Effect Size
"Statistically significant" results can be of trivial importance in the real world. Because of this, in recent years, many dissertation students have been required to report the effect size in addition to the significance level of their results.
Definition
Effect size is a measurement of how important an obtained effect is in reality, given that we have rejected the null hypothesis (i.e., obtained a significant result). Effect size can be thought of as the number of standard deviation units of difference. For example, for a t-test, effect size measures how different the (standardized) means of the two groups are. But if you don't understand this, don't worry about it, and please keep reading!
Note. Effect size is used in two ways. When planning your dissertation, you use an estimate of the typical effect size in similar studies in your field to determine the required sample size for your dissertation study. The effect size should represent the smallest effect that would be important for you to detect in your study. After you carry out the study, you calculate what its actual effect size was.
Tip
A free trial of the versatile effect size calculator Power and Precision is available at http://www.power-analysis.com.
For help with effect size calculators, contact us.
Example: Calculating Effect Size for a Simple Regression Analysis
Our simple regression analysis had an R2 of .20. For a simple regression, the effect size is f2 = R2 / (1 - R2). Thus, our effect size was f2 = .25. Using Cohen's (1988) conventions, this is a medium-to-large effect.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum.
For help with effect size calculation, contact us.
For other effect size reports on this site, see t-test and ANOVA.
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