Registry · Priors

construct.job_involvement predicts construct.task_performance

normal · informative · 2 studies · N = 10,175

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.0736, 0.116]; mean ≈ 0.0948.0.05150.07310.09480.1160.138z0density

mean ≈ 0.0948 · 95% CI ≈ [0.0736, 0.116]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.0735, 0.116]; mean ≈ 0.0945.0.05160.07300.09450.1160.137r0density

mean ≈ 0.0945 · 95% CI ≈ [0.0735, 0.116]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2005.01, ≈20.99000000000001 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.07, 0.12]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.09, 0.09]
I² (heterogeneity) — share of total variance from between-study differences
0% — studies largely agree

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=0.09 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.09k = 2 · N = 10,175

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 | 0.0947918, 0.0108319);
beta = pm.Normal("beta", mu=0.0947918, sigma=0.0108319)
brms::prior(normal(0.0947918, 0.0108319), class = "b")
# base R sample
rnorm(N, mean = 0.0947918, sd = 0.0108319)
np.random.normal(loc=0.0947918, scale=0.0108319, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = 0.09479, sigma = 0.01083, r_mean = 0.09451, k_studies = 2.000, tau_squared = 0.000, fisher_z_bias = 0.000002602, r_mean_bare_bones = 0.09451

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
10,175
Last updated
2026-07-16T01:08:29.504Z

Quality distribution

GradeCount
A1
B1
C0
D0

Source articles

The research this prior is synthesized from — each is a full dossier (findings, the models it informs, and what the literature says).

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.