SP 2021 Semester

Class times: Mon/Wed (Section A: 11:30-12:20 pm) or (Section B: 12:40-1:30 pm)

Lab Times: Tuesday (Y 1:55-2:25 pm) (Z 3:00-3:55 pm)

Office hours: Mon/Wed 12:20-12:40; Tues 2:25-3:00 pm; Thurs 12-2 pm

Peer Tutoring Times: Mon 4-5 PM, Thursday 3:30-4:30 and 7-8 pm

Who can help me?

Instructor: Erick Gong

Peer Tutors: Natalie Eddy and Kaitlyn Girouard

Individual Tutoring: Make a request by completing this CTLR form

Where do I go?

Class Meetings (Mon & Wed)

-Section A (11:30-12:20) Zoom link Password 210

-Section B (12:40-1:30) Zoom link Password 210

Lab Meetings and Office Hours

-Zoom Meeting ID 203 536 7203 (password: 210)

Tutoring:

  • Natalie Eddy’s Session (Thursday 3:30-4:30 PM): Zoom Meeting ID 810 498 4338 (password: econ210)
  • Kaitlyn Girouard’s Session (Monday 4-5 PM) and (Thursday 7-8 pm) : Zoom Meeting ID 918 7939 1620 (password: econ210)

What should I do?

All material is on the Econ 210 site here

All assignments should be submitted on Canvas

What should I expect?

Format: Lecture and lab times are quasi-flipped, meaning we’ll aspire to spend a lot of time in discussion (yes, a statistics class where discussion is central.) You’ll watch videos, skim a textbook, and work through labs before meeting for our lecture/lab times.

Deliverables: Weekly problem sets; weekly lab reports; short quizzes mostly linked to videos; challenge questions; UpShot-style blog post. NO EXAMS.

Grading: Labor-based contract (grades based largely on effort).

Note: This is version 1.1 of a redesigned ECON 210 course. Professor Rao, Bea Lee (2020.5) and I did this redesign with the aim of making ECON 210 more relevant and inclusive, and this is the second live run. We’re still ironing out the wrinkles. I would greatly appreciate your feedback on the course design, during and after this semester.

Course description and goals

This course is about asking and answering questions with data.

A lot of people think about research and statistical methods as something other people do. The goal of this course is for you to develop the capability to understand research other people have done, and to conduct independent research of your own. I want you to develop the confidence and knowledge to ask important questions and to generate scientific evidence. I strongly believe that regardless of your background, by the end of this course, you will be able to do quantitative research.

This course is also about building up your ability to read and write quantitatively. You will likely struggle at times. This is good! We often learn the most when we struggle a bit. Don’t shy away from trying, even if it doesn’t work out right away. Think of me as your statistics coach. I’ll challenge you a bit to help you learn and develop, but fundamentally my goal is to help you get the most out of this course. Don’t hesitate to ask for help.

Specific objectives:

By the end of this course, you will:

  • Be able to examine and describe data

  • Use two statistical programming languages (R and Stata)

  • Understand the connection between sample statistics and random variables

  • Be able to create compelling visualizations to describe data and convey insights

  • Understand the difference between correlation and causation

  • Appreciate the central limit theorem (the most important theorem in this class!)

  • Write models to frame a question

  • Estimate a multivariable linear regression

  • Test a hypothesis

  • Conduct empirical research

Course content

The major theme is statistics as a lens for understanding inequality. We will cover statistical content including descriptive analysis, visualization, hypothesis testing, inferential errors, and regression modeling. We will learn these topics by studying important and pressing questions relating to inequality, including:

  • Mass incarceration

  • Racial income gaps

  • Coronavirus case rates and income distributions

  • Income inequality and public sentiment

  • Climate change and public sentiment

  • Air pollution and environmental justice

Statistics offers one lens through which to view these topics. While it is a powerful lens, it is not the only one. The conclusions we can draw using statistics rely on our engagement with subject matter experts, particularly non-statistical accounts reflecting lived experiences. In econ-speak, quantitative analysis is a complement (not a substitute) to qualitative work. In addition to a useful statistical toolkit and the knowledge and confidence to apply it, I want you to take from this class a sense of humility about its limitations when used in isolation.

Positionality

The topics and examples in this class reflect, among other things, my desire to engage more with issues of inequality and social justice. I recognize that some of these topics may have deep emotional resonances. I care deeply about not perpetrating harms by discussing issues inappropriately. While it is not your responsibility to educate me when I slip, I hope we can develop the mutual trust to support calling each other into more inclusive and empowered discussions. The topics and examples in this class also reflect my academic US-centric perspective. I recognize that many of you bring other perspectives to our discussions. I aim to support you if and when you choose to express your different perspectives.

