Use of latent class regression models with a random intercept to remove the effects of the overall response rating level

Jay Magidson () and Jeroen K. Vermunt ()
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Jay Magidson: Statistical Innovations Inc.
Jeroen K. Vermunt: Tilburg niversity, Department of Methodology and Statistics

A chapter in Compstat 2006 - Proceedings in Computational Statistics, 2006, pp 351-360 from Springer

Abstract: Abstract Latent class regression models may be used to identify segments that differ with respect to the contribution of product attributes on their ratings of the associated products. However, such solutions tend be dominated by the overall liking (or the respondents’ response tendency) rather than differences in the liking of the presented products. In this paper, we show how to overcome this problem by including a continuous factor (CFactor) in the model to function as a random intercept. As such, it provides an elegant model-based alternative and general extension of the common practice of within-case ‘centering’ of the data. An application involving cracker products is used to illustrate the approach which results in segments that show clear differences in their sensory preferences.

Keywords: Latent class; random effects regression; data centering; random intercept; segmentation; response level effect (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1709-6_27

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DOI: 10.1007/978-3-7908-1709-6_27

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