Directed Research Group: The COVID-19 Information Landscape (Fall 2022)
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. This work builds from work conducted as part of a previous DRG in Fall 2020 and Winter 2021. 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
The Content Analysis Guidebook, Kimberly A. Neuendorf. SAGE Publications, Inc. (2017)
Available for free from the UW Library: https://dx-doi-org.offcampus.lib.washington.edu/10.4135/9781071802878
DRG Schedule
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 pre-print of the paper from the previous DRG
- Read this description of content analysis: https://en.wikipedia.org/wiki/Content_analysis
- Bring your questions!
Meeting Agenda
- Introductions
- Scope Review
- Commitments and communication ++ time tracking
- Content Analysis: What is it?
- 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
- Explore the week 1 sample.
- Brainstorm alternate/new research questions.
- Write up one (or more) of your questions in paragraph form (see example).
Week 2:
Key Questions:
Meeting Agenda
- Your sample: what trends did you see? Let's talk about the data!
After-Meeting Follow-Up
- Read pp. 1-2, 18-34 of Ch 1 of the online version of Neuendorf
- 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?
After-Meeting Follow-Up
Week 4:
Key Question: Is this working? Can we synthesize these codes into themes? Meeting Agenda
- Open codes discussion: what did you see?
- Prep for next phase.
After-Meeting Follow-Up
Week 5:
Key Question: Can we make a codebook out of what we've seen?
Meeting Agenda
- Codebook production discussion
After-Meeting Follow-Up
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, 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 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. 139-166.
- This is Pilot Round 2, and we are entity coding for agreement
Week 8:
Meeting Agenda
- Discuss coding progress
Follow-Up Items
- Keep Coding
- Read Neuendorff p.169-206
- If Agreement is sufficiently high, this is when we Begin independent coding
Week 9
Meeting Agenda
- Discuss coding progress
Follow-Up Items
- Keep Coding
- Read Neuendorff p. 221-230, 234-248 (through just the first paragraph)
Week 10:
Complete Writing and Coding Tasks
- Read Neuendorf p. 334-340, 394-403
Finals Week:
- Revision Task
- Dissemination Task
