Research Fundamentals #7
Effect Size Qualities
Small effect: A subtle but potentially meaningful difference.
Medium effect: A noticeable impact.
Large effect: A strong and substantial difference.
Conventional Measures of Effect Size
Pearson’s r (for correlation strength):
Small: 0.1
Medium: 0.3
Large: 0.5
Cohen’s d (for comparing two means):
Small: 0.2
Medium: 0.5
Large: 0.8
Eta-squared (η²) (for ANOVA effect size):
Small: 0.01
Medium: 0.06
Large: 0.14
Why Effect Size Matters
Practical Interpretation: Helps researchers assess whether findings are meaningful beyond mere significance.
Meta-Analysis: Allows for comparisons across studies. Study Design: Informs required sample size calculations.
Effect Size vs. Statistical Significance
Key Takeaways
Effect size measures the strength of an effect, not just its existence. Common metrics include Cohen’s d, Pearson’s r, and eta squared. Effect size provides context. Statistical significance alone is not enough Understanding effect size enhances the interpretation of research findings.
Critical Thinking Question
How does understanding effect size impact our ability to understand research?
Next Lesson: Practical vs. Statistical Significance
In the next lesson, we will explore the difference between practical and statistical significance, ensuring that research findings are not only mathematically valid but also meaningful in real-world applications.