| Resource | Link |
|---|---|
| Class website | https://anyone-can-cook.github.io/educ152/ |
| Class Zoom link | https://ucla.zoom.us/j/91707509593 |
| Class Slack workspace | https://app.slack.com/client/T01S30RQY2E/C01SVPEPHPT |
EDUC 152 introduces students to regression as a tool to answer questions about education. Regression is commonly used to answer questions about “association claims” – the relationship between variables – and “causal claims” – the causal effect of one variable on another. However, using regression appropriately requires thoughtfulness about what kinds of questions regression can answer, about the assumptions regression relies on, about the limitations of our data, and about how particular variables (e.g., “race” and “gender”) are incorporated into analyses. Otherwise, regression results may be biased and may reify rather than interrogate problematic ideas. Therefore, the course emphasizes learning fundamental concepts of regression analysis and how these concepts can be thoughtfully applied to address different kinds of questions about education. The course also trains students how to read and critically assess research that uses regression. ECUC 152 integrates theory and application using the R programming language. Students will be assessed through four substantive take-home assignments, including a final, capstone assignment.
Ozan Jaquette
Patricia Martín
Big-picture (conceptual) learning goals
Skill-based learning goals
Regression is a quantitative methodology commonly used to answer questions about the “association” between variables and questions about the causal effect of one variable on another. Association research questions examine the relationship between variables. For example, what is the relationship between property values and school expenditure per student. However, the existence of an association relationship does not necessarily mean that the value of one variable affects the value of the other. For example, we would expect to observe a positive relationship between ice cream sales and sunglasses sales in a city, but it is unlikely that ice cream sales have a positive causal effect on sunglasses sales. Rather, the temperature and intensity of the sun are likely driving sales of both ice cream and sunglasses. Causal research questions attempt to isolate the causal effect of one variable on another and can usually be stated in the form, “what is the effect of X on Y.” For example, what is the effect of reducing the number of students per class on student learning outcomes?
This course teaches the fundamental principles of regression analysis and the application of these principles to questions about education. Using regression appropriately requires thoughtfulness about what kinds of questions regression can answer, about the assumptions regression relies on, about the limitations of our data, and about how particular variables (e.g., “race” and “gender”) are incorporated into analyses. Therefore, the course emphasizes how the principles of regression can be thoughtfully applied to analyze association research questions and causal research questions.
The course integrates statistical theory and application using the R programming language. The course consists of three units: first, the basics of statistical inference necessary for hypothesis testing; second, the fundamental principles of regression, applied to questions of association; and, third, using regression to answer causal research questions.
The primary course assessments are four “problem sets.” Each problem set will require students to apply knowledge of statistical concepts, and conduct substantive statistical analyses around a particular research question. Students will complete the first three problem sets in groups. Students will complete the final capstone problem set, due during finals week, on their own.
Each week, the course will be structured around asynchronous (pre-class) lectures and one synchronous workshop-style class meeting per week. Weekly homework will consist of students working through the lectures on their own and a modest amount of required reading.
Prior to our in-class meetings, students should work through lecture materials on their own. We recommend treating the lecture materials as an active learning experience, in which students run R code on their computer instead of merely reading text on the slide. Additionally, we recommend that students ask questions on Slack when they are having difficulty with the material.
With respect to written work, the problem sets – described below – will be substantive and are intended to be challenging. Students who devote time each week working through the lecture materials will be better prepared for the problem sets. We recommend starting the problem sets early. This way students will have plenty of time to ask for help on questions they find challenging.
Concepts introduced earlier in the quarter will be utilized later in the quarter. For example, in the beginning of the quarter we learn how to test hypotheses about descriptive claims (e.g., test a hypothesis about average household income). Later in the quarter we will utilize these same concepts to test hypotheses about regression models. Therefore, please seek help if you feel confused about new concepts introduced in a given week so that you do not feel confused when these concepts reappear later in the quarter.
The prerequisites for EDUC 152 are:
Students who have not completed EDUC 150 or EDUC 151 but who have completed a different introductory statistics course can enroll in EDUC 152 with instructor consent.
