Statistics and Statistical Programming (Fall 2020)

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Statistics and Statistical Programming
Media, Technology & Society (MTS) 525 and Communication Studies 395
Tuesdays & Thursdays 1-2:50pm CT
Fall 2020
Northwestern University
Course websites
Canvas for announcements, assignments, and some files.
Zoom for synchronous course meetings.
Discord for discussions and chat.
This wiki page for nearly everything else.
Instructor: Aaron Shaw (aaronshaw@northwestern.edu)
Office Hours: Thursday 10am-12pm and by appointment
Please use office hours signups (with location information)
Also usually available via chat during "business hours."
Teaching Assistant: Nick Vincent (nickvincent@u.northwestern.edu)
Office Hours: Monday 10am-12pm and by appointment. I'll try to respond to any asynchronous questions in a timely fashion during "business hours" (9a-5p Central Time), and will also have OH by appointment. I'll respond best to email (above), but am also happy to use Discord for quicker back-and-forth.
I am happy to try out alternative communication software for OH!



Course information[edit]

Overview and learning objectives[edit]

This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming mostly for applications in the social sciences and social computing. My main objectives are for all participants to acquire the conceptual, technical, and practical skills to conduct your own statistical analyses and become more sophisticated consumers of quantitative research in communication, human computer interaction (HCI), and adjacent disciplines.

I will consider the course a complete success if every student is able to do all of the following things at the end of the quarter:

  • Design and execute a quantitative research project that involves statistical inference, start to finish.
  • Read, modify, and create short programs in the R statistical programming language.
  • Feel comfortable reading and interpreting papers that use basic statistical techniques.
  • Feel prepared to enroll in more specialized and advanced statistics courses.

The course will cover a number of techniques, likely including the following: t-tests; chi-squared tests; ANOVA; linear regression; and logistic regression. We will also consider salient issues in quantitative research such as reproducibility and "the statistical crisis in science." We may cover other topics as time and interest allow.

The course materials will consist of readings, problem sets, assessment exercises, and recorded lectures and screencasts (some created by me, some created by other people). The course requirements will emphasize active participation, self-evaluation, and will include a final project focused on the design and execution of an original piece of quantitative research. We will use the R programming language for all examples and assignments.

You are not required to know much about statistics or statistical programming to take this class. I will assume some (very little!) knowledge of the basics of empirical research methods and design, basic algebra and arithmetic, and a willingness to work to learn the rest. In general we are not going to cover most of the math behind the techniques we'll be learning. Although we may do some math, this is not a math class. This course will also not require knowledge of calculus or matrix algebra. I will *not* do proofs on the board. Instead, the class is unapologetically focused on the application of statistical methods. Likewise, while some exposure to R, other programming languages, or other statistical computing resources will be helpful, it is not assumed.

Why this course? Why statistical programming? Why R?

Many comparable courses in statistics and quantitative methods do not emphasize statistical programming. So why bother? By learning statistical programming you will gain a deeper understanding of both the principles behind your analysis techniques as well as the tools you use to apply those techniques. In addition, a solid grasp of statistical programming will prepare you to create reproducible research, avoid common errors, and enable both greater durability and validity of your work.

Other programming languages are also well suited to statistics, including Stata and Python. I do most of my work with R, so that guides my choice for the course. That said, I opt to use and teach with R for a few reasons:

  • R is freely available and open source.
  • R is the most widely used package in statistics and several social scientific fields.
  • R (along with Stata) will be used in most of the advanced stats classes I hope you will take after this course.
  • R is better general purpose programming language than Stata which means that R programming skills will let you solve non-statistical problems and may make it easier to learn other programming languages like Python.

Format and structure[edit]

This course will proceed in a remote format that includes asynchronous and synchronous elements (more on those below). In general, the organization of the course adopts a "flipped" approach where participants consume, discuss, and process instructional materials outside of "class" and we use synchronous meetings to answer questions, address challenges or concerns, work through solutions, and hold semi-structured discussions.

The course introduces both basic statistical concepts as well as applications of those concepts through statistical programming. As a result, we will usually dedicate part of each week to a particular set of concepts and part of each week to applied data analysis and/or interpretation. A brief description of how I expect it all to work follows below. We'll talk about it more during the first class session.

