Registry · Priors

construct.psychological_ownership predicts construct.organizational_commitment

normal · uninformative · 1 studies · N = 50

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.125, 0.517]; mean ≈ 0.321.-0.07950.1210.3210.5210.721z0density

mean ≈ 0.321 · 95% CI ≈ [0.125, 0.517]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.133, 0.487]; mean ≈ 0.310.-0.05160.1290.3100.4910.672r0density

mean ≈ 0.310 · 95% CI ≈ [0.133, 0.487]

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
[0.12, 0.48]
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=0.31 (k=1, replication: single); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.31k = 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 | 0.320545, 0.1);
beta = pm.Normal("beta", mu=0.320545, sigma=0.1)
brms::prior(normal(0.320545, 0.1), class = "b")
# base R sample
rnorm(N, mean = 0.320545, sd = 0.1)
np.random.normal(loc=0.320545, scale=0.1, size=N)

Parameters

FamilyParameters
normalmu = 0.3205, sigma = 0.1000, r_mean = 0.3100, k_studies = 1.000, tau_squared = 0.000

Synthesis

Method
single_study
Informativeness
uninformative
Replication status
single
k_studies
1
n_total
50
Last updated
2026-05-29T15:21:31.382Z

Quality distribution

GradeCount
A1
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.