Russel-Research-Method-in-Anthropology

Multivariate Analysis 669
under plow. And his eight independent variables were: the total cultivated land
area, in hectares; the number of years a farmer was experienced in using fertilizers;
whether the farming household usually brewed maize beer for sale;
whether farmers owned any cattle at all; whether farmers had had any training
in animal husbandry practices from the local extension agents; whether the
family had an improved house (this required an index consisting of items such
as a tin roof, cement floor, glass windows, and so on); whether the farmer
owned a bicycle; and whether the farmer owned a plow and oxcart. These
independent variables together accounted for 48% of the variance in the
dependent variable.
On Explaining Just a Little of Something
In social science research, multiple regression (including path analysis,
which is coming up next) typically accounts for between 30% and 50% of the
variance in any dependent variable, using between two and eight independent
variables. Like the equation from Graves and Lave (1972) above, you’ll see
plenty of regression equations with individual variables that explain 10% or
less of what people are trying to understand. Does accounting for 10% of the
variance in what you’re interested in seem feeble? Consider:
1. In 2005, the average white male baby had a life expectancy at birth of 75.4 years
in the United States, or 27,540 days. The life expectancy at birth for the average
African American male baby was 69.9 years, or 25,532 days. The difference is
2,008 days. (In 1970, the figures were 67.1 years vs. 60.0 years, or a difference
of 2,593 days.)
2. There were approximately 2.4 million births in Mexico in 2003, and around
52,000 infant deaths—that is, about 22.5 infant deaths per 1,000 live births.
Compare these figures to the United States, where there were 4.1 million births
and approximately 28,000 infant deaths, or about 6.8 per 1,000 live births. If the
infant mortality rate in Mexico were the same as that in the United States, the
number of infant deaths would be about 16,000 instead of 52,000. The difference
would be 37,000 infant deaths.
Suppose you could account for just 10% of the difference in longevity
among white and African American men in the United States (201 days) or
10% of the difference between the United States and Mexico in infant deaths
(3,700 babies). Would that be worth doing? In my view, the most important
contribution a social scientist can make to ameliorating a social problem is to
be right about what causes it, and ‘‘accounting for variance’’ is part of causal
modeling.