Registry · Priors

construct.psychological_empowerment predicts construct.organizational_citizenship_behavior_ocb

normal · weakly_informative · 1 studies · N = 1,299

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.117, 0.247]; mean ≈ 0.182.0.04920.1160.1820.2480.315z0density

mean ≈ 0.182 · 95% CI ≈ [0.117, 0.247]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.117, 0.243]; mean ≈ 0.180.0.05150.1160.1800.2440.309r0density

mean ≈ 0.180 · 95% CI ≈ [0.117, 0.243]

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
[0.12, 0.24]
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=0.18 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.18k = 1 · N = 1,299

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 | 0.181983, 0.0332008);
beta = pm.Normal("beta", mu=0.181983, sigma=0.0332008)
brms::prior(normal(0.181983, 0.0332008), class = "b")
# base R sample
rnorm(N, mean = 0.181983, sd = 0.0332008)
np.random.normal(loc=0.181983, scale=0.0332008, size=N)

Parameters

FamilyParameters
normalmu = 0.1820, sigma = 0.03320, r_mean = 0.1800, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
1,299
Last updated
2026-05-29T15:21:22.290Z

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.