Registry · Priors

construct.abusive_supervision predicts construct.counterproductive_work_behaviours

normal · informative · 2 studies · N = 1,765

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.390, 0.482]; mean ≈ 0.436.0.3420.3890.4360.4830.530z0density

mean ≈ 0.436 · 95% CI ≈ [0.390, 0.482]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.372, 0.448]; mean ≈ 0.410.0.3320.3710.4100.4490.488r0density

mean ≈ 0.410 · 95% CI ≈ [0.372, 0.448]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.37, 0.45]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.41, 0.41]
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=0.41 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.41k = 2 · N = 1,765

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 | 0.435611, 0.023492);
beta = pm.Normal("beta", mu=0.435611, sigma=0.023492)
brms::prior(normal(0.435611, 0.023492), class = "b")
# base R sample
rnorm(N, mean = 0.435611, sd = 0.023492)
np.random.normal(loc=0.435611, scale=0.023492, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = 0.4356, sigma = 0.02349, r_mean = 0.4100, k_studies = 2.000, tau_squared = 0.000

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
1,765
Last updated
2026-05-30T22:53:16.671Z

Quality distribution

GradeCount
A2
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.