Modeling random and non-random decision uncertainty in ratings data: A fuzzy beta model

Modeling human ratings data subject to raters' decision uncertainty is an attractive problem in applied statistics. In view of the complex interplay between emotion and decision making in rating processes, final raters' choices seldom reflect the true underlying raters' responses. Rather, they are imprecisely observed in the sense that they are subject to a non-random component of uncertainty, namely the decision uncertainty. The purpose of this article is to illustrate a statistical approach to analyse ratings data which integrates both random and non-random components of the rating process. In particular, beta fuzzy numbers are used to model raters' non-random decision uncertainty and a variable dispersion beta linear model is instead adopted to model the random counterpart of rating responses. The main idea is to quantify characteristics of latent and non-fuzzy rating responses by means of random observations subject to fuzziness. To do so, a fuzzy version of the Expectation-Maximization algorithm is adopted to both estimate model's parameters and compute their standard errors. Finally, the characteristics of the proposed fuzzy beta model are investigated by means of a simulation study as well as two case studies from behavioral and social contexts.Comment: 24 pages, 0 figures, 5 table

A psychometric modeling approach to fuzzy rating data

Modeling fuzziness and imprecision in human rating data is a crucial problem in many research areas, including applied statistics, behavioral, social, and health sciences. Because of the interplay between cognitive, affective, and contextual factors, the process of answering survey questions is a complex task, which can barely be captured by standard (crisp) rating responses. Fuzzy rating scales have progressively been adopted to overcome some of the limitations of standard rating scales, including their inability to disentangle decision uncertainty from individual responses. The aim of this article is to provide a novel fuzzy scaling procedure which uses Item Response Theory trees (IRTrees) as a psychometric model for the stage-wise latent response process. In so doing, fuzziness of rating data is modeled using the overall rater's pattern of responses instead of being computed using a single-item based approach. This offers a consistent system for interpreting fuzziness in terms of individual-based decision uncertainty. A simulation study and two empirical applications are adopted to assess the characteristics of the proposed model and provide converging results about its effectiveness in modeling fuzziness and imprecision in rating data

Szolgáltatásminőség keretrendszer kialakítása és fejlesztése – egy felsőoktatási tantárgy példája = Establishing and improving a service quality framework through – The example of a higher education course

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. ------ 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