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Research: Why, & What does it tell us?

Research is important because without it we’d never know anything. To conduct research we must have some notion that the world we experience is real and that there is some knowable truth we can arrive at through observation, questioning and experimentation. To do research we must be realists to a certain extent, otherwise it’d be impossible. Fortunately, the normal human experience leads us down the path of acceptance that what we experience as life is real and that what we observe is also observable by others. Now we can begin to ask questions about everything we encounter. If we can create good questions, then by seeking out the answers to them we can arrive at good answers, or the at least the best answers our human faculties allow us to arrive at.

When it comes to research, especially in the social sciences, we are presented with two main branches of scientific inquiry; quantitative and qualitative. The former has been in existence and acceptance for a longer period of time, it’s basically the reason we created the invisible college that lead to our modern scientific methods after all (Bryson, 2010) and the latter is a more recent late comer on the scene (Johnson & Christensen, 2014). Although many see the Quantitative Research as hard science, or more “real” science because of its reliance on numbers basically, Qualitative research is no less valid nor is it any less “scientific”. I believe both allow us to know truth and that both can be corrupted by our lack of understanding. Either one can teach us about the world or teach us about our own inadequacies. After-all, they are both based on questions produced by the naturally fallible human mind, the same mind that has lead us down the path of religion, blood-letting, witch-burning, and numerous other blunders of ignorance over the centuries.

First let’s take a look at quantitative research methods. Quantitative methods do exactly as its name implies, it quantifies things. It puts them into numbers and we as humans find numbers particularly appealing if not erroneously impartial and objective. To do quantitative research we must collect data. This data can be collected through both experimental and none experimental methods. To produce an experiment, researchers must come up with a research question where  separate groups share the same independent variable (IV) and a dependent variable (DV) that can show the results of the of the manipulations of the Independent variable by the researcher (Johnson & Christensen, 2014). This is what most people think of first when they hear the word “science” and from this type of experiment we get lots of data usually in the more of numbers representing changes and statistics about those changes.  There may also be intervening or mediating variables that comes into play between the independent and dependent variables and must also be accounted for measured by thee researcher (Johnson & Christensen, 2014).

To accomplish the task of producing a good experiment, one that would hopefully prove some causational relationship between the manipulations of the (IV) and the changes in the (DV), researchers have to take the utmost care that there are no extraneous variables at play or the whole experiment may prove use useless and very unscientific (Johnson & Christensen, 2014). Even when the scientific method has been followed to a “T”, perhaps the results are not actually proof of knowing what is real in this world. For example, the experiments on precognition carried out by Dr Daryl Bem who proved that ESP is real, but really proved that “Science is Broken” (Engber, 2017). Bem’s research in Parapsychology took the world by storm. Here was a scientist following the rigorous methods of experimental psychology without fail and he was “proving” something to be true that most Psychologists believed to be anything but “scientific”. In the end, after all the hype died down, it would be concluded that “no, ESP is not real”, but that so-called “hard science”  may not be as hard and factual as is commonly accepted (Engber, 2017).

Of course, there is quantitative research that does not rely on experiments, but rather by the gathering of statistics. Nonexperimental research does not involve manipulation of variables but rather on perceived links between various variables. The variables involved in such research are often categorical variables. Things like religion, gender, language etc., are taken as the meaningful differences between groups and then another categorical variable shared across groups is studied to see if there are any differences are observable. The results are co-relational and can be both positively and negatively correlated. By observing that relationship between these variables, knowledge can be gained and accurate predictions made (Johnson & Christensen, 2014).

