Editing Statistics and Statistical Programming (Winter 2021)

[[File:Datasaurus-300px.gif|right|300px|frame|Image from [https://www.autodeskresearch.com/publications/samestats Matejka and Fitzmaurice, ''CHI'', 2017]|link=https://www.autodeskresearch.com/publications/samestats]]

;Statistical Methods in Communication: Introductory Statistics and Statistical Programming
:COM 521 — Department of Communication, University of Washington
; Description in Course Catalog: Reviews the steps taken in social scientific research on communication, with emphasis on the conceptualization, operationalization, and analysis of quantifiable variables. Highlights understanding of computer application of univariate and bivariate statistics, focusing on both parametric and nonparametric tests.
;Dates: Mondays and Wednesday, 9:30-11:20am
;Course websites
:* [https://canvas.uw.edu/courses/1434003 Canvas] — for [https://canvas.uw.edu/courses/1434003/announcements announcements], [https://canvas.uw.edu/courses/1434003/assignments assignments], and some [https://canvas.uw.edu/courses/1434003/files files].
:* [https://discord.com Discord] — for synchronous course meetings as well as asyncronous discussion and chat.
:* [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Winter_2021) This syllabus wiki page] — for nearly everything else.

;Instructor: [[Benjamin Mako Hill]] ([mailto:makohill@uw.edu makohill@uw.edu])
:Office Hours: {{tbd}} and by appointment (I'm usually available via chat during "business hours.")

<br clear=all>

== Overview and learning objectives ==

<div style="float:right;" width=30%; class="toclimit-3">__TOC__</div>

This course provides a get-your-hands-dirty introduction to inferential statistics and statistical programming for applications in communication research. 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—from 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 and proceed onward on the [https://www.csss.washington.edu/academics/phd-tracks/communication Statistics Concentration in the Communication for the MA/PhD program] offered between the Department of Communication and [https://www.csss.washington.edu/ Center for Statistics and the Social Sciences].

There will be readings on conceptualization and operationalization in quantitative research although these will overlap with reading in COM 501. The course will focus on a number of techniques, 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 have any prior training in 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, but nothing it is not assumed.

== Why statistical programming? Why R? ==

Some courses in statistics and quantitative methods do not emphasize statistical programming and rely on point-and-click tools like SPSS instead. Why bother learning R?

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. Ultimately, I'm teaching R because R is ascendant (i.e., it is increasing and is well on its way to "taking over") and there was consensus among the faculty in the department who were likely to teach statistics classes in the future that this made the most sense. I also do quite a lot of my own statistical work with R, so that also guides my choice. 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.

For students with a strong psychometric focus or whose research will be limited to linear and logistic regression or ANOVA on small pre-collected datasets and similar, SPSS will likely be fine. R has a higher barrier to entry than SPSS but it's ceiling is much higher.

== Note About This Syllabus ==

You should expect this syllabus to be a dynamic document. Although the core expectations for this class are fixed, the details of readings and assignments ''will'' shift based on how the class goes, guest speakers that I might arrange, my own readings in this area, etc. As a result, there are three important things to keep in mind:

* Although details on this syllabus will change, I will try to ensure that I never change readings more than six days before they are due. We will send an announcement '''no later than before we go to sleep each Tuesday evening''' that fixes the schedule for the next week. This means that if I don't fill in a reading marked "{{tbd}}" six days before it's due, it is dropped. If we don't change something marked "{{tentative}}" before the deadline, then it is assigned. This also means that if you plan to read more than six days ahead, contact the teaching team first.
* Because this syllabus a wiki, you will be able to track every change by clicking the history button on this page when I make changes. I will summarize these changes in the weekly [https://canvas.uw.edu/courses/1369415/announcements an announcement on Canvas] sent that will be emailed to everybody in the class. Closely monitor your email or the announcements section on the [https://canvas.uw.edu/courses/1369415/announcements course website on Canvas] to make sure you don't miss these announcements.
* I will ask the class for voluntary anonymous feedback frequently — 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 to courses that I teach while the quarter progressed based on this feedback.
* Many readings are marked as "''[Available through UW libraries]''". Most of these will be accessible to anybody who connects from the UW network. This means that if you're on campus, it will likely work. Although you can go through the UW libraries website to get most of these, the easiest way to get things using the [https://www.lib.washington.edu/help/connect/tools UW library proxy bookmarklet]. This is a little button you can drag-and-drop onto your bookmarks toolbar on your browser. When you press the button, it will ask you to log in using your UW NetID and then will automatically send your traffic through UW libraries. You can also use the other tools on [https://www.lib.washington.edu/help/connect this UW libraries webpage].

