Print Syllabus

Syllabus

PVS0172 Multilevel Modelling, 4.0 Credits

Subjects

Veterinary Medicine

Education cycle

Postgraduate level

Grading scale

Prior knowledge

Admitted to a postgraduate program in natural sciences or a residency program in the European College of Veterinary Surgeons. A recommended prerequisite is that the participant is familiar with the basics of veterinary epidemiology, and basic statistics including linear and logistic regression techniques.

Objectives

On completion of the course, the participant should be: - Familiar with the basic theories for analysing multilevel data (including repeated measures) in veterinary epidemiology - Able to read and understand scientific papers using these models - Aware of different software options for analysing such data The participant should also be able to: - establish, fit and evaluate models for continuous and categorical data - present results from the analyses into a format fit for a scientific publication - Use advanced epidemiological methods in interpreting research data

Content

The course is led by Ian Dohoo from the University of Prince Edward Island, supported by staff from the epidemiology unit at SLU. The course consists of lectures (live and recorded), tutored exercises, independent work (reading, exercises as well as work with own data) and a final workshop. Online teaching will be combined with three consecutive days of teaching on campus. The course will be taught in three modules: The first module includes pre-reading followed by two assignments. The pre-reading and assignments will ensure that participants understand the basics of linear and logistic regression as well as interactions. In addition, they will help participants describe and prepare data to bring to the course. The second module includes theoretical lectures mixed with exercises as well as opportunity to work on own data under skilled supervision. The first two 2 lectures (introductions to clustered data and to linear mixed models) and the associated exercises will be completed online (reviewed during first day on-site) followed by lectures and tutored exercises on site. Opportunities to discuss and work on own data will be provided at the end of each day on site as well as digitally the days following the days on campus. The last module consists of a post-course assignment where the participant independently applies the skills acquired during the second module to analyse their own data. The last day of the course is a workshop where participants present and discuss their work. For participants who do not have any data of their own, we will provide a dataset and prepared assignment to be written-up and handed in. - Pre-course reading and 2 assignments 36 hours - Lectures 14 hours -Tutored exercises 11 hours - Scheduled time for working with own data 3 hours - POST-course assignment 32 hours - Final workshop with presentations 4 hours Total 100 hours

Additional information

To facilitate participation from other SLU sites this course is built up in three modules where two can be done anywhere. However, the second module consists of three days of supervised teaching placed on campus Ultuna. Due to the interactive nature of the tutored exercises as well as relatively heavy content these parts are not suitable for distant learning.

Responsible department

Department of Clinical Sciences