construct.job_satisfaction predicts construct.voluntary_turnover
normal · weakly_informative · 4 studies · N = 156,807
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
- [-0.30, -0.15]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [-0.35, -0.09]
- 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=-0.23 (k=4, high heterogeneity (I²=0.99)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | -0.23 | k = 4 · N = 156,807 |
- 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 | -0.229925, 0.0422352);beta = pm.Normal("beta", mu=-0.229925, sigma=0.0422352)brms::prior(normal(-0.229925, 0.0422352), class = "b")# base R sample
rnorm(N, mean = -0.229925, sd = 0.0422352)np.random.normal(loc=-0.229925, scale=0.0422352, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.9921, mu = -0.2299, sigma = 0.04224, r_mean = -0.2260, k_studies = 4.000, tau_squared = 0.005032, fisher_z_bias = 0.02939, r_mean_bare_bones = -0.2553 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- weakly_informative
- Replication status
- meta-analytic
- k_studies
- 4
- n_total
- 156,807
- Last updated
- 2026-07-16T01:07:47.234Z
Quality distribution
| Grade | Count |
|---|---|
| A | 3 |
| 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).
JOB SATISFACTION, ORGANIZATIONAL COMMITMENT, TURNOVER INTENTION, AND TURNOVER: PATH ANALYSES BASED ON META‐ANALYTIC FINDINGS
Surveying the forest: A meta‐analysis, moderator investigation, and future‐oriented discussion of the antecedents of voluntary employee turnover
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.