Statistical Analysis with Missing data using multiple Imputation
|University of Manchester|
Dr Karla DiazOrdaz
17/10/2016 - 18/10/2016
Cathie Marsh Institute
View in Google Maps (M13 9PL)
Claire Spencer, 0161 275 1980, firstname.lastname@example.org
In this course we begin by discussing the issues and problems raised by missing data, and introduce the key concepts required for classifying missing data mechanisms into one of three types. We then consider some of the frequently adopted ‘ad-hoc’ approaches for handling missing data, and discuss their limitations. Next, we introduce the method of multiple imputation, 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 apply the statistical methods introduced in the course to realistic datasets.
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.
Schafer JL (1999) Multiple imputation: a primer. Statistical Methods in Medical Research 8; 3-15.
9.45 – 10:00 Registration
10:00– 11:00 Lecture 1
11:00 – 11:30 Coffee/tea
11:30 – 13:15 Practical 1
13:15 – 14:15 Lunch
14:15 – 15:15 Lecture 2
15:15 – 15:45 Coffee/tea
15:45 – 17:30 Practical 2
9:00 – 10:00 Lecture 3
10:00 – 10:30 Coffee/tea
10:30 – 12:15 Practical 3
12:15 – 13:15 Lunch
13:15 – 14:15 Lecture 4
14:15 – 16:00 Practical 4
All fees include event materials, lunch and tea breaks. They do not include travel and accommodation costs.
Full refund for cancellation one month before the course, NO refunds can be made after this date.
Intermediate (some prior knowledge)
For UK registered postgraduate students £30 per day
Website and registration
Nonresponse , Missing data, Regression Methods, Stata
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