A Two-Step Approach to Analyze Satisfaction Data

Abstract

In this paper a two-step procedure based on Nonlinear Principal Component Analysis (NLPCA) and Multilevel models (MLM) for the analysis of satisfaction data is proposed. The basic hypothesis is that observed ordinal variables describe different aspects of a latent continuous variable, which depends on covariates connected with individual and contextual features. NLPCA is used to measure the level of a latent variable and MLM is adopted for detecting individual and environmental determinants of the level. This approach is suggested to analyze users’ satisfaction. In fact, NLPCA is used to create a synthetic continuous measure of satisfaction (first step) and MLM are used to detect the role of external (individual or environmental) variables that can affect the level itself (second step). The proposed two-step procedure is applied to the Eurobarometer survey data about opinion of European citizens on services of general interest (SGI) aiming to evaluate and compare the opinion about SGI in different countries. The focus is on overall level of satisfaction about four major public services: fixed telephone, electricity supply, postal and rail services. The item analyzed, which are named manifest variables, are: access easiness, price, quality, information clarity and contract fairness, as reported in the 2002 Eurobarometer survey. In the first step these variables are used to set up the synthetic indicator (the overall level) of satisfaction and, in the second step, a MLM is used to test the impact of some explanatory variables on this satisfaction.