Registry · Priors

construct.psychological_detachment correlates construct.contextual_performance

normal · weakly_informative · 1 studies · N = 2,106

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.173, -0.0880]; mean ≈ -0.131.-0.218-0.174-0.131-0.0871-0.0435z0density

mean ≈ -0.131 · 95% CI ≈ [-0.173, -0.0880]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.172, -0.0880]; mean ≈ -0.130.-0.216-0.173-0.130-0.0871-0.0442r0density

mean ≈ -0.130 · 95% CI ≈ [-0.172, -0.0880]

Weakly informative prior. This prior is weakly informative. It will nudge your posterior but won't overwhelm it; expect data to do most of the work in modest samples.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.17, -0.09]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
— needs ≥2 studies to estimate (k = 1)
I² (heterogeneity) — share of total variance from between-study differences
— needs ≥2 studies to estimate

k<2 — between-study heterogeneity not estimable; credibility interval / generalization not assessed

Evidence provenance

published ρ=-0.13 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.13k = 1 · N = 2,106

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 | -0.13074, 0.0218062);
beta = pm.Normal("beta", mu=-0.13074, sigma=0.0218062)
brms::prior(normal(-0.13074, 0.0218062), class = "b")
# base R sample
rnorm(N, mean = -0.13074, sd = 0.0218062)
np.random.normal(loc=-0.13074, scale=0.0218062, size=N)

Parameters

FamilyParameters
normalmu = -0.1307, sigma = 0.02181, r_mean = -0.1300, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
2,106
Last updated
2026-07-16T01:10:00.367Z

Quality distribution

GradeCount
A1
B0
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.