References
- Biffignandi, S. and Pratesi, M. (2003). Potentiality of propensity score matching in inference from web-surveys: a simulation study. Working Paper, Dipartimento di Statistica, Informatica a Applicazioni.
- Biffignandi, S., Pratesi, M. and Toninelli, D. (2003). Potentiality of propensity scores methods in weighting for Web surveys: a simulation study based on a statistical register. Proceedings of the ISI Conference, Berlin.
- Couper, M. P. (2000). Web survey: a review of issues and approaches. Public Opinion Quarterly, 64, 464–494.
- De Battisti F., Nicolini, G. and Salini, S. (2005). The rasch model to measure service quality. The ICFAI Journal of Services Marketing, 3, 58–80.
- De Battisti F., Nicolini, G. and Salini, S. (2010). The rasch model in customer satisfaction survey data. Quality Technology and Quantitative Management, 7, 15–34.
- Fay, R. E. and Herriot, R. A. (1979). Estimates of income for small places: an application of James-Stein procedures to census data. Journal of the American Statistical Association, 74, 269–277.
- Gamerman, D. and Lopes, H. F. (2006). Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference. Chapman & Hall.
- Gifi, A. (1990). Nonlinear Multivariate Analysis. John Wiley and Sons, New York.
- Groves, R. M. (1989). Survey Errors and Survey Costs. John Wiley and Sons, New York.
- Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.
- Hothum, C. and Spintig, S. (1998). Customer Satisfaction Research, in the Hesomar Handbook of Market and Opinion Research, 4th edition. (Edited by McDonald and Vangelder), Esomar, 853–890.
- Johnson, N. and Kotz, S. (1972). Distribution in Statistics: Continuous Multivariate Distributions. John Wiley and Sons, New York.
- Lee, S. (2006). Propensity score adjustment as a weighting scheme for volunteer panel web surveys. Journal of Official Statistics, 22, 329–349.
- Lessler, J. T. and Kalsbeek, W. D. (1992). Nonsampling Errors in Surveys. John Wiley and Sons, New York.
- Michailidis, G. and De Leeuw, J. (1998). The gifi system of descriptive multivariate analysis. Statistical Science, 13, 307–336.
- Nicolini, G. and De Battisti, F. (2008). Methods for Summarizing the Rasch Model Coefficients, in Metodi, Modelli e Tecnologie dell’Informazione a Supporto delle Decisioni. (Edited by D’Ambra, Rostirolla and Squillante), 284–290.
- Rao, J. N. K. (2003). Small Area Estimation. John Wiley and Sons, New York.
- Rosenbaum, P. and Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.
- Rossi, P. E., Allenby, G. M. and McCulloch, R. (2005). Bayesian Statistics and Marketing. John Wiley and Sons, New York.
- Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.
- Smits, J. (2003). Estimating the Heckman two-step procedure to control for selection bias with SPSS. Available at http://home.planet.nl/~smit9354/selbias/Heckman-SPSS.doc.
- Trevisani, M. and Torelli, N. (2006). Comparing Hierarchical Bayesian Models for Small Area Estimation. In Metodi statistici per l’integrazione di basi di dati da fonti diverse, (Edited by Franco Angeli), 17–36.