The Context
This is a reflection of how I developed over the past year, and the growth I experienced as a result of imagining, creating, designing, and teaching an advanced qualitative analysis course for an online doctoral program. But first, I need to recognize some #formative steps that had already been traversed.
Childhood
I’ve always enjoyed #statistics. As a kid, I read about statistics (and became obsessed with the #descriptive sort) in the 1973 World Book set I got for Christmas as it was published. Yes, I was *that* kid who requested a new encyclopedia set from Santa Claus. I’d been reading a 1962 set, and well, I was recognizing that things in the world were newer than the tales in the ones from which I’d learned to read. But that’s digressing; I mean to talk about learning the #probabilities and how to calculate those, too. I hid sets of dice and created my own games that only I ever played. I recorded rolls of dice in my notebooks. I enjoyed statistics.
I created complete fantasy college football conferences with two teams from every state. I got in trouble for recording my statistics and standings of every conference’s weekly games (for which I rolled dice to determine results of running plays, passing plays, and kicks; I based rolls and created rules on #constructs that those who understand #THACO would find intelligible) on my father’s business-statement pads. The lines and columns fit perfectly for listing team names with “wins-losses-ties” totals in rows. I didn’t appreciate how much Pop paid for those pre-printed statements until he found a gap in the numbering, totaling 500. I learned those were too expensive to continue my practice. But, I loved statistics. I didn’t really know anything #inferential, but I was apt at describing things with numbers.
Adulthood
At university, I took stats courses across disciplines such as sociology, archaeology, anthropology, political science, psychology, and mathematics. I learned how to analyze descriptives and draw inferences from samples to populations. I was still enjoying statistics as an undergraduate student. I learned as a university administrator that with appropriate research design and analysis, I could get funding for many under-resourced areas. I learned the mainframe versions of SAS and SPSSx, alongside the original student edition of Minitab for DOS.
I oversaw QA in an advanced manufacturing facility. I wrote the materials for achieving ISO 9000, 9001, and 9002 certification. *There is a weird story-line about how that position came about, but not for today’s tales*. I learned how to appropriately use Minitab Statistical Software, outside academia. After six years of factory-work I returned to university.
In graduate school however, I learned deeply about research design, and then that (as #learning does) naturally led to investigating some qualitative research methodologies about which I knew nothing. Subsequently, I learned quite a bit about qualitative research design. I thought it was fluffy; I said so (and wrote that) many times. I knew I could get funding for my programs by using quantitative methodologies that measured impact; not so much–from describing what we might have needed–funding was procured with words. I think I am not overstating it to claim that I rejected qualitative research as a serious undertaking. I asked to audit a (qualitatively-based) “dissertation course” because I thought it was a waste to take the course and really spend enough energy and resources on it to earn an “A” mark; request approved. I may have even written in my appeal, “I will never do qualitative research”.
The Turnaround
As it turned out I conducted a case study for a dissertation when earning my PhD. I had to invite a qualitative-person to serve on my committee as methodologist. *It turned out to be she who had taught the course I had already infamously and publicly audited.* She was well aware that “I will never do qualitative research” had been scrawled, as I crawled back seeking her assistance. So, I read everything about *case study*, *ethnography*, and *ethnomethodology* I could find, as I was deciding between those three basic qualitative methodologies for the design of my study. Much of the rationale for reading so much and digging so deeply was because I needed to prove to her that I could learn her craft. I may have been a bit ashamed, yet #fear of failure (not meeting her standards) was a major ***causal mechanism***. *Even that (bolded) phrase was chosen because of an influence while developing and teaching this course about which I will eventually say something!*
After I defended my study in 2009, Dr. Gail Furman asked me if she could use **my** methodology section, *to teach the course which years earlier, I had audited*. At that point, I think it was safe to say that I had achieved a level of #affective learning (Krathwohl et al. 1964) categorized as a *commitment* to qualitative research. I can also firmly state that I had not embraced–achieved the affective level of *characterization* at the pinnacle of the Krathwohl et al. (1964) taxonomy–qualitative research, even then.
The Task at Hand
Fast forward to fall 2022. I was asked by (now our College’s dean) the director of our Ed.D. program to “develop an advanced qualitative analysis course for our research core”. He’s since joked, “we hired Vince for his non-parametric quantitative and mixed method expertise, and I’m happy to report that he’s not just another quantite”. But, that’s the story about how we got here.
