Methodology 08

HPI Score Calculation

The HPI Score is calculated through a transparent and reproducible pathway from raw validated data to indicator scores, pillar scores, and the overall composite score—ensuring accuracy, consistency, and trust in every result.

Methodology
Score Confidence
91.0 /100
Excellent
Consistency
+4.4 Points
vs Last Assessment
Priority
Priority
Core
Composite Result
Overview Formula Components Controls Dashboard Improvement Actions
Pillar
Indicator Scores 90.7 /100
Excellent
Pillar
Section Scores 89.8 /100
Excellent
Pillar
Pillar Scores 91.3 /100
Excellent
Pillar
Composite Logic 90.2 /100
Excellent
Pillar
Score Confidence 91.0 /100
Excellent
What It Covers
  • Scoring pathway from data to composite score
  • Indicator scoring and standardization
  • Section score aggregation
  • Pillar score consolidation
  • Normalization across indicators and sections
  • Composite score calculation logic
  • Transparency and auditability of results
building
Core Sections
Raw Data
Raw Data
Indicator Scores
Indicator Scores
Section Scores
Section Scores
Pillar Scores
Pillar Scores
Composite Score
Composite Score
Score Confidence
Score Confidence
Key Elements
  • Formula and calculation pathway
  • Input validation and eligibility rules
  • Weight application and aggregation
  • Normalization and scaling approach
  • Composite score derivation
  • Rounding and display rules
  • Confidence score computation
  • Audit trail and reproducibility
Risk Signals
  • Weak documentation of calculation logic
  • Inconsistent recalculation results
  • Hidden assumptions or black-box steps
  • Low confidence due to sparse or unreliable data
Dashboard Preview
1. Raw Data
(Validated)
Validated and cleaned data inputs
2. Indicator
Scores
90.7/100
Standardized indicator performance
3. Section
Scores
89.8/100
Aggregated section performance
4. Pillar
Scores
91.3/100
Consolidated pillar performance
5. Composite
Score
90.2/100
Overall HPI composite score
Improvement Actions
  • Document and communicate calculation logic clearly
  • Perform regular recalculation and validation checks
  • Review normalization and weighting assumptions
  • Strengthen confidence reporting for sparse data
  • Improve transparency with audit trails and versioning
  • Provide user guidance on score interpretation
Pillar
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