In this course students will acquire knowledge about quantitative methods used in HCI research, including the design of research studies, metrics and scales, and concepts such as reliability and validity.
Besides acquiring knowledge, the course will include hands-on exercises with respect to hypothesis testing, including both data preparation as well as performing parametric and non-parametric tests.

Assessment
There is a group assignment which contributes 50% to the final grade, and a final exam which contributes 50% to the final grade.
Each of these two parts (both the final exam as well as the group assignment) are required to be positive to have a final positive score.
The group assignment consists of two parts. The poster report contributes 60% to the grade of the group assignment, and the poster presentation contributes 40% to it.

To qualify for the retake exam, the grade of the original must be at least a 4.

As with all empirical sciences, to assure valid outcomes, HCI studies heavily rely on research methods and statistics. This holds for the design of user interfaces, personalized recommender systems, and interaction paradigms for the internet of things. This course prepares you to do so by learning you to collect data, design experiments, and analyze the results. By the end of the course, you will have an understanding of how to select and apply quantitative research methods and analysis to address HCI challenges.

Course form
Lectures will be alternated with hands-on learning.
The basic concepts and how to's will be presented in a lecture style with sufficient time for questions and discussion. The concepts presented in the lectures will be applied in hands-on exercises in the context of state-of-the-art HCI challenges.

Literature
Will be added continuously.