Registry · Priors

construct.situational_interviews predicts construct.task_performance

normal · informative · 3 studies · N = 1,046

Distribution

Storage scale (Fisher z)
Prior PDF · normalnormal distribution. Storage scale (Fisher z). 95% CI ≈ [0.482, 0.600]; mean ≈ 0.541.0.4210.4810.5410.6010.661z0density

mean ≈ 0.541 · 95% CI ≈ [0.482, 0.600]

Reader scale (r)
Prior PDF · normalnormal distribution. Reader scale (r). 95% CI ≈ [0.449, 0.538]; mean ≈ 0.494.0.4030.4480.4940.5390.584r0density

mean ≈ 0.494 · 95% CI ≈ [0.449, 0.538]

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 1995.03, ≈30.970000000000027 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ
[0.45, 0.54]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
[0.49, 0.49]
I² (heterogeneity) — share of total variance from between-study differences
0% — studies largely agree

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=0.49 (k=3, replication: meta-analytic); no primary-deployment evidence yet

Sourceρ (r)Scope
Published literature0.49k = 3 · N = 1,046

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 | 0.541034, 0.0299745);
beta = pm.Normal("beta", mu=0.541034, sigma=0.0299745)
brms::prior(normal(0.541034, 0.0299745), class = "b")
# base R sample
rnorm(N, mean = 0.541034, sd = 0.0299745)
np.random.normal(loc=0.541034, scale=0.0299745, size=N)

Parameters

FamilyParameters
normalI2 = 0.000, mu = 0.5410, sigma = 0.02997, r_mean = 0.4938, k_studies = 3.000, tau_squared = 0.000, fisher_z_bias = -0.002787, r_mean_bare_bones = 0.4966

Synthesis

Method
random_effects_meta
Informativeness
informative
Replication status
meta-analytic
k_studies
3
n_total
1,046
Last updated
2026-07-16T01:07:41.274Z

Quality distribution

GradeCount
A2
B1
C0
D0

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.