An application of a computerized fuzzy graphic rating scale to the psychological measurement of individual differences

Hesketh, T.; Pryor, Robert; Hesketh, Beryl

doi:10.1016/s0020-7373(88)80029-4

Cited by 90 publications

(73 citation statements)

References 18 publications

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“…As a final remark, in the specific case of imprecision associated with the use in the evaluation process of linguistic term-based scales we can distinguish two fuzzy approaches: (a) fuzzy rating scale [80,105]; (b) Likert or associated fuzzy conversion scales [80]. Indeed, since nowadays an increasing amount of data arises from human judgements, perceptions and evaluations, it seems necessary to find suitable instruments able to handle and capture the imprecision contained in them.…”

Section: Discussionmentioning

confidence: 99%

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

D’Urso

Disegna

Massari

et al. 2015

Knowledge-Based Systems

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Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers' place preferences and implications for place marketing are offered.

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Section: Discussionmentioning

confidence: 99%

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

D’Urso

Disegna

Massari

et al. 2015

Knowledge-Based Systems

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“…An example of this kind of scale with five values (the most usual number of values) is shown in Figure 1 An increase of the number of choices is meant to provide more informative results 32 . Moreover, the concept of fuzzy rating scale was provided by Hesketh et al 16 in order to broaden this idea. They take advantage of the properties of fuzzy logic to extend the number of choices to a continuum.…”

Section: Survey Methodsmentioning

confidence: 99%

“…Fuzzy rating scales, introduced by Hesketh et al 16 in 1988, allow a respondent to give a flexible answer by selecting any value in a continuous line, along with two additional values which represent a certain interval of preference. Thus, it becomes straightforward the generation of a triangular fuzzy set which characterizes the uncertainty attached to the given answer.…”

Section: Introductionmentioning

confidence: 99%

Descriptive and Comparative Analysis of Human Perceptions expressed through Fuzzy Rating Scale-based Questionnaires

Quirós¹,

Alonso²,

Pancho³

2016

IJCIS

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Opinion surveys are widely admitted as a valuable source of information which becomes complementary to the information extracted from data by machine learning techniques. This paper focuses on a challenging and still open problem which is related to how to handle properly the inherent uncertainty of human perceptions. Namely, we propose new ways to interpret and analyze fuzzy data coming out from a special case of survey, the so-called fuzzy rating scale-based questionnaire. This kind of questionnaire is characterized by allowing expressing human perceptions in terms of fuzzy rating scales. The proposed methods are in charge of capturing and modeling the uncertainty of the answers by varying the heights of the related fuzzy sets. These methods have been validated in two case studies: (1) a descriptive survey related to the packaging design of gin bottles; and (2) a comparative survey related to 2015 IFSA-EUSFLAT conference.

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“…--Fuzzy rating scale This scale has been introduced by Hesketh et al (1988). See also, among others, Hesketh and Hesketh (1994), Matsui and Takeya (1994), Takemura (1999Takemura ( , 2007Takemura ( , 2012, Yamashita (2006), Hesketh et al (2011) and de la Rosa de Saa et al (2015), Lubiano et al (2016a, b) for some developments and applications.…”

Section: Fuzzy Data: Fuzzy Representation Of Linguistic Terms Ordinamentioning

confidence: 99%

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

D’Urso

2017

Granul. Comput.

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An application of a computerized fuzzy graphic rating scale to the psychological measurement of individual differences

Cited by 90 publications

References 18 publications

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

Bagged fuzzy clustering for fuzzy data: An application to a tourism market

Descriptive and Comparative Analysis of Human Perceptions expressed through Fuzzy Rating Scale-based Questionnaires

Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

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