Registry · Priors

construct.distributive_justice correlates construct.job_satisfaction

normal · weakly_informative · 1 studies · N = 57,515

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.625, 0.641]; mean ≈ 0.633.0.6160.6240.6330.6410.650z0density

mean ≈ 0.633 · 95% CI ≈ [0.625, 0.641]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.554, 0.566]; mean ≈ 0.560.0.5490.5540.5600.5660.571r0density

mean ≈ 0.560 · 95% CI ≈ [0.554, 0.566]

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.

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2001, ≈25 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.55, 0.57]
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=0.56 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.56k = 1 · N = 57,515

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 | 0.632833, 0.00416985);
beta = pm.Normal("beta", mu=0.632833, sigma=0.00416985)
brms::prior(normal(0.632833, 0.00416985), class = "b")
# base R sample
rnorm(N, mean = 0.632833, sd = 0.00416985)
np.random.normal(loc=0.632833, scale=0.00416985, size=N)

Parameters

FamilyParameters
normalmu = 0.6328, sigma = 0.004170, r_mean = 0.5600, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
57,515
Last updated
2026-07-16T01:09:31.645Z

Quality distribution

GradeCount
A1
B0
C0
D0

Source articles

The research this prior is synthesized from — each is a full dossier (findings, the models it informs, and what the literature says).

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.