Fuzzy vs Likert scale in Statistics * Mar´ ıa ´ Angeles Gil 1 andGilGonz´alez-Rodr´ ıguez 2 Abstract Likert scales or associated codings are often used in connection with opinions/valuations/ratings, and especially with questionnaires with a pre-specified response format. A guideline to design questionnaires allowing free fuzzy-numbered response format is now given, the fuzzy numbers scale being very rich and expressive and enabling to describe in a friendly way the usual answers in this context. A review of some techniques for the statistical analysis of the obtained responses is enclosed and a real-life example is used to illustrate the application. 1 Introduction Likert scales are widely used to measure attributes often associated with opinions/valuations/ratings, and so on, leading to ordinal/categorical data from a set of pre-fixed labels/categories/names. To facilitate the development of statistical data analysis in this setting, the usual way to proceed is to code each response category by means of an integer number (often by using the either the scale 1-5, or 1-7). More recently, some authors (see, for instance, Lalla et al. [9] Lazim and Osman [10], Bharadwaj [2]) have suggested to identify each Likert response category with a fuzzy subset from a class of operational and flexible fuzzy sets which have been stated by ‘experts’ either individually or by consensus. 1 University of Oviedo, 33071 Oviedo, Spain magil@uniovi.es · 2 European Centre for Soft Computing, 33600 Mieres, Spain gil.gonzalez@softcomputing.es * This paper has been written as a tribute to Professor Ebrahim Mamdani. We have had the great opportunity of meeting a unique outstanding person, during last years mainly because of him being a member of the Scientific Committee of the European Centre for Soft Computing. We have learned a lot from his lectures and conversations, and have enjoyed with the fruitful discussions around, so we will feel always indebted to him. 1