Missing Data and Multiple Imputation

About

This course will provide an introduction to the theory and application of Multiple Imputation (MI) (Rubin 1987), which has become a very popular way of handling missing data because it allows for correct statistical inference in the presence of missing data. With the advent of MI algorithms implemented in standard statistical software (such as R, SAS, Stata, or SPSS), the method has become more accessible to data analysts. For didactic purposes, we will start the course by introducing some naive ways of handling missing data, and we will use the examination of their weaknesses to create an understanding of the MI framework. The first day of this course will be of a somewhat theoretical nature, as we believe that a fundamental understanding of the MI principle helps adapt to a wider range of practical problems, rather than focusing on only a few specific situations. We will subsequently shift to the more practical aspects of statistical analysis with missing data, and we will address frequent problems like regression with missing data. Further examples will also be covered throughout the course, and they will be predominantly based on the statistical programming language R. We recommend basic R skills for this course, but it is possible to understand the course contents without prior knowledge in R, as the main MI algorithms are almost identical across all major software packages

Course leader

Florian Meinfelder, University of Bamberg and Doris Stingl, Leibniz Institute for Educational Trajectories (LlfBi)

Target group

You will find the course useful if:
- you are a survey methodologist working with incomplete data,
- you are a researcher who wants to learn more about the analysis of incomplete data in general,
- you are already aware of MI and its benefits but still feel uncomfortable about using MI algorithms implemented in statistical software

Course aim

By the end of the course, you will:
- be familiar with the theoretical implications of the MI framework and aware of its explicit and implicit assumptions (e.g. you will be able to explain within an article why MAR was assumed, etc.),
- know when to use MI (and when not!),
- know how to specify a "good" imputation model and how to use diagnostics,
- be familiar with the availability of the various MI algorithms,
- be able to not only replicate situations akin to the case studies covered in the course but also know how to handle incomplete data in general

Fee info

The rates include the tuition fee, course materials, the academic program, social and plenary program, and coffee/tea breaks.