On the other end of the spectrum is Qualitative Research. Qualitative research takes its subjects’ experience as evidence of reality and after collecting lots of information about various subjects, it is up to the researcher to connect the dots and derive meaning from that experience derived data (Johnson & Christensen, 2014). Because it is researcher who decides the meaning, and not a supposedly impartial and objective group of numbers, some people claim it not to be as scientific as the quantitative approach. However, this is not the case. While quantitative methods conform to the commonly accepted process of science, one of deductive reasoning that leads to theories, hypothesizes, observation, and knowledge, qualitative research is one that comes naturally to use and is no less capable of shedding light on the reality in which we find ourselves. Qualitative research relies on Inductive Reasoning, the observance of patterns that leads to questions and ultimately theories that explain the world around us. In many respects, this is the way our human and pre-human ancestors, and perhaps many animals, learn about, experience, and predict the occurrence of phenomena in our world. If every winter is cold for several years, we can predict that next winter will be cold too, and over time rightly arrive at the fact that winters are indeed cold. This is no less a hard fact than a set of temperatures derived from a thermometer taking the ambient temperature during different seasons of the year, it’s just a little bit less specific. We can’t know that one winter may have been colder on average than the next, but we do know the general truth that winters are cold due to our experience of them.

In the social sciences, much can be learned through these inductive participant reported or observer observed experiences (Johnson & Christensen, 2014). In some ways it goes deeper than quantitative methods as looking for quality in the nature of experience, great effort can be made to discern and infer the individual feelings, goals, and motivations of those we are studying. Rather than simple observable fact such as the height of the mercury in a thermometer and it’s correlation to a line etched thereon, we can arrive at knowledge and meaning of what it feels like to be cold as oppose to warm for example. To me, at least, these types of results feel more “human” and easier to relate too than a data set of impartial numbers.

However, because of the human element involved, mainly that of the researcher interpreting what they observe or the participant reporting what they have experienced, human errors in judgment can pollute the results arrived at. I am reminded of the ethnographic studies of the Yanomamo carried out by Gagnon (Eakin, 2013). The researcher’s conclusions that they were a fierce tribe constantly engaged in warfare may not have been skewed simply because Chagnon missed the intervening variable in his research, mainly whose presence among the tribes, but his results may have simply been reflections of his own inherit biases and ideas about humans and their motivation. Others looking at the same people came out with the opposite conclusions (Eakin, 2013).

A third path of scientific inquiry is available, suffering from the same drawbacks while possessing the same strengths of Quantitative and Qualitative research methods, that is, Mixed Research. Mixed research relies on both approaches and makes use of data driven quantitative research and experience/observation derived qualitative research (Johnson & Christensen, 2014). In mixed research a problem may be looked at from both traditional research perspectives simultaneously. What this allows the researcher(s) to do is gather more perspectives and information concerning a research question than either of the approaches would provide on their own. I like to think of it as being the best of both worlds.

Because it employs research methods from the opposite ends of the spectrum, I believe that any researcher or participant induced bias into the results or any unnoticed mediating variables corrupted the data as in looking at a single question in two ways, any conclusions drawn would have to be in agreement with data derived from both methods. In my view, considering the problems I highlighted in both quantitative and qualitative research studies, I think such errors could be avoided. If Chagnon’s subjects had been exposed to a psychologist experimental research lab, it might have been found that they acted no more or less aggressive than any other human being under similar conditions and if Bem’s ESP were submitted to ethnographic scrutiny, perhaps no evidence of precognition would have been observed. After-all, if people could “see the future” one would imagine that there’d be some phenomena available to a researcher to observe, say, someone who won the lottery several times, or an accurate prophet who explained in detail what would happen and then it did. None of these types of observations were available and that is why everyone was, and now is gain, so skeptical of parapsychological research findings.



Bryson, B. (Ed.). (2010). Seeing Further: The Story of Science, Discovery, and The Genius of the Royal Society. Great Britain: HarperPress.

Eakin, E. (2013, February 13). How Napoleon Chagnon became our most controversial anthropologist. The New York Times. Retrieved from http://www.nytimes.com/2013/02/17/magazine/napoleon-chagnon-americas-most-controversial-anthropologist.html

Engber, D. (2017, May 17). Daryl Bem proved ESP is real, which means science is broken. Slate Magazine. Retrieved from https://slate.com/health-and-science/2017/06/daryl-bem-proved-esp-is-real-showed-science-is-broken.html

Johnson, R.B., Christensen, L. (2014). Introduction to Educational Research. In Educational Research: Quantitative, Qualitative, and Mixed Approaches. (p. 2-28). Thousand Oaks, CA: Sage.

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