Standard Deviation Weight — objective weighting by column standard deviation
SD-WEIGHT (Standard Deviation Weight — objective weighting by column standard deviation) is a weight objective multi-criteria decision-making (MCDM) method introduced by Various in 1980. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.
Key highlights
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
Intuition
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How it works
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When to use it
SD weighting assigns higher weight to criteria with greater discrimination power (higher variance across alternatives after normalisation). Criteria where all alternatives perform similarly get low weight. Unlike CRITIC/CCSD it ignores inter-criterion correlation.
Strengths & limitations
- Follows a transparent, reproducible computational procedure that can be audited step by step.
- Handles multiple criteria of differing scales and units within a single decision matrix.
- Results depend on the chosen normalisation, weights, and parameter settings.
Common pitfalls
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Sources
- 1.(). UNCONFIRMED — SD-WEIGHT specific seminal not confirmed via systematic literature search. PENDING
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Cite this page
ScholarGate. (2026, June 2). SD-WEIGHT. ScholarGate. https://scholargate.app/decision-making/sd-weight