Registry · Priors

construct.leader_member_exchange_lmx predicts construct.task_performance

normal · weakly_informative · 3 studies · N = 150

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

[0.20, 0.42]

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

[0.31, 0.31]

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=0.31 (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 | 0.326028, 0.0608581);

beta = pm.Normal("beta", mu=0.326028, sigma=0.0608581)

brms::prior(normal(0.326028, 0.0608581), class = "b")

# base R sample
rnorm(N, mean = 0.326028, sd = 0.0608581)

np.random.normal(loc=0.326028, scale=0.0608581, size=N)

Parameters

Synthesis

Method

random_effects_meta

Informativeness

weakly_informative

Replication status

meta-analytic

k_studies

3

n_total

150

Last updated

2026-05-30T21:47:52.016Z

Quality distribution

Contributing effect sizes

• effect.6226975b9870dc0a
• effect.9101ef2bdbc233c6
• effect.84614e2d82c0f487

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