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Missing data in optimal scaling

Author

Listed:
  • Pieralda FERRARI
  • Paola ANNONI

Abstract

We propose a procedure to assess a measure for a latent phenomenon, starting from the observation of a wide set of ordinal variables affected by structured missing data. The proposal is based on Nonlinear PCA technique to be jointly used with an ad hoc imputation method for the treatment of missing data. The procedure is particularly suitable when dealing with ordinal, or mixed, variables, which are strongly interrelated and in the presence of specific patterns of missing observations

Suggested Citation

  • Pieralda FERRARI & Paola ANNONI, 2005. "Missing data in optimal scaling," Departmental Working Papers 2005-019, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    • Handle: RePEc:mil:wpdepa:2005-019
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