Which practice helps ensure fairness when rating performance?

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Fairness in performance ratings comes from anchoring judgments to clear, observable criteria and gathering evidence from multiple sources. When you use structured criteria, every rating is tied to predefined behaviors or outcomes, with specific anchors that describe what performance at each level looks like. This makes expectations explicit and reduces ambiguity in how ratings are applied.

Adding multiple data sources—such as self-assessments, manager observations, peer feedback, subordinate input, and objective results—gives a fuller, more balanced view of performance. It helps counteract individual biases, like recency or halo effects, and supports calibration across raters so similar performance receives similar ratings.

The other options rely too much on one perspective or introduce bias: a single subjective impression can be swayed by personal liking or memory gaps, ignoring valuable peer or outcomes data; excluding feedback loses important context; and letting personal relationships drive ratings is inherently unfair and undermines trust in the system.