comparative case study method

Last updated 09/07/24: Online ordering is currently unavailable due to technical issues. We apologise for any delays responding to customers while we resolve this. For further updates please visit our website: https://www.cambridge.org/news-and-insights/technical-incident

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings .

Login Alert

• > The Case for Case Studies
• > Selecting Cases for Comparative Sequential Analysis

Book contents

• The Case for Case Studies
• Strategies for Social Inquiry
• Copyright page
• Contributors
• Preface and Acknowledgments
• 1 Using Case Studies to Enhance the Quality of Explanation and Implementation
• Part I Internal and External Validity Issues in Case Study Research
• Part II Ensuring High-Quality Case Studies
• 6 Descriptive Accuracy in Interview-Based Case Studies
• 7 Selecting Cases for Comparative Sequential Analysis
• 8 The Transparency Revolution in Qualitative Social Science
• Part III Putting Case Studies to Work: Applications to Development Practice

7 - Selecting Cases for Comparative Sequential Analysis

Novel Uses for Old Methods

from Part II - Ensuring High-Quality Case Studies

Published online by Cambridge University Press:  05 May 2022

Pavone analyzes how our evolving understanding of case-based causal inference via process-tracing should alter how we select cases for comparative inquiry. The chapter explicates perhaps the most influential and widely used means to conduct qualitative research involving two or more cases: Mill’s methods of agreement and difference. It then argues that the traditional use of Millian methods of case selection can lead us to treat cases as static units to be synchronically compared rather than as social processes unfolding over time. As a result, Millian methods risk prematurely rejecting and otherwise overlooking (1) ordered causal processes, (2) paced causal processes, and (3) equifinality, or the presence of multiple pathways that produce the same outcome. To address these issues, the chapter develops a set of recommendations to ensure the alignment of Millian methods of case selection with within-case sequential analysis.

7.1 Introduction

In the lead article of the first issue of Comparative politics , Harold Lasswell posited that the “scientific approach” and the “comparative method” are one and the same ( Reference Lasswell Lasswell 1968 : 3). So important is comparative case study research to the modern social sciences that two disciplinary subfields – comparative politics in political science and comparative-historical sociology – crystallized in no small part because of their shared use of comparative case study research ( Reference Collier and Finifter Collier 1993 ; Reference Adams, Clemens, Orloff, Adams, Clemens and Orloff Adams, Clemens, and Orloff 2005 : 22–26; Reference Mahoney and Thelen Mahoney and Thelen 2015 ). As a result, a first-principles methodological debate emerged about the appropriate ways to select cases for causal inquiry. In particular, the diffusion of econometric methods in the social sciences exposed case study researchers to allegations that they were “selecting on the dependent variable” and that “selection bias” would hamper the “answers they get” ( Reference Geddes Geddes 1990 ). Lest they be pushed to randomly select cases or turn to statistical and experimental approaches, case study researchers had to develop a set of persuasive analytic tools for their enterprise.

It is unsurprising, therefore, that there has been a profusion of scholarship discussing case selection over the years. Footnote 1 Reference Gerring and Cojocaru Gerring and Cojocaru (2015) synthesize this literature by deriving no less than five distinct types (representative, anomalous, most-similar, crucial, and most-different) and eighteen subtypes of cases, each with its own logic of case selection. It falls outside the scope of this chapter to provide a descriptive overview of each approach to case selection. Rather, the purpose of the present inquiry is to place the literature on case selection in constructive dialogue with the equally lively and burgeoning body of scholarship on process tracing ( Reference George and Bennett George and Bennett 2005 ; Reference Brady and Collier Brady and Collier 2010 ; Reference Beach and Pedersen Beach and Pedersen 2013 ; Reference Bennett and Checkel Bennett and Checkel 2015 ). I ask a simple question: Should our evolving understanding of causation and our toolkit for case-based causal inference courtesy of process-tracing scholars alter how scholars approach case selection? If so, why, and what may be the most fruitful paths forward?

To propose an answer, this chapter focuses on perhaps the most influential and widely used means to conduct qualitative research involving two or more cases: Mill’s methods of agreement and difference. Also known as the “most-different systems/cases” and “most-similar systems/cases” designs, these strategies have not escaped challenge – although, as we will see, many of these critiques were fallaciously premised on case study research serving as a weaker analogue to econometric analysis. Here, I take a different approach: I argue that the traditional use of Millian methods of case selection can indeed be flawed, but rather because it risks treating cases as static units to be synchronically compared rather than as social processes unfolding over time. As a result, Millian methods risk prematurely rejecting and otherwise overlooking (1) ordered causal processes, (2) paced causal processes, and (3) equifinality, or the presence of multiple pathways that produce the same outcome. While qualitative methodologists have stressed the importance of these processual dynamics, they have been less attentive to how these factors may problematize pairing Millian methods of case selection with within-case process tracing (e.g., Reference Hall, Mahoney and Rueschemeyer Hall 2003 ; Reference Tarrow Tarrow 2010 ; Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 ). This chapter begins to fill that gap.

Taking a more constructive and prescriptive turn, the chapter provides a set of recommendations for ensuring the alignment of Millian methods of case selection with within-case sequential analysis. It begins by outlining how the deductive use of processualist theories can help reformulate Millian case selection designs to accommodate ordered and paced processes (but not equifinal processes). More originally, the chapter concludes by proposing a new, alternative approach to comparative case study research: the method of inductive case selection . By making use of Millian methods to select cases for comparison after a causal process has been identified within a particular case, the method of inductive case selection enables researchers to assess (1) the generalizability of the causal sequences, (2) the logics of scope conditions on the causal argument, and (3) the presence of equifinal pathways to the same outcome. In so doing, scholars can convert the weaknesses of Millian approaches into strengths and better align comparative case study research with the advances of processualist researchers.

Organizationally, the chapter proceeds as follows. Section 7.2 provides an overview of Millian methods for case selection and articulates how the literature on process tracing fits within debates about the utility and shortcomings of the comparative method. Section 7.3 articulates why the traditional use of Millian methods risks blinding the researcher to ordered, paced, and equifinal causal processes, and describes how deductive, processualist theorizing helps attenuate some of these risks. Section 7.4 develops a new inductive method of case selection and provides a number of concrete examples from development practice to illustrate how it can be used by scholars and policy practitioners alike. Section 7.5 concludes.

7.2 Case Selection in Comparative Research

7.2.1 case selection before the processual turn.

Before “process tracing” entered the lexicon of social scientists, the dominant case selection strategy in case study research sought to maximize causal leverage via comparison, particularly via the “methods of agreement and difference” of John Stuart Reference Mill Mill (1843 [1974] : 388–391).

In Mill’s method of difference, the researcher purposively chooses two (or more) cases that experience different outcomes, despite otherwise being very similar on a number of relevant dimensions. Put differently, the researcher seeks to maximize variation in the outcome variable while minimizing variation amongst a set of plausible explanatory variables. It is for this reason that the approach also came to be referred to as the ‘most-similar systems’ or ‘most-similar cases’ design – while Mill’s nomenclature highlights variation in the outcome of interest, the alternative terminology highlights minimal variation amongst a set of possible explanatory factors. The underlying logic of this case selection strategy is that because the cases are so similar, the researcher can subsequently probe for the explanatory factor that actually does exhibit cross-case variation and isolate it as a likely cause.

Mill’s method of agreement is the mirror image of the method of difference. Here, the researcher chooses two (or more) cases that experience similar outcomes despite being very different on a number of relevant dimensions. That is, the researcher seeks to minimize variation in the outcome variable while maximizing variation amongst a set of plausible explanatory variables. An alternative, independent variable-focused terminology for this approach was developed – the ‘most-different systems’ or ‘most-different cases’ design – breeding some confusion. The underlying logic of this case selection strategy is that it helps the researcher isolate the explanatory factor that is similar across the otherwise different cases as a likely cause. Footnote 2

Figure 7.1 Case selection setup under Mill’s methods of difference and agreement

Mill himself did not believe that such methods could yield causal inferences outside of the physical sciences ( Reference Mill Mill 1843 [1974] : 452). Nevertheless, in the 1970s a number of comparative social scientists endorsed Millian methods as the cornerstones of the comparative method. For example, Reference Przeworski and Teune Przeworski and Teune (1970) advocated in favor of the most-different cases design, whereas Reference Lijphart Lijphart (1971) favored the most-similar cases approach. In so doing, scholars sought case selection techniques that would be as analogous as possible to regression analysis: focused on controlling for independent variables across cases, maximizing covariation between the outcome and a plausible explanatory variable, and treating cases as a qualitative equivalent to a row of dataset observations. It is not difficult to see why this contributed to the view that case study research serves as the “inherently flawed” version of econometrics ( Reference Adams, Clemens, Orloff, Adams, Clemens and Orloff Adams, Clemens, and Orloff 2005 : 25; Reference Tarrow Tarrow 2010 ). Indeed, despite his prominence as a case study researcher, Reference Lijphart Lijphart (1975 : 165; Reference Lijphart 1971 : 685) concluded that “because the comparative method must be considered the weaker method,” then “if at all possible one should generally use the statistical (or perhaps even the experimental) method instead.” As Reference Hall, Mahoney and Rueschemeyer Hall (2003 : 380; 396) brilliantly notes, case study research

was deeply influenced by [Lijphart’s] framing of it … [where] the only important observations to be drawn from the cases are taken on the values of the dependent variable and a few explanatory variables … From this perspective, because the number of pertinent observations available from small-N comparison is seriously limited, the analyst lacks the degrees of freedom to consider more than a few explanatory variables, and the value of small-N comparison for causal inference seems distinctly limited.

In other words, the predominant case selection approach through the 1990s sought to do its best to reproduce a regression framework in a small-N setting – hence Lijphart’s concern with the “many variables, small number of cases” problem, which he argued could only be partially mitigated if, inter alia , the researcher increases the number of cases and decreases the number of variables across said cases ( Reference Lijphart 1971 : 685–686). Later works embraced Lijphart’s formulation of the problem even as they sought to address it: for example, Reference Eckstein, Greenstein and Polsby Eckstein (1975 : 85) argued that a “case” could actually be comprised of many “cases” if the unit of analysis shifted from being, say, the electoral system to, say, the voter. Predictably, such interventions invited retorts: Reference Lieberson Lieberson (1994) , for example, claimed that Millian methods’ inability to accommodate probabilistic causation, Footnote 3 interaction effects, and multivariate analysis would remain fatal flaws.

7.2.2 Enter Process Tracing

It is in this light that ‘process tracing’ – a term first used by Reference Hobarth Hobarth (1972) but popularized by Reference George and Lauren George (1979 ) and particularly Reference George and Bennett George and Bennett (2005) , Reference Brady and Collier Brady and Collier (2010) , Reference Beach and Pedersen Beach and Pedersen (2013) , and Reference Bennett and Checkel Bennett and Checkel (2015) – proved revolutionary for the ways in which social scientists conceive of case study research. Cases have gradually been reconceptualized not as dataset observations but as concatenations of concrete historical events that produce a specific outcome ( Reference Mahoney Goertz and Mahoney 2012 ). That is, cases are increasingly treated as social processes, where a process is defined as “a particular type of sequence in which the temporally ordered events belong to a single coherent pattern of activity” ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 214). Although there exist multiple distinct conceptions of process tracing – from Bayesian approaches ( Reference Bennett, Bennett and Checkel Bennett 2015 ) to set-theoretic approaches ( Reference Mahoney, Kimball and Koivu Mahoney et al. 2009 ) to mechanistic approaches ( Reference Beach and Pedersen Beach and Pedersen 2013 ) to sequentialist approaches ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 ) – their overall esprit is the same: reconstructing the sequence of events and interlinking causal logics that produce an outcome – isolating the ‘causes of effects’ – rather than probing a variable’s mean impact across cases via an ‘effects of causes’ approach. Footnote 4

For this intellectual shift to occur, processualist social scientists had to show how a number of assumptions underlying Millian comparative methods – as well as frequentist approaches more generally – are usually inappropriate for case study research. For example, the correlational approach endorsed by Reference Przeworski and Teune Przeworski and Teune (1970) , Reference Lijphart Lijphart (1971) , and Reference Eckstein, Greenstein and Polsby Eckstein (1975) treats observational units as homogeneous and independent ( Reference Hall, Mahoney and Rueschemeyer Hall 2003 : 382; Reference Mahoney Goertz and Mahoney 2012 ). Unit homogeneity means that “different units are presumed to be fully identical to each other in all relevant respects except for the values of the main independent variable,” such that each observation contributes equally to the confidence we have in the accuracy and magnitude of our causal estimates ( Reference Brady and Collier Brady and Collier 2010 : 41–42). Given this assumption, more observations are better – hence, Reference Lijphart Lijphart (1971) ’s dictum to “increase the number of cases” and, in its more recent variant, to “increase the number of observations” ( Reference King, Keohane and Verba King, Keohane, and Verba 1994 : 208–230). By independence, we mean that “for each observation, the value of a particular variable is not influenced by its value in other observations”; thus, each observation contributes “new information about the phenomenon in question” ( Reference Brady and Collier Brady and Collier 2010 : 43).

By contrast, practitioners of process tracing have shown that treating cases as social processes implies that case study observations are often interdependent and derived from heterogeneous units ( Reference Mahoney Goertz and Mahoney 2012 ). Unit heterogeneity means that not all historical events, and the observable evidence they generate, are created equal. Hence, some observations may better enable the reconstruction of a causal process because they are more proximate to the central events under study. Correlatively, this is why historians accord greater ‘weight’ to primary than to secondary sources, and why primary sources concerning actors central to a key event are more important than those for peripheral figures ( Reference Trachtenberg Trachtenberg 2009 ; Reference Tansey Tansey 2007 ). In short, while process tracing may yield a bounty of observable evidence, we seek not to necessarily increase the number, but rather the quality, of observations. Finally, by interdependence we mean that because time is “fateful” ( Reference Sewell Sewell 2005 : 6), antecedent events in a sequence may influence subsequent events. This “fatefulness” has multiple sources. For instance, historical institutionalists have shown how social processes can exhibit path dependencies where the outcome of interest becomes a central driver of its own reproduction ( Reference Pierson Pierson 1996 ; Reference Pierson Pierson 2000 ; Reference Mahoney Mahoney 2000 ; Reference Hall, Mahoney and Rueschemeyer Hall 2003 ; Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 ). At the individual level, processual sociologists have noted that causation in the social world is rarely a matter of one billiard ball hitting another, as in Reference Hume Hume’s (1738 [2003]) frequentist concept of “constant conjunction.” Rather, it hinges upon actors endowed with memory, such that the micro-foundations of social causation rest on individuals aware of their own historicality ( Reference Sewell Sewell 2005 ; Reference Abbott Abbott 2001 ; Reference Abbott 2016 ).

At its core, eschewing the independence and unit homogeneity assumptions simply means situating case study evidence within its spatiotemporal context ( Reference Hall, Mahoney and Rueschemeyer Hall 2003 ; Reference Falleti and Lynch Falleti and Lynch 2009 ). This commitment is showcased by the language which process-sensitive case study researchers use when making causal inferences. First, rather than relating ‘independent variables’ to ‘dependent variables’, they often privilege the contextualizing language of relating ‘events’ to ‘outcomes’ ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 ). Second, they prefer to speak not of ‘dataset observations’ evocative of cross-sectional analysis, but of ‘causal process observations’ evocative of sequential analysis ( Reference Brady and Collier Brady and Collier 2010 ; Reference Mahoney Goertz and Mahoney 2012 ). Third, they may substitute the language of ‘causal inference via concatenation’ – a terminology implying that unobservable causal mechanisms are embedded within a sequence of observable events – for that of ‘causal inference via correlation’, evocative of the frequentist billiard-ball analogy ( Reference Waldner and Kincaid Waldner 2012 : 68). The result is that case study research is increasingly hailed as a “distinctive approach that offers a much richer set of observations, especially about causal processes, than statistical analyses normally allow” ( Reference Hall, Mahoney and Rueschemeyer Hall 2003 : 397).

7.3 Threats to Processual Inference and the Role of Theory

While scholars have shown how process-tracing methods have reconceived the utility of case studies for causal inference, there remains some ambiguity about the implications for case selection, particularly using Millian methods. While several works have touched upon this theme (e.g., Reference Hall, Mahoney and Rueschemeyer Hall 2003 ; Reference George and Bennett George and Bennett 2005 ; Reference Levy Levy 2008 ; Reference Tarrow Tarrow 2010 ), the contribution that most explicitly wrestles with this topic is Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney (2015) , who acknowledge that “the application of Millian methods for sequential arguments has not been systematically explored, although we believe it is commonly used in practice” ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 226). Falleti and Mahoney argue that process tracing can remedy the weaknesses of Millian approaches: “When used in isolation, the methods of agreement and difference are weak instruments for small-N causal inference … small-N researchers thus normally must combine Millian methods with process tracing or other within-case methods to make a positive case for causality” ( Reference Falleti, Mahoney, Mahoney and Thelen 2015 : 225–226). Their optimism about the synergy between Millian methods and process tracing leads them to conclude that “by fusing these two elements, the comparative sequential method merits the distinction of being the principal overarching methodology for [comparative-historical analysis] in general” ( Reference Falleti, Mahoney, Mahoney and Thelen 2015 : 236).

Falleti and Mahoney’s contribution is the definitive statement of how comparative case study research has long abandoned its Lijphartian origins and fully embraced treating cases as social processes. It is certainly true that process-tracing advocates have shown that some past critiques of Millian methods may not have been as damning as they first appeared. For example, Reference Lieberson Lieberson’s (1994) critique that Millian case selection requires a deterministic understanding of causation has been countered by set-theoretic process tracers who note that causal processes can indeed be conceptualized as concatenations of necessary and sufficient conditions ( Reference Mahoney Goertz and Mahoney 2012 ; Reference Mahoney and Vanderpoel Mahoney and Vanderpoel 2015 ). After all, “at the individual case level, the ex post (objective) probability of a specific outcome occurring is either 1 or 0” ( Reference Mahoney Mahoney 2008 : 415). Even for those who do not explicitly embrace set-theoretic approaches and prefer to perform a series of “process tracing tests” (such as straw-in-the-wind, hoop, smoking gun, and doubly-decisive tests), the objective remains to evaluate the deterministic causal relevance of a historical event on the next linkage in a sequence ( Reference Collier Collier 2011 ; Reference Mahoney Mahoney 2012 ). In this light, Millian methods appear to have been thrown a much-needed lifeline.

Yet processualist researchers have implicitly exposed new, and perhaps more damning, weaknesses in the traditional use of the comparative method. Here, Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney (2015) are less engaged in highlighting how their focus on comparing within-case sequences should push scholars to revisit strategies for case selection premised on assumptions that process-tracing advocates have undermined. In this light, I begin by outlining three hitherto underappreciated threats to inference associated with the traditional use of Millian case selection: potentially ignoring (1) ordered and (2) paced causal processes, and ignoring (3) the possibility of equifinality. I then demonstrate how risks (1) and (2) can be attenuated deductively by formulating processualist theories and tweaking Millian designs for case selection.

