Registry · Priors

construct.leader_member_exchange_lmx predicts construct.organizational_citizenship_behavior_ocb

normal · informative · 4 studies · N = 20,493

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.344, 0.399]; mean ≈ 0.371.0.3160.3440.3710.3990.427z0density

mean ≈ 0.371 · 95% CI ≈ [0.344, 0.399]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.331, 0.379]; mean ≈ 0.355.0.3070.3310.3550.3790.404r0density

mean ≈ 0.355 · 95% CI ≈ [0.331, 0.379]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.33, 0.38]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.32, 0.38]
I² (heterogeneity) — share of total variance from between-study differences
51% — substantial heterogeneity

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=0.36 (k=4, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.36k = 4 · N = 20,493

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 | 0.37135, 0.0139144);
beta = pm.Normal("beta", mu=0.37135, sigma=0.0139144)
brms::prior(normal(0.37135, 0.0139144), class = "b")
# base R sample
rnorm(N, mean = 0.37135, sd = 0.0139144)
np.random.normal(loc=0.37135, scale=0.0139144, size=N)

Parameters

FamilyParameters
normalI2 = 0.5133, mu = 0.3713, sigma = 0.01391, r_mean = 0.3552, k_studies = 4.000, tau_squared = 0.0003037, fisher_z_bias = 0.001400, r_mean_bare_bones = 0.3538

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
4
n_total
20,493
Last updated
2026-07-16T01:06:06.135Z

Quality distribution

GradeCount
A4
B0
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.