Author ORCID Identifier
Year of Publication
Doctor of Philosophy (PhD)
Dr. Michael D. Toland
Under item response theory, three types of limited information goodness-of-fit test statistics – M2, Mord, and C2 – have been proposed to assess model-data fit when data are sparse. However, the evaluation of the performance of these GOF statistics under multidimensional item response theory (MIRT) models with polytomous data is limited. The current study showed that M2 and C2 were well-calibrated under true model conditions and were powerful under misspecified model conditions. Mord were not well-calibrated when the number of response categories was more than three. RMSEA2 and RMSEAC2 are good tools to evaluate approximate fit.
The second study aimed to evaluate the psychometric properties of the Religious Commitment Inventory-10 (RCI-10; Worthington et al., 2003) within the IRT framework and estimate C2 and its RMSEA to assess global model-fit. Results showed that the RCI-10 was best represented by a bifactor model. The scores from the RCI-10 could be scored as unidimensional notwithstanding the presence of multidimensionality. Two-factor correlational solution should not be used. Study two also showed that religious commitment is a risk factor of intimate partner violence, whereas spirituality was a protecting factor from the violence. More alcohol was related with more abusive behaviors. Implications of the two studies were discussed.
Digital Object Identifier (DOI)
Applied Psychometric Strategies (APS) Lab, P20 Motivation and Learning Lab, Robinson Scholarship Program, Center for Research on Violence Against Women, Quantitative and Psychometric Methods (QPM) program, Educational Psychology (EDP) program, College of Education
Li, Caihong Rosina, "ASSESSING THE MODEL FIT OF MULTIDIMENSIONAL ITEM RESPONSE THEORY MODELS WITH POLYTOMOUS RESPONSES USING LIMITED-INFORMATION STATISTICS" (2019). Theses and Dissertations--Education Science. 45.
Criminology Commons, Domestic and Intimate Partner Violence Commons, Educational Assessment, Evaluation, and Research Commons, Quantitative, Qualitative, Comparative, and Historical Methodologies Commons