There is no requirement in calculating and using effect sizes to assume a normal distribution. This may be required if statistical probability statements are made in relation to the findings which is not the task of effect sizes in meta-analysis. Similarly, there is no requirement that standard deviation is the same across studies (although this could be interesting), instead they depend more on the scale of the measures used within each study.
Articles in this section
- Why does the Visible Learning research use effect sizes?
- Why do you use an effect size of d=0.40 as a cut-off point and basically ignore effect sizes lower than 0.40?
- What is the preferred timescale over which an effect size can be calculated?
- Is there a bias when using effect sizes in favor of lower achieving students?
- What caution should I take when calculating an effect size?
- Why are effect sizes used when conducting meta-analysis?
- Why can an effect size of 0.40 be gained in a shorter timeframe?
- Can effect sizes be added (or averaged)?
- How accurate are the conclusions drawn from meta-analysis?
- How can the variability associated with each influence be evaluated?