Registry · Priors

construct.leader_member_exchange_lmx predicts construct.organizational_citizenship_behavior_ocb

normal · informative · 3 studies · N = 9,424

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.368, 0.408]; mean ≈ 0.388.0.3470.3680.3880.4090.429z0density

mean ≈ 0.388 · 95% CI ≈ [0.368, 0.408]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.353, 0.387]; mean ≈ 0.370.0.3350.3520.3700.3880.405r0density

mean ≈ 0.370 · 95% CI ≈ [0.353, 0.387]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.35, 0.39]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.37, 0.37]
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=0.37 (k=3, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.37k = 3 · N = 9,424

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 | 0.388308, 0.0102485);
beta = pm.Normal("beta", mu=0.388308, sigma=0.0102485)
brms::prior(normal(0.388308, 0.0102485), class = "b")
# base R sample
rnorm(N, mean = 0.388308, sd = 0.0102485)
np.random.normal(loc=0.388308, scale=0.0102485, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = 0.3883, sigma = 0.01025, r_mean = 0.3699, k_studies = 3.000, tau_squared = 0.000

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
3
n_total
9,424
Last updated
2026-05-30T21:47:51.547Z

Quality distribution

GradeCount
A3
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.