Assessment of Participation in Cultural Activities in Poland by Selected Multivariate Methods
DOI:
https://doi.org/10.26417/ejser.v6i1.p18-26Keywords:
participation in cultural activities, clustering methods, categorical data.Abstract
This paper presents analyses of participation in cultural activities by Poles. The analyses are carried out on the base of metric and non-metric data retrieved from the Eurobarometer survey. The study includes two main aspects: the comparison of the involvement in Poland with the situation in other European Union countries and the detection of similarities among various form of the engagement. The socio-economic background of the respondents is also taken into account, namely age, gender, place of residence and level in the society. Chosen multivariate methods are applied to identify regularities in the participation. The results of the analyses are presented graphically to facilitate the interpretation. A two-step procedure is used for a better understanding of the participation schemes. The first step includes the partition of qualitative variables into relatively homogenous groups leading to the reduction of the multidimensionality. The second step is focused on evaluating the participation with respect to the variables forming the identified clusters.References
Afifi, A., May, S., Clark, V. A. (2003). Computer-aided multivariate analysis. CRC Press.
Aggarwal, C. C., Reddy, C. K. (Eds.). (2014). Data clustering: algorithms and applications. CRC Press.
Anderberg, M. R. (1973). Cluster Analysis for Applications. Academic Press, Inc., New York.
Bartholomew, D. J., Steele, F., Galbraith, J., Moustaki, I. (2008). Analysis of multivariate social science data. CRC Press.
Chavent, M., Kuentz, V., Liquet, B., Saracco, L. (2013). Clustofvar: An R package for the clustering of variables. Journal of Statistical Software, 50(13), 1-16.
Chen, C. H., Härdle, W. K., Unwin, A. (2007). Handbook of data visualization. Springer Science & Business Media.
Czapi?ski J., Panek T. (Eds.). (2015). Diagnoza spo?eczna 2015. Warunki i jako?? ?ycia Polaków. Rada Monitoringu Spo?ecznego, Warszawa, www.diagnoza.com (retrieved 2015-12-29).
Dhillon, I. S., Marcotte, E. M., & Roshan, U. (2003). Diametrical clustering for identifying anti-correlated gene clusters. Bioinformatics, 19(13), 1612-1619.
European Commission. (2013). Cultural access and participation. Report. Special Eurobarometer 399 / Wave EB79.2 – TNS Opinion & Social, http://ec.europa.eu/public_opinion/archives/ebs/ebs_399_en.pdf (retrieved 2015-12-29).
European Commission. (2007). European Cultural Values. Special Eurobarometer 278 / Wave 67. – TNS Opinion & Social, http://ec.europa.eu/COMMFrontOffice/PublicOpinion/index.cfm/ResultDoc/download/DocumentKy/54572 (retrieved 2015-12-29).
European Union (2012) A report on policies and good practices in the public arts and in cultural institutions to promote better access to and wider participation in culture. Open Method Of Coordination (OMC) Working Group Of EU Member States’ Experts On Better Access To And Wider Participation In Culture http://ec.europa.eu/culture/policy/strategic-framework/documents/omc-report-access-to-culture_en.pdf (retrieved 2015-12-29).
Everitt, B. S., Landau, S., Leese, M., Stahl D. (2011). Cluster Analysis. Wiley.
GUS. (2012). Uczestnictwo ludno?ci w kulturze w 2009 r. Warszawa.
Hubert, L., Arabie, P. (1985). Comparing partitions. Journal of classification, 2(1), 193-218.
Kolde, R. (2015). pheatmap: pretty heatmaps. R package version 1.0.8.
Pryke, A., Mostaghim, S., Nazemi, A. (2007). Heatmap visualization of population based multi objective algorithms. In Evolutionary multi-criterion optimization pp. 361-375. Springer Berlin Heidelberg.
Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journal of the American Statistical association, 66(336), 846-850.
Rencher, A. C. (2003). Methods of multivariate analysis. John Wiley & Sons.
Romesburg, C. (2004). Cluster analysis for researchers. Lulu Press.
TNS Opinion. (2013). Eurobarometer 79.2. Basic bilingual questionnaire,
https://dbk.gesis.org/dbksearch/download.asp?db=E&id=52187 (retrieved 2015-12-29).
Vigneau, E., Qannari, E. M. (2003). Clustering of variables around latent components. Communications in Statistics-Simulation and Computation, 32(4), 1131-1150.
Wilkinson, L., Friendly, M. (2009). The history of the cluster heat map. The American Statistician, 63(2), 179-184.
Downloads
Published
Issue
Section
License
Copyright (c) 2021 European Journal of Social Science Education and Research
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
