Full metadata
Title
The impact of information quantity and quality on parameter estimation for a selection of dynamic Bayesian network models with latent variables
Description
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-phase Monte Carlo simulation study was conducted in order to evaluate parameter recovery for DBNs using maximum likelihood estimation with the Netica software package. Phase 1 included a limited number of conditions and was exploratory in nature while Phase 2 included a larger and more targeted complement of conditions. Manipulated factors included sample size, measurement quality, test length, the number of measurement occasions. Results suggested that measurement quality has the most prominent impact on estimation quality with more distinct performance categories yielding better estimation. While increasing sample size tended to improve estimation, there were a limited number of conditions under which greater samples size led to more estimation bias. An exploration of this phenomenon is included. From a practical perspective, parameter recovery appeared to be sufficient with samples as low as N = 400 as long as measurement quality was not poor and at least three items were present at each measurement occasion. Tests consisting of only a single item required exceptional measurement quality in order to adequately recover model parameters. The study was somewhat limited due to potentially software-specific issues as well as a non-comprehensive collection of experimental conditions. Further research should replicate and, potentially expand the current work using other software packages including exploring alternate estimation methods (e.g., Markov chain Monte Carlo).
Date Created
2018
Contributors
- Reichenberg, Raymond E (Author)
- Levy, Roy (Thesis advisor)
- Eggum-Wilkens, Natalie (Thesis advisor)
- Iida, Masumi (Committee member)
- DeLay, Dawn (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
xii, 160 pages : illustrations (some color)
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.50531
Statement of Responsibility
by Raymond E. Reichenberg
Description Source
Viewed on December 11, 2018
Level of coding
full
Note
thesis
Partial requirement for: Ph.D., Arizona State University, 2018
bibliography
Includes bibliographical references (pages 111-120)
Field of study: Family and human development
System Created
- 2018-10-01 08:03:40
System Modified
- 2021-08-26 09:47:01
- 3 years 3 months ago
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