Applying a fuzzy questionnaire in a peer review process

Abstract

This paper introduces the application and the results of a fuzzy number based questionnaire used for evaluating lecturers' performance in a newly launched peer review programme in order to enhance the reliability of peer evaluations. In our approach, the membership function of the fuzzy number is composed of an increasing and a decreasing sigmoid membership function conjuncted by Dombi's intersection operator. Compared to the traditional Likert scale-based evaluation, the proposed fuzzy number based methodology allows peer reviewers to express their uncertainty and the variability of the reviewed lecturer's performance within a single lecture and during a semester in a quantitative way. By utilising the principles of Dombi's Pliant Arithmetic, fuzzy Likert scale based evaluations given by peer reviewers can be cumulated by aggregating the parameters of the left-and right-hand sides of the fuzzy numbers separately. This property results in a much simpler statistical analysis of the gathered data than the methods previously proposed in the literature. A demonstrative case study illustrates our main contributions in peer evaluation.

A psychometric modeling approach to fuzzy rating data

“…Similarly, [55] developed a fuzzy system to measure xenophobia through pollster method and frequency-based fuzzy set assignment. Still, [56,57] and [58] proposed to generate fuzzy categories via Dombi-intersection of sigmoid-shaped functions based on the most likely, worst and best values assigned by raters. In a similar way, [59] derived fuzzy numbers using histograms of Likert-type responses and ideal histograms-based distances for modeling response-bias.…”

Conditions Influencing Salary of the Automotive Industry in Mexico City—A Linguistic Fuzzy-Set Approach

“…Furthermore, when it comes to statistical analysis of Likert-type data, the methodologies available are relatively limited. The fuzzy approach can simulate the imprecision of human rating evaluations, formalize them mathematically, continuously "precisiate" them, and construct mathematical computations with them (see, for example [40][41][42]).…”

Analisis Tingkat Kualitas Pelayanan Perizinan Menggunakan Metode Fuzzy Servqual

“…Bagaimana kita dapat melihat dengan andal apakah harapan pelanggan dan pemangku kepentingan lainnya, terutama pelaku usaha terpenuhi? Untuk menjawab pertanyaan ini, ada kebutuhan untuk merancang metodologi yang sesuai dan dapat diandalkan untuk mengevaluasi kinerja dan untuk mengidentifikasi unit pengukuran yang tepat untuk menyoroti kualitas kinerja layanan yang dicapai [3].…”

“…Dimana Σ merupakan Skor harapan per atribut pelayanan, Σ ℎ merupakan jumlah hasil harapan tidak puas, Σ ℎ merupakan jumlah hasil harapan kurang puas, Σ ℎ merupakan jumlah hasil harapan cukup puas, Σ merupakan jumlah hasil harapan puas, Σ merupakan jumlah hasil harapan sangat puas, dan koefisien ini artinya nilai skala tiap-tiap harapan. Kemudian, melakukan perhitungan skor average persepsi (Þ ) dan skor average harapan (Ê) dalam Persamaan (3).…”