I am committed to making this learning experience as fruitful as possible for you. Over my life I have struggled a lot with math. I recognize that my personal background shapes what I do and don’t immediately recognize as challenging. I hope you feel comfortable asking me to explain in more detail if something is confusing.

Etiquette and discourse

At Middlebury, we strive to make our campus a respectful, engaged community that embraces difference with all the complexity and individuality each person brings. Each student in this course is expected to contribute to an inclusive and respectful class environment. Students of all backgrounds are to be treated fairly and with honesty, integrity, and respect. Civil discourse without degrading, abusing, harassing, or silencing others is required of all students in this class.

Being intentional with our words and actions is always important, and particularly so when we discuss heavy topics like those in this class. Please, be mindful of how you frame points and arguments. Avoid pathologizing language in particular—it is often both harmful and a marker of a poorly-considered argument. Intentional and thoughtful discussions which do not perpetrate harms increase our collective freedom of speech and make it possible for us all to engage more fully. Similarly, when someone notifies us that we have perpetrated harms, robust intellectual discourse is best served when we listen, apologize, reflect, and adjust our behaviors moving forward.

Textbook and readings

The textbook is Introduction to Statistics with Randomization and Simulation (ISRS). The book is free and open source; just go to the link and download/stream what you want. If you want a paper copy, you can pay a low fee for printing and shipping.

The textbook is meant to be skimmed. Focus on topics covered in the videos - ignore concepts not covered in the videos (unless of course they interest you).

We may also read academic articles, essays, and book excerpts to complement our understanding of the substantive topics in the course.

Software

-R and RStudio: We’ll use this for the first half of the course. R is a free open-source statistical software package, and RStudio is a popular integrated development environment which makes it easier to work in R. Given its open-source nature, R is very popular in the data science world.

-STATA: You’ll be introduced to Stata in the 2nd half ot he course. It is not free and open source. STATA is the dominant statistical package used in economics and if you are continuing on to Econ 211 (required for the Econ major), you will be expected to know STATA. I will recommend that you purchase a discounted student license (~$43) to install on your laptop. Remote desktop access will also be available.

By the end of this course, you should be comfortable using either R or STATA. We are hoping that you become technology agnostic, and are able to use the best tool for the job (or what your workplace/supervisor/course expects of you).

Communication

I encourage you to direct all questions and communication to our Slack workspace. Slack is a popular communications platform that is more fluid than email. The major benefit is that you discussions are much more organized and less cumbersome than email.

Slack protocol and details

Using your @middlebury.edu email, go to this link to sign up for the workspace. You can download the app for free.

Use the appropriate channel for labs, problem sets, and general discussions. If you want to propose a new channel, send me a DM. If your question is about specific code or math, please include a copy of the code (as a .Rmd file) or the math (as a PDF, picture, screenshot, or text description in the email). Please follow the etiquette and discourse guide discussed earlier when posting anything on Slack.

If you are asking me a question on Slack or would like me to weigh in on a discussion, please tag me (type @egong once somewhere in your question). I’ll check Slack at least once per day during the week. I won’t be checking Slack over the weekend. If you ask me a question there and I don’t get to it right away, I’ll tag you in my reply.

Instead of email, you can also DM me with questions if you don’t want to post them in a public channel. I may post such questions (anonymized) to a public channel if I think it would help others in the class (unless of course you specify otherwise).

Email protocol and details

I will focus my attention on our Slack workspace and any emails I receive will have a longer response time (48 to 72 hours). In the subject line, please start with “Econ 210:”. I use email filters to prioritize among the many emails I receive each day; not including the “Econ 210:” subject line will delay my response. If your question is about specific code or math, please include a copy of the code (as a .Rmd file) or the math (as a PDF, picture, screenshot, or text description in the email).

Miscenalleneous notes

  • This course may seem daunting—we’ll be covering complicated concepts, sometimes at a very abstract level. Don’t panic! With practice, repetition, and patience, it will come together. I’m here to help—don’t hesitate to ask questions.

  • On asking questions: sometimes a concept feels fuzzy or like it isn’t quite clicking, but it’s hard to frame a question precisely. Don’t worry! Ask anyway. We may not get to the bottom of it right away, but we can make progress. Questions about material also tend to be correlated across students, so by asking (especially in a public Slack channel) you often provide a public good to your classmates.

  • This material goes deep. I may at times limit our inquiry to keep us moving along. If you’re curious, I’m happy to talk in more detail in office hours. Think of statistics as a very big onion, and this course as pulling the first few layers off—you can chop those layers up and cook with them, plenty of recipes won’t need any more!—but there’s a lot of onion left.

  • Statistical programming proficiency is an important outcome of this class. I strongly encourage you to work through the code examples in the textbook and lab notes (actually type the commands in, don’t just copy-paste). You can look at the lab notes attached to the textbook for more examples. Stack Overflow is a particularly helpful online resource for programming questions in R and other languages. I encourage you to spend some time getting comfortable with using the site.

