Registry · Priors

construct.positive_affect correlates construct.job_satisfaction

normal · informative · 2 studies · N = 3,376

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.301, 0.642]; mean ≈ 0.472.0.1240.2980.4720.6460.820z0density

mean ≈ 0.472 · 95% CI ≈ [0.301, 0.642]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.302, 0.577]; mean ≈ 0.440.0.1590.2990.4400.5800.720r0density

mean ≈ 0.440 · 95% CI ≈ [0.302, 0.577]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2000.09, ≈25.910000000000082 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.29, 0.57]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.26, 0.59]
I² (heterogeneity) — share of total variance from between-study differences
69% — substantial heterogeneity

The true effect varies across settings — moderators likely matter.

SD_ρ>0 — true effect varies across settings; likely moderated (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=0.44 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.44k = 2 · N = 3,376

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 | 0.471727, 0.0869932);
beta = pm.Normal("beta", mu=0.471727, sigma=0.0869932)
brms::prior(normal(0.471727, 0.0869932), class = "b")
# base R sample
rnorm(N, mean = 0.471727, sd = 0.0869932)
np.random.normal(loc=0.471727, scale=0.0869932, size=N)

Parameters

FamilyParameters
normalI2 = 0.6889, mu = 0.4717, sigma = 0.08699, r_mean = 0.4396, k_studies = 2.000, tau_squared = 0.01141, fisher_z_bias = -0.04818, r_mean_bare_bones = 0.4878

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
3,376
Last updated
2026-07-16T01:08:39.222Z

Quality distribution

GradeCount
A2
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.