Russel-Research-Method-in-Anthropology

638 Chapter 20
and r, because of certain assumptions that they made, require interval-level
data, and this became an almost magical prescription.
Thirty-four years later, Gaito (1980) surveyed the (by then voluminous)
mathematical statistics literature and found no support for the idea that measurement
properties have anything to do with the selection of statistical procedures.
Social scientists, said Gaito, confuse measurement (which focuses on
the meaning of numbers) with statistics (which doesn’t care about meaning at
all) (p. 566). So, treating ordinal variables as if they were interval, for purposes
of statistical analysis, is almost always a safe thing to do, especially with
five or more ordinal categories (Boyle 1970; Labovitz 1971a).
The important thing is measurement, not statistics. As I pointed out in chapter
2, many concepts, such as gender, race, and class are much more subtle
and complex than we give them credit for being. Instead of measuring them
qualitatively (remember that assignment of something to a nominal category
is a qualitative act of measurement), we ought to be thinking hard about how
to measure them ordinally.
Emil Durkheim was an astute theorist. He noted that the division of labor
became more complex as the complexity of social organization increased
(Durkheim 1933 [1893]). But he, like other theorists of his day, divided the
world of social organization into a series of dichotomous categories (Gemeinschaft
vs. Gesellschaft, or mechanical vs. organic solidarity).
Today, social theorists want to know how degrees of differences in aspects
of social organization (like the division of labor in society) are related to social
complexity. This requires some hard thinking about how to measure these two
variables with more subtlety. The meaning of the measurements is crucial.
Eliminating the Outliers
Another controversial practice in data analysis is called ‘‘eliminating the
outliers,’’ which means removing extreme values from data analysis. If there
are clear indications of measurement error (a person with a score of 600 on a
300-point test turns up in your sample), you can throw out the data that are in
error. The problem comes when outliers (so-called freak cases) are eliminated
just to smooth out data—to achieve better fits of regression lines to data. A
single wealthy household might be ignored in calculating the average household
income in a community on the theory that it’s a ‘‘freak case.’’ But what if
it isn’t a freak case? What if it represents a small, but substantively significant
proportion of cases in the community? Eliminating it only prevents the discovery
of that fact.
Trivially, you can always achieve a perfect fit to a set of data if you reduce
it to just two points. But is creating a good fit what you’re after? Don’t you