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.

The 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.

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.

A repair test requires at least a 4 for the original test.

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.

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
Mandatory:

• Denis, D.J "Applied univariate, bivariate, and multivariate statistics: Understanding statistics for social and natural scientists, with applications in SPSS and R " (2nd edition),  2021, Hoboken, NJ, USA: John Wiley & Sons, Inc. ISBN-13: 978-1-119-58304-2. Available via the UU: https://ebookcentral.proquest.com/lib/uunl/detail.action?docID=7103902  (1st edition)

Optional:

For those who prefer Python over SPSS and R:

• Denis, D.J. "Applied univariate, bivariate, and multivariate statistics using Python: A beginner’s guide to advanced data analysis", 2021, ISBN-13: 978-1-119-57814-7. Not available via the UU.

An additional resource for the first part of the course:

• Navarro, D. "Learning statistics with R". Online available: https://learningstatisticswithr.com

An additional resource for the second part of the course:

• Tabachnick, B.G. and Fidell, L.S.. "Using multivariate statistics" (7th edition), .2019, New York, NY, USA: Pearson Education, Inc. ISBN-13: 978-0-13-479054-1. Not available via the UU.