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.
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.
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.
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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