# UseR Group (Logan)

1. Customizing RStudio and ggplot2 PPTX and PDF

# Courses

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 Polynomials and Interactions (A Little Outdated)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 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.

### Resources

• 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).

### Lecture Slides

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:

### Intermediate

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.

### Book

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