ArticlePDF Available

Process-Tracing Research Designs: A Practical Guide

July 2018
Political Science and Politics 51(4):1-5

DOI:10.1017/S1049096518000975

Authors:

Jacob Ricks

Singapore Management University

Amy H. Liu

University of Texas at Austin

Process-tracing has grown in popularity among qualitative researchers. However, unlike statistical models and estimators—or even other topics in qualitative methods—process-tracing is largely bereft of guidelines, especially when it comes to teaching. We address this shortcoming by providing a step-by-step checklist for developing a research design to use process-tracing as a valid and substantial tool for hypothesis testing. This practical guide should be of interest for both research application and instructional purposes. An online appendix containing multiple examples facilitates teaching of the method.

Process-Tracing: The Checklist

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842 PS • October 2018 © American Political Science Association, 2018 doi:10.1017/S1049096518000975

The Teacher

Process-Tracing Research Designs:

A Practical Guide

Jacob I. Ricks, Singapore Management University

Amy H. Liu, University of Texas at Austin

ABSTRACT Process-tracing has grown in popularity among qualitative researchers. However,

unlike statistical models and estimators—or even other topics in qualitative methods—process-

tracing is largely bereft of guidelines, especially when it comes to teaching. We address this

shortcoming by providing a step-by-step checklist for developing a research design to use

process-tracing as a valid and substantial tool for hypothesis testing. This practical guide

should be of interest for both research application and instructional purposes. An online

appendix containing multiple examples facilitates teaching of the method.

How does one develop a research design based on

process-tracing? This question highlights a major

challenge in teaching and adopting process-tracing

methods. Although there is an expanding body

of work on the approach (Beach and Pedersen

2013; Bennett and Checkel 2015; Humphreys and Jacobs 2015;

Mahoney 2012; Rohlfing 2014), we are still faced with Collier’s

(2011, 823) lamentation: “Too often this tool is neither well

understood nor rigorously applied” (see also Blatter and Blume

2008, 318; Zaks 2017). One central concern is that there are few

instructional materials in the qualitative-methods canon (Elman,

Kapiszewski, and Kirilova 2015; Kapiszewski, MacLean, and Read

2014). This article provides a short, practical guide for develop-

ing a process-tracing research design. The corresponding online

appendix applies this guide to four examples, thereby offering

a tool for researchers seeking to employ and instructors planning

to teach this method.

The material is organized in the form of a checklist

that provides introductory guideposts to help researchers

structure their research designs. This article is not a compre-

hensive literature review (Kay and Baker 2015), and neither

is it the final word on what constitutes good process-tracing

(Waldner 2015). There remains much work to be done in defin-

ing, delineating, and developing process-tracing methods,

and we advise graduate students and advanced researchers to

become familiar with these debates (Beach and Pedersen 2013;

Bennett and Checkel 2015). Instead, our contribution is to

make process-tracing accessible and more readily applicable to

beginners without being distracted by ongoing methodological

discussions.

The discussion is limited to one type of process-tracing: theory

testing (Beach and Pedersen 2013). Speciﬁcally, we focus on the

systematic study of the link between an outcome of interest and

an explanation based on the rigorous assessing and weighting of

evidence for and against causal inference. By deﬁning process-

tracing in these terms, we emphasize the role of theory and the

empirical testing of hypotheses. The challenge is to assemble a

research design equipped to do so.

THE CHECKLIST

To craft a research design based on process-tracing, we suggest

that researchers must (1) define their theoretical expectations,

(2) give direction to their research, and (3) identify the types of

data necessary for testing a theory. Stated differently, the steps

outlined in ﬁgure 1 set the stage for implementing best practices

(Bennett and Checkel 2015). In the online appendix provided to

assist with teaching, we show how this checklist can be applied in

four diﬀerent examples: the rise of the Japanese developmental

state; the electoral success of the Thai Rak Thai party in Thailand;

the standardization of English in Singapore; and the bureaucratic

reforms of the Philippines irrigation agency. We recommend that

instructors start with the checklist before having students read

the appendix; these materials should be paired with Collier (2011).