Asynchronous elements of the course[edit]

These include all readings, recorded lectures/slides, tutorials, textbook exercises, problem sets, and other assignments. I expect you to complete (or at least attempt to complete!) these outside of our class meeting times. I also strongly encourage you to identify, submit, and discuss questions about the material before each class meeting whenever possible.

We will use Discord for everyday discussions and chat related to the course. In general, the teaching team will try to keep an eye on the various server channels during "business hours." To the extent that we can respond to questions and concerns there, we'll do so. We'll also use the discussion channels to identify topics that might benefit from synchronous conversation during the course meetings. Hopefully, writing and talking about questions and concerns outside of the synchronous course meetings will help support accountability, learning, and more effective use of our meeting time.

For nearly all of the "instructional" material introducing particular statistical concepts and techniques, you are assigned materials from the OpenIntro textbook and lecture materials created by the textbook authors. Please note that this means I will not deliver lectures during our class meetings. Please also note that this means you are responsible for coordinating your working groups and any collaborative work with other members of the class outside of our class meeting times.

Synchronous elements of the course[edit]

The synchronous elements of the course will be the two weekly class meetings that will happen via video conference (Zoom). These are scheduled to run for a maximum of 110 minutes. Each session will include multiple short breaks.

We will use the class meetings to discuss and work through any questions or challenges you encounter in the materials assigned for that day. This means that I encourage you to identify, submit, and discuss questions about the material before each class meeting whenever possible. Doing so will give the teaching team time to sift, sort, and organize the questions into a hopefully-cohesive plan for each class session that is tailored to the specific concerns you encounter in the material. Obviously, we anticipate that questions will arise during the class sessions too as well and we'll do our best to adapt as we go.

A couple of other notes about the synchronous course meetings:

  • Aaron plans to record the course meetings and have them available to class participants only via Zoom/Canvas. Please get in touch if you have concerns or requests about this.
  • The teaching team will do our best to notice and respond to any questions or comments that come up via Discord or Zoom during the class. Please do what you can to support these efforts.
  • You might want to create/acquire something like NU Mechanical Engineering Professor Michael Peshkin's homebrew document camera to facilitate sharing hand-written notes/drawings during class.

In addition, because randomness is extremely important in statistics, I may occasionally randomly assign different working groups to share and discuss their solutions to selected textbook exercises or problem set questions during class. These random assignments will be announced ahead of time so that the group has an opportunity to prepare. The idea here is to structure some participation in the synchronous sessions to ensure an equitable distribution of the responsibility to discuss questions, answers, points of confusion, and alternatives.

Working groups[edit]

At the start of the course you will be assigned to a small working group. This will be a group of 2-3 students (exact numbers will depend on the final enrollment) with whom you may meet outside of class time to discuss, complete, and/or review your weekly assignments (as well as some of the research project assignments). The groups will rotate at least once during the quarter to ensure that you get to work with different members of the class. The main idea is to support collaborative learning, peer support, and accountability. While the specifics of exactly when and how you work with your working group will largely be up to you, the teaching team will provide suggestions in the form of a template that you can use as a starting point.

As a general rule, we strongly encourage you to collaborate with members of your working group on any/all weekly (minor) assignments. You may, if you choose, also collaborate with others in your group or the class on your research project (major) assignments; however, collaborative research projects should be discussed with a member of the teaching team and all research project assignment submissions should include the names of all collaborators.


Textbook, readings, and resources[edit]

This class will use a freely-licensed textbook:

  • Diez, David M., Christopher D. Barr, and Mine Çetinkaya-Rundel. 2019. OpenIntro Statistics. 4th edition. OpenIntro, Inc.

The texbook (in any format) is required for the course. You can download it at no cost and purchase hard copy versions in either full color ($60) or in black and white ($20). The B&W version is very affordable and I strongly recommend buying a hard copy for the purposes of the course and subsequent reference use. The book is excellent and has been adopted widely. It has also developed a large online community of students and teachers who have shared other resources. Lecture slides, videos, notes, and more are all freely licensed (many through the website and others elsewhere).

