Methods for Missing Data Course

Description:

Outline: This module deals with the problem of missing data common in many social surveys; problems of bias and inefficiency of naive statistical methods; alternative procedures: basics and complications; MCAR, MAR and non-ignorable missing data; selection bias and the problem of 'dropout' in panel studies. The module will also cover appropriate statistical analysis in appropriate software. The methods will be illustrated by case study analyses.

Particular topics will be:

Assumptions for missing data methods; problems with conventional methods; Maximum Likelihood (ML) with missing data; ML with the EM algorithm; ML for contingency tables; multiple imputation (MI) for missing data; data augmentation; MI for the multivariate normal model; Markov Chain Monte Carlo (MCMC) approach; MICE and other R packages for missing data; MI with categorical and non-normal data; combining MI results; likelihood ratio tests; Bayesian statistics; bootstrap methods.

Learning: Students will learn through the application of concepts and techniques covered in the module to real data sets. Students will be encouraged to examine issues of substantive interest in these studies.

Successful students will be able to:

• understand the problems of missing data in social studies

• perform advanced statistical procedures

• apply theoretical concepts

• identify and solve problems

• analyse data and interpret statistical output

Cost:

External from industry/commerce - £255 per day;
External from academic institution/public sector/charity staff - £220 per day;
External postgraduate student - £150 per day.

Website and registration:

http://www.lancaster.ac.uk/maths/postgraduate/short-courses-and-cpd/

Region:

North West

Keywords:

Missing data

Related publications and presentations:

Missing data

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