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Research Data Management Qualitative Data

Managing qualitative data

Qualitative and mixed method research comes with unique challenges in terms of data management and sharing. This page includes topics to consider and resources to help you manage and share qualitative data responsibly. 

Qualitative research seeks to explore and understand people's experiences, perceptions, and behaviors, by collecting non-numerical data like text, audio, or video. Examples of qualitative data include:

  • Text files of transcripts from interviews, focus groups, and oral histories
  • Images, audio, or videos from the above interactions
  • Direct observations, such as field notes and ethnographic studies
  • Other written documents, such as books, news articles, and webpages

Management and analysis of qualitative data may use specialized software tools called Computer Assisted Qualitative Data Analysis (CAQDAS). These tools are used to annotate and tag the data using thematic codes. To determine which tool is right for you see this review of CAQDAS tool features and limitations which was conducted by researchers at the University of Surrey. 

Here is a brief comparison of popular CAQDAS tools: 



 

Atlas.ti

nVivo

MAXQDA

Dedoose

Taguette

Cost

$$$

$$$$*

$$

$

open source 

Learning curve

Steep

Steep 

Steep

Mid

Low

Ease of collaboration 

😃

😓

😐

😃

😓

Deidentification features

Advanced analysis features

🟨

*At UAB you have free access to NVivo, which can be downloaded from central IT.  See this NVivo LibGuide for more information on getting started.

Sharing qualitative data

Promises to sharing qualitative data

  • Funders and journal policies are increasingly requiring that underlying data be shared.
  • Open data will improve the transparency of research methods and findings.

Challenges to sharing qualitative data

  • The data sharing infrastructure not as advanced as it is for quantitative data. This means that while publishers and funders may expect you to share qualitative data, there is little practical guidance on how to do it.
  • Sharing deidentified information strips important context - Non numerical data is complex and heavily dependent on context including the environment which it was obtained.
  • Deidentification processes for free-form text entries 
  • The concepts of reproducibility and replication do not directly translate from quantitative studies.

For more information on the discourse surrounding the sharing of qualitative data see these articles:

Tsai et al. 2016. Promises and pitfalls of data sharing in qualitative research. Social Science & Medicine. https://doi.org/10.1016/j.socscimed.2016.08.004

DuBois et al. 2023. Exchanging words: Engaging the challenges of sharing qualitative research data. PNAS. https://doi.org/10.1073/pnas.2206981120


What data do I share?

The answer to this question will differ depending on the nature of the project and any sharing obligations from the study's funder or publication destination. The rule of thumb is to share the data as publicly as possible while being as secure as needed. Before the project begins, the research participant needs to give direct consent to sharing data. When the project is concluded, data should be deidentified prior to sharing. This table gives examples of file types and sharing norms.
A summary table of sharing expectations for qualitative data types
Data Types Recommended format Typically shared?
Text (transcripts, other documents) plain (.txt) or rich text (.rtf) Maybe
Images and video .tif and .mp4 No
Geospatial data ESRI shapefiles or GeoTIFF Maybe
Tabular data .csv, .tsv, Stata, SPSS, Rdata Yes
CAQDAS (NVivo, ATLAS.ti, MAXQDA etc.,) .rtf or REFI* Yes

*REFI = Rotterdam Exchange Format Initiative: https://www.qdasoftware.org/. A standard form of sharing qualitative data so that data from different CAQDAS can be harmonized.

Repositories for qualitative data

  • ICPSR is the Inter-University Consortium for Political and Social Research: a data archive of 350,000+ items in social and behavioral sciences
  • UAB is a member = you have access to all their collections and can deposit research data
  • To make data available on ICPSR there is either no or a low fee. Contact ICPSR customer service to get a quote during the data management planning process 
  • UAB library guide to ICPSR
  • Qualitative data management guidance from ICPSR.

  • The Qualitative Data Repository is the first US data repository specifically for qualitative data. It is run out of University of Syracuse and heavily supported by the NSF.
  • UAB is not an institutional member
  •  Depositing data to the QDR typically ranges from $300 to $1,000. If possible, factor this fee into the grant budget.
  • Qualitative data management guidance from QDR

Additional Resources

CARE principles of Indigenous Data Governance = Collective benefit, Authority to control, Responsibility, Ethics

Managing Qualitative Data online course. This interactive on-line course includes four modules with activities to walk you through the management and sharing stages throughout the research process.