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The Ecological Fallacy

Once you select your unit of analysis, remember it as you go through data analysis, or you’re likely to commit the dreaded ‘‘ecological fallacy.’’ This fallacy (also known as the Nosnibor effect, after W. S. Robinson [1950], who described it) comes from drawing conclusions about the wrong units of analysis—making generalizations about people, for example, from data about groups or places. For example, in 1930, 11% of foreign-born people in the United States were illiterate, compared with 3% of those born in the United States. The correlation between these two variables was .118. 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 (Further Reading: the ecological inference problem).

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.

 
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