When conducting meta-analysis it is important to understand the detail of how research has been grouped for analysis. For example, the research on feedback needs much within-group sorting and this has led to many key interpretations. This is always based on the criteria of the researcher conducting the analysis thus making the justifications for the groupings important to include. In Visible Learning (2009) Professor Hattie provides a detailed argument for each of the groupings used, which gives validity to the subsequent effect size analyses that is conducted.
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?