Russel-Research-Method-in-Anthropology

Multivariate Analysis 693
Lambros Comitas and I used DFA in our study of two groups of people in
Athens, Greece: those who had returned from having spent at least 5 years in
West Germany as labor migrants and those who had never been out of Greece.
We were trying to understand how the experience abroad might have affected
the attitudes of Greek men and women about traditional gender roles (Bernard
and Comitas 1978). Our sample consisted of 400 persons: 100 male migrants,
100 female migrants, 100 male nonmigrants, and 100 female nonmigrants.
Using DFA, we were able to predict with 70% accuracy whether an informant
had been a migrant on the basis of just five variables.
There are some things you need to be careful about in using DFA, however.
Notice that our sample in the Athens study consisted of half migrants and half
nonmigrants. That was because we used a disproportionate, stratified sampling
design to ensure adequate representation of returned migrants in the study.
Given our sample, we could have guessed whether one of our informants was
a migrant with 50% accuracy, without any information about the informant at
all.
Now, only a very small fraction of the population of Athens consists of former
long-term labor migrants to West Germany. The chances of stopping an
Athenian at random on the street and grabbing one of those returned labor
migrants was less than 5% in 1977 when we did the study.
Suppose that, armed with the results of the DFA that Comitas and I did, I
asked random Athenians five questions, the answers to which allow me to predict
70% of the time whether any respondent had been a long-term labor
migrant to West Germany. No matter what the answers were to those questions,
I’d be better off predicting that the random Athenian was not a returned
migrant. I’d be right more than 95% of the time.
Furthermore, why not just ask the random survey respondent straight out:
‘‘Are you a returned long-term labor migrant from West Germany?’’ With
such an innocuous question, presumably I’d have gotten a correct answer at
least as often as our 70% prediction based on knowing five pieces of information.
DFA is a powerful classification device, but it is not really a prediction
device. Still, many problems (like the one Comitas and I studied) are essentially
about understanding things so you can classify them correctly. Livingstone
and Lunt (1993) surveyed 217 people in Oxford, England, and divided
them into six types, based on whether or not people were in debt, whether or
not people had savings, and people who were living exactly within their
income (with neither savings nor debt). DFA, using a variety of variables (age,
class, education, income, expenses, attitudes toward debt, etc.) correctly classified
almost 95% of the cases into one of the six groups that Livingstone and
Lunt had identified.