Assignments

  • Quizzes: Quizzes will be based on videos and readings that are expected before our class meetings. There will be available on Canvas. Frequent quizzing is intended to stimulate effortful retrieval which can help cement understanding. You will receive full credit by simply taking the quiz. (Recommended time: 3 minutes)

  • Lab reports: The lab report will guide you through the mechanics of the new code we’re learning that week. The lab report for a week is an html file that results from “knitting” the r markdown file (.rmd). with your work on completing the exercises. (Recommended time: 2 hours)

  • Problem sets: These are weekly collections of problems to solve. They will typically follow the lab and videos leading into the problem set. A completed submission is an html file with your work towards solutions. When you are unable to completely solve a question, sketch out your work so far, your thoughts on what the answer ought to look like, and what steps you think need to be taken to get there. After you submit, you will receive access to the solutions. You then must review the solutions and submit a “redo” file explaining what you got wrong. If you got everything right, you can just say so. A complete problem set involves both submitting your initial answers AND a “redo” file. (Recommend time: 3 hours)

  • Challenge questions: These are more challenging/involved questions intended to test your understanding and stretch your skills. (Recommend time 4 hours)

  • UpShot-style blog post: These are the capstone research project for this class, blending statistical analysis and communication. The UpShot is widely read by academics and non-academics. Each post is generally well-written, has an interesting question and a key point, and gets to the point fast. It features statistical analysis and visualizations as needed to drive the key point home and address likely reader questions. A non-specialist could read an UpShot post and come away smarter. Whatever you do later on, it’s (probably) the kind of output you’ll need or want to produce to convey quantitative insights to a broad audience. A guide and rubric for the blog post will be available. (Recommend time: similar to what you would invest in a final research paper or project)

Please feel free to use Slack or discuss with your peers questions about any assignments. However, you will probably get the most out of questions if you ask them after working on them a bit and running into an issue.

Deadlines and Extensions

Students are expected to submit work by the deadlines. Why? It really helps facilitate our group discussions. It also keeps students on track for the course. We all know that students/professors prioritize what they need to do by deadlines.

Since you won’t be penalized for effortful-but-incorrect answers, please don’t take extensions if you’re at the wall and unsure if your answers are correct. I don’t mind extensions, but this kind of extension usually won’t do much for your learning beyond maybe getting you frustrated. You’re almost surely better off just submitting your effortful attempts and reviewing the solutions carefully.

Sometimes life happens, and you aren’t able to submit things on time. That’s ok. Just send me a Slack DM. You won’t be penalized. If you are experiencing a major challenge in life (i.e. the level of a Dean’s excuse), send me a Slack DM so we can schedule a time to chat. We can make accommodations.

Grades

Labor-Based Grading Contracts

Grading is problematic. It induces anxiety and stress, fosters competition amongst students, induces proclivities towards honor code violations, and focuses student time and attention on grades instead of learning. Many professors also think it is the worst part of our jobs. Grading takes up a lot of time and typically doesn’t result in timely or productive feedback. Finally, grading can create an adversarial relationship between professors and students; office hours discussions around grades (e.g. “What can I do to get a better grade?”, “Can I do extra credit?”) are generally unpleasant for all involved.

All that said, I still have to assign you a final grade in the course.

What to do?

Instead of receiving a final letter grade based on mastery of course materials, I will reward you based on the effort that you put into the class. In a traditional statistics course, students take exams and receive grades based on the number of correct responses (grades are based on knowledge). In this course, students will be given assignments, and receive credit based on the effort they put into these assignments (grades based on labor). Letter Grades are assigned on the amount of labor; students can see the minimum amount of effort/labor they need to exert to obtain a specific letter grade (this is the contract).

The underlying theory here is that exerting effort has benefits even when it feels like you’re not getting much out of it. I want to rewire the usual effort-output-reward loop a bit so that you’re more directly incentivized to do things that will (on average) help you learn, even if in the moment it feels like a struggle and you’re not getting anywhere. From what I’ve seen, all else equal,

  • an effortful attempt almost always teaches you more than no attempt,
  • an effortful-but-incorrect attempt often teaches you about as much as an effortful-but-correct attempt, and
  • the average effortful-but-incorrect attempt teaches you more than the average effortless-but-correct attempt.

Why do this?

I start from the premise that if you’re in this class, you want to (a) learn statistics and statistical programming and (b) achieve a certain grade. All too often, course (and other) incentives students guide students to focus on more on getting good grades than on actually learning. With this system, I hope to minimize uncertainty from your final grade (you can achieve the grade you want with a relatively well-understood effort allocation) and make it easier for you to learn (less anxiety about assessments and more focus on process). Think about this question: “If I just wanted to learn this material and wasn’t getting a grade for this assignment, would I still do it?” If the answer is “No”, then maybe the assignment isn’t the best use of your time. I want to take the emphasis away from grades, and put it back on where it belongs: the process of learning.