Alternatively, instructors can present both the checklist and the

appendix simultaneously and then assign students to use the

checklist to evaluate a separate article based on process-tracing

methods (e.g., Fairﬁeld 2013 and Tannenwald 1999). The goal is to

ingrain in students’ minds what process-tracing is and how it can

be used. In the following discussion, we reference the example of

Slater and Wong’s (2013) process-tracing analysis of why strong

authoritarian parties sometimes embrace democratization.

Step 1: Identify Hypotheses

We adopt the maxim “Theory saves us all.” Research designs and

empirical analyses for causal analysis should be theoretically guided.

Jacob I. Ricks is assistant professor of political science at Singapore Management University.

He can be reached at jacobricks@smu.edu.sg.

Amy H. Liu is associate professor in government at University of Texas at Austin.

She can be reached at amy.liu@austin.utexas.edu.

PS • October 2018 843

Therefore, establishing testable hypotheses based on our theories

is the first step in good process-tracing. In this sense, building

a research design for process-tracing is the same as in any other

attempt at causal inference. There is, however, one important

distinction. In process-tracing, we are concerned not only with

our theory of interest; we also must juxtapose rival explanations

that we intend to test (Hall 2013; Rohlﬁng 2014; Zaks 2017). It is

important that the concerned hypothesis is evaluated against

alternative(s) in a Lakatosian sense, creating a “three-cornered

ﬁght” that pits our observations against both our primary theory

and at least one alternative (Lakatos 1970).

The checklist is structured to allow for the testing of multiple—

that is, as many as required—rival hypotheses. In an oft-used

comparison, detectives in criminal cases begin their investigation

by focusing on those closest to the victim and then eliminating

suspects (i.e., hypotheses) along the way. Social scientists should

act similarly, remembering Ockham’s razor: seek ﬁrst hypotheses

that are clearly related to the outcome, simple, and testable before

employing more complex explanations. These theoretical expecta-

tions should be plainly established before moving to step 2.

Step 2: Establish Timelines

The second step is to sequence events. Timelines should be

bookended according to the theoretical expectations. The conclusion

of the timeline will be at or shortly after the outcome of interest—that

is, the dependent variable. The challenge is to identify how far back

in time we must go to seek out our cause. A good timeline begins

with the emergence of the theorized causal variable. For instance,

we hypothesize that the compounded eﬀect of antecedent party

strength, ominous signals, and legitimization strategies causes

strong authoritarian parties to embrace democratization (Slater and

Wong 2013). Therefore, we begin our timeline with the foundations

of the vital components of the theory—namely, the antecedent

strength of the party—and end it with the democratic transition.

The timeline has several purposes. First, it clariﬁes the research-

er’s thought process. Second, it establishes temporal precedence.

Third, it provides what can be constituted as a “face-validity” test

for the argument. Fourth, it helps to identify major events that

could have shaped the outcome of interest. In doing so, this allows

us to revisit our hypotheses and to ascertain whether we might be

missing an obvious probable cause for the concerned outcome.

In essence, we give ourselves the opportunity to verify whether

the events in question ﬁt the hypotheses. Analogously, criminal

investigators also use timelines to establish the victims’ histories

and points where they may have met foul play. Although these

timelines rarely ﬁnd their way into published works, they are an

imperative step in the research process. Researchers should keep

their timelines readily available with updates as they progress

through the many stages of ﬁeldwork. Timeline development is a

critical exercise before initiating evidence collection.

Step 3: Construct Causal Graph

After sequencing the timeline, the next step is to construct

a causal graph (Waldner 2015). This type of graph identifies

the independent variable(s) of

interest. It also provides struc-

ture, allowing us to focus on the

link between the explanation

and the concerned outcome. In

other words, a causal graph vis-

ually depicts the causal process

through which X causes Y. With

a causal graph, we can identify all

moments when the concerned

actor (e.g., individual, govern-

ment, party, or group) made a

choice that could have aﬀected

the result. This endogenous

choice need not be contentious,

but it does need to be theoreti-

cally relevant.

We depart slightly from

Waldner (2015), however, in

two ways. First, we contend that

just as not all choices are rele-

vant moments, not all relevant

moments are choices. They also

Figure 1

Process-Tracing: The Checklist

In an oft-used comparison, detectives in criminal cases begin their investigation by focusing

on those closest to the victim and then eliminating suspects (i.e., hypotheses) along the way. Social

scientists should act similarly, remembering Ockham’s razor: seek ﬁrst hypotheses that are clearly

related to the outcome, simple, and testable before employing more complex explanations.

