Welcome to EPsy 8251
In this class, we will work together to develop a learning community that is inclusive and respectful, and where every student is supported in the learning process. As a class full of diverse individuals (reflected by differences in race, culture, age, religion, gender identity, sexual orientation, socioeconomic background, abilities, professional goals, and other social identities and life experiences) I expect that different students may need different things to support and promote their learning. The TAs and I will do everything we can to help with this, but as we only know what we know, we need you to communicate with us if things are not working for you or you need something we are not providing. I hope you all feel comfortable in helping to promote an inclusive classroom through respecting one another’s individual differences, speaking up, and challenging oppressive/problematic ideas. Finally, I look forward to learning from each of you and the experiences you bring to the class.
Instructor
Andrew Zieffler (zief0002@umn.edu)
Physical Office: Education Sciences Building 178
Office Hours: Monday 9:00 AM–10:00 AM; and by appointment
Virtual Office: If you want to meet virtually, send me a Google calendar invite and include a Zoom link.
Teaching Assistant
Peter Li (lixx1474@umn.edu)
Physical Office: Education Sciences Building 193
Office Hours: Tuesday 10:00 AM–11:00 AM; and by appointment
Course Content and Syllabus
- The course syllabus is available here.
- Message from Snoop Dogg about the syllabus
Textbooks
The course textbook is available via the University of Minnesota library.
- Applied regression: An introduction [required]
Schedule
Below is the tentative schedule for the class. The dates are subject to change at the instructor’s discretion. When possible, required readings should be completed prior to class.
Calendar
Assignments
Below are the due dates for the assignments, as well as links to each assignment. The due dates may change at the instructor’s discretion. Any revised due dates will be announced in class and posted to the website.
Data
Below are the links to the data sets and data codebooks used in the notes, scripts, and assignments.
Readings
As part of the course, there are several articles, papers and technical reports that you will need to read during the semester. Most of the articles themselves are accessible through the University of Minnesota library website (http://www.lib.umn.edu). In order to access the full text of some of the articles, you will need to log in using your University x500 username and password. More detailed information, including references or links to specific readings, are given below.
Welcome to EPsy 8251
Required
- Download and read through the course syllabus
- Read Preface for Statistical Modeling and Computation for Educational Scientists [Computational Toolkit]
Introduction to R and RStudio
Required
Read the following:
- Getting Started with R [Computational Toolkit]
- If you will be using your computer for statistical computing (rather than the RStudio server), download and install the most recent versions of R (version 4.1.1; Kick Things) and RStudio Desktop (version 1.4.1717) on your computer.
R and RStudio onto your personal computer. See R and RStudio Installation and Setup for help. - Data Structures in R [Computational Toolkit]
Additional Resources
- RStudio Cheatsheets
- RStudio Keyboard Shortcuts
- R Cookbook [book; UMN Library]
- tidy data video [video]
- Teacups, Giraffes, and Statistics: Learn statistics & R coding through a series of modules that teach in an intuitive, playful, and approachable way
Data Wrangling with dplyr
Required
- Data Wrangling with dplyr [Computational Toolkit]
Additional Resources
- dplyr Cheatsheet
- Data Wrangling Part 1: Basic to Advanced Ways to Select Columns
- Data Wrangling Part 2: Transforming your columns into the right shape
- Data Wrangling Part 3: Basic and more advanced ways to filter rows
- Data Wrangling Part 4: Summarizing and slicing your data
- Create tidyverse inspired clothing. See how Amelia McNamara created a tidyverse dress and make your own clothing, shower curtain, etc. with the Spoonflower printed fabric she made available.
Plotting with ggplot2
Required
Here are two videos to watch that will help you learn ggplot2:
- Plotting with ggplot: Part 1 [video]
- Plotting with ggplot: Part 2 [video]
Additional Resources
Simple Linear Regression: Description
Required
- Lewis-Beck & Lewis-Beck [Chap. 1]
- Clayton, A. (2020, Oct. 28). How eugenics shaped statistics: Exposing the damned lies of three science pioneers. Nautilus, 92 (Frontiers).
Ordinary Least Squares (OLS) Estimation
Required
Correlation and Standardized Regression
Required
Additional Resources
- corrr Package Vignette
- Exploring correlations in R with corrr
- Guess the Correlation Game [fun game]
- Bring, J. (1994). How to standardize regression coefficients. The American Statistician, 48(3), 209–213.
Coefficient-Level Inference
Required
- Lewis-Beck & Lewis-Beck [Chap. 2]
- Amrhein, V., Greenland, S., & McShane, B. (2019). Comment: Retire statistical significance. Nature, 567, 305–307.
Additional Resources
- Dance of the p-values [video]
Introduction to Multiple Linear Regression
Required
- Lewis-Beck & Lewis-Beck [Chap. 3]
Additional Resources
- McDonald, J. H. (2014). Confounding variables. In Handbook of biological statistics (3rd ed., pp. 24–28). Sparky House Publishing.
Understanding Statistical Control
Required
- Pourhoseingholi, M. A.Baghestani, A. R., & Vahedi, M. (2012). How to control confounding effects by statistical analysis. Gastroenterology and hepatology from bed to bench, 5(2), 79–83.
Distributional Assumptions Underlying the Regression Model
Required
- Re-read Lewis-Beck & Lewis-Beck [Chap. 3]
Additional Resources
- Visualizing Residuals [blog post]
- Osborne, J. W., & Overbay, A. (2004). The power of outliers (and why researchers should ALWAYS check for them). Practical Assessment, Research & Evaluation, 9(6).
Polychotomous Categorical Predictors
Required
Additional Resources
- Gelman, A., Hill, J., & Yajima, M. (2012). Why we (usually) don’t have to worry about multiple comparisons. Journal of Research on Educational Effectiveness, 5, 189–211.
- Science isn’t Broken [web article]
- Veazie, P. J. (2006). When to combine hypotheses and adjust for multiple Tests. Health Services Research, 41(3 Pt 1), 804–818. doi: 10.1111/j.1475-6773.2006.00512.x
Introduction to Interaction Effects
Required
- Brambor, T., Clark, W. R., & Golder, M. (2006). Understanding interaction models: Improving empirical analyses. Political Analysis, 14, 63–82.
Additional resources
- Frost, J. (2018). Understanding Interaction Effects in Statistics. [blog post]
More Interaction Effects
Required
- Williams, R. (2015). Interpreting interaction effects; interaction effects and centering.