Effect sizes are evaluated as small (0.02), medium (0.15) or large (0.35) respectively, according to Cohen (1988). OR According to Cohen (1988; 2019), the f2 values of 0.10 represent small effect; while 0.30 represent medium effect; and 0.50 represent large effect.
Yes, I can confirm the benchmark values for effect size (ƒ2) in ANOVA.
Effect size (ƒ2) is a measure of the proportion of variance in the dependent variable that is explained by the independent variable(s).
Here are the benchmark values for effect size (ƒ2):
- Small effect: ƒ2 = 0.02
- Medium effect: ƒ2 = 0.15
- Large effect: ƒ2 = 0.35
These values were first proposed by Jacob Cohen in 1988 and are widely used as a reference for interpreting effect sizes.
Keep in mind that the interpretation of effect size depends on the context and research field. These benchmarks are general guidelines and may need to be adjusted based on the specific research question and area of study.
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