Registry · Priors

construct.power_distance predicts construct.organizational_citizenship_behavior_ocb

normal · uninformative · 1 studies · N = 50

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.0626, 0.406]; mean ≈ 0.172.-0.306-0.06740.1720.4110.650z0density

mean ≈ 0.172 · 95% CI ≈ [-0.0626, 0.406]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.0575, 0.397]; mean ≈ 0.170.-0.294-0.06210.1700.4020.634r0density

mean ≈ 0.170 · 95% CI ≈ [-0.0575, 0.397]

Uninformative prior. This prior is uninformative — too thin to dominate small-N posteriors. Treat as a placeholder until more evidence lands.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.06, 0.39]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
— needs ≥2 studies to estimate (k = 1)
I² (heterogeneity) — share of total variance from between-study differences
— needs ≥2 studies to estimate

k<2 — between-study heterogeneity not estimable; credibility interval / generalization not assessed

Evidence provenance

published ρ=0.17 (k=1, replication: single); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.17k = 1 · N = 50

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 | 0.171667, 0.119523);
beta = pm.Normal("beta", mu=0.171667, sigma=0.119523)
brms::prior(normal(0.171667, 0.119523), class = "b")
# base R sample
rnorm(N, mean = 0.171667, sd = 0.119523)
np.random.normal(loc=0.171667, scale=0.119523, size=N)

Parameters

FamilyParameters
normalmu = 0.1717, sigma = 0.1195, r_mean = 0.1700, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
uninformative
Replication status
single
k_studies
1
n_total
50
Last updated
2026-07-16T01:08:31.673Z

Quality distribution

GradeCount
A0
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.