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Analysis of nominal data – multi-way contingency table

100%
Stanimir A.
Mathematical Economics
|
2011
|
issue 7 (14)
229-240
EN
Presented in this paper the method of graphical presentation of the relationship between nominal variables and their categories gives the opportunity for an extensive diagnosis of dependence variables. Correspondence analysis and mosaic plots are based on the same grounds, i.e. contingency table or multi-way contingency table. Correspondence analysis can be used in the study of relationships between two or more nominal variables without limiting the number of categories. In the case of many variables, the multidimensional contingency table is used very often. Only the difficulty of construction of such a table and the combined variables can affect the decision of a researcher about the validity of using this solution. For mosaic plots the situation is different. These graphs represent very well the relationships between two categories of nominal variables with few categories. The introduction of another variable to the study, which is described by two or three categories, is also not too problematic, and the graph is easy to interpret. However, if in a multi-way contingency table variables are a combination of several primary variables, described with many categories, the mosaic plot is no longer as clear as the projection made in correspondence analysis.
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Visualisation of nominal data – practical and theoretical remarks

75%
Mącik R.
Econometrics. Ekonometria. Advances in Applied Data Analytics
|
2016
|
issue 2 (52)
22-34
EN
Nominal data, due to their nature, are often analysed statistically in a quite limited and traditional way. Usually they come from open-ended or simple/multiple choice questions. In typical research projects, such data are often presented in the form of more or less complex tables (including contingency tables) and standard charts. The author’s experience shows that such a visualisation is perceived as boring, especially by younger people, accustomed to the presentation of content in the form of infographics. The article presents examples of data analysis and a visualisation of the nominal data based on the results of the author’s research, including theoretical reflections on the techniques and tools used. The starting point is the raw text data from the responses to the open-ended questions subjected to analyses of the frequency of words and expressions, including its visualisation through word clouds. The next step is categorization and tabulation at the level of individual variables including the visualisation of categories, to assess the contingency between two nominal variables (or the nominal and the ordinal one), including visualising the relationships via chord diagrams and the correspondence analysis.
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