Full metadata
Title
A Bayesian Synthesis approach to data fusion using augmented data-dependent priors
Description
The process of combining data is one in which information from disjoint datasets sharing at least a number of common variables is merged. This process is commonly referred to as data fusion, with the main objective of creating a new dataset permitting more flexible analyses than the separate analysis of each individual dataset. Many data fusion methods have been proposed in the literature, although most utilize the frequentist framework. This dissertation investigates a new approach called Bayesian Synthesis in which information obtained from one dataset acts as priors for the next analysis. This process continues sequentially until a single posterior distribution is created using all available data. These informative augmented data-dependent priors provide an extra source of information that may aid in the accuracy of estimation. To examine the performance of the proposed Bayesian Synthesis approach, first, results of simulated data with known population values under a variety of conditions were examined. Next, these results were compared to those from the traditional maximum likelihood approach to data fusion, as well as the data fusion approach analyzed via Bayes. The assessment of parameter recovery based on the proposed Bayesian Synthesis approach was evaluated using four criteria to reflect measures of raw bias, relative bias, accuracy, and efficiency. Subsequently, empirical analyses with real data were conducted. For this purpose, the fusion of real data from five longitudinal studies of mathematics ability varying in their assessment of ability and in the timing of measurement occasions was used. Results from the Bayesian Synthesis and data fusion approaches with combined data using Bayesian and maximum likelihood estimation methods were reported. The results illustrate that Bayesian Synthesis with data driven priors is a highly effective approach, provided that the sample sizes for the fused data are large enough to provide unbiased estimates. Bayesian Synthesis provides another beneficial approach to data fusion that can effectively be used to enhance the validity of conclusions obtained from the merging of data from different studies.
Date Created
2017
Contributors
- Marcoulides, Katerina M (Author)
- Grimm, Kevin (Thesis advisor)
- Levy, Roy (Thesis advisor)
- MacKinnon, David (Committee member)
- Suk, Hye Won (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
viii, 128 pages : illustrations
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.44280
Statement of Responsibility
by Katerina M. Marcoulides
Description Source
Viewed on December 15, 2017
Level of coding
full
Note
thesis
Partial requirement for: Ph. D., Arizona State University, 2017
bibliography
Includes bibliographical references (pages 75-85)
Field of study: Psychology
System Created
- 2017-06-01 02:06:30
System Modified
- 2021-08-26 09:47:01
- 3 years 2 months ago
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