construct.general_mental_ability_gma predicts construct.overall_job_performance
normal · weakly_informative · 5 studies · N = 59,563
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
- [0.27, 0.57]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [0.08, 0.69]
- I² (heterogeneity) — share of total variance from between-study differences
- 100% — 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=0.43 (k=5, high heterogeneity (I²=1.00)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.43 | k = 5 · N = 59,563 |
- high heterogeneity (I²=1.00)
- 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 | 0.458197, 0.0946693);beta = pm.Normal("beta", mu=0.458197, sigma=0.0946693)brms::prior(normal(0.458197, 0.0946693), class = "b")# base R sample
rnorm(N, mean = 0.458197, sd = 0.0946693)np.random.normal(loc=0.458197, scale=0.0946693, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.9960, mu = 0.4582, sigma = 0.09467, r_mean = 0.4286, k_studies = 5.000, tau_squared = 0.03817, fisher_z_bias = 0.1327, r_mean_bare_bones = 0.2959 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- weakly_informative
- Replication status
- meta-analytic
- k_studies
- 5
- n_total
- 59,563
- Last updated
- 2026-07-16T01:10:38.164Z
Quality distribution
| Grade | Count |
|---|---|
| A | 4 |
| B | 1 |
| 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).
A Meta-Analytic Study of General Mental Ability Validity for Different Occupations in the European Community.
Meta-Analysis of the Validity of General Mental Ability for Five Performance Criteria: Hunter and Hunter (1984) Revisited
Revisiting meta-analytic estimates of validity in personnel selection: Addressing systematic overcorrection for restriction of range.
Validity of General Mental Ability for the Prediction of Job Performance and Training Success in Germany: A meta‐analysis<sup>1</sup>
Contributing effect sizes
- effect.1d77735bf95df79b
- effect.2a0e9697d5a6819c
- effect.8a90bc5772688794
- effect.d4e172e5cfc1f5a2
- effect.f7af2afbd436a7ec
Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.