Outline

Most researchers in the social and behavioural sciences have encountered the problem of missing data: It seriously complicates the statistical analysis of data, and simply ignoring it is not a good strategy. A general and statistically valid technique to analyze incomplete data is multiple imputation, which is rapidly becoming the standard in social and behavioural science research.

This course will explain a modern and flexible imputation technique that is able to preserve important features in the data. The aim of this course is to enhance participants’ knowledge in imputation methodology, and to provide a flexible solution to their incomplete data problems using R. The course will explain the principles of missing data theory, outline a step-by-step approach toward creating high quality imputations, and provide guidelines how the results can be reported. The course will use the authors’ MICE package in R, and explain how to bridge to mainstream analysis software such as SPSS and Mplus.

The lectures will follow the book “Flexible Imputation of Missing Data” by Stef van Buuren ( 2nd edition, Chapman & Hall, 2018). The book can be accessed online here.

Prerequisites:

Participants are requested to bring their own laptop for lab meetings.

Certificate

Participants will receive a certificate at the end of the course.

Course schedule

Lunch

A lunch buffet is served at the Educatorium. You will receive a bundle of lunch coupons at the start of the course. These coupons will give you access to the buffet and drinks.

Preparing your machine for the course

Dear all,

This summer you will participate in the USS28: Multiple Imputation in Practice course in Utrecht, the Netherlands. To realize a steeper learning curve, we will use some functionality that is not part of the base installation for R. The below steps guide you through installing both R as well as the necessary additions.

We look forward to see you all in Utrecht,

Stef, Gerko and Thom

System requirements

Bring a laptop computer to the course and make sure that you have full write access and administrator rights to the machine. We will explore programming and compiling in this course. This means that you need full access to your machine. Some corporate laptops come with limited access for their users, we therefore advice you to bring a personal laptop computer, if you have one.

1. Install R

R can be obtained here. We won’t use R directly in the course, but rather call R through RStudio. Therefore it needs to be installed.

2. Install RStudio Desktop

Rstudio is an Integrated Development Environment (IDE). It can be obtained as stand-alone software here. The free and open source RStudio Desktop version is sufficient.

3. Start RStudio and install the following packages.

Execute the following lines of code in the console window:

install.packages(c("ggplot2", "tidyverse", "magrittr", "micemd",
                   "jomo", "pan", "lme4", "mice", "ggmice", 
                   "mitml", "miceadds"), 
                 dependencies = TRUE)

If you are not sure where to execute code, use the following figure to identify the console:

Just copy and paste the installation command and press the return key. When asked

Do you want to install from sources the package which needs 
compilation? (Yes/no/cancel)

type Yes in the console and press the return key.

What if the steps to the left do not work for me?

If all fails and you have insufficient rights to your machine, the following web-based service will offer a solution.

1. You will receive an account to Utrecht University’s MyWorkPlace. You would have access to R and RStudio there. You may need to install packages for new sessions during the course.
2. Open a free account on rstudio.cloud. You can run your own cloud-based RStudio environment there.

Naturally, you will need internet access for these services to be accessed. Wireless internet access will be available at the course location.

Lectures

• All lectures can be obtained here as a single file pdf. We’ll proceed through each part of these slides over the course of the lectures.
• The slides for Thursday are here

A handout to the slides can be found here.

Practicals

A reprex detailing pool.scalar()

We advise to work from these online documents to make sure you are using the latest iteration. please use the stable mice version 3.14 from CRAN

Course Archive

We adapt the course as we go. To ensure that you work with the latest iteration of the course materials, we advice all course participants to access the materials online. At the end of the course, these materials will remain online.