Missing Data in R

Description

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.

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. So, familiarity with linear regression analysis will be helpful.

Day to Day Documents

Day program S007 - 2025.pdf

Lecturers

Kyle Lang

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:

• Describe the most important characteristics of a missing data problem and choose appropriate statistics, metrics, or visualizations to quantify/illustrate those characteristics.
• Describe multiple imputation (MI): what it is, why it works, and why it is superior to traditional, ad-hoc techniques.
• Write basic R scripts to do the following:
  - Explore a missing data problem with appropriate statistics, metrics, and visualizations.
  - Conduct an MI-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.

 

Tags

• winterschool
• missing data
• multiple imputation
• R programming
• M&S