Or A Blessedly Short Primer on Counting Things for Research

In the previous post, I discussed a video about arts-based research. For you readers who didn’t use it as a sleep aid, you might have picked up on the term qualitative research. I’m pretty sure it wrinkled a few brows, so I’m back to explain two terms that have wrinkled many other brows before yours.

Qualitative Versus Quantitative Research

If these terms confuse you, don’t feel bad. It took me nearly halfway through graduate school before the distinction finally clicked. Yet somehow, I still managed to write comprehensive papers and appear as though I knew what I was talking about. I suspect I’m in very good company, judging by my peers (and a few instructors).

Quantitative research deals with things that can be counted and measured. Numbers, percentages, frequencies—your concrete and tangible information. In other words, “How many?”

Qualitative research focuses more on meaning, experience, and interpretation. Feelings, stories, memories, symbols, and relationships that typically can’t be measured in numbers. In other words, “What was it like?”

So how do researchers gather this sort of qualitative information?

Well, as discussed previously, arts-based research is one way. You can ask students to create artwork about their experiences during, say, their final semester of graduate school. Researchers could then observe how many students chose abstract versus representational imagery, greyscale versus color, or traditional versus digital media. Follow-up interviews might reveal why each student made those choices and what the artwork means to them.

It’s very human-based and therefore seems deceptively easy—until you factor in the time spent analyzing the data, talking it over with your team (or yourself, if you’re flying solo), developing interview questions that respect participants’ privacy and rights, writing the paper for your supervisor or journal submission, and any other tasks required to complete the research. At that point, those warm and fuzzy human experiences begin to resemble work. A lot of work.

Why Not Both?

How about using both in the same study? Actually, that happens more often than you might think, and it’s one of the best ways to build as complete a picture as possible.

Think of the Likert scale. You’ve seen this before, even if you never knew what it was called. On a scale of 1 to 5…

Depending on the level of detail (or the pedantry) of whoever designed it, the options might look something like this:

• Strongly disagree
• Disagree
• Neutral
• Agree
• Strongly agree

Or, for those obsessed with nuance:

• Strongly disagree
• Somewhat disagree
• Disagree
• Neutral/Neither agree or disagree
• Agree
• Somewhat agree
• Strongly agree

Fortunately, we haven’t yet encountered:

• Emphatically disagree
• Not sure if I disagree
• Spiritually opposed
• It’s complicated
• Comfortably numb
• Might agree
• Totally agree
• Yeah, sure

You get the point.

The Likert scale has driven many a student insane, particularly in cases where the answers are reversed and the agrees come before the disagrees. I’ve accidentally given instructors negative reviews after a semester because some genius in the office decided to be quirky.

Regardless of their order, these responses are considered ordinal data, or they have a meaningful sequence and mutually exclusive (that’s academic speak for “cannot happen at the same time”) (Oxford Brookes University, n.d.). Technically, this is quantitative data, because the responses can be counted and compared.

Using our scale above, researchers could further report:

• 70% agreed
• The average score was 4.2 out of 5
• Most respondents selected “Agree”

That’s quantitative. But if researchers ask why people chose those answers and then analyze written explanations or interview responses, that becomes qualitative.

Those answers might come from interviews, focus groups, diaries, drawings, poems, stories, photographs, audio recordings. Here, the researchers want to understand experiences, patterns, meanings—the why behind the previous answers.

Of course, that raises an obvious question: if these approaches are so different, which one should you choose?

Yeah, What If I Have to Choose?

Great news: you might not have to.

Depending on the topic, the kind of data needed, and the strengths of the researcher, it may be perfectly acceptable to use just one approach. But if the situation calls for both, combining them is often easier than you might think. The example above is only one of countless ways researchers can bridge quantitative and qualitative methods.

Neither approach is superior, although researchers often have preferences. Scientific folks may feel at home with “countables”, while artsy folks who struggle to visualize more than five objects at a time (i.e., me) may be more comfortable with “feelies”.

The two actually complement each other rather well. But if you’re working with others, just make sure the team as a whole can do both, because explaining the holes in your research after the fact probably won’t be much fun.

“On a scale of 1 to 10, how would you rate the initial research process?…”

Science and the humanities have enough arguments already. There’s no reason the countables and the feelies can’t get along.

References

Oxford Brookes University. (n.d.). Types of data. https://www.brookes.ac.uk/students/academic-development/maths-and-stats/statistics/types-of-data