Introduction to Dealing with Missing Data

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Start - End
8 Mar - 10 Mar
Study Options
£ 225
Contact Name
Centre for Applied Statistics Courses
Contact Email

08 March 2021–10 March 2021, 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.

Book Now (Main Course)

Book Now (Main Course + optional SPSS supplement)

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 with an optional second day for practical application. The part 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 optional day of the course, the theory is put in practice with the use of SPSS and real-world datasets; particular emphasis is given to multiple imputation.

Everyone should ensure the SPSS software is installed and 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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