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
Concessions
A 50% discount is available for UCL staff, students, alumni. If you're eligible for a discount, email ich.statscou@ucl.ac.uk before booking to be sent the discount code.
The course is available for free to those associated with the Institute of Child Health or Great Ormond Street Hospital, and UCL doctoral students. Please also email ich.statscou@ucl.ac.uk to gain a booking code.
Find out about other statistics courses
CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses allow non-statisticians to interpret published research and/or undertake their own research studies.
Find out more about CASC's full range of statistics courses.
