construct.positive_affect predicts construct.task_performance
normal · informative · 2 studies · N = 14,767
Distribution
Intervals
- Confidence interval (95%) — uncertainty about the mean ρ
- [0.18, 0.22]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [0.20, 0.20]
- 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=0.20 (k=2, replication: meta-analytic); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.20 | k = 2 · N = 14,767 |
- 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 | 0.202633, 0.0098059);beta = pm.Normal("beta", mu=0.202633, sigma=0.0098059)brms::prior(normal(0.202633, 0.0098059), class = "b")# base R sample
rnorm(N, mean = 0.202633, sd = 0.0098059)np.random.normal(loc=0.202633, scale=0.0098059, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.000, mu = 0.2026, sigma = 0.009806, r_mean = 0.1999, k_studies = 2.000, tau_squared = 0.000, fisher_z_bias = -0.00004766, r_mean_bare_bones = 0.2000 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- informative
- Replication status
- meta-analytic
- k_studies
- 2
- n_total
- 14,767
- Last updated
- 2026-07-16T01:08:39.557Z
Quality distribution
| Grade | Count |
|---|---|
| A | 1 |
| B | 1 |
| 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.