Imputation Methods for Single Variables

  • Seppo Laaksonen


This chapter considers imputation methods for single variables. Naturally, it may be necessary to impute the values of several variables in each dataset and to carry out several imputations for each dataset. It is essential to understand the basics of Chap.  11, which presents the starting point for imputation methods. It is helpful to look at that chapter for the core terms, but an important question is also why one should, or should not, use imputation. Before answering this question, it is necessary to analyse the missingness and the reasons for it thoroughly. Then again, it is good to remember that the imputation methodology always depends on the case; thus, each variable should be separately imputed even though the principles of the method used can be similar. Successful imputation therefore is ‘tailored’ to the specific case, and the best results are obtained if the ‘imputation team’ has sound knowledge of the basis of the data and its quality.


  1. Allison, B. D. (2005). Imputation of categorical variables with PROC MI. In SUGI 30 Proceedings. Retrieved February 2015, from
  2. Björnstad, J. (2007). Non-Bayesian multiple imputation. Journal of Official Statistics, 23, 433–452.Google Scholar
  3. Carpenter, J., & Kenward, M. (2013). Multiple imputation and its application. Chichester, West Sussex: Wiley.CrossRefGoogle Scholar
  4. Chambers, R. L., Hoogland, J., Laaksonen, S., Mesa, D. M., Pannekoek, J., Piela, P., et al. (2001). The AUTIMP-Project: Evaluation of imputation software. Research Paper 0122. Statistics Netherlands, Voorburg.Google Scholar
  5. Enders, C. K. (2010). Applied missing data analysis. New York: Guilford Press.Google Scholar
  6. Laaksonen, S. (2000). Regression-based nearest neighbor hot decking. Computational Statistics, 15, 65–71.CrossRefGoogle Scholar
  7. Laaksonen, S. (2003). Alternative imputation techniques for complex metric variables. Journal of Applied Statistics, 30, 1009–1020.MathSciNetCrossRefGoogle Scholar
  8. Laaksonen, S. (2016a). Multiple imputation for a continuous variable. Journal of Mathematics and Statistical Science, 2016, 624–643. Science Signpost Publishing, Newark, DE.Google Scholar
  9. Laaksonen, S. (2016b). A new framework for multiple imputation and applications to a binary variable. Model Assisted Statistics and Applications, 11, 191–201.CrossRefGoogle Scholar
  10. Laaksonen, S. & Piela, P. (2003). Integrated modelling approach to imputation. In Euredit Project Documents. Standard Methods, D512 StatFI. Retrieved October 2004, from
  11. Laaksonen, S., Rässler, S., & Skinner, C. (2004). Documentation of pseudo code of imputation methods for the simulation study. In DACSEIS Project Research Papers under Workpackage 11.2 ‘Imputation and Nonresponse’. Retrieved March 2004, from
  12. Lago, L. P., & Clark, R. G. (2015). Imputation of household survey data using linear mixed models. Australian and New Zealand Journal of Statistics, 57, 169–187. Scholar
  13. Muñoz, J. F., & Rueda, M. M. (2009). New imputation methods for missing data using quantiles. Journal of Computational and Applied Mathematics, 232, 305–317.MathSciNetCrossRefGoogle Scholar
  14. Rubin, D. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.CrossRefGoogle Scholar
  15. Rubin, D. (1996). Multiple imputation after 18+ years. Journal of the American Statistical Association, 91, 473–489.CrossRefGoogle Scholar
  16. Rubin, D. (2004). Multiple imputation for nonresponse in surveys. Hoboken, NJ: Wiley-Interscience.zbMATHGoogle Scholar
  17. SAS/STAT 9.3 (2011). Help and Documentation, Users’ Guide for the MI Procedure. Details. SAS Institute Inc., Cary, NC.Google Scholar
  18. Wagstaff, H. (2003). Appendix B: data sets and perturbations. In Euredit project, vol. 2. Retrieved October 2004, from

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Seppo Laaksonen
    • 1
  1. 1.Social Research, StatisticsUniversity of HelsinkiHelsinkiFinland

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