Overview

This short course looks in depth at the problem of missing data in research studies.

You'll learn about different types of missing data, and the reasons for this, along with good and bad methods of dealing with them.

You can take this course either online or face to face. The third day is optional and will focus on practical application using SPSS.

Course content

Missing data are very common in research studies, but ignoring these cases can lead to invalid and misleading conclusions being drawn.

This workshop gives you guidance on how to deal with missing values and sets out the best ways of analysing an incomplete dataset.

The first two days cover the following topics:

• Reasons for missing data
• Types of missing data
• Simple methods for analysing incomplete data
• More sophisticated methods of dealing with missing data (simple and multiple stochastic imputation, weighting methods)

On the third (optional) day of the course, you'll put the first day's theory into practice using SPSS and real-world datasets. Particular emphasis is given to multiple imputation.

Computers and software

To take part in day three of the course, you'll need to have SPSS installed and licensed on your computer. Where possible, we recommend using a recent version of SPSS (e.g. version 25) for maximum compatibility with the notes provided during the course.

Learning outcomes

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

• understand the different types of missing data
• understand the advantages and disadvantages of some of the most commonly used, but less effective, applied methods of dealing with missing data
• understand the underlying theory of the principled methods of dealing with missing data
• be able to fully interpret and apply multiple imputation
• fully understand the advantages and disadvantages of multiple imputation

Certificates

You can request a certificate of attendance for this course once you've completed it. Please send your request to ich.statscou@ucl.ac.uk

Include the following in your email:

• the name of the completed course for which you'd like a certificate
• how you'd like your name presented on the certificate (if the name/format differs from the details you gave during registration)

Cost and concessions

The fees are as follows:

• External delegates (non UCL) - £150 (£225 for 3 days)
• UCL staff, students, alumni - £75* (£112.50 for 3 days)
• ICH/GOSH staff and doctoral students - free    

* valid UCL email address and/or UCL alumni number required upon registration

Cancellations

Read the cancellation policy for this course on the ICH website. Please send all cancellation requests directly to the course administrator

Find out about other CASC statistics courses

CASC's stats courses are for anyone requiring an understanding of research methodology and statistical analyses. The courses will allow non-statisticians to interpret published research and/or undertake their own research studies.

Find out more about CASC's full range of statistics courses, and the continuing statistics training scheme (book six one-day courses and get a seventh free.) 

Course team

Dr Chibueze Ogbonnaya

Since joining the teaching team at CASC in February 2019, Chibueze has contributed to the teaching and development of short courses. He currently leads and co-leads short courses on MATLAB, missing data, regression analysis and survival analysis. Chibueze has a BSc in Statistics from the University of Nigeria, where he briefly worked as a teaching assistant after graduation. He then moved to the University of Nottingham for his MSc and PhD in Statistics. His research interests include functional data analysis, applied machine learning and distribution theory.

Dr Eirini Koutoumanou

Eirini has a BSc in Statistics from Athens University of Economics and Business and an MSc in Statistics from Lancaster University (funded by the Engineering and Physical Sciences Research Council). She joined UCL GOS Institute of Child Health in 2008 to develop a range of short courses for anyone interested in learning new statistical skills. Soon after, CASC was born. In 2014, she was promoted to Senior Teaching Fellow. In 2019, she successfully passed her PhD viva on the topic of Copula models and their application within paediatric data. Since early 2020 she has been co-directing CASC with its founder, Emeritus Professor Angie Wade, and has been the sole Director of CASC since January 2022.

Course information last modified: 27 Mar 2024, 11:34