I had a lot of/still have some vestigial arrogance about quantitative methods over qualitative ones, probably in a combination of scientific misogyny + STEMlord superiority. But doing regression analysis and quant-heavy data analysis makes me realise more and more that you can justify basically any claim with numbers, and that you can construct your research in such a way as to output the numbers you want. which does not mean that all data are made up or that quantitative knowledge is all false. I think stories about scientists straight up inventing numbers or fudging experiments on purpose prove that there is a real difference between fraudulent and non-fraudulent research. but those data must always be narrativised & are always already narrativised. The act of presenting numbers itself is doing some of that narration because you’re already arguing that these numbers are worth presenting
People in the notes are rightfully pointing out common issues with data manipulation and pre-loaded conclusions in scientific research (i.e., the academic version of asking "so, how often do you beat your wife?" and so on), but I should have clarified that I'm not really talking about that. I'm talking about completely legitimate, above-board scientific research.
For example, I've had students ask me (in good faith) how it was possible for international medical bodies to report different counts of COVID-19 cases during the early years of the pandemic. And one of the answers is that you need to first define what you mean by a "COVID-19 case." Do you include self-reported incidences? Waste water data? Geo-fenced social media posts about people complaining about their coronavirus symptoms? Federal estimates? Hospital data? How do you compare countries/territories/substate entities with mandatory reporting mechanisms vs countries/territories/substate entities that rely only on voluntary self-reported cases? And what combination of these do you use? How you construct what you mean by "case" is going to impact the outcomes you report. These different counts of COVID-19 cases can all be true simultaneously, not because numbers are made up, but because they all come out of different methodologies that can be equally valid.
And this is true across all science, not just social science. Bill Clinton said it best lol: "it depends on what your definition of the word 'is' is." This feels obvious when you look at scientific research that uses "skull measurements" as their object of analysis - the concept itself is white supremacist, regardless of how "sound" the research is. But even something as apparently self-evident as a COVID-19 case still requires a definition, and how you define your variables is necessarily going to impact how the research goes and what conclusions are drawn.
These definitions are always embedded in political & social assumptions. And again, this does not mean that science is all made up or nonsense or whatever. There is a widespread fetishism of "objective knowledge" that is itself ideological - the idea that knowledge can be divorced from all historical and political contexts, that you can scrub bias from research and simply report the facts. Valuable, well-supported, well-constructed scientific research is always embedded in these contexts. Not just as a result of researcher assumptions, but of the material context it exists in - what research resources are available, how & what research gets funded, the academy's relationship to the state & non-government bodies that both provide data and use that research to inform policy, the historical relationships universities often have with settler-colonialism and imperialism that give them access to "foreign" research subjects, etc etc etc.
So my overall point (which I didn't communicate well) is that data can always say what you want to some extent, for good and for ill. And research results (at least in my experience) tend to surprise you in ways that require explanations, which themselves can be fully justified, but again, exist within many different contexts that influence how you interpret them - and not just the results themselves, but your own surprise at your results
