Registry · Priors

construct.work_engagement predicts construct.absenteeism

normal · informative · 2 studies · N = 138,076

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [-0.178, -0.167]; mean ≈ -0.173.-0.183-0.178-0.173-0.167-0.162z0density

mean ≈ -0.173 · 95% CI ≈ [-0.178, -0.167]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [-0.176, -0.166]; mean ≈ -0.171.-0.181-0.176-0.171-0.166-0.161r0density

mean ≈ -0.171 · 95% CI ≈ [-0.176, -0.166]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[-0.18, -0.17]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[-0.17, -0.17]
I² (heterogeneity) — share of total variance from between-study differences
0%

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=-0.17 (k=2, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature-0.17k = 2 · N = 138,076

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 | -0.172697, 0.00269123);
beta = pm.Normal("beta", mu=-0.172697, sigma=0.00269123)
brms::prior(normal(-0.172697, 0.00269123), class = "b")
# base R sample
rnorm(N, mean = -0.172697, sd = 0.00269123)
np.random.normal(loc=-0.172697, scale=0.00269123, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = -0.1727, sigma = 0.002691, r_mean = -0.1710, k_studies = 2.000, tau_squared = 0.000

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
2
n_total
138,076
Last updated
2026-05-30T18:11:39.392Z

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.