A latent class analysis of the public attitude towards the euro adoption in Poland

Ewa Genge ()

Advances in Data Analysis and Classification, 2014, vol. 8, issue 4, 427-442

Abstract: Latent class analysis can be viewed as a special case of model–based clustering for multivariate discrete data. It is assumed that each observation comes from one of a number of classes, groups or subpopulations, with its own probability distribution. The overall population thus follows a finite mixture model. When observed, data take the form of categorical responses—as, for example, in public opinion or consumer behavior surveys it is often of interest to identify and characterize clusters of similar objects. In the context of marketing research, one will typically interpret the latent number of mixture components as clusters or segments. In fact, LC analysis provides a powerful new tool to identify important market segments in target marketing. We used the model based clustering approach for grouping and detecting inhomogeneities of Polish opinions on the euro adoption. We analyzed data collected as part of the Polish General Social Survey using the R software. Copyright The Author(s) 2014

Keywords: Latent class analysis; Mixture model; Categorical data; Euro adoption; 62P20; 6207; 91B82 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11634-013-0156-0

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