Registry · Priors

construct.self_efficacy predicts construct.job_satisfaction

normal · informative · 2 studies · N = 12,953

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.467, 0.502]; mean ≈ 0.484.0.4490.4670.4840.5020.520z0density

mean ≈ 0.484 · 95% CI ≈ [0.467, 0.502]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.436, 0.464]; mean ≈ 0.450.0.4220.4360.4500.4640.478r0density

mean ≈ 0.450 · 95% CI ≈ [0.436, 0.464]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2001.13, ≈24.86999999999989 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.44, 0.46]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.45, 0.45]
I² (heterogeneity) — share of total variance from between-study differences
0%

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=0.45 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.45k = 2 · N = 12,953

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 | 0.484435, 0.00878072);
beta = pm.Normal("beta", mu=0.484435, sigma=0.00878072)
brms::prior(normal(0.484435, 0.00878072), class = "b")
# base R sample
rnorm(N, mean = 0.484435, sd = 0.00878072)
np.random.normal(loc=0.484435, scale=0.00878072, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = 0.4844, sigma = 0.008781, r_mean = 0.4498, k_studies = 2.000, tau_squared = 0.000

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
12,953
Last updated
2026-05-29T15:21:19.115Z

Quality distribution

GradeCount
A1
B1
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.