To send 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 sending content to .
To send content items to your Kindle, first ensure firstname.lastname@example.org
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 sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent 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.
Movement disorders associated with exposure to antipsychotic drugs are common and stigmatising but underdiagnosed.
To develop and evaluate a new clinical procedure, the ScanMove instrument, for the screening of antipsychotic-associated movement disorders for use by mental health nurses.
Item selection and content validity assessment for the ScanMove instrument were conducted by a panel of neurologists, psychiatrists and a mental health nurse, who operationalised a 31-item screening procedure. Interrater reliability was measured on ratings for 30 patients with psychosis from ten mental health nurses evaluating video recordings of the procedure. Criterion and concurrent validity were tested comparing the ScanMove instrument-based rating of 13 mental health nurses for 635 community patients from mental health services with diagnostic judgement of a movement disorder neurologist based on the ScanMove instrument and a reference procedure comprising a selection of commonly used rating scales.
Interreliability analysis showed no systematic difference between raters in their prediction of any antipsychotic-associated movement disorders category. On criterion validity testing, the ScanMove instrument showed good sensitivity for parkinsonism (90%) and hyperkinesia (89%), but not for akathisia (38%), whereas specificity was low for parkinsonism and hyperkinesia, and moderate for akathisia.
The ScanMove instrument demonstrated good feasibility and interrater reliability, and acceptable sensitivity as a mental health nurse-administered screening tool for parkinsonism and hyperkinesia.
A reliable biomarker signature for bipolar disorder sensitive to illness phase would be of considerable clinical benefit. Among circulating blood-derived markers there has been a significant amount of research into inflammatory markers, neurotrophins and oxidative stress markers.
To synthesise and interpret existing evidence of inflammatory markers, neurotrophins and oxidative stress markers in bipolar disorder focusing on the mood phase of illness.
Following PRISMA (Preferred Reporting Items for Systematic reviews and Meta-analyses) guidelines, a systematic review was conducted for studies investigating peripheral biomarkers in bipolar disorder compared with healthy controls. We searched Medline, Embase, PsycINFO, SciELO and Web of Science, and separated studies by bipolar mood phase (mania, depression and euthymia). Extracted data on each biomarker in separate mood phases were synthesised using random-effects model meta-analyses.
In total, 53 studies were included, comprising 2467 cases and 2360 controls. Fourteen biomarkers were identified from meta-analyses of three or more studies. No biomarker differentiated mood phase in bipolar disorder individually. Biomarker meta-analyses suggest a combination of high-sensitivity C-reactive protein/interleukin-6, brain derived neurotrophic factor/tumour necrosis factor (TNF)-α and soluble TNF-α receptor 1 can differentiate specific mood phase in bipolar disorder. Several other biomarkers of interest were identified.
Combining biomarker results could differentiate individuals with bipolar disorder from healthy controls and indicate a specific mood-phase signature. Future research should seek to test these combinations of biomarkers in longitudinal studies.
Over the last decade, organized criminal violence has reached unprecedented levels and has caused as much violent death globally as direct armed conflict. Nonetheless, the study of organized crime in political science remains limited because these organizations and their violence are not viewed as political. Building on recent innovations in the study of armed conflict, I argue that organized criminal violence should no longer be segregated from related forms of organized violence and incorporated within the political violence literature. While criminal organizations do not seek to replace or break away from the state, they have increasingly engaged in the politics of the state through the accumulation of the means of violence itself. Like other non-state armed groups, they have developed variously collaborative and competitive relationships with the state that have produced heightened levels of violence in many contexts and allowed these organizations to gather significant political authority. I propose a simple conceptual typology for incorporating the study of these organizations into the political violence literature and suggest several areas of future inquiry that will illuminate the relationship between violence and politics more generally.