Suffice to say, I had to dig in and make sure that the course was worthy of the name. Here’s [the syllabus]. Here’re [the evaluations] from the students. I’m claiming that it was a resounding success in terms of their self-reports, their marks on the culminating projects, and the weekly reflection activities I asked them to do. And finally, now, I want to shift a bit and talk about *how I evolved as an instructor and researcher*. Some of this *stuff* I *knew* had to be true, but I had never *felt* why nor how it was true. **I had never embraced qualitative research**. Given that (for now let’s call it a ) supposition, I’ll look for evidence to support, and additional corroboration that I do now–without room for questioning–embrace qualitative research.
At last, the Reflection!
Metamorphosis?
Was there a ‘transformational’ moment hidden somewhere in all of this? Perhaps, but I think it was a gradual recognition in my case.
First, as I was considering “measurement” in a new light (see Michell, 1997, 2003, 2005 2008, and 2011) I realized that I had never bought in to psychometrics because of Michell’s thesis, without knowing it was anybody’s thesis. As he put it, the quantitative rationale was always something like this: *if you cannot measure, you do not really know what you are talking about* but *if you can measure, you do know what you are talking about* (paraphrased); he claimed that this erroneous belief had a consequential outcome: qualitative research had no place in the realm of psychological sciences. Looking back, perhaps this is why I enjoyed my sociological coursework; I didn’t know it then, but sociology was explaining *why* and *how* the psychological findings surfaced.
Second, being raised in the tradition of Maimonides, I always had an appreciation of the metaphysical. None of my coursework in any of my academic programs had asked me to distinguish types of reasoning other than inductive–deductive. As I dug into measurement through Michell’s work, I encountered abductive logic. That led to reading about Charles Sanders Peirce . This led to a deeper understanding of “phenomenon” as it pertains to research; see the previously linked Bogen & Woodward (1988) piece. Almost none of what I consider to be metaphysical can be counted; however, I can measure its influence both by using and by analyzing rhetoric.
And the kicker, or the piece of straw that broke the proverbial Camel’s back was this: Bhaskar’s critical realism is interdisciplinary and includes hard science, social science, and the metaphysical. Unearthing and scrutinizing this ontological paradigm led me to believe that serious investigations cannot ignore stratified reality. The provincial demarcation of qualitative and quantitative had never been more blurred; however clarity materialized when explaining the real domain. Attitudes, beliefs, values, mindsets, a focus of more than half of my research agenda, are all qualitative. When I measure those, I don’t. I can estimate or have a participant estimate her perceived strength of a belief, *but the belief cannot be numerically measured*.
Think on these things
What does it mean to be qualitative? Imagine the word “embrace” for an exercise. What does it mean to *embrace* anything? Can I wholeheartedly embrace some thing but only halfheartedly embrace the other thing? Glancing at the first five hits on Google’s search engine, the top synonyms for “embrace” include: hug, cuddle, clutch, and hold. The second group (less common meanings?) of similar words include ideas like this: welcome, accept, take to one’s heart, champion, and espouse. In terms of non-verbs (nouns), I found a list that included these terms: hug, cuddle, nuzzle, caress, and clinch. Is it possible to partially do any of these things, or observe a minuscule *nuzzle* between friends?
Whatever your response, you might be hard-pressed to devise a psychometric-valid #scale that accurately and reliably measures “nuzzlement” or “strength of nuzzle”. Well, you ***should*** be hard pressed to do complete such a task. I think I am afraid to search, because I *might find such scales.*
**Exhibit A:**
It shouldn’t be acceptable to devise such a scale outside a relatively bounded #contextual #intensive research situation. Qualitative research would recognize the subjectivity of that task and phenomenalize the nuzzle; quantitative researchers could observe the significance of the nuzzle’s impact. Progressive multimethod research might even identify (and compare!) the effect sizes of the levels of nuzzle across contexts…achieving external validity for the #extensive research project!
Are any of those approaches less important (or less frivolous) than the other? I think not. The only way we could construct, develop, and explain the nuzzle phenomenon with a complete picture is by combining method. I’m not talking about mixing method, nor just mixing data, I mean integrating every available type of data into the research project so we can believe what we tell people, and people can believe us when we tell them what it means to “wholeheartedly nuzzle”.
**Exhibit B:**
There should be only two options for doing this research, choosing between conducting an *intensive* or *extensive* research project. Do I want to know intensively, how the people in the Nix family nuzzle, or do I want to find extensive similarities between the Nix and Song families? Do I want to extend this further, by extensively looking at Mississippi nuzzles versus Yunnan nuzzles?
Qualitatively, though, how can I *know* “nuzzle”?
Aftershocks?
