construct.work_engagement predicts construct.job_satisfaction
normal · highly_informative · 6 studies · N = 433,684
Distribution
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [0.58, 0.60]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [0.56, 0.61]
- I² (heterogeneity) — share of total variance from between-study differences
- 96%
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=0.59 (k=6, high heterogeneity (I²=0.96)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.59 | k = 6 · N = 433,684 |
- high heterogeneity (I²=0.96)
- 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 | 0.671496, 0.00821008);beta = pm.Normal("beta", mu=0.671496, sigma=0.00821008)brms::prior(normal(0.671496, 0.00821008), class = "b")# base R sample
rnorm(N, mean = 0.671496, sd = 0.00821008)np.random.normal(loc=0.671496, scale=0.00821008, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.9614, mu = 0.6715, sigma = 0.008210, r_mean = 0.5860, k_studies = 6.000, tau_squared = 0.0003731 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- highly_informative
- Replication status
- meta-analytic
- k_studies
- 6
- n_total
- 433,684
- Last updated
- 2026-05-30T23:52:09.100Z
Quality distribution
| Grade | Count |
|---|---|
| A | 6 |
| B | 0 |
| C | 0 |
| D | 0 |
Contributing effect sizes
- effect.88ea9cf6df84535d
- effect.0672982b80dab9a7
- effect.1c09a3a09d0a3c9b
- effect.b13b5df3888ce797
- effect.7dff39e632ac1025
- effect.3995c090d05c2e98
Effect-size detail pages land with a later sub-ticket; for now, ids link to the filtered list. Browse all rows via /registry/effects.