Editing User:Aaronshaw/Stats course (section)

== Assignments ==

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).

=== Weekly problem sets and participation ===

Each week I will post a problem set. Some of these will be taken from the textbooks and some will not. They will include:

* '''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 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). More on

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 [https://mako.cc/teaching/assessment.html Benjamin Mako Hill's assessment page] and [https://reagle.org/joseph/zwiki/Teaching/Assessment/Participation.html 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 [https://reagle.org/joseph/zwiki/Teaching/Best_Practices/Learning/Balance_in_Discussion.html balance in our classroom discussions].

=== 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'''
* '''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.

==== Project plan and dataset identification ====

;Due date: Thursday, April 18, 2019
;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 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.

==== Project planning document ====

;Due date: Thursday, May 16, 2019
;Maximum length: 5 pages

The project planing 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. Descriptions of each of these planning document section are available [[TODO-planningdoc|on this wiki page]].

An exemplary planning document from public health researcher Mika Matsuzaki is [https://canvas.northwestern.edu online in Canavs]. Your diagram will likely be much less complicated than Matsuzaki's. Also, please don't be distracted by the fact that Matsuzaki does public health research. You can (and should!) emulate the form rather than the content. You can also check out [http://ajcn.nutrition.org/content/99/6/1450.full the published paper] to see how the project wound up.

Please note that the Matsuzaki planning document includes everything except a "Measures" section. Your Measures section should include a two column table where column 1 is the name of each variable in your analysis and column 2 describes the operationalization of each measures and (if necessary) how you will create it.

==== Project presentation and paper ====

;Paper due date: Monday, June 10, 2019
;Maximum length: 6000 words (~20 pages)

;Presentation due date: Thursday, June 6, 2019
;Maximum length: 12 minutes


''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. This can happen through Github. 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., <TODO link> ACM SIGCHI CSCW format or <TODO link> APA 6th edition) 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 [https://canvas.northwestern.edu Creating a Successful Scholarly Presentation] (link is in Canvas) may be useful.

=== Grading ===

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.

* Participation: 40%
* Proposal identification: 5%
* Final project planning document: 5%
* Final project presentation: 10%
* Final project paper: 40%

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.