Making Sense of Your Results

30 May 2015 - 18:16

So you’ve successfully completed your study — all your participants have submitted their sorts and completed the post-sort interviews. You’ve munged your raw data using Q-Assessor’s online tools or you’ve downloaded your data and churned it with PQMethod or R. You’re now staring at some number of factors with reported eigenvalues and variances and whatnot. What does this all mean and how do you use it?

Well of course this is where the majesty or genius or mystery or bollocks (depending on your disposition) of Q stands forth. You can (and should) resort to the many obsessively detailed books and discussions out there where the vagaries of Q analysis and interpretation are scrutinized in densely-packed jargon. Since you’re doing a Q study, you will presumably want to report your results in similar fashion.

However en route, you will probably benefit if you spend a bit of time stepping back and thinking about your study from the simple first principles of Q. Recall that Q identifies types of people and their perspectives from the cross-correlations amongst their sorts. Each factor in your results thus represents a type of person and what they think. The task is to understand the nature of that person type by looking at two things: 1) the meaning of the statements that most define that type; and 2) the self-expressed features of people of that type as revealed in their answers to the post-sort interview. Once you understand the type, you can then provide it with an explanatory label that provides a useful shorthand as you compare that type to the others you discover and then interpret them all within the semantic arena of your study. But note: these factors can only identify the existence of such person/perspectives but not their prevalence, which requires an “R” methodology.

So for instance, if you were looking at “attitudes of college students to gender,” you might find that one factor represents “obnoxious fraternity ‘bros’ with mommy issues,” another factor “brilliant arts students who couldn’t get dates in high school,” and so on. Then you would use these discovered types to tell you something about the college and gender in the context of your research question. Note that one interesting result would be if you found individual respondents whose demographics would suggest they should belong to one type but their sorts place them in quite another type — e.g. a female violin major whose attitudes match best with the “bros” (analogous to the white supremacists whose DNA analysis shows large degrees of African DNA for instance). That’s the kind of revelation that Q supposedly can uniquely provide. What that might mean would be up to you to explain, but Q can at least discover such things for you.

Anyway, once you “explain and label” each of your factors so you’re no longer thinking about eigenvalues and loadings but instead are thinking of types of people and their perspectives, you are then in a better position to determine which provide meaningful insights into whatever fundamental question you had that led you to conduct the study. That would also provide a cogent rationale for why you choose the factors (viz, which types of people/perspective) that you report as significant answers to your question.

Once you personally understand something new and important about the world your study examined, you can either report it out clearly and understandably, or else you can follow conventional Q practice and wrap it in layers of verbose, opaque Q jargon in the fashion that Q traditionalists expect. That choice depends on your audience. But at the very least, you should understand what your study means.


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