Registry · Priors

construct.emotional_exhaustion predicts construct.turnover_intention

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

[0.26, 0.49]

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

[0.38, 0.38]

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

0%

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=0.38 (k=2, replication: replicated); no primary-deployment evidence yet

• replication: replicated
• aging literature (freshness=0.34)

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 | 0.401939, 0.0707107);

beta = pm.Normal("beta", mu=0.401939, sigma=0.0707107)

brms::prior(normal(0.401939, 0.0707107), class = "b")

# base R sample
rnorm(N, mean = 0.401939, sd = 0.0707107)

np.random.normal(loc=0.401939, scale=0.0707107, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

weakly_informative

Replication status

replicated

k_studies

2

n_total

100

Last updated

2026-05-30T22:23:32.103Z

Quality distribution

Contributing effect sizes

• effect.432b4882c87c01ca
• effect.08e314cfe442b719

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