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MORE GENERAL EXPLANATION

In another variety of process-tracing, the investigator constructs a general explanation rather than a detailed tracing of a causal process. The investigator may do this either because the data or theory and laws necessary for a detailed explanation are lacking or because an explanation couched at a higher level of generality and abstraction is preferred for the research objective. A decision to do so is consistent with the familiar practice in political science research of moving up the ladder of abstraction.425 Such process-tracing does not require a minute, detailed tracing of a causal sequence. One may opt for a higher level of generality of explanations in within-case analysis, just as researchers using statistical methods often create larger cells either to obtain categories of broader theoretical significance or to obtain enough cases (in a smaller number of larger cells) to permit statistical analysis.

Process-tracing can be applied also to the explanation of macro-phenomena, as it often is in economics, as well as to microprocesses. The method of process-tracing does not necessarily focus on the individual decision-making level of analysis.

Forms of Causal Processes

The process-tracing technique must be adapted to the nature of the causal process thought to characterize the phenomenon being investigated. Several different types of causal processes can be distinguished.426 The simplest form is linear causality, a straightforward, direct chain of events that characterizes simple phenomena. However, many or most phenomena of interest in international relations and comparative politics are characterized by more complex causality, for which the assumption of linearity is misplaced.

In a more complex form of causality the outcome flows from the convergence of several conditions, independent variables, or causal chains. An example of this type of complex explanation occurs in Theda Skocpol’s study of revolutions referred to in Chapter 8.

A still more complex form involves interacting causal variables that are not independent of each other. Case study methods provide opportunities for inductively identifying complex interaction effects. In addition, typological theories (discussed in Chapter 11) can capture and represent interaction effects particularly well. Statistical methods can also capture interaction effects, but they are usually limited to interactions that reflect simple and well-known mathematical forms.

Another type of causal process to which the technique of process-tracing can be applied occurs in cases that consist of a sequence of events, some of which foreclose certain paths in the development and steer the outcome in other directions. Such processes are path-dependent. A different kind of within-case analysis and process-tracing is needed for dealing with phenomena of this kind. The investigator must recognize the possibility of path dependency in order to construct a valid explanation. Path dependency can be dealt with in several ways, for example by identifying key decision points or branching points in a longitudinal case (as in Jack Levy’s study of developments during the six-week crisis that led to World War I and in Brent Sterling’s study of policy choices during limited wars).427 However, the investigator must avoid assuming that certain outcomes were necessarily excluded once and for all by the resolution of an earlier branching point. One or another final outcome may have become only less likely at that stage, but the way in which subsequent branching points were resolved may have increased its probability.

Such considerations are particularly relevant when the branching points are decisions taken by policymakers. A decision taken at one point that reduces the likelihood of achieving a desired policy goal may be recouped by changes in the situation that give policymakers a second chance to accomplish a desired goal or to avoid a poor outcome. In brief, path dependency at early points in the development of a longitudinal