Salmonella is a leading cause of bacterial foodborne illness. We report the collaborative investigative efforts of US and Canadian public health officials during the 2013–2014 international outbreak of multiple Salmonella serotype infections linked to sprouted chia seed powder. The investigation included open-ended interviews of ill persons, traceback, product testing, facility inspections, and trace forward. Ninety-four persons infected with outbreak strains from 16 states and four provinces were identified; 21% were hospitalized and none died. Fifty-four (96%) of 56 persons who consumed chia seed powder, reported 13 different brands that traced back to a single Canadian firm, distributed by four US and eight Canadian companies. Laboratory testing yielded outbreak strains from leftover and intact product. Contaminated product was recalled. Although chia seed powder is a novel outbreak vehicle, sprouted seeds are recognized as an important cause of foodborne illness; firms should follow available guidance to reduce the risk of bacterial contamination during sprouting.
To this point, we have mainly focused on the early stages of mechanism-centered research, particularly selecting cases for pathway analysis that explore the links underlying an association between some explanatory variable (X1) and an outcome (Y). However, the results of pathway analysis not only can provide a map of mechanisms underlying an X1/Y relationship, but also can be used to “look forward” to provide insight into the feasibility and nature of future studies of mechanisms. From this vantage, pathway analysis is best understood as part of a larger research agenda that spans multiple years, multiple research approaches, and multiple researchers.
The forward-looking purposes of pathway analysis are largely ignored in the literature for a variety of possible reasons. Partly this omission may reflect the limited state of knowledge about mechanisms in many areas. Quantitative studies of mechanisms require that a researcher has already identified some basic information about the mechanisms linking X1 and Y and understand the basic structure of the X1/Y relationship (see Table 2.2 in Chapter 2). This omission also may reflect that the literature on how to study mechanisms quantitatively is relatively new and rapidly evolving. Nevertheless, there are some areas in which researchers have accumulated a considerable amount of insight into mechanisms, and there are compelling reasons for pursuing further mechanism-centered research. For researchers working in such an area, the question to ask is: How might insights from pathway analysis inform future studies on mechanisms?
The allure of mixed-method research in the search for causal mechanisms
Scholars of judicial behavior have found time and time again an association between US Supreme Court justices’ political ideologies and their votes (e.g., Pritchett 1948; Rhode and Spaeth 1976; Schubert 1965; Segal and Cover 1989; Segal et al. 1995; Segal and Spaeth 1993, 1999, 2002). Scholars differ sharply, however, over the meaning of this finding. Behavioralists argue that the relationship between ideology and votes suggests that justices largely ignore the law and impose their personal preferences when deciding cases. “Simply put, Rehnquist votes the way he does because he is extremely conservative; Marshall voted the way he did because he is extremely liberal” (Segal and Spaeth 1993: 65). Postbehavioralists envisage a very different decision-making process (Gillman 2001). They argue that justices begin with a good faith understanding of legal rules and principles, and that those general legal principles meaningfully constrain justices’ discretion (e.g., Burton 1992; Gillman 1993, 1996; Cushman 1998; see also Dworkin 1978). From this perspective, conservative and liberal judges can end up voting quite differently from one another while still applying the same legal principle, just as two sergeants ordered to choose the “best” five soldiers from a platoon might both follow the order but still select different soldiers (Dworkin 1978).
Researchers have long recognized that “the cases you choose affect the answers you get” (Geddes 1990). Accordingly, it is critical to select cases carefully and in a transparent manner. This chapter lays out our general approach for selecting cases for pathway analysis. It begins by briefly reviewing the analytic goals of pathway analysis and how they relate to the general criteria for case selection. It then outlines some of the key challenges in applying these criteria and ends with practical advice for implementing these general principles.
The goals of pathway analysis and case selection
As discussed in the last chapter, pathway analysis ultimately has two goals: (1) to gain insight into the mechanisms that connect some explanatory variable (X1) and some outcome (Y) in specific cases; and (2) to use the insights from these cases to generate hypotheses about mechanisms in the unstudied population of cases that feature the X1/Y relationship.
These two goals, in turn, imply several principles for case selection (see Figure 3.1). The first goal of pathway analysis suggests the expected relationship criteria, which means the degree to which individual cases are expected to feature the relationship of interest between X1 and Y given existing theory, empirical knowledge, and large-N studies. It is perhaps obvious, but studying mechanisms that underlie the X1/Y relationship requires identifying cases where the X1 variable is related to the Y, controlling for possible confounds (X2) (Gerring 2007). If the relationship between X1 and Y differs based on the values of X1, then a researcher needs to understand how the relationship depends on the value of X1.
