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Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures

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  • Riccardo Borgoni
  • Ann Berrington

Abstract

Item nonresponse in survey data can pose significant problems for social scientists carrying out statistical modeling using a large number of explanatory variables. A number of imputation methods exist but many only deal with univariate imputation, or relatively simple cases of multivariate imputation, often assuming a monotone pattern of missingness. In this paper we evaluate a tree-based approach for multivariate imputation using real data from the 1970 British Cohort Study, known for its complex pattern of nonresponse. The performance of this tree-based approach is compared to mode imputation and a sequential regression based approach within a simulation study. Copyright Springer Science+Business Media B.V. 2013

Suggested Citation

  • Riccardo Borgoni & Ann Berrington, 2013. "Evaluating a sequential tree-based procedure for multivariate imputation of complex missing data structures," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 1991-2008, June.
    • Handle: RePEc:spr:qualqt:v:47:y:2013:i:4:p:1991-2008
    DOI: 10.1007/s11135-011-9638-3
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    References listed on IDEAS

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    1. Riccardo Borgoni & Ann Berrington & Peter Smith, 2012. "Selecting and fitting graphical chain models to longitudinal data," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(3), pages 715-738, April.
    2. George Petrakos & Claudio Conversano & Gregory Farmakis & Francesco Mola & Roberta Siciliano & Photis Stavropoulos, 2004. "New ways of specifying data edits," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 249-274, May.
    3. Jefferis, B.J.M.H. & Power, C. & Graham, H. & Manor, O., 2004. "Effects of Childhood Socioeconomic Circumstances on Persistent Smoking," American Journal of Public Health, American Public Health Association, vol. 94(2), pages 279-285.
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