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Date

Available Anytime

Session Type

Online

Topic

Data collection, quality and management

Book now

Course Provider


UCL, Centre for Applied Statistics


Overview


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


Learning outcomes


At the end of the course, delegates should understand potential reasons for missing data in research and be able to deal with it if they encounter missing data in their own analysis. In particular, delegates will be able to:



  • Understand the reasons for missing data in research

  • Differentiate between the different types of missing data including ‘’missing completely at random”, “missing at random” and “missing not at random”.

  • With the additional help additional software packages, report the extent of missing data in their analysis.

  • Employ simple and advanced methods for filling in missing data such as multiple imputation.

  • Comprehend the advantages and disadvantages of each imputation method


 


For further details, cost and bookings please visit website link here

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