Registry · Priors

construct.autonomy correlates construct.work_engagement

normal · weakly_informative · 3 studies · N = 68,405

Distribution

Storage scale (Fisher z)

mean ≈ 0.525 · 95% CI ≈ [0.273, 0.777]

Reader scale (r)

mean ≈ 0.482 · 95% CI ≈ [0.288, 0.675]

Weakly informative prior. This prior is weakly informative. It will nudge your posterior but won't overwhelm it; expect data to do most of the work in modest samples.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ: [0.27, 0.65]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [0.09, 0.74]
I² (heterogeneity) — share of total variance from between-study differences: 100% — the contributing studies disagree almost completely; read the pooled value as a midpoint of conflicting findings, not as one population's effect

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=0.48 (k=3, high heterogeneity (I²=1.00)); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.48	k = 3 · N = 68,405

high heterogeneity (I²=1.00)
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 | 0.525189, 0.128564);

beta = pm.Normal("beta", mu=0.525189, sigma=0.128564)

brms::prior(normal(0.525189, 0.128564), class = "b")

# base R sample
rnorm(N, mean = 0.525189, sd = 0.128564)

np.random.normal(loc=0.525189, scale=0.128564, size=N)

Parameters

Family	Parameters
normal	I2 = 0.9991, mu = 0.5252, sigma = 0.1286, r_mean = 0.4817, k_studies = 3.000, tau_squared = 0.04954, fisher_z_bias = -0.00009978, r_mean_bare_bones = 0.4818

Synthesis

Method: random_effects_meta
Informativeness: weakly_informative
Replication status: meta-analytic
k_studies: 3
n_total: 68,405
Last updated: 2026-07-16T01:10:32.239Z

Quality distribution

Grade	Count
A	3
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.