Risk 1: Ignoring Ordered Processes

Process-sensitive social scientists have long noted that “the temporal order of the events in a sequence [can be] causally consequential for the outcome of interest” ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 218; see also Reference Pierson Pierson 2004 : 54–78). For example, where individual acts of agency play a critical role – such as political elites’ response to a violent protest – “reordering can radically change [a] subject’s understanding of the meaning of particular events,” altering their response and the resulting outcomes ( Reference Abbott Abbott 1995 : 97).

An evocative illustration is provided by Reference Sewell Sewell’s (1996) analysis of how the storming of the Bastille in 1789 produced the modern concept of “revolution.” After overrunning the fortress, the crowd freed the few prisoners held within it; shot, stabbed, and beheaded the Bastille’s commander; and paraded his severed head through the streets of Paris ( Reference Sewell Sewell 1996 : 850). When the French National Assembly heard of the taking of the Bastille, it first interpreted the contentious event as “disastrous news” and an “excess of fury”; yet, when the king subsequently responded by retreating his troops to their provincial barracks, the Assembly recognized that the storming of the Bastille had strengthened its hand, and proceeded to reinterpret the event as a patriotic act of protest in support of political change ( Reference Sewell Sewell 1996 : 854–855). The king’s reaction to the Bastille thus bolstered the Assembly’s resolve to “invent” the modern concept of revolution as a “legitimate rising of the sovereign people that transformed the political system of a nation” ( Reference Sewell Sewell 1996 : 854–858). Proceeding counterfactually, had the ordering of events been reversed – had the king withdrawn his troops before the Bastille had been stormed – the National Assembly would have had little reason to interpret the popular uprising as a patriotic act legitimating reform rather than a violent act of barbarism.

Temporal ordering may also alter a social process’s political outcomes through macro-level mechanisms. For example, consider Reference Falleti Falleti’s (2005 , Reference Falleti 2010 ) analysis of the conditions under which state decentralization – the devolution of national powers to subnational administrative bodies – increases local political autonomy in Latin America. Through process tracing, Falleti demonstrates that when fiscal decentralization precedes electoral decentralization, local autonomy is increased, since this sequence endows local districts with the monetary resources necessary to subsequently administer an election effectively. However, when the reverse occurs, such that electoral decentralization precedes fiscal decentralization, local autonomy is compromised. For although the district is being offered the opportunity to hold local elections, it lacks the monetary resources to administer them effectively, endowing the national government with added leverage to impose conditions upon the devolution of fiscal resources.

For our purposes, what is crucial to note is not simply that temporal ordering matters, but that in ordered processes it is not the presence or absence of events that is most consequential for the outcome of interest. For instance, in Falleti’s analysis both fiscal and electoral decentralization occur. This means that a traditional Millian framework risks dismissing some explanatory events as causally irrelevant on the grounds that their presence is insufficient for explicating the outcome of interest (see Figure 7.2 ).

Figure 7.2 How ordered processes risk being ignored by a Millian setup

The way to deductively attenuate the foregoing risk is to develop an ordered theory and then modify the traditional Millian setup to assess the effect of ordering on an outcome of interest. That is, deductive theorizing aimed at probing the causal effect of ordering can guide us in constructing an appropriate Millan case selection design, such as that in Figure 7.3 . In this example, we redefine the fourth independent variable to measure not the presence or absence of a fourth event, but rather to measure the ordering of two previously defined events (in this case, events 1 and 2). This case selection setup would be appropriate if deductive theorizing predicts that the outcome of interest is produced when event 1 is followed by event 2 (such that, unless this specific ordering occurs, the presence of events 1 and 2 is insufficient to generate the outcome). In other words, if Millian methods are to be deductively used to select cases for comparison, the way to guard against prematurely dismissing the causal role of temporal ordering is to explicitly theorize said ordering a priori . If this proves difficult, or if the researcher lacks sufficient knowledge to develop such a theory, it is advisable to switch to the more inductive method for case selection outlined in the next section .

Figure 7.3 Deductively incorporating ordered processes within a Millian setup

Risk 2: Ignoring Paced Processes

Processualist researchers have also emphasized that, beyond temporal order, “the speed or duration of events … is causally consequential” ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 219). For example, social scientists have long distinguished an “eventful temporality” ( Reference Sewell Sewell 1996 ) from those “big, slow moving” incremental sequences devoid of rapid social change ( Reference Pierson, Mahoney and Rueschemeyer Pierson 2003 ). For historical institutionalists, this distinction is illustrated by “critical junctures” – defined as “relatively short periods of time during which there is a substantially heightened probability that agents’ choices will affect the outcome of interest” ( Reference Capoccia and Kelemen Capoccia and Kelemen 2007 : 348; Reference Capoccia, Mahoney and Thelen Capoccia 2015 : 150–151) – on the one hand, and those “causal forces that develop over an extended period of time,” such as “cumulative” social processes, sequences involving “threshold effects,” and “extended causal chains” on the other hand ( Reference Pierson Pierson 2004 : 82–90; Reference Mahoney, Thelen, Mahoney and Thelen Mahoney and Thelen 2010 ).

An excellent illustration is provided by Reference Beissinger Beissinger (2002) ’s analysis of the contentious events that led to the collapse of the Soviet State. Descriptively, the sequence of events has its origins in the increasing transparency of Soviet institutions and freedom of expression accompanying Gorbachev’s Glasnost ( Reference Beissinger Beissinger 2002 : 47). As internal fissures within the Politburo began to emerge in 1987, Glasnost facilitated media coverage of the split within the Soviet leadership ( Reference Beissinger 2002 : 64). In response, “interactive attempts to contest the state grew regularized and began to influence one another” ( Reference Beissinger 2002 : 74). These challenging acts mobilized around previously dormant national identities, and for the first time – often out of state incompetence – these early protests were not shut down ( Reference Beissinger 2002 : 67). Protests reached a boiling point in early 1989 as the first semicompetitive electoral campaign spurred challengers to mobilize the electorate and cultivate grievances in response to regime efforts to “control nominations and electoral outcomes” ( Reference Beissinger 2002 : 86). By 1990 the Soviet State was crumbling, and “in many parts of the USSR demonstration activity … had become a normal means for dealing with political conflict” ( Reference Beissinger 2002 : 90).

Crucially, Beissinger stresses that to understand the causal dynamics of the Soviet State’s collapse, highlighting the chronology of events is insufficient. The 1987–1990 period comprised a moment of “thickened history” wherein “what takes place … has the potential to move history onto tracks otherwise unimaginable … all within an extremely compressed period of time” ( Reference Beissinger 2002 : 27). Information overload, the density of interaction between diverse social actors, and the diffusion of contention engendered “enormous confusion and division within Soviet institutions,” allowing the hypertrophy of challenging acts to play “an increasingly significant role in their own causal structure” ( Reference Beissinger 2002 : 97, 27). In this light, the temporal compression of a sequence of events can bolster the causal role of human agency and erode the constraints of social structure. Proceeding counterfactually, had the exact same sequence of contentious events unfolded more slowly, it is doubtful that the Soviet State would have suddenly collapsed.

Many examples of how the prolongation of a sequence of events can render them invisible, and thus produce different outcomes, could be referenced. Consider, for example, how global climate change – which is highlighted by Reference Pierson Pierson (2004 : 81) as a prototypical process with prolonged time horizons – conditions the psychological response of social actors. As a report from the American Psychological Association underscores, “climate change that is construed as rapid is more likely to be dreaded,” for “people often apply sharp discounts to costs or benefits that will occur in the future … relative to experiencing them immediately” ( Reference Swim Swim et al. 2009 : 24–25; Reference Loewenstein and Elster Loewenstein and Elster 1992 ). This logic is captured by the metaphor of the “boiling frog”: “place a frog in a pot of cool water, and gradually raise the temperature to boiling, and the frog will remain in the water until it is cooked” ( Reference Boyatzis Boyatzis 2006 : 614).

What is important to note is that, once more, paced processes are not premised on the absence or presence of their constitutive events being causally determinative; rather, they are premised on the duration of events (or their temporal separation) bearing explanatory significance. Hence the traditional approach to case selection risks neglecting the causal impact of temporal duration on the outcome of interest (see Figure 7.4 ).

Figure 7.4 Paced processes risk being ignored by a Millian setup

Here, too, the way to deductively assess the causal role of pacing on an outcome of interest is to explicitly develop a paced theory before selecting cases for empirical analysis. On the one hand, we might theorize that it is the duration of a given event that is causally consequential; on the other hand, we might theorize that it is the temporal separation of said event from other events that is significant. Figure 7.5 suggests how a researcher can assess both theories through a revised Millian design. In the first example, we define a fourth independent variable measuring not the presence of a fourth event, but rather the temporal duration of a previously defined event (in this case, event 1). This would be an appropriate case selection design to assess a theory predicting that the outcome of interest occurs when event 1 unfolds over a prolonged period of time (such that if event 1 unfolds more rapidly, its mere occurrence is insufficient for the outcome). In the second example, we define a fourth independent variable measuring the temporal separation between two previously defined events (in this case, events 1 and 2). This would be an appropriate case selection design for a theory predicting that the outcome of interest only occurs when event 1 is temporally distant to event 2 (such that events 1 and 2 are insufficient for the outcome if they are proximate). Again, if the researcher lacks a priori knowledge to theorize how a paced process may be generating the outcome, it is advisable to adopt the inductive method of case selection described in Section 7.4 .

Figure 7.5 Deductively incorporating paced processes within a Millian setup

Risk 3: Ignoring Equifinal Causal Processes

Finally, researchers have noted that causal processes may be mired by equifinality: the fact that “multiple combinations of values … produce the same outcome” ( Reference Mahoney Mahoney 2008 : 424; see also Reference George and Bennett George and Bennett 2005 ; Reference Mahoney Goertz and Mahoney 2012 ). More formally, set-theoretic process tracers account for equifinality by emphasizing that, in most circumstances, “necessary” conditions or events are actually INUS conditions – individually necessary components of an unnecessary but sufficient combination of factors ( Reference Mahoney and Vanderpoel Mahoney and Vanderpoel 2015 : 15–18).

One of the reasons why processualist social scientists increasingly take equifinality seriously is the recognition that causal mechanisms may be context-dependent. Sewell’s work stresses that “the consequences of a given act … are not intrinsic to the act but rather will depend on the nature of the social world within which it takes place” ( Reference Sewell Sewell 2005 : 9–10). Similarly, Reference Falleti and Lynch Falleti and Lynch (2009 : 2; 11) argue that “causal effects depend on the interaction of specific mechanisms with aspects of the context within which these mechanisms operate,” hence the necessity of imposing “scope conditions” on theory building. One implication is that the exact same sequence of events in two different settings may produce vastly different causal outcomes. The flip side of this conclusion is that we should not expect a given outcome to always be produced by the same sequence of events.

For example, consider Sewell’s critique of Reference Skocpol Skocpol (1979) ’s States and Social Revolutions for embracing an “experimental temporality.” Skocpol deploys Millian methods of case selection to theorize that the great social revolutions – the French, Russian, and Chinese revolutions – were caused by a conjunction of three necessary conditions: “(1) military backwardness, (2) politically powerful landlord classes, and (3) autonomous peasant communities” ( Reference Sewell Sewell 2005 : 93). Yet to permit comparison, Skocpol assumes that the outcomes of one revolution, and the processes of historical change more generally, have no effect on a subsequent revolution ( Reference Sewell Sewell 2005 : 94–95). This approach amounts to “cutting up the congealed block of historical time into artificially interchangeable units,” ignoring the fatefulness of historical sequences ( Reference Sewell Sewell 2005 ). For example, the Industrial Revolution “intervened” between the French and Russian Revolutions, and consequently one could argue that “the revolt of the Petersburg and Moscow proletariat was a necessary condition for social revolution in Russia in 1917, even if it was not a condition for the French Revolution in 1789” ( Reference Sewell Sewell 2005 : 94–95). What Sewell is emphasizing, in short, is that peasant rebellion is an INUS condition (as is a proletariat uprising), rather than a necessary condition.

Another prominent example of equifinality is outlined by Reference Collier Collier’s (1999 : 5–11) review of the diverse pathways through which democratization occurs. In the elite-driven pathway, emphasized by Reference O’Donnell and Schmitter O’Donnell and Schmitter (1986 ), an internal split amongst authoritarian incumbents emerges; this is followed by liberalizing efforts by some incumbents, which enables the resurrection of civil society and popular mobilization; finally, authoritarian incumbents negotiate a pacted transition with opposition leaders. By contrast, in the working-class-driven pathway, emphasized by Reference Rueschemeyer, Stephens and Stephens Rueschemeyer, Stephens, and Stephens (1992) , a shift in the material balance of power in favor of the democracy-demanding working class and against the democracy-resisting landed aristocracy causes the former to overpower the latter, and via a democratic revolution from below a regime transition occurs. Crucially, Reference Collier Collier (1999 : 12) emphasizes that these two pathways need not be contradictory (or exhaustive): the elite-driven pathway appears more common in the Latin American context during the second wave of democratization, whereas the working-class-driven pathway appears more common in Europe during the first wave of democratization.

What is crucial is that Millian case selection is premised on there being a single cause underlying the outcome of interest. As a result, Millian methods risk dismissing a set of events as causally irrelevant ex ante in one case simply because that same set of events fails to produce the outcome in another case (see Figure 7.6 ). Unlike ordered and paced processes, there is no clear way to leverage deductive theorizing to reconfigure Millian methods for case selection and accommodate equifinality. However, I argue that the presence of equifinal pathways can be fruitfully probed if we embrace a more inductive approach to comparative case selection, as the next section outlines.

Figure 7.6 Equifinal causal processes risk being ignored by a Millian setup

7.4 A New Approach: The Method of Inductive Case Selection

If a researcher wishes to guard against ignoring consequential temporal dynamics but lacks the a priori knowledge necessary to develop a processual theory and tailor their case selection strategy, is there an alternative path forward? Yes, indeed: I suggest that researchers could wield most-similar or most-different cases designs to (1) probe causal generalizability, (2) reveal scope conditions, and (3) explore the presence of equifinality. Footnote 5 To walk through this more inductive case selection approach, I engage some case studies from development practice to illustrate how researchers and practitioners alike could implement and benefit from the method.

7.4.1 Tempering the Deductive Use of Millian Methods

To begin, one means to ensure against a Millian case selection design overlooking an ordered, paced, or equifinal causal process (in the absence of deductive theorizing) is to be wary of leveraging the methods of agreement and difference to eliminate potential explanatory factors ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 225–226). That is, the decision to discard an explanatory variable or historical event as causally unnecessary (via the method of agreement) or insufficient (via the method of difference) may be remanded to the process-tracing stage, rather than being made ex ante at the case selection stage.

Notice how this recommendation is particularly intuitive in light of the advances in process-tracing methods. Before this burgeoning literature existed, Millian methods were called upon to accomplish two things at once: (1) provide a justification for selecting two or more cases for social inquiry, and (2) yield causal leverage via comparison and the elimination of potential explanatory factors as unnecessary or insufficient. But process-tracing methodologists have showcased how the analysis of temporal variation disciplined via counterfactual analysis, congruence testing, and process-tracing tests renders within-case causal inference possible even in the absence of an empirical comparative case ( Reference George and Bennett George and Bennett 2005 ; Reference Gerring Gerring 2007 ; Reference Collier Collier 2011 ; Reference Mahoney Mahoney 2012 ; Reference Beach and Pedersen Beach and Pedersen 2013 ; Reference Bennett and Checkel Bennett and Checkel 2015 ; Reference Levy Levy 2015 ). That is, the ability to make causal inferences need not be primarily determined at the case selection stage.

The foregoing implies that if a researcher does not take temporal dynamics into account when developing their theory, the use of Millian methods should do no more than to provisionally discount the explanatory purchase of a given explanatory factor. The researcher should then bear in mind that as the causal process is reconstructed from a given outcome, the provisionally discounted factor may nonetheless be shown to be of causal relevance – particularly if the underlying process is ordered or paced, or if equifinal pathways are possible.

Despite these limitations, Millian methods might fruitfully serve additional functions from the standpoint of case selection, particularly if researchers shift (1) when and (2) why they make use of them. First, Millian methods may be as – if not more – useful after process tracing of a particular case is completed rather than to set the stage for within-case analysis. Such a chronological reversal – process tracing followed by Millian case selection, instead of Millian case selection followed by process tracing – inherently embraces a more inductive, theory-building approach to case study research ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 229–231) which, I suspect, is far more commonly used in practice than is acknowledged. I refer to this approach as the method of inductive case selection , wherein “theory-building process tracing” ( Reference Beach and Pedersen Beach and Pedersen 2013 : 16–18) of a single case is subsequently followed by the use of a most-similar or most-different cases design.

7.4.2 Getting Started: Selecting the Initial Case

The method of inductive case selection begins by assuming that the researcher has justifiable reasons for picking a particular case for process tracing and is subsequently looking to contextualize the findings or build a theory outwards. Hence, the first step involves picking an initial case. Qualitative methodologists have already supplied a number of plausible logics for selecting a single case, and I describe three nonexhaustive possibilities here: (1) theoretical or historical importance; (2) policy relevance and salience; and (3) empirically puzzling nature.

First, an initial case may be selected due to its theoretical or historical importance. Reference Eckstein, Greenstein and Polsby Eckstein (1975) , for example, defines an idiographic case study as a case where the specific empirical events/outcome serve as a central referent for a scholarly literature. As an illustration, Reference Gerring and Cojocaru Gerring and Cojocaru (2015 : 11) point to Reference North and Weingast North and Weingast (1989) ’s influential study of how the Glorious Revolution in seventeenth-century Britain favorably shifted the constitutional balance of power for the government to make credible commitments to protecting property rights (paving the way for the financial revolution of the early eighteenth century). Given that so much of the scholarly debate amongst economic historians centers on the institutional foundations of economic growth, North and Weingast’s case study was “chosen (it would appear) because of its central importance in the [historical political economy] literature on the topic, and because it is … a prominent and much-studied case” ( Reference Gerring and Cojocaru Gerring and Cojocaru 2015 : 11). In other words, Reference North and Weingast North and Weingast (1989) ’s study is idiographic in that it “aim[s] to explain and/or interpret a single historical episode,” but it remains “theory-guided” in that it “focuses attention on some theoretically specified aspects of reality and neglects others” ( Reference Levy Levy 2008 : 4).

