The q-method, as a graphic (visual) elicitation, has existed since the mid-1930s. Setting up a q-method, with q-sort capabilities, in an online survey platform, extends the reach of this method, even as data has to be processed in a quantitative data analytics suite. This chapter describes the setting up of a visual q-sort and the related debriefing on the Qualtrics Research Suite. The available data may be extracted and analyzed in a basic statistical analysis tool for factors and preference clusters.
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A q-methodology study (q-method, q-inquiry) elicits “operant subjectivity” through the presentation of various selected text and visual elements to a p-set (of respondents) to place into three general categories: agree strongly, neutral, or disagree strongly. The presented elements are selected from a “concourse” of elements from the particular relevant issue universe related to the particular research question. After select respondents (in the p-set) provide insights, they also are asked to debrief their responses for follow-on information. The q-method provides insights about general preferences around a particular issue but also individual human patterns of preferences, which may be studied as preference clusters.
Often, when researchers want to understand what people think of particular policies, practices, messaging, and in-world phenomena, among other things, they will conduct interviews, focus groups, surveys, and other research approaches. They will generalize from the findings and use their insights for awareness, decision making, policy making, policy implementation, marketing, and advertising. One lesser-known approach for understanding people’s thinking is the q-methodology, a “card sorting” approach based on various topic-related statements that involves selected insider participants (in small groups) to represent diverse opinions (to saturation) and to map various stances around particularized topics. Q-methodology enables the exploring of “tastes, preferences, sentiments, motives and goals, the part of personality that is of great influence on behaviour but that often remains largely unexplored” (van Exel & de Graaf, 2005, p. 2). Here the sampling is “strategic” vs. “random” (Armatas, et al., 2014, as cited in Sy, et al., 2018, p. 4). One key feature is the convenience of the setup for analysis.
Q-methodology (q-method, q-inquiry, q-technique) was first introduced back in 1935 by British physicist and psychologist William Stephenson (1902 – 1989). This approach enables data collection through a card sorting activity (known as the q-sort) by a group of selected “insider” respondents (known as the p-set). Here, the data analysis is described as an “inverted factor analysis” with “persons as the variables rather than the tests, and the population (as)…the group of tests rather than the group of persons; i.e. the rows of the matrix are correlated” (Stephenson, 1936).
These individual rankings (or viewpoints) are then subject to factor analysis. Stephenson (1935) presented Q methodology as an inversion of conventional factor analysis in the sense that Q correlates persons instead of tests; “(w)hereas previously a large number of people were given a small number of tests, now we give a small number of people a large number of test-items”. Correlation between personal profiles then indicates similar viewpoints, or segments of subjectivity which exist (Brown 1993). By correlating people, Q factor analysis gives information about similarities and differences in viewpoint on a particular subject. If each individual would have her/his own specific likes and dislikes, Stephenson (1935) argued, their profiles will not correlate; if, however, significant clusters of correlations exist, they could be factorised, described as common viewpoints (or tastes, preferences, dominant accounts, typologies, et cetera), and individuals could be measured with respect to them. (van Exel & de Graaf, 2005, p. 1)