Process-tracing can be used both for inductive (theory-generating) and deductive (theory-testing) purposes. Process tracing can be divided into three variants. Although all variants trace causal mechanisms, it is necessary to consider these variants in order to align our practices with what we preach. The three variants of process tracing are "theory-testing process tracing," "theory-building process tracing," and "explaining outcome process tracing”. Among themselves, these variants differ from each other on the fact that they are theory- or case-based designs, they test or build theoretical causal mechanisms, they understand the generality of causal mechanisms differently, and they make different inferences. In 'theory-testing process tracing,' the goal is to test existing theories and the causal mechanisms assumed therein. On the contrary, 'theory-building process tracing' involves constructing a theory about a causal mechanism that can be applied to a broader population of a particular phenomenon. Through empirical evidence, a theoretical explanation is developed about causal mechanisms. In "explaining outcome process tracing," it is not about testing or building a theoretical mechanism, but it is about finding a satisfactory explanation for a given outcome. This variant constructs a detailed narrative that explains the process through which a specific outcome or series of events came to be.
Process-tracing differs from other qualitative analysis methods because of its focus on "how" causal mechanisms work; other qualitative analysis methods focuses at the correlation between the dependent and independent variable (Beach & Pedersen, 2012). Process-tracing looks beyond the correlation of two variables.
In terms of theory-testing, the process-tracing method works by presenting the observable implications (hypotheses) of a theory, as well as alternative explanations that are inconsistent with the theory. These observable implications and alternative explanations are based on theory-based hypotheses and key events. Once these observable implications are presented, they are then tested empirically to see which of the observable implications can be observed and which cannot. It is also important to test if alternative explanations are present. Process-tracing emphasizes the temporal sequence of events, and requires fine-grained case knowledge.
For testing the hypothetical theories, there are different types of requirements within a causal mechanism. There are necessary requirements, where the presence of one variable will always lead to the effect on the dependent variable. This means that the lack of the necessary requirement will also mean a lack of the rest of the mechanism. The second type of requirement is a sufficient requirement, where the presence of the requirement confirms the existence of a possible mechanism. Stephen Van Evera's influential typology of process-tracing tests distinguishes tests depending on how they adjudicate between theoretical expectations:
It is often used to complement comparative case study methods. By tracing the causal process from the independent variable of interest to the dependent variable, it may be possible to rule out potentially intervening variables in imperfectly matched cases. This can create a stronger basis for attributing causal significance to the remaining independent variables.
One advantage to process-tracing over quantitative methods is that process-tracing provides inferential leverage. In addition to aiding uncovering and testing causal mechanisms, process-tracing also contributes descriptive richness. In addition to that, process-tracing can also present the contextual conditions within certain processes take place. Another important advantage is that process tracing can deal with theoretical pluralism, which means hypotheses or conceptual models have multiple (un)dependent variables and causal relationships. This method of analysis is therefore suitable for understanding inherent complexity (Kay & Baker, 2015). The reason why process-tracing differs from other qualitative research methods is also an advantage.
By assigning probabilities to outcomes under specific conditions, scholars can use Bayesian rules in their process tracing to draw robust conclusions about the causes of outcomes. For example, if a scholar's theory assumes that a number of observable implications will happen under certain conditions, then the repeated occurrence of those outcomes under the theorized conditions lends strong support for the scholar's theory because the observed outcomes would be improbable to occur in the manner expected by the scholar if the theory were false. By using Bayesian probability, it may be possible to make strong causal inferences from a small sliver of data. For example, a video recording of a person committing a bank robbery can be very strong evidence that a particular person committed the robbery while also ruling out that other potential suspects did it, even if it is only a single piece of evidence.
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