Directed Research Group: The COVID-19 Information Landscape (Fall 2020)

The Directed Research Group
The Covid-19 pandemic has required us to navigate a challenging information landscape. How have our institutions responded, and how have people made sense of the information provided to them? What role have search platforms played in shaping this terrain?

In this Directed Research Group, you'll conduct a content analysis on search engine results collected during the pandemic. The group will be run for 3-5 excellent students interested in engaging in faculty directed research. The research group will be organized by the Community Data Science Collective by Benjamin Mako Hill and Kaylea Champion and will be conducted for UW course credit. We'll analyze the data you collect and reflect on what it can tell us about our response to the crisis.

Prerequisites: Strong reading and writing skills in the English language, a computer you can use during the project, ability to attend team meetings through an online conferencing platform, and a commitment to high-quality results are required. Willingness to work both in a team and independently is required. We strongly prefer candidates with experience in social scientific research methods (such as COM 301). Familiarity with content analysis, R, and Python are all helpful but not required.

Applying: To apply to join this DRG, submit a cover letter and resume to covid-drg@communitydata.science. The resume might contain a brief list of recent courses and any relevant work experience, the cover letter could be a description of why you'd like to participate, including your academic or career interests, or what you hope to learn. If selected, you will be able to enroll for 4-5 credits, depending on how much time you can commit to the project (for 4 credits, you commit 12 hours, for 5 credits, you commit 15 hours per week).

DRG Responsibilities and Commitments
As a DRG member, you are joining an active research project, and you are expected to learn both through information presented to you as well as through your own initiative. What you get out of this project will match what you put into it. Communication is essential to keep our collaboration smooth, and your commitment to doing good quality work is essential. We are trying to develop knowledge that will be useful to the public, and that means holding ourselves to maximum standards of accuracy and ethics. Be honest and open about what you do and always do your best: life happens to all of us and we live in interesting times. Work that's a little late is understandable especially if you communicate early and often, but work that's not your best effort can't be accepted.

Key Text
Available for free from the UW Library: https://dx-doi-org.offcampus.lib.washington.edu/10.4135/9781071802878

Week 1:
Key Question: Who are we, what are we trying to do, and how are we going to go about it?

Preparation
 * Read through this wiki page!
 * Read the project outline (sent via email).
 * Read this description of thematic analysis: https://en.wikipedia.org/wiki/Thematic_analysis
 * Read this description of content analysis: https://en.wikipedia.org/wiki/Content_analysis
 * Make a Taguette Account: https://taguette.communitydata.science
 * Bring your questions!

Meeting Agenda
 * Introductions
 * Scope Review
 * Commitments and communication ++ time tracking
 * Theme Analysis and Content Analysis: What are they?
 * Important Issue: Qualitative versus Quantitative
 * Toy Example: "What's it been like living in Seattle this September?"
 * Tour and Overview of the Data

After-Meeting To-Do Items
 * Read through page 22 of Kaylea's [|annotated version of Braun and Clark 2006] -- note that there are both highlighted areas and comments. You may need to adjust your view so you can see the sidebar with comments. Feel free to add your own notes and questions for everyone to benefit.
 * First sample: Dive In To Trends
 * Set aside a solid block of time.
 * Open with a freewrite. What are you thinking and feeling? What are you bringing to the table as you sit down to read this body of data? Use a separate document as a personal journal, or whatever approach to journaling you like.
 * Read through the entire set of trends, writing down thoughts as you go; this will become the foundation of your 'memos'.
 * Read through the set of trends a second time, updating your thoughts as you go, in your memos.
 * Reflect. If someone asked you "what's that all about?" or "what did you see?", what's the story you'd tell them? Add your story to the memo.
 * Develop your memo further. Put thoughts you'd like to keep to yourself into a personal document. Edit the memo into something you're ok with sharing, and share it in the Google Docs folder.
 * Second Sample: Images
 * Set aside another solid block of time.
 * Same process as above. Freewrite in your personal journal to focus on the task.
 * Look through all of the images, write your notes as part of your memos.
 * Look a second time, add more notes as a memo.
 * Reflect, and add the story you'd tell into your memo.
 * Develop your memo -- private thoughts can be put into your journal, reflections for sharing into your memo, edited, shared to Google Docs.

Week 2:
Key Questions: What are your initial impressions of this data? What do Braun and Clark say about how can we turn those initial impressions into knowledge?

Meeting Agenda
 * Your sample: what trends did you see? Let's talk about the data!
 * Walk through Braun and Clark.

After-Meeting Follow-Up


 * Look back at your memos from the first week and read your colleagues' memos. What did they see that you didn't? Given our collective examination, what feels most important, useful, or surprising about this data?
 * There are 6 new days of data to look at. You might do a memo per day, or all days in a single memo, or some other approach: organize yourself in a way that works for you.
 * We'll follow our same memo process from last week: free write, immerse yourself in the data, make notes in multiple passes, synthesize and reflect. Remember Mako's comment that it's ok to treat your memo as a journal: cut and paste, scribble, sketch.

Week 3:
Key Question: What are we seeing in this data?

Meeting Agenda
 * Research Protocol -- what's coming up next?
 * Open codes discussion: what did you see?

After-Meeting Follow-Up
 * Braun & Clark phases 1&2 for a new set of data. This is codes development round 2.
 * Do your previous codes still work? Will you incorporate ideas from colleagues?

Week 4:
Key Question: Is this working? Can we make a codebook out of what we've seen?

Meeting Agenda
 * Share your codes so far
 * Codebook production discussion
 * Prep for next phase.

After-Meeting Follow-Up
 * Read pp. 1-2, 18-34 of Ch 1 of the online version of Neuendorf

Week 5:
Key Question: Does this codebook make sense?

Meeting Agenda
 * Codebook presentation, discussion, training.
 * Synchronized coding + discussion

After-Meeting Follow-Up
 * Read Neuendorff, Ch 2., pp. 36-44, and Ch 3. pp 70-84. Skim the rest of Ch3.
 * This is Pilot Round 1, and we are entity coding for agreement

Week 6:
Key Question: Is this working?

Meeting Agenda
 * Discuss your experience coding data
 * Discuss our agreement levels so far
 * Update codebook as needed

After-Meeting Follow-Up
 * Read Neuendorff p. 139-166.
 * This is Pilot Round 2, and we are entity coding for agreement

Week 7:
Key Question Still Is: Is this working?

Meeting Agenda
 * Discuss your experience coding data
 * Discuss our agreement levels so far
 * Update codebook as needed

Follow-up Items
 * Read Neuendorff p.169-206
 * If Agreement is sufficiently high, this is when we Begin independent coding

Week 8:
Meeting Agenda
 * Discuss coding progress

Follow-Up Items
 * Keep Coding
 * Read Neuendorff p. 221-230, 234-248 (through just the first paragraph)

Week 9
Meeting Agenda
 * Discuss coding progress

Follow-Up Items
 * Keep Coding
 * Read Neuendorf p. 334-340, 394-403

Week 10:
Complete Writing and Coding Tasks

Finals Week:

 * Revision Task
 * Dissemination Task