Abstract
Discovering the preferences and the behaviour of consumers is a key challenge in marketing. Information about such topics can be gathered through surveys in which the respondents must assign a score to a number of items. A strategy based on different latent class models can be used to analyze such data and achieve this objective: it consists in identifying groups of consumers whose response patterns are similar and characterizing them in terms of preferences and covariates. The basic latent class model can be extended by including covariates to model differences in (1) latent class probabilities and (2) conditional probabilities. A strategy for fitting and choosing a suitable model among them is proposed taking into account identifiability issues, the identification of potential covariates and the checking of goodness-of-fit. The tools to perform this analysis are implemented in the R package covLCA available from CRAN. We illustrate and explain the application of this strategy using data about the preferences of Belgian households for supermarkets.
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Notes
The category Bachelor is an approximate translation of the Belgian education level graduat, while we use Master to denote the Belgian education levels licence and maitrise.
p-value of the Pearson’s chi-squared test: \(<\!2.2\,\times \,10^{-16}\) for Age, Education and Profession, 0.04 for Gender.
In class GB-Carrefour: p value of the likelihood ratio test is \(<\)0.01.
p-value in latent class Lidl-Aldi: 0.03 ; p value in class Delhaize: 0.05.
p value of the Pearson’s chi-squared test: \(<\)2.2\(\,\times \,10^{-16}\).
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Acknowledgments
We would like to thank Eric Lecoutre for helpful discussions, and Business and Decision, Brussels, for providing the data. We also would like to thank the editor and two anonymous referees for helpful comments.
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Bertrand, A.M.E., Hafner, C.M. On heterogeneous latent class models with applications to the analysis of rating scores. Comput Stat 29, 307–330 (2014). https://doi.org/10.1007/s00180-013-0450-5
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DOI: https://doi.org/10.1007/s00180-013-0450-5