Registry · Priors

construct.perceived_overqualification predicts construct.counterproductive_work_behaviours

normal · uninformative · 1 studies · N = 50

Distribution

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

[-0.03, 0.34]

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=0.16 (k=1, replication: single); no primary-deployment evidence yet

• replication: single

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

target += normal_lpdf(beta | 0.161387, 0.1);

beta = pm.Normal("beta", mu=0.161387, sigma=0.1)

brms::prior(normal(0.161387, 0.1), class = "b")

# base R sample
rnorm(N, mean = 0.161387, sd = 0.1)

np.random.normal(loc=0.161387, scale=0.1, size=N)

Parameters

Synthesis

Method

single_study

Informativeness

uninformative

Replication status

single

k_studies

1

n_total

50

Last updated

2026-05-29T15:21:26.180Z

Quality distribution

Contributing effect sizes

• effect.96e14e53fb828021

Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.