I will also assigning several chapters from the following:

This book provides a readable conceptual introduction to some common failures in statistical analysis that you should learn to recognize and avoid. It was also written by a Ph.D. student. You have access to an electronic copy via the NU library (you'll need to sign-in and/or use the NU VPN to access it), but you may find it helpful to purchase as well.

A few other books may be useful resources while you're learning to analyze, visualize, and interpret statistical data with R. I will share some advice about these during the first class meeting:

  • Healy, Kieran. 2019. Data Visualization: A Practical Introduction. Princeton, NJ: Princeton UP. (via Healy's website)
  • Teetor, Paul. 2011. R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. 1 edition. Sebastopol, CA: O’Reilly Media. (Safari Proquest/NU Libraries; Various Sources; Amazon)
  • Verzani, John. 2014. Using R for Introductory Statistics, Second Edition. 2 edition. Boca Raton: Chapman and Hall/CRC. (Various Sources; Amazon)
  • Wickham, Hadley. 2010. ggplot2: Elegant Graphics for Data Analysis. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. (Springer/NU Libraries; Various Sources)
  • Wickham, Hadly and Grolemund, Garret. 2017. R for Data Science. Sebastopol, CA: O'Reilly. (Online version).

There are also some invaluable non-textbook resources:

  • Baggott's R Reference Card v2 — Print this out. Take it with you everywhere and look at it dozens of times a day. You will learn the language faster!
  • StackOverflow R Tag — Somebody already had your question about how to do X in R. They asked it, and several people have answered it, on StackOverflow. Learning to read this effectively will take time but as build up some basic familiarity with R and with StackOverflow, it will get easier. I promise.
  • Rseek — Rseek is a modified version of Google that just searches R websites online. Sometimes, R is hard to search because R is a common letter. This has become much easier over time as R has become more popular, but it can still be an issue sometimes and Rseek is a good solution.
  • ggplot2 documentation — ggplot is a powerful data visualization package for R that I recommend highly. The documentation is indispensable for learning how to use it.
  • Statistical Analysis and Reporting in R — A set of resources created and distributed by Jacob Wobbrock (University of Washington, School of Information) in conjunction with a MOOC he teaches. Contains cheatsheets, code snippets, and data to help execute commonly encountered statistical procedures in R.
  • DataCamp offers introductory R courses. Northwestern usually has some free accounts that get passed out via Research Data Services each quarter. Apparently, if you are taking or teaching relevant coursework, instructors can request free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.

Computing resources:

  • If you are planning to analyze large-scale data (i.e., data that won't fit in memory on your laptop) then you will want to sign up for a research allocation on Quest, which is Northwestern's high-performance computing cluster. Instructions on how to do that are here.

Weekly (minor) assignments[edit]

In order to support continuous progress towards the learning goals for the course, I have assigned some textbook exercises or a problem set ahead of every class. These assignments will provide the basis on which the teaching team will assess and provide feedback on your participation and engagement with the course material.

The first week or so of the course is textbook-focused to get us warmed up. Starting in week 2, we will do more statistical programming and apply the textbook concepts using R and RStudio. In general, we will cover the problem sets in the first session of the week and the textbook materials in the second session.

Textbook exercises[edit]

The focus is on self-assessment of your understanding of the textbook material and you do not need to hand in anything. I expect that you will work on the exercises, review and discuss solutions, and submit any questions ahead of or during class. Please note that solutions to odd-numbered problems appear in the back of the book. The teaching team will distribute solutions to even-numbered problems as well.

Problem sets[edit]

The course will include problem sets and these may incorporate several kinds of questions:

  • Statistics questions about statistical concepts and principles.
  • Programming challenges that you should solve using R.
  • Empirical paper questions about other assigned readings.

For the problem sets, I ask that you submit your work via Canvas 24 hours before class (i.e., Monday afternoon for our Tuesday class sessions). Details of exactly how this will work will be elaborated during the first class. For the programming challenges, you should submit code and text for your solutions (again, more on how later). If you get completely stuck on a problem, that's okay, but please provide whatever you have.

Problem sets will be evaluated on a complete/incomplete basis. Although the problem sets will not be assigned a letter grade, they are a central focus of the course and completing them will support your mastery of the material in multiple ways. Working through them on schedule will also make it possible for you to participate in the synchronous course meetings and online discussions of course material effectively. Your ability to do so will figure prominently in your participation grade for the course (see the section on grading and assessment below).

