Registry · Priors

construct.autonomy predicts construct.task_performance

normal · informative · 2 studies · N = 38,534

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.209, 0.314]; mean ≈ 0.262.0.1550.2080.2620.3150.369z0density

mean ≈ 0.262 · 95% CI ≈ [0.209, 0.314]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.207, 0.305]; mean ≈ 0.256.0.1560.2060.2560.3060.356r0density

mean ≈ 0.256 · 95% CI ≈ [0.207, 0.305]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.21, 0.30]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.19, 0.32]
I² (heterogeneity) — share of total variance from between-study differences
94%

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=0.26 (k=2, high heterogeneity (I²=0.94)); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.26k = 2 · N = 38,534

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 | 0.261816, 0.0267318);
beta = pm.Normal("beta", mu=0.261816, sigma=0.0267318)
brms::prior(normal(0.261816, 0.0267318), class = "b")
# base R sample
rnorm(N, mean = 0.261816, sd = 0.0267318)
np.random.normal(loc=0.261816, scale=0.0267318, size=N)

Parameters

FamilyParameters
normalI2 = 0.9443, mu = 0.2618, sigma = 0.02673, r_mean = 0.2560, k_studies = 2.000, tau_squared = 0.001351

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
38,534
Last updated
2026-05-30T16:21:34.608Z

Quality distribution

GradeCount
A2
B0
C0
D0

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.