© Grzegorz Kończak, Martyna Kosińska Artykuł udostępniony na licencji CC BY-SA 4.0
Multiple categories of qualitative variables are often identified in economic and social research. In these cases, contingency tables (two- or multidimensional) are frequently used to report the results of a study. Statistical inference based on data provided in contingency tables involves in most instances the use of the chi-square test of independence or the chi-square test of homogeneity. The form of the statistics in both tests is the same, but the method by which the data are obtained remains different. A sample of size n is drawn from a single population with two classification variables in the first case, and in the second samples from an r population of n1, n2,…, nr sizes are drawn independently, considering a single classifier variable only. It is much less common to make comparisons of structures for more than two qualitative variables simultaneously. The aim of the research described in this article is to present the usefulness of permutation methods in testing the presence of significant differences in the structures of the levels of participation in cultural events based on data contained in multidimensional contingency tables. The application of the permutation test was illustrated through the results of the authors’ own research examining the cultural participation of active users of Internet portals immediately before and during the COVID-19 pandemic. Participation in cinema screenings by gender, age and work status was analysed. The research results confirmed that the pandemic had a significant impact on participation in cinema screenings.
statistical inference, multidimensional contingency tables, permutation tests
C12, C15, C18
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