Statistical Methods with R
This site holds the teaching materials for a graduate course in statistical methods for the behavioral and social sciences: slides, in-class practice activities, assignments, readings, and datasets, covering fifteen chapters across five units.
Anyone is welcome to teach from these materials or work through them independently. Nothing here is tied to a particular semester, institution, or offering — schedules, deadlines, and grading belong wherever the course is actually being run.
Teach from these materials freely: assign the chapters, point your students at the slides, and build your own schedule, activities, and grading around them. Linking to a page here needs no permission at all.
Answer keys, practice solutions, and grading rubrics are deliberately not published, since students can read this site as easily as you can. If you are teaching a course and want them, email from your institutional address with a short note about the course.
These materials are licensed CC BY-NC-ND 4.0, which in plain terms means: link to them and teach from them, with credit. Please do not republish them under your own name, whether edited or verbatim, and do not sell them or fold them into paid training. If you need a change — a fix, a different dataset, a slide that would work better reordered — email me and I will consider making it here, where everyone gets it.
About the Course
A graduate introduction to statistical methods for the behavioral and social sciences, taught entirely in R.
The course opens with the R ecosystem itself — projects, Quarto, and the data wrangling and exploration needed to get real data ready to analyze. It then builds up statistical inference from sampling and estimation through hypothesis testing, correlation, t-tests, and one-way ANOVA. The largest part of the course develops the general linear model: simple and multiple regression, categorical predictors and dummy coding, interaction effects for testing moderation, assumption diagnostics, and polynomial regression for curvilinear relationships. It closes with research practices, power analysis, and how to report and critically review a linear model analysis.
Throughout, the emphasis is on estimating and interpreting effects rather than on hypothesis testing alone, and on writing code you could hand to someone else.
The material assumes a prior introductory statistics course, but starts from the beginning with R itself and expects no programming experience.
Learning Outcomes
By the end of the material, students should be able to:
- Describe the parts of the R ecosystem used in the course
- Understand and apply R code for basic programming tasks
- Accomplish basic data wrangling and exploration tasks in R
- Summarize the main roles that statistics play in psychology
- Discuss the logic of estimation and hypothesis testing
- Estimate group comparisons and variable associations
- Design, conduct, and interpret an effect size analysis
- Discuss the logic and steps of the general linear model
- Regress continuous outcomes on any predictor variables
- Apply interaction effects to test moderation hypotheses
- Design, conduct, and interpret a linear model analysis
- Discuss, diagnose, and address violated model assumptions
- Model curvilinear relationships with polynomial regression
- Conduct an a priori power analysis to determine sample size
- Report and review the results of a linear model analysis
Readings
There is no required textbook. Each chapter page lists its own readings: those drawn from open-access books and articles link straight to the source, while the two reference books below have to be obtained separately.
Freely available online
- R for Data Science (2nd ed.) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. The main source for Unit A.
- Learning Statistics with R by Danielle Navarro. The main source for Unit B.
Optional references
- Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.) by Jacob Cohen, Patricia Cohen, Stephen G. West, and Leona S. Aiken. Routledge, 2013. The main source for Unit C.
- Beyond Significance Testing: Statistics Reform in the Behavioral Sciences (2nd ed.) by Rex B. Kline. American Psychological Association, 2015.