Registry · Priors

construct.emotional_intelligence_ei predicts construct.organizational_commitment

normal · weakly_informative · 1 studies · N = 7,675

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.438, 0.482]; mean ≈ 0.460.0.4140.4370.4600.4830.506z0density

mean ≈ 0.460 · 95% CI ≈ [0.438, 0.482]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.412, 0.448]; mean ≈ 0.430.0.3930.4110.4300.4490.467r0density

mean ≈ 0.430 · 95% CI ≈ [0.412, 0.448]

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
[0.41, 0.45]
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=0.43 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.43k = 1 · N = 7,675

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 | 0.459897, 0.0114168);
beta = pm.Normal("beta", mu=0.459897, sigma=0.0114168)
brms::prior(normal(0.459897, 0.0114168), class = "b")
# base R sample
rnorm(N, mean = 0.459897, sd = 0.0114168)
np.random.normal(loc=0.459897, scale=0.0114168, size=N)

Parameters

FamilyParameters
normalmu = 0.4599, sigma = 0.01142, r_mean = 0.4300, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
weakly_informative
Replication status
meta-analytic
k_studies
1
n_total
7,675
Last updated
2026-05-29T15:21:27.687Z

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.