14 July 2022
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 analyse 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.
The lectures will follow the book “Flexible Imputation of Missing Data” by Stef van Buuren ( 2nd edition, Chapman & Hall, 2018). The book can be read online for free at https://stefvanbuuren.name/fimd/.
Participants should have a basic knowledge of scripting and programming in R. Participants who have limited experience with R are suggested to follow the Summer School 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. Software will be available online.
This course is part of a series of 5 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics. Please see here for more information about the full specialisation. This course can also be taken separately.
Summer School Data Science specialisation:
Data science: Statistical Programming with R (S24)
Data science: Introduction to Text Mining with R (S41)
Data science: Multiple Imputation in Practice
Data science: Data analysis (S31)
Data science: Applied Text Mining (S42)
Upon completing 3 out of 5 courses in the specialisation (no more than one text mining course), students can obtain a certificate. Each course may also be taken separately.
Dr. Gerko Vink
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.
A max. of 50 participants will be allowed in this course. Please note that the selection for this course will be done on a first-come-first-served basis.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
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.
EUR 615: Course + course materials
EUR 200: Housing fee (optional)