Registry · Priors

construct.safety_climate predicts construct.safety_participation

normal · weakly_informative · 1 studies · N = 58,118

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [1.09, 1.11]; mean ≈ 1.10.1.081.091.101.111.12z0density

mean ≈ 1.10 · 95% CI ≈ [1.09, 1.11]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.797, 0.803]; mean ≈ 0.800.0.7930.7960.8000.8040.807r0density

mean ≈ 0.800 · 95% CI ≈ [0.797, 0.803]

Weakly informative prior. This prior is weakly informative. It will nudge your posterior but won't overwhelm it; expect data to do most of the work in modest samples.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.80, 0.80]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
— needs ≥2 studies to estimate (k = 1)
I² (heterogeneity) — share of total variance from between-study differences
— needs ≥2 studies to estimate

k<2 — between-study heterogeneity not estimable; credibility interval / generalization not assessed

Evidence provenance

published ρ=0.80 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.80k = 1 · N = 58,118

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 | 1.09861, 0.004958);
beta = pm.Normal("beta", mu=1.09861, sigma=0.004958)
brms::prior(normal(1.09861, 0.004958), class = "b")
# base R sample
rnorm(N, mean = 1.09861, sd = 0.004958)
np.random.normal(loc=1.09861, scale=0.004958, size=N)

Parameters

FamilyParameters
normalmu = 1.099, sigma = 0.004958, r_mean = 0.8000, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
58,118
Last updated
2026-05-30T17:42:56.534Z

Quality distribution

GradeCount
A0
B1
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.