Course code NOVA-408

NOVA-408 Multilevel Modelling

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Showing course contents for the educational year 2019 - 2020 .

Course responsible: Eystein Skjerve
Teachers: Marit Stormoen, Ingrid Hunter Holmøy
ECTS credits: 5
Faculty: Faculty of Veterinary Medicine
Teaching language: EN
(NO=norsk, EN=Engelsk)
Teaching exam periods:

Teaching spring 2019.

Precourse preparations: 20 May - 14 Jun, on-site course: 17-21 Jun. in Sandnes, postcourse assigment: 24 Jun. - 19 Jul.

Course frequency: Spring 2019
First time: Study year 2018-2019
Last time: 2019V
Preferential right:
Students on PhD level have number one prioritiy. Master's students may inquire vacancy for PhD courses from the contact listed on the course's NOVA webpage.
Course contents:

Through the course you will be acquainted with the fundamental theories and application of multilevel models. The focus will be on using these methods for applied research in veterinary epidemiology. You will also gain practical competency in statistical software for analyzing data.

Introduction to clustered data; linear mixed models, using software (MLwiN, Stata, R); Generalized linear mixed models; GLMM in MLWiN, Stata, R; Multilevel model diagnostics; Diagnostics in MLWiN.
Random slopes and contextual effects; Alternative approaches for discrete data; Repeated measures for discrete and continuous outcomes.

Learning outcome:


  • Be familiar with the basic theories for analysing random effects and hierarchical data in veterinary epidemiology
  • Be able to read and understand scientific papers using these models
  • Be aware of different software options for analysing such data


  • Be able to establish, fit and evaluate models for continuous and categorical data
  • Be able to present results from the analyses into a format fit for a scientific publication


  • Be able to use advanced epidemiological methods in interpreting research data, to better utilise observational data within an intervention context
Teaching support:
The course consists of theoretical lectures mixed with exercises. The course will provide opportunity to work on own data under skilled supervision. A review of last day¿s topics is given every morning.
The students will receive the reading list in due time.
A prerequisite is that the students have an MSc within natural sciences. MSc students can occasionally be accepted to attend.
Recommended prerequisites:
The student needs to be familiar with the basics of veterinary epidemiology, and basic statistics including simple linear regression techniques.
Mandatory activity:
Participation at the on-site course.

Continuous interaction with the course teachers to provide supervision and ensure each student's progress. The students are required to actively take part in practical exercises. The final credits for the course will be given after evaluation of the post-campus assignment.

After the course, an assignment will be handed in by each student, based upon analysing own data.

Nominal workload:
Lectures 25 hours
Seminars and tutored exercises 25 hours
Independent work 100 hours (pre-course reading and assignment 40 hours, course exercises 15 hours, POST-course assignment 45 hours)
Total 150 hours
Entrance requirements:
deadline: 1. mars 2019
Type of course:
  • Monday: Introduction, clustered data and linear mixed models
  • Tuesday: Generalized linear mixed models and multilevel model diagnostics
  • Wednesday: Visualisation of models, random slopes and contextual effects
  • Thursday: Repeated measures, advanced models for discrete data
  • Friday: Course wrap up, assignments.

This course is a joint Nordic NOVA PhD course organised by Eystein Skjreve, NMBU. Teahcers from NMBU and University of Prince Edward Island will teach the course. Please see the course information webpage on NOVA's website for more information on the course:

Examination details: Continuous exam: Passed / Failed