844 PS • October 2018

The Teacher: Process-Tracing Research Designs: A Practical Guide

can be exogenous events—that is, critical junctures that emerge

from events such as the discovery of oil or a natural disaster. What

matters is that these moments are “collectively suﬃcient to gen-

erate the outcome” (Waldner 2015, 131). Second, our use of causal

graphs potentially includes events that may not fit clearly into

the causal process being identiﬁed. We distinguish these events

with dashed lines. In contrast, the causal process remains out-

lined with solid lines. This highlights and clariﬁes—especially for

students—that not all interesting events are variables of interest.

Figure 2

Causal Graph of Slater and Wong (2013)

These activities are part of the background work that must be accomplished before engaging

in any type of ﬁeldwork—from visiting archives to conducting interviews, from administering

surveys to observing participants.

For an example, we offer a simple causal graph of Slater and

Wong’s (2013) theory about why strong authoritarian-party

states democratize (figure 2). Slater and Wong began by pre-

senting their scope condition: democratic transitions under

the watch of dominant authoritarian ruling parties. Given

this situation, our theoretical expectation would be a low like-

lihood of democratization. Yet, Slater and Wong (2013, 719)

claimed that “dominant parties can be incentivized to con-

cede democratization from a position of exceptional strength”

under a set of three specific conditions. First, they must enjoy

a high degree of antecedent strengths—that is, confidence that

the party can still dominate post-transition politics. Second,

this strength, however, must have been challenged by ominous

signals that the party is past its authoritarian prime. Third,

leaders must strategically choose to adopt democratic legitima-

tion strategies.

Causal graphs follow the initial timeline; they build on the

series of events that are identified in the timeline. In other

words, we can pinpoint the hypothesized explanation and

the outcome in a temporal chain. We can specify where and

which types of empirical information are necessary for the

analysis. The timeline and the causal graph can be developed

together iteratively. Whereas the sequence of events will not

change, the creation of the causal graph might cause us to revisit

the timeline to clarify links or highlight important missing

information.

Step 4: Identify Alternative Choice or Event

At each relevant moment in the causal graph, a diﬀerent choice

could have been made or another event could have happened. For

each distinct moment, we identify these alternative(s). It is impor-

tant, however, that these alternatives are theoretically grounded.

There must be a reason that the choice could have been made or

that the event could have manifested diﬀerently.

Step 5: Identify Counterfactual Outcomes

Next, for each moment, we identify the counterfactual outcome

that would have happened if the alternative choice had been

taken or the alternative event had transpired. Counterfactuals

are vital to process-tracing, especially when no alternative cases

are considered (Fearon 1991). When treating hypothetical predic-

tions, it is imperative that another outcome was possible. If there

is no plausible theory-informed alternative outcome, then no real

choice or event has taken place. Thus, the link between the input

and the outcome was predetermined; hence, process-tracing pro-

vides little value added. Note that steps 4 and 5 are closely linked.

An approach in lieu of counterfactuals is the use of controlled

comparisons, wherein the case of interest is compared with empiri-

cal alternatives rather than a hypothetical counterfactual (Slater

and Ziblatt 2013). However, if a researcher is primarily focused

on one single case—or perhaps multiple cases that are not explic-

itly comparable via the research design—then this counterfactual

exercise is important. Even if a researcher does use controlled

comparisons, we still recommend considering counterfactuals.

Note, however, that counterfactuals are heuristic devices that allow

us to identify hypothesized outcomes and thus potential data

to collect; they are not evidence per se.

It is important that steps 1 through 5 be conducted before data

collection. These activities are part of the background work that

must be accomplished before engaging in any type of ﬁeldwork—

from visiting archives to conducting interviews, from administer-

ing surveys to observing participants. They are essential to the

process of theory testing because they establish expectations about

what researchers should encounter during their data-collection

process. Because process-tracing often is iterative, researchers

likely will revisit these steps throughout the research project—

especially in light of new data. However, an initial plan for data

collection should be designed based on these ﬁve steps.

