Process tracing is a qualitative research method used to develop and test theories. [1] [2] [3] Process-tracing can be defined as the following: it is the systematic examination of diagnostic evidence selected and analyzed in light of research questions and hypotheses posed by the investigator (Collier, 2011). Process-tracing thus focuses on (complex) causal relationships between the independent variable(s) and the outcome of the dependent variable(s), evaluates pre-existing hypotheses and discovers new ones. It is generally understood as a "within-case" method to draw inferences on the basis of causal mechanisms, but it can also be used for ideographic research or small-N case-studies. [4] [5] It has been used in social sciences (such as in psychology [2] ), as well as in natural sciences. [5]
Scholars that use process tracing evaluate the weight of evidence on the basis of the strength of tests (notably straw-in-the-wind tests, hoop tests, smoking gun tests, double decisive tests). [5] As a consequence, what matters is not solely the quantity of observations, but the quality and manner of observations. [5] [6] By using Bayesian probability, it may be possible to make strong causal inferences from a small sliver of data through process tracing. [5] [7] As a result, process tracing is a prominent case study method. [8] Process tracing can be used to study one or a few cases, in order to determine the changes that have occurred over time within these cases and causal mechanisms are responsible for this change. [1]
Process-tracing can be used both for inductive (theory-generating) and deductive (theory-testing) purposes. [5] 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. [9] 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. [9] In 'theory-testing process tracing,' the goal is to test existing theories and the causal mechanisms assumed therein. [9] [10] 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. [9] Through empirical evidence, a theoretical explanation is developed about causal mechanisms. [10] 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. [9] This variant constructs a detailed narrative that explains the process through which a specific outcome or series of events came to be. [10]
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. [11] Once these observable implications are presented, they are then tested empirically to see which of the observable implications can be observed and which cannot. [1] [12] It is also important to test if alternative explanations are present. [11] Process-tracing emphasizes the temporal sequence of events, and requires fine-grained case knowledge. [1]
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. [1] 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. [1] Stephen Van Evera's influential typology of process-tracing tests distinguishes tests depending on how they adjudicate between theoretical expectations: [5] [13]
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. [15]
A limitation to process-tracing is the problem of infinite regress. [16] [17] While some influential works by methods scholars have argued that the ability of process-tracing to make causal claims is limited by low degrees of freedom, [18] methodologists widely reject that the "degrees of freedom" problem applies to research that uses process-tracing, given that qualitative research entails different logics than quantitative research (where scholars do need to be wary of degrees of freedom). [16] [5] Some other disadvantages are:
One advantage to process-tracing over quantitative methods is that process-tracing provides inferential leverage. [1] In addition to aiding uncovering and testing causal mechanisms, process-tracing also contributes descriptive richness. [1] In addition to that, process-tracing can also present the contextual conditions within certain processes take place. [19] 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. [20] [21] [5] [8] [22] [23] [7] 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. [20] By using Bayesian probability, it may be possible to make strong causal inferences from a small sliver of data. [5] 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. [5]
Scholars can also use set theory in their process tracing. [24]
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.
An experiment is a procedure carried out to support or refute a hypothesis, or determine the efficacy or likelihood of something previously untried. Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale but always rely on repeatable procedure and logical analysis of the results. There also exist natural experimental studies.
A Bayesian network is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
A case study is an in-depth, detailed examination of a particular case within a real-world context. For example, case studies in medicine may focus on an individual patient or ailment; case studies in business might cover a particular firm's strategy or a broader market; similarly, case studies in politics can range from a narrow happening over time like the operations of a specific political campaign, to an enormous undertaking like world war, or more often the policy analysis of real-world problems affecting multiple stakeholders.
Quantitative research is a research strategy that focuses on quantifying the collection and analysis of data. It is formed from a deductive approach where emphasis is placed on the testing of theory, shaped by empiricist and positivist philosophies.
Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability, where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation, which views probability as the limit of the relative frequency of an event after many trials. More concretely, analysis in Bayesian methods codifies prior knowledge in the form of a prior distribution.
Mathematical statistics is the application of probability theory, a branch of mathematics, to statistics, as opposed to techniques for collecting statistical data. Specific mathematical techniques which are used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure theory.
Internal validity is the extent to which a piece of evidence supports a claim about cause and effect, within the context of a particular study. It is one of the most important properties of scientific studies and is an important concept in reasoning about evidence more generally. Internal validity is determined by how well a study can rule out alternative explanations for its findings. It contrasts with external validity, the extent to which results can justify conclusions about other contexts. Both internal and external validity can be described using qualitative or quantitative forms of causal notation.
Exploratory research is "the preliminary research to clarify the exact nature of the problem to be solved." It is used to ensure additional research is taken into consideration during an experiment as well as determining research priorities, collecting data and honing in on certain subjects which may be difficult to take note of without exploratory research. It can include techniques, such as:
External validity is the validity of applying the conclusions of a scientific study outside the context of that study. In other words, it is the extent to which the results of a study can generalize or transport to other situations, people, stimuli, and times. Generalizability refers to the applicability of a predefined sample to a broader population while transportability refers to the applicability of one sample to another target population. In contrast, internal validity is the validity of conclusions drawn within the context of a particular study.
Stephen William Van Evera is a professor of Political Science at the Massachusetts Institute of Technology, specializing in international relations. His research includes U.S. foreign and national security policy as well as causes and prevention of war. He is a member of the Council on Foreign Relations.
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In statistics, ignorability is a feature of an experiment design whereby the method of data collection does not depend on the missing data. A missing data mechanism such as a treatment assignment or survey sampling strategy is "ignorable" if the missing data matrix, which indicates which variables are observed or missing, is independent of the missing data conditional on the observed data. It has also been called unconfoundedness, selection on the observables, or no omitted variable bias.
In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints. One common observational study is about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator. This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group. Observational studies, for lacking an assignment mechanism, naturally present difficulties for inferential analysis.
Designing Social Inquiry: Scientific Inference in Qualitative Research is an influential 1994 book written by Gary King, Robert Keohane, and Sidney Verba that lays out guidelines for conducting qualitative research. The central thesis of the book is that qualitative and quantitative research share the same "logic of inference." The book primarily applies lessons from regression-oriented analysis to qualitative research, arguing that the same logics of causal inference can be used in both types of research.
In statistics, qualitative comparative analysis (QCA) is a data analysis based on set theory to examine the relationship of conditions to outcome. QCA describes the relationship in terms of necessary conditions and sufficient conditions. The technique was originally developed by Charles Ragin in 1987 to study data sets that are too small for linear regression analysis but large enough for cross-case analysis.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The study of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
Deductive pragmatism is a research method aiming at helping researchers communicate qualitative assumptions about cause-effect relationships (causality), elucidate the ramifications of such assumptions and drive causal inferences from a combination of assumptions, experiments, observations and case studies.
Necessary condition analysis (NCA) is a research approach and tool employed to discern "necessary conditions" within datasets. These indispensable conditions stand as pivotal determinants of particular outcomes, wherein the absence of such conditions ensures the absence of the intended result. For example, the admission of a student into a Ph.D. program necessitates a prior degree; the progression of AIDS necessitates the presence of HIV; and organizational change necessitates communication.
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