Retention Rate
operationalbehavioral · retention · rate · organization
Share of employees retained over a period (inverse of attrition).
How it’s computed
- Formula
- 1 - (separations / starting headcount)
- Measurement model
- operational (a formula over systems data) — not a reflective survey scale; “reliability” here means data quality, not internal consistency
What it operationalizes
This metric measures the construct(s) below from operational data. Through them, it connects to the meta-analytic relationship network — the bridge from what you measure operationally to what the science knows.
What we know
Synthesized Bayesian priors over relationships involving this metric’s construct(s) — the meta-analytic evidence, with 95% confidence intervals and between-study heterogeneity (I²). Follow a row for the full distribution, contributing studies, and code.
| Relationship | r (95% CI) | I² | k | Evidence |
|---|---|---|---|---|
| ← Met expectations (predicts) | [-0.00, 0.37] | — | 1 | single |
| ← Realistic Job Preview (predicts) | [0.03, 0.05] | — | 1 | meta-analytic |
The evidence
Attributed quotes from open-access papers, classified for stance against the relationship. Sourced and classified by Principia (no third-party feed) — displayable under each paper’s license.
“A realistic representation is critical for engineering as denying the agentic traits of the field would be an inaccurate representation of it—which would likely have negative consequences on retention (Earnest et al., 2011).” supports
Realistic Job Preview → New-Hire / First-Year Retention · The impact of changing engineering perceptions on women’s attitudes and behavioral intentions towards engineering pursuits (2024) · doi · CCBY
Use it
Track Retention Rate and its drivers in the People Analytics Toolbox — the registry’s evidence wired into deployable measurement.
Related metrics
Programmatic access: /api/v1/metrics/metric.retention_rate · /api/v1/evidence-statements?construct=construct.new_hire_retention.