Dealing with Missing Data in Research Studies: an Introduction

Missing data are very common in research studies, but ignoring these cases can lead to invalid and misleading conclusions being drawn.

This workshop gives you guidance on how to deal with missing values and sets out the best ways of analysing an incomplete dataset.

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 second (optional) day of the course, you'll put the first day's theory into practice using SPSS (v.17 or later) and real-world datasets - particular emphasis is given to multiple imputation.