Title: Essays on Applying Bayesian Data Analysis to Improve Evidence-based Decision-making in Education
Description: This three-article dissertation aims to apply Bayesian data analysis to improve the methodologies that process effectiveness findings, cost information and subjective judgments with the purpose of providing clear, localized guidance for decision makers in educational resource allocation. The first article shows how to use a Bayesian hierarchical model to capture the uncertainty of the effectiveness-cost ratio. The uncertainty information produced by the model may inform the decision makers of the best- and worst-case scenarios of the program efficiency if it is replicated. The second article introduces Bayesian decision theory to address a subset of methodological barriers that hamper the influence of research on educational decision-making, including how to generalize or extrapolate effectiveness and cost information from the evaluation site(s) to a specific context, how to incorporate information from multiple sources, and how to aggregate multiple consequences of an intervention into one framework.

The purpose of this article is to generate evidence of program comparison that applies to a specific school facing a decision problem by incorporating the decision-makers' subjective judgements and modeling their specific preference on multiple consequences. The third article proposes a randomized control trial to detect whether principals and practitioners update their beliefs on the effectiveness and cost of educational programs in the light of uncertainty information and localized evidence. Supplemented by a pilot qualitative study that guides decision makers to work on self-defined decision problems, the pilot testing of the experiment provides some evidence on the plausibility of using an experiment to identify the causal impact of research evidence on decision-making.