construct.employee_well_being predicts construct.burnout
normal · uninformative · 1 studies · N = 50
Distribution
Uninformative prior. This prior is uninformative — too thin to dominate small-N posteriors. Treat as a placeholder until more evidence lands.
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [-0.66, -0.17]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- — needs ≥2 studies to estimate (k = 1)
- I² (heterogeneity) — share of total variance from between-study differences
- — needs ≥2 studies to estimate
k<2 — between-study heterogeneity not estimable; credibility interval / generalization not assessed
Evidence provenance
published ρ=-0.45 (k=1, replication: single); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | -0.45 | k = 1 · N = 50 |
- replication: single
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 | -0.4847, 0.158114);beta = pm.Normal("beta", mu=-0.4847, sigma=0.158114)brms::prior(normal(-0.4847, 0.158114), class = "b")# base R sample
rnorm(N, mean = -0.4847, sd = 0.158114)np.random.normal(loc=-0.4847, scale=0.158114, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | mu = -0.4847, sigma = 0.1581, r_mean = -0.4500, k_studies = 1.000, tau_squared = 0.000 |
Synthesis
- Method
- single_study
- Informativeness
- uninformative
- Replication status
- single
- k_studies
- 1
- n_total
- 50
- Last updated
- 2026-07-16T01:08:33.602Z
Quality distribution
| Grade | Count |
|---|---|
| A | 0 |
| B | 0 |
| C | 1 |
| 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).
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.