1. Mediation Analysis
  2. Secure Data Collection
  3. Intro to the DSDU
  4. Intro to Connectivity

Academic Talks

  1. Health Data Science
  2. Data Joins

UseR Group (Logan)

  1. Customizing RStudio and ggplot2 PPTX and PDF


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


This class (Syllabus) 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).

Lecture Slides

  1. Introductions to the class PPTX or PDF and to R and RStudio HTML or RMD and Chapter 1 PPTX or PDF
  2. Chapter 2 PPTX or PDF
  3. Chapter 3 PPTX or PDF
  4. Chapter 4 PPTX or PDF
  5. Chapter 5 PPTX or PDF
  6. Chapter 6 PPTX or PDF
  7. Chapter 7 PPTX or PDF
  8. Chapter 8 PPTX or PDF
  9. Chapter 9 and 10 PPTX or PDF
  10. Chapter 11 PPTX or PDF
  11. Chapter 12 PPTX or PDF
  12. Chapter 13 and 14 PPTX or PDF
  13. Chapters 16 and 17 PPTX or PDF
  14. Chapter 18 PPTX or PDF
  15. Chapter 15 HTML

Homework Assignments

  1. Homework 1 HTML and RMD
  2. Homework 2 HTML and RMD
  3. Homework 3 HTML and RMD
  4. (Optional) Homework 4 HTML and RMD
  5. Final Project HTML
    • Example Final Project PDF and Word

Review Material

  1. General Interpretation - PPTX and PDF
  2. Interactions - PPTX and PDF
  3. Polynomials and Interactions (A Little Outdated)


This course (Syllabus) 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.


Lecture Slides

  1. 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)
  2. Chapter 2 (PDF) or Chapter 2 (HTML)
  3. Chapter 3 (PDF) or Chapter 3 (HTML)
  4. Chapter 4 (PDF) or Chapter 4 (HTML)
  5. Chapter 5 (PDF) or Chapter 5 (HTML)
  6. Chapter 6 (PDF) or Chapter 6 (HTML)
  7. Chapter 7 (PDF) or Chapter 7 (PowerPoint)
  8. Chapter 8 (PDF) or Chapter 8 (PowerPoint)
  9. Chapter 9 (PDF) or Chapter 9 (HTML)
  10. Chapter 10 (PDF) or Chapter 10 (HTML)
  11. Chapter 11 (PDF) or Chapter 11 (PowerPoint)
  12. Chapter 12 (PDF) or Chapter 12 (PowerPoint)
  13. Chapter 13 (PDF) or Chapter 13 (PowerPoint)
  14. Chapter 14 (PDF) or Chapter 14 (PowerPoint) and Chapter 14 Example (PDF) or Chapter 14 Example (PowerPoint)
  15. Chapter 15 (PDF) or Chapter 15 (PowerPoint)
    • An R Example of RM ANOVA HTML or RMD
  16. Chapter 16 (PDF) or Chapter 16 (PowerPoint)
    • An R Example of Mixed ANOVA HTML or RMD
  17. Chapter 19-20 (PDF) or Chapter 19-20 (PowerPoint)


This is an introduction to statistics for master’s level social scientists (Syllabus). 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).

Lecture Slides

  1. Week 1 - PDF and PPTX
  2. Week 2 - PDF and PPTX
  3. Week 3 - PDF and PPTX
  4. Week 4 - PDF and PPTX
  5. Week 5 - PDF and PPTX
  6. Week 6 - PDF and PPTX
  7. Week 7 - PDF and PPTX
  8. Week 8 - PDF and PPTX
  9. Week 9 - PDF and PPTX
  10. Week 10 - PDF and PPTX
  11. Week 11 - PDF and PPTX
    • Example data for class about poverty: OMV
  12. Week 12 - Chi Square Activity and Review (no slides)
  13. Week 13 - PDF and PPTX
  14. Week 14 - Review of Class Material - PDF and PPTX

These are introductory (Syllabus) and intermediate (Syllabus) R courses for researchers 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.

My most recent class material can be found below:


  1. Intro to R
  2. Intro to Tidyverse
  3. Exploring Data
  4. Basic Stats
  5. Generalized Linear Models
  6. Multilevel Models
  7. Other Models
  8. Functions, Functions, Functions
  9. More on Visualizations


  1. Review of Tidyverse
  2. Tables and Viz’s
  3. Replicable Workflow in R
  4. Basics of Git and GitHub
  5. Creating an R Package
  6. Miscellaneous Topics
  7. More on Functions


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

Lecture Slides

  1. Introduction HTML and Chapter 1 PDF
  2. Chapter 2 PDF and Ten Simple Rules for Structuring a Paper
  3. Chapter 3 PDF
  4. Chapter 6 PDF
  5. Chapter 4 PDF
  6. Chapter 5 PDF
  7. Chapter 9 PDF
  8. Chapter 10 PDF
  9. Chapter 11 PDF
  10. Chapter 7 PDF
  11. Methods Section PDF
  12. Chapter 8 PDF
  13. Chapter 12 PDF
  14. Chapter 13 PDF
  15. Chapter 14 PDF


  1. Plagiarism Form
  2. Literature Review
  3. Survey/Scale
  4. Consent Form
  5. Methods Section
  6. Abstract
  7. Final Proposal