PowerUp!: A Tool for Calculating Minimum Detectable Effect Sizes and Minimum Required Sample Sizes for Experimental and Quasi-Experimental Design Studies
Abstract
This paper and the accompanying tool are intended to complement existing supports for conducting power analysis tools by offering a tool based on the framework of Minimum Detectable Effect Sizes (MDES) formulae that can be used in determining sample size requirements and in estimating minimum detectable effect sizes for a range of individual- and group-random assignment design studies and for common quasi-experimental design studies. The paper and accompanying tool cover computation of minimum detectable effect sizes under the following study designs: individual random assignment designs, hierarchical random assignment designs (2-4 levels), block random assignment designs (2-4 levels), regression discontinuity designs (6 types), and short interrupted time-series designs. In each case, the discussion and accompanying tool consider the key factors associated with statistical power and minimum detectable effect sizes, including the level at which treatment occurs and the statistical models (e.g., fixed effect and random effect) used in the analysis. The tool also includes a module that estimates for one and two level random assignment design studies the minimum sample sizes required in order for studies to attain user-defined minimum detectable effect sizes.
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With a sample size of 198 patients at 2 weeks post-intervention (assuming 21% attrition), the trial will have 80% power (p = .05, two-tailed) to detect a Cohen's d of −.40 in a linear mixed model.
“With a sample size of 198 patients at 2 weeks post-intervention (assuming 21% attrition based on our pilot [23]), we will have 80% power (p = .05, two-tailed) to detect a Cohen’s d of −.40 in a linear mixed model [53].”
From Protocol of a randomized trial of acceptance and commitment therapy for fatigue interference in metastatic breast cancer (2020) · cites this paper as [53]
At the final 6-month follow-up with a sample size of 163 (assuming 35% attrition), the trial will have 80% power (p = .05, two-tailed) to detect a Cohen's d of −.44.
“At the final, 6-month follow-up with a sample size of 163 (assuming 35% attrition), we will have 80% power (p = .05, two-tailed) to detect a Cohen’s d of −.44 [53].”
From Protocol of a randomized trial of acceptance and commitment therapy for fatigue interference in metastatic breast cancer (2020) · cites this paper as [53]
The study was designed with 80% power (alpha = 0.05, two-tailed) to detect a large intervention effect (d = 0.77) for each primary outcome, based on a target of 55 dyads and 47 dyads at post-intervention assuming 15% attrition.
“We aimed to recruit 55 dyads and calculated power for 47 dyads at post-intervention (assuming 15% attrition). For each primary outcome, we had 80% power (alpha = 0.05, two-tailed) to detect a large intervention effect (d = 0.77) in a linear mixed model [41].”
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References 43 · Cited by 206
- Authors
- Dong, N., & Maynard, R.
- Affiliations
- Vanderbilt University (US)
- University of Pennsylvania (US)
- Venue
- Journal of Research on Educational Effectiveness
- Publisher
- Informa UK Limited
- Published
- 2013-01
- Volume
- 6
- Issue
- 1
- Pages
- 24-67
- ISSN
- 1934-5739 (ISSN-L)
- 1934-5747
- OpenAlex
- W2057769283
- Cited by
- 206
- References
- 43
- Citations by year
- 2026: 19 · 2025: 27 · 2024: 25 · 2023: 15 · 2022: 17 · 2021: 15 · 2020: 21 · 2019: 15 · 2018: 17 · 2017: 10 · 2016: 9 · 2015: 8 · 2014: 7 · 2013: 1
- FWCI
- 7.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Research on Educational Effectiveness (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- sample size determination, statistical power, quasi-experimental design, regression discontinuity design, interrupted time series design, fixed and random effects models
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