In this paper an integrated use of NLPCA (Nonlinear PCA) and multilevel models for the analysis of satisfaction data is proposed. The basic hypothesis is that observed ordinal variables describe different aspects of a latent unobservable continuous variable that depends on covariates connected with individual and contextual features. NLPCA is used to measure the level of a latent variable and multilevel model is adopted for detecting individual and enviromental determinants of its level. An application to Eurobarometer survey data concerning the satisfaction of European citizens for some Services of General Interest, is carried out.
A two-step procedure to analize users' satisfaction
PAGANI, Laura
2007-01-01
Abstract
In this paper an integrated use of NLPCA (Nonlinear PCA) and multilevel models for the analysis of satisfaction data is proposed. The basic hypothesis is that observed ordinal variables describe different aspects of a latent unobservable continuous variable that depends on covariates connected with individual and contextual features. NLPCA is used to measure the level of a latent variable and multilevel model is adopted for detecting individual and enviromental determinants of its level. An application to Eurobarometer survey data concerning the satisfaction of European citizens for some Services of General Interest, is carried out.File in questo prodotto:
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