construct.job_satisfaction predicts construct.voluntary_turnover
normal · highly_informative · 5 studies · N = 156,857
Distribution
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [-0.30, -0.16]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [-0.36, -0.10]
- I² (heterogeneity) — share of total variance from between-study differences
- 99%
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=5, high heterogeneity (I²=0.99)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | -0.23 | k = 5 · N = 156,857 |
- 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 | -0.236031, 0.0398578);beta = pm.Normal("beta", mu=-0.236031, sigma=0.0398578)brms::prior(normal(-0.236031, 0.0398578), class = "b")# base R sample
rnorm(N, mean = -0.236031, sd = 0.0398578)np.random.normal(loc=-0.236031, scale=0.0398578, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.9895, mu = -0.2360, sigma = 0.03986, r_mean = -0.2317, k_studies = 5.000, tau_squared = 0.005010 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- highly_informative
- Replication status
- meta-analytic
- k_studies
- 5
- n_total
- 156,857
- Last updated
- 2026-05-30T23:51:58.560Z
Quality distribution
| Grade | Count |
|---|---|
| A | 4 |
| B | 0 |
| C | 0 |
| D | 0 |
Contributing effect sizes
- effect.45e455e116c6efba
- effect.5064e36124e16c86
- effect.125175c59b1e32ac
- effect.256a70bd0e5f13c3
- effect.d1e05a9de704cc2a
Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.