Methodology and Statistics in Psychology (MSc)
The master's specialisation Methodology and Statistics in Psychology consists of three main parts: the mandatory and elective courses, a thesis and an internship.
Here you can find the proposed schedule for this master's specialisation. Click on the programme to view it at full-screen.
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 variables (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).
Psychological characteristics cannot be measured directly. They are latent variables, which can only be measured indirectly through, for example, tests or questionnaires. Responses to test or questionnaire items are then used as a measure, or indicator, of the psychological characteristic (construct) of interest. With latent variable models (LVMs) we can assess how well these latent variables are measured, how they change over time and/or how they are associated with other variables. LVMs are therefore an important tool for testing models and hypotheses in psychological research, for assessing the quality of psychological tests, or for interpreting the results of psychological tests. This will be the main focus in this course.
Statistical learning refers to a vast set of tools for understanding data. Two classes of such tools can be distinguished: “supervised” and “unsupervised”. Supervised statistical learning involves building a statistical model for predicting an output (response, dependent) variable based on one or more input (predictor) variables. There are many areas of psychology where such a predictive question is of interest. For example, finding early markers for Alzheimer’s or other diseases, selection studies for personnel or education, or prediction of treatment outcomes. In unsupervised statistical learning, there are only input variables but no supervising output (dependent) variable; nevertheless we can learn relationships and structures from such data using cluster analysis and methods for dimension reduction. In this course we aim to give the student a firm theoretical basis for understanding and evaluating statistical learning techniques and teach the students skills to apply statistical learning techniques in empirical research.
For more information about the courses, check out the Prospectus.
During this master's specialisation, at least 5 EC must consist of elective courses, of level 500. A full list of electives can be found here.