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Summer School - Data Science: Multiple Imputation in Practice, 22 - 25 July 2019, Utrecht University, Netherlands

Publish Date: Jan 14, 2019

Deadline: Jul 08, 2019

Event Dates: from Jul 22, 2019 12:00 to Jul 25, 2019 12:00

Data Science: Multiple imputation in practice

This 4-day course teaches you the basics in solving your own missing data problems appropriately. Participants will learn how to form imputation models, how to combine data sets, how to model non-response, how to use diagnostics to inspect the imputed values, how to obtain valid inference on incomplete data and how to avoid many of the pitfalls associated with real-life missing data problems.

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.

Participants should have a basic knowledge of scripting and programming in R. Participants who have limited experience with R are suggested to follow the course S24: Data Science:Statistical Programming in R, or a similar level course beforehand.
The theory and practice discussed in this course requires that participants are familiar with basic statistical concepts and techniques, such as linear modeling, least squares estimation and hypothesis testing. Participants are requested to bring their own laptop computer for lab meetings.
Summer School Data Science specialization:
1. Data science: Statistical Programming with R (S24: 15 – 19 July)
2. Data science: Multiple Imputation in Practice (this course) (S28: 22 – 25 July)
3. Data science: Data analysis & visualization (S31: 29 July – 2 August)
Upon completing all courses in the specialization, students can obtain a certificate. Each course may also be taken separately.

Course director

Dr. Gerko Vink


Prof. dr. Stef van Buuren (Netherlands Organization for Applied Scientific Research (TNO) and Utrecht University), dr. Gerko Vink (Utrecht University)

Target audience

This course is relevant for applied researchers or statistical researchers that would like to get acquainted with the theory and practice of multiple imputation. Participants should have basic understanding of statistical techniques (such as analysis of variance and (non)linear regression) and the concept of statistical inference. 

This course is suitable for students at Master level, Advanced master level en PhD level.

After registration we will ask you to briefly describe your missing data experience (none required) as well as your expectations from this course.

A max. of 40 participant will be allowed in this course.

Course aim

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.

Study load

Four days (09.00 – 17.00 hrs.)


Course fee
€ 550.00
Housing fee
€ 200.00

Housing through Utrecht Summer School

Tuition fee for PhD students from the Faculty of Social and Behavioural Sciences from Utrecht University will be funded by the Graduate School of Social and Behavioural Sciences.


Utrecht Summer School does not offer scholarships for this course.


Write a short description about your (scientific) background, and what you do expect to learn from this course (or would like to learn).

For more information click "LINK TO ORIGINAL" below.

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