Research project (major) assignments[edit]

Overview[edit]

As a demonstration of your learning in this course, you will design and carry out a quantitative research project, start to finish. This means you will all:

  • Design and describe a plan for a study — The study you design should involve quantitative analysis and should be something you can complete at least a first pass on during this quarter.
  • Find a dataset — Very quickly, you should identify a dataset you will use to complete this project. For most of you, I suspect you will be engaging in secondary data analysis or a analysis of a previously collected dataset.
  • Engage in descriptive data analysis — Use R to calculate descriptive statistics and visualizations to describe your data.
  • Motivate and test at least one hypothesis about relationships between two or more variables — I'm happy to discuss alternatives to formal hypothesis testing procedures (even if some of them are beyond the scope of this course).
  • Report and interpret your findings — You will do this in both a short paper and a short (recorded) presentation.
  • Ensure that your work is replicable — You will need to provide code and data for your analysis in a way that makes your work replicable by other researchers.

I strongly urge you to produce a project that will further your academic career outside of the class. There are many ways that this can happen. Some obvious options are to prepare a project that you can submit for publication, use as pilot analysis that you can report in a grant or thesis proposal, and/or use to fulfill a degree requirement.

There are several intermediate milestones, deliverables, and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas by 5pm CT on the day they are due.


Research project plan and dataset identification[edit]

Due date
October 9, 2020, 5pm CT
Maximum length
500 words (~1-2 pages)

Early on, I want you to identify and describe your final project. Your description should be short and can be either paragraphs or bullets. It should include the following:

  • An abstract of the proposed study including the topic, research question, theoretical motivation, object(s) of study, and anticipated research contribution.
  • An identification of the dataset you will use and a description of the rows and columns or type(s) of data it will include. If you do not currently have access to these data, explain why and when you will.
  • A short (several sentences?) description of how the project will fit into your career trajectory.


Notes on finding a dataset[edit]

In order to complete your final project, you will each need a dataset. If you already have a dataset for the project you plan to conduct, great! If not, fear not! There are many datasets to draw from. Some ideas are below (please suggest others, provide updated links, or report problems). The teaching team will also be available to help you brainstorm/find resources if needed:

  • Ask your advisor for a dataset they have collected and used in previous papers. Are there other variables you could use? Other relationships you could analyze?
  • If there's an important study you loved, you can send a polite email to the author(s) asking if they are willing and able to share an archival or replication version of the dataset used in their paper. Be very polite and make it clear that this is starting as a class project, but that it might turn into a paper for publication. Make your timeline clear. In Communication and HCI, replication datasets are still very rare, so be prepared for a negative answer and/or questions about your motives in conducting the analysis.
  • Do some Google Scholar and normal internet searching for datasets in your research area. You'll probably be surprised at what's available.
  • Take a look at datasets available in the Harvard Dataverse (a very large collection of social science research data) or one of the other members of the Dataverse network.
  • Look at the collection of social scientific datasets at ICPSR at the University of Michigan (NU is a member). There are an enormous number of very rich datasets.
  • Use the ISA Explorer to find datasets. Keep in mind the large majority of datasets it will search are drawn from the natural sciences.
  • The City of Chicago has one of the best data portal sites of any municipality in the U.S. (and better than many federal agencies). There are also numerous administrative datasets released by other public entities (try searching!) that you might find inspiring.
  • FiveThirtyEight.com has published a GitHub repository and an R package with pre-processed and cleaned versions of many of the datasets they use for articles published on their website.
  • If you interested in studying online communities, there are some great resources for accessing data from Reddit, Wikipedia, and StackExchange. See pushshift for dumps of Reddit data, here for an overview of Wikipedia's data resources, and Stack Exchange's data portal.
  • The NY Times is publishing a COVID-19 data repository that includes county-level metrics for deaths, mask usage, and other pandemic-related data. The release a lot of it as frequently updated .csv files and the repository includes documentation of the measurements, data collection details, and more.
  • The Community Data Science Collective and colleagues have created a COVID-19 digital observatory (hosted in part right here on this wiki!) that publishes a bunch of pandemic-related data as csv and json files.
  • The Stanford Open Policing project has published a huge archive of policing data related mostly to traffic stops in states and many cities of the U.S. We'll use at least one of these files for a problem set.

