Registry · Priors

construct.transformational_leadership predicts construct.organizational_commitment

normal · uninformative · 1 studies · N = 50

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.290, 0.682]; mean ≈ 0.486.0.08600.2860.4860.6860.886z0density

mean ≈ 0.486 · 95% CI ≈ [0.290, 0.682]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.295, 0.607]; mean ≈ 0.451.0.1320.2920.4510.6100.770r0density

mean ≈ 0.451 · 95% CI ≈ [0.295, 0.607]

Uninformative prior. This prior is uninformative — too thin to dominate small-N posteriors. Treat as a placeholder until more evidence lands.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.28, 0.59]
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=0.45 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.45k = 1 · N = 50

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 | 0.485955, 0.1);
beta = pm.Normal("beta", mu=0.485955, sigma=0.1)
brms::prior(normal(0.485955, 0.1), class = "b")
# base R sample
rnorm(N, mean = 0.485955, sd = 0.1)
np.random.normal(loc=0.485955, scale=0.1, size=N)

Parameters

FamilyParameters
normalmu = 0.4860, sigma = 0.1000, r_mean = 0.4510, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
uninformative
Replication status
meta-analytic
k_studies
1
n_total
50
Last updated
2026-05-29T15:21:23.530Z

Quality distribution

GradeCount
A1
B0
C0
D0

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.