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  • Writer's pictureLindsay Morgia

The case for using qualitative data for your nonprofit's next project

Updated: 2 days ago

Organizations are often asked to use quantitative data, or data involving numbers, to show their impact. And that makes complete sense. Numbers are a quick way to track all kinds of outputs, like program completion rates, revenue changes, or staff retention rates. Quantitative data is also instrumental in understanding trends and changes over time.

However, while numbers are a great way to see what is happening within an organization, they can’t always tell you why specific trends or problems are occurring. Also, quantitative data does not always explain how to fix emerging problems.

That’s where qualitative data comes in.

What is qualitative data?

A block of text in different sizes that includes words like "social," "data," "web," and "global" to demonstrate what qualitative data is.

Qualitative data is information based on words, texts, and observations. Usually gathered through interviews, focus groups, videos, and even social media, it can help us understand how people experience the world: their working environment, a program, a policy, or a community. Qualitative analysis often involves finding common themes that help explain why a problem exists and what can be done to correct it. It can also help us understand the personal and social contexts influencing our quantitative results.

Why is qualitative data awesome?

Like any good analyst, I must admit my bias here; I love qualitative work. I love listening to people’s stories and finding themes to solve problems. I also like qualitative work because, when done well, it can do certain things that quantitative can’t, such as:

  1. It creates safe places for people to express themselves freely.

  2. It is inclusive, encouraging multiple voices, perspectives, and experiences to be a part of the assessment or evaluation process.

  3. It gives community members and other stakeholders a chance to share, in detail, creative and innovative solutions to pressing problems.

  4. It creates opportunities for human connection and relationship-building in ways quantitative methods like surveys can't do.

So, despite all the great uses for qualitative data, why are organizations often asked for numbers instead?

The Big Myth: Quantitative data is more objective than qualitative data.

There are some long-held assumptions that numerical data and quantitative methods are more objective and scientific than their qualitative counterparts. In quantitative data collection, like surveys, the researcher is seen as detached with no relationship to the study participants. The logic is that this detachment ensures the researcher does not introduce personal or other biases into the project. However, because qualitative work is interpersonal and can involve emotions and opinions, there is more risk that the analysis will be too subjective and, therefore, not “real” science.

These assumptions are nonsense, and here’s why: all social research involves people, and people have biases. It doesn’t matter if we are counting things or recounting stories. There is always the risk that our personal biases influence our choices regarding the questions we ask, who we include as a part of our project, and how we understand the data in front of us.

For instance, think of all of the healthcare research that has excluded women and people of color. These studies involve quantitative methods but have significant flaws because someone decided to exclude specific groups along the way. Or consider debates about how the Census should “count” particular populations in the U.S., such as Native Americans or LGBTQ+ people. Making misguided decisions about counting community members can unknowingly exclude people who would otherwise consider themselves part of those communities. As a result, researchers inadvertently end up with incomplete data on these communities’ experiences.

My point is that no research project is perfect, no matter your chosen method. But stories and observations are just as valid and scientific as any other type of data, and just as prone to potential biases. As organizations using qualitative data for needs assessments and program evaluations, our job is to take steps to ensure that our work is as bias-free as possible.

Checking for bias in qualitative work

If you’re interested in qualitative work, there are steps you can take to limit the influence of biases in your analysis and reporting. These are the top ways that I check for biases when I work on qualitative projects:

  1. Ask clients to review interview or focus group questions I have written to ensure that a) the questions are straightforward and b) do not lead participants to give specific types of answers

  2. Write short memos with key points and follow-up questions after each interview or focus group (often referred to as reflexive work)

  3. When possible, go back to interview/focus group participants to confirm that my interpretation of their story is correct

4.      Work in pairs or teams to check each other’s analyses, see if our findings align, and work

through why we interpret the transcripts differently.

Ready to start scientifically using stories to solve problems? You don’t have to do it alone. Please reach out to me at, and I’ll be happy to answer your questions and work with you to plan your next assessment or evaluation project.


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