Social Sciences

Missing Data in R

In this workshop, we will discuss principled methods for treating missing data and how to apply these methods in R. We will cover some basic missing data theory and two principled methods for treating missing data: multiple imputation (MI) and full information maximum likelihood (FIML). Participants will practice what they learn via practical exercises.

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Course Level
ECTS credits
0.5 ECTS
Course location(s)
Utrecht, The Netherlands


Missing data are ubiquitous in nearly every data analytic enterprise. Simple ad-hoc techniques for dealing with missing values such as deleting incomplete cases or replacing missing values with the item mean can cause a host of (hidden) problems.

The workshop content will be presented via a combination of short lectures and live R analysis demonstrations. Workshop participants will practice what they learn on-the-fly by following along with the demonstration scripts and completing in-situ practical exercises. If the schedule permits, the participants are also welcome to ask the instructor for advice on their own data analyses.

Participants should install both R and Rstudio (the free desktop version) on their computers before the beginning of the course.

  • R can be downloaded here.
  • RStudio can be downloaded here.
  • We will not cover basic R usage. Participants should already know how to use R to read and write data, do basic data manipulations, run R functions, and work with the results returned by R functions.
  • We will use linear regression to demonstrate MI-based analyses and structural equation modeling to demonstrate FIML-based analyses. So, familiarity with these techniques will be helpful.


Target audience

Professionals who seek a master-level introduction to missing data analysis.

For an overview of all our winter school courses offered by the Department of Methodology and Statistics please click here.


Aim of the course

After completing this course, participants can:

  1. Describe the most important characteristics of a missing data problem and choose appropriate statistics, metrics, or visualizations to quantify/illustrate those characteristics.
  2. Describe multiple imputation (MI): what it is, why it works, and why it is superior to traditional, ad-hoc techniques.
  3. Describe full information maximum likelihood (FIML): what it is, why it works, and why it is superior to traditional ad-hoc techniques.
  4. Compare and contrast the relative strengths and weaknesses of MI and FIML.
  5. Write basic R scripts to do the following:

    Explore a missing data problem with appropriate statistics, metrics, and visualizations.

    Conduct an MI-based analysis.

    Conduct a FIML-based analysis.

Study load

Approximately eight classroom hours.

You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore, we cannot provide a transcript of grades.



  • Course fee: €175.00
  • Included: Course + course materials + lunch


The tuition fee for staff off the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by FSBS.

PhD students from the Faculty of Social and Behavioural Sciences at Utrecht University have the opportunity to attend three Winter/Summer School courses funded by the Graduate School of Social and Behavioural Sciences. Additionally, they may choose to take as many courses as they wish at their own expense from their personal budget.

Utrecht Summer School does not offer scholarships for this course.


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