Performance of the S[... formula ...]2 Statistic for the Multidimensional Graded Response Model |
| |
| Authors: | Shiyang Su Chun Wang David J Weiss |
| |
| Institution: | 1.University of Central Florida, Orlando, FL, USA;2.University of Washington, Seattle, WA, USA;3.University of Minnesota, Twin Cities, Minneapolis, MN, USA |
| |
| Abstract: | is a popular item fit index that is available in commercial software packages such as flexMIRT. However, no research has systematically examined the performance of for detecting item misfit within the context of the multidimensional graded response model (MGRM). The primary goal of this study was to evaluate the performance of under two practical misfit scenarios: first, all items are misfitting due to model misspecification, and second, a small subset of items violate the underlying assumptions of the MGRM. Simulation studies showed that caution should be exercised when reporting item fit results of polytomous items using within the context of the MGRM, because of its inflated false positive rates (FPRs), especially with a small sample size and a long test. performed well when detecting overall model misfit as well as item misfit for a small subset of items when the ordinality assumption was violated. However, under a number of conditions of model misspecification or items violating the homogeneous discrimination assumption, even though true positive rates (TPRs) of were high when a small sample size was coupled with a long test, the inflated FPRs were generally directly related to increasing TPRs. There was also a suggestion that performance of was affected by the magnitude of misfit within an item. There was no evidence that FPRs for fitting items were exacerbated by the presence of a small percentage of misfitting items among them. |
| |
| Keywords: | item response theory (IRT) item fit multidimensional graded response model (MGRM) |
|
|
