AI Summary & Key Takeaways
AI InsightFinally, this study provides convenient tables to guide sample size decisions for MRES values between 0.20 ≤ Cohen’s d ≤ 0.50.
📌 3 Key Takeaways
Abstract
A recent systematic review of experimental studies conducted in Turkey between 2010 and 2020 reported that small sample sizes had been a significant drawback (Bulus & Koyuncu, 2021). A small chunk of the studies in the review were randomized pretest-posttest control-group designs. In contrast, the overwhelming majority of them were non-equivalent pretest-posttest control-group designs (no randomization). They had an average sample size below 70 for different domains and outcomes. Designing experimental studies with such small sample sizes implies a strong (and perhaps an erroneous) assumption about the minimum relevant effect size (MRES) of an intervention; that is, a standardized treatment effect of Cohen’s d < 0.50 is not relevant to education policy or practice. Thus, an introduction to sample size determination for randomized/non-equivalent pretest-posttest control group designs is warranted. This study describes nuts and bolts of sample size determination (or power analysis). It also derives expressions for optimal design under differential cost per treatment and control units, and implements these expressions in an Excel workbook. Finally, this study provides convenient tables to guide sample size decisions for MRES values between 0.20 ≤ Cohen’s d ≤ 0.50.