All course material is shared under the GNU General Public License.

This class is all about applying regression analysis and linear models, including generalized linear models, mediation and moderation, with a little bit of machine learning techniques thrown in. The book we’ll use throughout the class, and that drives the structure of the lecture slides, is Regression Analysis and Linear Models by Richard Darlington and Andrew Hayes. This course uses R and RStudio for all data analyses. A subset of the General Social Survey data set, a data set used in Quas et al. about high risk youth data set, and a data set regarding poverty, violence, and teen birth rates per state will be used in the examples. We will also pull from FiveThirtyEight’s open data on GitHub occassionally throughout the class (many of these data sets can be used for your class project as well if they have both continuous and categorical predictors).

- Introductions to the class PPTX or PDF and to R and RStudio HTML or PDF and Chapter 1 PPTX or PDF
- Readings: Tidy Data and Data Guidelines
- Examples HTML

- Chapter 2 PPTX or PDF
- Readings: Plot Your Data
- Examples HTML or RMD

- Chapter 3 PPTX or PDF
- Chapter 4 PPTX or PDF
- Chapter 5 PPTX or PDF
- Chapter 6 PPTX or PDF
- Chapter 7 PPTX or PDF
- Chapter 8 PPTX or PDF
- Chapter 9 and 10 PPTX or PDF
- Chapter 11 PPTX or PDF
- Chapter 12 PPTX or PDF
- Chapter 13 and 14 PPTX or PDF
- Chapters 16 and 17 PPTX or PDF
- Examples HTML or RMD
- Measurement Error and Reproducibility
- Measurement Error in Practice
- Missing Data - see Little, R. J., & Rubin, D. B. (2014). Statistical analysis with missing data (Vol. 333). John Wiley & Sons.
- Missing Data Overview

- Chapter 18 PPTX or PDF
- Chapter 15 HTML

This is an introduction to statistics at a graduate level (generally PhD students take this course in preparation for further statistical training). Discussion includes topics such as data visualization, exploratory data analysis, and statistical tests (t-tests, ANOVA). This course uses R and RStudio for all data analyses.

- We are putting together a book that should help with the homework—especially with using R.
- A PDF with all the formulas we use in the class.
- A PDF with all the tables we use in the class.
- A Box Folder that contains nearly all the material used for the class (homework assignments, data, articles).

- Intro to the Class (PDF), Intro to the Textbook (PDF), APA Style Review (PDF), Getting Started with R, RStudio, and Latex (HTML), and Chapter 1 (HTML)
- Chapter 2 (PDF) or Chapter 2 (HTML)
- Chapter 3 (PDF) or Chapter 3 (HTML)
- Chapter 4 (PDF) or Chapter 4 (HTML)
- Chapter 5 (PDF) or Chapter 5 (HTML)
- Chapter 6 (PDF) or Chapter 6 (HTML)
- Chapter 7 (PDF) or Chapter 7 (PowerPoint)
- Chapter 8 (PDF) or Chapter 8 (PowerPoint)
- Chapter 9 (PDF) or Chapter 9 (HTML)
- Chapter 10 (PDF) or Chapter 10 (HTML)
- Chapter 11 (PDF) or Chapter 11 (PowerPoint)
- Chapter 12 (PDF) or Chapter 12 (PowerPoint)
- Chapter 13 (PDF) or Chapter 13 (PowerPoint)
- Chapter 14 (PDF) or Chapter 14 (PowerPoint) and Chapter 14 Example (PDF) or Chapter 14 Example (PowerPoint)
- Chapter 15 (PDF) or Chapter 15 (PowerPoint)
- Chapter 16 (PDF) or Chapter 16 (PowerPoint)
- Chapter 19-20 (PDF) or Chapter 19-20 (PowerPoint)

This is an introduction to statistics for master’s level social scientists. It focuses on the applied aspects of data analysis and statistics. Topics include working with data in spreadsheets and Jamovi, data visualization, exploratory data analysis, and statistical tests (t-tests, ANOVA, correlation, regression).

- Week 1 - PDF and PPTX
- Week 2 - PDF and PPTX
- Week 3 - PDF and PPTX
- Week 4 - PDF and PPTX
- Week 5 - PDF and PPTX
- Week 6 - PDF and PPTX
- Week 7 - PDF and PPTX
- Week 8 - PDF and PPTX
- Week 9 - PDF and PPTX
- Week 10 - PDF and PPTX
- Week 11 - PDF and PPTX
- Example data for class about poverty: OMV

- Week 12 - Chi Square Activity and Review (no slides)
- Week 13 - PDF and PPTX
- Week 14 - Review of Class Material - PDF and PPTX

This course is an undergraduate-level research methods class for psychology majors (although it is applicable to many other social sciences). It covers the entirety of the basic research process, from question formulation to communicating results. Below are the resources I provide for the class.

Research Methods for Psychology by Crump, Price, Jhangiani, Chiang, & Leighton

- Introduction HTML and Chapter 1 PDF
- Chapter 2 PDF and Ten Simple Rules for Structuring a Paper
- Chapter 3 PDF
- Chapter 6 PDF
- Chapter 4 PDF
- Chapter 5 PDF
- Chapter 9 PDF
- Chapter 10 PDF
- Chapter 11 PDF
- Chapter 7 PDF
- Methods Section PDF
- Chapter 8 PDF
- Chapter 12 PDF

These are introductory and intermediate graduate R courses for scientists in health, behavioral, social, and educational sciences. The material is taught to be used by very applied individuals with minimal statistical training. The overall goal is to empower reproducible research using R and RMarkdown.

I have lots of material that can be found at: Graduate R Courses–GitHub and Graduate R Courses.

My most recent class material can be found below:

- Review of Tidyverse
- Tables and Viz’s
- Replicable Workflow in R
- Basics of Git and GitHub
- Creating an R Package
- Miscellaneous Topics

- Intro to R
- Intro to Tidyverse
- Exploring Data
- Basic Stats
- Generalized Linear Models
- Multilevel Models
- Other Models
- Functions, Functions, Functions
- More on Visualizations

I will also be using Andrew Heiss’s great walkthrough of replicating Minard’s 1812 plot with `ggplot2`

(found here).