Research project planning document[edit]

Due date
October 30, 2020, 5pm CT
Suggested length
~5 pages

The project planning document is a shell/outline of an empirical quantitative research paper. Your planning document should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General objectives; (b.2) Specific objectives; (c) (Null) hypotheses; (d) Conceptual diagram and explanation of the relationship(s) you plan to test; (e) Measures; (f) Dummy tables/figures; (g) anticipated finding(s) and research contribution(s). Longer descriptions of each of these planning document sections (as well as a few others) can be found on this wiki page.

I will also provide three example planning documents via our Canvas site (links to-be-updated for 2020 edition of the course):

Research project presentation[edit]

Presentation due date
December 3, 2020, 5pm CT
Maximum length
10 minutes

You will also create and record a short presentation of your final project. The presentation will provide an opportunity to share a brief overview of your project and findings with the other members of the class. Since you will all give other research presentations throughout your career, I strongly encourage you to take the opportunity to refine your academic presentation skills. The document Creating a Successful Scholarly Presentation (file posted to Canvas) may be useful.

Additional details about the presentation goals, format suggestions, resources, and more will be provided later in the quarter.

Research project paper[edit]

Paper due date
December 8, 2020, 5pm CT
Maximum length
6000 words (~20 pages)

I expect you to produce a short, high quality research paper that you might revise, extend, and submit for publication and/or a dissertation milestone. I do not expect the paper to be ready for publication, but it should contain polished drafts of all the necessary components of a scholarly quantitative empirical research study. In terms of the structure, please see the page on the structure of a quantitative empirical research paper.

As noted above, you should also provide data, code, and any documentation sufficient to enable the replication of all analysis and visualizations. If that is not possible/appropriate for some reason, please talk to me so that we can find another solution.

Because the emphasis in this class is on statistics and methods and because I'm probably not an expert in the substance of your research domain, I'm happy to assume that your paper, proposal, or thesis chapter has already established the relevance and significance of your study and has a comprehensive literature review, well-grounded conceptual approach, and compelling reason why this research is important. As a result, you need not focus on these elements of the work in your written submission. Instead, feel free to start with a brief summary of the purpose and importance of this research followed by an introduction of your research questions or hypotheses. If you provide more detail, that's fine, but I won't give you detailed feedback on these parts and they will not figure prominently in my assessment of the work.

I have a strong preference for you to write the paper individually, but I'm open to the idea that you may want to work with others in the class. Please contact me before you attempt to pursue a collaborative final paper.

I do not have strong preferences about the style or formatting guidelines you follow for the paper and its bibliography. However, your paper must follow a standard format (e.g., ACM SIGCHI CSCW format or APA 6th edition (Word and LaTeX templates)) that is applicable for a peer-reviewed journal or conference proceedings in which you might aim to publish the work (they all have formatting or submission guidelines published online and you should follow them). This includes the references. I also strongly recommend that you use reference management software like Zotero to handle your bibliographic sources.


Human subjects research, IRB, and ethics[edit]

In general, you are responsible for making sure that you're on the right side of the IRB requirements and that your work meets applicable ethical norms and standards.

Class projects generally do not need IRB approval, but research for publications, dissertations, and sometimes even pilot studies do fall under IRB purview. You should not plan to seek IRB approval/determination retroactively. If your study may involve human subjects and you may ever publish it in any form, you will need IRB oversight of some sort.

Secondary analysis of anonymized data is generally not considered human subjects research, but I strongly suggest that you get a determination from the Northwestern IRB before you start. For work that is not considered human subjects research, this can often happen in a few hours or days. If you need to list a faculty sponsor or Principal Investigator, that should ideally be your advisor. If that doesn't make sense for some reason, please talk to me.

Research ethics are broad and complex topic. We'll talk about issues related to ethics and quantitative empirical research a bit more during class, but will likely only scratch the surface. I strongly encourage you to pursue further reading, conversation, coursework, and reflection as you consider how to understand and apply ethical principles in the context of your own research and teaching.

