Calibration Presets
Understanding the five preset configurations and choosing the right level of adjustment for your organisation.
Overview
Different organisations and situations require different levels of adjustment. We provide five preset configurations that range from minimal intervention to maximum correction.
Starting Point
Most organisations should start with the Balanced preset and adjust based on specific needs and stakeholder feedback after evaluating initial results.
The Five Presets
Very Conservative
Minimal adjustments, preserving original scores as much as possible
Conservative
Light adjustments for established reviewer-mean differences
Balanced (Default)
Moderate offset shrinkage with bounded spread correction
Aggressive
Strong corrections for variable reviewer patterns
Very Aggressive
Maximum correction for substantial reviewer variability
Very Conservative
When to Use
When you want to preserve original scores as much as possible, making only minimal adjustments for the largest reviewer-mean differences.
Characteristics
- Very high shrinkage factor (K=40): Strongly favours organisational patterns over individual reviewer patterns
- No spread adjustment: Only applies the offset calculation, not rating compression/expansion
Suitable For
- Organisations with highly trained reviewers
- Established rating standards
- Where preserving original scores is culturally important
Conservative
When to Use
When you trust your reviewers but want to address clear reviewer-mean differences.
Characteristics
- High shrinkage factor (K=25): Still favours organisational patterns
- No spread adjustment: Only applies the offset calculation
Suitable For
- Mature review systems with periodic reviewer calibration
- Organisations with established rating cultures
Balanced (Default)
When to Use
When you want moderate offset shrinkage and bounded spread correction as a starting policy.
Characteristics
- Moderate shrinkage factor (K=12): Balances individual and organisational patterns
- Spread adjustment enabled: Adjusts both reviewer-mean differences and compression/expansion
- Moderate spread limits: Scale factor between 0.88 and 1.15
Suitable For
- Most organisations
- Mixed reviewer experience levels
- Standard implementation scenarios
Why This Is Default
The balanced preset provides:
- Moderate offset shrinkage
- The narrowest enabled spread limits
- A predictable starting point that should still be validated against case mix and observed outcomes
Aggressive
When to Use
When observed reviewer-cohort means or spreads vary materially and you want stronger corrections after reviewing assignment context.
Characteristics
- Low shrinkage factor (K=5): Gives eligible reviewer-mean differences more formula weight
- Spread adjustment enabled: With tighter controls (K=16, minimum 10 reviews)
- Wider spread limits: Scale factor between 0.8 and 1.25
Suitable For
- Organisations deliberately testing stronger corrections after case-mix review
- Minimal reviewer training programmes
- Diverse international teams with different cultural norms
Very Aggressive
When to Use
When you deliberately choose maximum correction after reviewing substantial observed cohort differences and assignment context. Use with caution.
Characteristics
- No offset shrinkage (K=0): Applies the full eligible reviewer-mean difference once the minimum three total scores exists
- Aggressive spread adjustment: K=8, minimum 8 reviews
- Very wide spread limits: Scale factor between 0.7 and 1.35
Suitable For
- Organisations undergoing major review system changes
- Research and analysis purposes
- Controlled analysis of maximum permitted correction
Important Considerations
This preset can produce dramatic adjustments. Ensure stakeholders understand the rationale before implementation.
Comparison Table
| Preset | Offset shrinkage (K) | Spread adjustment | Three-score offset weight | Best for |
|---|---|---|---|---|
| Very Conservative | 40 | None | 0.048 | Highly trained reviewers |
| Conservative | 25 | None | 0.074 | Mature review systems |
| Balanced | 12 | Moderate | 0.143 | Default starting policy |
| Aggressive | 5 | Enabled | 0.286 | Variable reviewer means |
| Very Aggressive | 0 | Strong | 1.000 | Maximum correction needed |
Practical Comparison
For a reviewer mean of 62, organisation mean of 70, and 15 valid reviewer scores:
Balanced Preset
- Reviewer offset: +4.31 points (
8 × 14 / (14 + 12)) - Eligible scale is clamped to 0.88-1.15; the realised scale depends on the observed spreads
Aggressive Preset
- Reviewer offset: +5.89 points (
8 × 14 / (14 + 5)) - Eligible scale is clamped to 0.80-1.25; the realised scale depends on the observed spreads
Effective target adjustments can differ from these reviewer offsets because of co-reviewers, spread correction, or clamping.
Rule of Thumb
The balanced preset is more conservative and suitable for most situations. The aggressive preset produces stronger corrections and should only be used when you have clear evidence of substantial reviewer inconsistency.
Choosing the Right Preset
Consider these factors when selecting a preset:
1. Reviewer Training Level
- More training → Conservative presets
- Less training → Aggressive presets
2. Sample Sizes
- Smaller samples per reviewer → Conservative presets
- Larger samples → Aggressive presets
3. Organisational Culture
- Risk-averse culture → Conservative presets
- Data-driven culture → Aggressive presets
4. Known Variability
- Low variability → Conservative presets
- High variability → Aggressive presets
5. Stakes of Decisions
- High-stakes (e.g., promotions, redundancy) → Conservative presets
- Low-stakes (e.g., development planning) → Aggressive presets
Implementation Approach
1. Start with Balanced
Balanced is the default starting policy because it combines moderate offset shrinkage with the narrowest enabled spread limits. Validate it against reviewer case mix, raw scores, and observed outcomes before relying on adjusted results.
2. Gather Feedback
Collect feedback from stakeholders, reviewers, and those being reviewed. Monitor the size and distribution of adjustments.
3. Analyse Results
Review manager statistics, percentile shifts, and adjustment distributions. Look for patterns that suggest the preset needs adjustment.
4. Adjust if Needed
Move to a more conservative preset if adjustments seem too large, or a more aggressive preset if reviewer variability remains high.
5. Communicate Changes
Clearly explain any preset changes to all stakeholders, including the rationale and expected impact.
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