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- W163707675 abstract "This study compared the ability of hierarchical linear modeling (HLM) to detect differential item functioning (DIF) to standard DIF detection methods, such as Rasch difficulty difference. The big advantages to using HLM for DIF detection are that the person abilities so produced are adjusted for any DIF in the items, and the DIF can then be modeled as a function of other predictors at a lower level in the same analysis. Data were simulated, and 180 data sets were produced to compare DIF detection approaches. In most cases, the amount of root mean square error (rmse) for Rasch and HLM is similar, but consistent points of difference occur when the number of people is small and the proportion of people in the focal group is low. In that case, the HLM rmse is larger, and when the number of people is large, the size of the DIF is small, and the proportion of people in the focal group is small, the HLM rmse is smaller. The paper discusses reasons HLM can produce better estimates than conventional methods in some circumstances. (SLD) Reproductions supplied by EDRS are the best that can be made from the original document. PERMISSION TO REPRODUCE AND DISSEMINATE THIS MATERIAL HAS BEEN GRANTED BY S. Luppescu TO THE EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) 1" @default.
- W163707675 created "2016-06-24" @default.
- W163707675 creator A5017700432 @default.
- W163707675 date "2002-04-08" @default.
- W163707675 modified "2023-09-23" @default.
- W163707675 title "DIF Detection in HLM." @default.
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