While the causes of the Glorious Revolution are a much-debated topic amongst economic historians, they have less relevance to researchers and practitioners focused on assessing the effects of contemporary public policy interventions. Hence, a second logic for picking a first case for process tracing is its policy relevance and salience. Reference George and Bennett George and Bennett (2005 : 263–286) define a policy-relevant case study as one where the outcome is of interest to policy-makers and its causes are at least partially amenable to policy manipulation. For example, one recent World Bank case study ( Reference El-Saharty and Nagaraj El-Saharty and Nagaraj 2015 ) analyzes how HIV/AIDS prevalence amongst vulnerable subpopulations – particularly female sex workers – can be reduced via targeted service delivery. To study this outcome, two states in India – Andhra Pradesh and Karnataka – were selected for process tracing. There are three reasons why this constitutes an appropriate policy-relevant case selection choice. First, the outcome of interest – a decline in HIV/AIDS prevalence amongst female sex workers – was present in both Indian states. Second, because India accounts for almost 17.5 percent of the world population and has a large population of female sex workers, this outcome was salient to the government ( Reference El-Saharty and Nagaraj El-Saharty and Nagaraj 2015 : 3). Third, the Indian government had created a four-phase National AIDS Control Program (NACP) spanning from 1986 through 2017, meaning that at least one set of possible explanatory factors for the decline in HIV/AIDS prevalence comprised policy interventions that could be manipulated. Footnote 6

A third logic for picking an initial case for process tracing is its puzzling empirical nature. One obvious instantiation is when an exogenous shock or otherwise significant event/policy intervention yields a different outcome from the one scholars and practitioners expected. Footnote 7 For example, in 2004 the federal government of Nigeria partnered with the World Bank to improve the share of Nigeria’s urban population with access to piped drinking water. This partnership – the National Urban Water Sector Reform Project (NUWSRP1) – aimed to “increase access to piped water supply in selected urban areas by improving the reliability and financial viability of selected urban water utilities” and by shifting resources away from “infrastructure rehabilitation” that had failed in the past ( Reference Hima and Santibanez Hima and Santibanez 2015 : 2). Despite $200 million worth of investments, ultimately the NUWSRP1 “did not perform as strongly on the institutional reforms needed to ensure sustainability” ( Reference Hima and Santibanez Hima and Santibanez 2015 ). Given this puzzling outcome, the World Bank conducted an intensive case study to ask why the program did “not fully meet its essential objective of achieving a sustainable water delivery service” ( Reference Hima and Santibanez Hima and Santibanez 2015 ). Footnote 8

The common thread of these three logics for selecting an initial case is that the case itself is theoretically or substantively important and that its empirical dynamics – underlying either the outcome itself or its relationship to some explanatory events – are not well understood. That being said, the method of inductive case selection merely presumes that there is some theoretical, policy-related, empirical, or normative justification to pick the initial case.

7.4.3 Probing Generalizability Via a Most-Similar Cases Design

It is after picking an initial case that the method of inductive case selection contributes novel guidelines for case study researchers by reconfiguring how Millian methods are used. Namely, how should one (or more) additional cases be selected for comparison, and why? This question presumes that the researcher wishes to move beyond an idiographic, single-case study for the purposes of generating inferences that can travel. Yet in this effort, we should take seriously process-tracing scholars’ argument that causal mechanisms are often context-dependent. As a result, the selection of one or more comparative cases is not meant to uncover universally generalizable abstractions; rather, it is meant to contextualize the initial case within a set or family of cases that are spatiotemporally bounded.

That being said, the first logical step is to understand whether the causal inferences yielded by the process-traced case can indeed travel to other contexts ( Reference Goertz Goertz 2017 : 239). This constitutes the first reconfiguration of Millian methods: the use of comparative case studies to assess generalizability. Specifically, after within-case process tracing reveals a factor or sequence of factors as causally important to an outcome of interest, the logic is to select a case that is as contextually analogous as possible such that there is a higher probability that the causal process will operate similarly in the second case. This approach exploits the context-dependence of causal mechanisms to the researcher’s advantage: Similarity of context increases the probability that a causal mechanism will operate similarly across both cases. By “context,” it is useful to follow Reference Falleti and Lynch Falleti and Lynch (2009 : 14) and to be

concerned with a variety of contextual layers: those that are quite proximate to the input (e.g., in a study of the emergence of radical right-wing parties, one such layer might be the electoral system); exogenous shocks quite distant from the input that might nevertheless effect the functioning of the mechanism and, hence, the outcome (e.g., a rise in the price of oil that slows the economy and makes voters more sensitive to higher taxes); and the middle-range context that is neither completely exogenous nor tightly coupled to the input and so may include other relevant institutions and structures (the tax system, social solidarity) as well as more atmospheric conditions, such as rates of economic growth, flows of immigrants, trends in partisan identification, and the like.

For this approach to yield valuable insights, the researcher focuses on ‘controlling’ for as many of these contextual explanatory factors (crudely put, for as many independent variables) as possible. In other words, the researcher selects a most-similar case: if the causal chain similarly operates in the second case, this would support the conclusion that the causal process is likely at work across the constellation of cases bearing ‘family resemblances’ to the process-traced case ( Reference Soifer Soifer 2020 ). Figure 7.7 displays the logic of this design:

Figure 7.7 Probing generalizability by selecting a most-similar case

As in Figure 7.7 , suppose that process tracing of Case 1 reveals that some sequence of events (in this example, event 4 followed by event 5) caused the outcome of interest. The researcher would then select a most-similar case (a case with similar values/occurrences of other independent variables/events (here, IV1–IV3) that might also influence the outcome). The researcher would then scout whether the sequence in Case 1 (event 4 followed by event 5) also occurs in the comparative case. If it does, the expectation for a minimally generalizable theory is that it would produce a similar outcome in Case 2 as in Case 1. Correlatively, if the sequence does not occur in Case 2, the expectation is that it would not experience the same outcome as Case 1. These findings would provide evidence that the explanatory sequence (event 4 followed by event 5) has causal power that is generalizable across a set of cases bearing family resemblances.

For example, suppose a researcher studying democratization in Country A finds evidence congruent with the elite-centric theory of democratization of Reference O’Donnell and Schmitter O’Donnell and Schmitter (1986 ) described previously. To assess causal generalizability, the researcher would subsequently select a case – Country B – that is similar in the background conditions that the literature has shown to be conducive to democratization, such as level of GDP per capita ( Reference Przeworski and Limongi Przeworski and Limongi 1997 ; Reference Boix and Stokes Boix and Stokes 2003 ) or belonging to the same “wave” of democratization via spatial and temporal proximity ( Reference Collier, Rustow and Erickson Collier 1991 ; Reference Huntington Huntington 1993 ). Notice that these background conditions in Case B have to be at least partially exogenous to the causal process whose generalizability is being probed – that is, they cannot constitute the events that directly comprise the causal chain revealed in Case A. One way to think about them is as factors that in Case A appear to have been necessary, but less proximate and important, conditions for the outcome. Here, importance is determined by the “extent that they are [logically/counterfactually] present only when the outcome is present” ( Reference Mahoney, Kimball and Koivu Mahoney et al. 2009 : 119), whereas proximity is determined by the degree to which the condition is “tightly coupled” with the chain of events directly producing the outcome ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 233).

An example related to the impact of service delivery in developmental contexts can be drawn from the World Bank’s case study of HIV/AIDS interventions in India. Recall that this case study actually spans across two states: Andhra Pradesh and Karnataka. In a traditional comparative case study setup, the selection of both cases would seem to yield limited insights. After all, they are contextually similar: “Andhra Pradesh and Karnataka … represent the epicenter of the HIV/AIDS epidemic in India. In addition, they were early adopters of the targeted interventions”; and they also experience a similar outcome: “HIV/AIDS prevalence among female sex workers declined from 20 percent to 7 percent in Andhra Pradesh and from 15 percent to 5 percent in Karnataka between 2003 and 2011” ( Reference El-Saharty and Nagaraj El-Saharty and Nagaraj 2015 : 7; 3). In truth, this comparative case study design makes substantial sense: had the researchers focused on the impact of the Indian government’s NACP program only in Andhra Pradesh or only in Karnataka, one might have argued that there was something unique about either state that rendered it impossible to generalize the causal inferences. By instead demonstrating that favorable public health outcomes can be traced to the NACP program in both states, the researchers can support the argument that the intervention would likely prove successful in other contexts to the extent that they are similar to Andhra Pradesh and Karnataka.

One risk of the foregoing approach is highlighted by Reference Sewell Sewell (2005 : 95–96): contextual similarity may suggest cross-case interactions that hamper the ability to treat the second, most-similar case as if it were independent of the process-traced case. For example, an extensive body of research has underscored how protests often diffuse across proximate spatiotemporal contexts through mimicry and the modularity of repertoires of contention ( Reference Tilly Tilly 1995 ; Reference Tarrow Tarrow 1998 ). And, returning to the World Bank case study of HIV/AIDS interventions in Andhra Pradesh and Karnataka, one concern is that because these states share a common border, cross-state learning or other interactions might limit the value-added of a comparative design over a single case study, since the second case may not constitute truly new data. The researcher should be highly sensitive to this possibility when selecting and subsequently process tracing the most-similar case: the greater the likelihood of cross-case interactions, the lesser the likelihood that it is a case-specific causal process – as opposed to cross-case diffusion mechanism – that is doing most of the explanatory work.

Conversely, if the causal chain is found to operate differently in the second, most-similar case, then the researcher can make an argument for rejecting the generalizability of the causal explanation with some confidence. The conclusion would be that the causal process is sui generis and requires the “localization” of the theoretical explanation for the outcome of interest ( Reference Tarrow Tarrow 2010 : 251–252). In short, this would suggest that the process-traced case is an exceptional or deviant case, given a lack of causal generalizability even to cases bearing strong family resemblances. Here, we are using the ‘strong’ notion of ‘deviant’: the inability of a causal process to generalize to similar contexts substantially decreases the likelihood that “other cases” could be explained with reference to (or even in opposition to) the process-traced case.

There is, of course, the risk that by getting mired in the weeds of the first case, the researcher is unable to recognize how the overall chronology of events and causal logics in the most-similar case strongly resembles the process-traced case. That is, a null finding of generalizability in a most-similar context calls on the researcher to probe whether they have descended too far down the “ladder of generality,” requiring more abstract conceptual categories to compare effectively ( Reference Sartori Sartori 1970 ; Reference Collier and Levitsky Collier and Levitsky 1997 ).

7.4.4 Probing Scope Conditions and Equifinality Via a Most-Different Cases Design

A researcher that has process-traced a given case and revealed a factor or sequence of factors as causally relevant may also benefit from leveraging a most-different cases approach. This case selection technique yields complementary insights to the most-similar cases design described in the previous section , but its focus is altogether different: instead of uncovering the degree to which an identified causal process travels, the objective is to try to understand where and why it fails to travel and whether alternative pathways to the same outcome may be possible.

More precisely, by selecting a case that differs substantially from the process-traced case in background characteristics, the researcher maximizes contextual heterogeneity and the likelihood that the causal process will not generalize to the second case ( Reference Soifer Soifer 2020 ). Put differently, the scholar would be selecting a least-likely case for generalizability, because the context-dependence of causal mechanisms renders it unlikely that the same sequence of events will generate the same outcome in the second case. This would offer a first cut at establishing “scope conditions” upon the generalizability of the theory ( Reference Tarrow Tarrow 2010 : 251) by isolating which contextual factors prevented the process from producing the outcome in the most-different case.

Figure 7.8 provides a visual illustration of what this design could look like. Suppose, once more, that process tracing in Case 1 has revealed that some event 4 followed by event 5 generated the outcome of interest. To maximize the probability that we will be able to place scope conditions on this finding, we would select a comparative case that is most different to the process-traced case (a case with different values/occurrences of other independent variables/events [denoted as IV1–IV3 in Figure 7.8 ] that might also influence the outcome) but which also experienced the sequence of event 4 followed by event 5. Given the contextual differences between these two cases, the likelihood that the same sequence will produce the same outcome in both is low, which then opens up opportunities for the researcher to probe the logic of scope conditions. In this endeavor, temporality can serve as a useful guide: a means for restricting the set of potential contextual factors that prevented the causal process from reproducing the outcome in Case 2 is to identify at what chronological point the linkages between events 4 and 5 on the one hand and the outcome of interest on the other hand branched off from the way they unfolded in Case 1. The researcher can then scout which contextual factors exuded the greatest influence at that temporal location and identify them as central to the scope conditions to be placed upon the findings.

Figure 7.8 Probing scope conditions by selecting a most-different case

To provide an example for how this logic of inquiry can work, consider a recent case study focused on understanding the effectiveness of Mexico’s conditional cash transfer program – Opportunitades , the first program of its kind – in providing monetary support to the female heads of Indigenous households ( Reference Alva Estrabridis and Ortega Nieto Alva Estrabridis and Ortega Nieto 2015 ). The program suffered from the fact that Indigenous beneficiaries dropped out at higher rates than their non-Indigenous counterparts. In 2009 the World Bank spearheaded an Indigenous Peoples Plan (IPP) to bolster service delivery of cash transfers to Indigenous populations, which crucially included “catering to indigenous peoples in their native languages and disseminating information in their languages” ( Reference Alva Estrabridis and Ortega Nieto Alva Estrabridis and Ortega Nieto 2015 : 2). A subsequent impact evaluation found that “[w]hen program messages were offered in beneficiaries’ mother tongues, they were more convincing, and beneficiaries tended to participate and express themselves more actively” ( Reference Alva Estrabridis and Ortega Nieto Alva Estrabridis and Ortega Nieto 2015 ; Reference Mir, Gámez, Loyola, Martí and Veraza Mir et al. 2011 ).

Researchers might well be interested in the portability of the foregoing finding, in which case the previously described most-similar cases design is appropriate – for example, a comparison with the Familias en Accion program in Colombia may be undertaken ( Reference Attanasio, Battistin, Fitzsimons, Mesnard and Vera-Hernandez. Attanasio et al. 2005 ). But they might also be interested in the limits of the policy intervention – in understanding where and why it is unlikely to yield similar outcomes. To assess the scope conditions upon the “bilingualism” effect of cash transfer programs, a most-different cases design is appropriate. Thankfully, conditional cash transfer programs are increasingly common even in historical, cultural, and linguistic contexts markedly different from Mexico, most prominently in sub-Saharan Africa ( Reference Lagarde, Haines and Palmer Lagarde et al. 2007 ; Reference Garcia and Moore Garcia and Moore 2012 ). Selecting a comparative case from sub-Saharan Africa should prove effective for probing scope conditions: the more divergent the contextual factors, the less likely it is that the policy intervention will produce the same outcome in both contexts.

On the flip side, in the unlikely event that part or all of the causal process is nonetheless reproduced in the most-different case, the researcher would obtain a strong signal that they have identified one of those rare causal explanations of general scope. In coming to this conclusion, however, the researcher should be wary of “conceptual stretching” ( Reference Sartori Sartori 1970 : 1034), such that there is confidence that the similarity in the causal chain across the most-different cases lies at the empirical level and is not an artificial by-product of imprecise conceptual categories ( Reference Bennett and Checkel Bennett and Checkel 2015 : 10–11). Here process tracing, by pushing researchers to not only specify a sequence of “tightly-coupled” events ( Reference Falleti, Mahoney, Mahoney and Thelen Falleti and Mahoney 2015 : 233), but also to collect observable implications about the causal mechanisms concatenating these events, can guard against conceptual stretching. By opening the “black box” of causation through detailed within-case analysis, process tracing limits the researcher’s ability to posit “pseudo-equivalences” across contexts ( Reference Sartori Sartori 1970 : 1035).

Selecting a most-different case vis-à-vis the process-traced case is also an excellent strategy for probing equifinality – for maximizing the likelihood that the scholar will be able to probe multiple causal pathways to the same outcome. To do so, it is not sufficient to merely ensure divergence in background conditions; it is equally necessary to follow Mill’s method of agreement by ensuring that the outcome in the process-traced case is also present in the second, most-different case. By ensuring minimal variation in outcome, the scholar guarantees that process tracing the second case will lead to the desired destination; by ensuring maximal variation in background conditions, the scholar substantially increases the likelihood that process tracing will reveal a slightly or significantly different causal pathway to said destination. Should an alternative route to the outcome be found, then its generalizability could be assessed using the most-similar cases approach described previously.

Figure 7.9 visualizes what this case selection design might look like. Here, as in previous examples, suppose process tracing in Case 1 provides evidence that event 4 followed by event 5 produced the outcome of interest. The researcher then selects a case with the same outcome, but with different values/occurrences of some independent variables/events (in this case, IV1–IV3) that may influence the outcome. Working backwards from the outcome to reconstruct the causal chain that produced it, the researcher then probes whether (i) the sequence (event 4 followed by event 5) also occurred in Case 2, and (ii) whether the outcome of interest can be retraced to said sequence. Given the contextual dissimilarities between these most-different cases, such a finding is rather unlikely, which would subsequently enable to the researcher to probe whether some other factor (perhaps IV2/event 2 in the example of Figure 7.9 ) produced the outcome in the comparative case instead, which would comprise clear evidence of equifinality.

Figure 7.9 Probing equifinality by selecting a most-different case with the same outcome

To return to the concrete example of Mexico’s conditional cash transfer program’s successful outreach to marginalized populations via bilingual service provision, an alternative route to the same outcome might be unearthed if a cash transfer program without bilingual outreach implemented in a country characterized by different linguistic, gender, and financial decision-making norms proves similarly successful in targeting marginalized populations. Several factors – including recruitment procedures, the size of the cash transfers, the requirements for participation, and the supply of other benefits ( Reference Lagarde, Haines and Palmer Lagarde et al. 2007 : 1902) – could interact with the different setting to produce similar intervention outcomes, regardless of whether multilingual services are provided. Such a finding would suggest that these policy interventions can be designed in multiple ways and still prove effective.

To conclude, the method of inductive case selection complements within-case analysis by supplying a coherent logic for probing generalizability, scope conditions, and equifinality. To summarize, Figure 7.10 provides a roadmap of this approach to comparative case selection.

Figure 7.10 Case selection roadmap to assess generalizability, scope conditions, equifinality

In short, if the researcher has the requisite time and resources, a multistage use of Millian methods to conduct four comparative case studies could prove very fertile. The researcher would begin by selecting a second, most-similar case to assess causal generalizability to a family of cases similar to the process-traced case; subsequently, a third, most-different case would be selected to surface possible scope conditions blocking the portability of the theory to divergent contexts; and a fourth, most-different case experiencing the same outcome would be picked to probe equifinal pathways. This sequential, four-case comparison would substantially improve the researcher’s ability to map the portability and contours of both their empirical analysis and their theoretical claims. Footnote 9

7.5 Conclusion

The method of inductive case selection converts process tracing meant to simply “craft a minimally sufficient explanation of a particular outcome” into a methodology used to build and refine a causal theory – a form of “theory-building process-tracing” ( Reference Beach and Pedersen Beach and Pedersen 2013 : 16–18). Millian methods are called upon to probe the portability of a particular causal process or causal mechanism and to specify the logics of its relative contextual-dependence. In so doing, they enable theory-building without presuming that the case study researcher holds the a priori knowledge necessary to account for complex temporal dynamics at the deductive theorizing stage. Both of these approaches – deductive, processualist theorizing on the one hand, and the method of inductive case selection on the other hand – provide some insurance against Millian methods leading the researcher into ignoring the ordered, paced, or equifinal structure that may underlie the pathway(s) to the outcome of interest. But, I would argue, the more inductive approach is uniquely suited for research that is not only process-sensitive, but also open to novel insights supplied by the empirical world that may not be captured by existing theories.

