Statistical Analysis with Missing Data using Multiple Imputation


  • 12 December - 13 December 2012
  • Manchester, UK
  • Organiser: Cathie Marsh Centre for Census and Survey Research
  • Contact: Nasira Asghar
  • E-mail:
  • Event Details

Course Summary

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, such as the National Child Development Study.

Course Objectives:

The course will:

• 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 (e.g. mean imputation) for handling missing data

introduce the method of multiple imputation as a practical and principled approach for handling missing data

Target Audience

The course is designed for researchers involved in social science and epidemiology who face the problem of missingness in their data analyses.

Preliminary Reading

Schafer JL (1999) Multiple imputation: a primer. Statistical Methods in Medical Research 8; 3-15.

Sterne JAC, White IR, Carlin JB et al (2009) Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. British Medical Journal 338; b2393.'

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