Registry · Priors

construct.psychological_contract_breach predicts construct.organizational_commitment

normal · weakly_informative · 1 studies · N = 19,407

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.426, -0.398]; mean ≈ -0.412.-0.441-0.426-0.412-0.397-0.383z0density

mean ≈ -0.412 · 95% CI ≈ [-0.426, -0.398]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.402, -0.378]; mean ≈ -0.390.-0.414-0.402-0.390-0.378-0.366r0density

mean ≈ -0.390 · 95% CI ≈ [-0.402, -0.378]

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
[-0.40, -0.38]
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=-0.39 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.39k = 1 · N = 19,407

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 | -0.4118, 0.00717884);
beta = pm.Normal("beta", mu=-0.4118, sigma=0.00717884)
brms::prior(normal(-0.4118, 0.00717884), class = "b")
# base R sample
rnorm(N, mean = -0.4118, sd = 0.00717884)
np.random.normal(loc=-0.4118, scale=0.00717884, size=N)

Parameters

FamilyParameters
normalmu = -0.4118, sigma = 0.007179, r_mean = -0.3900, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
19,407
Last updated
2026-05-29T15:21:25.135Z

Quality distribution

GradeCount
A1
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.