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.

This course will give a short but advanced introduction to R and extendedfurther expands on computational statistics, handing a toolkit of theory and practice of the environmentfor programming, optimization, and matrix algebra using R, making it both possible to use R in a variety of statistical analysis, and creating a base for acquiring further knowledge and skillskills.

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.

Consulting is an integral part of an applied statistician’s life, whether working in academia, government or industry. Statistical and methodological consultation may involve brief interactions to respond to very specific technical questions of a single client, or long term associations as a member of research projects. It always involves a form of ‘reverse reasoning’, that is, figuring out the conclusion you want to come to, and finding a way to get there. Specifically, the consultant’s problem is: What statistical procedure(s) could provide an appropriate answer to the client’s research question, within all practical constraints? During the course, every two students will carry out a statistical consultation project with an actual client.