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On heterogeneous latent class models with applications to the analysis of rating scores

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  • Bertrand, Aurelie
  • Hafner, Christian

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. In this article we suggest a strategy 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. We use latent class models allowing for heterogeneity of both latent class and within-class probabilities across individuals. We illustrate the proposed methodology using data about the preferences of Belgian households for supermarkets.
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Suggested Citation

  • Bertrand, Aurelie & Hafner, Christian, 2011. "On heterogeneous latent class models with applications to the analysis of rating scores," LIDAM Discussion Papers ISBA 2011028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvad:2011028
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    References listed on IDEAS

    as
    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.
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    5. Paul S. Albert, 2007. "Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard," Biometrics, The International Biometric Society, vol. 63(2), pages 593-602, June.
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    7. van Herk, H. & Poortinga, Y.H. & Verhallen, T.M.M., 2004. "Response styles in rating scales : Evidence of method bias in data from 6 EU countries," Other publications TiSEM c8befc7a-f2f4-44cf-b2fc-b, Tilburg University, School of Economics and Management.
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    Cited by:

    1. Sunil Kumar & Zakir Husain & Diganta Mukherjee, 2015. "Assessing Consistency of Consumer Confidence Data using Dynamic Latent Class Analysis," Papers 1509.01215, arXiv.org.
    2. Bart Neuts & João Romão & Peter Nijkamp & Asami Shikida, 2016. "Market segmentation and their potential economic impacts in an ecotourism destination," Tourism Economics, , vol. 22(4), pages 793-808, August.
    3. Kumar, Sunil & Husain, Zakir & Mukherjee, Diganta, 2017. "Assessing consistency of consumer confidence data using latent class analysis with time factor," Economic Analysis and Policy, Elsevier, vol. 55(C), pages 35-46.

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    More about this item

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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