Russel-Research-Method-in-Anthropology

694 Chapter 21
Gans and Wood (1985) used DFA technique for classifying Samoan women
as ‘‘traditional’’ or ‘‘modern’’ with respect to their ideal family size. If women
stated that they wanted three or fewer children, Gans and Wood placed them
in a category they labeled ‘‘modern.’’ Women who said they wanted four or
more children were labeled ‘‘traditional.’’ DFA showed that just six of the
many variables that Gans and Wood had collected allowed them to classify
correctly which category a woman belonged to in 75% of all cases. The variables
were such things as age, owning a car, level of education, etc.
It would have been ridiculous for Gans and Wood to have asked women
straight out: ‘‘Are you traditional or modern when it comes to the number of
children you’d like?’’ DFA (combined with on-the-ground ethnography) gave
them a good picture of the variables that go into Samoan women’s desired
family size.
Similarly, Comitas and I were able to describe the attitudinal components
of gender role changes by using DFA, and our prediction rate of 70% was
significantly better than the 50% we’d have gotten by chance, given our sampling
design. If you’re careful about how you interpret the results of a discriminant
function analysis, it can be a really important addition to your statistical
tool kit.
And Finally . . .
In a world of thousands of variables and millions of combinations of variables,
how do you decide what to test? There is no magic formula. My advice
is to follow every hunch you get. Some researchers insist that you have a good
theoretical reason for including variables in your design and that you have a
theory-driven reason to test for relations among variables once you have data.
They point out that anyone can make up an explanation for any relation or
lack of relation after seeing a table of data or a correlation coefficient.
This is very good advice, but I think it’s a bit too restrictive, for three reasons:
1. I think that data analysis should be lots of fun, and it can’t be unless it’s based
on following your hunches. Most relations are easy to explain, and peculiar relations
beg for theories to explain them. You just have to be very careful not to
conjure up support for every statistically significant relation, merely because it
happens to turn up. There is a delicate balance between being clever enough to
explain an unexpected finding and just plain reaching too far. As usual, there is
no substitute for thinking hard about your data.
2. It is really up to you during research design to be as clever as you can in thinking
up variables to test. You’re entitled to include some variables in your research