The course is build around three themes. First, we will discuss the methodology of scientific research involving human behavior, covering the following aspects: deriving a verifiable research idea, selecting data collection methods, and determining reliability and validity. In part two, we will introduce different statistical philosophies for analyzing behavioral data. The following topics will be addressed: descriptive statistics, frequentist hypothesis testing, Bayesian hypothesis testing, cross-validation, and design analyses. To this end, we will use the statistical programming language R. Finally, in part three, meta-scientific themes inclunding pre-registration, reproducibility, and replicability will be discussed.
Understanding key concepts regarding methods and techniques of behavioral data science.
Applying different statistical philosophies (i.e., frequentist hypothesistesting, Bayesian hypothesistesting, and cross-validation).
Analyzing data in R.
Undertsanding key meta-scientific concepts.
Mode of instruction
A two-hour lecture and a two-hour work group session per week.
The assessment involves
A written, clossed-book exam consisting of 40 multiple choice question with four alternatives each, covering both theoretical knowledge as well as statistical caculations discussed in the lectures, and work group sessions.
An R skills test covering the various aspects of students’ skills in working with R as well as in describing and interpreting statistical output.
The final grade is a weighted average of the examination grade (70%) and the grade for the R skills test (30%).
The teacher will inform the students how the inspection of and follow-up discussion of the exams will take place.
Course material includes slides, exercises, and articles that will be made available via the online course platform.
Dr. T.D.P. Heyman firstname.lastname@example.org
Dr. S.M.H. Huisman email@example.com