Registry · Priors

construct.transformational_leadership predicts construct.job_satisfaction

normal · weakly_informative · 3 studies · N = 37,996

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.475, 0.725]; mean ≈ 0.600.0.3450.4720.6000.7280.855z0density

mean ≈ 0.600 · 95% CI ≈ [0.475, 0.725]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.448, 0.626]; mean ≈ 0.537.0.3550.4460.5370.6280.719r0density

mean ≈ 0.537 · 95% CI ≈ [0.448, 0.626]

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
[0.44, 0.62]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.39, 0.66]
I² (heterogeneity) — share of total variance from between-study differences
98% — the contributing studies disagree almost completely; read the pooled value as a midpoint of conflicting findings, not as one population's effect

The true effect varies across settings — moderators likely matter.

SD_ρ>0 — true effect varies across settings; likely moderated (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=0.54 (k=3, high heterogeneity (I²=0.98)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.54k = 3 · N = 37,996

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 | 0.600052, 0.0638372);
beta = pm.Normal("beta", mu=0.600052, sigma=0.0638372)
brms::prior(normal(0.600052, 0.0638372), class = "b")
# base R sample
rnorm(N, mean = 0.600052, sd = 0.0638372)
np.random.normal(loc=0.600052, scale=0.0638372, size=N)

Parameters

FamilyParameters
normalI2 = 0.9776, mu = 0.6001, sigma = 0.06384, r_mean = 0.5371, k_studies = 3.000, tau_squared = 0.009442, fisher_z_bias = 0.04311, r_mean_bare_bones = 0.4940

Synthesis

Method
random_effects_meta
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
3
n_total
37,996
Last updated
2026-07-16T01:06:13.231Z

Quality distribution

GradeCount
A2
B1
C0
D0

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.