This course provides a first introduction to quantitative data analysis in education and the social sciences. It is part of a three course sequence intended to provide a toolkit of statistical concepts, methods and their implementation to producers of applied research in education and other social sciences. This course is intended for those who will pursue additional statistics courses with the aim of ultimately conducting their own, independent research. The course is organized around the principle that research design depends in part on researchers’ substantive questions and their quantitative data available to answer these question. In this introductory course, we will focus on describing categorical and continuous data and quantifying the relationship between categorical and continuous data. Students will form a solid foundation for frequentist, inferential statistics (and some of critiques of this model). The course seeks to blend a conceptual, mathematical and applied understanding of basic statistical concepts. At the core of our pedagogical approach is the belief that students learn statistical analysis by doing statistical analysis. This course (or substitute) is a pre-requisite for EDUC 643.
By the end of this term, it is expected that students will be able to:
We will primarily be referring to chapters in Learning Statistics with R (LSWR) by Danielle Navarro. This textbook is available for free online. You may choose to purchase a paper copy if you wish, but it is not required.
There are additional readings assignments on the troubling historical origins of modern statistics that appear in the detailed schedule.
We will generally ask that you complete the readings after the concepts have been introduced in class. See detailed schedule.
There are literally dozens of high-quality introductory statistics textbooks. We have particularly found the following to be helpful:
Howell, D.C. (2013). Statistical Methods for Psychology. Cengage Learning: Belmont, CA. [A balanced book in terms of technical details and conceptual discussion. A lot of examples and discussions with a context in psychology and social science.]
Fox, J. (2015) Applied Regression Analysis and Generalized Linear Models. 3rd Ed. SAGE Publications: Thousand Oaks, CA. [Focused only on regression and a lot more detail in regression diagnostics, remedial measures, etc. compared to Howell]
Darlington, R.B. & Hayes, A.F. (2016) Regression Analysis and Linear Models. Guilford Press: New York, NY. [Strong conceptual discussion and many examples]
Gelman, A., Hill, J. & Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press: Cambridge, UK. [Focused on applied problems of data analysis, estimation, prediction and causal inference. Computation in R with code available online. Note: this book primarily takes a Bayesian approach to inference and so will be more appropriate as you advance in your learning.]
Students must have the latest version of R, which can be downloaded here. It is strongly recommended that students also download the RStudio GUI, available here. Both softwares are free. The R bootcamp will provide a tutorial on R/RStudio installation and we also provide a short demonstration of R/Rstudio installation here.
While we will teach you how to effectively use R and RStudio to conduct analyses, one of the key skills required to use R is the ability to find answers on your own. Many common questions or problems are available on blogs, have been asked and answered in discussion forums already, or can be problem-solved using the assistance of artificial intelligence chat bots (more below). Finding and deciphering those answers is an important skill you should seek to hone. You will never remember all of the programming commands!
Here are some sites where you can find the answers to many R questions and learn new tricks:
Artificial Intelligence (AI) chatbots and the large language models (LLMs) on which they rely have dramatically increased the speed and efficiency of many programmers. Members of the teaching team regularly use such tools in their analytic and drafting tasks. That said, they are not (as least currently) substitutes for skilled analysts and writers. Beyond AI chatbots’ known proclivity for “hallucinating” facts and reproducing social biases, their solutions to programming tasks often require adaptation and revision by a knowledgeable human. Further, because their ability to generate text relies on using billions of phrase chunks in the public domain to predict the next word, their language on technical topics can be imprecise when many other writers on these topics are also imprecise. Thus, while we encourage you to investigate how AI chatbots can help improve your programming and statistical analysis skills, we caution you to skeptically review all code and language produced to ensure its alignment to the course expectations. To be explicit: you may use AI chatbots for assistance with your assignments. If you use one to generate your responses, you must indicate so on your assignment. You do not need to do so if you have used these tools only to help with coding tasks and/or light editing of your written responses. You are likely already familiar with OpenAI’s ChatGPT. As you gain more experience as a programmer, you may want to use an AI tool that is designed specifically to help with coding such as GitHub Copilot or AskCodi.
