Case Studies and Theory Development in the Social Sciences - Alexander L. George [95]
The congruence method offers considerable flexibility and adaptability. It can contribute to theory development in several ways; it can be employed in a disciplined-configurative type of case study, a plausibility probe, or in a crucial case (or tough test) of an existing theory.377 The theory employed in the congruence method may be well-established and highly regarded, or it may be formulated or postulated by the investigator for the first time on the basis of a hunch that it may turn out to be important.
Often, however, available theories lack clarity and internal consistency so that they cannot make specific predictions and thus cannot be tested in any rigorous way. Nonetheless, investigators often succumb to the temptation to attribute predictive or explanatory power to such theories, leading to spurious or inconclusive tests of loosely formulated theories. The priority is not to test such theories, but to refine them if possible so that they can be tested. The congruence method may contribute to such refinement and development. An investigator may be able to clarify and refine a theory through its use in case studies, making it more nearly testable. As noted in Chapter 4, an investigator must establish the level of concreteness and differentiation with which variance in the dependent variable will be measured. How well this task is performed may well determine whether one can find congruence between the independent variable in the theory and outcomes on the dependent variable. This point is demonstrated later in this chapter.
A final attractive feature of the congruence method is that it can be used either as a within-case method or, when coupled with a counterfactual case, as a form of controlled comparison. The latter possibility is discussed later in this chapter.378
An important general standard for congruence tests is “congruity”: similarities in the relative strength and duration of hypothesized causes and observed effects.379 This does not mean that causes must resemble their effects or be on the same scale, and researchers must avoid the common bias toward assuming this should be the case. For example, there is a temptation to assume that large or dramatic effects must have large and dramatic causes, but this is not necessarily true. Researchers must take into account theoretical reasons why the effects of hypothesized causes might be amplified, diminished, delayed, or sped up (through expectations effects). Once this has been done, it is possible to address the question of whether the independent and dependent variables are congruent; that is, whether they vary in the expected directions, to the expected magnitude, along the expected dimensions, or whether there is still unexplained variance in one or more dimensions of the dependent variable.
Although consistency between a theory’s predictions and case outcomes is often taken as providing support for a causal interpretation (and, for that matter, for assessing deductive theories generally), researchers must guard against unjustified, questionable imputation of a causal relationship on the basis of mere consistency, just as safeguards have been developed in statistical analysis to deal with the possibility of spurious correlation.
There are several ways in which this problem can be addressed. The investigator can employ process-tracing to attempt to identify a causal path (the causal chain) that depicts how the independent variable leads to the outcome of the dependent variable. (We note the close connection between process-tracing and causal mechanisms in Chapter 7.)
The usefulness of combining the congruence