Chapter 10 Review Questions

An important distinction between the failed most-likely and successful least-likely approaches is the role of falsification. Falsification is an empirical refutation of a theoretical proposition. A failed most-likely case study may falsify a theory if it posits a narrow causality: if a theory argues that A causes B even though our instance of B is caused by something else, the study would falsify the assertion that A is a necessary and sufficient cause of B. This situation is not terribly common in practice, as most theories stipulate complex causal relations. For this reason, a failed most-likely case will usually question the degree of probability or determinism that a theory asserts. In a successful least-likely case, falsification is rarely the goal because the phenomenon is already expected to fail. This type of study often seeks to relax the scope conditions of the theory, that is, to show that the theory explains more than its proponents claim.

In evaluating a case study, there are four factors to consider:

1. Are there clear definitions of the subject and object of the case study? This component is an important distinction, as it moves case studies from questions of “what” to questions of “why” and “how.”
2. Does the case study have rigorous and clear conceptualization? Case studies are often highly qualitative and therefore depend on concepts in order to generate meaningful insight.
3. Does the case study properly employ process tracing? Process tracing is the primary means by which case study research generates causal reasoning. It starts with breaking the phenomenon being studied into a series of observations (e.g., decisions, speech acts, events, procedures, and anything else that explains how something came to be). These observations are then used to make an argument that explains the “how” and “why” questions.
4. Does the case study make applicable generalizations to a wider population of cases? Essentially, this question asks whether the findings of this case are true in other similar cases. The best approach is to qualify the generalization: the case study is certain in its explanation of the phenomenon being studied; it is highly probable to increase understanding on a set of similar cases; and it suggests some interesting questions to ask in the widest population of cases.

Comparative research is a form of small-n research. This type systematically contrasts a number of cases in order to create stronger generalizations and thus broaden our knowledge of the political world. Such generalizations, when supported, allow greater explanatory power and prediction. There are two advantages to choosing comparative research:

1. It highlights the degree to which our conclusions about life and politics are filtered through our own preconceptions and are therefore culture-bound.
2. It enables the researcher to examine more complex, system-level traits. Examples of such characteristics include federalism, authoritarianism, democracies, revolutionary movements, and developing economies.

The most similar systems design allows researchers to eliminate the many shared characteristics as explanation for variation in outcomes and thereby focus on the small number of differences between them and use them to explain the differences. In a most different systems design, researchers seek out cases with similar outcomes but differ in other ways. This allows the researcher to eliminate possible sources of explanation: any variable that does not exist in all systems under the study is eliminated.

When reading and evaluating comparative research, it is important to consider measurement validity, and the extent to which the measurement of concepts is appropriate for each context. A measure that is appropriate in one culture or society may not be the same concept in another culture or society. Therefore, in designing a comparative study, researchers must be highly cognizant of the social and political context. The goal is to have equivalent measures. This may mean using different indicators in different cases or working hard to ensure that the indicators chosen do, in fact, measure the same concept in all the cases under study.