construct.impression_management predicts construct.task_performance
normal · weakly_informative · 4 studies · N = 25,012
Distribution
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
- [0.00, 0.33]
- Credibility interval (95%) — distribution of the true effect across settings (the Bayesian prior)
- [-0.11, 0.42]
- I² (heterogeneity) — share of total variance from between-study differences
- 98% — 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=0.17 (k=4, high heterogeneity (I²=0.98)); no primary-deployment evidence yet
| Source | ρ (r) | Scope |
|---|---|---|
| Published literature | 0.17 | k = 4 · N = 25,012 |
- high heterogeneity (I²=0.98)
- 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.
target += normal_lpdf(beta | 0.171691, 0.0875036);beta = pm.Normal("beta", mu=0.171691, sigma=0.0875036)brms::prior(normal(0.171691, 0.0875036), class = "b")# base R sample
rnorm(N, mean = 0.171691, sd = 0.0875036)np.random.normal(loc=0.171691, scale=0.0875036, size=N)Parameters
| Family | Parameters |
|---|---|
| normal | I2 = 0.9820, mu = 0.1717, sigma = 0.08750, r_mean = 0.1700, k_studies = 4.000, tau_squared = 0.02008, fisher_z_bias = 0.07839, r_mean_bare_bones = 0.09164 |
Synthesis
- Method
- random_effects_meta
- Informativeness
- weakly_informative
- Replication status
- meta-analytic
- k_studies
- 4
- n_total
- 25,012
- Last updated
- 2026-07-16T01:07:30.396Z
Quality distribution
| Grade | Count |
|---|---|
| A | 1 |
| B | 3 |
| 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).
Impression Management and Interview and Job Performance Ratings: A Meta-Analysis of Research Design with Tactics in Mind
Influence tactics and work outcomes: a meta‐analysis
What you see may not be what you get: Relationships among self-presentation tactics and ratings of interview and job performance.
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.