PRESENTING RESULTS: CAUSAL FLOW CHARTS
Causal maps represent theories about how things work. They are visual representations of ideas that emerge from studying data, seeing patterns, and 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.’’
Plattner's model for how merchants in the Soulard Market in St. Louis decide what and how much produce to buy.
SOURCE: S. Plattner, "Economic Decision Making in a Public Marketplace." American Ethnologist, Vol. 9, p. 404, 1982. Reproduced by permission of the American Anthropological Association. Not for further reproduction.
Real life is usually much, much more complicated than that. Look at figure 15.3. 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 15.3 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).
Now, on to the nuts and bolts of data analysis.