Russel-Research-Method-in-Anthropology

676 Chapter 21
Of course, people are free to mix and match their opinions on any of these
issues, but overall, there is a strong association of opinions across these issues.
Factor analysis assumes that this association is the result of an underlying,
hypothetical variable, a general attitude orientation. We can use a set of statistical
techniques to identify both the existence and the content of this underlying
variable. Naming the variable is a strictly qualitative exercise.
Many studies have demonstrated the existence of an underlying value orientation
usually called ‘‘liberal vs. conservative.’’ Liberals and conservatives
exhibit packages of attitudes about things like personal freedom (as with the
right to own a handgun or the right to an abortion), about foreign policy (as
with involvement in peacekeeping missions), and about domestic policies (as
with affirmative action).
This idea of multidimensional supervariables that underlie a set of observations
was first articulated in 1904 by Charles E. Spearman (he for whom
Spearman’s rank-order correlation coefficient is named—see chapter 20).
Spearman noticed that the scores of students on various exams (classics,
French, English, math, etc.) were correlated. He suggested that the exam
scores were correlated with each other because they were all correlated with
an underlying factor, which he labeled g, for general intelligence.
The single-factor theory of intelligence has been repudiated many times
since then, as have been the IQ tests that were developed to measure intelligence
as a single factor. But the idea of factors—multidimensional variables—that
underlie and give rise to a set of correlated events became one of
the most important developments in all the social sciences.
The data for factor analysis are often attitude items, like the kind you see
in Likert scales, but can just as well be about artifacts (movies, songs, buildings,
cars, brands of beer . . .), people (movie stars, politicians, fashion models,
criminals, classical musicians . . .), or even countries. Factor analysis is
used across the social sciences in data reduction—to explore large data sets
with dozens or even hundreds of variables in order to extract a few variables
that tell a big story.
In political science, for example, Lieske (1993) partitioned the 3,164 counties
in the United States into 10 distinctive regional subcultures by looking
for common, underlying factors in the correlations among 45 racial, ethnic,
religious, and social structural variables. In social psychology, Stein et al.
(1991) used factor analysis to test the influence of ethnicity, socioeconomic
status, and various anxieties as barriers to the use of mammography among
white, African American, and Hispanic women. Factor analysis confirmed the
influence of five kinds of anxiety: fear of radiation, fear of pain, embarrassment
about breast exams, anxiety about what might be found, and concerns
about cost.