Registry · Priors

construct.impression_management predicts construct.task_performance

normal · weakly_informative · 2 studies · N = 24,912

Distribution

Storage scale (Fisher z)

mean ≈ 0.160 · 95% CI ≈ [-0.0576, 0.378]

Reader scale (r)

mean ≈ 0.159 · 95% CI ≈ [-0.0535, 0.371]

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.

Historical evidence. This prior's contributing evidence is older than 15 years on average (centroid year 2005.1, ≈20.90000000000009 years old); treat the estimate as historical.

Intervals

Confidence interval (95%) — uncertainty about the mean ρ: [-0.06, 0.36]
Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior): [-0.12, 0.42]
I² (heterogeneity) — share of total variance from between-study differences: 99%

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=0.16 (k=2, high heterogeneity (I²=0.99)); no primary-deployment evidence yet

Source	ρ (r)	Scope
Published literature	0.16	k = 2 · N = 24,912

high heterogeneity (I²=0.99)
replication: meta-analytic
aging literature (freshness=0.37)

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 | 0.160093, 0.111079);

beta = pm.Normal("beta", mu=0.160093, sigma=0.111079)

brms::prior(normal(0.160093, 0.111079), class = "b")

# base R sample
rnorm(N, mean = 0.160093, sd = 0.111079)

np.random.normal(loc=0.160093, scale=0.111079, size=N)

Parameters

Family	Parameters
normal	I2 = 0.9939, mu = 0.1601, sigma = 0.1111, r_mean = 0.1587, k_studies = 2.000, tau_squared = 0.02083

Synthesis

Method: random_effects_meta
Informativeness: weakly_informative
Replication status: meta-analytic
k_studies: 2
n_total: 24,912
Last updated: 2026-05-29T15:21:32.686Z

Quality distribution

Grade	Count
A	1
B	1
C	0
D	0

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.