In Walesiak [1993], pp. 44-45 the distance measure was proposed, which can be used for the ordinal data. In the paper the proposal of the general distance measure is given. This measure can be used for data measured in ratio, interval and ordinal scale. The proposal is based on the idea of the generalised correlation coefficient.
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Kamila Migdał-Najman, Krzysztof Najman: Hierarchiczne deglomeracyjne sieci SOM w analizie skupień / The hierarchical divisive SOM in the cluster analysis .
Streszczenie: Metody porządkowania liniowego są stosowane na gruncie ekonomii w badaniach rankingowych i klasyfikacyjnych dotyczących obiektów i zjawisk opisanych zmiennymi o różnych rozkładach i charakterystykach statystycznych. W literaturze przedmiotu z zakresu wielowymiarowej analizy porównawczej opracowano wiele procedur porządkowania liniowego. Różnią się one m.in. metodami wyznaczania wag zmiennych, metodami normalizacji zmiennych oraz metodami szacowania wartości zmiennych syntetycznych. W związku z tym pojawia się problem wyboru optymalnej procedury porządkowania liniowego do analizy danych statystycznych. Celem pracy jest prezentacja wyników badań dotyczących oceny jakości wybranych procedur porządkowania liniowego w odniesieniu do danych o rozkładzie normalnym i różnym od rozkładu normalnego. Do oceny jakości rankingów wykorzystane zostały wybrane mierniki jakości metod porządkowania liniowego.Słowa kluczowe: porządkowanie liniowe, analiza porównawcza, program R.
Summary:Linear ordering methods are used in economic studies to determine the order or classification of objects described using variables with different distributions and statistical characteristics. In the literature on the subject in the field of multidimensional comparative analysis, many linear ordering procedures have been developed. They differ, among others using the methods for determining variable weights, normalization methods, and methods for estimating the values of synthetic variables. Therefore, there is a problem of choosing the optimal procedure for the analysis of statistical data with specified statistical characteristics. The purpose of this article is to present the results of research concerning the quality evaluation of selected linear ordering procedures in relation to data about normal distribution and different from normal distribution. To assess the quality of rankings, selected measures of the quality of linear ordering methods were used.
(1) Background: Uber Technologies are currently changing the pattern of urban transport. Statista reports that in the period 2017–2019 alone, the average monthly number of active Uber users worldwide increased by 126.5%, and the average monthly number of Uber trips grew by 115%. The purpose of this article is to identify the most important motives encouraging both current and potential customers to use Uber “taxi” services. Particular attention was paid to the factor of perceiving these services as a more sustainable way of meeting transport needs. Uber creates its image specifically on the idea of sustainability. (2) Methods: The operationalization of the sustainability concept was based on three dimensions: ecological, social and economic. The CAWI (Computer-Assisted Web Interview) technique was used to collect the research data. The representative research sample covered 1003 Poles. A logistic regression model was used to analyze empirical data collected based on the survey. The data analysis used R program and the selected packages for this program. (3) Results: Among the most important motives, sustainability is the most frequently indicated. (4) Conclusions: The choices of Uber services are significantly influenced by the reasons related to two sustainability pillars—one social and one economic. The factors significantly influencing consumer decision-making processes related to the use of shared mobility services belong to the following groups of motives: sustainable development, knowledge of information and communication technologies (ICT), innovation, user convenience and savings. The findings from the study can become the basis for organizations and local authorities to undertake appropriate marketing activities to promote shared-mobility services (SMS) and support sustainable and environmentally friendly development.
Two groups of research methods are used in the decompositional approach to stated preferences-conjoint analysis methods and discrete choice methods. The most commonly applied traditional conjoint analysis method is an example of the first group. Because of its computational complexity, its practical application requires using appropriate commercial or non-commercial computer software. The purpose of the article is to present the traditional conjoint analysis method and discuss its implementation in the form of the conjoint package for R program, which with CRAN packages is currently one of the most important non-commercial computing environments for statistical data analysis. In addition to the detailed characteristics of the individual conjoint R package functions, the paper also presents the application of the conjoint package in marketing research, along with the interpretation of the selected results, based on the example of measuring and analysing stated preferences of beer consumers.
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