Taking 'don't knows' as valid responses: A multiple complete random imputation of missing data

Quality and Quantity

Volumn 40, Issue 2, 2006, Pages 225-244

  Kroh, Martin ^a  

^a DIW BERLIN   (Germany)

Author keywords

Incomplete data; Missing data; Mixture regression models; Multiple imputation; Non response; Survey methodology; Vote choice

Indexed keywords

EID: 33644945906     PISSN: 00335177     EISSN: 15737845     Source Type: Journal    
DOI: 10.1007/s11135-005-5360-3     Document Type: Article

References (22)

1. Allison, P. D. (2002). Missing Data. Sage Series: Quantitative applications in the social sciences. Thousand Oaks: Sage.
2. Alvarez, R. & Franklin, C. (1994). Uncertainty and political perceptions. Journal of Politics 56: 671-688.
3. Anderweg, R. B. & Irwin, G. A. (2002). Governance and Politics of the Netherlands. New York: Palgrave.
4. Anker, H. & Oppenhuis, E. (1997). Dutch Parliamentary Election Study 1994. Ann Arbor: ICPSR (Study Nr. 6740).
5. Arminger, G., Clogg, C. C. & Sobel, M. E. (eds.) (1995). Handbook of Statistical Modeling for the Social and Behavioral Sciences. New York: Plenum.
6. Bartels, L. (1996). Uninformed votes: information effects in presidential elections. American Journal of Political Science 40: 194-230.
7. Böhning. D.& Seidel, W. (eds.) (2003). Recent developments in mixture models. Computational Statistics & Data Analysis 41 (Special Issue): 349-678.
8. van der Brug, W., van der Eijk, C. & Franklin, M. (2003). Designs for the empirical analysis of electoral preferences, utilities and choice. Paper prepared for the joint sessions of workshops of the ECPR in Edinburgh, March 2003.
9. van Buuren, S., & Oudshoorn C. G. (2000). Multivariate imputation by chained equations: MICE V1.0 User's Manual. Leiden: TNO Preventie en Gezondheid.
10. van der Eijk, C. (2002). Design issues in electoral research: taking care of (core) business. Electoral Studies 21: 189-206.
11. Greene, W. (2000). Econometric Analysis. 4th edn. London: Prentice Hall.
12. Heckman, J. (1979). Sample selection bias as a specification error. Econometrica 47: 153-161.
13. Honaker, J., Joseph, A., King, G. Scheve, K. & Singh, N. (1999). Amelia: A Program for Missing Data. Cambridge: Harvard University.
14. King, G., Honacker, J., Joseph, A. & Scheve, K. (2001). Analyzing incomplete political science data: an alternative algorithm for multiple imputation. American Political Science Review 95: 49-69.
15. Kroh, M. & Eijk, C., van der. (2003). Utilities, Preferences and Choice. Paper presented at the joint sessions of workshops of the ECPR in Edinburgh.
16. Laird, N. (1978). Nonparamteric maximum likelihood estimation of a mixture distribution. Journal of the American Statistical Association 73: 805-811.
17. Little, R. (1992). Regression with missing X's: a review. Journal of the American Statistical Association 87: 1227-1237.
18. Little, R. J. & Rubin, D. B. (1987). Statistical Analysis with Missing Data. New York: John Wiley.
19. Raghunathan, T. E., Solenberger, P. & Hoewyk, J., van. (2000). IVEware: Imputation and Variance Estimation Software: Installation Instructions and User Guide. Survey Research Center, Institute of Social Research, University of Michigan.
20. Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. New York: John Wiley.
21. Schafer, J. L. (1997). Analysis of Incomplete Multivariate Data. London: Chapman & Hall.
22. Tourangeau, R., Rips, L. J. & Rasinski, K. (2000). The Psychology of Survey Response. Cambridge: Cambridge University Press.