A novel Likert scale based on fuzzy sets theory

The Likert method is commonly used as a standard psychometric scale to measure responses. This measurement scale has a procedure that facilitates survey construction and administration, and data coding and analysis. However, there are some drawbacks in the Likert scaling. This paper addresses the information distortion and information lost arising from the closed-form scaling and the ordinal nature of this measurement method. To overcome these problems, a novel fuzzy Likert scale developed based on the fuzzy sets theory has been proposed. The major contribution of the fuzzy Likert approach is that it permits partial agreement of a scale point. By incorporating this capability into the measurement process, the new scale can capture the lost information and regulate the distorted information. A quantitative analysis based on the concept Consensus has proven that the new scale can provide a more accurate measurement. The implementation feasibility and the improved measurement performance of the fuzzy Likert scale have been demonstrated via a simulation study on a low birth weight analysis.

► A novel Likert scale based on fuzzy sets theory is proposed.
► The new scale is proven to be more accurate using a consensus model.
► Information distortion/lost can be reduced significantly.
► The implementation feasibility is demonstrated via a low birth weight analysis.