== Class format and structure ==
<!---
I expect everybody to come to class, every week, with a laptop and a power cord, ready to answer any question on the problem set and having uploaded code related the the programming questions. The class is listed as nearly 3 hours long and, with the exception of short breaks, I intend to use the entire period. Please be in class on time, plugged in, and ready to go.
--->

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.

===Asynchronous elements of the course===

These include all readings, recorded lectures/slides, tutorials, textbook exercises, problem sets, and other assignments. I expect you to complete—or put a good effort into attempting to complete so you can share your progress—these problem sets 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, I will try to keep an eye on the various server channels during "business hours." To the extent that I can respond to questions and concerns there, I'll do so. I strongly benefit that you raise issues in the "public" channels so that your classmates can answer the questions if they are struggling with similar issues.

We'll also use the discussion channels to identify topics that might benefit from conversation during synchronous course meetings. Hopefully, writing and talking about questions and concerns outside of our formal course meetings will help support accountability, learning, and more effective use of our limited time together.

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 any formal lectures during our class meetings. Please also note that this means you are responsible for coordinating any collaborative work with other members of the class outside of our class meeting times.

===Synchronous elements of the course===

The synchronous elements of the course will be the two weekly class meetings that will happen via video conference in Discord on the "Classroom Voice" channel on the course Discord. These are scheduled to run for a maximum of 110 minutes. I plan to use the entire time.

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''' over Discord whenever possible. Doing so will give me time to sift, sort, and organize your questions into a plan for each class session that is tailored to the specific concerns you have encountered in the material. Obviously, 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:
* I plan to record the course meetings and have them available to class participants in an access-control-restricted fashion. Please get in touch if you have concerns or requests about this. 
* I will do my 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 [https://www.mccormick.northwestern.edu/news/articles/2020/08/back-to-school-hack-shares-students-handwritten-work-and-teacher-response-in-real-time.html 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 plan to '''randomly call on students''' to share and discuss their solutions to selected textbook exercises or problem set questions during class. 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.

Although the day-to-day routine will vary, class session will include some combination of the following:

* Quick updates about assignments, projects, and meta-discussion about the class.
* Discussion of '''programming challenges''' due that day (and related to the previous week's R lecture materials).
* Discussion of  '''statistics questions''' related to new material in we've covered.
<!--* Discussion of any exemplary empirical paper we have read and the '''empirical paper questions'''. -->

== Textbook, readings, and resources ==

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. {{avail-free|https://www.openintro.org/book/os/}}

The texbook (in any format) is required for the course. You can [https://www.openintro.org/go?id=os4&referrer=/book/os/index.php download it] at no cost and purchase hard copy versions in either [https://www.openintro.org/go?id=os4_color_pb&referrer=/book/os/index.php full color ($60)] or in [https://www.openintro.org/go?id=os4_bw_pb&referrer=/book/os/index.php 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:

* Reinhart, Alex. 2015. ''Statistics Done Wrong: The Woefully Complete Guide''. SF, CA: No Starch Press. {{avail-uw|1=https://alliance-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=CP71226818410001451&context=L&vid=UW&lang=en_US&search_scope=all&adaptor=Local%20Search%20Engine&tab=default_tab&query=any,contains,statistics%20done%20wrong}}

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:

