Registry · Priors

construct.job_satisfaction predicts construct.organizational_citizenship_behavior_ocb

normal · weakly_informative · 3 studies · N = 58,027

Distribution

Storage scale (Fisher z)

mean ≈ 0.273 · 95% CI ≈ [0.243, 0.303]

Reader scale (r)

mean ≈ 0.266 · 95% CI ≈ [0.238, 0.294]

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: [0.24, 0.29]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [0.22, 0.31]
I² (heterogeneity) — share of total variance from between-study differences: 91% — 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 is ~constant across settings — it generalizes.

SD_ρ≈0 — true effect is ~constant across settings; generalizes (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=0.27 (k=3, high heterogeneity (I²=0.91)); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.27	k = 3 · N = 58,027

high heterogeneity (I²=0.91)
replication: meta-analytic
aging literature (freshness=0.49)

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.

StanPyMCbrmsRNumPy

target += normal_lpdf(beta | 0.273005, 0.0154004);

beta = pm.Normal("beta", mu=0.273005, sigma=0.0154004)

brms::prior(normal(0.273005, 0.0154004), class = "b")

# base R sample
rnorm(N, mean = 0.273005, sd = 0.0154004)

np.random.normal(loc=0.273005, scale=0.0154004, size=N)

Parameters

Family	Parameters
normal	I2 = 0.9116, mu = 0.2730, sigma = 0.01540, r_mean = 0.2664, k_studies = 3.000, tau_squared = 0.0006368, fisher_z_bias = -0.001399, r_mean_bare_bones = 0.2678

Synthesis

Method: random_effects_meta
Informativeness: weakly_informative
Replication status: meta-analytic
k_studies: 3
n_total: 58,027
Last updated: 2026-07-16T01:07:02.335Z

Quality distribution

Grade	Count
A	3
B	0
C	0
D	0

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.