Step 6: Finding Evidence for Primary Hypothesis

After we have established a timeline, outlined our causal graphs,

and identified our theoretical expectations, we can design the

data-collection portion of our research project. At each iden-

tified relevant moment, we must plan to systematically find

evidence that the variable germane to the primary hypothesis

was the reason the concerned actor pursued the timeline path.

It is important that as we design our data collection, we must

recognize that not all evidence types are the same (Bennett

2014; Collier 2011; Mahoney 2012; Rohlﬁng 2014). Some data are

necessary to establish causation; others suﬃcient—and then there

are data that are neither or both. We suggest drawing on Van

Evera’s (1997) four types of evidence, summarized in table 1:

straw-in-the-wind, hoops, smoking gun, and doubly decisive.

Due to space constraints, we do not explain these evidence types

in detail (see Collier 2011 for an extensive discussion). Figure 1

utilizes these evidence types and the appendix demonstrates

their application.

PS • October 2018 845

Process-tracing involves rigor and attention to details and logic of causal inference similar to

that of a detective or a medical examiner.

When creating a data-collection plan, it is common for

researchers—especially those who spend time in the field—to

accumulate data in a “soak-and-poke” fashion. We do not con-

demn such efforts; however, we encourage researchers to think

carefully about the evidence types they are collecting because

most information gathered will be of the straw-in-the-wind

type. Stated differently, whereas much data gathered may offer

weak support for—or at least not negate—the primary hypothesis,

it is not the most useful for testing purposes. When designing

research, it is absolutely vital to remain cognizant of the evidence

type collected and its ability to support or negate the larger claims

(Fairfield 2013). The causal graph is particularly useful at this

point because it identifies the links that must be made between

Table 1

Types of Evidence for Process-Tracing

Sucient for Arming Causal Inference

No Yes

Necessary for Arming

Causal Inference

1. Straw-in-the-Wind 3. Smoking Gun

a. Passing: Arms relevance of hypothesis but does not conrm it. a. Passing: Conrms hypothesis.

b. Failing: Hypothesis is not eliminated but is slightly weakened. b. Failing: Hypothesis is not eliminated but is

somewhat weakened.

c. Implications for rival hypothesis:

Passing slightly weakens them.

Failing slightly strengthens them.

c. Implications for rival hypothesis:

Passing substantially weakens them.

Failing somewhat strengthens them.

Ye s

2. Hoops 4. Doubly Decisive

a. Passing: Arms relevance of hypothesis but does not conrm it. a. Passing: Conrms hypothesis and eliminates

others.

b. Failing: Eliminates hypothesis. b. Failing: Eliminates hypothesis.

c. Implications for rival hypothesis:

Passing somewhat weakens them.

Failing somewhat strengthens them.

c. Implications for rival hypothesis:

Passing eliminates them.

Failing substantially strengthens them.

Source: Collier (2011, 825)

interviews—for example, with military advisers from the authori-

tarian period who relayed growing disloyalty among the armed

forces and recommended the leadership to concede. It also can

be ascertained from archival documents—for example, minutes

from cabinet meetings discussing diﬀerent electoral rules for the

party to adopt on transition. Conversely, evidence describing the

personalities active in alternative rival parties might be consid-

ered straw-in-the-wind. Although interesting, these data are not

vital to establishing the strength of the theory; more important is

information on the level of threat they posed to the ruling party.

When we design data collection, we must be careful to focus on the

evidence types that matter lest we be left building our evidentiary

house with a pile of straw.

our variables of interest to establish causation. For instance, certain

evidence types simultaneously can support our proposed theory

and eliminate a rival one. Van Evera (1997) called this doubly-

decisive evidence. If such a datum is found, then we can exclude

all other hypotheses and step 6 becomes the final one in

our process-tracing efforts. Unfortunately, these cases are rare.

Therefore, we must increase our evidence pool to demonstrate

that our hypothesis is the best fit from a set of possible expla-

nations. This is outlined in step 7.

