Handbook of Organization Measurement
Procedural Fairness
construct.procedural_fairness
§3The math on what we know
Synthesized priors from the live registry: pooled r, studies (k), total N, top study grade, and heterogeneity. Rows flagged for heterogeneity or single-study evidence are directional, not settled — read them as a central tendency.
What drives procedural fairness
| Predictor | r | k | N | Grade | Confidence |
|---|---|---|---|---|---|
| Predictive Validity Perceptions | +0.63 | 1 | 8,695 | A | single study |
What procedural fairness predicts
| Outcome | r | k | N | Grade | Confidence |
|---|---|---|---|---|---|
| Organizational Attractiveness | +0.49 | 1 | 15,033 | A | single study |
§4As a model node
Where procedural fairness sits when you drop it into a model — what feeds it, what it moves. Read left to right as a small, actionable causal claim.
Inputs (drivers)
- Predictive Validity Perceptions+0.63
→
Procedural Fairness
→
Outputs (outcomes)
- Organizational Attractiveness+0.49