Russel-Research-Method-in-Anthropology

690 Chapter 21
I’m going to explain cluster analysis in some detail. It’s a very important
descriptive tool, but as we go through the next few paragraphs, keep in mind
two things: First, clustering is just a technique for finding the similarity
chunks. It doesn’t label those chunks. That part is also a Rorschach test: You
stare at the output and decide what the meaning is. Second, as with so many
methods, different treatments of your data produce different outcomes. The
next few pages will make it very clear just how much you and only you are
responsible for every choice you make in data analysis.
How Cluster Analysis Works
Consider the following example from de Ghett (1978:121):
1 3 7 9 14 20 21 25
This set of numbers has no meaning at all, so we can concentrate on the
method of clustering them, without any interpretation getting in the way.
When we get through with this example, we’ll move on to a set of numbers
that does have meaning. The distance between 1 and 3 is 2. The distance
between 21 and 25 is 4. So, in a numerical sense, 1 and 3 are twice as similar
to one another as 21 and 25 are to one another. Table 21.29 shows the dissimilarity
matrix for these numbers.
TABLE 21.29
Dissimilarity Matrix for Clustering
1 3 7 9 14 20 21 25
1 0
3 2 0
7 6 4 0
9 8 6 2 0
14 13 11 7 5 0
20 19 17 13 11 6 0
21 20 18 14 12 7 1 0
25 24 22 18 16 11 5 4 0
SOURCE: ‘‘Hierarchical Cluster Analysis’’ by V. J. de Ghett, 1978, Quantitative Ethology, ed. by P. W. Colgan.
Reprinted by permission of John Wiley & Sons, Inc.
There are several ways to find clusters in this matrix. Two of them are called
single-link or closest-neighbor analysis and complete-link or farthestneighbor
analysis (there are others, but I won’t go into them here). In singlelink
clustering, we use only the numbers adjacent to the diagonal: 2, 4, 2, 5,