Russel-Research-Method-in-Anthropology

672 Chapter 21
Path analysis lets you test a particular theory about the relations among a
system of variables, but it doesn’t produce the theory; that’s your job. In the
case of Niwan Witz, for example, Thomas specified that he wanted his path
analysis to test a particular model in which wealth causes leadership. The
results were strong, leading Thomas to reject the null hypothesis that there
really is no causal relation between wealth and leadership. But even the strong
results that Thomas got don’t prove anything. In fact, Thomas noted that an
alternative theory is plausible. It might be that leadership in individuals (wherever
they get it from) causes them to get wealthy rather than the other way
around.
Path analysis often serves as reality therapy for social scientists. It’s fun to
build conceptual models—to think through a problem and hypothesize how
variables are linked to each other—but our models are often much more complicated
than they need to be. Path analysis can help us out of this fix. We can
test several plausible theories and see which is most powerful. But in the end,
you’re left out there, all by yourself, defending your theory on the basis of
whatever data are available right now.
Multicollinearity
Multivariate models are subject to a problem that simply can’t exist when
you have one independent variable: Independent variables can be correlated.
In fact, when two variables both strongly predict a third, you’d expect the first
two to be correlated. This multicollinearity means that you may not be able
to tell the influence of one independent variable free from the influence of the
independent variables with which it is correlated.
One way to avoid this problem is to conduct true experiments and nothing
but true experiments. By assigning research participants randomly to control
and experimental groups, we ensure that any correlation between independent
variables is the result of chance. Nice work if you can get it, but most of what
we want to study in anthropology requires fieldwork or survey research where
things get messy.
Fortunately, multicollinearity distorts the findings of multiple regression
only occasionally, and multicollinearity problems show up very clearly. The
most dramatic sign of a multicollinearity problem is when your statistical program
tells you that it cannot solve the equation. Some statistical programs calculate
a condition index that diagnoses multicollinearity. A condition index
between 10 and 30 signals moderate to strong multicollinearity, and an index
over 30 signals a severe multicollinearity problem (Gujarati 1995).
But basic regression output shows the most common sign of a multicollin-