I will conclude my involvement in this week’s discussion by looking at the place of theory building in qualitative research designs. I’m doing this following reflections on the week’s required resource readings with comparisons to last week’s discussion on quantitative models. Some researchers may consider qualitative designs “easier” (Laureate Online Education, 2012) than quantitative ones because of the lack of statistical skills needed. Hatch (2002) reports that some students “openly confess that they have never felt comfortable with math, especially statistics” (p. 1). However, this position fails to acknowledge both the role of statistics and the necessity for theory generation. These two aspects overlap significantly in quantitative research. I suspect that many researchers—if not most—who undertake statistical analyses cannot reproduce the underlying mathematics beyond simple arithmetical averages, standard deviations, variances and so on. The mathematics behind ANOVAs and linear regressions may be beyond many. The ready existence of statistical programmes that produce advanced statistical output make such analyses easier, and the task of researchers is limited to understanding the statistical assumptions of the tools they use (Cohen et al., 2011). I highlight this to make the point that, due to statistical software packages, quantitative models may actually be far easier than qualitative ones. Qualitative designs, especially grounded theory, often require the researcher to build theoretical models from scratch, from emerging meanings found in the data itself (Charmaz, 2006; Cohen et al., 2011; Silverman, 2010). Cohen, Manion and Morrison (2011) emphasise the need for strict content analysis and outline an eleven-step process to achieve this. However, their short exposition omits much, including the need to explicate base ontological and epistemological positions (Hatch, 2002) and a longer discussion of the iterative nature of qualitative data analysis (Charmaz, 2006).
Hadley’s (2015) grounded theory study of “blended” professionals, i.e. neither academic nor administrative staff, and their attitudes of English for academic purposes in Japanese, American and English universities presents a tripartite division of a grounded theory into initial, middle and final stages. Each stage represents a phase in the development of an emergent theory, a set of explanations that aim to fulfill Hammersley’s (1992) criteria of validity, plausibility and credibility in relation to how the collected data can match the researcher’s theory.
In other words, the sheer amount of data that interviews and other sources generate (Silverman, 2010) combined with the iterative constant comparison (to borrow a grounded theory term, but can be appropriate for other data analysis methods) and the necessity to develop a theory from the given data makes qualitative research a very tough proposition. Compare that process to a few clicks of a computer button that can generate a T-test statistic. I, for one, would find it challenging to describe succinctly and exactly how a T-test statistic worked including its underlying mathematical model of the bell curve, and that task is largely avoided in quantitative research. Its equivalent task is a necessity in qualitative research. And that makes it tough.
Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. London: Sage.
Cohen, L., Manion, L., & Morrison, K. (2011). Research Methods in Education. Professional Development in Education (7th ed.). Abingdon: Routledge.
Hadley, G. (2015). English for academic purposes in neoliberal universities: A critical grounded theory. Cham: Springer.
Hammersley, M. (1992). What’s Wrong with Ethnography? London and New York: Routledge.
Hatch, A. J. (2002). Doing qualitative research in education settings. Albany: State University of New York Press.
Laureate Online Education. (2012). Week 5: Qualitative Data Analysis. Educational Research Methods Weekly Notes. Laureate Online Education B.V.
Silverman, D. (2010). Doing qualitative research (3rd ed.). London: Sage Publications.