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The Impact of Information Quantity and Quality on Parameter Estimation for a Selection of Dynamic Bayesian Network Models with Latent Variables

Abstract Dynamic Bayesian networks (DBNs; Reye, 2004) are a promising tool for modeling student proficiency under rich measurement scenarios (Reichenberg, in press). These scenarios often present assessment conditions far more complex than what is seen with more traditional assessments and require assessment arguments and psychometric models capable of integrating those complexities. Unfortunately, DBNs remain understudied and their psychometric properties relatively unknown. If the apparent strengths of DBNs are to be leveraged, then the body of literature surrounding their properties and use needs to be expanded upon. To this end, the current work aimed at exploring the properties of DBNs under a variety of realistic psychometric conditions. A two... (more)
Created Date 2018
Contributor Reichenberg, Raymond E. (Author) / Levy, Roy (Advisor) / Eggum-Wilkens, Natalie (Advisor) / Iida, Masumi (Committee member) / DeLay, Dawn (Committee member) / Arizona State University (Publisher)
Subject Educational tests & measurements / Educational psychology / Quantitative psychology / dynamic Bayesian networks / educational measurement / game-based assessment
Type Doctoral Dissertation
Extent 173 pages
Language English
Note Doctoral Dissertation Family and Human Development 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis