EDUC 152

EDUC 152
Introduction to Quantitative Research in Education: Regression Analysis

Spring 2021

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.

Syllabus & Resources

Class materials including the syllabus, class Zoom link, Slack workspace, and course textbook are linked below.

Lecture Materials

Lecture materials are organized by topic. All lectures will have associated materials for that lecture topic linked below. This includes lecture notes (html), the R markdown file used to create the lectures (.Rmd), pre-recorded video(s), and any materials used during synchronous class sessions.

Unit 1: Fundamentals of Statistical Inference

Introduction

HTML

Introduction

.Rmd

Introduction

Video recordings

Lecture for Week 1, Introduction - Section 1 & 2: Introduction and College Scorecard Data
Lecture for Week 1, Introduction - Section 3: Review of Statistics 3.1-3.4
Lecture for Week 1, Introduction - Section 3: Review of Statistics 3.5

In-class exercise

Statistical inference

HTML

Statistical inference

.Rmd

Statistical inference

Video recordings

Lecture for Week 2, Statistical Inference I - Sections 1-3: Introduction, IPEDS data, and Distributions
Lecture for Week 2, Statistical Inference I - Section 4: Sampling distribution
Lecture for Week 3, Statistical Inference II - Sections 5.1-5.4: Overview of hypothesis testing through test statistic
Lecture for Week 3, Statistical Inference II - Section 5.5-5.7: p-value through Assumptions

In-class exercise

Fundamental concepts in causal inference and comparing two groups

HTML

Fundamental concepts in causal inference and comparing two groups

.Rmd

Fundamental concepts in causal inference and comparing two groups

Video recordings

Lecture for Week 4, Fundamental concepts in causal inference and comparing two groups - Sections 1-2: Introduction, Fundamentals of Causal Inference
Lecture for Week 4, Fundamental concepts in causal inference and comparing two groups - Section 3: Experiments
Lecture for Week 4, Fundamental concepts in causal inference and comparing two groups - Sections 4-5: User defined functions & Hypothesis testing about two populations

Unit 2: Fundamentals of Regression

Introduction to bivariate regression

HTML

Introduction to bivariate regression

.Rmd

Introduction to bivariate regression

Video recordings

Lecture for Week 5, Bivariate Regression- Sections 1-2: Introduction, Review: scatterplot, covariance, correlation
Lecture for Week 5, Bivariate Regression- Section 3: Population linear regression model
Lecture for Week 5, Bivariate Regression- Sections 4-5: Regression in R, Estimation
Lecture for Week 6, Bivariate Regression- Section 6.1: Model Fit - R2 ,
Lecture for Week 6, Bivariate Regression- Section 6.2: Model Fit - Standard Error of the Regression (SER)
Lecture for Week 6, Bivariate Regression- Section 7: Hypothesis testing about B1
Lecture for Week 7, Bivariate Regression- Section 8: Factor Variables ,
Lecture for Week 7, Bivariate Regression- Section 9: Interpretation of Beta hat, continuous X
Lecture for Week 7, Bivariate Regression- Section 10: Interpretation of Beta hat , categorical X
Lecture for Week 7, Bivariate Regression- Section 11: Confidence Intervals

Multivariate regression

HTML

Multivariate regression

.Rmd

Multivariate regression

Video recordings

Lecture for Week 8, Multivariate Regression- Sections 1-2: Introduction; Bias and efficiency
Lecture for Week 8, Multivariate Regression- Section 3: OLS Assumptions
Lecture for Week 8, Multivariate Regression- Sections 4-5: Conditional Independence Assumption (CIA); Omitted Variable Bias
Lecture for Week 9, Multivariate Regression- Section 6: Intro to Multivariate Regression
Lecture for Week 9, Multivariate Regression- Sections 7: Reading Empirical Regression Results
Lecture for Week 10, Multivariate Regression- Sections 8: Linear Probability Model

In-class exercise

Readings & Assignments

Weekly readings will be assigned to supplement the lectures. There will also be four substantive take-home problem sets, including a final, capstone assignment. Links to the readings and assignments can be found below.

Week 1 (due 04/02)

Required readings

Encouraged readings

Week 2 (due 04/09)

Required readings

Encouraged readings

Week 3 (due 04/16)

Required readings

Statistical Inference lecture, section 5 ("Hypothesis testing"):

Encouraged readings

Problem set 1 due 04/16/21 @ 11:59pm

Problem set files

Problem set solutions

Week 4 (due 04/23)

Required readings

Fundamental concepts in causal inference and comparing two groups (work through lecture and video)
Thornhill (2019)- We want black students, just not you: How white admissions counselors screen Black prospective students

Week 5 (due 04/30)

Required readings

Bivariate regression - (sections 1-5) (work through lecture and video)
Stock and Watson, ch.4 - (section 4.1 - 4.2)

Encouraged readings

Problem set 2 due 04/30/21 @ 11:59pm

Problem set files

Problem set solutions

Week 6 (due 05/07)

Required readings

Bivariate regression - (sections 6-7) (work through lecture and video)
Stock and Watson, ch.4 - (section 4.3)
Stock and Watson, ch.5 - section 5.1 (ignore one-sided hypothesis)

Week 7 (due 05/14)

Required readings

Bivariate regression - (sections 8-11) (work through lecture and video)
Stock and Watson, ch.5 - (section 5.2 - 5.3)

Problem set 3 due 05/14/21 @ 11:59pm

Problem set files

Problem set solutions

Week 8 (due 05/21)

Required readings

Multivariate regression - (sections 1-5) (work through lecture and video)
Stock and Watson, ch.4 - (section 4.4)
Stock and Watson, ch.6 (section 6.1)

Encouraged readings

Covarrubias, A., & Vélez, V. (2013). Critical race quantitative intersectionality. Handbook of Critical Race Theory in Education: Routledge.

Week 9 (due 05/28)

Required readings

Multivariate regression - (sections 6-7) (work through lecture and video)
Short exercise (due 05/28 before class)

Encouraged readings

Powers (2004)- High-Stakes Accountability and Equity: Using Evidence From California’s Public Schools Accountability Act to Address the Issues in Williams v. State of California
Stock and Watson, ch.6 (section 6.2-6.3)

Short exercise due 05/28/21 @ 9:00am

Exercise files

Short exercise solutions

Week 10 (due 06/04)

Required readings

Multivariate regression - (sections 8) (work through lecture and video)
Cabrera et al. (2014)- Missing the (Student Achievement) Forest for All the (Political) Trees: Empiricism and the Mexican American Studies Controversy in Tucson

Problem set 4 due 06/11/21 @ 11:59pm

Problem set files

Course Communication

We will use Slack, an online collaborative workspace, for course communication and discussion.

Ask course questions on Slack

You will be using Slack to post any questions you have relating to the course material. We encourage students to answer each other's questions as well and discuss ideas.

Email instructors for private questions

If you have a personal question or issue, you can email the instructor or TA directly. Additionally, we are available for office hours or by appointment if there is anything you would like to discuss with us in private.