Furthermore, case study research often does (and should!) proceed with the scholar outlining why an outcome is of interest, and then seeking ways to not only make inferences about what produced said outcome (via process tracing) but situating it within a broader empirical and theoretical landscape (via the method of inductive case selection). This approach pushes scholars to answer that pesky yet fundamental question – why should we care or be interested in this case/outcome? – before disciplining their drive for generalizable causal inferences. After all, the deductive use of Millian methods tells us nothing about why we should care about the cases selected, yet arguably this is an essential component of any case selection justification. By deploying a most-similar or most-different cases design after an initial case has been justifiably selected due to its theoretical or historical importance, policy relevance, or puzzling empirical nature, the researcher is nudged toward undertaking case study research yielding causal theories that are not only comparatively engaged, but also substantively interesting.

The method of inductive case selection is most useful when the foregoing approach constitutes the esprit of the case study researcher. Undoubtedly, deductively oriented case study research (see Reference Lieberman Lieberman 2005 ; Reference Lieberman, Mahoney and Thelen 2015 ) and traditional uses of Millian methods will continue to contribute to social scientific understanding. Nevertheless, the perils of ignoring important sequential causal dynamics – particularly in the absence of good, processualist theories – should caution researchers to proceed with the greatest of care. In particular, researchers should be willing to revise both theory building and research design to its more inductive variant should process tracing reveal temporal sequences that eschew the analytic possibilities of the traditional comparative method.

I would like to thank Jennifer Widner and Michael Woolcock for the invitation to write this chapter, and Daniel Ortega Nieto for pointing me to case studies conducted by the World Bank’s Global Delivery Initiative that I use as illustrative examples, as well as Jack Levy, Hillel Soifer, Andrew Moravcsik, Cassandra Emmons, Rory Truex, Dan Tavana, Manuel Vogt, and Killian Clarke for constructive feedback.

1 See, for example, Reference Przeworski and Teune Przeworski and Teune (1970) , Reference Lijphart Lijphart (1971) , Reference Eckstein, Greenstein and Polsby Eckstein (1975) , Reference Yin Yin (1984) , Reference Geddes Geddes (1990) , Reference Collier and Finifter Collier (1993) , Reference Faure Faure (1994) , Reference George and Bennett George and Bennett (2005) , Reference Flyvbjerg Flyvbjerg (2006) , Reference Levy Levy (2008) , Reference Seawright and Gerring Seawright and Gerring (2008) , Reference Gerring Gerring (2007) , Reference Brady and Collier Brady and Collier (2010) , and Reference Tarrow Tarrow (2010) .

2 Some scholars, such as Reference Faure Faure (1994) , distinguish Mill’s dependent-variable driven methods of agreement and difference from the independent-variable driven most-similar and most-different systems designs, suggesting they are distinct. But because, as Figure 7.1 shows, Mill’s dependent-variable driven methods also impose requirements on the array of independent variables to permit causal inference via exclusion, this distinction is not particularly fertile.

3 In Mill’s method of difference, factors present in both cases are eliminated for being insufficient for the outcome (in the method of agreement, factors that vary across the cases are eliminated for being unnecessary).

4 Note that Mill himself distinguished between deductively assessing the average “effect of causes” and inductively retracing the “causes of effects” using the methods of agreement and disagreement ( Reference Mill Mill 1843 [1974] , pp. 449, 764).

5 The proposed approach bears several similarities to Reference Soifer Soifer’s (2020) fertile analysis of how “shadow cases” in comparative research can contribute to theory-building and empirical analysis.

6 This study found that the expansion of clinical services into government facilities embedded in the public health system, the introduction of peer educators, and the harmonization of large quantities of public health data underlay the timing and breadth of the decline in HIV/AIDS amongst female sex workers.

7 What Reference Levy Levy (2008 :13) calls a “deviant” case – which “focus[es] on observed empirical anomalies in existing theoretical propositions” – would also fit within the category of a puzzling case.

8 Process tracing revealed that a conjunction of factors – management turnover and a lackluster culture of staff performance at the state level, inadequate coordination at the federal level, premature disbursement of funds, and citizen aversion to the commercialization of the public water supply – underlay the initially perplexing underperformance of the urban water delivery project.

9 Many thanks to Rory Truex for highlighting this implication of the roadmap in Figure 7.5 .

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle .

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service .

• Selecting Cases for Comparative Sequential Analysis
• By Tommaso Pavone
• Edited by Jennifer Widner , Princeton University, New Jersey , Michael Woolcock , Daniel Ortega Nieto
• Book: The Case for Case Studies
• Online publication: 05 May 2022
• Chapter DOI: https://doi.org/10.1017/9781108688253.008

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox .

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive .

Breadcrumbs Section. Click here to navigate to respective pages.

Comparative case study analysis

DOI link for Comparative case study analysis

Click here to navigate to parent product.

Chapter 20 deals with the set of methods used in comparative case study analysis, which focuses on comparing a small or medium number of cases and qualitative data. Structured case study comparisons are a way to leverage theoretical lessons from particular cases and elicit general insights from a population of phenomena that share certain characteristics. The chapter discusses variable-oriented analysis (guided by frameworks), formal concept analysis and qualitative comparative analysis. It goes on to discuss the types of social-ecological systems (SES) problems and research questions commonly addressed by this set of methods, as well as their limitations, resource implications and new emerging research directions. The chapter also includes an in-depth case study showcasing the application of comparative case study analyses, and suggested further readings on these methods.

• Privacy Policy
• Terms & Conditions
• Cookie Policy
• Taylor & Francis Online
• Taylor & Francis Group
• Students/Researchers
• Librarians/Institutions

Connect with us

Registered in England & Wales No. 3099067 5 Howick Place | London | SW1P 1WG © 2024 Informa UK Limited

This page deals with the set of methods used in comparative case study analysis, which focuses on comparing a small or medium number of cases and qualitative data. Structured case study comparisons are a way to leverage theoretical lessons from particular cases and elicit general insights from a population of phenomena that share certain characteristics. The content on this page discusses variable-oriented analysis (guided by frameworks), formal concept analysis and qualitative comparative analysis.

The Chapter summary video gives a brief introduction and summary of this group of methods, what SES problems/questions they are useful for, and key resources needed to conduct the methods. The methods video/s introduce specific methods, including their origin and broad purpose, what SES problems/questions the specific method is useful for, examples of the method in use and key resources needed. The Case Studies demonstrate the method in action in more detail, including an introduction to the context and issue, how the method was used, the outcomes of the process and the challenges of implementing the method. The labs/activities give an example of a teaching activity relating to this group of methods, including the objectives of the activity, resources needed, steps to follow and outcomes/evaluation options.

More details can be found in Chapter 20 of the Routledge Handbook of Research Methods for Social-Ecological Systems.

Chapter summary:

Method Summaries

Case studies, comparative case study analysis: comparison of 6 fishing producer organizations.

Dudouet, B. (2023)

Lab teaching/ activity

Tips and tricks.

• Basurto, X., S. Gelcich, and E. Ostrom. 2013. ‘The Social-Ecological System Framework as a Knowledge Classificatory System for Benthic Small-Scale Fisheries.’ Global Environmental Change 23(6):  1366–1380.
• Binder, C., J. Hinkel, P.W.G. Bots, and C. Pahl-Wostl. 2013. ‘Comparison of Frameworks for Analyzing Social-Ecological Systems.’ Ecology and Society 18(4): 26. 
• Ragin, C. 2000. Fuzzy-Set Social Science . Chicago: University of Chicago Press.
• Schneider C.Q., and C. Wagemann. 2012. Set-theoretic Methods for the Social Sciences. A Guide to Qualitative Comparative Analysis . Cambridge: Cambridge University Press.
• Villamayor-Tomas, S., C. Oberlack, G. Epstein, S. Partelow, M. Roggero, E. Kellner, M. Tschopp, and M.  Cox. 2020. ‘Using Case Study Data to Understand SES Interactions: A Model-centered Meta-analysis of SES Framework Applications.’ Current Opinion in Environmental Sustainability .

Sign up for our newsletter

• Evidence & Evaluation
• Evaluation guidance
• Impact evaluation with small cohorts (evaluation guidance)
• Get started with small n evaluation

Comparative Case Study

Share content.

A comparative case study (CCS) is defined as ‘the systematic comparison of two or more data points (“cases”) obtained through use of the case study method’ (Kaarbo and Beasley 1999, p. 372). A case may be a participant, an intervention site, a programme or a policy. Case studies have a long history in the social sciences, yet for a long time, they were treated with scepticism (Harrison et al. 2017). The advent of grounded theory in the 1960s led to a revival in the use of case-based approaches. From the early 1980s, the increase in case study research in the field of political sciences led to the integration of formal, statistical and narrative methods, as well as the use of empirical case selection and causal inference (George and Bennett 2005), which contributed to its methodological advancement. Now, as Harrison and colleagues (2017) note, CCS:

“Has grown in sophistication and is viewed as a valid form of inquiry to explore a broad scope of complex issues, particularly when human behavior and social interactions are central to understanding topics of interest.”

It is claimed that CCS can be applied to detect causal attribution and contribution when the use of a comparison or control group is not feasible (or not preferred). Comparing cases enables evaluators to tackle causal inference through assessing regularity (patterns) and/or by excluding other plausible explanations. In practical terms, CCS involves proposing, analysing and synthesising patterns (similarities and differences) across cases that share common objectives.

What is involved?

Goodrick (2014) outlines the steps to be taken in undertaking CCS.

Key evaluation questions and the purpose of the evaluation: The evaluator should explicitly articulate the adequacy and purpose of using CCS (guided by the evaluation questions) and define the primary interests. Formulating key evaluation questions allows the selection of appropriate cases to be used in the analysis.

Propositions based on the Theory of Change: Theories and hypotheses that are to be explored should be derived from the Theory of Change (or, alternatively, from previous research around the initiative, existing policy or programme documentation).

Case selection: Advocates for CCS approaches claim an important distinction between case-oriented small n studies and (most typically large n) statistical/variable-focused approaches in terms of the process of selecting cases: in case-based methods, selection is iterative and cannot rely on convenience and accessibility. ‘Initial’ cases should be identified in advance, but case selection may continue as evidence is gathered. Various case-selection criteria can be identified depending on the analytic purpose (Vogt et al., 2011). These may include:

• Very similar cases
• Very different cases
• Typical or representative cases
• Extreme or unusual cases
• Deviant or unexpected cases
• Influential or emblematic cases

Identify how evidence will be collected, analysed and synthesised: CCS often applies mixed methods.

Test alternative explanations for outcomes: Following the identification of patterns and relationships, the evaluator may wish to test the established propositions in a follow-up exploratory phase. Approaches applied here may involve triangulation, selecting contradicting cases or using an analytical approach such as Qualitative Comparative Analysis (QCA). Download a Comparative Case Study here Download a longer briefing on Comparative Case Studies here

Useful resources

A webinar shared by Better Evaluation with an overview of using CCS for evaluation.

A short overview describing how to apply CCS for evaluation:

Goodrick, D. (2014). Comparative Case Studies, Methodological Briefs: Impact Evaluation 9 , UNICEF Office of Research, Florence.

An extensively used book that provides a comprehensive critical examination of case-based methods:

Byrne, D. and Ragin, C. C. (2009). The Sage handbook of case-based methods . Sage Publications.

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Comparative Case Studies: An Innovative Approach

What is a case study and what is it good for? In this article, we argue for a new approach—the comparative case study approach—that attends simultaneously to macro, meso, and micro dimensions of case-based research. The approach engages two logics of comparison: first, the more common compare and contrast; and second, a 'tracing across' sites or scales. As we explicate our approach, we also contrast it to traditional case study research. We contend that new approaches are necessitated by conceptual shifts in the social sciences, specifically in relation to culture, context, space, place, and comparison itself. We propose that comparative case studies should attend to three axes: horizontal, vertical, and transversal comparison. We conclude by arguing that this revision has the potential to strengthen and enhance case study research in Comparative and International Education, clarifying the unique contributions of qualitative research.

Related Papers

Lesley Bartlett

Higher Education Quarterly

Anna Kosmützky , Terhi Nokkala

Abstract Finding the balance between adequately describing the uniqueness of the context of studied phenomena and maintaining sufficient common ground for comparability and analytical generalization has widely been recognized as a key challenge in international comparative research. Methodological reflections on how to adequately cover context and comparability have extensively been discussed for quantitative survey or secondary data research. In addition, most recently, promising methodological considerations for qualitative comparative research have been suggested in comparative fields related to higher education. The article's aim is to connect this discussion to comparative higher education research. Thus, the article discusses recent advancements in the methodology of qualitative international comparative research, connects them to older analytical methods that have been used within the field in the 1960s and 1970s, and demonstrates their analytical value based on their application to a qualitative small-N case study on research groups in diverse organizational contexts in three country contexts.

John C Weidman

This is the inaugural volume in the PSCIE (Pittsburgh Studies in Comparative and International Education) Series which expands on the life work of University of Pittsburgh professor Rolland G. Paulston (1929-2006). Recognized as a stalwart in the field of comparative and international education, Paulston's most widely recognized contribution is social cartography. He demonstrated that mapping comparative, international, and development education is no easy task and, depending on the perspective of the mapper, there may be multiple cartographies to chart. This collection of nineteen essays and research studies is a festschrift celebrating and developing Robert Paulston's scholarship in comparative, international, and development education (CIDE). Considering key international education issues, national education systems, and social and educational theories, essays in this volume explore and go beyond Paulston's seminal works in social cartography. Organized into three sec...

Ben Hawbaker , Candace Jones , Brooke Boren , Reut Livne-Tarandach

Qualitative researchers utilize comparative and case-based methods to develop theory through elaboration or abduction. They pursue research in intermediate fields where some but not all relevant constructs are known (Edmonson & McManus, 2007). When cases and comparisons move beyond a few, it threatens researchers with information overload. Qualitative Comparative Analysis (QCA) is a novel method of analysis that is appropriate for larger case or comparative studies and provides a flexible tool for theory elaboration and abduction. Building on recently published exemplars from organizational research, we illuminate three key benefits of QCA: (1) allows researchers to examine cases as wholes, effectively addressing the complexity of action embedded in organizational phenomena; (2) provides indicators of whether results are reliable and valid so qualitative researchers, and others, can assess their findings within a study and across studies; and (3) explores potentially overlooked connections between qualitative and quantitative research.

Eleanor Knott

This course focuses on how to design and conduct small-n case study and comparative research. Thinking outside of students' areas of interest and specialisms and topics, students will be encouraged to develop the concepts and comparative frameworks that underpin these phenomena. In other words, students will begin to develop their research topics as cases of something. The course covers questions of design and methods of case study research, from single-n to small-n case studies including discussions of process tracing and Mill's methods. The course addresses both the theoretical and methodological discussions that underpin research design as well as the practical questions of how to conduct case study research, including gathering, assessing and using evidence. Examples from the fields of comparative politics, IR, development studies, sociology and European studies will be used throughout the lectures and seminars.

Reut Livne-Tarandach , Candace Jones

Qualitative researchers utilize comparative and case-based methods to develop theory through elaboration or abduction. They pursue research in intermediate fields where some but not all relevant constructs are known (Edmonson & McManus, 2007). When cases and comparisons move beyond a few, it threatens researchers with information overload. Qualitative Comparative Analysis (QCA) is a novel method of analysis that is appropriate for larger case and comparative studies and provides a flexible tool for theory elaboration and abduction. Building on recently published exemplars from organizational research, we illuminate three key benefits of QCA: (1) allows researchers to examine cases as wholes, effectively addressing the complexity of action embedded in organizational phenomena; (2) provides indicators of whether results are reliable and valid so qualitative researchers, and others, can assess their findings within a study and across studies; and (3) explores potentially overlooked connections between qualitative and quantitative research.

Bedrettin Yazan

Case study methodology has long been a contested terrain in social sciences research which is characterized by varying, sometimes opposing, approaches espoused by many research methodologists. Despite being one of the most frequently used qualitative research methodologies in educational research, the methodologists do not have a full consensus on the design and implementation of case study, which hampers its full evolution. Focusing on the landmark works of three prominent methodologists, namely Robert Yin, Sharan Merriam, Robert Stake, I attempt to scrutinize the areas where their perspectives diverge, converge and complement one another in varying dimensions of case study research. I aim to help the emerging researchers in the field of education familiarize themselves with the diverse views regarding case study that lead to a vast array of techniques and strategies, out of which they can come up with a combined perspective which best serves their research purpose.

The SAGE Handbook of Qualitative Data Analysis

Monika Palmberger

KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie

Markus Siewert

This article presents the case study as a type of qualitative research. Its aim is to give a detailed description of a case study-its definition, some classifications, and several advantages and disadvantages-in order to provide a better understanding of this widely used type of qualitative approac h. In comparison to other types of qualitative research, case studies have been little understood both from a methodological point of view, where disagreements exist about whether case studies should be considered a research method or a research type, and from a content point of view, where there are ambiguities regarding what should be considered a case or research subject. A great emphasis is placed on the disadvantages of case studies, where we try to refute some of the criticisms concerning case studies, particularly in comparison to quantitative research approaches.

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

Dr. BABOUCARR NJIE

johnpatrick gonzales

Romina Costa , Stephanie M Hall

Gerald LeTendre

Quality & Quantity

Sofia Pagliarin

Edgar Ojeda

A. Biba Rebolj

Philosophy of the Social Sciences

Attilia Ruzzene

Erasmus Journal for Philosophy and Economics

International Journal of Research & Method in Education

Linda Graham

IOSR Journals publish within 3 days

Anna Kosmützky

Current Issues in Comparative Education

Handbook of Qualitative Research Methods for International Business

Paul Matthyssens

Teachers College Record

Merli Tamtik

Anju Chhetri

Frances Vavrus , Lesley Bartlett

Oxford Research Encyclopedia of Education

Veritas: The Academic Journal of St Clements Education Group

Mohamed A Eno , abderrazak dammak

Qualitative Inquiry, vol. 12, no. 2, pp. 219-245

Bent Flyvbjerg

COMPARE - journal of comparative and international education

Karen Evans

Martin Oller Alonso , Daniel Barredo Ibáñez

Working Papers

Ipsita Sapra

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Transl Behav Med
• v.4(2); 2014 Jun

Using qualitative comparative analysis to understand and quantify translation and implementation

Heather kane.

RTI International, 3040 Cornwallis Road, Research Triangle Park, P.O. Box 12194, Durham, NC 27709 USA

Megan A Lewis

Pamela a williams, leila c kahwati.

