Registry · Priors

construct.need_satisfaction predicts construct.work_engagement

normal · weakly_informative · 2 studies · N = 51,124

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

[0.41, 0.71]

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

[0.34, 0.75]

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=0.58 (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 | 0.662302, 0.112996);

beta = pm.Normal("beta", mu=0.662302, sigma=0.112996)

brms::prior(normal(0.662302, 0.112996), class = "b")

# base R sample
rnorm(N, mean = 0.662302, sd = 0.112996)

np.random.normal(loc=0.662302, scale=0.112996, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

weakly_informative

Replication status

meta-analytic

k_studies

2

n_total

51,124

Last updated

2026-05-30T22:23:37.855Z

Quality distribution

Contributing effect sizes

• effect.cae093f5726755a5
• effect.b8fa237afa74f4d7

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