Russel-Research-Method-in-Anthropology

Multivariate Analysis 689
‘‘childhood disorders.’’ Those, at least, are the labels that struck Weller as
appropriate. I agree with her intuition about this. What do you think?
How about the arrays, or dimensions? We’re accustomed to looking for vertical
and horizontal axes in graphs, but this convention is irrelevant for interpreting
MDS output. Dimensions in MDS graphs don’t necessarily have to be
at right angles to each other. To me, it looks like informants distinguish
between chronic and acute illnesses (from top to bottom in figure 21.6) and
between infectious and noninfectious illnesses (from left to right). The fact
that I can come up with labels, though, says absolutely nothing about whether
I understand what is going on. It is possible to label anything, once you’re
confronted with the task. This means that you must be especially careful in
the use of factor analysis, MDS, and other dredging techniques that present
you with something to explain.
On the other hand, the mere fact that I might make a mistake in my interpretation
of the results doesn’t stop me from using these techniques. Interpretation
of numerical results is always a brazen, flat-out qualitative exercise, a
Rorschach test for social scientists—which is why I like it so much. Use every
technique you can think of in data analysis, and let your experience guide your
interpretation. Interpretation of results is where data analysis in all science
ultimately becomes a humanistic activity. (For more examples of MDS in
anthropology, see Johnson and Griffith 1995, Magaña et al. 1995, and Bolton
and Vincke 1996.)
Cluster Analysis
Like factor analysis and MDS, cluster analysis is a descriptive tool for
exploring relations among items in a matrix—for finding what goes with what.
You start with a similarity matrix, like a matrix of Pearson correlation coefficients.
If you factor the matrix, you find underlying variables that may encompass
the variables in the original data. If you scale the matrix (MDS), you get
a map that shows you graphically the relations among the items. Clustering
tells you which items go together and in what order. And, of course, the output
from all these analyses should agree, more or less. Thus, in the MDS example
from Weller (1983) above, a cluster analysis would let us check the guess
about that chunk of illnesses labeled ‘‘gastrointestinal disorders.’’
To get an idea of where we’re going with this, take another look at figure
21.6. Does kidney pain go with arthritis-rheumatism-allergies group of
chronic illnesses or does it go with the diarrhea etc. group of intestinal infections?
Or is it somehow a cognitive bridge between these groups? This is the
sort of thing we want cluster analysis to help us determine.