construct.job_embeddedness predicts construct.turnover_intention
normal · informative · 5 studies · N = 29,507
Distribution
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [-0.51, -0.44]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [-0.52, -0.42]
- I² (heterogeneity) — share of total variance from between-study differences
- 82% — considerable heterogeneity; moderators likely dominate
The true effect is ~constant across settings — it generalizes.
SD_ρ≈0 — true effect is ~constant across settings; generalizes (observed-score scale until artifact correction, PRN-058)
Evidence provenance
published ρ=-0.47 (k=5, high heterogeneity (I²=0.82)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | -0.47 | k = 5 · N = 29,507 |
- high heterogeneity (I²=0.82)
- 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 | -0.515262, 0.0224309);beta = pm.Normal("beta", mu=-0.515262, sigma=0.0224309)brms::prior(normal(-0.515262, 0.0224309), class = "b")# base R sample
rnorm(N, mean = -0.515262, sd = 0.0224309)np.random.normal(loc=-0.515262, scale=0.0224309, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.8190, mu = -0.5153, sigma = 0.02243, r_mean = -0.4740, k_studies = 5.000, tau_squared = 0.001008, fisher_z_bias = -0.0007042, r_mean_bare_bones = -0.4733 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- informative
- Replication status
- meta-analytic
- k_studies
- 5
- n_total
- 29,507
- Last updated
- 2026-07-16T01:06:44.080Z
Quality distribution
| Grade | Count |
|---|---|
| A | 0 |
| B | 5 |
| 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‐analysis on predictors of turnover intention of hospital nurses in South Korea (2000–2020)
Nurses’ job embeddedness and turnover intention: A systematic review and meta-analysis
The effectiveness of job embeddedness in turnover studies
When and how is job embeddedness predictive of turnover? A meta-analytic investigation.
Contributing effect sizes
- effect.36762416e80fadc3
- effect.487226dec979450f
- effect.6077044bc3906939
- effect.9225bb9a0e0b564c
- effect.bc02561a7ef1d75f
Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.