Overview

This course looks at the problem of missing data in research studies in detail. Reasons and different types of missing data are discussed as well as bad and good methods of dealing with them.

Content

Missing data are very common in research studies, but ignoring these cases can lead to invalid and misleading conclusions being drawn. This course provides guidance on how to deal with missing values and the best ways of analysing a dataset that is incomplete.

The course covers 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)

Course structure and teaching

This is an online, self-paced course that includes:

• Full electronic notes
• Short lecture videos that follow closely with the notes.
• Accessible materials with alternative text for images, and captions/transcripts for each video.
• Interactive quizzes for each chapter.
• Support will also be available through a forum, where you can ask questions related to the course materials.

Learning outcomes

At the end of the course, delegates should understand potential reasons for missing data in research and be able to deal with it if they encounter missing data in their own analysis. In particular, delegates will be able to:

• Understand the reasons for missing data in research
• Differentiate between the different types of missing data including ‘’missing completely at random”, “missing at random” and “missing not at random”.
• With the additional help additional software packages, report the extent of missing data in their analysis.
• Employ simple and advanced methods for filling in missing data such as multiple imputation.
• Comprehend the advantages and disadvantages of each imputation method

Entry requirements

A basic level of statistical literacy is required as a prerequisite.

It is desirable for the course participants to have basic knowledge of statistics, i.e. notion of statistical inference, p-values and Confidence intervals.

Those who have completed the five-day Introduction to Statistics and Research Methods course run frequently by the Centre for Applied Statistics Courses (CASC) team will be equipped.

Cost and concessions

The standard price is £75.

A 50% discount is available for UCL staff, students, alumni. If you're eligible for a discount, email ich.statscou@ucl.ac.uk before booking to be sent the discount code.

The course is available for free to those associated with the Institute of Child Health or Great Ormond Street Hospital, and SLMS doctoral students. Please also email ich.statscou@ucl.ac.uk to receive a booking code.

Certificates

You can download a certificate of participation once you have completed all the session quizzes.

Find out about other statistics courses

CASC's stats courses are suitable for anyone requiring an understanding of research methodology and statistical analyses. The courses 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.

Course team

Dr Dean Langan

Dean works as a lecturer, jointly based within the School of Life and Medical Sciences (SLMS) and the Centre for Applied Statistics Courses (CASC) at UCL. He has a Bachelor’s degree in Mathematics from University of Liverpool, a Master's degree in Medical Statistics from University of Leicester, and a PhD from University of York for his research in statistical methods for random-effects meta-analysis. He's worked as a statistician on a number of clinical trials related to stroke and myeloma at the Clinical Trials Research Unit in Leeds. His specialist areas include statistical methods for meta-analysis, R programming, clinical trial methodology and research design.

Course information last modified: 23 Oct 2023, 16:02