The Extension And Application Of MIMIC Model In Detecting Differential Testlet Functioning

When the test contains testlets,the differential item functioning(DIF)detection method based on the single-dimension IRT is no longer applicable to testlet-based data.In order to ensure the fairness of testlets tests,the current study attempts to explore and develop a series of detection methods to test the differential bundle functioning(DBF)and the testlet’s DIF for those testlet-based data.In addition,the number of individual’s background information which can be selected in a real-world test situation is usually far more the one type(gender),so the current also analysis and discusses the detection methods for the differential bundle funcitoning in multiple background variables.In this study,the DBF detection methods based on the bundle level and the DIF detection method based on the item level were first reviewed respectively.At bundle level,the SIBTEST method and the MIMIC-Bundle method are selected;at item level,the Rasch testlet response model(RTRM)and the bi-factor model for testlets with covariates are selected to test the testlet’s DIF.All above methods are introduced and evaluated respectively,and the concepts and models involved in the method are summarized.Then,this study reviews the previous researches on multi-group DIF detection methods,and summarizes similarities and differences in the multi-group expansion methods.Finally,on the basis of literature review,this study proposes using the MIMIC-Bundle model to detect the DBF in multigroup simultaneously,and then tests the performance of the above two extensions simulation studies.Through analysis of simulation studies,it can be found that:(1)When the proportion of samples is balanced,the Type I error and the type three error rate of DBF detection using the MIMIC-Bundle model can be controlled at acceptable levels in most situations(within 0.1),these power rates also reached 0.9 or more in most situations;(2)When the proportion of the sample is unbalanced,there is no significant increase in the Type I error,but the Type III error rates are inflated to above 0.6 in some situations.The power rate has a significant decrease under the condition of DBF amount of 0.3;(3)When the potential traits of the main dimension and the testlets dimension are respectively independent,MIMIC-Bundle model can better control Type I errorr(lower than 0.05)under all conditions compared with MIMIC method.The power rates of the two methods are equivalent;(4)When the potential traits of the main dimension and the testlets dimension are related,the power rate of the MIMIC-Bundle method is obviously higher than the MIMIC method in some situations,but inflated Type I error would be observed in most situations.