Registry · Priors

construct.need_satisfaction predicts construct.job_satisfaction

normal · weakly_informative · 2 studies · N = 25,038

Distribution

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

[0.50, 0.74]

Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)

[0.44, 0.77]

I² (heterogeneity) — share of total variance from between-study differences

100%

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=0.63 (k=2, high heterogeneity (I²=1.00)); no primary-deployment evidence yet

• high heterogeneity (I²=1.00)
• replication: meta-analytic

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

target += normal_lpdf(beta | 0.747739, 0.100216);

beta = pm.Normal("beta", mu=0.747739, sigma=0.100216)

brms::prior(normal(0.747739, 0.100216), class = "b")

# base R sample
rnorm(N, mean = 0.747739, sd = 0.100216)

np.random.normal(loc=0.747739, scale=0.100216, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

weakly_informative

Replication status

meta-analytic

k_studies

2

n_total

25,038

Last updated

2026-05-30T22:23:37.330Z

Quality distribution

Contributing effect sizes

• effect.2b5e0c403c1a91ac
• effect.5401b4dfd001e98b

Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.