Others have investigated this issue and with one major exception quality of study is just not a big enough variable to make a difference to the conclusions. The difference is when the effect size is very small (<0.10) then quality of the studies becomes more important. In the second book Visible Learning for Teachers (2012) some of the low-quality meta-studies have been discarded. Again, the effects of low and high quality were investigated and in general across the meta-analysis level (certainly not necessarily at the individual study level) it is rare to find quality making a difference (except when the effect is low).
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?