Russel-Research-Method-in-Anthropology

670 Chapter 21
Path Analysis
Path analysis is a particular application of multiple regression. In multiple
regression, we know (1) which independent variables help to predict some
dependent variable and (2) how much variance in the dependent variable is
explained by each independent variable. But multiple regression is an inductive
technique: It does not tell us which are the antecedent variables, which
are the intervening variables, and so on.
Path analysis is the application of multiple regression for testing conceptual
models of multivariate relations—that is, for testing specific theories
about how the independent variables in a multiple regression equation may be
influencing each other—and how this ultimately leads to the dependent variable
outcome.
The method was developed by the geneticist Sewall Wright in 1921 and
became very popular in the social sciences in the 1960s (see Duncan 1966). It
fell out of favor for a while (isn’t it nice to know that even in statistics there
are fads and fashions?), but it’s making a comeback as full-featured statistics
packages become more widely available.
I rather like the method because it depends crucially on the researcher’s
best guess about how a system of variables really works. It is, in other words, a
nice combination of quantitative and qualitative methods. Here’s an example.
John Thomas (1981) studied leadership in Niwan Witz, a Mayan village.
He was interested in understanding what causes some people to emerge as
leaders, while others remain followers. From existing theory, Thomas thought
that there should be a relation among leadership, material wealth, and social
resources. He measured these complex variables for all the household heads in
Niwan Witz (using well-established methods) and tested his hypothesis using
Pearson’s r. Pearson correlations showed that, indeed, in Niwan Witz leadership
is strongly and positively related to material wealth and control of social
resources.
Since the initial hypothesis was supported, Thomas used multiple regression
to look at the relation of leadership to both types of resources. He found
that 56% of the variance in leadership was explained by just three variables in
his survey: wealth (accounting for 46%), family size (accounting for 6%), and
number of close friends (accounting for 4%). But, since multiple regression
does not, as Thomas said, ‘‘specify the causal structure among the independent
variables’’ (ibid.:132), he turned to path analysis.
From prior literature, Thomas conceptualized the relation among these three
variables as shown in figure 21.2. He felt that leadership, L, was caused by all
three of the independent variables he had tested, that family size (fs) influ-