A dimensional versus attribute approach for disaggregate choice models
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A fundamental issue facing choice modelers is to make a decision on what kind of independent variables to include in a choice model. With survey data, the two immediate options are: actual product attributes or underlying latent dimensions (factor scores). Using behavioral logic we argue that heterogeneity of consumer perceptions of variables and their saliences should be the key items moderating such a decision. We present empirical evidence to support our theory that dimensional (factor score) based models do better in terms of predictions than attribute based models in more heterogeneous populations. Empirical analysis shows that in segments (where consumer heterogeneity is lower) the predictive performance of attribute based models improves relative to the factor score model and may actually have a better predictive fit when the respondents are relatively homogeneous with respect to attribute ratings and saliences.
Key wordsActual product attributes underlying latent dimensions consumer perceptions
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