Description

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 dataTypes of missing dataSimple methods for analysing incomplete dataMore 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 (v.17 or later) 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 (e.g., 19-22) for maximum compatibility with the notes provided during the course..