We all have a responsibility to ensure that every member of the class feels valued and safe. Be mindful that our words and body language affects others in ways we might not fully understand. We have a responsibility to express our ideas in a way that doesn’t make disparaging generalizations and doesn’t make people feel excluded. As an instructor, I am responsible for setting an example through my own conduct.
Learning regression, while trying to get a handle on R and unfamiliar data can feel overwhelming! We must create an environment where students feel comfortable asking questions and talking about what they did not understand. Discomfort is part of the learning process. Unburdern yourself from the weight of being an “expert.” Focus your energy on improving and helping your classmates improve.
Every course should be an anti-racist course, even when the subject matter is broadly oriented. In this course we’ll engage with research that reflect systemic gaps based on race, ethnicity, immigration status, and gender identity, among other aspects of identity. We will discuss whether the language, the framing, the analyses, and even the research question of a study contribute to problematic beliefs. If so, how can we do better? It is also critical that we acknowledge that the social and economic marginalization reflected in data is rooted in systemic oppression that upholds opportunity for some at the expense of others. We should all be thinking about our own role in upholding these systems.
All course related material can be found on the course website. Pre-recorded lecture videos, lecture slides (PDF, HTML), and .Rmd files will be posted on the class website under the associated sections. Additional resources (e.g., syllabus) may also be posted on the class website.
Instead of communicating via email, we’ll use Slack, an online collaborative workspace. You can access it through a web browser or by downloading the Slack app on your phone or computer. Our workspace is called “EDUC 152, Spring 2021” and the link is at the top of the syllabus. The best way to communicate with the instructor, TAs, and your classmates is via Slack, instead of email. All class announcements (e.g., “the due date for the assignment has changed” or “I’ve posted a study guide – check it out!”) will be made with Slack. This means it’s important that you check regularly for communication.
If you have a personal question or issue, please send a direct message to the instructor and/or TA via Slack. Additionally, we are available for office hours or by appointment if there is anything you would like to discuss with us in private.
Required book to buy (or get from library)
Free, online books/resources we will use for assigned reading
Other free resources we may draw from
Links to other required and optional reading will be on the course website.
Required software
Link to tips for software installation HERE.
Course grade will be based on the following components:
The primary course assessments are four problem sets and a short exercise. The first three problem sets are each worth 15% of the course grade and students will work in groups. Additionally, there is a short exercise worth 5% of the course grade. The final, capstone problem set is worth 30% and students will work alone.
Each problem set will require students to apply knowledge of statistical concepts, conduct substantive statistical analyses, and present and interpret results. Other questions will introduce students to some of the thorny data challenges that inevitably arise in real research projects. The capstone problem set will require students to conduct the major components of an empirical regression analysis, from research question and variable collection to modeling, presentation, and interpretation. Additionally, the capstone problem set will require students to critically evaluate an empirical journal article that utilized the same data sources to answer the same research question.
You will work in groups for the first three problem sets. However, it is important that you understand how to do the problem set on your own, rather than copying the solution developed by group members. Each student will submit their own R script or .Rmd file. Since you will be working together, it is understandable that answers for some questions will be the same as your group members. However, if I find compelling evidence that a student merely copied solutions from a classmate, I will consider this a violation of academic integrity and that student will receive a zero for the homework assignment.
Late submissions will lose 20% (i.e., max grade becomes 80%). Problem sets submitted a week late (after the problem set is due) will not receive points. You will not lose points for late submission if you cannot submit a problem set due to an unexpected emergency. Extensions can be granted, but only with prior approval by the instructor(s). But please contact the instructor by email as soon as you can so we can work out a plan.
We strongly recommend using the course Slack workspace to ask questions that you have about problem sets. Instructors will do our best to reply quickly with helpful hints/explanations and we encourage members of the class to do the same.
Link to problem set expectations and helpful resources HERE.
During most synchronous class sessions, students will work on a group activity or challenge that applies concepts and skills that we are learning. For example, you may be asked to run a statistical test in R and use the test results object to create a formatted table that you insert into a .Rmd file. Or, you might write a draft critique of the methodology of a published research paper. Students will submit their work at the end of class. Most of these tasks cannot be fully completed during the duration of the class. The goal is to get students thinking. Students will be graded largely based on effort.
