Understanding the relationship between person, item, and testlet covariates and person, item, and testlet parameters may offer considerable benefits to both test development and test validation efforts. The Bayesian TRT models proposed by Wainer, Bradlow, and Wang (2007) offer a unified structure within which model parameters may be estimated simultaneously with model parameter covariates. This unified approach represents an important advantage of these models: theoretically correct modeling of the relationship between covariates and their respective model parameters. Analogous analyses can be performed via conventional post-hoc regression methods, however, the fully Bayesian framework offers an important advantage over the conventional post-hoc methods by reflecting the uncertainty of the model parameters when estimating their relationship to covariates. The purpose of this study was twofold. First was to conduct a basic simulation study to investigate the accuracy and effectiveness of the Bayesian TRT approach in estimating the relationship of covariates to their respective model parameters. Additionally, the Bayesian TRT results were compared to post-hoc regression results, where the dependent variable was the point estimate of the model parameter of interest. Second, an empirical study applied the Bayesian TRT model to two real data sets: the Step 3 component of the United States Medical Licensing Examination (USMLE), and the Posttraumatic Growth Inventory (PTGI) by Tedeschi and Calhoun (1996). The findings of both simulation and empirical studies suggest that the Bayesian TRT performs very similarly to the post-hoc approach. Detailed discussion is provided and potential future studies are suggested in chapter 5.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:dissertations-5044 |
| Date | 01 January 2008 |
| Creators | Baldwin, Su G |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Language | English |
| Detected Language | English |
| Type | text |
| Source | Doctoral Dissertations Available from Proquest |
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