Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology
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Background and Objectives: Researchers typically use Cohen’s guidelines of Pearson’s r = .10, .30, and .50, and Cohen’s d = 0.20, 0.50, and 0.80 to interpret observed effect sizes as small, medium, or large, respectively. However, these guidelines were not based on quantitative estimates and are only recommended if field-specific estimates are unknown. This study investigated the distribution of effect sizes in both individual differences research and group differences research in gerontology to provide estimates of effect sizes in the field. Research Design and Methods: Effect sizes (Pearson’s r, Cohen’s d, and Hedges’ g) were extracted from meta-analyses published in 10 top-ranked gerontology journals. The 25th, 50th, and 75th percentile ranks were calculated for Pearson’s r (individual differences) and Cohen’s d or Hedges’ g (group differences) values as indicators of small, medium, and large effects. A priori power analyses were conducted for sample size calculations given the observed effect size estimates. Results: Effect sizes of Pearson’s r = .12, .20, and .32 for individual differences research and Hedges’ g = 0.16, 0.38, and 0.76 for group differences research were interpreted as small, medium, and large effects in gerontology. Discussion and Implications: Cohen’s guidelines appear to overestimate effect sizes in gerontology. Researchers are encouraged to use Pearson’s r = .10, .20, and .30, and Cohen’s d or Hedges’ g = 0.15, 0.40, and 0.75 to interpret small, medium, and large effects in gerontology, and recruit larger samples.
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The PCIT-T group's AQS-security effect size changes place it in a comparable range to other brief to medium-term attachment-based interventions, e.g., VIPP-SD (overall combined effect size r = 0.23, medium effect size).
“placing it in a comparable range to other brief to medium-term attachment-based interventions, e.g., VIPP-SD (overall combined effect size r = 0.23, medium effect size; [13]).”
From Optimizing parenting and child outcomes following parent–child interaction therapy – toddler: a randomized controlled trial (2024) · cites this paper as [13]
The Results (Individual Differences Research; page unavailable) classify Pearson’s r = .20 as a medium effect in gerontology, which supports only the general description of r = .23 as medium under those guidelines. The paper does not report a VIPP-SD combined effect of r = .23 or compare attachment-based interventions; its gerontology guideline also does not establish that classification for this intervention. (LLM, conf=0.85)
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Masticatory performance in oral function assessment: Alternative methods
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Statistical Inferences Using Effect Sizes in Human Endothelial Function Research
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Enhancing pre-school teachers’ competence in managing pediatric injuries in Pemba Island, Zanzibar
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Subspecialized radiological reporting reduces radiology report turnaround time
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Social Psychology and Fabrication
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- Authors
- Brydges, C. R.
- Affiliations
- Colorado State University (US)
- Corresponding author
- Brydges, Christopher R
- Venue
- Innovation in Aging
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-01
- Volume
- 3
- Issue
- 4
- Pages
- igz036-igz036
- ISSN
- 2399-5300 (ISSN-L)
- PMC
- PMC6736231
- OpenAlex
- W2972140183
- Cited by
- 826
- References
- 32
- Citations by year
- 2026: 212 · 2025: 212 · 2024: 168 · 2023: 90 · 2022: 68 · 2021: 56 · 2020: 18 · 2019: 1 · 2014: 1
- FWCI
- 23.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Innovation in Aging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- effect size interpretation, Cohen's d, Hedges' g, Pearson correlation, sample size determination, statistical power, gerontology, individual differences, group differences, meta-analysis, effect size distribution, aging research
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