Published correlational effect sizes in social and developmental psychology
Published correlational effect sizes in social and developmental psychology is a dataset published in Royal Society Open Science (2022). On theSindex it has a DataRank of 0.553, placing it in the top 47.8% of the data-sharing corpus. It has been cited 16 times, with 15 citing works in its 1-hop citation network. Its calibrated FAIR score is 50/100.
Abstract
The distribution of effect sizes may offer insights about the research done and reported in a scientific field. We have evaluated 12 412 manually collected correlation effect sizes (Sample 1) and 31 157 computer-extracted correlation effect sizes (Sample 2) published in journals focused on social or developmental psychology. Sample 1 consisted of 243 studies from six journals published in 2010 and 2019. Sample 2 consisted of 5012 papers published in 10 journals between 2010 and 2019. The 25th, 50th and 75th effect size percentiles were 0.08, 0.17 and 0.33, and 0.17, 0.31 and 0.52 in Samples 1 and 2, respectively. Sample 2 percentiles were probably larger because Sample 2 only included effect sizes from the text but not from tables. In text authors may have emphasized larger correlations. Large sample sizes were associated with smaller reported correlations. In Sample 1 about 70% of studies specified a directional hypothesis. In 2010 no papers had power calculations, while in 2019 14% of papers had power calculations. These data offer empirical insights into the distribution of reported correlations and may inform the interpretation of effect sizes. They also demonstrate the importance of computation of statistical power and highlight potential reporting bias.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.406
From this paper's citation signal
Citation Network Contribution
0.147
From 7 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- A power primer.Psychological Bulletin199241,798 citationsDataRank 1.6
- Why Most Published Research Findings Are FalsePLoS Medicine200510,409 citationsDataRank 1.4
- Estimating the reproducibility of psychological scienceScience20158,592 citationsDataRank 1.4
- Power failure: why small sample size undermines the reliability of neuroscienceNature Reviews Neuroscience20137,742 citationsDataRank 1.3
- Why Most Discovered True Associations Are InflatedEpidemiology20081,529 citationsDataRank 1.1
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 73% comes from its base citations and 27% from the citation network (7 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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