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Regression Modelling

Vak 2018-2019

Entry requirements

Only open to Master’s and Research Master’s students from Psychology.

Description

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.

Course objectives

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

Timetable

For the timetables of your lectures, workgroups, and exams, select your study programme.
Psychology timetables

Lectures

Registration

Course

Students need to enroll for lectures (and work group sessions). Please consult the Instructions registration.

Examination

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.

Registering for exams

Mode of instruction

12 2-hour working groups.

Assessment method

Two graded assignments and a written exam. The final grade is the average of the 3 grades obtained.

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.

Reading list

Fox, J. (2016). *Applied regression analysis & generalized linear models (Third Edition). *Sage publications.

Contact information

Prof.dr. Mark de Rooij