Grading and assessment[edit]

I will assign grades (usually a numeric value ranging from 0-10) for each of the following aspects of your performance. The percentage values in parentheses are weights that will be applied to calculate your overall grade for the course.

  • Weekly participation: 40%
  • Proposal identification: 5%
  • Final project planning document: 5%
  • Final project presentation: 10%
  • Final project paper: 40%

The teaching team will jointly and holistically evaluate your participation along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of Benjamin Mako Hill's assessment page and Joseph Reagle's participation assessment rubric. Exceptional participation means excelling along all four dimensions. Please note that participation ≠ talking/typing more and I encourage all of us to seek balance in our discussions.

The teaching team's assessment of your final project proposal, planning document, presentation, and paper will reflect the clarity of the work, the effective execution and presentation of quantitative empirical analysis, as well as the quality and originality of the analysis. A more detailed assessment rubric will be provided. Throughout the quarter, we will talk about the qualities of exemplary quantitative research. In general, I expect your final project to embody these exemplary qualities.

Policies[edit]

General course policies[edit]

General policies on a wide variety of topics including classroom equity, attendance, academic integrity, accommodations, late assignments, and more are provided on Aaron's class policies page. Below are some policy statements specific to this course and quarter.

Teaching and learning in a pandemic[edit]

The Covid-19 pandemic will impact this course in various ways, some of them obvious and tangible and others harder to pin down. On the obvious and tangible front, we have things like a mix of remote and (a)synchronous instruction, the fact that many of us will not be anywhere near campus or each other this year, and the unusual academic calendar. These will reshape our collective "classroom" experience in major ways.

On the "harder to pin down" side, many of us may experience elevated levels of exhaustion, stress, uncertainty and/or distraction. We may need to provide unexpected support to family, friends, or others in our communities. I have personally experienced all of these things at various times over the past six months and I expect that some of you have too. It is a difficult time.

I believe it is important to acknowledge these realities of the situation and create the space to discuss and process them in the context of our class throughout the quarter. As your instructor and colleague, I commit to do my best to approach the course in an adaptive, generous, and empathetic way. I will try to be transparent and direct with you throughout—both with respect to the course material as well as the pandemic and the university's evolving response to it. I ask that you try to extend a similar attitude towards everyone in the course. When you have questions, feedback, or concerns, please try to share them in an appropriate way. If you require accommodations of any kind at any time (directly related to the pandemic or not), please contact the teaching team.

Expectations for synchronous remote sessions[edit]

The following are some baseline expectations for our synchronous remote class sessions. I expect that these can and will evolve. Please feel free to ask questions, suggest changes, or raise concerns during the quarter. I welcome all input.

  • All members of the class are expected to create a supportive and welcoming environment that is respectful of the conditions under which we are participating in this class.
  • All members of the class are expected to take reasonable steps to create an effective teaching/learning environment for themselves and others.

And here are suggested protocols for any video/audio portions of our class:

  • Please mute your microphone whenever you're not speaking and learn to use "push-to-talk" if/when possible.
  • Video is optional for all students at all times, although if you're willing/able to keep the instructor company in the video channel that would be nice.
  • If you need to excuse yourself at any time and for any reason you may do so.
  • Children, family, pets, roommates, and others with whom you may share your workspace are welcome to join our class as needed.

Syllabus revisions[edit]

This syllabus will be a dynamic document that will evolve throughout the quarter. Although the core expectations are fixed, the details will shift. As a result, please keep in mind the following:

  1. Assignments and readings are frozen 1 week before they are due. I will not add readings or assignments less than one week before they are due. If I forget to add something or fill in a "To Be Determined" less than one week before it's due, it is dropped. If you plan to read or work more than one week ahead, contact me first.
  2. Substantial changes to the syllabus or course materials will be announced. Please closely monitor your email and/or the announcements section on the course website on Canvas. When I make changes, these changes will be recorded in the edit history of this page so that you can track what has changed. I will also do my best to summarize these changes in an announcement on Canvas that will be emailed to everybody in the class.
  3. The course design may adapt throughout the quarter. As this is a new format for this course, I may iterate and prototype course design elements rapidly along the way. To this end, I will ask you for voluntary anonymous feedback — especially toward the beginning of the quarter. Please let me know what is working and what can be improved. In the past, I have made many adjustments based on this feedback and I expect to do so again.

