Source: https://wiki.communitydata.cc/Statistics_and_Statistical_Programming_(Spring_2019)
Timestamp: 2019-04-22 13:58:44+00:00

Document:
2 Why this course? Why statistical programming? Why R?
We will use Canvas for announcements, turning in most assignments, and discussions.
This course provides a get-your-hands-dirty introduction to 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, HCI, and adjacent disciplines.
Design and execute a complete quantitative research project, 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 comfortable and prepared to enroll in more specialized and advanced statistics courses.
The course will cover the following techniques: t-tests; chi-squared tests; ANOVA, MANOVA, and related methods; 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, 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 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 absolutely not assumed.
Why this course? Why statistical programming? Why R?
Many comparable courses in statistics and quantitative methods do not focus on statistical programming and use easier-to-learn statistical software than R. 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.
R is freely available and open source.
R is becoming the most widely used package in statistics and many social science fields.
R (along with Stata) will be used in most other advanced stats classes I hope you will take after this course.
R is better general purpose programming language than software like Stata which means that R programming skills will let you solve non-statistical problems and will make it easier to learn other programming languages like Python.
I will not add readings or assignments less than one week before they are due. If I don't add something or fill in a "To Be Determined" one week before it's due, it is dropped. If you plan to read more than one week ahead, contact me first.
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 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.
I will ask the class 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.
The texbook (in any format) is required material for the course. You can download it at no cost and/or buy (affordable!) hard copy versions in either full color hardcover or in black and white paperback. 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).
This book provides a 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, but you may find it helpful to purchase.
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 search R websites online. Sometimes, R is hard to search before because R is a common letter. This has become much easier over time as R has become more popular but it might still be the case 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.
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.
The assignments in this class focus on applied statistical research design, analysis, and interpretation. There will be no graded exams or quizzes. Unless otherwise noted, all assignments are due at the end of the day (i.e., 11:59pm on the day they are due).
Statistics questions about statistical concepts, principles, and interpretation.
Programming challenges that you must solve using R.
Empirical paper questions about other assigned readings.
You should submit your solutions to the programming challenges (feel free to submit the others if you like, but they're not required!) ahead of each class session. While I will not grade them, we will spend a good chunk of class going through the answers to the assignment due on that day.
Because randomness is extremely important in statistics, I will use a small R program to randomly call on students to walk through your answer to statistics questions and empirical paper questions in class. We'll then discuss the answers, address points of confusion, and consider alternative approaches as a group.
For the programming challenges, you should submit code for your solutions before class (more on how in a moment) so we can walk through the material together. If you get completely stuck on a problem, that's okay, but please share whatever code you have so that you can tell us what you did and what you were thinking.
Coming to class will be profoundly important to learning the material and to your final grade. Although the problem sets will not be graded, it is critical that you be present and able to discuss your answers to each of the questions. Your ability to do so will figure prominently in your participation grade for the course (40% of your final grade).
I strongly encourage you to form groups to work on the problem sets if you find that helpful; however, you must still submit your work individually and respond to my cold-call prompts in class individually to help ensure that you learn and understand the material.
I evaluate 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 more and I encourage all of us to seek balance in our classroom discussions.
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.
Report and interpret your findings — You will do this in both a short paper and a short 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 that fulfills a degree requirement.
There are several intermediate milestones and deadlines to help you accomplish a successful research project. Unless otherwise noted, all deliverables should be submitted via Canvas.
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 columns or type 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.
The project planning document is a basic 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/or explanation of the relationship you plan to test; (e) Measures; (e) Dummy tables/figures. Descriptions of each of these planning document sections as well as an exemplary example will be available on this wiki page.
The paper: Ideally, I expect you to produce a high quality short research paper that you might revise 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 not an expert in each of your fields, 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 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 to handle your bibliographic sources.
The presentation: The presentation will provide an opportunity to share a brief summary 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 will be posted to Canvas) may be useful.
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.
My assessment of your paper will reflect the clarity of the written 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 a lot about the qualities of exemplary quantitative research. I expect your final project to embody these exemplary qualities.
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.
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 generally 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.
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.
When it comes to the statistics material, this will mostly be a so-called "flipped" classroom. This means we will rely on the textbook and other resources to introduce the material and we will use the class sessions to discuss questions as they come up.
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 Diez, Barr, and Çetinkaya-Rundel.
Discussion of any exemplary empirical paper we have read and the empirical paper questions.
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 of chapter n.
Please complete the readings and assignment prior to class so that we can discuss them and start talking through some of the examples in R together.
Questions? Topics you'd like to discuss? Add them to the Canvas discussion for this week's material.
Seeing Theory §1 (Basic Probability) and §2 (Compound Probability). (Note: this site provides a beautiful visual introduction to core concepts in probability and statistics).
Diez, Barr, and Çetinkaya-Rundel: §3.1-3.2, §3.4: You should read the rest of the chapter (§3.3 and §3.5). I won't assign problem set questions about it but it's still important to be familiar with.
Seeing Theory §3 (Probability Distributions).
Reinhart, §1, §4, and §5.
OpenIntro eschews a mathematical introduction to correlation. Look over the Wikipedia article on correlation and dependence and pay attention to the formulas. It's tedious to compute, but I'd like to you to at least see what goes into it.
Lesson 8: Categorical Predictors and Lesson 9: Data Transformations from the PennState Eberly College of Science STAT 501 Regression Methods Course. There are several subparts (many quite short), please read them all carefully.
Reserved for catch-up, supplementary topics, and maybe some final presentations.
Throughout the course, you may receive, read, collaborate, and/or comment on classmates’ work. These assignments are for class use only. You may not share them with anybody outside of class without explicit written permission from the document’s author and pertaining to the specific piece.
This syllabus has, in ways that should be obvious, borrowed and built on the OpenInto Statistics curriculum. I also based nearly every aspect of the course design on Benjamin Mako Hill's COM 521 class.

References: §1
 §2
 §3
 §3
 §3
 §3
 §1
 §4
 §5