Use Bayesian adaptive designs to channel growing public-program data streams into evaluation¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article recommends that evaluators of public programs adopt novel statistical methods, such as the Bayesian approach, to exploit increasingly rich data streams. It notes that public programs will capture more and richer data, "including data from web-based tools used by participants in employment services, from tablet-based educational curricula, and from electronic health records for Medicaid beneficiaries", and that taking full advantage of these streams will require such methods.

Design Implications¶

Context¶

Requirements¶

• Access to accumulating program data streams and capacity to run randomized evaluations with adaptive allocation

Constraints¶

• The recommendation is framed for public program evaluation contexts such as employment services, education, and Medicaid

Target Learners¶

• program evaluators
• education researchers
• health services evaluators

Target Learning Goals¶

• efficient evaluation of public programs using large and rich data streams

Affordances¶

• Bayesian Adaptive Program Evaluation Framework

Related Strategies¶

Examples¶

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Key Sources¶

• Mariel McKenzie Finucane, Ignacio Martinez, Scott Cody. (2018). What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Public Program Evaluation. American Journal of Evaluation, vol. 39, issue 1. https://journals.sagepub.com/loi/aje