For step 6, we exhort researchers to make clear their expec-

tations about the evidence types needed to (1) further support

their argument, and (2) negate the rival hypotheses. For instance,

consider Slater and Wong’s (2013) assertion that democratiza-

tion can emerge from strategic decisions by a ruling party. Here,

we want smoking-gun evidence that links antecedent strength,

ominous signals, and legitimation strategies directly to the

decision to democratize. This type of evidence can be found in

Step 7: Find Evidence for Rival Hypothesis

Our final step is to repeat step 6; at each choice node, the focus

now should be on alternative explanations. This step may

require multiple iterations depending on the number of rival

hypotheses. The objective is to dismiss as many explanations as

possible, leaving only one hypothesis as the most likely. Here,

the most important evidence type is the exclusionary or—per

Van Evera (1997)—the hoops test. Hoops evidence, if absent, can

eliminate a hypothesis from consideration. If the hypothesized

variable was not present when the event happened, then we can

dismiss the rival hypothesis.

If the rival explanation is not easily discarded, we must move

on to other data types. Wherever possible, we look for opportuni-

ties to dismiss the hypothesis. However, if at some point we ﬁnd

evidence to the contrary, we cannot reject it. Instead, we must

consider that a rival hypothesis could explain the phenomenon of

interest better than the primary one.

846 PS • October 2018

The Teacher: Process-Tracing Research Designs: A Practical Guide

Because political phenomena are complex, it is possible

that the different explanations may not be mutually exclusive

(Zaks 2017). Therefore, pitting competing hypotheses against

one another can result in instances in which multiple hypotheses

all seem to have explanatory leverage. When these conditions

manifest, we must rely on a deep understanding of our cases to

weigh the evidence and judge which hypothesis best explains the

outcome. As in a criminal investigation, we must discern which

theory of the crime has the strongest evidence and proceed as

best we can to trial.

CONCLUSION

Despite the popularity of process-tracing in empirical research,

discussions on how to develop effective research designs based

on the method are largely absent in political science—especially

when we consider teaching materials. Frequently, there is a

disjuncture between theoretically driven research designs and

rigorously evaluated empirics. Beyond this, to those who do

not regularly engage in process-tracing, the method can be poorly

understood. The prime advocates of process-tracing continue

to make strides in pushing methodological understanding and

boundaries. This work, however, does not necessarily lend itself

to introducing the tool to the uninitiated. As a result, critics have

dismissed process-tracing as being ineﬀective in explaining polit-

ical phenomena beyond a singular case—if even that. We under-

stand but do not agree with these positions.

Process-tracing involves rigor and attention to details and

logic of causal inference similar to that of a detective or a medical

examiner. It requires establishing a sequence of events and iden-

tifying a suspect pool. With each piece of evidence, we can elimi-

nate a variable and/or strengthen one hypothesis against another.

We conduct this iterative process until we are ready for trial.

In this spirit, we oﬀer our checklist to help researchers develop

a causal research design and then evaluate pieces of evidence sys-

tematically against it. Such practical guidance is largely missing

in the process-tracing literature. This guide and the applications

in the online appendix attempt to address this shortcoming and

to demonstrate how process-tracing can be done rigorously.

We challenge advocates to adopt these standards in their own work

and skeptics to conceptualize process-tracing as more than glori-

ﬁed storytelling. We also hope that the method can be integrated

easily and clearly as a component of political science courses—not

only in methods classes but also in substantive courses. Indeed,

through careful application, process-tracing can serve as a strong

tool for hypothesis testing.

SUPPLEMENTARY MATERIAL

To view supplementary material for this article, please visit

https://doi.org/10.1017/S1049096518000975

ACKNOWLEDGMENTS

We thank Marissa Brookes, Jason Brownlee, José Cheibub,

Travis Curtice, Jennifer Cyr, John Donaldson, Richard Doner,

Zach Elkins, Michael Giles, Anna Gunderson, Nicholas Harrigan,

Abigail Heller, Allen Hicken, Laura Huber, Kendra Koivu, James

Mahoney, Eddy Malesky, Joel Moore, Ijlal Naqvi, Sari Niedzwiecki,

Rachel Schoner, Dan Slater, Hillel Soifer, Kurt Weyland, and

the anonymous reviewers for helpful comments on this article.

