being passed by one person cannot mean the same about its difficulty level as being passed by any other person, so percent of persons passing it regardless of which persons these are cannot indicate the item’s difficulty level. Percent of items passed by a person and percent of persons passing an item are incommensurate quantities not expressible in terms of the same quality or dimension. Both such percents are dependent on what sample of items and of persons are used. A person’s attribute level is not demonstrably probabilistic, because truly independent replicate occasions of a person responding to an item are impossible. Passing an item depends on more than a person’s single attribute level, the item’s difficulty level, and random chance. On all these matters Item Response Theory relies on assumptions that are logically unjustifiable.

Suggested Citation

  • Merton S. Krause, 2017. "Item response theory requires logically unjustifiable assumptions," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(4), pages 1549-1561, July.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:4:d:10.1007_s11135-016-0351-0
    DOI: 10.1007/s11135-016-0351-0
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    References listed on IDEAS

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    1. Ferdinand A. Gul & Judy S. L. Tsui, 2004. "Introduction and overview," Palgrave Macmillan Books, in: The Governance of East Asian Corporations, chapter 1, pages 1-26, Palgrave Macmillan.
    2. van Schuur, Wijbrandt H., 2003. "Mokken Scale Analysis: Between the Guttman Scale and Parametric Item Response Theory," Political Analysis, Cambridge University Press, vol. 11(2), pages 139-163, April.
    3. Paul Holland, 1990. "On the sampling theory roundations of item response theory models," Psychometrika, Springer;The Psychometric Society, vol. 55(4), pages 577-601, December.
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    1. Francesca Fortuna & Fabrizio Maturo, 2019. "K-means clustering of item characteristic curves and item information curves via functional principal component analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2291-2304, September.

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