Daniel Litwok, Austin Nichols, Azim Shivji, and Robert Olsen

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Experimental studies of educational interventions are rarely designed to produce impact evidence, justified by statistical inference, that generalizes to populations of interest to education policymakers.  This simulation study explores whether formal sampling strategies for selecting districts and schools improve the generalizability of impact evidence from experimental studies.

Which selection strategies produced samples with the greatest generalizability to the target population?

Kaitlyn G. Fitzgerald & Elizabeth Tipton

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What type of data do clearinghouses communicate?

As the body of scientific evidence about what works in education grows, so does the need to effectively communicate that evidence to policy-makers and practitioners. Clearinghouses, such as the What Works Clearinghouse (WWC), have emerged to facilitate the evidence-based decision-making process and have taken on the non-trivial task of distilling often complex research findings to non-researchers. Among other things, this involves reporting effect sizes, statistical uncertainty, and meta-analytic summaries. This information is often reported visually. However, existing visualizations often do not follow data visualization best practices or take the statistical cognition of the audience into consideration.

Wendy Chan

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Will this intervention work for me?

This is one of the questions that make up the core of generalization research. Generalizations focus on the extent to which the findings of a study apply to people in a different context, in a different time period, or in a different study altogether. In education, one common type of generalization involves examining whether the results of an experiment (e.g., the estimated effect of an intervention) apply to a larger group of people, or a population.