Registry · Priors

construct.pay_for_performance predicts construct.employee_performance

normal · informative · 3 studies · N = 41,039

Distribution

Intervals

Confidence interval (95%) — uncertainty about the mean ρ

[0.21, 0.24]

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

[0.22, 0.24]

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

1%

The true effect is ~constant across settings — it generalizes.

SD_ρ≈0 — true effect is ~constant across settings; generalizes (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=0.23 (k=3, 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 | 0.230991, 0.00704917);

beta = pm.Normal("beta", mu=0.230991, sigma=0.00704917)

brms::prior(normal(0.230991, 0.00704917), class = "b")

# base R sample
rnorm(N, mean = 0.230991, sd = 0.00704917)

np.random.normal(loc=0.230991, scale=0.00704917, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

informative

Replication status

meta-analytic

k_studies

3

n_total

41,039

Last updated

2026-05-30T23:52:03.593Z

Quality distribution

Contributing effect sizes

• effect.c814705049815c32
• effect.5ece879070a8d269
• effect.762b7afd1334ed34

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