* Babbie, Earl R. 2015. '''The Practice of Social Research''', 14th edition. Boston, MA: Cengage Learning. [''Chapters will be made available in Canvas'']
* Healy, Kieran. 2019. ''Data Visualization: A Practical Introduction''. Princeton, NJ: Princeton University Press.{{avail-free|https://kieranhealy.org/publications/dataviz/}}
* Teetor, Paul. 2011. ''R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics''. 1 edition. Sebastopol, CA: O’Reilly Media. {{avail-uw|http://proquest.safaribooksonline.com/9780596809287}}; [''[https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Available for purchase through various sources]'']; [''[https://www.amazon.com/Cookbook-Analysis-Statistics-Graphics-Cookbooks/dp/0596809158/ref=sr_1_1?ie=UTF8&qid=1482802812&sr=8-1&keywords=r+cookbook Available for purchase through Amazon]'']
* Verzani, John. 2014. ''Using R for Introductory Statistics, Second Edition''. 2 edition. Boca Raton: Chapman and Hall/CRC. [''[https://en.wikipedia.org/wiki/Special:BookSources/978-1-4665-9073-1 Available for purchase through various sources]'']; [''[https://www.amazon.com/Using-Introductory-Statistics-Second-Chapman/dp/1466590734/ref=mt_hardcover?_encoding=UTF8&me= Available for purchase through Amazon]'']
* Wickham, Hadley. 2010. ''ggplot2: Elegant Graphics for Data Analysis''. 1st ed. 2009. Corr. 3rd printing 2010 edition. New York: Springer. {{avail-uw|https://link.springer.com/book/10.1007%2F978-3-319-24277-4}}; [''[https://en.wikipedia.org/wiki/Special:BookSources/978-0-596-80915-7 Available for purchase through various sources]'']
* Wickham, Hadly and Grolemund, Garret. 2017. ''R for Data Science''. Sebastopol, CA: O'Reilly. {{avail-free|https://r4ds.had.co.nz/}}

There are also some invaluable non-textbook resources:

* [ftp://cran.r-project.org/pub/R/doc/contrib/Baggott-refcard-v2.pdf 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!
* [https://stackoverflow.com/questions/tagged/r 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.
* [http://rseek.org/ 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.
* [https://ggplot2.tidyverse.org/ 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.
* [https://depts.washington.edu/acelab/proj/Rstats/index.html 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.
* [https://www.datacamp.com 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 [https://www.datacamp.com/groups/education request] free access to DataCamp for their courses from DataCamp. If folks are interested in this, I can reach out.

== Assignments ==

There are two types of assignments in the course: (a) problem sets that we will discuss during each class session; and (b) a large course project.

=== Problem Sets===

In order to support continuous progress towards the learning goals for the course, I have assigned problem sets for each class. These problem sets include some textbook exercises, some programming challenges, and some other questions.

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 [https://canvas.uw.edu/courses/1434003/assignments 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. -->

Although you will never hand these in to be graded, I will ''randomly'' call on students to share your answers to these questions and I will assess your preparedness after every single class meeting. I will ''not'' grade you on whether you get these answers correct or incorrect. Although the problem sets will not be assigned a letter grade, they are the central focus of the course and completing them will support your mastery of the material in multiple ways. These assignments will provide the basis on which I will assess and provide feedback on your participation and engagement with the course material.

For the programming challenges, be ready to share code and text for your solutions via screen share. If you get completely stuck on a problem, that's okay, but be ready to provide whatever you have and describe what tripped you up. In general, we will cover the problem sets in the first session of the week and the textbook materials in the second session.

=== Research project ===

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. The last time I taught a statistical course, a majority of students in the class used their course projects either to satisfy a general examination requirement, as a published paper, or both.

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 at 11:59pm Seattle time on the day they are due.

