Registry · Priors

construct.job_satisfaction correlates construct.task_performance

normal · informative · 2 studies · N = 54,467

Distribution

Storage scale (Fisher z)

mean ≈ 0.328 · 95% CI ≈ [0.286, 0.370]

Reader scale (r)

mean ≈ 0.316 · 95% CI ≈ [0.279, 0.354]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2002.06, ≈23.940000000000055 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ: [0.28, 0.35]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [0.27, 0.36]
I² (heterogeneity) — share of total variance from between-study differences: 82%

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=0.32 (k=2, high heterogeneity (I²=0.82)); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.32	k = 2 · N = 54,467

high heterogeneity (I²=0.82)
replication: meta-analytic
aging literature (freshness=0.30)

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 | 0.327677, 0.0214269);

beta = pm.Normal("beta", mu=0.327677, sigma=0.0214269)

brms::prior(normal(0.327677, 0.0214269), class = "b")

# base R sample
rnorm(N, mean = 0.327677, sd = 0.0214269)

np.random.normal(loc=0.327677, scale=0.0214269, size=N)

Parameters

Family	Parameters
normal	I2 = 0.8164, mu = 0.3277, sigma = 0.02143, r_mean = 0.3164, k_studies = 2.000, tau_squared = 0.0007704

Synthesis

Method: random_effects_meta
Informativeness: informative
Replication status: meta-analytic
k_studies: 2
n_total: 54,467
Last updated: 2026-05-29T17:32:05.338Z

Quality distribution

Grade	Count
A	2
B	0
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.