It provides insights based on non-statistical and semi-structured data.Ī report published by Open University Press states. To understand these concepts better, let’s have a look at them in detail: What is Qualitative Research?Īs the name suggests, qualitative research focuses on the quality of the data, the non-numerical aspect of the research. #Qual vs quant manualRequires manual efforts (except for online surveys) Online surveys (using Sentiment Analysis) Help verify speculations and hypotheses made during qualitative research. Measures the past and predicts the future. Note to profs: New technologies (like dscout) enable researchers to combine the two seamlessly.Aims to understand human behavior, preferences, and needs from an interviewee’s point of view.įocuses on collecting facts about social phenomenaĬoncerned with discovering social phenomena, unconscious or conscious.Įxplores the psychological mechanisms via interaction with individuals or analyzing individual responses online.Ĭollects quantifiable data, which is easy to visualize. When the same group of participants demonstrates patterns and frequencies across more than one research study, it lessens the data fuzziness and disconnects. It is likely that some opinions, ideas, and beliefs vary between the two groups, which leads to a bit of a disconnect. The disadvantage, however, is using data collected from two entirely different groups of participants to support a larger story. The advantage, of course, is that Starbucks has different types of data to analyze. 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. Say Starbucks wants to better understand daily coffee drinking routines. Researchers can often conduct qualitative and qualitative research simultaneously. Since joining the team at dscout, I’ve come to understand this choice is a false one. 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. Analyzing that deep dive is time consuming as well - so much so that most of the data captured goes unused. Sample sizes tended to be smaller, as researchers like time to get to know their participants. It captures perspective and experience, which of course is more difficult to analyze than hard and fast numbers. Qual data explains and exposes trends in underlying opinions, motives, and behaviors. Through conversation and observation, via in-home interviews, shop alongs, and focus groups, we could get those answers. 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. Qualitative research, at the other end of the spectrum, was positioned as purely exploratory. To be statistically representative, these rigidly structured studies required hundreds or thousands of participants. We analyzed using statistical models, numerical comparisons and inferences to quantify opinions, motives, and behavior patterns. #Qual vs quant freeIn her free time, my stats professor would conduct her own quant research to study other statisticians. Quantitative research - questionnaires, surveys, and polls designed to collect concrete numerical data - was billed as a statistician’s bread and butter. So I focused my efforts on understanding the two methodologies, how they differ, and when is best to choose one over the other. 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. Professors would paint the research method picture as an either-or decision: either qualitative or quantitative. Mobile research technology alters the “either or” decisionĪs 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.
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