==== Research project plan and dataset identification ====

;Due date: Friday January 8, 2021
;Maximum length: 500 words (~1-2 pages)

Very 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 =====

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 [https://dataverse.harvard.edu/ Harvard Dataverse] (a very large collection of social science research data) or one of the other members of the [http://dataverse.org/ Dataverse network].
* Look at the collection of social scientific datasets at [https://www.icpsr.umich.edu/icpsrweb/ICPSR/ ICPSR at the University of Michigan] (NU is a member). There are an enormous number of very rich datasets.
* Use the [http://scientificdata.isa-explorer.org/index.html 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 Seattle has one of the best [https://data.seattle.gov/ 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.
* [http://fivethirtyeight.com FiveThirtyEight.com] has published a [https://cran.r-project.org/web/packages/fivethirtyeight/vignettes/fivethirtyeight.html 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 [https://files.pushshift.io/reddit/ pushshift] for dumps of Reddit data, [https://meta.wikimedia.org/wiki/Research:Data here] for an overview of Wikipedia's data resources, and [https://data.stackexchange.com/ Stack Exchange's data portal].
* The NY Times is publishing a [https://github.com/nytimes/covid-19-data 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| 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 [https://openpolicing.stanford.edu 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 ====

;Due date: January 31, 2021
;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 [[CommunityData:Planning document|on this wiki page]].

I will also provide example planning documents via our Canvas site:
* [https://canvas.northwestern.edu/files/9439380/download?download_frd=1 One by public health researcher Mika Matsuzaki]. The first planning document I ever saw and still one of the best. It's missing a measures section. It's also focused on a research context that is probably very different from yours, but try not to get bogged down by that and imagine how you might map the structure of the document to your own work.
* [One by Jim Maddock] created as part of a qualifying exam early in 2019. Jim doesn't provide dummy tables or anticipated findings/contributions, but he has an especially phenomenal explanation of the conceptual relationships and processes he wants to test. {{tentative}}
* [One provided as an appendix to Gerber and Green's excellent textbook, ''Field Experiments: Design, Analysis, and Interpretation'' (FEDAI)]. It's over-detailed and over-long for the purposes of this assignment, but nevertheless an exemplary approach to planning empirical quantitative research in a careful, intentional way that is worthy of imitation. {{tentative}}

==== Research project presentation ====

;Presentation due date: March 11, 2021
;Maximum length: 15 minutes

<!-- TODO revisit old presentations page to update/adapt 
[[Statistics_and_Statistical_Programming_(Spring_2019)/Final_project_presentations]]
--->
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. {{tentative}}

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

==== Research project paper ====

;Paper due date: March 19, 2021
;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 like a methods general examination. 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., [https://www.apastyle.org/index APA 6th edition] ([https://templates.office.com/en-us/APA-style-report-6th-edition-TM03982351 Word] and [https://www.overleaf.com/latex/templates/sample-apa-paper/fswjbwygndyq LaTeX] templates) or [https://cscw.acm.org/2019/submit-papers.html ACM SIGCHI CSCW format]) 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 ====
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 [https://www.washington.edu/research/hsd/ Human Subjects Division] (the UW 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 ===

I will assign grades (typically on the UW 4.0 grade scale) 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.

* Problem set discussion: 40%
* Proposal identification: 5%
* Final project planning document: 5%
* Final project presentation: 15%
* Final project paper: 35%

I will jointly and holistically evaluate your participation in problem set discussions along four dimensions: attendance, preparation, engagement, and contribution. These are quite similar to the dimensions described in the "Participation Rubric" section of [[User:Benjamin Mako Hill/Assessment|my assessment page]]. 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.

My 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. 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.

== Schedule (with all the details) ==

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) ===
==== September 17: Intro and setup ====

;[[Statistics_and_Statistical_Programming_(Fall_2020)/w01_session_plan|Session plan]]

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

'''Required'''
* Read this syllabus, discuss any questions/concerns with the teaching team.
* Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html 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 [https://www.openintro.org/book/os/ 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 [https://wiki.communitydata.science/Statistics_and_Statistical_Programming_(Fall_2020)/pset0 problem set #0] 

