Abstract
Whether widely used measures in the human sciences—e.g., measures of intelligence, happiness, empowerment, depression, etc.—count as quantitative remains a battlefront. Practitioners commonly analyze their data assuming that their measures are quantitative, but many methodologists reject this presupposition. Other authors acknowledge that current measures might not be strictly quantitative, but taking inspiration from recent philosophy of measurement, they express optimism about future human science measurements. Is the optimism of the latter camp warranted? Joel Michell’s more recent work (2012) provides reasons to the contrary. He argues not only that current measures aren’t quantitative, but that the attributes at stake (intelligence, etc.) are themselves not quantitative. Hence, these attributes cannot (thus, will not) afford quantitative measurement. Michell’s influential argument draws from a long tradition (including von Kries and Keynes). But I focus on Michell’s argument, because its scope is wider. My goal is to demonstrate his argument fails in showing that common human science attributes are not-quantitative. Michell argues that the key feature indicating that attributes are not quantitative is their lack of “pure homogeneity.” When we consider the different degrees of some quantity—e.g., 3cms, 4cms, and 6cms—we realize that they are all degrees of the very same kind; they differ only quantitatively, but not qualitatively. The same is true for the differences between these degrees—e.g., the interval between 6cms and 4cms and the interval between 4cms and 3cms don’t differ in kind, their only difference is that the former is twice the latter. In contrast, in not-quantitative (but ordinal) attributes, says Michell, we don’t observe proper homogeneity: although we can order different degrees, we cannot order the differences between these degrees. Crucially, Michell’s point (in this more recent work) is not epistemic. His claim is that these differences do not stand in ordering relations because the attribute is not purely homogeneous (i.e. the differences between degrees are qualitatively different). Michell believes common attributes in the human sciences are heterogeneous in this sense. He illustrates the argument with the attribute ‘functional independence’. He considers a typical scale for measuring functional independence, and concludes that functional independence is merely ordinal since the differences between degrees indicated in the scale are qualitatively different. However, Michell’s argument misses the mark. We should distinguish between the actual target of our measurements—the theoretical attribute to be measured, the ‘measurand’—and the (empirically accessible) measuring attribute we use to infer values of the measurand. This distinction and the working assumption that (some) measurands are quantitative lie behind psychometricians’ understanding of measurement that Michell targets. Those assumptions are also part of influential contemporary accounts of measurement such as Eran Tal’s and David Sherry’s. Yet Michell’s argument overlooks this distinction, conflating the measurand with the measuring attribute—Michell’s argument only demonstrates heterogeneity in the scale for measuring functional independence, leaving open whether functional independence itself is heterogeneous. I show that the former doesn’t entail the latter and suggest that this generalizes to other attributes.
