Registry · Priors

construct.entrepreneurial_orientation predicts construct.financial_performance

normal · weakly_informative · 1 studies · N = 19,514

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.292, 0.325]; mean ≈ 0.308.0.2740.2910.3080.3260.343z0density

mean ≈ 0.308 · 95% CI ≈ [0.292, 0.325]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.284, 0.314]; mean ≈ 0.299.0.2680.2830.2990.3150.330r0density

mean ≈ 0.299 · 95% CI ≈ [0.284, 0.314]

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.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.28, 0.31]
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 literature0.30k = 1 · N = 19,514

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 | 0.308421, 0.0085568);
beta = pm.Normal("beta", mu=0.308421, sigma=0.0085568)
brms::prior(normal(0.308421, 0.0085568), class = "b")
# base R sample
rnorm(N, mean = 0.308421, sd = 0.0085568)
np.random.normal(loc=0.308421, scale=0.0085568, size=N)

Parameters

FamilyParameters
normalmu = 0.3084, sigma = 0.008557, r_mean = 0.2990, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
19,514
Last updated
2026-07-16T01:08:38.888Z

Quality distribution

GradeCount
A0
B1
C0
D0

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