Use the BASIE framework to interpret impact estimates from evaluations¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The BASIE (BAyeSian Interpretation of Estimates) framework is Deke's recommended approach for researchers drawing inferences about causal relationships from impact evaluations. The commentary states he "recommends researchers use the BASIE framework to interpret impact estimates from evaluations," positioning it as a way to "more productively use evidence while avoiding the types of inferential mistakes" examined by Sims et al (2022) and Simpson (2022). A related Mathematica publication describes BASIE as a framework for interpreting findings from impact evaluations as an alternative to statistical-significance-based interpretation.

Design Implications¶

Context¶

Requirements¶

• Researchers interpreting impact estimates from evaluations adopt the BASIE framework in place of filtering findings by statistical significance

Constraints¶

Target Learners¶

• education researchers
• decision makers using evaluation evidence

Target Learning Goals¶

• drawing valid causal inferences from impact evaluations
• avoiding inferential errors from the statistical significance filter

Related Strategies¶

Examples¶

-

Key Sources¶

• John Deke. (2023). A Strong Case for Rethinking Causal Inference. Journal of Research on Educational Effectiveness (online ahead of print). https://www.mathematica.org/publications/a-strong-case-for-rethinking-causal-inference