Statistics and power[edit]

The subject matter of this course—statistics and statistical programming—has historical and present-day affinities with a variety of oppressive ideologies and projects, including white supremacy, discrimination on the basis of gender and sexuality, state violence, genocide, and colonialism. It has also been used to challenge and undermine these projects in various ways. I will work throughout the quarter to acknowledge and represent these legacies accurately, at the same time as I also strive to advance equity, inclusion, and justice through my teaching practice, the selection of curricular materials, and the cultivation of an inclusive classroom environment. Please see my general classroom policies for more on some of these topics.

Schedule (with all the details)[edit]

When reading the schedule below, the following key might help resolve ambiguity: §n denotes chapter n; §n.x denotes section x of chapter; §n.x-y denotes sections x through y (inclusive) of chapter n.

Week 1 (9/17)[edit]

September 17: Intro and setup[edit]

Session plan

Note: Aaron doesn't actually expect you to complete these before class on September 17

Required

  • Read this syllabus, discuss any questions/concerns with the teaching team.
  • Complete pre-course assessment of statistical concepts (access code TBA via email). Estimated time to do this is 30-40 minutes. Submission deadline: September 18, 11:00pm Chicago time
  • Confirm course registration and access to the textbook (pdf download available for $0 and b&w paperbacks for $20) as well as any software and web-services you'll need for course (Zoom, Discord, Canvas, this wiki, R, RStudio). Discord invites will be sent via email.
  • Complete problem set #0

Recommended

Week 2 (9/22, 9/24)[edit]

Session plans

September 22: Data and variables[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data).
  • Watch Lecture materials for §1.1-3 (Videos 1-4 in the playlist).
  • Complete exercises from OpenIntro §1: 1.6, 1.9, 1.10, 1.16, 1.21, 1.40, 1.42, 1.43 (and remember that solutions to odd-numbered problems are in the book!)
  • Submit, review, and respond to questions or requests for discussion via Discord or some other means.

September 24: Numerical and categorical data[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data).
  • Review Lecture materials for §2.1 and §2.2 (Videos 6-7 in the playlist).
  • Complete exercises from OpenIntro §2: 2.12, 2.13, 2.16, 2.20, 2.23, 2.30 (and remember that solutions to odd-numbered problems are in the book!)
  • Submit, review, and respond to questions or requests for discussion via Discord or some other means.

Week 3 (9/29, 10/1)[edit]

Session plans

September 29: R fundamentals: Import, transform, tidy, and describe data[edit]

Required

Recommended

  • Week 3 R tutorial (note that you can access .rmd or .pdf versions by replacing the suffix of the URL accordingly).
  • Additional material from any of the recommended R learning resources suggested last week or elsewhere in the syllabus. In particular, you may find the ModernDive, RYouWithMe, Healy, and/or Wickham and Grolemund resources valuable.

October 1: Probability[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability).
  • Watch Probability introduction and Probability trees OpenIntro lectures (just videos 1 and 2 in the playlist).
  • Complete exercises from OpenIntro §3: 3.12, 3.15, 3.22, 3.28, 3.34, 3.38

Resources

Week 4 (10/6, 10/8)[edit]

Session plans

October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions[edit]

Required

Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” Proceedings of the National Academy of Sciences 111(24):8788–90. [Open access]

Recommended

October 8: Distributions[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions).
  • Watch normal and binomial distributions OpenIntro lectures (videos 1-3 in the playlist).
  • Complete exercises from OpenIntro §4: 4.4, 4.6, 4.15, 4.22

Resources

October 9: Research project plan and dataset identification due by 5pm CT[edit]

  • Submit via Canvas (due by 5pm CT)

Week 5 (10/13, 10/15)[edit]

Session plans

October 13: Descriptive analysis and visualization of data[edit]

Required

Recommended

October 15: Foundations for (frequentist) inference[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference).
  • Watch foundations for inference (videos 1-3 in the playlist) OpenIntro lectures.
  • Complete Why .05? OpenIntro video/exercise.
  • Complete exercises from OpenIntro §5: 5.4, 5.8, 5.10, 5.17, 5.30, 5.35, 5.36