Understanding the factors that facilitate implementation of behavioral medicine programs into practice can advance translational science. Often, translation or implementation studies use case study methods with small sample sizes. Methodological approaches that systematize findings from these types of studies are needed to improve rigor and advance the field. Qualitative comparative analysis (QCA) is a method and analytical approach that can advance implementation science. QCA offers an approach for rigorously conducting translational and implementation research limited by a small number of cases. We describe the methodological and analytic approach for using QCA and provide examples of its use in the health and health services literature. QCA brings together qualitative or quantitative data derived from cases to identify necessary and sufficient conditions for an outcome. QCA offers advantages for researchers interested in analyzing complex programs and for practitioners interested in developing programs that achieve successful health outcomes.

INTRODUCTION

In this paper, we describe the methodological features and advantages of using qualitative comparative analysis (QCA). QCA is sometimes called a “mixed method.” It refers to both a specific research approach and an analytic technique that is distinct from and offers several advantages over traditional qualitative and quantitative methods [ 1 – 4 ]. It can be used to (1) analyze small to medium numbers of cases (e.g., 10 to 50) when traditional statistical methods are not possible, (2) examine complex combinations of explanatory factors associated with translation or implementation “success,” and (3) combine qualitative and quantitative data using a unified and systematic analytic approach.

This method may be especially pertinent for behavioral medicine given the growing interest in implementation science [ 5 ]. Translating behavioral medicine research and interventions into useful practice and policy requires an understanding of the implementation context. Understanding the context under which interventions work and how different ways of implementing an intervention lead to successful outcomes are required for “T3” (i.e., dissemination and implementation of evidence-based interventions) and “T4” translations (i.e., policy development to encourage evidence-based intervention use among various stakeholders) [ 6 , 7 ].

Case studies are a common way to assess different program implementation approaches and to examine complex systems (e.g., health care delivery systems, interventions in community settings) [ 8 ]. However, multiple case studies often have small, naturally limited samples or populations; small samples and populations lack adequate power to support conventional, statistical analyses. Case studies also may use mixed-method approaches, but typically when researchers collect quantitative and qualitative data in tandem, they rarely integrate both types of data systematically in the analysis. QCA offers solutions for the challenges posed by case studies and provides a useful analytic tool for translating research into policy recommendations. Using QCA methods could aid behavioral medicine researchers who seek to translate research from randomized controlled trials into practice settings to understand implementation. In this paper, we describe the conceptual basis of QCA, its application in the health and health services literature, and its features and limitations.

CONCEPTUAL BASIS OF QCA

QCA has its foundations in historical, comparative social science. Researchers in this field developed QCA because probabilistic methods failed to capture the complexity of social phenomena and required large sample sizes [ 1 ]. Recently, this method has made inroads into health research and evaluation [ 9 – 13 ] because of several useful features as follows: (1) it models equifinality , which is the ability to identify more than one causal pathway to an outcome (or absence of the outcome); (2) it identifies conjunctural causation , which means that single conditions may not display their effects on their own, but only in conjunction with other conditions; and (3) it implies asymmetrical relationships between causal conditions and outcomes, which means that causal pathways for achieving the outcome differ from causal pathways for failing to achieve the outcome.

QCA is a case-oriented approach that examines relationships between conditions (similar to explanatory variables in regression models) and an outcome using set theory; a branch of mathematics or of symbolic logic that deals with the nature and relations of sets. A set-theoretic approach to modeling causality differs from probabilistic methods, which examines the independent, additive influence of variables on an outcome. Regression models, based on underlying assumptions about sampling and distribution of the data, ask “what factor, holding all other factors constant at each factor’s average, will increase (or decrease) the likelihood of an outcome .” QCA, an approach based on the examination of set, subset, and superset relationships, asks “ what conditions —alone or in combination with other conditions—are necessary or sufficient to produce an outcome .” For additional QCA definitions, see Ragin [ 4 ].

Necessary conditions are those that exhibit a superset relationship with the outcome set and are conditions or combinations of conditions that must be present for an outcome to occur. In assessing necessity, a researcher “identifies conditions shared by cases with the same outcome” [ 4 ] (p. 20). Figure  1 shows a hypothetical example. In this figure, condition X is a necessary condition for an effective intervention because all cases with condition X are also members of the set of cases with the outcome present; however, condition X is not sufficient for an effective intervention because it is possible to be a member of the set of cases with condition X, but not be a member of the outcome set [ 14 ].

Necessary and sufficient conditions and set-theoretic relationships

Sufficient conditions exhibit subset relationships with an outcome set and demonstrate that “the cause in question produces the outcome in question” [ 3 ] (p. 92). Figure  1 shows the multiple and different combinations of conditions that produce the hypothetical outcome, “effective intervention,” (1) by having condition A present, (2) by having condition D present, or (3) by having the combination of conditions B and C present. None of these conditions is necessary and any one of these conditions or combinations of conditions is sufficient for the outcome of an effective intervention.

QCA AS AN APPROACH AND AS AN ANALYTIC TECHNIQUE

The term “QCA” is sometimes used to refer to the comparative research approach but also refers to the “analytic moment” during which Boolean algebra and set theory logic is applied to truth tables constructed from data derived from included cases. Figure  2 characterizes this distinction. Although this figure depicts steps as sequential, like many research endeavors, these steps are somewhat iterative, with respecification and reanalysis occurring along the way to final findings. We describe each of the essential steps of QCA as an approach and analytic technique and provide examples of how it has been used in health-related research.

QCA as an approach and as an analytic technique

Operationalizing the research question

Like other types of studies, the first step involves identifying the research question(s) and developing a conceptual model. This step guides the study as a whole and also informs case, condition (c.f., variable), and outcome selection. As mentioned above, QCA frames research questions differently than traditional quantitative or qualitative methods. Research questions appropriate for a QCA approach would seek to identify the necessary and sufficient conditions required to achieve the outcome. Thus, formulating a QCA research question emphasizes what program components or features—individually or in combination—need to be in place for a program or intervention to have a chance at being effective (i.e., necessary conditions) and what program components or features—individually or in combination—would produce the outcome (i.e., sufficient conditions). For example, a set theoretic hypothesis would be as follows: If a program is supported by strong organizational capacity and a comprehensive planning process, then the program will be successful. A hypothesis better addressed by probabilistic methods would be as follows: Organizational capacity, holding all other factors constant, increases the likelihood that a program will be successful.

For example, Longest and Thoits [ 15 ] drew on an extant stress process model to assess whether the pathways leading to psychological distress differed for women and men. Using QCA was appropriate for their study because the stress process model “suggests that particular patterns of predictors experienced in tandem may have unique relationships with health outcomes” (p. 4, italics added). They theorized that predictors would exhibit effects in combination because some aspects of the stress process model would buffer the risk of distress (e.g., social support) while others simultaneously would increase the risk (e.g., negative life events).

Identify cases

The number of cases in a QCA analysis may be determined by the population (e.g., 10 intervention sites, 30 grantees). When particular cases can be chosen from a larger population, Berg-Schlosser and De Meur [ 16 ] offer other strategies and best practices for choosing cases. Unless the number of cases relies on an existing population (i.e., 30 programs or grantees), the outcome of interest and existing theory drive case selection, unlike variable-oriented research [ 3 , 4 ] in which numbers are driven by statistical power considerations and depend on variation in the dependent variable. For use in causal inference, both cases that exhibit and do not exhibit the outcome should be included [ 16 ]. If a researcher is interested in developing typologies or concept formation, he or she may wish to examine similar cases that exhibit differences on the outcome or to explore cases that exhibit the same outcome [ 14 , 16 ].

For example, Kahwati et al. [ 9 ] examined the structure, policies, and processes that might lead to an effective clinical weight management program in a large national integrated health care system, as measured by mean weight loss among patients treated at the facility. To examine pathways that lead to both better and poorer facility-level weight loss, 11 facilities from among those with the largest weight loss outcomes and 11 facilities from among those with the smallest were included. By choosing cases based on specific outcomes, Kahwati et al. could identify multiple patterns of success (or failure) that explain the outcome rather than the variability associated with the outcome.

Identify conditions and outcome sets

Selecting conditions relies on the research question, conceptual model, and number of cases similar to other research methods. Conditions (or “sets” or “condition sets”) refer to the explanatory factors in a model; they are similar to variables. Because QCA research questions assess necessary and sufficient conditions, a researcher should consider which conditions in the conceptual model would theoretically produce the outcome individually or in combination. This helps to focus the analysis and number of conditions. Ideally, for a case study design with a small (e.g., 10–15) or intermediate (e.g., 16–100) number of cases, one should aim for fewer than five conditions because in QCA a researcher assesses all possible configurations of conditions. Adding conditions to the model increases the possible number of combinations exponentially (i.e., 2 k , where k = the number of conditions). For three conditions, eight possible combinations of the selected conditions exist as follows: the presence of A, B, C together, the lack of A with B and C present, the lack of A and lack of B with C present, and so forth. Having too many conditions will likely mean that no cases fall into a particular configuration, and that configuration cannot be assessed by empirical examples. When one or more configurations are not represented by the cases, this is known as limited diversity, and QCA experts suggest multiple strategies for managing such situations [ 4 , 14 ].

For example, Ford et al. [ 10 ] studied health departments’ implementation of core public health functions and organizational factors (e.g., resource availability, adaptability) and how those conditions lead to superior and inferior population health changes. They operationalized three core public functions (i.e., assessment of environmental and population public health needs, capacity for policy development, and authority over assurance of healthcare operations) and operationalized those for their study by using composite measures of varied health indicators compiled in a UnitedHealth Group report. In this examination of 41 state health departments, the authors found that all three core public health functions were necessary for population health improvement. The absence of any of the core public health functions was sufficient for poorer population health outcomes; thus, only the health departments with the ability to perform all three core functions had improved outcomes. Additionally, these three core functions in combination with either resource availability or adaptability were sufficient combinations (i.e., causal pathways) for improved population health outcomes.

Calibrate condition and outcome sets

Calibration refers to “adjusting (measures) so that they match or conform to dependably known standards” and is a common way of standardizing data in the physical sciences [ 4 ] (p. 72). Calibration requires the researcher to make sense of variation in the data and apply expert knowledge about what aspects of the variation are meaningful. Because calibration depends on defining conditions based on those “dependably known standards,” QCA relies on expert substantive knowledge, theory, or criteria external to the data themselves [ 14 ]. This may require researchers to collaborate closely with program implementers.

In QCA, one can use “crisp” set or “fuzzy” set calibration. Crisp sets, which are similar to dichotomous categorical variables in regression, establish decision rules defining a case as fully in the set (i.e., condition) or fully out of the set; fuzzy sets establish degrees of membership in a set. Fuzzy sets “differentiate between different levels of belonging anchored by two extreme membership scores at 1 and 0” [ 14 ] (p.28). They can be continuous (0, 0.1, 0.2,..) or have qualitatively defined anchor points (e.g., 0 is fully out of the set; 0.33 is more out than in the set; 0.66 is more in than out of the set; 1 is fully in the set). A researcher selects fuzzy sets and the corresponding resolution (i.e., continuous, four cutoff points, six cutoff) based on theory and meaningful differences between cases and must be able to provide a verbal description for each cutoff point [ 14 ]. If, for example, a researcher cannot distinguish between 0.7 and 0.8 membership in a set, then a more continuous scoring of cases would not be useful, rather a four point cutoff may better characterize the data. Although crisp and fuzzy sets are more commonly used, new multivariate forms of QCA are emerging as are variants that incorporate elements of time [ 14 , 17 , 18 ].

Fuzzy sets have the advantage of maintaining more detail for data with continuous values. However, this strength also makes interpretation more difficult. When an observation is coded with fuzzy sets, a particular observation has some degree of membership in the set “condition A” and in the set “condition NOT A.” Thus, when doing analyses to identify sufficient conditions, a researcher must make a judgment call on what benchmark constitutes recommendation threshold for policy or programmatic action.

In creating decision rules for calibration, a researcher can use a variety of techniques to identify cutoff points or anchors. For qualitative conditions, a researcher can define decision rules by drawing from the literature and knowledge of the intervention context. For conditions with numeric values, a researcher can also employ statistical approaches. Ideally, when using statistical approaches, a researcher should establish thresholds using substantive knowledge about set membership (thus, translating variation into meaningful categories). Although measures of central tendency (e.g., cases with a value above the median are considered fully in the set) can be used to set cutoff points, some experts consider the sole use of this method to be flawed because case classification is determined by a case’s relative value in regard to other cases as opposed to its absolute value in reference to an external referent [ 14 ].

For example, in their study of National Cancer Institutes’ Community Clinical Oncology Program (NCI CCOP), Weiner et al. [ 19 ] had numeric data on their five study measures. They transformed their study measures by using their knowledge of the CCOP and by asking NCI officials to identify three values: full membership in a set, a point of maximum ambiguity, and nonmembership in the set. For their outcome set, high accrual in clinical trials, they established 100 patients enrolled accrual as fully in the set of high accrual, 70 as a point of ambiguity (neither in nor out of the set), and 50 and below as fully out of the set because “CCOPs must maintain a minimum of 50 patients to maintain CCOP funding” (p. 288). By using QCA and operationalizing condition sets in this way, they were able to answer what condition sets produce high accrual, not what factors predict more accrual. The advantage is that by using this approach and analytic technique, they were able to identify sets of factors that are linked with a very specific outcome of interest.

Obtain primary or secondary data

Data sources vary based on the study, availability of the data, and feasibility of data collection; data can be qualitative or quantitative, a feature useful for mixed-methods studies and systematically integrating these different types of data is a major strength of this approach. Qualitative data include program documents and descriptions, key informant interviews, and archival data (e.g., program documents, records, policies); quantitative data consists of surveys, surveillance or registry data, and electronic health records.

For instance, Schensul et al. [ 20 ] relied on in-depth interviews for their analysis; Chuang et al. [ 21 ] and Longest and Thoits [ 15 ] drew on survey data for theirs. Kahwati et al. [ 9 ] used a mixed-method approach combining data from key informant interviews, program documents, and electronic health records. Any type of data can be used to inform the calibration of conditions.

Assign set membership scores

Assigning set membership scores involves applying the decision rules that were established during the calibration phase. To accomplish this, the research team should then use the extracted data for each case, apply the decision rule for the condition, and discuss discrepancies in the data sources. In their study of factors that influence health care policy development in Florida, Harkreader and Imershein [ 22 ] coded contextual factors that supported state involvement in the health care market. Drawing on a review of archival data and using crisp set coding, they assigned a value of 1 for the presence of a contextual factor (e.g., presence of federal financial incentives promoting policy, unified health care provider policy position in opposition to state policy, state agency supporting policy position) and 0 for the absence of a contextual factor.

Construct truth table

After completing the coding, researchers create a “truth table” for analysis. A truth table lists all of the possible configurations of conditions, the number of cases that fall into that configuration, and the “consistency” of the cases. Consistency quantifies the extent to which cases that share similar conditions exhibit the same outcome; in crisp sets, the consistency value is the proportion of cases that exhibit the outcome. Fuzzy sets require a different calculation to establish consistency and are described at length in other sources [ 1 – 4 , 14 ]. Table  1 displays a hypothetical truth table for three conditions using crisp sets.

Sample of a hypothetical truth table for crisp sets

1 fully in the set, 0 fully out of the set

QCA AS AN ANALYTIC TECHNIQUE

The research steps to this point fall into QCA as an approach to understanding social and health phenomena. Analysis of the truth table is the sine qua non of QCA as an analytic technique. In this section, we provide an overview of the analysis process, but analytic techniques and emerging forms of analysis are described in multiple texts [ 3 , 4 , 14 , 17 ]. The use of computer software to conduct truth table analysis is recommended and several software options are available including Stata, fsQCA, Tosmana, and R.

A truth table analysis first involves the researcher assessing which (if any) conditions are individually necessary or sufficient for achieving the outcome, and then second, examining whether any configurations of conditions are necessary or sufficient. In instances where contradictions in outcomes from the same configuration pattern occur (i.e., one case from a configuration has the outcome; one does not), the researcher should also consider whether the model is properly specified and conditions are calibrated accurately. Thus, this stage of the analysis may reveal the need to review how conditions are defined and whether the definition should be recalibrated. Similar to qualitative and quantitative research approaches, analysis is iterative.

Additionally, the researcher examines the truth table to assess whether all logically possible configurations have empiric cases. As described above, when configurations lack cases, the problem of limited diversity occurs. Configurations without representative cases are known as logical remainders, and the researcher must consider how to deal with those. The analysis of logical remainders depends on the particular theory guiding the research and the research priorities. How a researcher manages the logical remainders has implications for the final solution, but none of the solutions based on the truth table will contradict the empirical evidence [ 14 ]. To generate the most conservative solution term, a researcher makes no assumptions about truth table rows with no cases (or very few cases in larger N studies) and excludes them from the logical minimization process. Alternately, a researcher can choose to include (or exclude) rows with no cases from analysis, which would generate a solution that is a superset of the conservative solution. Choosing inclusion criteria for logical remainders also depends on theory and what may be empirically possible. For example, in studying governments, it would be unlikely to have a case that is a democracy (“condition A”), but has a dictator (“condition B”). In that circumstance, the researcher may choose to exclude that theoretically implausible row from the logical minimization process.

Third, once all the solutions have been identified, the researcher mathematically reduces the solution [ 1 , 14 ]. For example, if the list of solutions contains two identical configurations, except that in one configuration A is absent and in the other A is present, then A can be dropped from those two solutions. Finally, the researcher computes two parameters of fit: coverage and consistency. Coverage determines the empirical relevance of a solution and quantifies the variation in causal pathways to an outcome [ 14 ]. When coverage of a causal pathway is high, the more common the solution is, and more of the outcome is accounted for by the pathway. However, maximum coverage may be less critical in implementation research because understanding all of the pathways to success may be as helpful as understanding the most common pathway. Consistency assesses whether the causal pathway produces the outcome regularly (“the degree to which the empirical data are in line with a postulated subset relation,” p. 324 [ 14 ]); a high consistency value (e.g., 1.00 or 100 %) would indicate that all cases in a causal pathway produced the outcome. A low consistency value would suggest that a particular pathway was not successful in producing the outcome on a regular basis, and thus, for translational purposes, should not be recommended for policy or practice changes. A causal pathway with high consistency and coverage values indicates a result useful for providing guidance; a high consistency with a lower coverage score also has value in showing a causal pathway that successfully produced the outcome, but did so less frequently.

For example, Kahwati et al. [ 9 ] examined their truth table and analyzed the data for single conditions and combinations of conditions that were necessary for higher or lower facility-level patient weight loss outcomes. The truth table analysis revealed two necessary conditions and four sufficient combinations of conditions. Because of significant challenges with logical remainders, they used a bottom-up approach to assess whether combinations of conditions yielded the outcome. This entailed pairing conditions to ensure parsimony and maximize coverage. With a smaller number of conditions, a researcher could hypothetically find that more cases share similar characteristics and could assess whether those cases exhibit the same outcome of interest.

