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An alternative procedure for imputing missing data based on principal components analysis

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  • Giovanni Di Franco

Abstract

This work entailed tackling the significant problem of missing data which was solved by identifying a new substitution procedure, following an empirical approach based on the analysis of the information contained in the entire set of data collected. This procedures offers a number of advantages compared to other techniques commonly mentioned in the statistical–methodological literature. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Giovanni Di Franco, 2014. "An alternative procedure for imputing missing data based on principal components analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1149-1163, May.
  • Handle: RePEc:spr:qualqt:v:48:y:2014:i:3:p:1149-1163
    DOI: 10.1007/s11135-013-9826-4
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    References listed on IDEAS

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    1. Michael G. Akritas & Jouni Kuha & D. Wayne Osgood, 2002. "A Nonparametric Approach to Matched Pairs with Missing Data," Sociological Methods & Research, , vol. 30(3), pages 425-454, February.
    2. Mark Huisman & Boudien Krol & Eric Van Sonderen, 1998. "Handling Missing Data by Re-approaching Non-respondents," Quality & Quantity: International Journal of Methodology, Springer, vol. 32(1), pages 77-91, February.
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    Cited by:

    1. Antonio Zinilli, 2021. "Imputation methods for estimating public R&D funding: evidence from longitudinal data," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 707-729, April.
    2. Merton S. Krause, 2019. "Randomness is problematic for social science research purposes," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(3), pages 1495-1504, May.

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