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

Schedule, syllabus and examination date

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Course content

Update April 2021: The course in June 21 is CANCELLED! The course will be postponed to autumn 2021. The course will not be offered digitally in Zoom. If corona travel restrictions will prevent the lecturer to come to Norway, the course will be cancelled/postponed. All applicants will be notified. 

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.

Admission

PhD candidates at the Faculty of Educational Sciences will be given priority, but it is also possible for others to apply for the course. As a minimum requirement, all participants must hold at least a Master's degree.

Candidates admitted to the PhD programme at the Faculty of Educational Sciences should apply through Studentweb

Other applicants apply through this application form

Registration deadline: May 7, 2021. 

Prerequisites

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.

Teaching

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

Important: All participants need to bring a laptop that has SPSS on it to use on the course. 

You will find the timetable on the semester webpage for this course.

Examination

As of January 2021 ECTS credits will no longer be awarded based on attendance only for PhD courses at the Faculty of Educational Sciences.

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: Coming soon. 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.

Evaluation

Evaluation form. 

Facts about this course

Credits

3

Level

PhD

Teaching language

English