Introduction to Dealing with Missing Data

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Organisation
UCL
Locations
Start - End
7 Dec - 9 Dec
Study Options
Remote
Fee
£ 75 - 150
Contact Name
Centre for Applied Statistics Courses
Contact Email
ich.statscou@ucl.ac.uk

07 December 2020–09 December 2020, 9:30 am–1:00 pm

This course looks at the problem of missing data in research studies in detail. Reasons and different types of missing data are discussed as well as bad and good methods of dealing with them.

NOTE: Due to the coronavirus outbreak, all courses will now be delivered online through a live video feed. You can expect the same level of group and individual support as you would have received in our face-to-face courses.

Missing data are very common in research studies, but ignoring these cases can lead to invalid and misleading conclusions being drawn. This workshop provides guidance on how to deal with missing values and the best ways of analysing a dataset that is incomplete.

The course runs over a full day, with an optional second half day for practical application. The first day covers the following topics:

  • Reasons for missing data
  • Types of missing data
  • Simple methods for analysing incomplete data
  • More sophisticated methods of dealing with missing data (simple and multiple stochastic imputation, weighting methods)

On the 2nd, optional, half-day of the course, the theory of day 1 is put in practice with the use of SPSS and real-world datasets; particular emphasis is given to multiple imputation.

The 2nd half-day of the course will take place in a cluster room. Delegates are welcome to bring their own laptops and access to the UCL Guest network will also be provided. Everyone wishing to bring their own computer should ensure the software is licensed before attending. Where possible, we recommend using a recent version of SPSS for maximum compatibility with the notes provided during the course..


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