Resources

Week 6 (10/20, 10/22)[edit]

Session plans

October 20: Reinforced foundations for inference[edit]

Required

  • Complete problem set #4
  • Read Reinhart, §1.
  • Revisit the Kramer et al. (2014) paper we read a few weeks ago:
Kramer, Adam D. I., Jamie E. Guillory, and Jeffrey T. Hancock. 2014. “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks.” Proceedings of the National Academy of Sciences 111(24):8788–90. [Open access]

October 22: Inference for categorical data[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data).
  • Watch inference for categorical data (videos 1-3 in the playlist) OpenIntro lectures.
  • Complete exercises from OpenIntro §6: 6.10, 6.16, 6.22, 6.30, 6.40 (just parts a and b; part c gets tedious)

Resources

Week 7 (10/27, 10/29)[edit]

Session plans

October 27: Applied inference for categorical data[edit]

Required

  • Read Reinhart, §4 and §5 (both are quite short).
  • Skim the following (all are referenced in the problem set)
    • Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. PLoS ONE 13(1): e0189693. https://doi.org/10.1371/journal.pone.0189693
    • Buechley, Leah and Benjamin Mako Hill. 2010. “LilyPad in the Wild: How Hardware’s Long Tail Is Supporting New Engineering and Design Communities.” Pp. 199–207 in Proceedings of the 8th ACM Conference on Designing Interactive Systems. Aarhus, Denmark: ACM. [PDF available on Hill's personal website]
    • Shaw, Aaron and Yochai Benkler. 2012. A tale of two blogospheres: Discursive practices on the left and right. American Behavioral Scientist. 56(4): 459-487. [available via NU libraries]
  • Complete problem set #5

Resources

October 29: Inference for numerical data (part 1)[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means).
  • Watch inference for numerical data (videos 1-4 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).
  • Complete exercises from OpenIntro §7: 7.12, 7.24, 7.26

Resources

October 30: Research project planning document due 5pm CT[edit]

  • Submit via Canvas (due by 5pm CT)

Week 8 (11/3, 11/5)[edit]

November 3: U.S. election day (no class meeting)[edit]

November 4: Interactive self-assessment due[edit]

November 5: Inference for numerical data (part 2)[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons).
  • Watch inference for numerical data (videos 4-8 in the playlist) OpenIntro lectures (and featuring one of the textbook authors!).
  • Complete exercises from OpenIntro §7: 7.42, 7.44, 7.46

Resources

Week 9 (11/10, 11/12)[edit]

November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA)[edit]

Required

  • Complete problem set #6

Resources

November 12: Linear regression[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).
  • Watch linear regression (videos 1-4 in the playlist) OpenIntro lectures.
  • Read More inference for linear regression (OpenIntro supplement).
  • Complete exercises from OpenIntro §8:
  • Complete exercises from OpenIntro supplement:

Resources

Week 10 (11/17, 11/19)[edit]

November 17: <Topic>[edit]

Required

  • Complete Problem set #7

Resources

November 19: Multiple and logistic regression[edit]

Required

  • Read Diez, Çetinkaya-Rundel, and Barr: §9 (Multiple and logistic regression). (Skim §9.2-9.4)
    • Disclaimer: Aaron doesn't like §9.2-9.3, but it should be useful to understand and discuss them, so we'll do that.
  • Watch multiple and logistic regression (videos 1-4 in the playlist) OpenIntro lectures.
  • Read Interaction terms (OpenIntro supplement).
  • Read Fitting models for non-linear trends (OpenIntro supplement).
  • Complete exercises from OpenIntro §9:'
  • Complete exercises from OpenIntro supplements:'

Resources

Week 11 (11/24)[edit]

November 24: <Topic> and assessment[edit]

Required

Resources

Week 12+[edit]

December 3: Research project presentation due by 5pm CT[edit]

December 10: Research project paper due by 5pm CT[edit]

Credit and Notes[edit]

This syllabus has, in ways that should be obvious, borrowed and built on the OpenInto Statistics curriculum. Most aspects of this course design extend Benjamin Mako Hill's COM 521 class from the University of Washington as well as a prior iteration of the same course offered at Northwestern in Spring 2019.