Our team was hired by a Brazilian college to investigate why it was losing students. When starting the project, our first challenge was identifying the main reason for this dropout. We had a list of 63 possible causes, compiled over several years through qualitative research. Yes, 63.
These factors ranged from teaching quality and professors, through the physical environment, the existence of laboratories and the institution's reputation, to issues like MEC (Ministry of Education, in Brazil) certification. There were, in total, 63 variables that could influence a student's decision to choose one college over another.
Imagine presenting a list of 63 reasons to a university rector and saying: "The problem is one of these." The response would be, predictably: "That doesn't help me make a decision." The information was vast, but hardly actionable.
That's when we turned to quantitative research. First, based on desk research and academic literature review, we reduced the number of hypotheses from 63 to a more focused group of 10 main factors. Then, we applied a statistical technique called binary logistic regression. For this, we created an experiment in which students were divided into two groups: those who remained at the college (our client) and those who transferred to another institution. Experienced interviewers went to the field with structured forms to approach both groups.
When running the regression analysis, the result was categorical: the determining factor, with the greatest variation power, was location.
At this point, we could conclude that quantitative research is superior, since it reduced 63 possibilities to just one. Right? Wrong. The analysis told us what, but not why. It pointed to location as the problem, but didn't explain the reason for its importance.
To discover the "why," we returned to qualitative research. We went to investigate what made location such a critical aspect in that city. We discovered that the municipality was cut in half by a train line. In small cities, when the train passes, life stops: traffic is interrupted and no one can cross from one side to the other. This daily disruption influenced residents' behavior, who avoided as much as possible having to move to the opposite side of the railway.
Our client's college, which had existed for decades, noticed that dropout began when a competitor opened a new campus. When we investigated, we saw that this competitor's campus was built exactly on the other side of the train line. If the traditional college was on the "right" side, the new one was installed on the "left" side. Naturally, students who lived on the left side began enrolling at the new institution, to avoid the daily hassle of crossing.
The solution became obvious: to compete effectively, the university would need to have a campus on each side of the train line.
What does this teach us about research? The moral of the story is that the fight between qualitative and quantitative research doesn't make sense, because, as the case demonstrates, both were indispensable.
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Without the initial qualitative research, we wouldn't have had hypotheses about the factors that influence college choice.
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Without quantitative research, we wouldn't have been able to reduce a universe of 63 reasons to a single manageable factor.
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And, again, without qualitative research, we wouldn't have been able to interpret the numerical result and understand the true context behind the data.
The lesson is clear: instead of getting stuck on labels like "positivist" or "constructivist," be pragmatic. Use the research methodology most appropriate for the challenge at hand, at the right time.