Quantitative measurement in the human sciences remains both widespread and controversial. Are depression scales\, intelligence tests\, etc. valid measurement instruments? Do they deliver quantitative or merely ordinal information? I discuss two approaches for understanding practices of quantitative measurement of theoretical attributes. One uses causal notions to characterize dispositional attributes and to understand how they relate to measurement indications. It aims at standard epistemic desiderata in science (discovery\, explanation\, prediction) and offers good answers to traditional worries about human attributes (namely\, are they really quantitative?) and about their measurement instruments (namely\, are they valid?). A second approach uses the notion of \;value \;(as in Dan Hausman's 2015 \;Valuing \;Health) to make sense of quantification practices. This approach does not resemble what scientists think of their measurement practices: it is not designed for the testing of tentative concepts but rather to standardize political decision making. Yet\, I argue\, this approach is the most plausible candidate for making sense of some human sciences&rsquo\; measurement practices as quantifying anything. Such is the case for measurements that (i) combine distinct dimensions of the phenomena at stake and (ii) for which we don&rsquo\;t observe serious efforts aiming at embedding such measurements in predictive and explanatory networks. I illustrate with two examples: depression severity (HAMD) and the Human Development Index (HDI).
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ORGANIZER;CN=Sandra Leonie Field: METHOD:PUBLISH END:VEVENT END:VCALENDAR