Summary
Various probabilistic ideal point and vector models have been proposed for the analysis of paired comparisons data. In order to show whether older sequential approaches (where an a priori clustering of respondents is used) are outperformed by newer simultaneous approaches (where clustering and choice model parameters are estimated simultaneously), a framework for empirical comparisons is developed. A formulation is presented, which includes sequential and simultaneous approaches as special cases. An application to the analysis of preference judgments related to print ads for beer brands shows advantages of the simultaneous approaches.
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Baier, D., Gaul, W. (1996). Analyzing Paired Comparisons Data Using Probabilistic Ideal Point Models and Probabilistic Vector Models. In: Bock, HH., Polasek, W. (eds) Data Analysis and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80098-6_14
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DOI: https://doi.org/10.1007/978-3-642-80098-6_14
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