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| Effect Size in Education Research× | Effect Size× | |
|---|---|---|
| Campo≠ | Education | Statistica per la ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine | 1988 | 1988 |
| Ideatore≠ | Statistical methodology (Cohen; Glass; Hedges & Olkin) applied in education | Jacob Cohen |
| Tipo≠ | Standardized index of the magnitude of an effect or difference | Concept |
| Fonte seminale | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 9780805802832 | Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5 |
| Alias | Educational Effect Size, Standardized Mean Difference in Education, Hedges' g in Education, Effect Size Reporting | ES, Cohen's d, standardized effect, practical significance |
| Correlati≠ | 2 | 4 |
| Sintesi≠ | 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. | Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings. |
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