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Applications and Extensions of Multiple Imputation for Survey Research

Kurs Winter 2011/2012, Mittwochs 12:00-14:00, Bibliothek

Overview of the course

Multiple imputation is a procedure for filling in missing data with a set of plausible values obtained from a predictive model. These multiply imputed data sets are then analyzed as if the data were complete by using standard procedures and combining the results from these analyses. Multiple imputation is frequently used in survey research where missing data is a ubiquitous problem. In addition to missing data problems, statistical methodologists are increasingly using multiple imputation for other purposes, including the creation of synthetic data sets for protecting data confidentiality, combining multiple data sources, and correcting for measurement error just to name a few. This seminar will explore a variety of uses and methods of performing multiple imputation in survey research, with a particular focus on missing data and data confidentiality applications. Students will be responsible for carefully reading the literature assigned and discussing it in class. Particular attention will be paid to identifying research gaps and new potential uses of multiple imputation in survey applications.

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02.02.2012 reading assignment 14. week online
25.01.2012 reading assignment 13. week online
18.01.2012 reading assignment 12. week online
22.12.2011 reading assignment 10/11. week online
14.12.2011 reading assignment 9. week online
07.12.2011 reading assignment 8. week online
30.11.2011 reading assignment 7. week online
23.11.2011 reading assignment 6. week online
16.11.2011 reading assignment 5. week online
09.11.2011 reading assignment 4. week online
03.11.2011 reading assignment 3. week online
20.10.2011 reading assignment 1./2. week online
20.10.2011 Homepage online


Reading assignments

1./2. week
  • Rässler, S., Rubin, D.B., Zell, E.R (2007). Incomplete data in epidemology and medical statistics. In: Rao CR, Miller J, Rao DC (eds) Handbook of Statistics, 27, Elsevier, pp 569-601.
  • Rubin, D.B. (1986). Basic ideas of multiple imputation for nonresponse. Survey Methodology,12, 37-47.
  • Rubin, D.B., and Schenker, N. (1991), Multiple imputation in health-care databases: An overview and some applications. Statistics in Medicine, 10, 585-598.
3. week
  • Schenker, N., Borrud, L.G., Burt, V.L., Curtin, L.R., Flegal, K.M., Hughes, J. , Johnson, C.L., Looker, A.C. and Mire,L. (2011), Multiple imputation of missing dual-energy X-ray absorptiometry data in the National Health and Nutrition Examination Survey, Statistics in Medicine, 260-276.
  • Schenker, N., Raghunathan, T. E., Chiu, P. L., Makuc, D. M., Zhang, G., and Cohen, A. J. (2006). Multiple imputation of missing income data in the National Health Interview Survey. Journal of the American Statistical Association 101, 924-933.
4. week
  • Meng, X.-L. (1994). Multiple-imputation inferences with uncongenial sources of input (disc: P558-573). Statistical Science 9, 538-558.
  • Little R.J.A. and Raghunathan, T.E. (1997). Should imputation of missing data condition on all observed variables? Proceedings of the Survey Research Methods Section, American Statistical Association 1997, 617-622.
  • Reiter, J.P., Raghunathan, T.E., and Kinney, S. (2006), The importance of modeling the sampling design in multiple imputation for missing data, Survey Methodology 32, 143-150.
5. week
  • Raghunathan, T.E., Lepkowski, J.M., van Hoewyk, J., and Solenberger, P. (2001). A multivariate technique for multiply imputing missing values using a series of regression models. Survey Methodology 27, 85-96.
  • Schimert, J., Schafer, J.L., Hesterberg, T., Fraley, C., Clarkson, D.B. (2001). Analyzing Data with Missing Values in S-PLUS. Insightful Corporation, Seattle, WA.
    Chapter 2, Chapter 4 (p.37-47), Chapter 5.
6. week
  • Abayomi, K., Gelman, A., and Levy, M. (2008). Diagnostics for multivariate imputations. Journal of the Royal Statistical Society, Series C 57, 273-291.
  • Giusti, C. and Little, R.J.A. (2011): An Analysis of Nonignorable Nonresponse to Income in a Survey with a Rotating Panel Design, Journal of Official Statistics 27, 211-229.
7. week
  • Honaker, J., King, G. (2010). What to do about missing values in time-series cross-section data, American Journal of Political Science 54, 561-581. (only p. 561-565 relevant for the seminar).
  • Drechsler, J. (2011). Multiple imputation in practice - a case study using a complex German establishment survey, Advances in Statistical Analysis 95, 1-26.
8. week
  • Rubin, D.B. (1993). Discussion: statistical disclosure limitation, Journal of Official Statistics 9, 461-468.
  • Raghunathan, T.E., Reiter, J.P., Rubin, D.B. (2003). Multiple imputation for statistical disclosure control, Journal of Official Statistics 19, 1-16.
9. week
  • Little, R.J.A. (1993). Statistical analysis of masked data. Journal of Official Statistics 9, 407-426.
  • Reiter, J.P. (2005). Releasing multiply-imputed, synthetic public use micro-data: An illustration and empirical study. Journal of the Royal Statistical Society, Series A 168, 185-205.
10. week
  • An, D., Little, R. (2007). Multiple imputation: an alternative to top coding for statistical disclosure control. Journal of the Royal Statistical Society, Series A 170, 923-940.
  • Kinney, S.K, Reiter, J.P., Reznek, A.P., Miranda, J., Jarmin, R.S., Abowd, J.M. (2011). Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database. International Statistical Review 79, 362-384.
11. week
  • Schenker, N., Raghunathan, T.E., Bondarenko, I. (2010). Improving on analyses of self-reported data in a large-scale health survey by using information from an examination-based survey. Statistics in Medicine 29, 533-545
  • Blackwell, M., Honaker, J., King, G. (2011). Multiple Overimputation: A Unified Approach to Measurement Error and Missing Data. Working Paper, 2011. copy at http://j.mp/jqdj72
12. week
  • Schenker, N., Parker, J.D. (2003). From Single-Race Reporting to Multiple-Race Reporting: Using Imputation Methods to Bridge the Transition, Statistics in Medicine 22, 1571-1587.
  • Yucel, R.M., Zaslavsky, A.M. (2005). Imputation of binary treatment variables with measurement error in administrative data. Journal of the American Statistical Association 100, 1123-1132.
13. week
  • Rubin, D.B. (1986). Statistical matching using file concatenation with adjusted weights and multiple imputations. Journal of Business and Economic Statistics 4, 87-94.
  • Moriarity, C., Scheuren, F. (2003): A Note on Rubin's Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations, Journal of Business & Economic Statistics 21, 65-73.
14. week
  • Raghunathan, T.E., Grizzle, J.E. (1995). A Split Questionnaire Survey Design. Journal of the American Statistical Association 90, 54-63.

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