Recalibrating Effect Size Benchmarks in Social Science Research
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- Опубліковано 29 лис 2024
- Deep Dive Podcast: Recalibrating Effect Size Benchmarks in Social Science Research
Effect size (ES) benchmarks play a critical role in research design and interpretation. Here's what a large-scale study of over 147,000 correlations revealed:
1️⃣ Cohen’s Benchmarks Need Updating: Cohen’s traditional small, medium, and large effect size benchmarks (|r| = .1, .3, .5) don’t align well with findings in applied psychology. This study found that medium effect sizes typically fall between |r| = .09 and .26, much lower than Cohen’s thresholds.
2️⃣ Context Matters for Effect Sizes: Effect size benchmarks vary significantly across research domains. For instance, behavioral relations (e.g., attitudes to behavior) show smaller effect sizes than attitudinal relations (e.g., attitudes to attitudes), emphasizing the need for tailored benchmarks.
3️⃣ Practical Implications for Research Design: Using outdated benchmarks inflates expectations and can lead to underpowered studies. Accurate, domain-specific benchmarks improve power analyses, guiding better research planning and resource allocation.
4️⃣ Understanding Variability for Deeper Insights: High heterogeneity (I²) in effect size distributions suggests that moderation effects are common. This variability provides opportunities to explore boundary conditions and refine theoretical frameworks.
5️⃣ A Step Toward Transparency: The study's comprehensive taxonomy and shared database enable researchers to compare their findings with refined benchmarks. This fosters transparency, reproducibility, and cumulative progress in applied psychology research.
Precision in research methodology drives impactful results. How do you incorporate context-specific benchmarks in your work? Let’s discuss!
Get article: Bosco, F. A., Aguinis, H. Singh, K., Field, J. G., & Pierce, C. A. 2015. Correlational effect size benchmarks. Journal of Applied Psychology, 100(2): 431-449. doi.org/10.103...