A multilevel logistic regression approach provides an attractive and practical alternative for the study of Differential Item Functioning (DIF). It is not only useful for identifying items with DIF but also for explaining the presence of DIF. Kamata and Binici (2003) first attempted to identify group unit characteristic variables explaining the variation of DIF by using hierarchical generalized linear models. Their models were implemented by the HLM-5 software, which uses the penalized or predictive quasi-likelihood (PQL) method. They found that the variance estimates produced by HLM-5 for the level 3 parameters are substantially negatively biased. This study extends their work by using a Bayesian approach to obtain more accurate parameter estimates. Two different approaches to modeling the DIF will be presented. These are referred to as the relative and mixture distribution approach, respectively. The relative approach measures the DIF of a particular item relative to the mean overall DIF for all items in the test. The mixture distribution approach treats the DIF as independent values drawn from a distribution which is a mixture of a normal distribution and a discrete distribution concentrated at zero. A simulation study is presented to assess the adequacy of the proposed models. This work also describes and studies models which allow the DIF to vary at level 3 (from school to school). In an example using real data, it is shown how the models can be applied to the identification of items with DIF and the explanation of the source of the DIF. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Spring Semester, 2005. / April 1, 2005. / Bayesian, Multilevel Logistic Regression, DIF / Includes bibliographical references. / Fred W. Huﬀer, Professor Co-Directing Thesis; Akihito Kamata, Professor Co-Directing Thesis; Richard Tate, Outside Committee Member; Xu-Feng Niu, Committee Member; Daniel McGee, Committee Member.
|Contributors||Chaimongkol, Saengla (authoraut), Huﬀer, Fred W. (professor co-directing thesis), Kamata, Akihito (professor co-directing thesis), Tate, Richard (outside committee member), Niu, Xu-Feng (committee member), McGee, Daniel (committee member), Department of Statistics (degree granting department), Florida State University (degree granting institution)|
|Publisher||Florida State University, Florida State University|
|Source Sets||Florida State University|
|Format||1 online resource, computer, application/pdf|
|Rights||This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). The copyright in theses and dissertations completed at Florida State University is held by the students who author them.|
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