'''Recommended'''
* Work through one (or more) introduction(s) to R and Rstudio so that you can complete problem set 0. Here are several suggestions:
** '''From Aaron:''' The [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.html Week 01 R tutorial] (you should also download the [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w01-R_tutorial.rmd .rmd version of the tutorial] that you can open and read/edit in RStudio). These are accompanied by the R and Rstudio intro screencasts ([https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s01-intro.webm Part 1] and [https://communitydata.cc/~ads/teaching/2019/stats/screencasts/w01-s02-intro.webm Part 2]) Aaron created for the 2019 version of the course. 
** Modern Dive [https://moderndive.netlify.app/index.html Statistical inference via data science] Chapter 1: [https://moderndive.netlify.app/1-getting-started.html Getting started with R].
** [https://rladiessydney.org/courses/ryouwithme/ RYouWithMe] course [https://rladiessydney.org/courses/ryouwithme/01-basicbasics-0/ "Basic basics" 1 & 2] (and maybe 3 if you're feeling ambitious).
** Verzani §1 (Getting started).
** Healy §2 (Get started).

=== Week 2 (9/22, 9/24) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w02_session_plan|Session plans]]
==== September 22: Data and variables ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §1.1-1.3 (Introduction to data). 
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 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 ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §2.1-2 (Numerical and categorical data). 
* Review [https://www.youtube.com/playlist?list=PLkIselvEzpM6pZ76FD3NoCvvgkj_p-dE8 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) ===

;[[Statistics_and_Statistical_Programming_(Fall_2020)/w03_session_plan|Session plans]]

==== September 29: R fundamentals: Import, transform, tidy, and describe data ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset1|problem set #1]] (due Monday, September 28 at 1pm Central)

'''Recommended'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w03-R_tutorial.html 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.
<!---
'''Resources'''
* [https://science.sciencemag.org/content/187/4175/398 UCB admissions paper]
* [https://openpolicing.stanford.edu Stanford OpenPolicing Project]
--->

==== October 1: Probability ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §3 (Probability). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&v=rG-SLQ2uF8U Probability introduction] and [https://www.youtube.com/watch?v=HxEz4ZHUY5Y&list=PLkIselvEzpM5EgoOajhw83Ax_FktnlD6n&index=2 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'''
* [https://seeing-theory.brown.edu/index.html#secondPage Seeing Theory §1-2 (Basic Probability and Compound Probability)]

=== Week 4 (10/6, 10/8) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w04_session_plan|Session plans]]

==== October 6: Emotional contagion and more advanced R fundamentals: import, tidy, transform, and simulate data; write functions ====
'''Required'''
* Read the paper below as well as the attendant [https://www.pnas.org/content/111/29/10779.1 "Expression of editorial concern"] and [https://www.pnas.org/content/111/29/10779.2 "Correction"] that were subsequently appended to it.
: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. [[http://www.pnas.org/content/111/24/8788.full Open access]]
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset2|problem set #2]] (due Monday, October 5 at 1pm CT)

'''Recommended'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w04-R_tutorial.html Week 4 R tutorial] (as usual, also available as .rmd or .pdf)

==== October 8: Distributions ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §4.1-3 (Normal and binomial distributions). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM6V9h55s0l9Kzivih9BUWeW&v=S_p5D-YXLS4 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'''
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter3 Seeing Theory §3 (Probability distributions)]

==== October 9: [[#Research project plan and dataset identification|Research project plan and dataset identification]] due by 5pm CT ====
*'''Submit via [https://canvas.uw.edu/courses/1434003/assignments Canvas]''' (due by 5pm CT)

=== Week 5 (10/13, 10/15) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w05_session_plan|Session plans]]
==== October 13: Descriptive analysis and visualization of data ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset3|problem set #3]] (due Monday, October 12 at 1pm CT)

'''Recommended'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05-R_tutorial.html Week 5 R tutorial] and [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w05a-R_tutorial.html Week 5 R tutorial supplement] (both, as usual, also available as .rmd or .pdf).

