Fuzzy vs. Likert Scale in Statistics

Gil, María Ángeles; González‐Rodríguez, Gil

doi:10.1007/978-3-642-24666-1_27

Cited by 38 publications

(28 citation statements)

References 17 publications

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“…the fuzzy number), rather than only one score, per each individual answer. Note that, in order to avoid this problem, when designing the questionnaire a fuzzy rating scale could be adopted [23,80,81].…”

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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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mentioning

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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“…A szakirodalmi áttekintésben bemutatott modellek -és az általunk bemutatott módszertan is -Likert-skála alkalmazására épül. A Likert-skálán alapuló értékelés öszszetett feladat: egyetlen értékelés során az értékelők több döntést hoznak -bizonytalansággal terhelt helyzetbenaz adott szempont szerinti értékelés során az értékelőnek viszonylag kevés érték közül kell választania (Gil & González-Rodríguez, 2012), így a pontos, precíz értékeléshez kapcsolódó szóródás, diverzitás és szubjektivitás elvész. Az alkalmazott statisztikai módszerek köre meglehetősen korlátozott, és az értékelés során releváns ismeretek veszhetnek el (Lubiano et al, 2016).…”

Section: További Módszertani Kérdések Kutatási Irányokunclassified

Szolgáltatásminőség keretrendszer kialakítása és fejlesztése – egy felsőoktatási tantárgy példája

Tóth¹,

Surman²

2020

Veztud

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A felsőoktatás fokozódó társadalmi-gazdasági szerepének és az intenzívebbé váló hazai és nemzetközi versenynek köszönhetően az intézmények menedzsmentje egyre inkább felismeri, hogy ahhoz, hogy az intézményi szolgáltatásokat igénybe vevők elvárásait a lehető legteljesebb mértékben ki tudják elégíteni, különböző menedzsmentmódszerek, köztük minőségmenedzsment-módszerek alkalmazása válik szükségessé. Jelen tanulmány célja a szolgáltatásminőség-mérési és -értékelési törekvések bemutatása olyan projektfeladat-kurzusok esetében, amelyek nem részei a rendszeres, szemeszter végi értékeléseknek -hagyományos kurzusoktól eltérő jellegzetességeiknek köszönhetően. A PDCA-filozófiát követve a szerzők kvantitatív és kvalitatív elemzéseinek célja, hogy a hallgatóktól és a konzulensektől származó visszajelzéseket felhasználva fejlesszék a konzultációs és az azt támogató folyamatokat, továbbá megfelelő inputokat szolgáltassanak az alkalmazott szolgáltatásminőség-mérési és -értékelési módszertan fejlesztéséhez is. Ehhez az elmúlt két év során gyűjtött tapasztalataikat foglalják össze, és vázolják a továbblépés lehetőségeit.Kulcsszavak: felsőoktatás, szolgáltatásminőség, kurzusértékelés, PDCA Due to the increasing social and economic role of higher education and to the intensifying national and international competition in the sector, institutional management boards gradually accept that the application of various management tools and methods including quality management ones is a necessity in order to fulfil the needs and expectations of those who 'consume' the services provided by higher education institutions. The primary aim of this paper is to introduce our service quality measuring and evaluating efforts in case of project work courses which are not part of the regular end-of-semester course evaluation system due to their highly special characteristics. Following the PDCA philosophy, the purpose of the conducted quantitative and qualitative analyses is to continuously improve not only the supervising and related supporting and administrative processes, but also to ensure inputs for the further improvement of the applied framework and methodology. These efforts are supported by the experiences and data gained during the implementation efforts of the last few semesters. Future improvement efforts and further research directions are also outlined.

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“…It is a discrete scale by choosing the most appropriate 'values' within a class according to the rater's judgement, opinion, valuation (Gil and González-Rodríguez, 2012) and lead to ordinal data from a set of pre-fixed categories. When Likert-type data are analysed for statistical purposes, the techniques to analyse them are quite limited (Lubiano et al, 2016).…”

Section: Research Questionsmentioning

confidence: 99%

“…Likert-type scales have several weaknesses and do not ease the task of the rater (Jamieson, 2004;Carifio and Perla, 2007). The number of 'values' to choose from is small (Gil and González-Rodríguez, 2012) which means that the variability, diversity and subjectivity associated with an accurate rating is usually lost. Another disadvantage originates from the fact that when values are encoded by their relative position in accordance with a certain ranking, differences between codes cannot be interpreted as differences in their magnitude.…”

Section: Research Questionsmentioning

confidence: 99%

“…Hesketh et al (1988) proposed the fuzzy rating scale without respondents being constrained to choose among a few pre-specified questions. This kind of scale has the ability to model the imprecision of human rating evaluations, formalize them mathematically, to 'precisiate' them in a continuous way, and to develop mathematical computation with them Calcagní and Lombardi, 2014;Gil and González-Rodríguez, 2012). This approach leads to a fuzzy-valued response format enabling a http: //dx.doi.org/10.15405/epsbs.2017.10.61 Corresponding Author: Zsuzsanna Eszter Tóth Selection and peer-review under …”

Section: Research Questionsmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing The Reliability Of Measurements And Evaluations Based On Service Quality Models

Tóth¹,

Árva²,

Surman³

2017

European Proceedings of Social and Behavioural Sciences

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Problem statement: The application of different service quality measurements and evaluations in higher educational context and the utilization and reliability of results are examined.Research questions: What are the difficulties in the measurement and evaluation of specific service quality dimensions? How can the reliability of such service quality measurements be enhanced?Purpose of the study: This paper introduces the application and the results of a student satisfaction questionnaire based on fuzzy Likert scale used for evaluating lecturers' performance at the Budapest University of Technology and Economics.Research methods: In order to capture and accurately measure the diversity, subjectivity and imprecision inherent to students' evaluations, a methodology based on fuzzy numbers having sigmoid membership functions has been proposed. By applying the principles of Dombi's Pliant Arithmetics, the evaluations can be aggregated and statistically analysed in a convenient way.Findings: Fuzzy-numbers based questionnaires result in a more precise reflection of human thinking and judgement. The Pliant Arithmetic-based approach allows us to aggregate the parameters of the left and right hand sides of the fuzzy number separately. This property results in a much simpler statistical analysis of the gathered data than the methods previously proposed in the literature.Conclusions: The proposed methodology can be utilized in two ways. It can either be applied to identify lecturers' strengths and weaknesses in order to develop their teaching skills or to compare and analyse various students' evaluations. The results can serve as a base for establishing teaching regulations and discovering best practices as well.

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Fuzzy vs. Likert Scale in Statistics

Cited by 38 publications

References 17 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

Szolgáltatásminőség keretrendszer kialakítása és fejlesztése – egy felsőoktatási tantárgy példája

Enhancing The Reliability Of Measurements And Evaluations Based On Service Quality Models

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