Registry · Priors

construct.ethical_leadership predicts construct.counterproductive_work_behaviours

normal · informative · 2 studies · N = 12,696

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.538, -0.308]; mean ≈ -0.423.-0.658-0.541-0.423-0.306-0.189z0density

mean ≈ -0.423 · 95% CI ≈ [-0.538, -0.308]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.496, -0.303]; mean ≈ -0.400.-0.597-0.498-0.400-0.301-0.203r0density

mean ≈ -0.400 · 95% CI ≈ [-0.496, -0.303]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.49, -0.30]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.51, -0.27]
I² (heterogeneity) — share of total variance from between-study differences
94%

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=-0.40 (k=2, high heterogeneity (I²=0.94)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.40k = 2 · N = 12,696

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 | -0.423373, 0.0586304);
beta = pm.Normal("beta", mu=-0.423373, sigma=0.0586304)
brms::prior(normal(-0.423373, 0.0586304), class = "b")
# base R sample
rnorm(N, mean = -0.423373, sd = 0.0586304)
np.random.normal(loc=-0.423373, scale=0.0586304, size=N)

Parameters

FamilyParameters
normalI2 = 0.9444, mu = -0.4234, sigma = 0.05863, r_mean = -0.3998, k_studies = 2.000, tau_squared = 0.005489

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
12,696
Last updated
2026-05-30T22:53:17.163Z

Quality distribution

GradeCount
A1
B1
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.