==== October 15: Foundations for (frequentist) inference ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §5 (Foundations for inference). 
* Watch [https://www.youtube.com/watch?v=oLW_uzkPZGA&list=PLkIselvEzpM4SHQojH116fYAQJLaN_4Xo foundations for inference] (videos 1-3 in the playlist) OpenIntro lectures.
* Complete [https://www.openintro.org/book/stat/why05/ 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'''
* Kelly M., [https://rss.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1740-9713.2013.00693.x Emily Dickinson and monkeys on the stair Or: What is the significance of the 5% significance level?] ''Significance'' 10:5. 2013.
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter4 Seeing Theory §4 (Frequentist Inference)]

=== Week 6 (10/20, 10/22) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w06_session_plan|Session plans]]
==== October 20: Reinforced foundations for inference ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset4|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. [[http://www.pnas.org/content/111/24/8788.full Open access]]  

==== October 22: Inference for categorical data ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §6 (Inference for categorical data). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5Gn-sHTw1NF0e8IvMxwHDW&v=_iFAZgpWsx0 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'''
* [https://gallery.shinyapps.io/CLT_prop/ OpenIntro Central limit theorem for proportions demo].

=== Week 7 (10/27, 10/29) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w07_session_plan|Session plans]]
==== October 27: Applied inference for categorical data ====
'''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 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. [[https://mako.cc/academic/buechley_hill_DIS_10.pdf 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. [[https://doi.org/10.1177%2F0002764211433793 available via NU libraries]]
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset5|problem set #5]]
'''Resources'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w06-R_tutorial.html Week 06 R tutorial] (it's very short!)

==== October 29: Inference for numerical data (part 1) ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §7.1-3 (Inference for numerical data: differences of means). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 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'''
* [https://gallery.shinyapps.io/CLT_mean/ OpenIntro Central limit theorem for means demo].

==== October 30: [[#Research project planning document|Research project planning document]] due 5pm CT====
* Submit via [https://canvas.uw.edu/courses/1434003/assignments/ Canvas] (due by 5pm CT)

=== Week 8 (11/3, 11/5) ===
==== November 3: U.S. election day (no class meeting) ====

==== November 4: Interactive self-assessment due ====
* Please submit results [https://canvas.uw.edu/courses/1434003/assignments/799630 (via Canvas)] [FIXME] from the [https://communitydata.science/~ads/teaching/2020/stats/assessment/interactive_assessment.rmd interactive self-assessment] by 5pm CT.

==== November 5: Inference for numerical data (part 2) ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §7.4-5 (Inference for numerical data: power calculations, ANOVA, and multiple comparisons). 
* Watch [https://www.youtube.com/watch?list=PLkIselvEzpM5G3IO1tzQ-DUThsJKQzQCD&v=uVEj2uBJfq0 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'''
* [https://www.openintro.org/go/?id=stat_better_understand_anova&referrer=/book/os/index.php OpenIntro supplement on ANOVA calculations] (useful if you think you'll be doing more ANOVAs).

=== Week 9 (11/10, 11/12) ===
==== November 10: Applied inference for numerical data (t-tests, power analysis, ANOVA) ====
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w09_session_plan|Session plans]]

'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset6|problem set #6]]

'''Resources'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w09-R_tutorial.html Week 09 R tutorial]

==== November 12: Linear regression ====
'''Required'''
* Read Diez, Çetinkaya-Rundel, and Barr: §8 (Linear regression).
* Watch [https://www.youtube.com/playlist?list=PLkIselvEzpM63ikRfN41DNIhSgzboELOM linear regression] (videos 1-4 in the playlist) OpenIntro lectures.
* Read [https://www.openintro.org/go/?id=stat_more_inference_for_linear_regression&referrer=/book/os/index.php More inference for linear regression] (OpenIntro supplement).
* Complete '''exercises from OpenIntro §8:''' 8.6, 8.36, 8.40, 8.44
* Complete '''exercises from OpenIntro supplement:''' 4 and 5 (answers provided in the supplement).
 
