Registry · Priors

construct.demographic_diversity predicts construct.team_performance

normal · informative · 3 studies · N = 8,857

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.0307, 0.0108]; mean ≈ -0.00998.-0.0524-0.0312-0.009980.01120.0324z0density

mean ≈ -0.00998 · 95% CI ≈ [-0.0307, 0.0108]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.0307, 0.0108]; mean ≈ -0.00998.-0.0524-0.0312-0.009980.01120.0324r0density

mean ≈ -0.00998 · 95% CI ≈ [-0.0307, 0.0108]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.03, 0.01]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.01, -0.01]
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=-0.01 (k=3, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.01k = 3 · N = 8,857

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 | -0.00997789, 0.0105976);
beta = pm.Normal("beta", mu=-0.00997789, sigma=0.0105976)
brms::prior(normal(-0.00997789, 0.0105976), class = "b")
# base R sample
rnorm(N, mean = -0.00997789, sd = 0.0105976)
np.random.normal(loc=-0.00997789, scale=0.0105976, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = -0.009978, sigma = 0.01060, r_mean = -0.009978, k_studies = 3.000, tau_squared = 0.000, fisher_z_bias = 0.00001112, r_mean_bare_bones = -0.009989

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
3
n_total
8,857
Last updated
2026-07-16T01:08:14.074Z

Quality distribution

GradeCount
A2
B0
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.