A Two-Step Approach to Analyze Satisfaction Data

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.

[1]  Harvey Goldstein,et al.  League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance , 1996 .

[2]  Michel Tenenhaus,et al.  PLS path modeling , 2005, Comput. Stat. Data Anal..

[3]  Silvia Salini,et al.  European Consumers’ Attitudes on Services of General Interest: Accessibility, Price and Quality , 2007 .

[4]  I. Jolliffe,et al.  Nonlinear Multivariate Analysis , 1992 .

[5]  J. Leeuw,et al.  The Gifi system of descriptive multivariate analysis , 1998 .

[6]  B. Muthén.  Latent Variable Mixture Modeling , 2001 .

[7]  H. Goldstein.  Multilevel Statistical Models , 2006 .

[8]  F. Padovano,et al.  Servizi Pubblici. Nuove Tendenze nella Regolamentazione, nella Produzione e nel Finanziamento , 2007 .

[9]  Carla Rampichini,et al.  Alternative Specifications of Multivariate Multilevel Probit Ordinal Response Models , 2003 .

[10]  Alessandra Petrucci,et al.  Analysis of university course evaluations: from descriptive measures to multilevel models , 2004 .

[11]  G. A. Marcoulides,et al.  New developments and techniques in structural equation modeling , 2001 .

[12]  A. Cameron,et al.  Microeconometrics: Methods and Applications , 2005 .

[13]  Sreenivasan Ravi,et al.  Multilevel Statistical Models, 3rd edn , 2005 .

[14]  M. Greenacre,et al.  Correspondence analysis in the Social Sciences : recent developments and applications , 1994 .

[15]  M. Hill,et al.  Nonlinear Multivariate Analysis. , 1990 .

[16]  Carla Rampichini,et al.  Multilevel Factor Models for Ordinal Variables , 2007 .

[17]  Roel Bosker,et al.  Multilevel analysis : an introduction to basic and advanced multilevel modeling , 1999 .

[18]  HanafiMohamed.  PLS Path modelling , 2007 .

[19]  Paul Conway,et al.  Product Market Regulation in the Non-Manufacturing Sectors of OECD Countries: Measurement and Highlights , 2006 .

[20]  Paola Annoni,et al.  Evaluation and comparison of European countries: public opinion on services , 2007 .