The DOI matches its Crossref record.
Contingency Table Models for Assessing Item Bias
Abstract
Two strategies for assessing item bias are discussed: Methods based on Item x Group interaction and methods that compare the probabilities of a correct response for different groups conditional on ability level. In latent trait models, correct response probabilties are compared conditional on latent ability; in Scheuneman’s (1979) method these probabilities are compared conditional on the observed test score. Scheuneman’s method is modified to fit the general theory of loglinear and logit models for contingency tables. A distinction is made between uniform and nonuniform item bias, and a method to assess item bias and distinguish between uniform and nonuniform bias is described. Data reported by Scheuneman are used to demonstrate the method in detail; the differences obtained with Scheuneman’s method are discussed. In addition, the method is applied to two tests administered by van der Flier (1980) in Kenya and Tanzania, and the results are compared to those obtained with Scheuneman’s approach.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Detecting Differential Item Functioning Using Logistic Regression Procedures
Swaminathan, H., & Rogers, H. J. (1990) · Journal of Educational Measurement · doi:10.1111/j.1745-3984.1990.tb00754.x · Cited by 1,073
Rutkowski, L., & Svetina, D. (2014) · Educational and Psychological Measurement · doi:10.1177/0013164413498257 · Cited by 767
Shealy, R., & Stout, W. (1993) · Psychometrika · doi:10.1007/bf02294572 · Cited by 657
Item bias and item response theory
Mellenbergh, G. J. (1989) · International Journal of Educational Research · doi:10.1016/0883-0355(89)90002-5 · Cited by 553
Self-citationThe Issue Of Measurement Invariance Revisited
Byrne, B. M., & Watkins, D. (2003) · Journal of Cross-Cultural Psychology · doi:10.1177/0022022102250225 · Cited by 489
Metadata
- Authors
- Mellenbergh, G. J.
- Venue
- Journal of Educational Statistics
- Publisher
- JSTOR
- Published
- 1982
- Volume
- 7
- Issue
- 2
- Pages
- 105
- ISSN
- 0362-9791 (ISSN-L)
- OpenAlex
- W2034931437
- Cited by
- 249
- References
- 0
- FWCI
- 4.12
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Contingency Table Models for Assessing Item Bias. https://app.cochat.ai/papers/10.2307/1164960
- The paper
- Mellenbergh, G. J. (1982). Contingency Table Models for Assessing Item Bias. Journal of Educational Statistics. https://doi.org/10.2307/1164960
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.