Research on Data Normalization Methods in Multi-Attribute Evaluation

Three major principles for the selection of indicator data normalization methods in multi-attribute evaluation are presented in this paper. Principle 1: The relative gap between the data for the same indicator should remain constant; Principle 2: The relative gap between different indicators should remain variable ; and Principle 3: The maximum values after normalization should be equal. According to these three major principles, a normalization method for positive indicators is screened out from several alternatives, and a new normalization method for negative indicators is proposed. These two methods are very good for the comparison among panel data. The requirement for data normalization methods is different when the evaluation goals are different, ranking-order-based evaluation is insensitive to data normalization methods.