construct.abusive_supervision predicts construct.task_performance
normal · informative · 2 studies · N = 4,062
Distribution
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [-0.22, -0.16]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [-0.19, -0.19]
- I² (heterogeneity) — share of total variance from between-study differences
- 0%
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=-0.19 (k=2, replication: meta-analytic); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | -0.19 | k = 2 · N = 4,062 |
- 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 | -0.192337, 0.0156003);beta = pm.Normal("beta", mu=-0.192337, sigma=0.0156003)brms::prior(normal(-0.192337, 0.0156003), class = "b")# base R sample
rnorm(N, mean = -0.192337, sd = 0.0156003)np.random.normal(loc=-0.192337, scale=0.0156003, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.000, mu = -0.1923, sigma = 0.01560, r_mean = -0.1900, k_studies = 2.000, tau_squared = 0.000 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- informative
- Replication status
- meta-analytic
- k_studies
- 2
- n_total
- 4,062
- Last updated
- 2026-05-30T22:23:35.292Z
Quality distribution
| Grade | Count |
|---|---|
| A | 2 |
| B | 0 |
| C | 0 |
| D | 0 |
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.