Fair, Consistent Interview Scoring

Compare how different panelists score candidates. Detect scoring bias, normalize evaluations, and ensure every candidate gets a fair assessment regardless of who interviews them.

  • Cross-panelist score comparison
  • Bias detection and alerts
  • Score normalization
  • Calibration session planning
  • Historical scoring trends
  • Fair evaluation assurance

Sound Familiar?

  • Panelist A always scores 8/10, Panelist B always scores 5/10: Bias detection flags inconsistencies and normalizes scores
  • No way to ensure scoring consistency: Calibration reports show patterns and trigger training sessions
  • Unfair admission decisions from biased scoring: Normalized scores ensure candidates are evaluated equally

Complete Panelist Calibration Features

Everything you need to modernize your school's panelist calibration.

  • Score Comparison: Side-by-side charts comparing each panelist's scoring distribution.
  • Bias Detection: Statistical analysis identifies panelists who score consistently higher or lower than average.
  • Score Normalization: Optional auto-normalization adjusts for known panelist biases.
  • Calibration Sessions: System suggests when calibration training is needed based on scoring divergence.
  • Trend Analysis: Track how panelist scoring patterns change over time and after calibration.
  • Reports: Detailed calibration reports for admission committee review.

Get Started in 4 Simple Steps

  1. Collect Scores: Multiple panelists score interviews independently using the scorecard system.
  2. Analyze Patterns: System compares scoring distributions across panelists statistically.
  3. Flag Biases: Alerts when any panelist's scores deviate significantly from peers.
  4. Calibrate: Conduct calibration sessions and track improvement over time.

Frequently Asked Questions

How is bias detected?

Statistical analysis compares each panelist's average scores, standard deviation, and distribution against the group. Significant deviations are flagged.

What is score normalization?

An optional feature that adjusts raw scores based on known panelist biases. If Panelist A consistently scores 15% higher, their scores are adjusted downward for comparison.

How often should calibration happen?

The system recommends calibration sessions when scoring divergence exceeds thresholds — typically quarterly or at the start of each admission season.

Can I see improvement after calibration?

Yes. Trend analysis shows how scoring consistency improves after calibration training sessions.