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Quantitative research is qualitative research in disguise
Whether you’re a market, academic, or social researcher, most human behavior researchers have a preference and expertise for quantitative or qualitative data collection tools. We tend to have preferences between focus groups and questionnaires, individual interviews and eye-tracking, ethnography and biometrics. We have a well-developed hammer, and we know how to make it solve most of our research problems.
However, the human experience is 100% qualitative, and quantitative research is really qualitative research in disguise. Researchers ask people to provide answers using distinct answer boxes, not realizing that they’re asking participants to pre-code highly subjective interpretations of complex experiences into imperfectly operationalized answer options. Those pre-coded answers are neither more precise nor valid than answering open-ended verbatims which are subsequently coded by the researcher. Whether the participant codes them, or the researcher codes them, both are representations of qualitative personal experiences crammed into a box.
We like to differentiate quantitative research as being measurable, structured, and controlled when qualitative research is also very much measurable, structured, and controlled. We like to say qualitative research is rich and in-depth when quantitative research can also be rich and in-depth. When well conducted, both qualitative and quantitative research give people the opportunity to reveal their underlying emotions and motivations. Quantitative research sometimes offers scale and statistical power, but the rest can be quite similar.
What can research users and practitioners do? Most importantly, we need to recognize that neither method is more valid, useful, nor important. It’s irrational to prioritize results from one method over the other. Second, research practitioners and users should have more than a basic level of training in both qualitative and quantitative research. It’s never a flex to be fluent in one method and mostly ignorant of the other. This only limits a researcher's perspective, problem-solving capabilities, and robustness of their findings.
Probability Sampling: A More Rigorous Form of Nonprobability Sampling
When it comes to choosing between probability and nonprobability sampling of human beings, the reality is that just about all sampling is nonprobability sampling. There are very few cases in which every member of population is known and every randomly selected participant consents. Probability sampling exists further along the continuum of nonprobability sampling.
For example, every student registered at a school can be identified but forcing a random sample of that population to participate in a study is impossible. Similarly, even with birth, death, driving, and voting records, it’s impossible to have a perfect list of every citizen in a city and subsequently force a random sample of those people to participate in a study. People will always be accidentally excluded and many who are included will not consent to participate. Nearly every attempt to achieve probability sampling with people is in fact an example of more rigorous nonprobablity sampling.
Regardless, probability sampling isn’t inherently superior to non probabliity sampling. Errors of sampling, data analysis, and interpretation creep into both methods. All participants behave on a continuum of honesty, attention, and care.
What can research users and practitioners do? In the end, the best sample is that one that is best suited for the job. Nonprobability samples are ideal for exploratory research, pilot studies, case studies, niche populations, trending, product testing, and, of course, working within time and budget constraints. If you require more precise statistical extrapolation such as for political polling, policy evaluation, market entry analysis, or demand forecasting, methods that approach probability sampling are preferred.
Every extrovert is an introvert
We love to classify people as male or female, introverted or extroverted, or online or offline shoppers. Our research results are massive collections of artificial, human-made binaries. But the human experience, even the most discrete physical attribute, exists on a continuum.
Binary groupings have a purpose and can be extremely helpful but it’s important to remember that we arbitrarily create the cut-points that become those binary groupings. We choose those cut points out of convenience not because they’re ‘true.’
No matter how we classify people into personality, demographic, shopping, social, or other groups, those groupings are artificial and they exist on a continuum. A group of ‘introverts’ could be subdivided into introverts and extroverts. And that subgroup of introverts could again be subdivided into introverts and extroverts, rinse and repeat. Being classified as a premium or budget shopper differs by who you’re shopping with, the product category, the time of day, and whether you’re hungry. Being classified as rural or urban can depend on political, national, local, and other characteristics.
What can research users and practitioners do? Remember that data tabulations are arbitrary and changeable. They can be redesigned once, twice, and thrice after the preliminary data has been reviewed. Design your initial tables with twice as many groups as will be necessary even if the sample sizes will be too small. With tables in hand, then you can evaluate the results and decide how many groups make sense and whether those groups should be equally sized.
Conclusion
Most binaries are arbitrary. They are human defined, human applied, and can be recoded into innumerable meaningful groups. While they are essential for simplifying the complexities of our world, every binary representation gives researchers a fresh opportunity to pause and question other equally valid categorizations that might exist. Questioning binaries is an important technique for researchers and marketers who want to reveal and explain the true complexities of consumer behaviors and preferences, ultimately improving the accuracy and relevance of marketing insights.
