Welcome to 8252
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: Tuesday
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: Monday
9:00 AM–10:00 AM; Thursday 9:00 AM–10:00 AM; and by appointment
Class Meeting Times and Zoom Links
- Tuesday/Thursday (11:15–12:30): 315 Peik Hall
- Monday/Wednesday (2:30–3:45): B80 Ford Hall
Textbooks
The following textbook is required:
- Fox, J. (2009). A mathematical primer for social statistics. Thousand Oaks, CA: Sage.
There is also an optional textbook:
- Anderson, D. R. (2008). Model based inference in the life sciences: A primer on evidence. New York: Springer.
Prerequisites
Prerequisites include EPsy 8251: Methods in Data Analysis for Educational Research I, or a sound conceptual understanding of the topics of:
- Foundational topics in data analysis;
- Design (e.g., random assignment and random sampling)
- Descriptive statistics and plots
- One- and two-sample tests
- Correlation;
- Simple regression analysis;
- Model-level and coefficient-level interpretation
- Ordinary least squares estimation
- Model-level and coefficient-level inference
- Assumption checking/residual analysis
- Multiple linear regression
- Model-level and coefficient-level interpretation and inference
- Assumption checking/residual analysis
- Working with categorical predictors (including adjusting p-values for multiple tests)
- Interaction effects
For the topics listed, students would be expected to be able to carry out an appropriate data analysis and properly interpret the results. It is also assumed that everyone enrolled in the course has some familiarity with using R.
Miscellaneous Resources
Here are two resources that might be useful:
Schedule
Below is the tentative schedule for the class. The dates are subject to change at the instructor’s discretion. Preparation should be completed prior to class.
Assignments
Below are the due dates for the assignments, as well as links to the RMD and PDF files for 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 8252
Required
- Download and read through the course syllabus
- Make sure you have the most recent versions of R (version 4.1.2; Bird
Hippie) and RStudio (version
2021.09.1+372 “ghost Orchid”) installed on your computer. You can
update RStudio by “Checking for Updates” within RStudio. To determine
which version of R you have installed, type
R.versionin the console. To update R, you will have to download the most current version and install it from the website.
Introduction to R Markdown/Tables with kable()
Required
- Read YAML: An Overview
- Download the s22-00-rmarkdown directory and unzip it. This directory includes class notes and examples of several R Markdown documents.
Probability Distributions
Required
Read the following:
- Refresh your knowledge about probability distributions by going though the Kahn Academy: Random Variables and Probability Distributions tutorial.
- Fox (Section 3.1.1) Probability Basics [Required Textbook]
- Fox (Sections 3.3.1–3.3.4)Continuous Distributions [Required Textbook]
Additional Resources
In addition to the notes and what we cover in class, there are many other resources for learning about probability distributions. Here are some resources that may be helpful in that endeavor:
- Common probability distributions: The data scientist’s cribsheet
- Leemis, L. M., & McQueston, J. T. (2008). Univariate distribution relationships. The American Statistician, 62(1), 45–53.
- Leemis, L. M. (n.d.) Univariate distribution relationships: Interactive website.
Likelihood: A Framework of Evidence and Estimation
Required
Read the following:
- Refresh your knowledge about logarithms by going though the Khan Academy Intro to Logarithms tutorial.
- Johansson, T. (2011). Hail the impossible: p-values, evidence, and likelihood. Scandinavian Journal of Psychology, 52, 113–125
Additional Resources
In addition to the notes and what we cover in class, there are many other resources for learning about likelihood. Here are some resources that may be helpful in that endeavor:
- Myung, J. (2003). Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology, 47, 90–100.
- Royall, R. (1997). Statistical evidence: A likelihood paradigm. Chapman & Hall/CRC.
Information Criteria and Model Selection
Required
Read the following:
- Elliott, L. P., & Brook, B. W. (2007). Revisiting Chamberlin: Multiple working hypotheses for the 21st century. BioScience, 57(7), 608–614.
Additional Resources
In addition to the notes and what we cover in class, there are many other resources for learning about information criteria and model selection. Here are some resources that may be helpful in that endeavor:
- [Optional Textbook] Anderson, David R. (2008). Model based inference in the life sciences: A primer on evidence. New York: Springer.
- Burnham, Kenneth P., & Anderson, David R. (2002). Model selection and multimodel inference: A practical information-theoretic approach. New York: Springer.
- Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2010). AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behavioral Ecology and Sociobiology, 65(1), 23–35.
Polynomial Effects
Required
- Refresh your knowledge about parabolas and quadratic functions by going though the Khan Academy Parabolas Intro
Additional Resources
In addition to the notes and what we cover in class, there are many other resources for learning about polynomial functions. Here are some resources that may be helpful in that endeavor:
Log-Transforming the Predictor
Required
- Refresh your knowledge about logarithms by going though the Khan Academy Intro to Logarithms tutorial.
Additional Resources
In addition to the notes and what we cover in class, there are many other resources for learning about log-transformations. Here are some resources that may be helpful in that endeavor:
Log-Transforming the Outcome
Required
Read the following:
- Osborne, Jason (2002). Notes on the use of data transformations. Practical Assessment, Research & Evaluation, 8(6).
Additional Resources
In addition to the notes and what we cover in class, there are many other resources for learning about log-transformations. Here are some resources that may be helpful in that endeavor:
- UCLA IDRE: Interpreting Log-Transformed Variables in Regression
- Interpret Regression Coefficient Estimates
Logistic Regression Models
Required
- Watch the YouTube video: 3Blue1Brown. (2020, March 8). Exponential growth and epidemics [Video file]
Additional Resources
In addition to the notes and what we cover in class, there many other resources for learning about using logistic regression models. Here are some resources that may be helpful in that endeavor:
- Fox (Section 3.2.1) The Binomial and Bernoulli Distributions [Required Textbook]
- Dunteman, G. H., & Ho, M.-H. R. (2005). An introduction to generalized linear models. Sage.
- Fox, J. (2015). Applied regression analysis and generalized linear models (3rd ed.). Sage
Introduction to Linear Mixed-Effects Regression Models
Required
- Read Grolemund, G., & Wickham, H. (2017). Relational data. In R for data science: Visualize, model, transform, tidy, and import data. O’Reilly.
LMER: More Random Effects and Covariates
Required
- Read: Uchikoshi, Y. (2005). Narrative development in bilingual kindergarteners: Can Arthur help? Developmental Psychology, 41(3), 464–478. doi: 10.1037/0012-1649.41.3.464
Additional Resources
In addition to the notes and what we cover in class, there many other resources for learning about using linear mixed-effects models for longitudinal analysis. Here are some resources that may be helpful in that endeavor:
- Long, J. D. (2012). Longitudinal data analysis for the behavioral sciences using R. Thousand Oaks, CA: Sage.
- Estimation of the Log-Likelihood in lmer
LMER: Alternative Representations and Model Assumptions
Required
- Read the section on Residual Analysis in: Loy, A. (2014). HLMdiag: A suite of diagnostics for hierarchical linear models in R. Journal of Statistical Software, 56(5), 1–28.