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From multiple choice questionnaires to synthetic indicators: jointly use of Rasch analysis and NonLinear PCA


  • Paola Annoni

    (Dep. of Economics, Business and Statistics - University of Milan)

  • Pieralda Ferrari

    (Dep. of Economics, Business and Statistics - University of Milan)


The jointly use of two latent factors methods is proposed to assess a measurement instrument for underlying phenomena. In particular the decay status of valuable Italian buildings is quantified on the basis of a wide set of observed categorical variables. At the purpose, Rasch analysis is used to properly calibrate the data, to discard non informative variables and redundant categories. As a second step, an optimal scaling technique, Nonlinear PCA, is applied to quantify variable categories and to compute a quantitative decay indicator, which could be independently used for units of future inventory. On the overall, similarities and different capabilities of the two techniques are analyzed and discussed.

Suggested Citation

  • Paola Annoni & Pieralda Ferrari, 2006. "From multiple choice questionnaires to synthetic indicators: jointly use of Rasch analysis and NonLinear PCA," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1038, Universit√° degli Studi di Milano.
  • Handle: RePEc:bep:unimip:unimi-1038 Note: oai:cdlib1:unimi-1038

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    References listed on IDEAS

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    6. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    7. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
    8. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
    9. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November.
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