Russel-Research-Method-in-Anthropology

21
◆
Multivariate Analysis
Most of the really interesting dependent variables in the social world—
things like personality type, amount of risk-taking behavior, level of
wealth accumulation, attitudes toward women or men—appear to be caused by
a large number of independent variables, all of which are dependent variables
themselves. The goal of multivariate analysis is to test hypotheses about how
variables are related, based on a theory of causation. This is called causal
modeling.
Multivariate analysis involves an array of statistical procedures—things like
multiple regression, partial regression, factor analysis, multidimensional scaling,
and so on. I’ll introduce you to the conceptual basis of some of these
methods here. I hope that this will give you an idea of the range of tools available
and enough information so you can read and understand research articles
in which these techniques are used. I also hope that this will arouse your curiosity
enough so that you’ll study these methods in more advanced classes.
This is the fun part.
Elaboration: Controlling for Independent Variables
We begin with the elaboration method developed by Paul Lazarsfeld and
his colleagues (1972) for analyzing data from surveys (see especially Rosenberg
1968). This method involves teasing out the complexities in a bivariate
relation by controlling for the effects of a third (antecedent or intervening)
variable. It was popular in sociology until the mid-1970s, but fell out of favor
when computers became common and made lighter work of multivariate analysis.
649