Linearity vs. Circularity? On Some Common Misconceptions on the Differences in the Research Process in Qualitative and Quantitative Research

Abstract

Methodological discussions often oversimplify by distinguishing between "the" quantitative and "the" qualitative paradigm and by arguing that quantitative research processes are organized in a linear, deductive way while qualitative research processes are organized in a circular and inductive way. When comparing two selected quantitative traditions (survey research and big data research) with three qualitative research traditions (qualitative content analysis, grounded theory and social-science hermeneutics), a much more complex picture is revealed: The only differentiation that can be upheld is how "objectivity" and "intersubjectivity" are defined. In contrast, all research traditions agree that partiality is endangering intersubjectivity and objectivity. Countermeasures are self-reflexion and transforming partiality into perspectivity by using social theory. Each research tradition suggests further countermeasures such as falsification, triangulation, parallel coding, theoretical sensitivity or interpretation groups. When looking at the overall organization of the research process, the distinction between qualitative and quantitative research cannot be upheld. Neither is there a continuum between quantitative research, content analysis, grounded theory and social-science hermeneutics. Rather, grounded theory starts inductively and with a general research question at the beginning of analysis which is focused during selective coding. The later research process is organized in a circular way, making strong use of theoretical sampling. All other traditions start research deductively and formulate the research question as precisely as possible at the beginning of the analysis and then organize the overall research process in a linear way. In contrast, data analysis is organized in a circular way. One consequence of this paper is that mixing and combining qualitative and quantitative methods becomes both easier (because the distinction is not as grand as it seems at first sight) and more difficult (because some tricky issues of mixing specific to mixing specific types of methods are usually not addressed in mixed methods discourse).

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