Registry · Priors

construct.self_efficacy predicts construct.task_performance

normal · informative · 3 studies · N = 22,788

Distribution

Storage scale (Fisher z)

mean ≈ 0.305 · 95% CI ≈ [0.160, 0.451]

Reader scale (r)

mean ≈ 0.296 · 95% CI ≈ [0.163, 0.429]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 1998.16, ≈27.839999999999918 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ: [0.16, 0.42]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [0.08, 0.48]
I² (heterogeneity) — share of total variance from between-study differences: 94%

The true effect varies across settings — moderators likely matter.

SD_ρ>0 — true effect varies across settings; likely moderated (observed-score scale until artifact correction, PRN-058)

Evidence provenance

published ρ=0.30 (k=3, high heterogeneity (I²=0.94)); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.30	k = 3 · N = 22,788

high heterogeneity (I²=0.94)
replication: meta-analytic
aging literature (freshness=0.28)

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.

StanPyMCbrmsRNumPy

target += normal_lpdf(beta | 0.305282, 0.0743042);

beta = pm.Normal("beta", mu=0.305282, sigma=0.0743042)

brms::prior(normal(0.305282, 0.0743042), class = "b")

# base R sample
rnorm(N, mean = 0.305282, sd = 0.0743042)

np.random.normal(loc=0.305282, scale=0.0743042, size=N)

Parameters

Family	Parameters
normal	I2 = 0.9370, mu = 0.3053, sigma = 0.07430, r_mean = 0.2961, k_studies = 3.000, tau_squared = 0.01284

Synthesis

Method: random_effects_meta
Informativeness: informative
Replication status: meta-analytic
k_studies: 3
n_total: 22,788
Last updated: 2026-05-30T21:47:55.229Z

Quality distribution

Grade	Count
A	2
B	1
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.