Registry · Priors

construct.work_engagement correlates construct.job_satisfaction

normal · highly_informative · 5 studies · N = 349,168

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.646, 0.686]; mean ≈ 0.666.0.6250.6450.6660.6870.708z0density

mean ≈ 0.666 · 95% CI ≈ [0.646, 0.686]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.569, 0.596]; mean ≈ 0.582.0.5550.5690.5820.5960.610r0density

mean ≈ 0.582 · 95% CI ≈ [0.569, 0.596]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.57, 0.60]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.55, 0.61]
I² (heterogeneity) — share of total variance from between-study differences
97%

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=0.58 (k=5, high heterogeneity (I²=0.97)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.58k = 5 · N = 349,168

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 | 0.666104, 0.0103948);
beta = pm.Normal("beta", mu=0.666104, sigma=0.0103948)
brms::prior(normal(0.666104, 0.0103948), class = "b")
# base R sample
rnorm(N, mean = 0.666104, sd = 0.0103948)
np.random.normal(loc=0.666104, scale=0.0103948, size=N)

Parameters

FamilyParameters
normalI2 = 0.9688, mu = 0.6661, sigma = 0.01039, r_mean = 0.5824, k_studies = 5.000, tau_squared = 0.0005041

Synthesis

Method
random_effects_meta
Informativeness
highly_informative
Replication status
meta-analytic
k_studies
5
n_total
349,168
Last updated
2026-05-30T22:23:38.231Z

Quality distribution

GradeCount
A5
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.