Course: Statistical Analysis with Missing data using multiple Imputation
Venue: CMIST, Humanities Bridgford Street, The University of Manchester
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This course begins by discussing the issues and problems raised by missing data, and introduces the key concepts required for classifying missing data mechanisms into one of three types. Some of the frequently adopted 'ad-hoc' approaches for handling missing data shall be considered, and their limitations shall be discussed. The method of multiple imputation shall be introduced, a practical and principled approach for handling missing data. Through computer practicals using Stata, participants will learn how to investigate missingness in their data and how to apple the statistical methods introduced in the course to realistic datasets.
- Provide an introduction to the issues raised by missing data, and the associated statistical jargon (missing completely at random, missing at random, missing not at random)
- Illustrate the shortcomings of ad-hoc methods (eg mean imputation) for handling missing data
- Introduce multiple imputation, as a practical and principled approach for handling missing data.
Participants should be familiar with regression models, such as linear and logistic regression, and have a working knowledge of STATA.
The course is designed for researchers involved in social science and epidemiology who are faced with missing data in their analyses.