Students of psychology and other social sciences are trained to analyze data. But the data we learn to work with (e.g., in courses on statistics and empirical research methods) is typically provided to us and structured in a (rectangular or “tidy”) format that presupposes many steps of data processing regarding the aggregation and spatial layout of variables. When beginning to collect their own data, students inevitably struggle with these pre-processing steps which — even for experienced data scientists — tend to require more time and effort than choosing and conducting statistical tests.
This course develops the foundations of data analysis that allow students to collect data from real-world sources and transform and shape such data to answer scientific and practical questions. Although there are many good introductions to data science (e.g., Grolemund & Wickham, 2016; Zumel & Mount, 2014) they typically do not take into account the special needs (and often anxieties and reservations) of psychology students. As social scientists are not computer scientists, we introduce new concepts and commands without assuming a mathematical or computational background. Adopting a task-oriented perspective, we begin with a specific problem and then solve it with some combination of data collection, manipulation, modeling, and visualization.
Our main goal is to develop a set of useful skills in analyzing real-world data and conducting reproducible research. Upon completing this course, you will be able to read, transform, analyze, and visualize data of various types. Many interactive exercises will allow students to check their understanding, monitor their progress, and practice their skills.
This course assumes some basic familiarity with statistics and the R programming language, but enthusiastic programming novices are also welcome.
Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. Sebastopol, Canada: O’Reilly Media, Inc.
[Available online at http://r4ds.had.co.nz.]