990052 Missing Data (WS) (WiSe 2025/2026)

In this workshop, participants will receive an introduction to missing data analysis in R. The workshop
begins with a theoretical overview of missing data, before we consider two state-of-the-art methods for
handling missing data: maximum likelihood estimation and multiple imputation. Later on, we will also
discuss more specific challenges in the treatment of missing data such as multilevel data, questionnaire
and longitudinal data, or nonlinear effects. The aim of the workshop is to develop a basic understanding
of the problem of missing data and to gain practical experience with modern methods for handling them
through a series of examples and hands-on exercises.

Content
9:00 Missing data
9:30 Maximum likelihood estimation
10:30 Coffee break
10:45 Multiple imputation
12:30 Lunch break
13:15 Multiple imputation for questionnaire/multilevel data
15:00 Coffee break
15:30 Multiple imputation in applications with nonlinear effects
16:45 Summary

Software
For the excercise sessions, the following software packages will be required:
– R (version 4.5.0 or newer)
– R packages: mice, miceadds, mitml, mdmb, lme4, lavaan, semTools
– RStudio

Please ensure beforehand that you have an up-to-date version of these packages installed and install or
update these packages as needed.

Suggested reading
Participants who would like to prepare for this workshop, can do so, for example, by reading the following
papers by Schafer & Graham (2002) and Hayes & Enders (2023):

Hayes, T., & Enders, C. K. (2023). Maximum likelihood and multiple imputation missing data handling:
How they work, and how to make them work in practice. In H. Cooper, M. N. Coutanche, L. M. McMullen,
A. T. Panter, D. Rindskopf, & K. J. Sher (Eds.), APA handbook of research methods in psychology: Data
analysis and research publication (Vol. 3) (2nd ed.). (pp. 27–51). American Psychological Association.
https://doi.org/10.1037/0000320-002

Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods,
7, 147–177. https://doi.org/10.1037//1082-989X.7.2.147

Anmeldung bitte per Mail an pprogramm-psy@uni-bielefeld.de
Registration via E-Mail at pprogramm-psy@uni-bielefeld.de