Bringing together numbers and feelings

Illustration by Tay Tuteur. Learn more about our illustrators.

Bringing together numbers and feelings

In our process, we’re most interested in feelings, stories, and the complex messiness of experiences that happen in people’s lives. We want to get beyond the objective knowing of how many and what into understanding the subjective answers to questions like who, why, and how. For example, when we learn that a quantitative study found that “youngish adults in the St. Louis Jewish community had more trouble coping with the psychological effects of the pandemic despite having similar or greater social support than adults of other ages,” we get excited to dig into the reasons behind this, with  questions about the behaviors, thoughts, expectations, and feelings of those youngish adults. 

Some partners we work with have historically utilized quantitative data as their primary tool for setting directions and making decisions. While it can be a valuable input, quantitative data on its own often misses the nuances of context, reasoning, and feelings – the messy and sometimes contradictory stuff that make up our experiences. The tendency to value quantitative data (and a lot of it) over stories and lived experiences can be traced to characteristics of White Supremacy Culture, as defined by Tema Okun, including Quantity over Quality, One Right Way, and The Binary. We’re working to learn from other, non-White cultures that elevate wisdom of experience in decision making, and exploring roles that both qualitative and quantitative information can play in processes. 

Here are a few practices we’re working on when it comes to the quantitative and qualitative information that guides the direction and decisions our partners make: 

Use quantitative data to hone where we pay attention, then add qualitative context
My mom has a favorite joke: A man goes to the doctor complaining that every part of his body hurts. The doctor tells the man to show him where it hurts. The man points to his elbow, and says ‘ow!’ Then he points to his legs and exclaims ‘ow!’, and then he points to his stomach, and everywhere else, and each place hurts. Seems pretty bad right? 

We think of quantitative data as part of the diagnostic process. It helps determine where the big problems are, whether that is the coping experiences of youngish adults as documented in a survey, the accessibility issues of a particular building as documented by complaints, or the health concerns documented by population-level data. 

For the man in the doctor’s office, the data about being in pain all over his body brought him in — the quantitative data is telling him that everything hurts. The punchline tells a different story: The doctor observes all of this, and says, “It looks like you have a broken finger.” The qualitative data involves seeing the context of the patient’s statement, and observing the behavior (poking oneself), and being able to make an accurate diagnosis of the problem. 

Review all data with a critical eye to who’s voice and experience is missing 
All data, whether qualitative or quantitative, is biased. We can’t remove our own biases entirely, nor the biases that creep in from who we learn from and how we learn from them. Anytime we’re dealing with information, we have to raise the question: who’s missing from this data? 

This means that when we’re talking with folks, we look carefully at the relevant demographic information to the question. If we see there’s a particular group that isn’t represented, we raise why. Data about people, both qualitative and quantitative, cannot be separated from the identities and experiences of these people. We follow the Forward Through Ferguson Calls to Action and disaggregate our data, identifying who’s missing and who hasn’t been part of the story. 

Check assumptions, and raise more questions 
In a recent engagement process, we had a lot of qualitative survey feedback about parking — it was identified by many, many people as one of the biggest challenges. We were confused. The quantitative data showed that there was plenty of parking, and good transit access. But the survey feedback, pointing to parking as a problem, included stories about walking through huge, unshaded parking lots alone, or struggling to interpret policies for different lots, or finding new, special places by parking somewhere new. The challenge wasn’t the amount of parking (often the go-to answer to parking complaints), but the experience of parking. By combining the quantitative and qualitative information, we could dig into the nuance of why parking was such a negative, and frame the opportunity to improve the experience. We had to check our own assumptions: we assumed that technically having enough parking spots would *feel* like there were enough. 

If this has piqued your interest, you might like Why Big Data Needs Thick Data by Tricia Wang, which really looks at the need for both those big quantitative data sets and the feelings, texture, and context that makes qualitative data so valuable. 

What do you think about quantitative and qualitative data? How do you bring them together? We’re excited to hear from you. Send us an email at or leave a comment below.

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