Russel-Research-Method-in-Anthropology

20
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Bivariate Analysis:
Testing Relations
This chapter is about describing relations between pairs of variables—
covariations—and testing the significance of those relations.
The qualitative concept of covariation creeps into ordinary conversation all
the time: ‘‘If kids weren’t exposed to so much TV violence, there would be
less crime.’’ Ethnographers also use the concept of covariation in statements
like: ‘‘Most women said they really wanted fewer pregnancies, but claimed
that this wasn’t possible so long as the men required them to produce at least
two fully grown sons to work the land.’’ Here, the number of pregnancies is
said to covary with the number of sons husbands say they need for agricultural
labor.
The concept of statistical covariation, however, is more precise. There are
two primary and two secondary things we want to know about a statistical
relation between two variables:
The primary questions are these:
1. How big is it? That is, how much better could we predict the score of a dependent
variable in our sample if we knew the score of some independent variable? Correlation
coefficients answer this question.
2. Is the covariation due to chance, or is it statistically significant? In other words,
is it likely to exist in the overall population to which we want to generalize?
Statistical tests answer this question.
For many problems, we also want to know:
3. What is its direction? Is it positive or negative?
4. What is its shape? Is it linear or nonlinear?
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