Registry · Priors

construct.job_resources predicts construct.work_engagement

normal · informative · 3 studies · N = 117,061

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.376, 0.393]; mean ≈ 0.384.0.3670.3760.3840.3930.402z0density

mean ≈ 0.384 · 95% CI ≈ [0.376, 0.393]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.359, 0.374]; mean ≈ 0.367.0.3510.3590.3670.3740.382r0density

mean ≈ 0.367 · 95% CI ≈ [0.359, 0.374]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.36, 0.37]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.36, 0.37]
I² (heterogeneity) — share of total variance from between-study differences
28%

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=0.37 (k=3, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.37k = 3 · N = 117,061

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 | 0.3844, 0.00439375);
beta = pm.Normal("beta", mu=0.3844, sigma=0.00439375)
brms::prior(normal(0.3844, 0.00439375), class = "b")
# base R sample
rnorm(N, mean = 0.3844, sd = 0.00439375)
np.random.normal(loc=0.3844, scale=0.00439375, size=N)

Parameters

FamilyParameters
normalI2 = 0.2804, mu = 0.3844, sigma = 0.004394, r_mean = 0.3665, k_studies = 3.000, tau_squared = 0.00001859

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
3
n_total
117,061
Last updated
2026-05-30T23:52:03.131Z

Quality distribution

GradeCount
A3
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.