Registry · Priors

construct.job_satisfaction predicts construct.turnover_intention

normal · weakly_informative · 2 studies · N = 100

Distribution

Storage scale (Fisher z)

mean ≈ -0.564 · 95% CI ≈ [-0.818, -0.310]

Reader scale (r)

mean ≈ -0.511 · 95% CI ≈ [-0.699, -0.323]

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.

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2004.53, ≈21.470000000000027 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ: [-0.67, -0.30]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [-0.68, -0.29]
I² (heterogeneity) — share of total variance from between-study differences: 65%

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=-0.51 (k=2, replication: replicated); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	-0.51	k = 2 · N = 100

replication: replicated
aging literature (freshness=0.36)

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.

StanPyMCbrmsRNumPy

target += normal_lpdf(beta | -0.564128, 0.129515);

beta = pm.Normal("beta", mu=-0.564128, sigma=0.129515)

brms::prior(normal(-0.564128, 0.129515), class = "b")

# base R sample
rnorm(N, mean = -0.564128, sd = 0.129515)

np.random.normal(loc=-0.564128, scale=0.129515, size=N)

Parameters

Family	Parameters
normal	I2 = 0.6493, mu = -0.5641, sigma = 0.1295, r_mean = -0.5110, k_studies = 2.000, tau_squared = 0.01852

Synthesis

Method: random_effects_meta
Informativeness: weakly_informative
Replication status: replicated
k_studies: 2
n_total: 100
Last updated: 2026-05-30T21:47:58.040Z

Quality distribution

Grade	Count
A	1
B	1
C	0
D	0

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.