Editing Statistics and Statistical Programming (Winter 2017) (section)

== Assignments ==

The assignments in this class are designed to give you an opportunity to try your hand at using the conceptual material taught in the class. There will be no 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 with a list of questions. Some of these will be drawn from the textbooks and some will be ones I design or write. The questions will cover:

* '''Statistics questions''' — These will be questions about statistics from the OpenIntro sections as well as any empirical papers that are listed as required for that that day.
* '''Programming challenges''' — These will be R programming problems that cover material from the Verzani text that was listed as required from the previous session.

I won't be grading these assignment and I won't be asking you to turn in anything for the ''statistics questions'' portion of the weekly assignment. That said, we will spend a good chunk of class each day going through the answers to the questions due on that day.

Because randomness is an extremely important concept in statistics, I will use a small R program to '''randomly cold call''' on students in the class to walk through your "answer" to each question and explain your reasoning to the class. We'll then have an opportunity to discuss the different approaches as a group. I don't promise to ask all of these questions in class (especially if it's clear that folks get the point). Although I might ask them, I won't cold call for questions that are not on the list.

For the programming challenges, I will ask that everybody shares code for any solutions to programming problems before class so we can walk through in class. If you get completely stuck on a problem and cannot "solve" it, that's OK, but share the code that you do have so that you can walk us through what you did and what you were thinking.

Although the problem sets are not going to be graded, it is critical that you be at class and that you be able to discuss your answers to each of the questions. Your ability to do these latter two things will be reflected in your participation grade for the course which makes a full 40% of your grade. I can't emphasize enough how important it will be to be in class.

I'm not going to form groups for you but it's totally fine with me if you want to work on these problem sets in small groups.

The "Participation Rubric" section of [https://mako.cc/teaching/assessment.html my page on assessment] gives the details on how I evaluate participation in my classes. If you sense a conflict between material in this section and material on that page, you can safely assume that the syllabus takes precedence.

=== 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 social scientific study''' —  You should all have experience doing this at least once in COM520. The study you design should involves quantitative analysis and should be something you can complete at least a first pass at over the course of 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 re-analysis of a previously collected dataset.
* '''Engage in descriptive data analysis''' — Use R to create descriptive statistics and visualization to describe your data.
* '''Test a hypotheses about relationships between two or more variables'''
* '''Report your findings''' — I'll expect you all to report your findings in both a short paper and a short presentation.
* '''Ensure replicability''' — I'll expect you all to provide code and data for your analysis in a way that makes your work replicable by other researchers.

Although it's not required, I ''strongly urge each of you'' to take this opportunity to produce a document that will further your academic career outside of the class. There are many ways that this can happen but the obvious ones are that the paper is something you can submit for publication to a journal or conference, that provides primarily analysis for or acts as a pilot analysis that you can report in a grant proposal or thesis proposal, and/or that serves as part of your masters thesis or dissertation.

==== Project and Dataset Identification ====

;Due Date: January 17
;Maximum paper length: 500 words (~1-2 page)
;Deliverables: Turn in in Canvas

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

* A one paragraph abstract of the proposed study and research question, theory, community, and/or groups you plan to study.
* A short description of how the project will fit into your career trajectory.
* 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 when you will have access to the data.

==== Final Project Ouline ====

;Outline Due Date: February 21
;Maximum outline length: 5 pages
;Deliverables: Turn in in Canvas

The outline should should have the following sections: (a) Rationale, (b) Objectives; (b.1) General Objectives; (b.2) Specific Objectives; (c) Null hypotheses; (d) Conceptual Diagram; (e) Measures; (e) Dummy Tables.

An excellent example from my partner Mika Matsuzakis is [https://canvas.uw.edu/courses/1098035/files/40388318/download?wrap=1 online in Canavs]. Your diagram will likely be much less complicated than Matsuzaki's. Also, please don't be distracted by the fact that Mika does public health. It's the basic form I want you all to emulate, not the content. You can read [http://ajcn.nutrition.org/content/99/6/1450.full the published paper] to compare.

The example includes everything except a "Measures" section. Your Measures section only needs to include two column table where column 1 is the name of each variable in your analysis and 2 is the specific operationalization of this measures and a description of how you will create it.

==== Final Project ====

;Paper Due Date: March 19
;Maximum length: 6000 words (~20 pages)
;Presentation Date: March 14
;All Deliverables: Turn in in Canvas

I'm expecting you to produce a draft of a short research paper that, after some additional work, you could consider submitting for publication. I'm also very open to the project being a part of a dissertation. I don't expect the papers to be ''publication ready'' but I do expect them to have well considered drafts of all the necessary pieces in terms of quantitative methodology.

Because the emphasis in this class is on statistics and methodology and because I'm not an expert in each of your areas or 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 so important. Instead of providing all of these details, feel free to start with a brief summary of the purpose and importance of this research, and an introduction of your research questions or hypotheses. If your provide more detail, that's fine, but I won't give you detailed feedback on these parts.

I have a strong preference for you to write this paper individually but I'm open to the idea that you may want to work with others in the class.

In terms of content:

* In terms of the structure of the paper, please see the page that I've written on the [[structure of a quantitative empirical research paper]].
* In terms of the structure of your presentation, you've got some latitude but this document on [https://canvas.uw.edu/files/40848246/download?download_frd=1 Creating a Successful Scholarly Presentation] (link is in Canvas) will likely be useful.

=== Grading ===

I have put together a very detailed page that describes [https://mako.cc/teaching/assessment.html grading rubric] I will be using in this course. Please read it carefully I will assign grades for each of the following items on the UW 4.0 grade scale according to the weights below:

* Participation: 40%
* Proposal identification: 5%
* Final paper outline: 5%
* Final Presentation: 10%
* Final Paper: 40%