Registry · Priors

construct.autonomy predicts construct.job_satisfaction

normal · weakly_informative · 2 studies · N = 87,883

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.367, 1.00]; mean ≈ 0.685.0.03540.3600.6851.011.34z0density

mean ≈ 0.685 · 95% CI ≈ [0.367, 1.00]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.389, 0.801]; mean ≈ 0.595.0.1750.3810.5880.7941.00r0density

mean ≈ 0.595 · 95% CI ≈ [0.389, 0.801]

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
[0.35, 0.76]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.23, 0.81]
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=0.59 (k=2, high heterogeneity (I²=1.00)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.59k = 2 · N = 87,883

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 | 0.685368, 0.162486);
beta = pm.Normal("beta", mu=0.685368, sigma=0.162486)
brms::prior(normal(0.685368, 0.162486), class = "b")
# base R sample
rnorm(N, mean = 0.685368, sd = 0.162486)
np.random.normal(loc=0.685368, scale=0.162486, size=N)

Parameters

FamilyParameters
normalI2 = 0.9991, mu = 0.6854, sigma = 0.1625, r_mean = 0.5950, k_studies = 2.000, tau_squared = 0.05276, fisher_z_bias = 0.08508, r_mean_bare_bones = 0.5099

Synthesis

Method
random_effects_meta
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
2
n_total
87,883
Last updated
2026-07-16T01:06:08.635Z

Quality distribution

GradeCount
A2
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.