An earlier version was presented at the 2016 Southwest Mixed

Methods Research Workshop at University of Arizona. We were

able to convert this project from an idea to an article under the

auspices of the Short-Term Research Collaboration Program at

Singapore Management University. Any errors belong to the

authors. n

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Perspect Polit

Ryan Tans

Anti-reclamation movements are common in Indonesia, but their effectiveness varies. Such movements, which oppose the infilling of coastal waters and wetlands, consistently draw support from environmentalists, fisherfolk, and coastal residents. To succeed, however, they must transcend these constituencies and mobilize broad coalitions. In this paper, I apply the concept of political opportunity to explain variation in the ability of anti-reclamation movements to achieve this goal. Specifically, I argue that the opportunity to build broad coalitions depends on the positioning of political, economic, and communal elites. Disagreement among these groups creates opportunities for activists to recruit some of them as allies in the construction of economically diverse, cross-class coalitions. Consensus, by contrast, excludes elites as potential allies, forcing activists to build geographically expansive but class-based coalitions. To develop my argument, I draw on local news archives and primary source documents to compare similarly situated anti-reclamation movements in Bali and Makassar. In Bali, the movement flourished by cultivating an alliance with communal elites and local businesses. In Makassar, the movement withered because public officials, local businesses, and communal elites all welcomed reclamation. My findings imply that anti-reclamation movements are most likely to succeed when they emphasize communal identities with cross-class appeal. Yet such tactics alienate parallel movements from one another and undermine national activism. As a result, anti-reclamation movements fight the same battles over and over without achieving national reforms that would empower coastal communities to participate in coastal planning. Under such conditions, reclamation deepens the vulnerability of coastal communities to climate change.

Capabilities, place, and wellbeing in conservation spaces: An empirical analysis

Article

Dec 2024

Using case studies from Namibia and Mozambique, we examine how regulations against hunting impact person–place relationships and affect multidimensional wellbeing in conservation spaces. We combine Amartya Sen's capability approach with theories of place, using Chris de Wet's concept of disemplacement to investigate the ways conservation efforts affect rural quality of life. We find conservation and place-making become incompatible if people are prevented from adapting lifestyles and livelihoods to accommodate changing circumstances. By tracing distinct dimensions of the disemplacement process, we demonstrate the adverse and compounding effects of wildlife regulations associated with nature tourism. Disruptions to economic livelihoods and physical security destabilize person–place bonds that enhance wellbeing. Material losses and economic hardship are accompanied by institutional disruptions that contribute to marginalization and social exclusion. We provide a detailed illustration of how conservation regulations constrain agency and contribute to a growing sense of powerlessness by decreasing local control over wildlife, which consequently weakens place attachment and diminishes wellbeing. Our study demonstrates how people become more multidimensionally impoverished as conservation initiatives change the places they value while simultaneously limiting their capabilities to maintain place attachment.

How is process tracing applied in health research? A systematic scoping review

Article

Nov 2024

Quality not quantity: Lobbying institutions and the influence of asylum rights groups

Article

Nov 2024
Br J Polit Int Relat

Alex Hartland

With participation in electoral politics limited, asylum seekers rely on lobbying to influence policy, but the factors which facilitate or constrain this process remain unclear. I interview asylum rights lobbyists in Germany and the United Kingdom to study the effects of lobbying institutions, using process tracing to identify influence mechanisms. Contrary to previous research, I find that successful lobbying stems from the quality not the quantity of institutions. I show how Germany’s corporatist institutions provide stable lobbying structures, building trustful working relationships. This enables collaborative policymaking and increases opportunities for influence. Informal, inconsistent structures in the United Kingdom’s pluralist system impair trust, reducing collaborative policymaking and influence. UK institutions are more policy responsive, however, enabling groups to capitalise on favourable public opinion, whereas the German system remains stable. The results demonstrate the effects of institutional arrangements and identify mechanisms which can improve policy outcomes for asylum seekers and other disadvantaged groups.

