Registry · Priors

construct.employee_engagement predicts construct.quality_defects

normal · weakly_informative · 1 studies · N = 2,320

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

[-0.27, -0.17]

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=-0.22 (k=1, 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 | -0.223656, 0.0248306);

beta = pm.Normal("beta", mu=-0.223656, sigma=0.0248306)

brms::prior(normal(-0.223656, 0.0248306), class = "b")

# base R sample
rnorm(N, mean = -0.223656, sd = 0.0248306)

np.random.normal(loc=-0.223656, scale=0.0248306, size=N)

Parameters

Synthesis

Method

single_study

Informativeness

weakly_informative

Replication status

meta-analytic

k_studies

1

n_total

2,320

Last updated

2026-05-30T11:46:55.682Z

Quality distribution

Contributing effect sizes

• effect.f4546d0c560cc16e

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