Registry · Priors

construct.organizational_commitment predicts construct.voluntary_turnover

normal · informative · 3 studies · N = 99,452

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.333, -0.212]; mean ≈ -0.273.-0.395-0.334-0.273-0.211-0.150z0density

mean ≈ -0.273 · 95% CI ≈ [-0.333, -0.212]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.322, -0.210]; mean ≈ -0.266.-0.380-0.323-0.266-0.209-0.152r0density

mean ≈ -0.266 · 95% CI ≈ [-0.322, -0.210]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.32, -0.21]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.35, -0.18]
I² (heterogeneity) — share of total variance from between-study differences
98%

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=-0.27 (k=3, high heterogeneity (I²=0.98)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.27k = 3 · N = 99,452

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 | -0.272542, 0.0307225);
beta = pm.Normal("beta", mu=-0.272542, sigma=0.0307225)
brms::prior(normal(-0.272542, 0.0307225), class = "b")
# base R sample
rnorm(N, mean = -0.272542, sd = 0.0307225)
np.random.normal(loc=-0.272542, scale=0.0307225, size=N)

Parameters

FamilyParameters
normalI2 = 0.9759, mu = -0.2725, sigma = 0.03072, r_mean = -0.2660, k_studies = 3.000, tau_squared = 0.002024

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
3
n_total
99,452
Last updated
2026-05-30T23:51:59.090Z

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.