Establishment of a New Planning System in Ukraine: Institutional Change Between Europeanization and Post-Socialist Path Dependence

Article

Nov 2024

Learning through Research: Using Data to Train Undergraduates in Qualitative Methods

Article

Full-text available

Jan 2015

We anticipate students will acquire research skills most effectively when they engage in “active learning,” rather than passively absorbing information. The question raised by Druckman’s call to integrate research and undergraduate teaching is to what extent active learning can or should take the form of direct involvement in faculty members’ research projects. To be sure, significant benefits to such participation exist. Collaborative research can strengthen mentoring relations. It can also teach students about teamwork and group dynamics, about discipline and rigor, about listening and compromise, and about the rhythm and mechanics of carrying out social inquiry. Students may be able to acquire substantive knowledge about their subject area as well. And participating in faculty research can empower and inspire undergraduates to want to engage in research, an unquestionably positive outcome. Nonetheless, we believe that undergraduates can only meaningfully collaborate in a research project—and can only learn from doing so—if they first receive a solid grounding in the methods being used in the project. Collaborative research involves a range of individual research tasks at different levels of sophistication and with varying degrees of proximity to a project’s underlying epistemology. Undergraduates with little or no methodological preparation can undertake simple tasks with straightforward scripts. Yet a research task is not by itself an act of social inquiry. It only becomes so by virtue of its placement in a project’s larger methodological context. Moreover, simply undertaking research tasks does not allow students to grasp the methods being used in a project, or to understand their rules, why they must be followed, what it means to follow them, and what failing to do so implies for the resulting scholarship.

What Can Causal Process Tracing Offer to Policy Studies? A Review of the Literature

Article

Full-text available

Feb 2015
POLICY STUD J

Causal process tracing (CPT) has emerged as an important method of causal inference in qualitative social science research, most notably in case study research designs. There is now a considerable literature on the aims, philosophical groundings, and methods of process tracing. This paper reviews the CPT literature to assess what new directions it may suggest for policy studies. The first part of the paper sets out the methodological advantages CPT offers in building and testing theories of policy change, most notably in supporting a theoretical pluralism to address the problem of complexity in policy studies. Building on recent scholarship across the social sciences, the second part examines step by step the recently minted “best practice” for undertaking CPT in policy studies. This part includes discussion of the possible pitfalls of CPT as a method; common errors involved in its use are set out and minimization strategies offered. In particular, while acknowledging the usefulness of Bayesian tests for causality as heuristic devices, we emphasize the limitations of applying such tests in practice. Possible correctives are suggested. The final part of the paper speculates more generally on the potential of CPT to improve our investigation of patterns of policy change over time.

What makes process tracing good?: Causal mechanisms, causal inference, and the completeness standard in comparative politics

Chapter

Nov 2014

David Waldner

Advances in qualitative methods and recent developments in the philosophy of science have led to an emphasis on explanation via reference to causal mechanisms. This book argues that the method known as process tracing is particularly well suited to developing and assessing theories about such mechanisms. The editors begin by establishing a philosophical basis for process tracing - one that captures mainstream uses while simultaneously being open to applications by interpretive scholars. Equally important, they go on to establish best practices for individual process-tracing accounts - how micro to go, when to start (and stop), and how to deal with the problem of equifinality. The contributors then explore the application of process tracing across a range of subfields and theories in political science. This is an applied methods book which seeks to shrink the gap between the broad assertion that 'process tracing is good' and the precise claim 'this is an instance of good process tracing'.

Falsification and the Methodology of Scientific Research Programmes

Chapter

Sep 1970

Imre Lakatos

Two books have been particularly influential in contemporary philosophy of science: Karl R. Popper's Logic of Scientific Discovery, and Thomas S. Kuhn's Structure of Scientific Revolutions. Both agree upon the importance of revolutions in science, but differ about the role of criticism in science's revolutionary growth. This volume arose out of a symposium on Kuhn's work, with Popper in the chair, at an international colloquium held in London in 1965. The book begins with Kuhn's statement of his position followed by seven essays offering criticism and analysis, and finally by Kuhn's reply. The book will interest senior undergraduates and graduate students of the philosophy and history of science, as well as professional philosophers, philosophically inclined scientists, and some psychologists and sociologists.

Process-Tracing Methods

Book

Jan 2019

Process-tracing in social science is a method for studying causal mechanisms linking causes with outcomes. This enables the researcher to make strong inferences about how a cause (or set of causes) contributes to producing an outcome. Derek Beach and Rasmus Brun Pedersen introduce a refined definition of process-tracing, differentiating it into three distinct variants and explaining the applications and limitations of each. The authors develop the underlying logic of process-tracing, including how one should understand causal mechanisms and how Bayesian logic enables strong within-case inferences. They provide instructions for identifying the variant of process-tracing most appropriate for the research question at hand and a set of guidelines for each stage of the research process. © 2019 by Derek Beach and Rasmus Brun Pedersen. All rights reserved.