Here is the real (seismic?) shift in my understanding. This requires an understanding of #ontology, or what I believe is learnable and knowable. As I described earlier, using statistics, I was learning and knowing “the things” from a #positivist perspective. I’d since been indoctrinated into a #constructivist way of knowing as a consequence of both graduate degrees. Dabbling into #realist conceptions of learning and knowing led me to read and investigate both #scientific and #critical realism ontological approaches. I believe that both the positivist and constructivist ontological paradigms reduce the “real” world to the knowledge we have about the world. I am committed to the layered (or stratified) ontological lens of critical realism; the #empirical is what we observe; the #actual is a series of events that occur to produce the empirical, and the #real is why it happened; resources and other *mechanisms of cause* are real. The deeper levels provide the necessary (physical or metaphysical resources) conditions; they are the catalysts. The stratified nature of reality is akin to water being derived from hydrogen and oxygen, yet irreducible to to its components. For example, political power may be derived from economic resources, but cannot be reduced back to the resources; reductionism fails at attempts to do so.
So, to describe “nuzzle” as it was observed empirically, I could ask people to rate the observed nuzzle on a scale from one to nine, with nine being “an absolute wholehearted nuzzle”, five being a “halfhearted nuzzle”, and one being “a meaningless or false nuzzle”. From positivists’ perspectives I could imagine what sort of characteristics (of the nuzzled, of the nuzzler, and of the recorder of the nuzzle) impacted the perceived strength of the observed nuzzle. I could eventually develop the *Kunming Shi Nuzzle Scale*, but it wouldn’t be valid for measuring nuzzles in Tupelo Mississippi. I could spend a lot of hours and dollars improving my scale so that I would eventually produce a scale that could reliably predict from whom we expected the warmest nuzzles across the two groups from Yunnan and Mississippi. That’s what we’d expect to see. In sum, nuzzles are empirically observable and eventually could be empirically predictable. *Heartfelt*, as a construct, is subjective; I might need to establish cross-rater coding reliability for something that nebulous.
Rich, Thick Descriptions
But to accomplish the tasks set out in the previous paragraph, I would need #subjective evaluations of nuzzling; in essence, I need *qualitative judgments* **of and by** the people who I chose to rate nuzzles. From which conditions do nuzzles emerge? How do we describe the catalysts of nuzzles between friends in Yunnan versus nuzzles between friends in Mississippi? How are they different? Why are they the same? Why is perceived privacy important in one area? How is social influencing or acceptance important in another context? If I want to know how and why people nuzzle, I *must need* qualitative data. How do I describe the necessary conditions of nuzzling? How are nuzzles influenced? Why do people choose to nuzzle? How do I know *when* a halfhearted nuzzle morphs into a playful headbutt?
If all of this has you thinking of writing a grant to subjectively assess *nuzzling and its levels of heartedness*, then hit me up! But this project will require *qualitative data*, reams of it.
**Exhibit C:**
Identifying and qualitatively describing any phenomenon is imperative to knowing anything beyond the empirical domain of said phenomenon; #empirical knowledge is in essence analogous to descriptive statistics. #Actual knowledge is analogous to inferential statistics; given a specific set of circumstances or the proper selection of participants, I might predict what happens. But #real knowledge emerges only after acknowledging that the world is intelligible and open to investigation. *Meaningful knowledge is the structure of the world, itself*. Human practices are mediating influences which link empirically observed action to structural mechanisms.
In Summation
Thus, *knowing the real* requires theoretical (qualitative) abstraction ( including #abductive and #retroductive reasoning) as well as conceptual (qualitative) analyses ( again including #abductive and #retroductive reasoning).
Therefore, I have *embraced* qualitative research.
~~~
Bogen, James, and James Woodward. 1988. “Saving the Phenomena.” The Philosophical Review 97 (3): 303–52. https://doi.org/10.2307/2185445.
Michell, Joel. 1997. “Quantitative Science and the Definition of Measurement in Psychology.” British Journal of Psychology 88 (3): 355–83. https://doi.org/10.1111/j.2044-8295.1997.tb02641.x.
———. 2003. “The quantitative imperative: Positivism, naïve realism and the place of qualitative methods in psychology.” Theory Psychol. 13 (1): 5–31. https://journals.sagepub.com/doi/10.1177/0959354303013001758
———. 2005. “The Logic of Measurement: A Realist Overview.” Measurement 38 (4): 285–94. https://doi.org/10.1016/j.measurement.2005.09.004
———. 2008. “Is Psychometrics Pathological Science?” Measurement: Interdisciplinary Research and Perspectives 6 (1–2): 7–24. https://doi.org/10.1080/15366360802035489.
———. 2011. “Qualitative Research Meets the Ghost of Pythagoras.” Theory & Psychology 21: 241–59. https://doi.org/10.1177/0959354310391351.