'''Resources'''
* [https://seeing-theory.brown.edu/index.html#secondPage/chapter6 Seeing Theory §6 (Regression analysis)]

=== Week 10 (11/17, 11/19) ===
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w10_session_plan|Session plans]]
==== November 17: Applied linear regression ====
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset7|Problem set #7]]

'''Resources'''
* [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/w10-R_tutorial.html Week 10 R tutorial]
==== November 19: Multiple and logistic regression ====
'''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 [https://www.youtube.com/playlist?list=PLkIselvEzpM5f1HYzIjFt52SD4izsJ2_I multiple and logistic regression] (videos 1-4 in the playlist) OpenIntro lectures.
* Read [https://www.openintro.org/go/?id=stat_interaction_terms&referrer=/book/os/index.php Interaction terms] (OpenIntro supplement).
* Read [https://www.openintro.org/go/?id=stat_nonlinear_relationships&referrer=/book/os/index.php Fitting models for non-linear trends] (OpenIntro supplement).
* Complete '''exercises from OpenIntro §9:''' 9.4, 9.13, 9.16, 9.18, 

'''Resources'''

=== Week 11 (11/24) ===
==== November 24: Applied multiple and logistic regression ====
;[[Statistics_and_Statistical_Programming_(Fall_2020)/w11_session_plan|Session plans]]
'''Required'''
* Complete [[Statistics_and_Statistical_Programming_(Fall_2020)/pset8|Problem set #8]]
'''Resources'''
* Mako Hill created (and Aaron updated) a brief tutorial on [https://communitydata.science/~ads/teaching/2020/stats/r_tutorials/logistic_regression_interpretation.html interpreting logistic regression coefficients with examples in R]

=== Week 12+ ===

==== December 3: [[#Research project presentation|Research project presentation]] due by 5pm CT ====
'''[https://canvas.uw.edu/courses/1434003/discussion_topics/856868 Post your video via this "Discussion" on Canvas]'''. Please view and provide constructive feedback on other's videos!

* '''Post videos directly to the "Discussion."''' The Canvas text editor has an option to upload/record a video. That's what you want.
* '''Please remember not to over-work/think this.''' I mentioned this in class, but just to reiterate, the focus of this assignment should not be your video editing skills. Please do what you can to record and convey your ideas clearly without devoting insane hours to creating the perfect video. 
* '''Some resources for recording presentations:''' There are a bunch of ways you might record/share your video. Some ideas include using the embedded media recorder in Canvas (!) that can record with with your webcam (maybe attach a few visuals to accompany this?); recording a "meeting" with yourself in Zoom; and "Panopto," a piece of high-end video recording, sharing, and editing software that NU licenses for campus use. Here are some pointers:
** NU has a "digital learning resource hub" which provides some [https://digitallearning.northwestern.edu/resource-hub#for-students resources for students]. The first item in that list has pointers for recording yourself and posting to Canvas and includes info about the Canvas media recorder and Panopto.
** You should be able to use your NU zoom account to create a zoom meeting, record your meeting (in which you deliver your presentation and share your screen with any visuals), and then share a link to the recording via the "Recordings" item in the left-hand menu of your [https://northwestern.zoom.us/ https://northwestern.zoom.us/] account page.
** If nothing works, please get in touch.

==== December 4: Post-course assessment of statistical concepts due by 11pm CT ====
Complete [https://apps3.cehd.umn.edu/artist/user/scale_select.html post-course assessment] (access code TBA VIA email). Submission deadline: December 4, 11:00pm Chicago time.

==== December 10: [[#Research project paper|Research project paper]] due by 5pm CT ====
'''[https://canvas.uw.edu/courses/1434003/assignments/812317 Submit your paper, data, and code via Canvas].''' [FIXME]

== Credit and Notes ==

This syllabus has, in ways that should be obvious, borrowed and built on the [https://www.openintro.org/stat/index.php OpenInto Statistics curriculum]. Most aspects of this course design extend Benjamin Mako Hill's [[Statistics_and_Statistical_Programming_(Winter_2017)|COM 521 class]] from the University of Washington as well as a [[Statistics_and_Statistical_Programming_(Spring_2019)|prior iteration of the same course]] offered at Northwestern in Spring 2019.