Social phenomena are often the consequence of many influences. As scientists, we are interested in isolating and explaining the many causes of an effect. How can we make such ceteris paribus statements? To which extent does one event cause another event? And how much of a particular outcome can we account for? This workshop shows how to answer such questions with the help of multivariate statistics. The first day focuses on the necessary theoretical foundations.
It sets out with the visual and mathematical tools to describe the central tendency and spread of data. It then turns to statistical inference and explains in which way samples help making statements about a population. In a final step, the lecture introduces multivariate statistical models for explanation and prediction. During the second day, students learn how to apply core concepts from the lecture to real world data. The computer lab relies on the software R, which is a free, open-source language that is becoming the lingua franca for statistical analysis.