General diagnostic models (GDMs) and Bayesian networks are mathematical frameworks that cover a wide variety of psychometric models. Both extend latent class models, and while GDMs also extend item response theory (IRT) models, Bayesian networks can be parameterized using discretized IRT. The purpose of this study is to examine similarities and differences between GDMs and Bayesian networks using both simulated data and real test data sets. The performances of the two frameworks in data generation and estimation under various possible conditions are investigated. Several indices for accuracy and precision are examined as well as the agreement between the GDM and Bayesian network for simulated data and a real data set in this study. Both have problems with identifiability and high-level proficiency variables. Bayesian network slightly better with small samples and can use correlations among proficiency variables to stabilize estimates for scales with few items. / A Dissertation submitted to the Department of Educational Psychology and Learning Systems in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester, 2013. / November 1, 2013. / Bayesian network, Cognitive Diagnosis Model, Evidence-centered Assessment Design, Evidence Model, General Diagnostic Model, Proficiency Model / Includes bibliographical references. / Russell Almond, Professor Directing Dissertation; Diana Rice, University Representative; Betsy Becker, Committee Member; Valerie Shute, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_185205 |
| Contributors | Wu, Haiyan (authoraut), Almond, Russell (professor directing dissertation), Rice, Diana (university representative), Becker, Betsy (committee member), Shute, Valerie (committee member), Department of Educational Psychology and Learning Systems (degree granting department), Florida State University (degree granting institution) |
| Publisher | Florida State University, Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text |
| 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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