Numbers Aren't Everything: 6 Things to Know About Qualitative Data
A while back, we shared a list of 10 things to know about quantitative data in order to interpret quantitative findings with confidence. Quantitative data is numerical and measurable (think counts, percentages, statistics)—you might see quantitative data in a report presenting findings from a survey or timing and tracking study. On the other hand, qualitative data is descriptive and interpretive (think quotations and narrative summaries of trends). Interviews, focus groups, and write-in survey questions all generate qualitative data.
While there is often a bias toward quantitative data, numbers aren’t everything. I love the complexity and nuance revealed through qualitative data. Because qualitative data is open-ended, it helps you understand peoples’ thoughts and experiences in their own words–this can reveal interesting, profound, funny, and unexpected insights that would be lost in quantitative methods. Qualitative data deserves its own list, so here it is!
1. The purpose of qualitative data is typically to explore a question or phenomenon in detail.
Qualitative data answers the questions “why?” and “how?” from your audiences’ perspective, in their own voice. For example, we often want to know what was most interesting about a person’s experience in a program or exhibition, but more important is why it was most interesting. Hearing from people in their own words is key to uncovering the meaning-making that happens during a museum experience. Meaning-making is deeply personal, built at the intersections of each person’s own experiences in the past and their museum experience in the present. Qualitative methods pull at these threads to uncover the detail of why and how meaning-making happens.
2. Sample size for qualitative studies is much smaller than for quantitative studies, and for good reason!
I like to think of qualitative data as small but mighty. A small amount of qualitative data can reveal a huge amount of detail and subtleties. Depending on who you are interested in hearing from and the scope of the question, a sample size of 10 or 20 interviews or a couple of focus groups may be sufficient to give you the information you need. Again, the goal of qualitative data is exploratory–seeking to understand people’s motivations, thoughts, and ideas about a topic or experience in detail. We are not trying to provide exhaustive or generalizable information about a larger population (for which a larger sample size would make sense).
3. Open-ended questions are the foundation of qualitative data.
An open-ended approach means asking broad questions and allowing the answers to emerge organically from participants in their own words rather than predetermined answer choices (such as multiple-choice in a survey). Hearing straight from participants is powerful–their answers are often contextualized in personal experiences and feelings and can lead you in unexpected new directions that push your work forward in meaningful ways. One of my favorite parts of qualitative data collection is watching people make the connection between a feeling or experience from their museum visit and a personal experience or memory. This richness can’t be replicated in a multiple choice question.
4. Getting high quality data depends on a skilled interviewer or facilitator.
Most qualitative data is collected through conversations between an interviewer or facilitator and a participant. During these conversations, we ask open-ended questions, actively listen, and probe the participant to elaborate or clarify their responses. Becoming a skilled interviewer or facilitator takes practice! It can be a lot to juggle, trying to cover all of your questions, listen closely to the responses, and prepare follow up questions in the moment that will draw out the details you need during an interview. That challenge is multiplied in a focus group with multiple people, differing opinions, and different levels of enthusiasm to participate. However, with practice, facilitation is a muscle that grows the more you use it.
5. Qualitative analysis can be time-consuming and messy, but oh-so-gratifying.
So, you’ve finished data collection, and now you have a mountain of transcripts or written responses to sift through. Figuring out where to start analysis can feel overwhelming, but like facilitation, analysis is a skill you can build over time as you find approaches that work for you. Coding is one process we often use to start making sense of the mess by looking for trends and patterns among responses to our questions. Each trend or pattern becomes a “code” used to group like responses from the data together. Coding helps organize the big ideas emerging from the data. It is time-consuming and often requires more than one review of the data to hone in on the essence of each code; however, getting to the heart of the data while still preserving important nuances is extremely satisfying and makes the data more meaningful and useful.
6. Trends emerging from qualitative data are presented descriptively and according to substantive significance—don’t expect to see numbers!
Rather than assigning numerical values or percentages to the trends that emerge from qualitative data, we often talk about the data using qualifiers like “most,” “many,” “about half” or “a few.” This provides a sense of the most- to least-occuring trends; however, we always suggest qualitative trends should be interpreted relative to one another rather than thought of strictly in terms of proportions. While it is important to keep major trends in mind, it is also good practice to remember that sometimes a response from just one person can be extremely insightful and important.
Next time you are considering doing some research or evaluation, don’t forget that there is more out there than just surveys and percentages. Don’t be afraid to dive in and hear from people in their own words with some qualitative data!
