construct.organizational_learning predicts construct.job_satisfaction
normal · weakly_informative · 2 studies · N = 100
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
- [0.39, 0.63]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [0.52, 0.52]
- I² (heterogeneity) — share of total variance from between-study differences
- 0% — studies largely agree
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=0.52 (k=2, replication: replicated); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.52 | k = 2 · N = 100 |
- replication: replicated
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 | 0.577221, 0.0845154);beta = pm.Normal("beta", mu=0.577221, sigma=0.0845154)brms::prior(normal(0.577221, 0.0845154), class = "b")# base R sample
rnorm(N, mean = 0.577221, sd = 0.0845154)np.random.normal(loc=0.577221, scale=0.0845154, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.000, mu = 0.5772, sigma = 0.08452, r_mean = 0.5206, k_studies = 2.000, tau_squared = 0.000, fisher_z_bias = 0.0006425, r_mean_bare_bones = 0.5200 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- weakly_informative
- Replication status
- replicated
- k_studies
- 2
- n_total
- 100
- Last updated
- 2026-07-16T01:07:31.609Z
Quality distribution
| Grade | Count |
|---|---|
| A | 0 |
| B | 2 |
| 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).
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.