At the completion of the truth table analysis, Kahwati et al. [ 9 ] used the qualitative data from site interviews to provide rich examples to illustrate the QCA solutions that were identified, which explained what the solutions meant in clinical practice for weight management. For example, having an involved champion (usually a physician), in combination with low facility accountability, was sufficient for program success (i.e., better weight loss outcomes) and was related to better facility weight loss. In reviewing the qualitative data, Kahwati et al. [ 9 ] discovered that involved champions integrate program activities into their clinical routines and discuss issues as they arise with other program staff. Because involved champions and other program staff communicated informally on a regular basis, formal accountability structures were less of a priority.

ADVANTAGES AND LIMITATIONS OF QCA

Because translational (and other health-related) researchers may be interested in which intervention features—alone or in combination—achieve distinct outcomes (e.g., achievement of program outcomes, reduction in health disparities), QCA is well suited for translational research. To assess combinations of variables in regression, a researcher relies on interaction effects, which, although useful, become difficult to interpret when three, four, or more variables are combined. Furthermore, in regression and other variable-oriented approaches, independent variables are held constant at the average across the study population to isolate the independent effect of that variable, but this masks how factors may interact with each other in ways that impact the ultimate outcomes. In translational research, context matters and QCA treats each case holistically, allowing each case to keep its own values for each condition.

Multiple case studies or studies with the organization as the unit of analysis often involve a small or intermediate number of cases. This hinders the use of standard statistical analyses; researchers are less likely to find statistical significance with small sample sizes. However, QCA draws on analyses of set relations to support small-N studies and to identify the conditions or combinations of conditions that are necessary or sufficient for an outcome of interest and may yield results when probabilistic methods cannot.

Finally, QCA is based on an asymmetric concept of causation , which means that the absence of a sufficient condition associated with an outcome does not necessarily describe the causal pathway to the absence of the outcome [ 14 ]. These characteristics can be helpful for translational researchers who are trying to study or implement complex interventions, where more than one way to implement a program might be effective and where studying both effective and ineffective implementation practices can yield useful information.

QCA has several limitations that researchers should consider before choosing it as a potential methodological approach. With small- and intermediate-N studies, QCA must be theory-driven and circumscribed by priority questions. That is, a researcher ideally should not use a “kitchen sink” approach to test every conceivable condition or combination of conditions because the number of combinations increases exponentially with the addition of another condition. With a small number of cases and too many conditions, the sample would not have enough cases to provide examples of all the possible configurations of conditions (i.e., limited diversity), or the analysis would be constrained to describing the characteristics of the cases, which would have less value than determining whether some conditions or some combination of conditions led to actual program success. However, if the number of conditions cannot be reduced, alternate QCA techniques, such as a bottom-up approach to QCA or two-step QCA, can be used [ 14 ].

Another limitation is that programs or clinical interventions involved in a cross-site analysis may have unique programs that do not seem comparable. Cases must share some degree of comparability to use QCA [ 16 ]. Researchers can manage this challenge by taking a broader view of the program(s) and comparing them on broader characteristics or concepts, such as high/low organizational capacity, established partnerships, and program planning, if these would provide meaningful conclusions. Taking this approach will require careful definition of each of these concepts within the context of a particular initiative. Definitions may also need to be revised as the data are gathered and calibration begins.

Finally, as mentioned above, crisp set calibration dichotomizes conditions of interest; this form of calibration means that in some cases, the finer grained differences and precision in a condition may be lost [ 3 ]. Crisp set calibration provides more easily interpretable and actionable results and is appropriate if researchers are primarily interested in the presence or absence of a particular program feature or organizational characteristic to understand translation or implementation.

QCA offers an additional methodological approach for researchers to conduct rigorous comparative analyses while drawing on the rich, detailed data collected as part of a case study. However, as Rihoux, Benoit, and Ragin [ 17 ] note, QCA is not a miracle method, nor a panacea for all studies that use case study methods. Furthermore, it may not always be the most suitable approach for certain types of translational and implementation research. We outlined the multiple steps needed to conduct a comprehensive QCA. QCA is a good approach for the examination of causal complexity, and equifinality could be helpful to behavioral medicine researchers who seek to translate evidence-based interventions in real-world settings. In reality, multiple program models can lead to success, and this method accommodates a more complex and varied understanding of these patterns and factors.

Implications

Practice : Identifying multiple successful intervention models (equifinality) can aid in selecting a practice model relevant to a context, and can facilitate implementation.

Policy : QCA can be used to develop actionable policy information for decision makers that accommodates contextual factors.

Research : Researchers can use QCA to understand causal complexity in translational or implementation research and to assess the relationships between policies, interventions, or procedures and successful outcomes.

How to investigate a constitutional culture?: the case for the focus group method in comparative constitutional studies

48 Pages Posted: 3 Jul 2024

Eoin Carolan

University College Dublin (UCD) - School of Law

Silvia Gagliardi

Sutherland School of Law, University College Dublin

Daniela Rodriguez Gutierrez

University College Dublin (UCD)

Date Written: May 06, 2024

This paper makes the case for the use of focus groups as a method with particular relevance to the field of comparative constitutional studies.  The paper begins with a brief overview of the most common approaches to accounts of constitutional culture. It then explains how the focus group method may,in theory, address some of the limitations of these techniques. By contrast with quantitatively-oriented techniques, focus groups offer a context-sensitive, participant driven and in-depth insights into how ordinary citizens understand, think about and discuss constitutional issues. This, it is argued, provides novel and valuable information about the functional and sociological value of constitutions, as hypothesised in much of the recent literature on social imaginaries and sociological constitutionalism. Having made the theoretical case for the method, the paper deals in its final sections with the authors' experience in making use of this method. It identifies the challenges and limitations that may arise when applying the method to constitutional questions and offers conclusions and guidance on its potential utility for future research in the area.

Keywords: Comparative constitutional studies, Constitutional culture, Socio-legal methods, Comparative law, Comparative constitutional methodology

Suggested Citation: Suggested Citation

Eoin Carolan (Contact Author)

University college dublin (ucd) - school of law ( email ).

Belfield Dublin 4 Ireland

Sutherland School of Law, University College Dublin ( email )

University college dublin (ucd) ( email ), do you have a job opening that you would like to promote on ssrn, paper statistics, related ejournals, comparative law & trans-national studies ejournal.

Subscribe to this fee journal for more curated articles on this topic

Law & Society: Public Law - Constitutional Law eJournal

Law & society: international & comparative law ejournal, political institutions: constitutions ejournal, legal anthropology: laws & constitutions ejournal, legal anthropology: law in global context ejournal.

Subscribe to this journal for more curated articles on this topic

Login | Register

• Browse Recent Issues (2022-)
• Search Recent Issues (2022-)
• Browse Back Issues (2009-2021)
• Harvard Citation Style
• Vancouver Citation Style
• APA Citation Style
• Download RIS
• Download BibTeX

Quo Vadis, Paleontology?

This is an accepted article with a DOI pre-assigned that is not yet published.

Studies of the history of life provide an interesting case study of how the questions scientists can ask, and from which they expect reliable answers, change over time. Some of these changes reflect the introduction of new technology or methodological advances in other fields that open new opportunities; other changes reflect the evolution of views of important questions the integration of multiple streams of information. In this contribution I consider the changing nature of questions in paleontology, largely focusing on English-speaking paleontologists. Rather than bemoan its limitations, paleontologists have pioneered techniques to identify and often correct preservation and collecting biases in the fossil record. The spread of rigorous methods to infer and test phylogenies has been integrated with molecular clock studies to infer branchpoints in phylogeny and insights from comparative developmental studies, which together inform our understanding of evolutionary dynamics, particularly novelty.  Together these advances have changed the questions paleontologists can address about the history of life, eliminating some questions (particularly in paleoecology), but greatly expanding research programs in other areas and collaborations with biologists and other Earth scientists.  The questions driving paleontologists have evolved from primarily descriptive and explanatory to increasingly analytical and integrative. These trends are briefly illustrated with examples from studies of the Ediacaran-Cambrian diversification of animals, and from studies of mass extinctions.

Keywords: paleontology, mass extinction, fossil record, taphonomy, Cambrian Radiation

Accepted on 30 May 2024

Peer reviewed, creative commons attribution 4.0, harvard-style citation.

Erwin, D. () 'Quo Vadis, Paleontology?', Philosophy, Theory, and Practice in Biology . 16(2) doi: 10.3998/ptpbio.5626

Show: Vancouver Citation Style | APA Citation Style

Vancouver-Style Citation

Erwin, D. Quo Vadis, Paleontology?. Philosophy, Theory, and Practice in Biology. ; 16(2) doi: 10.3998/ptpbio.5626

Show: Harvard Citation Style | APA Citation Style

APA-Style Citation

Erwin, D. (, ). Quo Vadis, Paleontology?. Philosophy, Theory, and Practice in Biology 16(2) doi: 10.3998/ptpbio.5626

Show: Harvard Citation Style | {% trans 'Vancouver Citation Style' %}

Non Specialist Summary

This article has no summary

Comparative landslide susceptibility assessment using information value and frequency ratio bivariate statistical methods: a case study from Northwestern Himalayas, Jammu and Kashmir, India

• Original Paper
• Published: 10 July 2024
• Volume 17 , article number  231 , ( 2024 )

Cite this article

• Imran Khan   ORCID: orcid.org/0000-0001-7377-5251 1 , 2 ,
• Ashutosh Kainthola 2 ,
• Harish Bahuguna 3 &
• Md. Sarfaraz Asgher 4  

68 Accesses

Explore all metrics

In the northwestern Himalayas, including Jammu and Kashmir (J&K), frequent landslides pose significant risks, necessitating proactive zoning to mitigate potential damage through effective land-use planning. Fourteen causative and two triggering factors, such as slope, aspect, curvature, relative relief (RR), terrain ruggedness index (TRI), geomorphon, dissection index (Di), lithology, structural tectonic, drainage density (Dd), stream power index (SPI), topographic wetness index (TWI), land use land cover (LULC), road density (Rd), earthquake density (Ed), and rainfall density (Rd), were selected based on terrain conditions to assess landslide susceptibility. Utilizing frequency ratio (FR) and information value (IV) approaches, a comprehensive landslide susceptibility mapping (LSM) study covered 54,922 km 2 , incorporating 6669 landslide instances. This dataset was split into 70% (4659 landslides) for modeling and 30% (2010 landslides) for validation. The landslide susceptibility map, classified into five categories (very low, low, moderate, high, and very high), delineates varying proportions of the study area. Using the FR approach, these zones cover 12.9% (7063 km 2 ), 25.7% (14,101 km 2 ), 25.6% (14,049 km 2 ), 24.7% (13,586 km 2 ), and 11.1% (6123 km 2 ) of the area, respectively. Meanwhile, employing the IV approach, the coverage percentages are 5.7% (3119 km 2 ), 11.0% (6063 km 2 ), 20.1% (11,057 km 2 ), 38.9% (21,373 km 2 ), and 24.1% (13,310 km 2 ). Validation using receiver operating characteristic curves revealed high correlations for both FR (AUC: 0.809) and IV (AUC: 0.778) models, indicating their effectiveness. The FR model, characterized by simplicity and higher accuracy, outperformed the IV model, offering valuable insights for local, regional, and governments in land-use planning, disaster prevention, and mitigation efforts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria

Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at wadi itwad basin, asir region, saudi arabia.

Landslide Susceptibility Modeling Using the Index of Entropy and Frequency Ratio Method from Nefas-Mewcha to Weldiya Road Corridor, Northwestern Ethiopia

Data availability.

Data used in this study were provided by the Geological Survey of India (lithological data and fault data), Indian Meteorological Department (rainfall data data), National Centre of Seismology (earthquake data), and digital elevation data from the USGS websites. The above datasets can be access from their respective website at free of cost.

Abija FA, Nwosu JI, Ifedotun AI, Osadebe CC (2019) Landslide susceptibility assessment of Calabar, Nigeria using geotechnical, remote sensing and multi-criteria decision analysis: implications for urban planning and development. SDRP J Earth Sci Environ Stud 4(6):774–788. https://doi.org/10.25177/jeses.4.6.ra.617

Article   Google Scholar  

Abraham MT, Satyam N, Pradhan B, Segoni S (2022) Proposing an easy-to-use tool for estimating landslide dimensions using a data-driven approach. All Earth 34(1):243–258

Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine. Algeria Arab J Geosci 10(8):194. https://doi.org/10.1007/s12517-017-2980-6

Aditian A, Kubota T, Shinohara Y (2018) Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology 318:101–111. https://doi.org/10.1016/j.geomorph.2018.06.006

Akgün A, Bulut F (2007) GIS-based landslide susceptibility for ArsinYomra (Trabzon, North Turkey) region. Environ Geol 51(7):1377–1387. https://doi.org/10.1007/s00254-006-0435-6

Akgün A, Dag S, Bulut F (2008) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood frequency ratio and weighted linear combination models. Environ Geol 54:1127–1143. https://doi.org/10.1007/s00254-007-0857-1

Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir. Turkey Landslides 9(1):93–106. https://doi.org/10.1007/s10346-011-0275-6

Akgun A, Kıncal C, Pradhan B (2012) Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey). Environ Monit Assess 184(9):5453–5470. https://doi.org/10.1007/s10661-011-2352-8

Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Env 58(1):21–44

Ali S, Biermanns P, Haider R, Reicherter K (2019) Landslide susceptibility mapping by using a (GIS) along CPEC (Karakoram Highway), Pakistan. Nat Hazards Earth Syst Sci 19(5):999–1022

Alkhasawneh MS, Ngah UK, Tay LT, Isa NAM, Al-batah MS (2013) Determination of important topographic factors for landslide mapping analysis using MLP network. Sci World J 2013:1–12

Al-Najjar HA, Pradhan B, Beydoun G et al (2022) A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset. Gondwana Res. https://doi.org/10.1016/j.gr.2022.08.004

Ambrosi C, Strozzi T, Scapozza C, Wegmuller U (2018) Landslide hazard assessment in the Himalayas (Nepal and Bhutan) based on Earth-Observation data. Eng Geol 237:217–228. https://doi.org/10.1016/j.enggeo.2017.10.026

Anbalagan R (1992) Landslide hazard evaluation and zonation mapping in mountainous terrain. Eng Geol 32:269–277. https://doi.org/10.1016/0013-7952(92)90053-2

Anbalagan R, Kumar R, Lakshmanan K et al (2015) Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim. Geoenviron Disasters 2:6. https://doi.org/10.1186/s40677-014-0009-y

Anbazhagan S, Ramesh V (2014) Landslide hazard zonation mapping in ghat road section of Kolli hills. India J Mt Sci 11(5):1308–1325. https://doi.org/10.1007/s11629-012-2618-9

Andreani L, Stanek K, Gloaguen R, Krentz O, Domínguez-González L (2014) DEM-based analysis of interactions between tectonics and landscapes in the Ore Mountains and Eger Rift (East Germany and NW Czech Republic). Remote Sens 6(9):7971–8001. https://doi.org/10.3390/rs6097971

Arabameri A, Pradhan B, Pourghasemi HR et al (2018) Spatial modelling of gully erosion using GIS and R programming: a comparison among three data mining algorithms. App Sci 8(8):1369. https://doi.org/10.3390/app8081369

Asmare D (2022) Landslide hazard zonation and evaluation around Debre Markos town, NW Ethiopia — a GIS-based bivariate statistical approach. Sci Afr 15:e01129. https://doi.org/10.1016/j.sciaf.2022.e01129

Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–23. https://doi.org/10.1016/j.geomorph.2018.06.006

Ayalew L, Yamagishi H, Ugawa N (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides 1:73–81. https://doi.org/10.1007/s10346-003-0006-9

Azemeraw W, Meten M (2020) Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci 2:807. https://doi.org/10.1007/s42452-020-2563-0

Aziz K, Sarkar S, Sahu P (2023) Geo-investigation and slope stability analysis of debris slides along Ramban-Gool Road Network, Jammu and Kashmir, India. J Geol Soc India 99:986–994. https://doi.org/10.1007/s12594-023-2419-5

Balamurugan G, Ramasamy SM (2006) Tectonic grains and slope with special reference to landslide in Palani to Kodaikanal Ghat road using remote sensing and GIS techniques. In: Basavaraiappa et al (eds) Remote sensing and GIS applications. Bellur Prakashana, Mysore, pp 96–102

Banshtu RS, Versain LD, Pandey DD (2020) Risk assessment using the quantitative approach: central Himalaya, Kullu, Himachal Pradesh, India. Arab J Geosci 13:1–11. https://doi.org/10.1007/s12517-020-5143-0

Barredo JI, Benavidesz A, Herhl J et al (2000) Comparing heuristic landslide hazard assessment techniques using GIS in the Tirajana basin, Gran Canaria Island, Spain. Int J Appl Earth Obs Geoinf 2(1):9–23

Google Scholar  

Bchari EF, Theilen-Willige B, Malek HA (2019) Landslide hazard zonation assessment using GIS analysis at the coastal area of Safi (Morocco). In Proceedings of the ICA, pp. 1–7. 2(July). https://doi.org/10.5194/ica-proc-2-24-2019

Bednarik M, Yilmaz I, Marschalko M (2012) Landslide hazard and risk assessment: a case study from the Hlohovec–Sered’ landslide area in south-west Slovakia. Nat Hazards 64:547–575. https://doi.org/10.1007/s11069-012-0257-7

Berhane G, Kebede M, Alfarah N, Hagos E, Grum B, Giday A, Abera T (2020) Landslide susceptibility zonation mapping using GIS-based frequency ratio model with multi-class spatial datasets in the Adwa-Adigrat Mountain chains, northern Ethiopia. J Afr Earth Sc 164:103795. https://doi.org/10.1016/j.jafrearsci.2020.103795

Beven K, Kirkby N (1979) A physically based variable contributing area model of basin hydrology. Hydrol Sci Bull 24:43–69

Bhandari RK, Gupta C (1985) Problems of landslides in the Himalaya and future directions. In: Singh JS (ed) Environmental Regeneration in Himalaya, Concepts and Strategies. Gyanodaya Parkashan, Nainital, pp 39–57

Bhatt BP, Awasthi KD, Heyojoo BP, Silwal T, Kafle G (2013) Using geographic information system and analytical hierarchy process in landslide hazard zonation. Appl Ecol Environ Sci 1(2):14–22. https://doi.org/10.12691/aees-1-2-1

Brunsden D (1979) Mass movement. In: Embleton C, Thornes J (eds) Processes in geomorphology. Edward Arnold Ltd, London, UK, p p436

Brunsden D (1985) Landslide types, mechanisms, recognition, identification, in: Landslides in the South Wales coalfield, edited by: Morgan CS, In: Proceedings Symposium, April 1–3, The Polytechnic of Wales, 19–28

