Outline

Usually, when we fit linear models, we don't know the 'truth' about the data we analyse, and hope that the model uncovers this 'truth' (or at least reveals something related to it). However, this is not necessarily the case, e.g. in case that a model lacks statistical power or if type I errors occur.

The primary aim of this course is to teach how to simulate data in a framework of linear models and using R. In case of simulated data, one knows exactly what is 'true' because one has generated the data. Hence, one can then compare what a statistical analysis reveals with truth. In addition to power analyses, data simulation can also be used to address questions such as 'Does the method do what I think it does?', 'Does bias in sampling translate into bias in the estimates?', 'How does an analysis behave when its assumptions are violated?', and many others.

The course has three main aims:

A) teaching how to simulate data and conduct power analyses in a wider sense. Participants will, for instance, learn how to simulate data sets with respect to various predictors, different response variables, effects of fixed effects predictors and grouping factors.

B) Teaching the basic tools for programming simulations in R

C) enhancing the understanding of linear models and what the link between them and 'life' (in either direction) is.

Participation in the workshop requires registration: https://s.gwdg.de/ccyh3N; registration is possible from August 12 to September 8, 2024.

The maximum number of participants is 35. Priority of access is given to, first, members of the Leibniz ScienceCampus Primate Cognition, second, staff, members, and students of the University of Göttingen and the German Primate Center, and finally (third), to everyone else. Within each group, priority of access is given in the order in which people have signed up.