Abstract
Structural Equation Modelling methods traditionally assume the homogeneity of all the units on which a model is estimated. In many cases, however, this assumption may turn to be false; the presence of latent classes not accounted for by the global model may lead to biased or erroneous results in terms of model parameters and model quality. The traditional multi-group approach to classification is often unsatisfying for several reasons; above all because it leads to classes homogeneous only with respect to external criteria and not to the theoretical model itself.
In this paper, a prediction-oriented classification method in PLS Path Modelling is proposed. Following PLS Typological Regression, the proposed methodology aims at identifying classes of units showing the lowest distance from the models in the space of the dependent variables, according to PLS predictive oriented logic. Hence, the obtained groups are homogeneous with respect to the defined path model. An application to real data in the study of customers’ satisfaction and loyalty will be shown.
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Squillacciotti, S. (2010). Prediction Oriented Classification in PLS Path Modeling. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_10
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DOI: https://doi.org/10.1007/978-3-540-32827-8_10
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