Only open to Master’s and Research Master’s students from Psychology.
In this course the focus will be on a whole range of regression models. Starting with the basic regression model we expand to categorical predictors (anova), to categorical response variable (logistic regression and generalized linear models), to nonlinear regression models, to regression models for nested data (multilevel or linear mixed models) and to regression models for nested data with a categorical outcome (generalized linear mixed models). For all cases assumptions and diagnostics to verify assumptions will be discussed.
On completion of the course, students:
Have a good understanding of the framework of regression models
Are able to fit the various regression models using statistical software
Can assess whether a model fits the data well or not
For the timetables of your lectures, workgroups, and exams, select your study programme.
Students need to enroll for lectures (and work group sessions). Please consult the Instructions registration.
Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date; students who are not registered will not be permitted to take the examination.
Mode of instruction
12 2-hour working groups where students give a short presentation about a chapter and subsequently we discuss about the chapter.
Two graded assignments made in pairs and a written exam. The final grade is the weighted average of the 3 grades obtained, where the written assignments each weigh 20%, and the exam 60%.
The Institute of Psychology follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of this fraud policy.
Fox, J. (2016). *Applied regression analysis & generalized linear models (Third Edition). *Sage publications.