Russel-Research-Method-in-Anthropology

52 Chapter 2
United States were illiterate, compared with 3% of those born in the United
States. The correlation between these two variables appeared to be positive. In
other words, across 97 million people (the population of the United States at
the time), being foreign born was a moderately strong predictor of being illiterate.
But when Robinson looked at the data for the (then) 48 states in the
United States, he got an entirely different result. The correlation between the
percent illiterate and the percent of foreign-born people was –.526. That minus
sign means that the more foreign born, the less illiteracy.
What’s going on? Well, as Jargowsky (2005) observes, immigrants went
mostly to the big industrial states where they were more likely to find jobs.
Those northern and midwestern states had better schools and, of course,
higher literacy—along with a lot of immigrants, many of whom were illiterate.
And that was Robinson’s point: if you only looked at the state-by-state averages
(the aggregated units of analysis) instead of at the individual data, you’d
draw the wrong conclusion about the relationship between the two variables.
(For reviews of the ecological inference problem, see King 1997, Freedman
2001, and Jargowsky 2005.)
This is an important issue for anthropologists. Suppose you do a survey of
villages in a region of southern India. For each village, you have data on such
things as the number of people, the average age of men and women, and the
monetary value of a list of various consumer goods in each village. That is,
when you went through each village, you noted how many refrigerators and
kerosene lanterns and radios there were, but you do not have these data for
each person or household in the village because you were not interested in that
when you designed your study. (You were interested in characteristics of villages
as units of analysis.)
In your analysis, you notice that the villages with the population having the
lowest average age also have the highest average dollar value of modern consumer
goods. You are tempted to conclude that young people are more interested
in (and purchase) modern consumer goods more frequently than do older
people.
But you might be wrong. Villages with greater employment resources (land
and industry) will have lower levels of labor migration by young people.
Because more young people stay there, this will lower the average age of
wealthier villages. Though everyone wants household consumer goods, only
older people can afford them, having had more time to accumulate the funds.
It might turn out that the wealthy villages with low average age simply have
wealthier older people than villages with higher average age. It is not valid to
take data gathered about villages and draw conclusions about villagers, and
this brings us to the crucial issue of validity.