Registry · Priors

construct.safety_climate predicts construct.accidents

normal · informative · 2 studies · N = 19,485

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.473, -0.152]; mean ≈ -0.312.-0.640-0.476-0.312-0.1480.0155z0density

mean ≈ -0.312 · 95% CI ≈ [-0.473, -0.152]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.449, -0.157]; mean ≈ -0.303.-0.601-0.452-0.303-0.154-0.00471r0density

mean ≈ -0.303 · 95% CI ≈ [-0.449, -0.157]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.44, -0.15]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.48, -0.10]
I² (heterogeneity) — share of total variance from between-study differences
81%

The true effect varies across settings — moderators likely matter.

SD_ρ>0 — true effect varies across settings; likely moderated (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=-0.30 (k=2, high heterogeneity (I²=0.81)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.30k = 2 · N = 19,485

No primary-deployment evidence yet — this prior rests on published literature alone. As anonymized, aggregated effect sizes from real deployments are contributed, they appear here as a distinct, publication-bias-free source, fused with the literature into a posterior estimate.

Code

Drop this prior straight into your model. Snippets generated from the synthesized distribution + parameters.

target += normal_lpdf(beta | -0.312434, 0.0819945);
beta = pm.Normal("beta", mu=-0.312434, sigma=0.0819945)
brms::prior(normal(-0.312434, 0.0819945), class = "b")
# base R sample
rnorm(N, mean = -0.312434, sd = 0.0819945)
np.random.normal(loc=-0.312434, scale=0.0819945, size=N)

Parameters

FamilyParameters
normalI2 = 0.8065, mu = -0.3124, sigma = 0.08199, r_mean = -0.3026, k_studies = 2.000, tau_squared = 0.01125

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
19,485
Last updated
2026-05-30T23:52:05.066Z

Quality distribution

GradeCount
A2
B0
C0
D0

Contributing effect sizes

Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.