Bui DT, Lofman O, Revhaug I, Dick O (2011) Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards 59:1413–1444. https://doi.org/10.1007/s11069-011-9844-2

Bui DT, Tuan TA, Klempe H et al (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13:361–378. https://doi.org/10.1007/s10346-015-0557-6

Bureau of Indian Standards (2002) Criteria for earthquake resistant design of structures; IS 1893(1) 5. https://law.Resource.org/pub/in/bis/S03/is.1893.1.2002.pdf . Accessed 20 Oct 2023

Canoglu MC (2017) Deterministic landslide susceptibility assessment with the use of a new index (factor of safety index) under dynamic soil saturation: an example from Demircikoy watershed (Sinop/Turky). Carpathian J Earth Environ Sci 12:423–436

Cao Y, Wei X, Fan W, Nan Y, Xiong W, Zhang S (2021) Landslide susceptibility assessment using the Weight of Evidence method: a case study in Xunyang area, China. PLoS ONE 16(1):e0245668. https://doi.org/10.1371/journal.pone.0245668

Carrara A, Cardinali M, Detti R et al (1991) GIS techniques and statistical models in evaluating landslide hazard. Earth Surf Proc Land 16:427–445. https://doi.org/10.1002/esp.3290160505

Chandak PG, Sayyed SS, Kulkarni YU, Devtale MK (2016) Landslide hazard zonation mapping using information value method near Parphi village in Garhwal Himalaya. Ljemas 4:228–236

Chen Z, Wang J (2007) Landslide hazard mapping using a logistic regression model in Mackenzie Valley. Can Nat Hazards 42(1):75–89

Chen CY, Yu FC (2011) Morphometric analysis of debris flows and their source areas using GIS. Geomorphology 129(3–4):387–397

Chen T, Niu R, Jia X (2016) A comparison of information value and logistic regression models in landslide susceptibility mapping by using GIS. Environ Earth Sci 75:867. https://doi.org/10.1007/s12665-016-5317-y

Chen W, Pourghasemi HR, Kornejady A, Zhang N (2017) Landslide spatial modeling: introducing new ensembles of ANN, maxent, and SVM machine learning techniques. Geoderma 305:314–327

Chen F, Yu B, Li B (2018) A practical trial of landslide detection from single-temporal Landsat8 images using contour-based proposals and random forest: a case study of national Nepal. Landslides 15(3):453–464

Chen W, Yan X, Pradhan B (2019) Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes, and RBF network for Long County area (China). Bull Eng Geol Env 78:247–266

Chen W, Fan L, Li C, Pham BT (2020) Spatial prediction of landslides using hybrid integration of artificial intelligence algorithms with frequency ratio and index of entropy in Nanzheng County. China Appl Sci 10(1):29. https://doi.org/10.3390/app10010029

Article   CAS   Google Scholar  

Chimidi G, Raghuvanshi TK, Suryabhagavan KV (2017) Landslide hazard evaluation and zonation in and around Gimbi town, western Ethiopia – a GIS-based statistical approach. Appl Geomat (Springer) 9(4):219–236

Chingkhei RK et al (2013) Landslide hazard zonation in NH-1A in Kashmir Himalaya, India. Int J Geosci 4:1501–1508

Choi J, Oh HJ, Lee HJ, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23

Chung CJ, Fabbri AG (1999) Probabilistic prediction models for landslide hazard mapping. Photogramm Eng Remote Sens 65(12):1389–1399

Chung CF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472. https://doi.org/10.1023/B:NHAZ.0000007172.62651.2b

Ciurleo M, Cascini L, Calvello M (2017) A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils. Eng Geol 2017:223

Conforti M, Aucelli PP, Robustelli G, Scarciglia F (2011) Geomorphology and GIS analysis for mapping gully erosion susceptibility in the Turbolo stream catchment (northern Calabria, Italy). Nat Hazards 56(3):881–898

Corominas J, van Westen C, Frattini P, Cascini L, Malet JP, Fotopoulou S, Van Den Jia N, Xie M, Mitani Y, Ikemi H, Djamaluddin I (2010) A GIS-based spatiala data processing system for slope monitoring. Int J Geo-Inf Res Dev 1(4):1

Costanzo D, Rotigliano E, Irigaray C, Jiménez-Perálvarez JD, Chacón J (2012) Factors selection in landslide susceptibility modelling on large scale following the GIS matrix method: application to the river Beiro basin (Spain). Nat Hazard 12(2):327–340. https://doi.org/10.5194/nhess-12-327-2012

Cruden DM (1991) A simple definition of a landslide. Bull Eng Geol Env 43(1):27–29

Cruden DM, Varnes DJ (1996) Landslides: Investigation and mitigation. In: National Research Council Transportation Research Board Special Report (Book 247); Turner AK, Schuster RL (eds); Transportation Research Board: Washington, DC, USA, 1996; pp. 36–75

Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102(3–4):496–510. https://doi.org/10.1016/j.geomorph.2008.05.041

Dai FC, Lee CF (2002) Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology 42:213–228

Dai FC, Xu C, Yao X et al (2011) Spatial distribution of landslides triggered by the 2008 Ms 8.0 Wenchuan earthquake. China J Asian Earth Sci 40:883–895. https://doi.org/10.1016/j.jseaes.2010.04.010

Demir G, Aytekin M, Akgün A, Ikizler SB, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65:1481–1506. https://doi.org/10.1007/s11069-012-0418-8

Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF (2013) Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya. Nat Hazards 65:135–165. https://doi.org/10.1007/s11069-012-0347-6

Donati L, Turrini MC (2002) An objective method to rank the importance of the factors predisposing to landslides with the GIS methodology application to an area of the Apennines (Valnerina; Perugia, Italy). Eng Geol 63:277–289

Du G, Zhang Y, Iqbal J et al (2017) Landslide susceptibility mapping using an integrated model of information value method and logistic regression in the Bailongjiang watershed, Gansu Province, China. J Mt Sci 14:249–268. https://doi.org/10.1007/s11629-016-4126-9

Dube F, Nhapi I, Murwira A, Gumindoga W, Goldin J, Mashauri DA (2014) Potential of weight of evidence modeling for gully erosion hazard assessment in Mbire District—Zimbabwe. Phys Chem Earth, Parts a/b/c 67–69:145–152

Duman TY, Can T, Gökceoglu C, Nefesliogocu HA, Sonmez H (2006) Application of logistic regression for landslide susceptibility zoning of Cekmee area, Istanbul, Turkey. Environ Geol 51:242–256

Erener A, Mutlu A, Sebnem Düzgün H (2016) A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM). Eng Geol. https://doi.org/10.1016/j.enggeo.2015.09.007

Fanos AM, Pradhan B, Mansor S, Yusof ZM, Abdullah AFb (2018) A hybrid model using machine learning methods and GIS for potential rockfall source identifcation from airborne laser scanning data. Landslides 15(9):1833–1850

Farhan Y, Anbar A, Enaba O, Al-Shaikh N (2015) Quantitative analysis of geomorphometric parameters of Wasi Kerak, Jordan, using remote sensing and GIS. J Water Resour Prot 7:456–475

Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874. https://doi.org/10.1016/j.patrec.2005.10.010

Fayaz M, Meraj G, Khader SA et al (2022) ARIMA and SPSS statistics based assessment of landslide occurrence in western Himalayas. Environ Challenges 9:100624. https://doi.org/10.1016/j.envc.2022.100624

Feizizadeh B, Blaschke T, Nazmfar H, Rezaeimoghaddam MH (2013) Landslide susceptibility mapping for the Urmia Lake basin, Iran: a multi-criteria evaluation approach using GIS. Int J Environ Res 7(2):319–336

Fell R, Corominas J, Bonnard C, Cascini L, Leroi E, Savage WZ (2008) Guidelines for landslide susceptibility, hazard and risk zoning for land use planning. Eng Geol 102:85–98

García S, Fernández A, Luengo J, Herrera F (2009) A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput 13:959

Ghosh D (2015) Landslide susceptibility analysis from morphometric parameter analysis of Riyong Khola basin, West Sikkim, India: a geospatial approach. Int J Geol 5(1):54–65

Glade T (2003) Landslide occurrence as a response to land-use change: a review of evidence from New Zealand. CATENA 51(3–4):297–314

Glade T, Crozier M, Smith P (2000) Applying probability determination to refine landslide-triggering rainfall thresholds using an empirical antecedent daily rainfall model. Pure Appl Geophys 157:1059–1079

Gorum T, Fan X, van Westen CJ, Huang RQ, Xu Q, Tang C, Wang G (2011) Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake. Geomorphology 133(3):152–167

Gumindoga W, Rwasoka DT, Murwira A (2011) Simulation of streamflow using TOPMODEL in the Upper Save River catchment of Zimbabwe. Phys Chem Earth Parts a/b/c 36(14–15):806–813

Guo C et al (2015) Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology 248:93–110

Gupta RP et al (1999) Landslide hazard zonation in a part of the Bhagirathi Valley Garhwal Himalayas using integrated remote sensing–GIS. Himalayan Geol 20:71–85

Gutiérrez F, Linares R, Roqué C, Zarroca M, Carbonel D, Rosell J et al (2015) Large landslides associated with a diapiric fold in Canelles reservoir (Spanish Pyrenees): detailed geological–geomorphological mapping, trenching, and electrical resistivity imaging. Geomorphology 241:224–242

Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study. Cent Italy Geomorphol 31(1–4):181–216

Guzzetti F, Reichenbach P, Ardizzone M, Cardinali M, Galli M (2006) Estimating the quality of landslides susceptibility models. Geomorphology 81(1–2):166–184. https://doi.org/10.1016/j.geomorph.2006.04.007

Hasegawa S, Nonomura A, Nakai S, Dahal RK (2014) Drainage density as rainfall-induced landslides susceptibility index in small catchment area. Int J Landslide Environ 1(1):27–28

Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City. China Arab J Geosci 9:112. https://doi.org/10.1007/s12517-015-2094-y

Hong H, Miao Y, Liu J, Zhu AX (2019) Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping. CATENA 176:45–64

Hong H, Junzhi L, Zhu AX (2020) Modeling landslide susceptibility using logitboost alternating decision trees and forest by penalizing attributes with the bagging ensemble. Sci Total Environ 718:3–15

Horton RE (1932) Drainage-basin characteristics. Trans Am Geophys Union 13(1):350. https://doi.org/10.1029/tr013i001p00350

Hyun-Joo Oh, Pradhan B (2011) Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci 37(9):1264–1276. https://doi.org/10.1016/j.cageo.2010.10.012

Ibrahim J (2011) Landslide assessment and hazard zonation in Mersa and Wurgessa, North Wollo, Ethiopia. Unpublished Master Thesis, School of Graduate Studies, Addis Ababa University, Addis Ababa, Ethiopia, pp 1–10

Irimuș IA, Roșca S, Rus ML, Marian FL, Bilașco S (2017) Landslide susceptibility assessment in Almas basin by means of the frequency rate and GIS techniques. Geografia Tehnologii 12(2):97–109

Jaafari A, Panahi M, Mafi-Gholami D et al (2022) Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides. Appl Soft Comput 116:108254. https://doi.org/10.1016/j.asoc.2021.108254

Jade S, Sarkar S (1993) Statistical models for slope instability classification. Eng Geol 36:91–98

Jasiewicz J, Stepinski TF (2013) Geomorphons—a pattern recognition approach to classification and mapping of landforms. Geomorphology 182:147–156. https://doi.org/10.1016/j.geomorph.2012.11.005

Jazouli E, Barakat A, Khellouk R (2019) GIS-multicriteria evaluation using AHP for landslide susceptibility mapping in Oum Er Rbia high basin (Morocco). Geoenviron Dis 6:3. https://doi.org/10.1186/s40677-019-0119-7

Jeganathan C, Chauniyal DD (2000) An evidential weighted approach for landslide hazard zonation from geo-environmental characterization: a case study of Kelani area. Curr Sci 79(2):238–243

Kakembo V, Xanga WW, Rowntree K (2009) Topographic thresholds in gully development on the hillslopes of communal areas in Ngqushwa Local Municipality, Eastern Cape, South Africa. Geomorphology 110:188–195

Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomat Nat Haz Risk 9(1):49–69. https://doi.org/10.1080/19475705.2017.1407368

Kannan M, Saranathan E, Anabalagan R (2013) Landslide vulnerability mapping using frequency ratio model: a geospatial approach in Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu. India Arab J Geosci 6(8):2901–2913. https://doi.org/10.1007/s12517-012-0587-5

Kanungo DP, Arora MK, Sarkar S, Gupta RP (2009) Landslide susceptibility zonation mapping a review. J South Asia Disaster Stud 2:81–105

Kanungo DP, Sarkar S, Sharma S (2011) Combining neural network with fuzzy, certainty factor south-facing, and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards 59(3):1491–1512

Karimi Nasab S, Ranjbar H, Akbar S (2010) Susceptibility assessment of the terrain for slope failure using remote sensing and GIS, a case study of Maskoon area. Iran Int J Geoinformation Res Dev 1(3):1–13

Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11:425–439. https://doi.org/10.1007/s10346-013-0391-7

Kayastha P, Dhital MR, De Smedt F (2013) Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal. Comput Geosci 52:398–408

Khan H et al (2019) Landslide susceptibility assessment using frequency ratio, a case study of northern Pakistan. Egyp J Remote Sens Space Sci 22(1):11–24

Khan I, Mandotra V (2019) Landslide susceptibility mapping using remote sensing, GIS and field inputs: a case study from Basohli-Bani Road corridor, Northwest Himalaya, Jammu and Kashmir, India. Conference: INDOROCK-2019 (8th Indian Rock Conference), New Delhi. https://www.researchgate.net/publication/341271501_Landslide_Susceptibility_Mapping_Using_Remote_Sensing_GIS_and_Field_Inputs_A_case_study_from_Basohli-_Bani_Road_corridor_Northwest_Himalaya_Jammu_and_Kashmir_India

Khan I, Bahuguna H, Kumar PA (2023) Qualitative slope stability assessment of hill slopes using multiple integrated approaches along Bafliaz-Poshiana (Mughal) Road, Jammu and Kashmir. J Geol Soc India 99:1511–1520. https://doi.org/10.1007/s12594-023-2503-x

Kouhpeima A, Feizniab S, Ahmadib H, Moghadamniab AR (2017) Landslide susceptibility mapping using logistic regression analysis in Latyan catchment. Arab J Geosci 22:85–95

Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin. Korean Environ Geol 40:1095–1113

Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island Malaysia. J Earth Sys Sci 115(6):661–672. https://doi.org/10.1007/s12040-006-0004-0

Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Earth Sci 47:982–990

CAS   Google Scholar  

Lei X, Chen W, Mohammadtaghi A et al (2020) GIS-based machine learning algorithms for gully erosion susceptibility mapping in a semi-arid region of Iran. Remote Sens 12(15):2478

Lin ML, Tung CC (2003) A GIS-based potential analysis of the landslides induced by the Chi-Chi Earthquake. Eng Geol 71:63–77

Luelseged A (2005) Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Cent Japan Geomorphol 65:15–31

Mahdadi F, Boumezbeur A, Hadji R, Kanungo DP, Zahri F (2018) GIS-based landslide susceptibility assessment using statistical models: a case study from Souk Ahras province. NE Algeria Arab J Geosci 11:476. https://doi.org/10.1007/s12517-018-3770-5

Martel S (2004) Mechanics of landslide initiation as a shear fracture phenomenon. Mar Geol 203(3–4):319–339

Martha TR, Van et al (2013) Landslide hazard and risk assessment using semi automatically created landslide inventories. Geomorphology 184:139–150

Meena SR, Ghorbanzadeh O, Blaschke T (2019) Validation of spatial prediction models for landslide susceptibility mapping by considering structural similarity. ISPRS Int J Geo Inform 8:94

Meten M, Bhandary NP (2015) Yatabe R (2015) GIS-based frequency ratio and logistic regression modeling for landslide susceptibility mapping of Debre Sina area in central Ethiopia. J Mt Sci 12(6):1355–1372

Mir RA, Habib Z, Kumar A et al (2024) (2024) Landslide susceptibility mapping and risk assessment using total estimated susceptibility values along NH44 in Jammu and Kashmir. Western Himalaya Nat Hazards. https://doi.org/10.1007/s11069-023-06363-6

Mohammad M, Pourghasemi HR (2012) Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci 61:22136

Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30. https://doi.org/10.1002/hyp.3360050103

Moore ID, O’loughlin EM, Burch GJ (1988) A contour-based topographic model for hydrological and ecological applications. Earth Surf Process Landf 13(4):305–320

Nagarajan R, Roy A, Kumar RV et al (2000) Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions. Bull Eng Geol Env 58:275–287. https://doi.org/10.1007/s002540050348

Nandi A, Shakoor A (2009) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1–2):11–20. https://doi.org/10.1016/j.enggeo.2009.10.001

Nefeslioglu HA, Gokceoglu C, Sonmez H (2008) An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Eng Geol 97(3–4):171–191

Nepal NJ, Chen H, Chen X, Wang TP, Sharma P (2019) Assessment of landslide susceptibility along the Araniko Highway in Poiqu/Bhote Koshi/Sun Koshi Watershed. Nepal Himalaya, Prog Disaster Sci 3:100037. https://doi.org/10.1016/j.pdisas.2019.100037

Neuhäuser B, Terhorst B (2007) Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology 86:12–24

Nhu VH, Shirzadi A, Shahabi H, Singh SK et al (2020) (2020) Shallow landslide susceptibility mapping: a comparison between logistic model tree, logistic regression, naïve bayes tree, artificial neural network, and support vector machine algorithms. Int J Environ Res Public Health 17:2749

Nir D (1957) The ratio of relative relief and absolute altitude of Mt Carmel. Geogr Rev 47:564–569

Onda Y (1993) Underlying rock type controls of hydrological processes and shallow landslide occurrence. Sedimentol: Probl Strateg, Monit, Prediction, Control 217:47–55

Pachauri AK, Gupta PV, Chander R (1998) Landslide zoning in a part of the Garhwal Himalayas. Environ Geol 36:325–334. https://doi.org/10.1007/s100649900032

Park NW (2011) (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62:367–376

Park S, Choi C, Kim B, Kim J (2013) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area Korea. Environ Earth Sci 68:1443–1464

Park S, Kim J (2019) Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl Sci 9. https://doi.org/10.3390/app9050942

Patil AS, Bhadra BK, Panhalkar SS et al (2020) Landslide susceptibility mapping using landslide numerical risk factor model and landslide inventory prepared through OBIA in Chenab Valley, Jammu and Kashmir (India). J Indian Soc Remote Sens 48:431–449. https://doi.org/10.1007/s12524-019-01092-5

Patil Abhijit S et al (2021) The landslide susceptibility assessment using bi-variate statistical information value model of Chenab River Valley, Jammu and Kashmir (India). Disaster Adv 14(11):44–56

