Qualitative Research, Part IV

In general, the thing I struggle with most is that qualitative research has no expectation of impartiality. I am beginning to understand that this is because at least some of the proponents of qualitative research explicitly reject the possibility of impartiality. I guess I’m a positivist by nature and my natural Libra inclination is to see and hear both sides of arguments. Anyway, I struggle with the idea of deliberate bias in research, of prejudged advocacy. I don’t know where it leaves us if everything we read takes a side, how do we get to truth? Yes, I know many of the folks who advocate for qualitative research reject the concept of truth, but honestly I don’t see how to move forward if there’s no truth.

Qualitative and Quantitative Research

Qualitative and qunatitative research both have their uses as well as their misuses. I think quantitative research is somewhat harder to misuse because the structural constraints and expectations are much clearer. Any partisan instinct, when exposed, seriously damages the weight of the paper. This seems to not be the case in qualitative research where it appears that advocacy is not only tolerated but, in some quarters, cheered. I find that disturbing. Science is a service business, service to the human race. It cannot serve two masters, the human race and a partisan cause. There is a role for partisan advocacy in serving the human race, we see it nightly on MSNBC, et al. But there it is clearly understood to be the free competition of partisan ideas.

I was particularly tweaked by the second paper in our text because I think the instinct to second guess as if “nothing bad should ever happen in life and if it does it’s somebody’s fault” is a generally unwise. Thus, the author’s making an issue of the fact that the university didn’t appear to be making a plan to deal with shooting incidents when few of the participants felt the same way was a misuse of the platform. Of course, the same happens in quantitative research. The confusion over global warming is a great example. It seems clear to me that many of the advocates of global warming didn’t like industrialization in the first place. They’re thrilled to “find” ecological issues to stop with science what they couldn’t stop with advocacy. And it is equally clear that there is a great deal of money being poured into both sides of the argument, paying for research. Neither Exxon nor the Sierra Club is all that interested in paying for research that contradicts its advocacy. The result is a blizzard of claim and counter claim, all backed by “science.” When I was a kid, the buzz was the coming of the new ice age. Today, it’s global warming. How do I know what to believe and therefore how to act? It’s unnerving and a disservice to us all.

When we go into a jury box, we are sworn to put our biases aside. I think most people do the best they can, and I think their best is pretty good. I believe that is what science demands: rigorous impartiality. That is hard enough to obtain in quantitative research, but qualitative research seems to not even have that as an expectation. That’s a problem for me.

Quantitative Research

What are some strengths and weaknesses of quantitative research?

Quantitative research is a fantastic tool for expanding human understanding.  By using the time-honored tenets of scientific inquiry, it pushes back the dark curtains of ignorance in our lives.  It is “systematic and purposeful.”  It “is conducted and reported in such a way that the argument can be examined painstakingly.  The report does not depend for its appeal on the eloquence of the writer or any surface plausibility.” It usually “assumes there are stable, social facts with a single reality, separated from the feelings and beliefs of individuals.” In quantitative research, “there is an established set of procedures and steps that guide the researcher.”  The researcher must remain “detached from the study to avoid bias.”  By doing all these things, quantitative research sheds informational light on the subject under study, allowing improved predictability and suggesting how changes in process could result in changes in outcomes.  It specific, practical, and observable.  By “observable,” I mean that the process by which a conclusion is reached and the facts upon which that conclusion is made are exposed to the reader and subject to independent scrutiny.  The conclusions can be judged in the context of the process, the facts, the methodology, and the solidity of the statistical outcomes.  It can also be replicated, checked against differing situations, places, people, and times for verification of universality.  It is an incredibly powerful tool to refine and improve outcomes.

The famous quote “there are lies, damn lies, and statistics” captures the flavor of the weakness of quantitative research.  It is possible to design studies such that essential causal factors are missed.  It is very easy to confuse correlation for causation in such studies (though after many years of use, I would like to think this is not such an easy mistake to sell anymore).  It is not hard to “data mine” samples for facts which support the desired argument while ignoring facts which do not. Finally, there are subjects, which are inaccessible to statistical methods, or are anyway better portrayed in a holistic representation of the words and experiences of the participants.  I think Mark Twain compared humor to dissecting frogs, claiming “both suffer from the experience.”  Likewise, many aspects of human existence lose a great measure of “truth” when broken into constituent parts.  Certainly, it takes a particular sensitivity to run the wheels of the quantitative research machine along the grain of life such that the reductionist techniques leave the reality of life intact.