1 of 18

Qualitative Data Tools Workshop

Presented by Rachel Deblinger (THI) and Kristy Golubiewski-Davis (DSC) for the SSRC DPD program

Slideshow with links available at:

http://bit.ly/DSC-SSRC

2 of 18

Brief Overview:

I. Why Data?�Imagining Research Material as Data�Snowflakes and Spreadsheets �

II. Best Practices �Elements of a Data Set�Tidy Data�Data Ethics

​

III. Nitty Gritty�Example Studies�Picking the Best Tool��

IV. Your Research �Revisit Form Questions (and more!)�Issues�Q&A / Tool demonstration

​

3 of 18

Why Data?

​

Doing Research

What materials you collect & how you read them determines the kinds of arguments you can make

​

How you organize your research material similarly determines the kinds of arguments you can make.

​

We want to open up the possibility of unexpected links between ideas and objects. And, we want to find new ways of knowing and seeing.

Find Patterns �+ Build Narratives

Time + Scale�Toggle between intimate & global �Track time, dates, places�Events + ideas as overlapping

Research is a Long Process�Assure access + findability�Match notes with citation details�Invite collaboration�

There is no magic tool.

​

This takes time, planning, organization, and a commitment to maintaining consistency.

This is - obviously - not always possible.

Create structures that help us focus on and achieve our goals.

I.

4 of 18

Snowflakes & Spreadsheets

EPHEMERA

ARCHIVAL DOCUMENTS

DIGITIZED MATERIAL

OBJECTS

5 of 18

Snowflakes & Spreadsheets

SHORT FILMS

RADIO BROADCASTS

6 of 18

Snowflakes & Spreadsheets

Google Drive

Devon Think

Google Sheets

Google Sheets

Notes

7 of 18

Elements of a data set

Data Files

  • The jpgs / pdfs / movs / etc.
  • Naming scheme (make it consistent)
  • File Structure (how your folders are saved)

​

​

Metadata

  • Author name
  • Interviewee / Interviewer
  • GPS coordinates
  • Etc.
  • Put these in a spreadsheet to easily keep track of them.

8 of 18

Tidy Data & Data Cleaning

Data Cleaning: Dirty Data

  • Incorrect spelling or punctuation
  • Missing Data
  • Data in the wrong field
  • Incomplete Data
  • Duplicated Data
  • Non-standardized data

Tidy Data

  • Each variable you measure should be in one column
  • Each different observation of that variable should be in a different row
  • There should be one table for each “kind” of variable
  • If you have multiple tables, they should include a column in the table that allows them to be linked (i.e. a unique ID)

http://curatingmenus.org/articles/against-cleaning/

https://blog.xkcd.com/2010/05/03/color-survey-results/

9 of 18

Ethics of data

In defense of Dirty Data

Croll and Gerteis, “Race as an Open Field: Eploring Identity beyond Fixed Choices”. Aociology of Race and Ethnicity. Pg 2-15. 2017 http://journals.sagepub.com/doi/pdf/10.1177/2332649217748425

10 of 18

Ethics of data

Confidential / Restricted Data

  • Personal identifiers
  • Do you need IRB (Institutional Review Board) approval?
  • UCSC Information Technology Services recommendations for protecting Restricted Data
  • If you think this applies to you, you should discuss your data practices with your advisor.

Greeking / Obscuring Data

  • Stripping sensitive data by using dummy text to obscure it’s true nature

Considerations

  • Who has access to your working files?
  • What rights and permissions have you obtained when collecting your data?
  • These apply not just for the short term, but also for long term (archiving?)
  • What kinds of permissions were previously given to the institution? What was left out?

11 of 18

Example Study 1:��Rachel Deblinger

12 of 18

Example Study 2:�Kristy Golubiewski-Davis

13 of 18

Useful Tools for Project Management

UCSC Data Management Guides

  • Lifecycle of a Digital Project
    • Taking Inventory, Digitizing, Scanning, Exhibition, Preservation, and Promotion
    • File naming structures
  • Research Data Management
    • Data Management Plans, Best Practices, and Tools

Project Management Tools

  • Trello
  • Asana
  • Task Management tools:
  • Evernote

Analysis

  • R / R Studio
  • Tableau
  • Palladio

Other Guides

​

14 of 18

Useful Tools

For Data Collection

Software

  • Mendeley: Reference management with network visualizations
  • Zotero: Robust Citation Management + links to library catalogues
  • DevonThink (Mac Only): Research + Document manager
  • Tropy: Research Photo Management
  • Evernote
  • Scrivener
  • Google Forms

Useful Processes to Know

​

15 of 18

Focus on OCR

16 of 18

Other Tools and Resources

Analysis

  • R / R Studio
  • Tableau
  • Voyant (DSC Guide to Voyant)

Digital Exhibit Tools

Further Reading

17 of 18

Your Research Materials

Exploring your own data

15 min: Visit http://bit.ly/DSC-SSRC to work on your own research plan.�

20 min: Work through common issues with Kristy and Rachel�

15 min: Open for questions or a tool demo

18 of 18

Follow Up

​

​

Contact Us for Consultation

Kristy Golubiewski-Davis, Digital Humanities Librarian | kristy.gd@ucsc.edu

Rachel Deblinger, Research Program Manager, THI | rdebling@ucsc.edu

Lucia Orlando, Social Sciences & Gov Info Librarian in Research Support Services | research@library.ucsc.edu ��Ann Hubble, Digital Initiatives Librarian | diginit@ucsc.edu

​

​