Registry · Priors

construct.learning_orientation predicts construct.turnover_intention

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

[-0.55, 0.00]

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: 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 | -0.30952, 0.158114);

beta = pm.Normal("beta", mu=-0.30952, sigma=0.158114)

brms::prior(normal(-0.30952, 0.158114), class = "b")

# base R sample
rnorm(N, mean = -0.30952, sd = 0.158114)

np.random.normal(loc=-0.30952, scale=0.158114, size=N)

Parameters

Synthesis

Method

single_study

Informativeness

uninformative

Replication status

single

k_studies

1

n_total

50

Last updated

2026-07-16T01:08:37.534Z

Quality distribution

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.720c3f240d47d2f6

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