Project Activities
People and institutions involved
IES program contact(s)
Products and publications
Book chapter
French, B. F., and Finch, W. H (2016). Detecting Differential Item Functioning. In K. Schweizer and C. DiStefano (Eds.), Principles and Methods of Test Construction (pp. 197-217). Hogrefe Publishing.
French, B. F., Finch, W. H., & Immekus, J. C. (2019, June). Multilevel generalized Mantel-Haenszel for differential item functioning detection. In Frontiers in Education (Vol. 4, p. 47). Frontiers Media SA.
Journal article, monograph, or newsletter
Beaver, J., French, B. F., Finch, W. H., and Ullrich-French, S. C. (2014). Sex Differential Item Functioning in the Inventory of Early Development III Social-Emotional Skills. Journal of Psychoeducational Assessment, 32(8): 775-780.
French, B. F., and Finch, W. H. (2015). Transforming SIBTEST to Account for Multilevel Data Structures. Journal of Educational Measurement, 52(2): 159-180.
French, B.F., and Finch, W.H. (2013). Extensions of Mantel-Haenszel for Multilevel DIF Detection. Educational and Psychological Measurement, 73(4): 648-671.
French, B.F., Finch, W.H., Randel, B., Hand, B., and Gotch, C.M. (2016). Measurement Invariance Techniques to Enhance Measurement Sensitivity. International Journal of Quantitative Research in Education, 3(1-2), 79-93.
French, B.F., Finch, W.H., and Vazquez, J.A.V. (2016). Differential Item Functioning on Mathematics Items Using Multilevel SIBTEST. Psychological Test and Assessment Modeling, 58(3), 471-483.
Supplemental information
Co-Principal Investigator: Finch, W. Holms
Two popular methods for DIF detection are SIBTEST and the Mantel-Haenszel (MH) statistic. These methods have proven to be effective at detecting DIF when present, while maintaining control of the Type I error rate. MH and SIBTEST have both been shown to be especially effective with small samples, particularly when compared with more complex, model-based approaches.
Neither method has been adapted for use with multilevel data and thus may exhibit poor performance when such a data structure is present. Multilevel data is prevalent in DIF studies (e.g., examinees sampled within classrooms which are sampled from within schools). If standard analyses at the individual level are used ignoring the multilevel structure, variation due to higher levels of the data structure (e.g., classrooms) could be conflated with that due to student level variations. This can lead to incorrect parameter estimates and standard errors thereby degrading DIF detection.
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