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A Didactic Explanation of Item Bias, Item Impact, and Item Validity From a Multidimensional Perspective
Abstract
Many researchers have suggested that the main cause of item bias is the misspecification of the latent ability space, where items that measure multiple abilities are scored as though they are measuring a single ability. If two different groups of examinees have different underlying multidimensional ability distributions and the test items are capable of discriminating among levels of abilities on these multiple dimensions, then any unidimensional scoring scheme has the potential to produce item bias. It is the purpose of this article to provide the testing practitioner with insight about the difference between item bias and item impact and how they relate to item validity. These concepts will be explained from a multidimensional item response theory (MIRT) perspective. Two detection procedures, the Mantel‐Haenszel (as modified by Holland and Thayer, 1988) and Shealy and Stout's Simultaneous Item Bias (SIB; 1991) strategies, will be used to illustrate how practitioners can detect item bias.
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Hambleton, R. K., & Swaminathan, H. (1985) · doi:10.1007/978-94-017-1988-9 · Cited by 1,777
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Shealy, R., & Stout, W. (1993) · Psychometrika · doi:10.1007/bf02294572 · Cited by 657
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Metadata
- Authors
- Ackerman, T. A.
- Venue
- Journal of Educational Measurement
- Publisher
- Wiley
- Published
- 1992-03
- Volume
- 29
- Issue
- 1
- Pages
- 67-91
- ISSN
- 0022-0655 (ISSN-L)
- 1745-3984
- OpenAlex
- W2035774867
- Cited by
- 389
- References
- 1
- FWCI
- 10.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ackerman, T. A. (1992). A Didactic Explanation of Item Bias, Item Impact, and Item Validity From a Multidimensional Perspective. Journal of Educational Measurement. https://doi.org/10.1111/j.1745-3984.1992.tb00368.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