Relationships Among Rivals (RAR): A Framework for Analyzing Contending Hypotheses in Process Tracing

Article

Jul 2017

Sherry Zaks

Methodologists and substantive scholars alike agree that one of process tracing’s foremost contributions to qualitative research is its capacity to adjudicate among competing explanations of a phenomenon. Existing approaches, however, only provide explicit guidance on dealing with mutually exclusive explanations, which are exceedingly rare in social science research. I develop a tripartite solution to this problem. The Relationships among Rivals (RAR) framework (1) introduces a typology of relationships between alternative hypotheses, (2) develops specific guidelines for identifying which relationship is present between two hypotheses, and (3) maps out the varied implications for evidence collection and inference. I then integrate the RAR framework into each of the main process-tracing approaches and demonstrate how it affects the inferential process. Finally, I illustrate the purchase of the RAR framework by reanalyzing a seminal example of process-tracing research: Schultz’s (2001) analysis of the Fashoda Crisis. I show that the same evidence can yield new and sometimes contradictory inferences once scholars approach comparative hypothesis testing with this more nuanced framework.

Field Research in Political Science: Practices and Principles

Book

Apr 2015

Field research - leaving one’s home institution in order to acquire data, information or insights that significantly inform one’s research - remains indispensable, even in a digitally networked era. This book, the first of its kind in political science, reconsiders the design and execution of field research and explores its role in producing knowledge. First, it offers an empirical overview of fieldwork in the discipline based on a large-scale survey and extensive interviews. Good fieldwork takes diverse forms yet follows a set of common practices and principles. Second, the book demonstrates the analytic benefits of fieldwork, showing how it contributes to our understanding of politics. Finally, it provides intellectual and practical guidance, with chapters on preparing for field research, operating in the field and making analytic progress while collecting data, and on data collection techniques including archival research, interviewing, ethnography and participant observation, surveys, and field experiments. © Diana Kapiszewski, Lauren M. MacLean and Benjamin L. Read 2015.

Mixing Methods: A Bayesian Approach

Article

Nov 2015

We develop an approach to multimethod research that generates joint learning from quantitative and qualitative evidence. The framework—Bayesian integration of quantitative and qualitative data (BIQQ)—allows researchers to draw causal inferences from combinations of correlational (cross-case) and process-level (within-case) observations, given prior beliefs about causal effects, assignment propensities, and the informativeness of different kinds of causal-process evidence. In addition to posterior estimates of causal effects, the framework yields updating on the analytical assumptions underlying correlational analysis and process tracing. We illustrate the BIQQ approach with two applications to substantive issues that have received significant quantitative and qualitative treatment in political science: the origins of electoral systems and the causes of civil war. Finally, we demonstrate how the framework can yield guidance on multimethod research design, presenting results on the optimal combinations of qualitative and quantitative data collection under different research conditions.

The Logic of Process Tracing Tests in the Social Sciences

Article

Oct 2012

James Mahoney

This article discusses process tracing as a methodology for testing hypotheses in the social sciences. With process tracing tests, the analyst combines preexisting generalizations with specific observations from within a single case to make causal inferences about that case. Process tracing tests can be used to help establish that (1) an initial event or process took place, (2) a subsequent outcome also occurred, and (3) the former was a cause of the latter. The article focuses on the logic of different process tracing tests, including hoop tests, smoking gun tests, and straw in the wind tests. New criteria for judging the strength of these tests are developed using ideas concerning the relative importance of necessary and sufficient conditions. Similarities and differences between process tracing and the deductive-nomological model of explanation are explored.

Tracing the Progress of Process Tracing

Article

Mar 2013

Peter Hall

Building on a Lakatosian approach that sees Social Science as an endeavour that confronts rival theories with systematic empirical observations, this article responds to probing questions that have been raised about the appropriate ways in which to conduct systematic process analysis and comparative enquiry. It explores varieties of process tracing, the role of interpretation in case studies, and the relationship between process tracing and comparative historical analysis.