Registry · Priors

construct.psychological_capital_psycap correlates construct.subjective_well_being

normal · uninformative · 1 studies · N = 50

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.280, 0.900]; mean ≈ 0.590.-0.04230.2740.5900.9061.22z0density

mean ≈ 0.590 · 95% CI ≈ [0.280, 0.900]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.307, 0.753]; mean ≈ 0.530.0.07520.3030.5300.7570.985r0density

mean ≈ 0.530 · 95% CI ≈ [0.307, 0.753]

Uninformative prior. This prior is uninformative — too thin to dominate small-N posteriors. Treat as a placeholder until more evidence lands.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.27, 0.72]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
— needs ≥2 studies to estimate (k = 1)
I² (heterogeneity) — share of total variance from between-study differences
— needs ≥2 studies to estimate

k<2 — between-study heterogeneity not estimable; credibility interval / generalization not assessed

Evidence provenance

published ρ=0.53 (k=1, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.53k = 1 · N = 50

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 | 0.590145, 0.158114);
beta = pm.Normal("beta", mu=0.590145, sigma=0.158114)
brms::prior(normal(0.590145, 0.158114), class = "b")
# base R sample
rnorm(N, mean = 0.590145, sd = 0.158114)
np.random.normal(loc=0.590145, scale=0.158114, size=N)

Parameters

FamilyParameters
normalmu = 0.5901, sigma = 0.1581, r_mean = 0.5300, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
uninformative
Replication status
meta-analytic
k_studies
1
n_total
50
Last updated
2026-05-29T15:21:18.610Z

Quality distribution

GradeCount
A0
B0
C1
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.