construct.emotional_intelligence_ei predicts construct.overall_job_performance
normal · weakly_informative · 5 studies · N = 41,308
Distribution
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
- [0.25, 0.47]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [0.14, 0.55]
- I² (heterogeneity) — share of total variance from between-study differences
- 99% — the contributing studies disagree almost completely; read the pooled value as a midpoint of conflicting findings, not as one population's effect
The true effect varies across settings — moderators likely matter.
SD_ρ>0 — true effect varies across settings; likely moderated (observed-score scale until artifact correction, PRN-058)
Evidence provenance
published ρ=0.36 (k=5, high heterogeneity (I²=0.99)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.36 | k = 5 · N = 41,308 |
- high heterogeneity (I²=0.99)
- replication: meta-analytic
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 | 0.382068, 0.0628385);beta = pm.Normal("beta", mu=0.382068, sigma=0.0628385)brms::prior(normal(0.382068, 0.0628385), class = "b")# base R sample
rnorm(N, mean = 0.382068, sd = 0.0628385)np.random.normal(loc=0.382068, scale=0.0628385, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.9872, mu = 0.3821, sigma = 0.06284, r_mean = 0.3645, k_studies = 5.000, tau_squared = 0.01497, fisher_z_bias = -0.0006271, r_mean_bare_bones = 0.3651 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- weakly_informative
- Replication status
- meta-analytic
- k_studies
- 5
- n_total
- 41,308
- Last updated
- 2026-07-16T01:10:39.420Z
Quality distribution
| Grade | Count |
|---|---|
| A | 5 |
| B | 0 |
| C | 0 |
| D | 0 |
Source articles
The research this prior is synthesized from — each is a full dossier (findings, the models it informs, and what the literature says).
Emotional intelligence and job performance: a meta-analysis
Emotional intelligence: An integrative meta-analysis and cascading model.
The relation between emotional intelligence and job performance: A meta‐analysis
Why does self-reported emotional intelligence predict job performance? A meta-analytic investigation of mixed EI.
Contributing effect sizes
- effect.0e19847bf0da66b1
- effect.2f620a1688204f31
- effect.63864d16ccd8413a
- effect.c87320ddc9b591fb
- effect.cc0ad111c56c16b3
Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.