| Presenter(s): | Florian Meinfelder, University of Bamberg |
| e-mail(s): | florian.meinfelder@uni-bamberg.de |
| Date: | 23 May 2018 |
| EMOS learning outcome addressed | Statistical methods |
| Webinar aims | The course aims to give a fundamental understanding of methods and techniques to handle missing data in general. This course addresses missing data mechanisms such as MCAR, MAR, and NMAR as well as missingness patterns like monotone, non-monotone or missing-by-design ones. Likelihood-based inference with missing data are shortly introduced, available case analysis as well as single and multiple imputation approaches. Finally the course focusses on multiple imputation (Rubin 1987) as a general method to analyze incomplete data sets. |
| Webinar learning outcomes | To understand fundamental problem of data that are missing, and its mechanisms. To be aware of the crucial role of statistical analysis with missing data, including its aim and limitations. To gain insights into the assumptions made for multiple imputation and selected imputation methods to handle missing data. |
Webinar content | – Are missing data a problem? Where do missing data occur? – Missing data mechanisms – Overview of statistical analysis with missing data – Multiple Imputation – Conclusions |
| Difficulty level | Intermediate |
| Prerequisites for the webinar | Literature recommended to be read in advance of the lecture: Rässler, S., Rubin, D.B., Schenker, N. (2008) Incomplete Data: Diagnosis, Imputation, and Estimation, International Handbook of Survey Methodology, de Leeuw, E.D., Hox, J.J., Dillman, D.A. (eds.), Lawrence Erlbaum Associates, New York, 370-386. |
| Further readings and resources | Little, R.J.A. and Rubin, D.B. (1987, 2002) Statistical Analysis with Missing Data. New York: Wiley. Rässler, S., Rubin, D.B., Zell, E.R. (2008). Incomplete Data in Epidemiology and Medical Statistics, Handbook of Statistics 27: Epidemiology and Medical Statistics, Rao, C.R., Miller, J.P., Rao, D.C. (eds.) Elsiver, 569-601. Raghunthan, T.E. (2015) Missing Data Analysis in Practice. London: Chapman & Hall. Rubin, D.B. (1987, 2004) Multiple Imputation for Nonresponse in Surveys. New York: Wiley. Rubin, D.B. (1976) Inference and Missing Data. Biometrika, 63, 581-592. Schafer, J.L. (1997) Analysis of Incomplete Multivariate Data. London: Chapman & Hall. Van Buuren, S. (2012) Flexible Multiple Imputation. London: Chapman & Hall. |
| Presentation material | Webinar_MissData_May23 |
| Recording of the webinar | EMOSWebinar11-Recording |
