How closely linked are students’ scores on standardized tests to their socio-economic status? Do individuals with disordered eating behaviors have lower self esteem? Applied data analysis can answer these and other sorts of questions in educational, social and behavior research. This course is the second in 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.
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 intermediate course, we will focus on applying the General Linear Model to Ordinary Least Squares regression analysis. Students will progress from bivariate to multiple regression, developing an understanding of the associated assumptions of these models and tools to solve instances in which those assumptions are unmet. 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. EDUC 641 (or a similar introductory statistics course) is a pre-requisite as is a basic familiarity with a statistical programming language (preferably R). This course (or substitute) is a pre-requisite for EDUC 645.
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 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. We will provide tutorials on R/RStudio installation and they are also accessible 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 either available on blogs or have been asked and answered in discussion forums already. 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.
| Units | Weeks | Topics | Required Readings | Assignments |
|---|---|---|---|---|
| 1 | 1 - 2 | Introduction to the General Linear Model and bivariate regression refresher | LSWR Ch 15.1-15.2 and 15.4-15.7; Ch. 5.7 Hu 2021 |
- |
| 2 | 3 | Regression assumptions, diagnostics and residuals | LSWR Ch 15.8-15.9 | Assignment 1 |
| 3 | 4 | Multiple regression | LSWR Ch 15.3 | Assignment 2 |
| 4 | 5 - 6 | Categorical predictors and ANOVA | LSWR Ch 14 and 16.6 | Assignment 3 |
| 5 | 7 - 9 | Interactions and non-linearity | LSWR Ch 16.2 McIntosh et al 2021 |
Assignment 4 |
| 6 | 10 | Model building and regression taxonomies | LSWR Ch 15.10-15.11 | - |
| Final | - | - | - | Final |
Final grades will be based on the following components:
We will have five (5) short quizzes that are designed to test your knowledge of the theoretical principles underlying the statistics and analytic strategies we have covered in the previous classes. The goal of these quizzes is to serve as a gentle nudge to you to make sure you have fully understood and internalized the content we have covered. 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). After we have covered each topic, you should review your lecture notes and ensure that you understand the core substantive content, referencing LSWR to help clarify any fuzziness. The quizzes will cover the substance of material introduced in class only. Based on student feedback, quizzes are to be completed at home. The quizzes are intended to be completed in less than 15 minutes. Quizzes will go live 15 minutes prior to the start of class on the designated date on which they are to occur and will remain open until 5pm the following day. Outside of the case of an emergency, quizzes not completed by 5pm the following day will receive a 0. Quizzes are open book, notes and computer, but should be completed independently. If students attempt all questions on a quiz, they will minimally earn a score of 1 out of 2.
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) partner. Please make sure that you engage in a 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. For the final assignment, you will receive a compact dataset which has several potential outcomes, predictors and covariates. It will be your task to construct a research question, identify the relevant measures for inclusion in your analysis, develop a sensible analytic plan, and write up your results for dissemination.
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 * 90 min classes |
| 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 |
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. In case of unavoidable absence, students must inform the instructor as early as possible to discuss make-up work.
Final grades are based on the following scale: A+ 98-100%, A 94-97%, A- 90-93%; B+ 87-89%, B 83-86%, B- 80-82%, C+ 77-79%, C 73-76%, C- 70-72%, D+ 67-69%, D 63-66%, D- 60-62%, F 0-59%.
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.
It is the policy of the University of Oregon to support and value equity and diversity and to provide inclusive learning environments for all students. To do so requires that we:
In this course, class discussions, projects/activities and assignments will challenge students to think critically about and be sensitive to the influence, and intersections, of race, ethnicity, nationality, documentation, language, religion, gender, socioeconomic background, physical and cognitive ability, sexual orientation, and other cultural identities and experiences. Students will be encouraged to develop or expand their respect and understanding of such differences.
Maintaining an inclusive classroom environment where all students feel able to talk about their cultural identities and experiences, ideas, beliefs, and values will not only be my responsibility, but the responsibility of each class member as well. Behavior that disregards or diminishes another student will not be permitted for any reason. This means that no racist, ableist, transphobic, xenophobic, chauvinistic or otherwise derogatory comments will be allowed. It also means that students must pay attention and listen respectfully to each other’s comments
The College of Education is always working to include and engage everyone. One way we can do this is to share your pronouns, or the words you want to be called when people aren’t using your name. Like names, pronouns are an important part of how we identify that deserves to be respected. And we recognize that assuming someone’s gender can be hurtful, especially to members of our community who are transgender, genderqueer, or non-binary. As a community, we are all learning together about the importance of pronouns and being better allies to the trans community on campus. Please discuss the pronouns you wish to be used with your professor to help them be aware of how to address you respectfully. Please visit this university website for more information.
The University of Oregon is working to create inclusive learning environments. Please notify me if there are aspects of the instruction or design of this course that result in disability-related barriers to your participation. Participation includes access to lectures, web-based information, in-class activities, and exams. The Accessible Education Center works with students to provide an instructor notification letter that outlines accommodations and adjustments to class design that will enable better access. Contact the Accessible Education Center in 360 Oregon Hall at 541-346-1155 or uoaec@uoregon.edu for assistance with access or disability-related questions or concerns.
