GESIS Training Courses

Scientific Coordination

Sebastian E. Wenz
Tel: +49 221 47694-159

Administrative Coordination

Jacqueline Schüller
Tel: +49 0221 47694-160

Course 8: Missing Data and Multiple Imputation

Online via Zoom
Course duration:
9:00-16:00 CEST
General Topics:
Course Level:
Software used:
Students: 500 €
Academics: 750 €
Commercial: 1500 €
Additional links
Lecturer(s): Florian Meinfelder, Angelina Hammon

About the lecturer - Florian Meinfelder

About the lecturer - Angelina Hammon

Course description

This online course provides an introduction to the theory and application of Multiple Imputation (MI) (Rubin 1987) which has become a very popular way for handling missing data, because it allows for correct statistical inference in the presence of missing data. With the advent of MI algorithms implemented in statistical standard software (R, SAS, Stata, SPSS,…), the method has become more accessible to data analysts. For didactic purposes, we start by introducing some naive ways of handling missing data, and we use the examination of their weaknesses to create an understanding of the framework of Multiple Imputation. The first day of this course is of a somewhat theoretical nature, but we believe that a fundamental understanding of the MI principle helps to adapt to a wider range of practical problems than focusing on a few select 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 be covered throughout the course, which are predominantly based on the statistical 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.
A detailed syllabus will soon be available for download here.

Target group

Participants will find the course useful if they:
  • are survey methodologists working with incomplete data.
  • are researchers who want to learn more about the analysis of incomplete data in general.
  • are already aware of MI and its benefits but feel uncomfortable about the available parameter settings in MI algorithms implemented in their preferred statistical software.

  • Learning objectives

    By the end of the course participants should be:
  • be familiar with the theoretical implications of the MI framework and will be aware of the explicit and implicit assumptions (e.g., will be able to explain within an article why MAR was assumed, etc.).
  • know when to use MI (and when not).
  • be aware 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.
    Organizational structure of the course
    We aim to include many smaller breaks so that lecture-style teaching will be no longer than about an hour at a time. Besides the pure teaching part, there will also be several virtual lab sessions per day, so that you have the opportunity to directly implement and practice the covered material. In addition, there will be room for individual consultations on the treatment of missing data in your own projects. Course notes and other material (videos, R Markdown documents,…) will be made available via the e-learning platform ILIAS.


  • General knowledge of data preparation and data analysis
  • An advanced understanding of the (generalized) linear model
  • Familiarity with statistical distributions
  • Basic knowledge of matrix algebra is helpful
  • Solid skills in either R or Stata (recommended for exercises)
    Software and hardware requirements
    Course participants will need a computer or laptop with R ( and RStudio installed ( Both programs are free and open source. We recommend using the Zoom desktop client for the best online teaching experience in Zoom.