Replication Rate by Author h-index
Are papers by eminent researchers more replicable? This page plots the replication rate of original papers in the database against the h-index of their authors — the mean across the byline, the most-cited coauthor, or the first or last author alone — using author metrics from SciSciNet (derived from OpenAlex). Note that h-indexes are the authors’ current values, not their values when the original paper was published (see methodology below).
Rate = success / (success + failure + reversal); inconclusive and unrecorded outcomes excluded, where success means the outcome recorded in the database. Unit: replication effect (n = 7,015 — originals matched in the author h-index dataset). How this is defined.
Replication rate by mean h-index of all authors
All determinate effect-level replications whose original paper matched in the SciSciNet snapshot, grouped into fixed h-index ranges. Each replication attempt counts once. Hover a bar for its 95% interval from a paper-cluster bootstrap (1,000 resamples of original papers with replacement), which accounts for multiple replications of the same paper not being independent.
Modeled replication probability by mean h-index of all authors
A probit regression of replication success on log₁₀(1 + h), plotted as a predicted-probability curve with a 95% confidence band — the same data as the bars above, without the arbitrary bin edges. Bin rates are overlaid as dots for comparison.
Slope β₁ = -0.408 (cluster-robust SE 0.078, z = -5.24, p < 0.001) per tenfold increase in 1 + h. On average across the sample, a doubling of 1 + h shifts the predicted replication probability by -4.8 percentage points. n = 6,108 replications across 4,211 original papers; standard errors are clustered on the original paper.
6,108 replications plotted · 907 determinate rows excluded (no SciSciNet match)
h-index source. Author h-indexes come from a SciSciNet-v2 snapshot (derived from OpenAlex; snapshot 2026-01-29, lookup generated 2026-07-14). Papers are matched by DOI, then joined to their authors and each author’s h-index; authors missing from the snapshot are dropped from the mean and max.
Current, not contemporaneous. An author’s h-index is their value today, not at the time the original paper was published. It therefore bakes in everything that happened since — including citations to the original paper itself and to the replication debate around it — and is confounded with career stage, field citation norms, and team size. Treat these charts as descriptive, not causal.
Matching. Original papers are matched by DOI: 7,241 of 8,445 replication rows (85.7%) have a matched original with author data; rows without a DOI or without a SciSciNet record are excluded from the charts.
Units. The chart counts every determinate replication attempt once. Reversals count as determinate non-replications; inconclusive rows are excluded. First-author h-index is used as the last-author value for single-author papers.
Probit model. The curve is a maximum-likelihood probit fit, P(replicated) = Φ(β₀ + β₁ · log₁₀(1 + h)), on the same determinate rows as the bars. Standard errors use a sandwich estimator clustered on the original paper (with the usual G/(G−1) small-sample correction), and the band is the delta-method 95% interval on the linear predictor pushed back through Φ, so it cannot leave 0–100%. This is the equivalent of Stata’s probit, vce(cluster) followed by margins / marginsplot. The model assumes the probit link is the right shape; where the curve and the binned dots disagree, the dots are the less model-dependent summary.
Data: replications_database_2026_08_01_120356.csv.