Yöntem Karşılaştırma
Seçtiğiniz yöntemleri yan yana inceleyin; farklı satırlar vurgulanır.
| What Works Clearinghouse Standards× | Effect Size in Education Research× | |
|---|---|---|
| Alan | Education | Education |
| Aile | Process / pipeline | Process / pipeline |
| Köken yılı≠ | 2022 | 1988 |
| Köken≠ | Institute of Education Sciences (IES), U.S. Department of Education | Statistical methodology (Cohen; Glass; Hedges & Olkin) applied in education |
| Tür≠ | Standards and procedures for assessing the causal credibility of education studies | Standardized index of the magnitude of an effect or difference |
| Seminal kaynak≠ | What Works Clearinghouse. (2022). What Works Clearinghouse Procedures and Standards Handbook, Version 5.0. Institute of Education Sciences, U.S. Department of Education. link ↗ | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 9780805802832 |
| Diğer adlar | WWC Standards, WWC Evidence Standards, What Works Clearinghouse Review, WWC Study Rating | Educational Effect Size, Standardized Mean Difference in Education, Hedges' g in Education, Effect Size Reporting |
| İlişkili≠ | 3 | 2 |
| Özet≠ | The What Works Clearinghouse (WWC) standards are the protocol the U.S. Institute of Education Sciences uses to judge how much confidence an education study's findings deserve as causal evidence. They specify which designs can support causal claims, how to screen for threats such as attrition and confounding, and how to rate each study — Meets Standards Without Reservations, With Reservations, or Does Not Meet Standards — before synthesizing the body of evidence. The standards are a cornerstone of evidence-based education policy in the United States. | An effect size is a standardized, scale-free measure of the magnitude of a difference or relationship — how big an effect is, not just whether it is statistically significant. In education research it is the common currency for reporting intervention impacts and for combining studies in meta-analysis, with the standardized mean difference (Cohen's d, or its bias-corrected form Hedges' g) the most familiar. Effect sizes let researchers compare effects across studies, outcomes, and scales, and translate statistical results into terms practitioners can weigh. |
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