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While Bayesian meta-analysis has flourished both in methodological and substantive work, group-specific Bayesian modeling remains scarce. Common practice for choosing prior distributions entails using typical non-informative priors. Currently, there is a push to use more informative prior distributions. In this dissertation I propose a group specific weakly informative prior distribution. The new prior distribution uses a frequentist estimate of between-studies heterogeneity as the noncentrality parameter in a folded noncentral t distribution. This new distribution is then modeled individually for groups based on some categorical factor. An extensive simulation study was performed to assess the performance of the new group-specific prior distribution to several non-informative prior distributions in a variety of meta-analytic scenarios. An application using data from a previously published meta-analysis on dynamic geometry software is also provided.
A Dissertation submitted to the Department Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
Includes bibliographical references.
Betsy Jane Becker, Professor Directing Dissertation; Kathy Clark, University Representative; Russell Almond, Committee Member; Ariel M. Aloe, Committee Member; Yanyun Yang, Committee Member.
Florida State University
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