Yes, where it assists in understanding of the underlying story and the many nuances around this story. In Visible Learning (2009), Professor Hattie chose to rank the relative effect sizes of 138 influences that related to student learning and achievement. This list provided a visual presentation of the effect sizes for each influence in order address and understand some of those with low effects (e.g., teacher subject matter knowledge), to understand some that are lower than expected (e.g., class size), and those with much variance (e.g., feedback).
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?