Students are expected to participate in the weekly class meetings by being attentive, supportive, by asking questions, or by answering questions posed by others on Zoom. Additionally, students can receive strong participation grades by asking questions and answering questions posed by classmates on Slack.
| Letter Grade | Percentage |
|---|---|
| A+ | 99-100% |
| A | 93<99% |
| A- | 90<93% |
| B+ | 87<90% |
| B | 83<87% |
| B- | 80<83% |
| C+ | 77<80% |
| C | 73<77% |
| C- | 70<73% |
| D | 60<70% |
| F | 0<60% |
Below is an overview of the tentative course schedule, which is subject to change at the discretion of the instructor. Topics may be cut if we need to devote more time to learning the most central topics. It is unlikely that additional topics will be added. The official course schedule, required reading, and optional reading will be posted on the course website.
Problem set distribution and due dates are tentative and may be subject to change
UNIT 1: FUNDAMENTALS OF STATISTICAL INFERENCE
WEEK 1 (04/02/21)
WEEK 2 (04/09/21)
WEEK 3 (04/16/21)
WEEK 4 (04/23/21)
UNIT 2: FUNDAMENTALS OF REGRESSION
WEEK 5 (04/30/21)
WEEK 6 (05/07/21)
WEEK 7 (05/14/21)
WEEK 8 (05/21/21)
WEEK 9 (05/28/21)
WEEK 10 (06/04/21)
With the ongoing spread of the COVID-19 pandemic, we understand that right now is a challenging time for everybody. Many of us may be experiencing added stress or responsibilities that make learning and completing classwork difficult. If you are having trouble keeping up with the class, please reach out to the teaching team and we will help work out a plan with you. We understand that right now is a precarious time and in the event that you or someone in your family and/or shared living space gets sick, we ask that you please reach out to us as soon as you are able to. We want to be accommodating to everyone’s unique situation and hope to make this class an enjoyable learning experience for all.
You will communicate with instructors and peers virtually through a variety of tools such as Slack, email, and Zoom web conferencing. The following guidelines will enable everyone in the course to participate and collaborate in a productive, safe environment.
Class Zoom guidelines
All synchronous class sessions will be held online, via Zoom. Below, we have outlined some general guidelines about Zoom learning. As we continue learning together, we can add to and change the below list. I’m open to your feedback and your experiences as we continue to learn how to learn via Zoom.
Center for Accessible Education
Students needing academic accommodations based on a disability should contact the Center for Accessible Education (CAE). When possible, students should contact the CAE within the first two weeks of the term as reasonable notice is needed to coordinate accommodations. For more information visit https://www.cae.ucla.edu/.
Located in A255 Murphy Hall: (310) 825-1501, TDD (310) 206-6083; http://www.cae.ucla.edu/
UCLA policy
This class
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CONFIDENTIAL RESOURCES:You can receive confidential support and advocacy at the CARE Advocacy Office for Sexual and Gender-Based Violence, A233 Murphy Hall, CAREadvocate@careprogram.ucla.edu, (310) 206-2465. Counseling and Psychological Services (CAPS) also provides confidential counseling to all students and can be reached 24/7 at (310) 825-0768.
NON-CONFIDENTIAL RESOURCES: You can also report sexual violence or sexual harassment directly to the University’s Title IX Coordinator, 2255 Murphy Hall, titleix@conet.ucla.edu, (310) 206-3417. Reports to law enforcement can be made to UCPD at (310) 825-1491. These offices may be required to pursue an official investigation.
Faculty and TAs are required under the UC Policy on Sexual Violence and Sexual Harassment to inform the Title IX Coordinator should they become aware that you or any other student has experienced sexual violence or sexual harassment.
Peer learning facilitators (PLFs) are undergraduates who understand the challenges of writing at UCLA. Scheduled appointment and walk-in options are available, see https://uwc.ucla.edu/ for more information about writing programs and to get assistance with your writing.
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