As a college student, a lot of what I came to understand about conducting research was gleaned by flipping through texts or sitting through classroom lectures. Professors would paint the research method picture as an either-or decision: either qualitative or quantitative. I had little knowledge of an alternative and was in no position to declare that forcing either-or decisions, in general, seemed a bit antiquated. So I focused my efforts on understanding the two methodologies, how they differ, and when is best to choose one over the other.
Quantitative research—questionnaires, surveys, and polls designed to collect concrete numerical data—was billed as a statistician’s bread and butter. In her free time, my stats professor would conduct her own quant research to study other statisticians. So meta!
We analyzed using statistical models, numerical comparisons and inferences to quantify opinions, motives, and behavior patterns. To be statistically representative, these rigidly structured studies required hundreds or thousands of participants.
Qualitative research, at the other end of the spectrum, was positioned as purely exploratory. In my marketing capstone class, the professor asked us: "Why do you all choose to drink Natural Ice?" and suddenly, I was wondering that myself. Through conversation and observation, via in-home interviews, shopalongs, and focus groups, we could get those answers.
Qual data explains and exposes trends in underlying opinions, motives, and behaviors. It captures perspective and experience, which of course is more difficult to analyze than hard and fast numbers. There's so much gray area. Sample sizes tended to be smaller, as researchers like time to get to know their participants. Analyzing that deep dive is time consuming as well -- so much so that most of the data captured goes unused.
In retrospect, it seems many professors, including my Stats and Marketing capstone professors, believe researchers are still stuck with the false dichotomy of qual vs. quant. Since joining the team at dscout, I’ve come to understand this choice is a false one.
Researchers can often conduct qualitative and qualitative research simultaneously.
Say Starbucks wants to better understand daily coffee drinking routines. A survey or questionnaire with questions targeting these habits is sent to one group of participants, while other participants discuss their habits in a focus group. The advantage, of course, is that Starbucks has different types of data to analyze.
The disadvantage, however, is using data collected from two entirely different groups of participants to support a larger story. It is likely that some opinions, ideas, and beliefs vary between the two groups, which leads to a bit of a disconnect. It gets a bit fuzzy.
When the same group of participants demonstrates patterns and frequencies across more than one research study, it lessens the data fuzziness and disconnects. Note to profs: New technologies (like dscout) enable researchers to combine the two seamlessly.
With dscout, for example, hundreds of participants can provide researchers with thousands of data points by answering structured and unstructured questions with photos, videos, and text responses. This allows for uniquely rich qualitative data, as the captured moments and observations—actual data points—can be captured in real-time.
Our online platform then enables researchers to conduct a quantitative analysis on their qualitative research data, with tagging and coding capabilities available directly in the platform. Frequencies and crosstabs develop a numerical spine for the overall story.
Then, by immersing ourselves in the individual entries from each participant—in the quotes, videos, and text responses specifically—the qualitative data begins to show its shape. This provides the context necessary to help bring the story to life.