UV9213 – Analysing Intervention Studies: How Analysis of covariance works and how to use and interpret it

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Course content

Analysis of covariance (ANCOVA) is the preferred approach to analysing experimental intervention studies. It provides a direct test of the hypothesis under consideration (i.e. if the change in scores from baseline to post-test is larger in the intervention- compared to the control group) and increases the power in detecting intervention effects. Analysis of covariance is essentially a type of regression analysis where continuous and discrete predictors are mixed.

Many people think of regression/correlation as only applying to analyses involving continuous variables (for example height and weight). However, there are a variety of ways of coding categorical variables so that they can be used as predictors in regression models. Perhaps the simplest approach is with dummy coding in which 0,1 codes are used to represent different contrasts between pairs of groups. Dummy coding allows us to analyse data from many different designs in a flexible manner. Such coding schemes are the basis of Analysis of Covariance models.

Learning outcome

In this course you will learn how ANCOVA works and how to use and interpret it as well as some of the basic issues involved in using dummy coding and how to interpret the results. Dummy coding becomes particularly important when we move on to more sophisticated forms of analysis (eg. Multi-level modelling, and Structural Equation Modelling) which have developed from classical multiple regression modelling.


Ph.d.-students at The Faculty of Education will be given priority, but it is also possible for other Ph.d.-students to apply.

Ph.d.-students affiliated with the Faculty of Educational Sciences register through Studentweb

Others may apply through the application form.

Registration deadline: August 30, 2019.


Formal prerequisite knowledge

A basic familiarity with analysing quantitative data. Analyses will be run in SPSS so some familiarity with this programme is required. We will also show you some basics of using Stata software but do not assume any knowledge of this.


This is an intensive course over two days, each with 6 hours of lectures, totaling 12 hours. Attendance is mandatory (80%).

Date: September 24-25, 2019.

Place: Room 235, Helga Engs's building.

You will find the timetable on the semester web-site for this course.


To obtain 1 study point 80% attendance in the lectures is required.

To obtain 3 study points the students will do some data analyses and present and discuss the result of this in a short paper. 

Deadline: October 8, 2019. The paper shall be submitted electronically to kathrine.hoegh-omdal@isp.uio.no

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

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