Registry · Priors

construct.person_organization_fit predicts construct.task_performance

normal · informative · 2 studies · N = 2,910

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.0379, 0.272]; mean ≈ 0.155.-0.08390.03550.1550.2740.394z0density

mean ≈ 0.155 · 95% CI ≈ [0.0379, 0.272]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.0394, 0.268]; mean ≈ 0.154.-0.07950.03710.1540.2700.387r0density

mean ≈ 0.154 · 95% CI ≈ [0.0394, 0.268]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2005.02, ≈20.980000000000018 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.04, 0.27]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.03, 0.27]
I² (heterogeneity) — share of total variance from between-study differences
44% — moderate heterogeneity

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=0.15 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.15k = 2 · N = 2,910

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 | 0.154883, 0.0596973);
beta = pm.Normal("beta", mu=0.154883, sigma=0.0596973)
brms::prior(normal(0.154883, 0.0596973), class = "b")
# base R sample
rnorm(N, mean = 0.154883, sd = 0.0596973)
np.random.normal(loc=0.154883, scale=0.0596973, size=N)

Parameters

FamilyParameters
normalI2 = 0.4352, mu = 0.1549, sigma = 0.05970, r_mean = 0.1537, k_studies = 2.000, tau_squared = 0.003987, fisher_z_bias = 0.02208, r_mean_bare_bones = 0.1316

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
2,910
Last updated
2026-07-16T01:06:23.050Z

Quality distribution

GradeCount
A1
B1
C0
D0

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.