Finally, labor-based contracts create a more (but not totally) equitable environment. Using a traditional grading system, students that have prior knowledge of the topic have a distinct advantage. With labor-based contracts, all students must put in time and effort; you cannot coast based on what you already know.

What does this look like?

There will be a set of assignments that you can choose to do. When you submit an assignment (quiz, lab, problem set), it is either accepted (receive credit) or declined (no credit). Anything that requires effort will be eligible for credit. Since we are focused on your effort and not knowledge, there will be no exams in this course. (You heard that correctly. No exams. A godsend to both students and professors.)

The mechanics

Each grade requires you to obtain a minimum number of submitted assignments in each grouping. For the UpShot-style blog posts only, I will track variation in output quality. The tiers of blog post quality are discussed in the blog post rubric. Every assignment except the blog posts will be assessed on an accept/decline basis.

Letter Grade Assignments

The table shows the minimum number of assignments in each grouping that is required for each final letter grade.

For example, a B requires 16 quizzes, 5 labs in R, 2 labs in STATA, 5 problem sets in R, 2 problem sets in STATA, and a “Basic” Blog post.

You should pay attention to the assignments in each grouping. For example, let’s say you are targeting an A-, and you have done 25 quizzes, 7 lab reports in R, but none in STATA. Should you do another lab report in R? NO!!! You should focus all of your attention on the STATA lab reports, because you need 3 of them to obtain an A-.

Frequently Asked Questions

  • If everything is based on effort and not knowledge, will I learn anything in this course?

Yes! True understanding of something is a result of the effort you put in, not a 24 hour cramming session before an exam. In fact, our (informal) best guess is that students forget most crammed things within a week after an exam.

  • What happens if there’s a question on an assignment that I cannot answer?

The beauty of this system is that you get credit if you put effort into answering the question, and not the actual answer. I will challenge your thinking with questions that require a lot of effort. I expect you to put in the effort. However, I want to minimize any anxiety about asking you tough questions because you don’t have to worry about getting the correct answer. As long as you show a good-faith effort, you’ll get credit.

  • This sounds pretty radical, will it work?

Great question. I hope so.

Course Calendar

All assignment due dates will be listed on Canvas.

Peer Groups

Peer groups consisting of 3-4 randomly assigned individuals will be created. You are encouraged to work on labs and problem sets together in your peer group. The groupings are here

The Secret to Doing Well

The course is front-end loaded and the course builds on itself. I strongly recommend to keep up with course deadlines, especially in the first-half of the semester.

One of your most valuable assets is your attention - and everything is vying for it. Here’s a plan to put you in a position to succeed

  • Each week, dedicate days and times when you work solely on Econ 210 in a distraction free environment. For example, on your calendar, block out Tuesdays and Thursdays from 8:30-10:30 am to work on Econ 210

  • When you work on Econ 210: Focus on this class and don’t multitasking (i.e. checking email , talking with friends, working on another course at the same time, etc . . .). Give your phone to a friend or leave it in your room and go to the library.

  • Use Freedom, Cold Turkey Blocker, SelfControl or another app to block web and app access on all your devices. When you work on this course, you want to eliminate access to anything that can distract you.

If you are interested in discovering more learning techniques and strategies, read up on Cal Newport. He’s a successful computer science professor at Georgetown who has interviewed a number of top undergraduate students.

College resources

The college has many resources to help you when you are struggling. You can find a list of resources here.

I’d like to make a particular mention of the Anderson Freeman Resource Center. The AFC has some great programming, including a peer mentorship program, peer writing tutoring, wellness support, counseling, and social events (granted we’re in a pandemic, but even online events can be nice). The AFC is physically located at Carr Hall and can be reached at or 802-443-2214.

Accommodations

Students who have Letters of Accommodation in this class are encouraged to contact me as early in the semester as possible to ensure that such accommodations are implemented in a timely fashion. For those without Letters of Accommodation, assistance is available to eligible students through Student Accessibility Services. Please contact Jodi Litchfield or Peter Ploegman, the ADA Coordinators, for more information: Peter Ploegman can be reached at or 802-443-2382 and Jodi Litchfield can be reached at or 802-443-5936. All discussions will remain confidential.I will make best efforts to provide accommodations but, given the current circumstances, I can’t guarantee the requirements of the letter will be met.

Honor code

I expect you to adhere to the Middlebury Honor Code. The Middlebury Honor Code is described at go/honorcode. You can collaborate on all assignments except the blog post. If you collaborate with someone, you must acknowledge those you worked with and submit your own final writeup.