Pattusamy S, Purusothaman BM (2014) Disaster management and mapping using remote sensing and GIS. J Comput Biosci Eng 1:1–7

Peruccacci S, Brunetti MT, Luciani S, Vennari C, Guzzetti F (2012) Lithological and seasonal control on rainfall thresholds for the possible initiation of landslides in central Italy. Geomorphology 139:79–90

Petley DN (2008) The global occurrence of fatal landslides in 2007. Geophysical Research Abstracts, vol 10, EGU General Assembly 2008. p 3

Pham BT, Tien Bui D, Indra P, Dholakia MB (2015) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS-based statistical approach of frequency ratio method. Int J Eng Res Technol 4(11):338–344

Pham BT, Bui DT, Pourghasemi HR, Indra P (2017) Dholakia MB (2017) Landslide susceptibility assessment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of Naïve Bayes, Multilayer Perceptron Neural Networks, and Azemeraw Wubalem Functional Trees methods. Theoret Appl Climatol 128:255–273

Pike RJ, Evans IS, Hengl T (2009) Chapter 1 geomorphometry: A Brief Guide. In: Hengl T and Reuter HI, (eds), Developments in Soil Science, Elsevier, 33, 3–30.  https://doi.org/10.1016/S0166-2481(08)00001-9

Polemio M, Petrucci O, (2010) Occurrence of landslide events and the role of climate in the twentieth century in Calabria, southern Italy. Quart J Eng Geol Hydro 43(4):403–415

Poudyal C, Chang C, Oh H-J, Lee S (2010) Landslide susceptibility maps comparing frequency ratio and artificial neural networks: a case study from the Nepal Himalaya. Environ Earth Sci 61(5):1049–1064. https://doi.org/10.1007/s12665-009-0426-5

Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed. Iran Arab J Geosci 6(7):2351–2365

Pourghasemi HR, Pradhan B, Gokceoglu C, Moezzi KD (2012) Landslide susceptibility mapping using a spatial multi criteria evaluation model at Haraz Watershed, Iran. In: Pradhan B, Buchroithner M (eds), Terrigenous Mass Movements. Springer, Berlin, Heidelberg, pp. 23–49

Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365

Pradhan AMS, Kim YT (2014) Relative effect method of landslide susceptibility zonation in weathered granite soil: A case study in Deokjeok-ri Creek. South Korea Nat Hazards 72(2):1189–1217

Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis by using an artificial neural network model. Earth Sci Front 14(6):143–152

Pradhan B, Lee S, Buchroithner MF (2009) Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping. Appl Geomat 1:3–15. https://doi.org/10.1007/s12518-009-0001-5

Pradhan B, Lee S, Buchroithner MF (2010a) A GIS-based backpropagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34:216–235

Pradhan B, Lee S, Buchroithner MF (2010b) Remote sensing and GIS-based landslide susceptibility analysis and its cross-validation in three test areas using a frequency ratio model. Photogramm Fernerkun 1:17–32. https://doi.org/10.1127/14328364/2010/0037

Pradhan B, Youssef A, Varathrajoo R (2010c) Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model. Geo-Spatial Inform Sci 13(2):93–102

Pradhan B, Mansor S, Pirasteh S, Buchroithner M (2011) Landslide hazard and risk analyses at a landslide-prone catchment area using the statistical-based geospatial model. Int J Remote Sens 32(14):4075–4087. https://doi.org/10.1080/01431161.2010.484433

Pradhan B, Chaudhari A, Adinarayana J et al (2012) Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia. Environ Monit Assess 184:715–727. https://doi.org/10.1007/s10661-011-1996-8

Pradhan B, Sameen MI, Al-Najjar HA et al (2021) A meta-learning approach of optimization for spatial prediction of landslides. Remote Sens 13(22):4521. https://doi.org/10.3390/rs13224521

Pradhan B, Dikshit A, Lee S, Kim H (2023) An explainable AI (XAI) model for landslide susceptibility modeling. Appl Soft Comput 142:110324. https://doi.org/10.1016/j.asoc.2023.110324

Qiqing W, Wenping L, Wei C, Hanying B (2015) GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city. China J Earth Syst Sci 124(7):1399–1415

Raghuvanshi TK (2019) Governing factors influence on rock slope stability — statistical analysis for plane mode of failure. J King Saud Univ - Sci 31:1254–1263. https://doi.org/10.1016/j.jksus.2019.01.002

Rai PK, Mohan K, Kumra VK (2014) Landslide hazard and its mapping using remote sensing and GIS. J Sci Res 58:1–13

Ramakrishnan D, Singh TN, Verma AK, Gulati A, Tiwari KC (2013) Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Nat Hazards 65:315–330. https://doi.org/10.1007/s11069-012-0365-4

Raman R, Punia M (2012) The application of GIS-based bivariate statistical methods for landslide hazards 415 assessment in the upper Tons river valley, Western Himalaya, India. Georisk: Assess 416 Manag Risk Eng Syst Geohaz 6(3):145–161

Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain. Indonesia Geoenv Disas 3(1):1–16

Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: western Colorado, USA. Geomorphology 115:172–187

Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2013) Application of frequency ratio, statistical index, and weightsof-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742. https://doi.org/10.1007/s12517-012-0807-z

Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014a) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742

Regmi AD, Yoshida K, Pourghasemi HR, Dhital MR, Pradhan B (2014b) Landslide susceptibility mapping along Bhalubang-Shiwapur area of mid-western Nepal using frequency ratio and conditional probability models. J Mt Sci 11(5):1266–1285

Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91. https://doi.org/10.1016/j.earscirev.2018.03.001

Reis S, Yalcin A, Atasoy M, Nisanci R, Bayrak T, Erduran M, Sancar C, Ekercin S (2012) Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey). Environ Earth Sci 66:2063–2073

Riaz MT, Basharat M, Hameed N, Shafique M, Luo J (2018) A data-driven approach to landslide-susceptibility mapping in mountainous terrain: a case study from the Northwest Himalayas. Pakistan Nat Hazards Revi 19:05018007. https://doi.org/10.1061/(asce)nh.1527-6996.0000302

Riley SJ, DeGloria SD, Elliot R (1999) A terrain ruggedness index that quantifies topographic heterogeneity. Intermountain J Sci 5:23–27

Roşca S, Bilaşco S, Petrea D, Fodorean I, Vescan I, Filip S (2015) Large scale landslide susceptibility assessment using the statistical methods of logistic regression and BSA — study case: the sub-basin of the small Niraj (Transylvania Depression, Romania). Nat Hazards Earth Syst Sci 3:7171–7201. https://doi.org/10.5194/nhessd-3-7171-2015

Saaty T (2008) Decision-making with the analytical hierarchy process. Int J Serv Sci 1(1):83–98

Saha AK, Gupta RP, Sarkar I, Arora KM, Csaplovics E (2005) An approach for GIS-based statistical landslide susceptibility zonation with a case study in the Himalayas. Landslides 2(1):61–69

Sahana M, Sajjad H (2017) Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district. India J Mountain Sci 14(11):2150–2167. https://doi.org/10.1007/s11629-017-4404-1

Sahin EK, Colkesen I, Kavzoglu T (2018) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int 1–23:341–363. https://doi.org/10.1080/10106049.2018.1516248

Sangeeta MBK (2022) Landslide susceptibility, social vulnerability, and risk assessment in Kumaun Himalaya, Uttarakhand. India Arab J Geosci 15:1600. https://doi.org/10.1007/s12517-022-10869-x

Sarkar S, Kanungo DP (2004) An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogramm Eng Remote Sens 70(5):617–625

Sarkar S, Kanungo DP, Patra AK, Kumar P (2008) GIS based spatial data analysis for landslide susceptibility mapping. J Mt Sci 85(5):52–62

Sarkar S, Kanungo DP, Mehrotra GS (1995) Landslide hazard zonation: a case study in Garhwal Himalaya. India Int Mountain Soc 15(4):301–309

Sarkar S, Rjan Martha T, Roy A (2013) Landslide susceptibility assessment using information value method in parts of the Darjeeling Himalayas. Geol Soc India 82:351–362

Sarkar S, Kanungo D, Ptra A, Kumar P (2006) Disaster mitigation of debris fow, slope failure, and landslides. GIS-based landslide susceptibility case study in Indian Himalaya. Universal Academy Press, Tokyo, pp 617–624

Senanayake S, Pradhan B, Huete A, Brennan J (2020) Assessing soil erosion hazards using land-use change and landslide frequency ratio method: a case study of Sabaragamuwa province Sri Lanka. Remote Sens 12(9):1483. https://doi.org/10.3390/rs12091483

Sensing R, Analysis B, Gashaw T, Bantider A, Mahari A (2014) Evaluations of land use/land cover changes and land degradation in Dera District, Ethiopia: GIS and remote sensing based analysis. https://doi.org/10.12983/ijsres-2014-p0199-0208

Sharma M, Upadhyay RK, Tripathi G, Kishore N, Shakya A, Meraj G, Kanga S, Singh SK, Kumar P, Johnson BA et al (2023) Assessing landslide susceptibility along India’s National Highway 58: a comprehensive approach integrating remote sensing, GIS, and logistic regression analysis. Conservation 3(3):444–459. https://doi.org/10.3390/conservation3030030

Smith GH (1935) The relative relief of Ohio. Geog Rev 25:272–284

Strahler AN (1964) Quantitative geomorphology of drainage basin and channel networks. In: Chow V (ed), Handbook of Applied Hydrology, McGraw Hill, New York, 439–476

Srivastava V, Srivastava H, Lakhera RC (2010) Fuzzy gamma based geomatic modeling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya. India Geomat Nat Hazards Risk 1(3):225–242

Sujatha ER, Rajamanickam GV, Kumaravel P (2012) Landslide susceptibility analysis using probabilistic certainty factor approach: a case study on Tevankarai stream watershed. India J Earth Syst Sci 121(5):1337–1350

Tella A, Balogun AL (2021) GIS-based air quality modeling: spatial prediction of PM10 for Selangor State, Malaysia using machine learning algorithms. Environ Sci Pollut Res 28(33):44157–44168. https://doi.org/10.1007/s11356-021-14730-7

Temesgen B, Mohammed U, Korme T (2001) Natural hazard assessment using GIS and remote sensing methods, with particular reference to the Landslides in the Wondogenet Area, Ethiopia. Phys Chem Earth Part C: Sol Terrestrials Planet Sci (C) 26:615–665

Tesfa C, Woldearegay K (2021) Characteristics and susceptibility zonation of landslides in Wabe Shebelle Gorge, southeastern Ethiopia. J Afr Earth Sc 182(May):104275

Tibaldi A, Ferrari L, Pasquarè G (1995) Landslides triggered by earthquakes and their relations with faults and mountain slope geometry: an example from Ecuador. Geomorph 11:215–226

Tie Bui D, Shahabi H, Geertsema M, Omidvar E, Clagu JJ, Thai Pham B (2019) new ensemble models for shallow landslide susceptibility modeling in a semi-arid watershed. Forests 10(9):743

Tsegaratos P, Ilia I, Hong H, Chen W, Xu C (2017) Applying information theory and GIS based quantitative methods to produce landslide susceptibility maps in Mancheang County, China. Landslides 14:1091–1111

Tuan T, Dan N (2012) Landslide susceptibility mapping and zoning in the Son La hydropower catchment area using the analytical hierarchy process. J Sci Earth 3:223–232

Valdiya KS (1985) Accelerated erosion and landslide prone zone in the Central Himalayan Region. In: Singh JS (ed) Environmental regeneration in Himalaya. Gyanodaya Prakashan, Nainital, pp 12–33

Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30(3):399–419

Varnes DJ (1984) Landslide hazard zonation, a review of principles and practice. UNESCO, Paris, p 63

Wang HB, Wu SR, Shi JS, Li B (2011) Qualitative hazard and risk assessment of landslides: a practical framework for a case study in China. Nat Hazards 69:1281–1294. https://doi.org/10.1007/s11069-011-0008-1

Wang Q, Guo Y, Li W, He J, Wu Z (2019) Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor. Geomat Nat Hazards Risk 10(1):820–835. https://doi.org/10.1080/19475705.2018.1549111

Wang Y, Zhice F, Mao W, Ling P, Hong H (2020) Comparative study of landslide susceptibility mapping with different recurrent neural networks. Comp Geosci 138:10445

Weirich F, Blesius L (2007) Comparison of satellite and air photo-based landslide susceptibility maps. Geomorph 87:352–364. https://doi.org/10.1016/j.geomorph.2006.10.003

Weng Q (2010) Remote sensing and GIS integration: theories, methods, and applications. McGraw-Hill, New York

Wilson JP, Gallant JC (eds) (2000) Terrain analysis: principles and applications. Wiley, Hoboken

Woldearegay K (2008) Characteristics of a large-scale landslide triggered by heavy rainfall in Tarmaber area, central highlands of Ethiopia. Geophys Res Abstr 10:EGU2008-A-04506-EGU02008

Wubalem A, Meten M (2020) Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Appl Sci 2. https://doi.org/10.1007/s42452-020-2563-0

Wubalem A (2021) Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia. Geoenviron Disaster 8. https://doi.org/10.1186/s40677-020-00170-y

Wubalem A (2020) Modeling of landslide susceptibility in a part of Abay Basin, northwestern Ethiopia. Open Geosci 12(1):1440–1467.  https://doi.org/10.1515/geo-2020-0206

Xu C, Dai FC, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145–146:70–80

Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41(1):201–226

Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85:274–287. https://doi.org/10.1016/j.catena.2011.01.014

Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101:572–582

Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio logistic regression artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci 35:1125–1138

Yin KJ, Yan TZ (1988) Statistical prediction model for slope instability of metamorphosed rocks. In: Proceedings of the ffth international symposium on landslides, Lausanne, Switzerland, vol 2, pp 1269–1272

Youssef AM, Al-Kathery M, Pradhan B (2015) Landslide susceptibility mapping at Al-Hasher Area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models. Geosci J 19(1):113–134

Youssef AM, Pradhan B, Dikshit A, Al-Katheri MM, Matar SS, Mahdi AM (2022) Landslide susceptibility mapping using CNN-1D and 2D deep learning algorithms: comparison of their performance at Asir Region, KSA”. Bull Eng Geol Env 81(4):2022. https://doi.org/10.1007/s10064-022-02657-4

Zaz SN, Romshoo SA (2022) Landslide susceptibility assessment of Kashmir Himalaya. India Arab J Geosci 15:552. https://doi.org/10.1007/s12517-022-09699-8

Zhao X, Chen W (2020) GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Appl Sci 10:16. https://doi.org/10.3390/app10010016

Zhao X, Chen W (2020) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens 12:2180

Zhu AX, Miao Y, Wang R, Zhu T, Deng Y, Liu J et al (2018) A comparative study of an expert knowledge-based model and two data-driven models for landslide susceptibility mapping. CATENA 166:317–327

Download references

Acknowledgements

The authors would like to acknowledge all of the agencies and individuals, Geological Survey of India (GSI), Indian Meteorological Department (IMD), and USGS for obtaining the maps and data required for the study.

Author information

Authors and affiliations.

Geological Survey of India Northern Region, Jammu, Jammu and Kashmir, 180006, India

Department of Geology, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, 221005, India

Imran Khan & Ashutosh Kainthola

Geological Survey of India, CHQ, Kolkata, West Bengal, 700091, India

Harish Bahuguna

Department of Geography, University of Jammu, Jammu, Jammu and Kashmir, 180006, India

Md. Sarfaraz Asgher

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Imran Khan .

Ethics declarations

Conflict of interest.

The authors declare no competing interests.

Additional information

Responsible Editor: Biswajeet Pradhan

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Khan, I., Kainthola, A., Bahuguna, H. et al. Comparative landslide susceptibility assessment using information value and frequency ratio bivariate statistical methods: a case study from Northwestern Himalayas, Jammu and Kashmir, India. Arab J Geosci 17 , 231 (2024). https://doi.org/10.1007/s12517-024-12022-2

Download citation

Received : 06 November 2023

Accepted : 13 June 2024

Published : 10 July 2024

DOI : https://doi.org/10.1007/s12517-024-12022-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Landslide susceptibility mapping
• Bivariate statistical methods
• Information value
• Frequency ratio
• Northwestern Himalayas
• Find a journal
• Publish with us
• Track your research

Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods: A case study in China

• Nong, Xizhi
• Chen, Lihua
• Wei, Jiahua

Dissolved oxygen (DO) is an essential indicator for assessing water quality and managing aquatic environments, but it is still a challenging topic to accurately understand and predict the spatiotemporal variation of DO concentrations under the complex effects of different environmental factors. In this study, a practical prediction framework was proposed for DO concentrations based on the support vector regression (SVR) model coupling multiple intelligence techniques (i.e., four data denoising techniques, three feature selection rules, and four hyperparameter optimization methods). The holistic framework was tested using a data matrix (17532 observation data in total) of 12 indicators from three vital water quality monitoring stations of the longest inter-basin water diversion project in the world (i.e., the Middle-Route of the South-to-North Water Diversion Project of China), during the year 2017 to 2020 period. The results showed that the framework we advocated for could successfully and accurately predict DO concentration variations in different geographical locations. The model used the "wavelet analysis-LASSO regression-random search-SVR" combination of the Waihuanhe station has the best prediction performance, with the Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2) values of 0.251, 0.063, 0.190, and 0.911, respectively. The combined methods using feature selection and hyperparameter optimization techniques can significantly promote the robustness and accuracy of the prediction model and can provide a new universal and practical way for investigating and understanding the environmental drivers of DO concentration variations. For the water quality management department, this proposed comprehensive framework can also identify and reveal the key parameters that should be concerned and monitored under different environmental factors change. More studies in terms of assessing potential integrated water quality risk using multi-indicators in mega water diversion projects and/or similar water bodies are required in the future.

No Sources Found

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Quantitative analysis of peri-urbanization: developing a peri-urban index for medium-sized cities using the analytic hierarchy process—a case study of yozgat, turkey.

Share and Cite

Demiroğlu İzgi, B. Quantitative Analysis of Peri-Urbanization: Developing a Peri-Urban Index for Medium-Sized Cities Using the Analytic Hierarchy Process—A Case Study of Yozgat, Turkey. Sustainability 2024 , 16 , 6002. https://doi.org/10.3390/su16146002

Demiroğlu İzgi B. Quantitative Analysis of Peri-Urbanization: Developing a Peri-Urban Index for Medium-Sized Cities Using the Analytic Hierarchy Process—A Case Study of Yozgat, Turkey. Sustainability . 2024; 16(14):6002. https://doi.org/10.3390/su16146002

Demiroğlu İzgi, Begüm. 2024. "Quantitative Analysis of Peri-Urbanization: Developing a Peri-Urban Index for Medium-Sized Cities Using the Analytic Hierarchy Process—A Case Study of Yozgat, Turkey" Sustainability 16, no. 14: 6002. https://doi.org/10.3390/su16146002

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals