Registry · Priors

construct.interactional_justice predicts construct.counterproductive_work_behaviours

normal · weakly_informative · 1 studies · N = 2,707

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.347, -0.272]; mean ≈ -0.310.-0.386-0.348-0.310-0.271-0.233z0density

mean ≈ -0.310 · 95% CI ≈ [-0.347, -0.272]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.334, -0.266]; mean ≈ -0.300.-0.370-0.335-0.300-0.265-0.230r0density

mean ≈ -0.300 · 95% CI ≈ [-0.334, -0.266]

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.

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2001, ≈25 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.33, -0.27]
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=-0.30 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.30k = 1 · N = 2,707

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 | -0.30952, 0.0192308);
beta = pm.Normal("beta", mu=-0.30952, sigma=0.0192308)
brms::prior(normal(-0.30952, 0.0192308), class = "b")
# base R sample
rnorm(N, mean = -0.30952, sd = 0.0192308)
np.random.normal(loc=-0.30952, scale=0.0192308, size=N)

Parameters

FamilyParameters
normalmu = -0.3095, sigma = 0.01923, r_mean = -0.3000, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
2,707
Last updated
2026-05-30T18:11:40.299Z

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.