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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.
This course takes place online, over two or three mornings (9:30am to 1pm). The third day is optional and will focus on practical application using SPSS.
Due to the coronavirus outbreak, this course will be run online through a live video feed.
This course is delivered by the Centre for Applied Statistics Courses (CASC), part of the UCL Great Ormond Street Institute of Child Health (ICH).
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.
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
You can request a certificate of attendance for this course once you've completed it. Please send your request to firstname.lastname@example.org
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 students - free
* valid UCL email address and/or UCL alumni number required upon registration
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.)
Dr Eirini Koutoumanou - Course Lead
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’s been co-directing CASC with its founder, Professor Angie Wade.
"Thank for all efforts and everybody. I think this kind of courses are provided good opportunity to the clinicians to learn statistics, design new studies, interpretation of the results and will improve their research skills."
"Good course to attend."
"Very informative and well-presented course."
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Course information last modified: 26 May 2021, 08:16