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

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  • Ewa Genge

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

Suggested Citation

  • Ewa Genge, 2014. "A latent class analysis of the public attitude towards the euro adoption in Poland," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 427-442, December.
    • Handle: RePEc:spr:advdac:v:8:y:2014:i:4:p:427-442
    DOI: 10.1007/s11634-013-0156-0
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    References listed on IDEAS

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    1. Vermunt, Jeroen K., 2010. "Latent Class Modeling with Covariates: Two Improved Three-Step Approaches," Political Analysis, Cambridge University Press, vol. 18(4), pages 450-469.
    2. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    3. Grün, Bettina & Leisch, Friedrich, 2008. "FlexMix Version 2: Finite Mixtures with Concomitant Variables and Varying and Constant Parameters," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i04).
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    1. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 209-224, June.
    2. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
    3. Hui Zhang & Huguang Rao & Junzheng Feng, 2018. "Product innovation based on online review data mining: a case study of Huawei phones," Electronic Commerce Research, Springer, vol. 18(1), pages 3-22, March.
    4. A. Felipe & N. Martín & P. Miranda & L. Pardo, 2018. "Statistical inference in constrained latent class models for multinomial data based on $$\phi $$ ϕ -divergence measures," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 605-636, September.

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