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Methods for Missing Data

Date: 2nd - 3rd May 2012
Duration: 2 days
Delivered by: Dr Dennis Prangle

Registration deadline has passed.
Please contact psc@lancaster.ac.uk for more information about this course.

Cost
The course fees include all supporting documentation and refreshments.
  • External from industry/commerce - £510
  • External from academic institution/public sector/charity - £440
  • External postgraduate student - £300
  • Lancaster University staff - £120
  • Lancaster University postgraduate student - £60
  • Members of Mathematics and Statistics at Lancaster University - £ 0
Course description

This course 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 course will also cover appropriate statistical analysis in appropriate software. The methods will be illustrated by case study analyses.

Topics covered
  • 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;
  • MI with SAS;
  • MI with categorical and non-normal data;
  • combining MI results;
  • likelihood ratio tests;
  • nonparametric methods;
  • Bayesian statistics;
  • bootstrap methods.
Learning
Students will learn through the application of concepts and techniques covered in the course 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
Cancellation Policy
Registrations are transferable to another course or individual at any time. Full refunds will be given for cancellation 10 or more working days before the course start date. Otherwise the full course fee will be charged.