The sad state of the social fields of study (the "non-hard" sciences) is not so much due to the belief in data as the final (if not only) arbiter of knowledge and truth, but that the nature of this belief is indistinguishable from religious dogma. It is, from my point of view,
perfectly alright to, as a researcher, be a "believer" in empirical data or even think that, rather ridiculously, data "speak" to you. But such a standpoint needs to be backed up by a sound argument to be valid and not "religious." And to produce such an argument, one must at
least be aware of alternative points of view and counter-arguments. Yet this is hardly ever the case: what's taught in advanced programs in the social sciences is not whether and how truth can be uncovered, but how to use data to get published. Consequently, most researchers are
not even aware of arguments that data may not be useful, or that they may even be deceptive or harmful, for finding the truth and generating knowledge. When encountering an argument that, for example, inductive analysis and empirical "testing" of hypotheses is invalid and
inconsequential, the most common response is: "but how do you know if you don't use data?" This question might seem, to the empiricist, both warranted and a severe blow to the argument for pure deduction, but would make any philosopher of science laugh (or cry - or both). It's a
retort that signals fundamental ignorance of both ontology and epistemology as they apply to studying the social world. And it signals a baffling lack of understanding for what research supposedly is and does. It is not a retort; it is an embarrassment. Again, it may be a valid
standpoint to be convinced of the value of using data in studying the social world, perhaps even inductively, but it is not, and cannot be, a respectable standpoint to believe that data is the *only* and *final* arbiter of truth. To study the complex and always-endogenous
causalities that shape the social world using data means those data first need to be both interpreted and selected. There is no such thing as a well demarcated and controlled experiment for studying social phenomena. There is also learning and intentionality that add further
complexity. Whereas the natural (or, as I prefer to call them, "simple") sciences can, to a great extent, produce controlled environments in which all or most factors can be objectively measured, social sciences can rely on neither. The data can, Indeed, "speak" to you in the
social sciences, but they "say" many things at the same time, and how many of those things you hear is limited by your imagination and further restricted by your own biases, intentions, and wishes. In other words, what they "say" cannot be trusted and we should, in our aim to
generate knowledge, try hard not to listen. Data are, from my perspective, more likely to deceive than to tell a truth in the social sciences. Consequently, I refrain from using data to the degree possible. While I know where I stand, and that I disagree with the main stream,
I fully recognize that this is a discussion worth having, and that the discussion itself, if thoughtful, generates important insights. But we're not having it, and most scholars are not even aware that there should be one. Instead, most effort is invested into "listening" to the
data as they're being tortured to fit different types of models and test hypotheses, even though the data themselves are fundamentally a result of both selection and interpretation (not to mention "measurement" errors and biases). I wish for the new year that we will finally
begin to see more of a discussion about what data actually mean in social science, if anything, and how to generate actual knowledge and not simply "support" in the data for a set of hypotheses. Briefly, social scientists need to become scholars.