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:
Students who are active participants in certain types of military or government service are afforded particular rights under state statute and university policy. Students who are afforded these rights should file documentation with the registrar and inform instructors of modifications they might need due to their service. Instructors should not on their own deny modifications for students under this policy. Instructors should contact the Office of the Provost if there are questions about a student’s requests under this policy.
I am a designated reporter. For information about my reporting obligations as an employee, please see Employee Reporting Obligations on the Office of Investigations and Civil Rights Compliance (OICRC) website. Students experiencing sex- or gender-based discrimination, harassment or violence should call the 24-7 hotline 541-346-SAFE [7244] or visit safe.uoregon.edu. Students experiencing all form of prohibited discrimination or harassment may may contact the Dean of Students Office at 541-346-3216 or the non-confidential Title IX Coordinator/OICRC at 541-346-3123 to request information and resources. Students are not required to participate in an investigation to receive support, including requesting academic supportive measures. Additional resources are available at investigations.uoregon.edu/how-get-support.
I am also a mandatory reporter of child abuse. Please find more information at Mandatory Reporting of Child Abuse and Neglect.
The University Student Conduct Code (available at conduct.uoregon.edu) defines academic misconduct. Students are prohibited from committing or attempting to commit any act that constitutes academic misconduct. By way of example, students should not give or receive (or attempt to give or receive) unauthorized help on assignments or examinations without express permission from the instructor. Students should properly acknowledge and document all sources of information (e.g. quotations, paraphrases, ideas) and use only the sources and resources authorized by the instructor. If there is any question about whether an act constitutes academic misconduct, it is the students’ obligation to clarify the question with the instructor before committing or attempting to commit the act. Additional information about a common form of academic misconduct, plagiarism, is available at https://researchguides.uoregon.edu/citing-plagiarism
It is generally expected that class will meet unless the University is officially closed for inclement weather. If it becomes necessary to cancel class while the University remains open, this will be announced on Canvas and by email. Updates on inclement weather and closure are also communicated in other ways described here.
Life at college can be very complicated. Students often feel overwhelmed or stressed, experience anxiety or depression, struggle with relationships, or just need help navigating challenges in their life. If you’re facing such challenges, you don’t need to handle them on your own–there’s help and support on campus.
As your instructor if I believe you may need additional support, I will express my concerns, the reasons for them, and refer you to resources that might be helpful. It is not my intention to know the details of what might be bothering you, but simply to let you know I care and that help is available. Getting help is a courageous thing to do—for yourself and those you care about. University Health Services help students cope with difficult emotions and life stressors. If you need general resources on coping with stress or want to talk with another student who has been in the same place as you, visit the Duck Nest (located in the EMU on the ground floor) and get help from one of the specially trained Peer Wellness Advocates. Find out more at health.uoregon.edu/ducknest.
University Counseling Services (UCS) has a team of dedicated staff members to support you with your concerns, many of whom can provide identity-based support. All clinical services are free and confidential. Find out more at counseling.uoregon.edu or by calling 541-346-3227 (anytime UCS is closed, the After-Hours Support and Crisis Line is available by calling this same number).
Any student who has difficulty affording groceries or accessing sufficient food to eat every day, or who lacks a safe and stable place to live and believes this may affect their performance in the course is urged to contact the Dean of Students Office (346-3216, 164 Oregon Hall) for support. This UO webpage includes resources for food, housing, healthcare, childcare, transportation, technology, finances, and legal support: https://blogs.uoregon.edu/basicneeds/food/
The university makes reasonable accommodations, upon request, for students who are unable to attend a class for religious obligations or observance reasons, in accordance with the university discrimination policy which says “Any student who, because of religious beliefs, is unable to attend classes on a particular day shall be excused from attendance requirements and from any examination or other assignment on that day. The student shall make up the examination or other assignment missed because of the absence.” To request accommodations for this course for religious observance, visit the Office of the Registrar’s website (https://registrar.uoregon.edu/calendars/religious-observances) and complete and submit to the instructor the “Student Religious Accommodation Request” form prior to the end of the second week of the term.
Several options, both informal and formal, are available to resolve conflicts for students who believe they have been subjected to or have witnessed bias, unfairness, or other improper treatment.
It is important to exhaust the administrative remedies available to you including discussing the conflict with the specific individual, contacting the Department Head, or within the College of Education, fall term you can contact the Associate Dean for Academic Affairs, Sylvia Linan-Thompson, sthomps5@uoregon.edu. Outside the College, you can contact:
A student or group of students of the College of Education may appeal decisions or actions pertaining to admissions, programs, evaluation of performance and program retention and completion. Students who decide to file a grievance should follow University student grievance procedures and/or consult with the College Associate Dean for Academic Affairs (Sylvia Linan-Thompson, sthomps5@uoregon.edu.
Students are expected to be familiar with university policy regarding grades of “incomplete” and the time line for completion. For details on the policy and procedures regarding incompletes, Please see: https://education.uoregon.edu/academics/incompletes-courses