Overview

This online course is for anyone needing to address the issue of missing information in their quantitative data. It covers the most important principles of missing data analysis and how to effectively address the issues in analyses. 
 
The course will consist of pre-recorded lectures, a computer practical exercise with a real data set to analyse and solutions. 

This course is specifically designed to enhance knowledge, self-confidence and expertise among researchers who wish to develop their skills in analysing complex longitudinal biosocial data. It makes key concepts and approaches accessible to quantitative researchers from a wide range of disciplines and sectors, with particular emphasis on how these can be practically applied in their work. It is part of the RADIANCE programme, which has been developed by an expert team from UCL and the University of Manchester to offer comprehensive, state-of-the-art longitudinal data science training all in one place.

Who this course is for

This course is aimed at quantitative researchers working with biomedical and social data who wish to use Multiple Imputation of Missing Data in their research and would like a short introduction to this topic.

Course content

This course provides an insight to the following topics:

• Address the issue of missing information in quantitative data.
• An introduction to the most important principles of missing data analysis and how to effectively address the issues in analyses.
• Perform multiple imputation methods and understand the assumptions in depth.
• Look at some of the issues and considerations in developing imputation models.

Teaching and structure

The course consists of 3 pre-recorded lectures, 2 computer practicals (for which either R or Stata can be used) and solutions. 

Participants will need to be familiar with either R or Stata (the course can be followed using either software).

Certificates

A certificate of completion will be available to download once the required activities have been completed.

Learning outcomes

This course will enable you to develop skills in conducting multiple imputation analysis for cross-sectional data.

By the end of this course you will be able to:

• identify different mechanisms of missing data
• use a multiple imputation method for dealing with missing data in cross-sectional studies
• specify perform and select models

Cost and concessions

The standard fee for this course is £125.

A 10% discount is available for UCL students and staff – please email radiance@ucl.ac.uk for details.

Course team

Prof Paola Zaninotto

Paola is a Professor of Medical and Social Statistics at UCL. Paola’s areas of expertise include longitudinal data analysis, time-to-event methods, structural equation models, multiple imputation, causal inference, and multistate life tables methods.

Dr Tra My Pham

Tra is a Senior Research Fellow at MRC Clinical Trials Unit at UCL. Tra’s areas of expertise include Statistics, Epidemiology and Public Health. 

Dr Tim Morris

Tim is Principle Research Fellow at MRC Clinical Trials Unit at UCL. Tim’s areas of expertise include Biostatistics, Statistics, Epidemiology and Statistical data.

Course information last modified: 2 Feb 2024, 17:21