Descriptors are a game-changer for researchers conducting qualitative data analysis. Descriptors - categorical, quantitative, or demographic data – that are tied to qualitative media, allow for more complex analysis across your dataset. Whether you’re tracking survey responses or exploring differences between groups, descriptors add depth to your research.

Descriptors are any categorical, demographic, or quantitative data that you attach to your qualitative media. Some common Descriptor Field categories include:

• Demographics: Gender, age, race, location, role, occupation, etc.

• Survey Data: Likert scale responses, continuous or categorical variables.

• Time Frames: Pre and post-intervention time points or developmental stages.

Descriptors are a great tool for doing comparative or relational analyses across a dataset. For example, if you’re analyzing interviews with students, descriptors can track characteristics like gender or educational background, allowing you to ask, “Do these experiences and my qualitative themes vary across groups?”

Descriptors allow researchers to:

• Compare Across Groups: Explore variations by demographics or other factors

• Visualize Data: View and create charts and graphs for a deeper understanding of patterns and relationships between descriptor categories and your coding

• Track Changes Over Time: Perfect for longitudinal studies or pre/post analysis

However, descriptors aren’t mandatory for qualitative research. If your study does not have any natural descriptor fields, you can conduct meaningful analyses without them.

Descriptor profiles encapsulate all the relevant information about a particular participant or case in your study. For instance, in a study of college athletes, each participant might have a descriptor profile containing descriptor field data such as their age, race, gender, sport played, and career status. These profiles are linked to corresponding qualitative media, such as interview transcripts or photos, enabling researchers to connect demographic or contextual data with the content being analyzed. By structuring data this way, descriptor profiles make it easier to identify patterns, conduct comparisons, and generate insights that align with the study’s analytical goals.

Creating and managing descriptor profiles requires thoughtful planning. Researchers should include only fields that are analytically significant to avoid unnecessary complexity or a drift away from their study’s purpose and focus. Dedoose makes it simple to import all descriptor profiles from an Excel spreadsheet, ensuring consistency and saving time. Moreover, profiles are flexible—they can be updated or expanded as new lines of inquiry emerge during the research process. By systematically linking data to profiles, researchers can enhance their ability to explore relationships and draw meaningful conclusions.

Creating Descriptor Profiles: Best Practices

Setting up descriptors using a Descriptor Profile Excel sheet is an efficient and reliable method to organize and streamline descriptors. A descriptor catalog is a spreadsheet where each column represents a descriptor field (e.g., gender, age, location), and each row corresponds to a participant or case in your study. This approach ensures consistency, reduces errors, and saves time compared to manually entering descriptors. Once the Excel sheet is prepared, it can be imported into Dedoose, which automatically generates a descriptor profile for each person/case according to the data in the spreadsheet. Researchers can also pre-define options within fields, such as specific age ranges or numerical categories, to maintain uniformity across data. This method not only simplifies initial setup but also makes it easier to add new profiles or adjust descriptors later as the study evolves. By starting with a well-structured Descriptor Profile Excel sheet, researchers can focus more on analysis and less on administrative tasks, ensuring a smoother and more organized workflow.

1. Unit of Study: Begin by identifying your unit of study (e.g, individuals, a process, organization, etc.) that is the primary entity or focus of your investigation.

2. Identify Categories of Interest: Use a whiteboard or note sheet to sketch out what demographic, categorical, or quantitative data is important to link to your qualitative unit of study. These are called Descriptor Fields in Dedoose.

3. Use Spreadsheets: Prepare a descriptor profile in a spreadsheet itemizing each piece of media/qualitative data in your dataset and its associated Descriptor Field data. Each row of your spreadsheet will form a unique Descriptor Profile for that media/case in your study.

Guides and corresponding videos demonstrating the upload and linking process are linked here in the Learning Center and a brief outline is below:

• Import Data: Import your descriptor catalogue spreadsheet to populate descriptor sets and descriptor fields.

• Link Media: Link media/qualitative data, like interviews or images, to the correct profiles.

• Utilize Visualizations: Analyze patterns and variations using built-in data visualization tools.

Final Thoughts

Descriptors transform qualitative research by bringing structure and depth to your data. Whether you’re a seasoned researcher or just starting, these tools enhance analysis and visualization. With thoughtful planning and tools like Dedoose, you can efficiently incorporate descriptors to uncover richer insights. Start small, stay focused, and let descriptors add new dimensions in your qualitative and mixed methods studies.  

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