construct.unstructured_employment_interviews predicts construct.task_performance
normal · weakly_informative · 4 studies · N = 16,738
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.
Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 1994.14, ≈31.8599999999999 years old); treat the estimate as historical.
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [0.14, 0.35]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [0.06, 0.41]
- I² (heterogeneity) — share of total variance from between-study differences
- 96% — 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=0.25 (k=4, high heterogeneity (I²=0.96)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.25 | k = 4 · N = 16,738 |
- high heterogeneity (I²=0.96)
- replication: meta-analytic
- aging literature (freshness=0.28)
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 | 0.250541, 0.0576451);beta = pm.Normal("beta", mu=0.250541, sigma=0.0576451)brms::prior(normal(0.250541, 0.0576451), class = "b")# base R sample
rnorm(N, mean = 0.250541, sd = 0.0576451)np.random.normal(loc=0.250541, scale=0.0576451, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.9633, mu = 0.2505, sigma = 0.05765, r_mean = 0.2454, k_studies = 4.000, tau_squared = 0.009278, fisher_z_bias = -0.02707, r_mean_bare_bones = 0.2725 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- weakly_informative
- Replication status
- meta-analytic
- k_studies
- 4
- n_total
- 16,738
- Last updated
- 2026-07-16T01:07:42.018Z
Quality distribution
| Grade | Count |
|---|---|
| A | 3 |
| 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).
Hunter and Hunter (1984) revisited: Interview validity for entry-level jobs.
Revisiting meta-analytic estimates of validity in personnel selection: Addressing systematic overcorrection for restriction of range.
The validity of employment interviews: A comprehensive review and meta-analysis.
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.