Registry · Priors

construct.abusive_supervision predicts construct.organizational_citizenship_behavior_ocb

normal · informative · 2 studies · N = 2,892

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.281, -0.209]; mean ≈ -0.245.-0.319-0.282-0.245-0.208-0.171z0density

mean ≈ -0.245 · 95% CI ≈ [-0.281, -0.209]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.274, -0.206]; mean ≈ -0.240.-0.310-0.275-0.240-0.205-0.170r0density

mean ≈ -0.240 · 95% CI ≈ [-0.274, -0.206]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.27, -0.21]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.24, -0.24]
I² (heterogeneity) — share of total variance from between-study differences
0%

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=-0.24 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.24k = 2 · N = 2,892

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 | -0.244774, 0.0184459);
beta = pm.Normal("beta", mu=-0.244774, sigma=0.0184459)
brms::prior(normal(-0.244774, 0.0184459), class = "b")
# base R sample
rnorm(N, mean = -0.244774, sd = 0.0184459)
np.random.normal(loc=-0.244774, scale=0.0184459, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = -0.2448, sigma = 0.01845, r_mean = -0.2400, k_studies = 2.000, tau_squared = 0.000

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
2,892
Last updated
2026-05-30T22:23:34.916Z

Quality distribution

GradeCount
A2
B0
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.