Russel-Research-Method-in-Anthropology

462 Chapter 16
coming to conclusions about what causes what. Causal maps do not have to
have numbers attached to them, although that is where causal modeling eventually
leads. After all, it is better to know how much one thing causes another
than to know simply that one thing does cause another. With or without numbers,
though, causal models are best expressed as a flow chart.
A causal flow chart consists of a set of boxes connected by a set of arrows.
The boxes contain descriptions of states (like being the youngest child, or
owning a tractor, or being Catholic, or feeling angry), and the arrows tell you
how one state leads to another. The simplest causal map is a visual representation
of the relation between two variables
A V B
which reads: ‘‘A leads to or causes B.’’
Of course, real life is usually much, much more complicated than that. Look
at figure 16.2. It is Stuart Plattner’s algorithm, based on intensive interviews
and participant observation at produce markets in St. Louis, for how merchants
decide what stock to buy. An algorithm is a set of ordered rules that
tell you how to solve a problem—like ‘‘find the average of a list of numbers,’’
or, in this case, ‘‘determine the decisions of produce merchants.’’ (The capital
letter Q in figure 16.2 stands for ‘‘quantity.’’)
Read the flow chart from top to bottom and left to right, following the
arrows. At the beginning of each week, the merchants seek information on the
supply and cost of produce items. After that, the algorithm gets complicated.
Plattner notes that the model may seem ‘‘too complex to represent the decision
process of plain folks at the marketplace.’’ However, Plattner says, the chart
‘‘still omits consideration of an enormous amount of knowledge pertaining to
qualities of produce at various seasons from various shipping areas’’ (Plattner
1982:405).
More about all this in chapter 18 when we get to models and matrices. Now,
on to the nuts and bolts of data analysis.