For more details, see here.
| Unit | Weeks | Topics | Required Readings | Assignments | Quizzes |
|---|---|---|---|---|---|
| 0 | 1 | Intro to scientific principles and data analysis | LSWR Ch 2 and 3 Light, Singer & Willet 1990, Ch. 2 |
- | Quiz 1 |
| 1 | 2 | Summarizing and displaying categorical data | LSWR Ch 6 Clayton 2020 |
Assignment 1 | Quiz 2 |
| 2 | 2-3 | Relationships between categorical variables | LSWR Ch 11 and 12 Evans 2020 |
Assignment 2 | Quiz 3 |
| 3 | 4-6 | Summarizing and displaying continuous data | LSWR Ch 5 and 10 | Assignment 3 | Quiz 4 |
| 4 | 7-8 | Relationships between continuous data | LSWR Ch 15.1-15.2 | Assignment 4 | Quiz 5 |
| 5 | 9-10 | Threats to quantitative inference and wrap-up | - | - | - |
| Final | 11 | - | - | Final Project | - |
Final grades will be based on the following components:
There will be five (5) very short quizzes that are designed to test your knowledge of the theoretical principles underlying the statistics we are studying for the week. While some may feel that this is overly paternalistic, research evidence shows that frequent quizzing increases learning (see a summary of one study from the University of Texas). Quizzes will be open book, notes and computer, and may (at your discretion) be completed in consultation with other students in the class. We will leave 15 minutes of class time available to complete each quiz, and any student who would benefit from additional time has until 5pm the following day to submit the quiz on Canvas. It is each student’s responsibility to submit the quiz, and the teaching team will not be able to send individualized reminders to do so.
The goal of the assignments is to practice the concepts and vocabulary we have been modeling in class and implement some of the techniques we have learned. Each assignment has an associated data source, short codebook and detailed instructions for the required data and analytic tasks. You may work on your own or collaborate with one (1) or two partner(s). No groups of more than three, please. Please make sure that you engage in a full, fair and mutually-agreeable collaboration if you do choose to collaborate. If you do collaborate, you should plan, execute and write-up your analyses together, not simply divide the work. Please make sure to indicate clearly when your work is joint and any other individual or resource (outside of class material) you consulted in your response. Further assignment details are available here
The final assignment involves a more extended application and synthesis of the concepts of descriptive and relational applied data analysis covered in this course.
Graduate students are expected to perform work of high quality and quantity, typically with forty hours of student engagement for each student credit hour. For this course, the following table shows the number of hours a typical student would expect to spend in each of the following activities:
| Educational activity | Hours | Explanatory comments |
|---|---|---|
| Class attendance | 30 | 20 sessions * 1.5 hours |
| Class reading and prep | 20 | Includes reading and review of slides |
| Homework Assignments | 40 | Homework assignments will take 10 hours each (on avg.) |
| Final | 30 | Includes familiarization with data, data analysis, preparation of displays and writing |
| Total hours | 120 | These are approximations. Reading and especially analytic time will vary per individual |
The University of Oregon is located on Kalapuya Ilihi, the traditional indigenous homeland of the Kalapuya people. Today, descendants are citizens of the Confederated Tribes of the Grand Ronde Community of Oregon and the Confederated Tribes of Siletz Indians of Oregon, and they continue to make important contributions in their communities, at UO, and across the land we now refer to as Oregon.
If you are concurrently taking any courses with the GEs assigned to this course, please let David Liebowitz know. The GEs will not be involved with any review of assignments for students in this course who are taking other courses concurrently.
This is a face-to-face course. Attendance is important because we will develop our knowledge through in-class activities that require your active engagement. We’ll have discussions and group activities that will be richer for your presence, and that you won’t be able to benefit from if you are not there. While there is not an automatic grade deduction for missing classes, we hope to create a value proposition by which attending class will help you learn more and excessive absences will make it challenging to succeed in the course.
We know our UO community will continue to navigate illness, and some students will need to rest at home if they become sick. Please take absences only when necessary, so when they are necessary, your prior attendance will have positioned you for success. There may be situations beyond the control of individual students that lead to multiple absences such as becoming seriously ill or caring for others. Please communicate with me if such events occur for you.
Federal Title IX regulations provide pregnant and parenting students with certain rights to modifications that may impact attendance, coursework and/or exams. Students needing these modifications are asked to fill out this form with OICRC. OICRC will work with the student and the instructor to determine appropriate modifications.
As a parent of three young children, I understand the difficulty in balancing academic, work, and family commitments. Here are my policies (with credit to Daniel Anderson) regarding children in class: