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A recipe for disappointment: policy, effect size and the winner’s curse

Adrian Simpson

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Effect size and policy

Standardized effect size estimates are commonly used by the ‘evidence-based education’ community as a key metric for judging relative importance, effectiveness, or practical significance of interventions across a set of studies: larger effect sizes indicate more effective interventions. However, this argument applies rarely; only when linearly equatable outcomes, identical comparison treatments and equally representative samples are used in every study.

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The Meta-Analytic Rain Cloud (MARC) Plot: A New Approach to Visualizing Clearinghouse Data

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.

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Technology-assisted Vocabulary Learning for EFL Learners: A Meta-analysis

Tao Hao, Zhe Wang, Yuliya Ardasheva

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A core component to learning a second language (L2) is vocabulary acquisition. New technologies have the potential to substantially improve vocabulary acquisition. Technological activities can elicit L2 learners’ interest, provide more verbal and multimedia exposure to the target language, and present opportunities to interact with the target language using various technological devices.

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