Registry · Priors

construct.conscientiousness predicts construct.task_performance

normal · highly_informative · 5 studies · N = 17,387

Distribution

Storage scale (Fisher z)

mean ≈ 0.183 · 95% CI ≈ [0.137, 0.229]

Reader scale (r)

mean ≈ 0.181 · 95% CI ≈ [0.137, 0.225]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 1993.61, ≈32.3900000000001 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ: [0.14, 0.22]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [0.11, 0.25]
I² (heterogeneity) — share of total variance from between-study differences: 73%

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=0.18 (k=5, replication: meta-analytic); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.18	k = 5 · N = 17,387

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 | 0.183049, 0.0232873);

beta = pm.Normal("beta", mu=0.183049, sigma=0.0232873)

brms::prior(normal(0.183049, 0.0232873), class = "b")

# base R sample
rnorm(N, mean = 0.183049, sd = 0.0232873)

np.random.normal(loc=0.183049, scale=0.0232873, size=N)

Parameters

Family	Parameters
normal	I2 = 0.7337, mu = 0.1830, sigma = 0.02329, r_mean = 0.1810, k_studies = 5.000, tau_squared = 0.001486

Synthesis

Method: random_effects_meta
Informativeness: highly_informative
Replication status: meta-analytic
k_studies: 5
n_total: 17,387
Last updated: 2026-05-30T22:53:20.946Z

Quality distribution

Grade	Count
A	5
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.