Missing Data Analytics (MDAR01R) NOT AVAILABLE
5th May 2025 - 7th May 2025£300.00
September, January 8th, 2021
This course is not available in a recorded format.
Please email email@example.com to be notified of the next edition.
About This Course
This course will cover introductory modelling for the analysis of missing data. Missing data is extremely common in all areas of science so this course will be of use to a wide variety of practitioners. The methods are presented both at a theoretical level and also with practical examples where all code is available. The practical classes include instructions on how to use the popular mice package.
The course is structured over 3 days and includes classes on:
- An introduction to missing data analysis terminology, missing completely at random, missing at random, not missing at random
- A revision of likelihood and regression approaches
- The Fully Conditional Specification (FCS) approach
- An introduction to the mice package
- The use of Bayesian and likelihood-based methods in missing data analysis
- Bayesian missing data analysis using JAGS
- More advanced missing data analysis including non-ignorable and not missing at random methods
Research postgraduates, practicing academics, or other professionals from any field who would like to learn about missing data analysis and how it can help them produce better quality information from their data.
Last Up-Dated – N/A
Duration – Approx. 21 hours
ECT’s – Equal to 2 ECT’s
Language – English
A mixture of lectures and hands-on practicals. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.
Assumed quantitative knowledge
A basic understanding of regression methods and generalised linear models.
Assumed computer background
Some familiarity with R including the ability to import/export data, manipulate data frames, fit basic statistical models, and generate simple exploratory and diagnostic plots.
Equipment and software requirements
A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.
All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.
A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.
Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience
PLEASE READ – CANCELLATION POLICY
Cancellations/refunds are accepted as long as the course materials have not been accessed,.
There is a 20% cancellation fee to cover administration and possible bank fess.
If you need to discuss cancelling please contact firstname.lastname@example.org.
If you are unsure about course suitability, please get in touch by email to find out more email@example.com
Day 1 – approx. 7 hours
How to run a missing data analysis in mice
• Introduction to Bayesian analysis and missing data
• The use of Bayesian and likelihood-based methods in missing data analysis
• The fully conditional specification approach to missing data analysis
Day 2 – approx. 7 hours
Including missing data in JAGS and Stan
• An introduction to the mice package
• Bayesian software tools JAGS/Stan for missing data analysis
Day 3 – approx. 7 hours
• Advanced missing data analysis methods
• More advanced missing data analysis including non-ignorable and not missing at random methods
• Missing data analysis in machine learning