Registry · Priors

construct.work_engagement predicts construct.task_performance

normal · informative · 4 studies · N = 79,719

Distribution

Storage scale (Fisher z)

mean ≈ 0.502 · 95% CI ≈ [0.456, 0.547]

Reader scale (r)

mean ≈ 0.463 · 95% CI ≈ [0.428, 0.499]

Intervals

Confidence interval (95%) — uncertainty about the mean ρ: [0.43, 0.50]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [0.40, 0.52]
I² (heterogeneity) — share of total variance from between-study differences: 90% — considerable heterogeneity; moderators likely dominate

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=0.46 (k=4, high heterogeneity (I²=0.90)); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.46	k = 4 · N = 79,719

high heterogeneity (I²=0.90)
replication: meta-analytic

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 | 0.501595, 0.0232364);

beta = pm.Normal("beta", mu=0.501595, sigma=0.0232364)

brms::prior(normal(0.501595, 0.0232364), class = "b")

# base R sample
rnorm(N, mean = 0.501595, sd = 0.0232364)

np.random.normal(loc=0.501595, scale=0.0232364, size=N)

Parameters

Family	Parameters
normal	I2 = 0.8993, mu = 0.5016, sigma = 0.02324, r_mean = 0.4634, k_studies = 4.000, tau_squared = 0.001538, fisher_z_bias = -0.01536, r_mean_bare_bones = 0.4787

Synthesis

Method: random_effects_meta
Informativeness: informative
Replication status: meta-analytic
k_studies: 4
n_total: 79,719
Last updated: 2026-07-16T01:10:09.249Z

Quality distribution

Grade	Count
A	4
B	0
C	0
D	0

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.