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# Syllabus
## Qualitative Data Analysis
Spring 2022, University of Colorado Boulder
Tuesdays, 10am-12:30pm Mountain Time meeting remotely
## Class Zoom
- https://cuboulder.zoom.us/j/94682755692
- Passcode: 816529
## Facilitators
### Nickoal Eichmann-Kalwara (call me Nickoal, she/her)
[email protected] <br>
Assistant Professor, Digital Scholarship Librarian <br>
Director of Digital Scholarship <br>
Center for Research Data & Digital Scholarship <br>
### Jordan Wrigley (call me Jordan, she/her)
[email protected] <br>
Data Librarian, Feral Researcher, and Free-range Data Consultant <br>
Center for Research Data & Digital Scholarship <br>
## Course Description
Qualitative data research and analysis includes growing technical aspects with numerous tools. This is a practical crash course in qualitative data tools focused on maximizing the efficacy of tool selection and usage through critical assessment of research goals for analysis. Topics will include data collection through the lens of planned analysis, QDA open-source and paid tools, and visualization for data reporting. Learners will complete a final project portfolio of resources and exploratory tool analyses.
## Required Materials
None other than devices that will enable you to remotely attend and participate in class. Please contact us if you’re concerned about your tech equipment. In following the data practices of of open scholarship and science, all materials will be available for free online or via the University Libraries, etc. They all will be hyperlinked in this syllabus. For some materials, you will need to have your proxy settings setup for off-campus access (see setup instructions). You are not required to purchase anything from the CU Bookstore.
## [Final Project](https://github.com/jwrigs/QDA22_gitpage/blob/68229effc29a5be2729d38533a8f4b0288eca2dd/_pages/week5.md)
## Modules
- [Week 1](https://github.com/jwrigs/QDA22_gitpage/blob/2366a7da63664c90f07b66f4312429e947e767b8/_pages/week1.md):
- Qualitative Types and Considerations
- What research approaches and types of data require which tools? This session will examine the planning phase of qualitative data research methods and how to develop processes which guide ethical data collection, analysis, and dissemination.
- [Week 2](https://github.com/jwrigs/QDA22_gitpage/blob/8c23213765c25918db2b8e5723c78009497a28ca/_pages/week2.md)::
- Qualitative Data Collection
- Qualitative data collection methods and tools are numerous. Selecting ethical and effective collection sources, instruments, and tools requires parallel critical assessment of project goals, data, and resources. This session will overview exploratory collection methods and tools including webscraper.io, APIs, and others.
- [Week 3](https://github.com/jwrigs/QDA22_gitpage/blob/8c23213765c25918db2b8e5723c78009497a28ca/_pages/week3.md):
- Qualitative Data Analysis
- Analysis is often a technical and tool heavy phase of modern qualitative data research. A variety of tools may be applicable to a given qualitative research project. This session will tour several survey-based, text mining, qualitative coding, and other tools including R and Python packages. This will also include a deeper dive into particular tools and techniques to upskill learners' practical knowledge.
- [Week 4](https://github.com/jwrigs/QDA22_gitpage/blob/8c23213765c25918db2b8e5723c78009497a28ca/_pages/week4.md):
- Communicating Qualitative Data Analysis
- Being able to communicate complex qualitative concepts in data findings is a key aspect of research dissemination. This session will examine current common and popular tools for qualitative data visualization and reporting. It will also provide examples of data communication platforms (ex. story maps) and language for presentations and manuscripts.
- [Week 5](https://github.com/jwrigs/QDA22_gitpage/blob/8c23213765c25918db2b8e5723c78009497a28ca/_pages/week5.md):
- Project Presentations and Peer Feedback
- Learners will present a lightning talk presentation overviewing their project including: data types, sources, and collection instruments; selected analysis methods and tools; initial and exploratory findings and visualizations. Presenters will receive peer feedback and initiate discussion around their projects.
## COVID 19
As your instructors, our first responsibility is to provide you a safe environment in which to learn, which is why we have chosen to hold this course remotely. If you anticipate this online class will cause you difficulties, please reach out and we will collaborate on solutions. If you experience pandemic-related (or other!) challenges to your ability to participate in this course, please also let us know how we can support you, regardless of what point in the semester issues arise. We are in a continuing global crisis. Be on Team You.
## Accomodation for Disabilities
Above all else, we honor your humanity and respect your privacy. You are not required to self-disclose your disabilities. Since not all disabilities are recognized by Disability Services, please know I am willing to support you and accommodate as much as possible.
"If you need accommodations for a disability, it is recommended that you submit an accommodation letter from Disability Services. Disability Services determines accommodations based on documented disabilities in the academic environment. Information on requesting accommodations is located on the Disability Services website. Contact Disability Services at 303-492-8671 or [email protected] for further assistance. If you have a temporary medical condition or injury, see Temporary Medical Conditions under the Students tab on the Disability Services website."
## Classroom Behavior
Data science and qualitative data analysis has always been an evolving field, making us constant learners. I aim to honor us as co-learners as we grow in this area together.
"Students and faculty each have responsibility for maintaining an appropriate learning environment. Those who fail to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with race, color, national origin, sex, pregnancy, age, disability, creed, religion, sexual orientation, gender identity, gender expression, veteran status, political affiliation or political philosophy. Class rosters are provided to the instructor with the student's legal name. I will gladly honor your request to address you by an alternate name or gender pronoun. Please advise me of this preference early in the semester so that I may make appropriate changes to my records. For more information, see the policies on classroom behavior and the Student Code of Conduct."
## Honor Code
"All students enrolled in a University of Colorado Boulder course are responsible for knowing and adhering to the Honor Code. Violations of the policy may include: plagiarism, cheating, fabrication, lying, bribery, threat, unauthorized access to academic materials, clicker fraud, submitting the same or similar work in more than one course without permission from all course instructors involved, and aiding academic dishonesty. All incidents of academic misconduct will be reported to the Honor Code ([email protected]; 303-492-5550). Students who are found responsible for violating the academic integrity policy will be subject to nonacademic sanctions from the Honor Code as well as academic sanctions from the faculty member. Additional information regarding the Honor Code academic integrity policy can be found at the Honor Code Office website."