Deciphering the Quantitative and Qualitative Discourse: A Postgraduate Narrative

Since starting my graduate studies, I have become acutely aware of the mobile environment. Now, social media algorithms, recognizing my behavior patterns as a graduate student, frequently show me content about 'what a graduate student should know.' I am grateful for these unsolicited yet beneficial information feeds.

In particular, I find interesting facts when browsing advertisements and posts related to research methodology. Ads promoting specialized lectures on quantitative research methodologies such as 'big data statistical analysis using R and Python,' or 'mastering regression analysis and structural equation modeling in a day,' charge hundreds of dollars per session. They even emphasize that these prices are steeply discounted. The splendid profiles of lecturers, who have learned advanced techniques from American universities and lecture at renowned domestic universities, also catch my eye.

I recall news of a professor from the Department of Statistics at a university in South Korea, who criticized the false hype of the term 'big data.' Another professor in the Sociology Department of a different university delivered a somewhat radical diagnosis in a recently published book, stating that anyone who advocates education reform in the name of the 'Fourth Industrial Revolution' is a businessperson, neither a scholar nor educator.

However, one cannot deny that we are living in an age of data. In this context, 'data' mainly refers to 'quantitative data.' Due to this dominant social atmosphere, there's a prevailing 'quantitative vibe' in graduate school as well. In a somewhat embarrassing confession, even as a doctoral student conducting qualitative research, I've felt unknown hesitation when referring to interview transcripts and literature as 'data.'

The research methodology-related class I attended after entering graduate school well demonstrated the overpowering force of 'quantitative' methodologies. The professor of one classs I took, a proud quantitative researcher, occasionally mentioned during class that "for details, you can attend the off-campus quantitative research workshop held during vacation." He/she did not mention that the workshop which was co-organized by a private third party provider cost as much as the classes advertised on social media—hundreds of dollars a day.

One student, who was highly active in this class, frequently used terms like HLM, EFA/CFA, and LCA/LTA. The vigor seemed like peer pressure to master advanced statistics. As someone who believes that genuine capability and insight as a researcher can be demonstrated through qualitative research, I couldn't help but feel a sense of discomfort throughout that semester.

Research methodologies may vary depending on the field and period. However, in the context of my major, educational administration and higher education, issues have been raised regarding the binary exclusivity towards positivism and constructivism and the dichotomic interpretation and application of 'quantitative' vs. 'qualitative' research methodologies rampant in academia.

This involves a misunderstanding of "incommensurability", often mistaken by viewing social science as equivalent to natural science and tying positivism, which seeks universal theories, to research methodologies using quantitative data and advanced statistical techniques. Mechanically linking interpretivism or constructivism, which values various perspectives and interpretations of actors and members, to qualitative research methods blocks the potentially complementary functions of these two different paradigms.

The incommensurability proposed by Paul Feyerabend and Thomas Kuhn implies that different paradigms cannot be commonly measured. It explains why there can't be a clear comparison or evaluation between paradigms based on widely accepted scientific standards in a particular era and society.

In other words, incommensurability only means that different paradigms cannot be measured or evaluated by a single standard, not that it proves the superiority of a certain paradigm or denies the potential for developing a co-generative or dialectic relationship.

Viewing the knowledge base and research methods of a discipline from a biased paradigmatic perspective can produce unintended side effects. It should not be mistakenly interpreted as signifying that a certain paradigm is unequivocally superior when compared to other competing ones.

The complementary nature of paradigms is the source of the power that produces new theoretical and practical propositions through a dialectical process between competing paradigms, leading to academic and intellectual advancement. Of course, it's important to be aware of the potential for the dichotomous distinction between 'quantitative' and 'qualitative' to lead to a mistaken belief that the empirical methodology is more scientific, and therefore superior, than the interpretative perspective.

A renowned professor in the Department of Physics at a major research university in my country, who I know well, left a famous quote about the 'significance level of 0.05,' a golden rule of quantitative research methodology, saying that "it seems like wordplay." As someone who respects the spirit and usefulness of quantitative methodology, and as a social science researcher who wants to achieve a high level of competence in quantitative research methods, I do not entirely agree with this.

However, as an academic researcher of the next generation, rather than determining the superiority between 'quantitative' and 'qualitative,' I constantly remind myself to bear in mind 'what I want to know as a researcher' and to continue introspection persistently between the two, adopting an attitude that recognizes their values.

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* Disclaimer

This content is a translation of the article in Korean, published on the Faculty Newspaper in June 2023. For the original article, please visit https://www.kyosu.net/news/articleView.html?idxno=105900.