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Lim, Hwanggyu; Choe, Edison M.; Han, Kyung T. – Journal of Educational Measurement, 2022
Differential item functioning (DIF) of test items should be evaluated using practical methods that can produce accurate and useful results. Among a plethora of DIF detection techniques, we introduce the new "Residual DIF" (RDIF) framework, which stands out for its accessibility without sacrificing efficacy. This framework consists of…
Descriptors: Test Items, Item Response Theory, Identification, Robustness (Statistics)
Drabinová, Adéla; Martinková, Patrícia – Journal of Educational Measurement, 2017
In this article we present a general approach not relying on item response theory models (non-IRT) to detect differential item functioning (DIF) in dichotomous items with presence of guessing. The proposed nonlinear regression (NLR) procedure for DIF detection is an extension of method based on logistic regression. As a non-IRT approach, NLR can…
Descriptors: Test Items, Regression (Statistics), Guessing (Tests), Identification
Finkelman, Matthew; Kim, Wonsuk; Roussos, Louis A. – Journal of Educational Measurement, 2009
Much recent psychometric literature has focused on cognitive diagnosis models (CDMs), a promising class of instruments used to measure the strengths and weaknesses of examinees. This article introduces a genetic algorithm to perform automated test assembly alongside CDMs. The algorithm is flexible in that it can be applied whether the goal is to…
Descriptors: Identification, Genetics, Test Construction, Mathematics

Sotaridona, Leonardo S.; Meijer, Rob R. – Journal of Educational Measurement, 2002
Studied the statistical properties of the K-index (P. Holland, 1996) that can be used to detect copying behavior on a test through a simulation study of the use of the K-statistic with small, medium, and large datasets. Also compared the Type I error rate and detection rate of this index with those of the copying index (J. Wollack, 1997).…
Descriptors: Cheating, Identification, Plagiarism, Sample Size

Roussos, Louis A.; Stout, William F. – Journal of Educational Measurement, 1996
Investigated Type I error performances of the SIBTEST and Mantel-Haenszel procedures for detecting differential item functioning (DIF) using simulation. Results of small sample simulation show no large differences in Type I error performances for the two techniques. Discusses results of a second simulation which shows some advantages for SIBTEST.…
Descriptors: Identification, Item Bias, Sample Size, Simulation

Chang, Hua-Hua; And Others – Journal of Educational Measurement, 1996
An extension to the SIBTEST procedure of R. Shealy and W. Stout (1993) to detect differential item functioning (DIF) is proposed to handle polytomous items. Results of two simulations suggest that the modified SIBTEST performs reasonably well and sometimes can provide better control of impact-induced Type I error inflation. (SLD)
Descriptors: Comparative Analysis, Identification, Item Bias, Simulation

French, Ann W.; Miller, Timothy R. – Journal of Educational Measurement, 1996
A computer simulation study was conducted to determine the feasibility of using logistic regression procedures to detect differential item functioning (DIF) in polytomous items. Results indicate that logistic regression is powerful in detecting most forms of DIF, although it requires large amounts of data manipulation and careful interpretation.…
Descriptors: Computer Simulation, Identification, Item Bias, Test Interpretation

Parshall, Cynthia G.; Miller, Timothy R. – Journal of Educational Measurement, 1995
Exact testing was evaluated as a method for conducting Mantel-Haenszel differential item functioning (DIF) analyses with relatively small samples. A series of computer simulations found that the asymptotic Mantel-Haenszel and the exact method yielded very similar results across sample size, levels of DIF, and data sets. (SLD)
Descriptors: Comparative Analysis, Computer Simulation, Identification, Item Bias