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.
Summer School Data Science specialisation:
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.
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.
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.
For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.
Four days (09.00 – 17.00 hrs.).
Please note that there are no graded activities included in this course. Therefore, we are not able to provide students with a transcript of grades. You will obtain a certificate upon completion of this course.
Housing through: Utrecht Summer School.
You can choose between two options for participating in this course, but please note that there is always the possibility that we have to change the course pending COVID19-related developments:
If you are interested in the campus option, let us know via a message in the application form under ‘Student Comment’.
The physical course costs €615, but if you participate via the livestream you will get a 80 euro discount. Note that if you choose the campus option, you will be asked to first pay the livestream-fee (€535) and, when we have permission from the university to actually organise classes on location, we will send a second invoice for the remainder of the fee. This way, you will be ensured to have at least a spot for the livestream.
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.
Please include a short description about your (scientific) background, and what you expect to learn from this course (or would like to learn).
Irma Reyersen | E: email@example.com