The importance of subscores in educational and psychological assessments is undeniable. Subscores yield diagnostic information that can be used for determining how each examinee's abilities/skills vary over different content domains. One of the most common criticisms about reporting and using subscores is insufficient reliability of subscores. This study employs a new reliability approach that allows the evaluation of between-person subscore reliability as well as within-person subscore reliability. Using this approach, the unidimensional IRT (UIRT) and multidimensional IRT (MIRT) models are compared in terms of subscore reliability in simulation and real data studies. Simulation conditions in the simulation study are subtest length, correlations among subscores, and number of subtests. Both unidimensional and multidimensional subscores are estimated with the maximum a posteriori probability (MAP) method. Subscore reliability of ability estimates are evaluated in light of between-person reliability, within-person reliability, and total profile reliability. The results of this study suggest that the MIRT model performs better than the UIRT model under all simulation conditions. Multidimensional subscore estimation benefits from correlations among subscores as ancillary information, and it yields more reliable subscore estimates than unidimensional subscore estimation. The subtest length is positively associated with both between-person and within-person reliability. Higher correlations among subscores improve between-person reliability, while they substantially decrease within-person reliability. The number of subtests seems to influence between-person reliability slightly but it has no effect on within-person reliability. The two estimation methods provide similar results with real data as well.
University of Minnesota Ph.D. dissertation. June 2013. Major: Educational Psychology. Advisor: Michael C. Rodriguez. 1 computer file (PDF); vii, 197 pages, appendices A-C.
Between-person and within-person subscore reliability: comparison of unidimensional and multidimensional IRT models.
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