Registry · Priors

construct.organizational_commitment predicts construct.organizational_citizenship_behavior_ocb

normal · weakly_informative · 2 studies · N = 31,633

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.162, 0.489]; mean ≈ 0.326.-0.008710.1580.3260.4930.660z0density

mean ≈ 0.326 · 95% CI ≈ [0.162, 0.489]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.167, 0.462]; mean ≈ 0.315.0.01330.1640.3150.4650.616r0density

mean ≈ 0.315 · 95% CI ≈ [0.167, 0.462]

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
[0.16, 0.45]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.11, 0.49]
I² (heterogeneity) — share of total variance from between-study differences
99% — the contributing studies disagree almost completely; read the pooled value as a midpoint of conflicting findings, not as one population's effect

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=0.31 (k=2, high heterogeneity (I²=0.99)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.31k = 2 · N = 31,633

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 | 0.325588, 0.0835751);
beta = pm.Normal("beta", mu=0.325588, sigma=0.0835751)
brms::prior(normal(0.325588, 0.0835751), class = "b")
# base R sample
rnorm(N, mean = 0.325588, sd = 0.0835751)
np.random.normal(loc=0.325588, scale=0.0835751, size=N)

Parameters

FamilyParameters
normalI2 = 0.9879, mu = 0.3256, sigma = 0.08358, r_mean = 0.3146, k_studies = 2.000, tau_squared = 0.01175, fisher_z_bias = -0.04259, r_mean_bare_bones = 0.3571

Synthesis

Method
random_effects_meta
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
2
n_total
31,633
Last updated
2026-07-16T01:07:01.950Z

Quality distribution

GradeCount
A1
B1
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.