Encyclopedia of Personality and Individual Differences

Living Edition
| Editors: Virgil Zeigler-Hill, Todd K. Shackelford

Differential Item Functioning

  • Eunike WetzelEmail author
  • Jan R. Böhnke
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-28099-8_1297-1



A questionnaire or test item shows differential item functioning (DIF) if the probability of responding correctly to the item differs between individuals belonging to different groups although they have been matched on the underlying latent trait.


It is essential to ensure that a test is fair to all test takers. A person’s observed item response should depend only on that person’s latent trait level, but not additionally on, for example, whether they are male or female. Whether this is the case can be investigated by testing items for DIF. Typically, DIF investigations compare items between groups (e.g., gender, age categories, or other relevant demographic variables). When no DIF is found, this indicates that the response categories of an item relate in the same way to the latent trait in the groups that were compared. This is important, since in this case manifest differences in...


Differential Item Functioning Item Response Theory Latent Trait Item Response Theory Model Differential Item Functioning Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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  1. Choi, S. W., Gibbons, L. E., & Crane, P. K. (2011). lordif: An R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. Journal of Statistical Software, 39(8), 1–30.CrossRefPubMedPubMedCentralGoogle Scholar
  2. Crane, P. K., Gibbons, L. E., Ocepek-Welikson, K., Cook, K., Cella, D., Narasimhalu, K., et al. (2007). A comparison of three sets of criteria for determining the presence of differential item functioning using ordinal logistic regression. Quality of Life Research, 16(Suppl 1), 69–84. doi:10.1007/s11136-007-9185-5.CrossRefPubMedGoogle Scholar
  3. Hambleton, R. K. (2006). Good practices for identifying differential item functioning. Medical Care, 44(11 Suppl 3), S182–S188. doi:10.1097/01.mlr.0000245443.86671.c4.CrossRefPubMedGoogle Scholar
  4. Liu, I. M., & Agresti, A. (1996). Mantel-Haenszel-type inference for cumulative odds ratios with a stratified ordinal response. Biometrics, 52(4), 1223–1234. doi:10.2307/2532838.CrossRefPubMedGoogle Scholar
  5. Mantel, N., & Haenszel, W. (1959). Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the Natlonal Cancer Institute, 22(4), 719–748.Google Scholar
  6. Penfield, R. D., & Algina, J. (2003). Applying the Liu-Agresti estimator of the cumulative common odds ratio to DIF detection in polytomous items. Journal of Educational Measurement, 40(4), 353–370. doi:10.1111/j.1745-3984.2003.tb01151.x.CrossRefGoogle Scholar
  7. Penfield, R. D., & Camilli, G. (2007). Differential item functioning and item bias. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics (Vol. 26, pp. 125–167). Amsterdam, The Netherlands: North-Holland.Google Scholar
  8. Rost, J., & von Davier, M. (1995). Mixture distribution Rasch models. In G. H. Fischer & I. W. Molenaar (Eds.), Rasch Models: Foundations, recent developments, and applications (pp. 257–268). New York: Springer.CrossRefGoogle Scholar
  9. Zieky, M. (1993). Practical questions in the use of DIF statistics in item development. In P. W. Holland & H. Wainer (Eds.), Differential item functioning (pp. 337–347). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
  10. Zwick, R. (2012). A review of ETS differential item functioning assessment procedures: Flagging rules, minimum sample size requirements, and criterion refinement ETS research report (Vol. ETS RR-12-08). Princeton, NJ: Educational Testing Service.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universität KonstanzKonstanzGermany
  2. 2.University of DundeeDundeeUK

Section editors and affiliations

  • Matthias Ziegler
    • 1
  1. 1.Humboldt University, GermanyBerlinGermany