Registry · Priors

construct.perceived_organizational_support predicts construct.turnover_intention

normal · weakly_informative · 3 studies · N = 67,823

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.610, -0.236]; mean ≈ -0.423.-0.804-0.613-0.423-0.232-0.0410z0density

mean ≈ -0.423 · 95% CI ≈ [-0.610, -0.236]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.556, -0.242]; mean ≈ -0.399.-0.720-0.560-0.399-0.239-0.0783r0density

mean ≈ -0.399 · 95% CI ≈ [-0.556, -0.242]

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
[-0.54, -0.23]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.61, -0.13]
I² (heterogeneity) — share of total variance from between-study differences
100% — the contributing studies disagree almost completely; read the pooled value as a midpoint of conflicting findings, not as one population's effect

The true effect varies across settings — moderators likely matter.

SD_ρ>0 — true effect varies across settings; likely moderated (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=-0.40 (k=3, high heterogeneity (I²=1.00)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.40k = 3 · N = 67,823

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 | -0.422584, 0.0954006);
beta = pm.Normal("beta", mu=-0.422584, sigma=0.0954006)
brms::prior(normal(-0.422584, 0.0954006), class = "b")
# base R sample
rnorm(N, mean = -0.422584, sd = 0.0954006)
np.random.normal(loc=-0.422584, scale=0.0954006, size=N)

Parameters

FamilyParameters
normalI2 = 0.9970, mu = -0.4226, sigma = 0.09540, r_mean = -0.3991, k_studies = 3.000, tau_squared = 0.02176, fisher_z_bias = 0.06046, r_mean_bare_bones = -0.4596

Synthesis

Method
random_effects_meta
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
3
n_total
67,823
Last updated
2026-07-16T01:05:59.034Z

Quality distribution

GradeCount
A1
B2
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.