Registry · Priors

construct.psychological_capital_psycap predicts construct.job_satisfaction

normal · weakly_informative · 2 studies · N = 100

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

[0.46, 0.74]

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

[0.44, 0.75]

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

62%

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=0.62 (k=2, replication: meta-analytic); no primary-deployment evidence yet

• 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 | 0.719436, 0.11528);

beta = pm.Normal("beta", mu=0.719436, sigma=0.11528)

brms::prior(normal(0.719436, 0.11528), class = "b")

# base R sample
rnorm(N, mean = 0.719436, sd = 0.11528)

np.random.normal(loc=0.719436, scale=0.11528, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

weakly_informative

Replication status

meta-analytic

k_studies

2

n_total

100

Last updated

2026-05-30T22:23:39.406Z

Quality distribution

Contributing effect sizes

• effect.95d529fcba59f459
• effect.75bab35107b556e9

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