construct.servant_leadership predicts construct.task_performance
normal · informative · 2 studies · N = 9,788
Distribution
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [0.23, 0.27]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [0.25, 0.25]
- I² (heterogeneity) — share of total variance from between-study differences
- 0% — studies largely agree
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=0.25 (k=2, replication: meta-analytic); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.25 | k = 2 · N = 9,788 |
- 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
Drop this prior straight into your model. Snippets generated from the synthesized distribution + parameters.
target += normal_lpdf(beta | 0.252049, 0.0104486);beta = pm.Normal("beta", mu=0.252049, sigma=0.0104486)brms::prior(normal(0.252049, 0.0104486), class = "b")# base R sample
rnorm(N, mean = 0.252049, sd = 0.0104486)np.random.normal(loc=0.252049, scale=0.0104486, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.000, mu = 0.2520, sigma = 0.01045, r_mean = 0.2468, k_studies = 2.000, tau_squared = 0.000, fisher_z_bias = 0.00001652, r_mean_bare_bones = 0.2468 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- informative
- Replication status
- meta-analytic
- k_studies
- 2
- n_total
- 9,788
- Last updated
- 2026-07-16T01:06:07.966Z
Quality distribution
| Grade | Count |
|---|---|
| A | 1 |
| B | 1 |
| C | 0 |
| D | 0 |
Source articles
The research this prior is synthesized from — each is a full dossier (findings, the models it informs, and what the literature says).
Contributing effect sizes
Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.