The primary purpose of pathway analysis is to build knowledge about the causal mechanisms that link X1 to Y across settings. This implies the need to perform comparative analyses that can generate knowledge and/or hypotheses about the broader population of cases that feature the X1/Y relationship. Before delving into the details of case selection, it is important to consider how researchers should prepare for pathway analysis. As an initial matter, researchers must clarify their specific goals. Once oriented to the basic task, they should: (1) determine whether the basic analytic requisites of pathway analysis are met; (2) identify what is already known about the X1/Y relationship; and (3) take stock of relevant measures.
The threshold task: clarifying the goals of pathway analysis
The purpose of using pathway analysis differs from other, more familiar types of research, which is why it is important for researchers to clarify these goals from the start. On this score, James Mahoney and Gary Goertz (2006) usefully distinguish between two ideal types of research in the social sciences: causes of effects and effects of causes. Causes-of-effects research seeks to provide “thick description” of the emergence of singular events or outcomes in particular settings (Mahoney and Goertz 2006; see Brady and Collier 2004; Geertz 1973). For US politics or history research, for example, possible questions might include: Why World War II? Why did the New Deal coalition collapse? What factors caused the most recent financial crisis? Why did the US Supreme Court decide Bush v. Gore as it did? For these types of questions, case selection is driven mostly by the substantive importance of the outcome to be explained, and the research primarily emphasizes case-specific internal validity (“Did the research get the story right?”), and not external validity (“Do the story's lessons apply in other contexts?”), although scholars often try to draw some broader lessons from their detailed descriptions.
So far in this book we have focused on using various quantitative methods to guide case selection in order to explore the substantive relationship between a key explanatory variable (X1) and an outcome (Y). The discussion assumes that, in some settings, researchers do not have strong reasons for selecting particular cases, and so are using quantitative data to determine case selection. However, in other cases, researchers often have compelling reasons for examining certain cases, including theoretical, empirical, and practical reasons (such as access to data sources, cost effectiveness, and preexisting expertise). Under these circumstances, the issue is not which cases to choose initially, but how to gain perspective on the cases already chosen and perhaps how to choose cases for additional analysis.
This chapter illustrates how large-N data can help gain perspective on previously chosen cases by shedding light on how they fit within the broader universe of cases that could have been chosen, thus providing some leverage over how the extant case studies compare to unstudied cases. This can help a researcher understand how to generalize to unstudied cases and how to identify future cases that might offer promising comparisons.
The use of large-N data to gain perspective on previously selected cases is best explored by example. In this chapter we focus on the relationship between natural resources (X1) and civil conflict (Y), which was also the focus of Chapter 6.We first explain why this example is useful, then we review the existing pathway analyses conducted by Michael Ross (2004) and the large-N data analysis of Paul Collier and Anke Hoeffler (2004), and finally we demonstrate how to use the large-N data to gain some perspective on the cases selected by Ross and how to identify avenues for future inquiry.
In the previous chapters, we used a regression-based approach for case selection. Like any approach, relying on regression has important trade-offs that researchers need to be aware of. Perhaps most importantly, this approach relies on the functional form assumptions of the underlying regression analysis, which researchers may not want to adopt in selecting cases for pathway analysis. The good news is that there is nothing about pathway analysis that requires the use of regression analysis. We have concentrated on regression analysis because it is well known by researchers, and we think likely to be used in practice. We recognize that there are a host of other options to leverage existing large-N data to select cases for comparative pathway analysis.
This chapter illustrates one alternative to regression-based case selection. It explores how a researcher might use a common matching approach to select cases, and we apply the technique to an example we also discuss in the next chapter: the relationship between natural resource exports and civil conflict. A matching approach is useful, because it is explicitly designed for making comparisons. This is valuable in our context, because it fits well with the underlying importance of comparative research strategies. It is worth noting that others have also combined matching approaches with case studies, but the goal in prior approaches has been to understand causal effects (Nielsen 2014; Rosenbaum and Silber 2001); in general, matching approaches aimed at improving causal inference focus on identifying comparable treatment and control cases based on pretreatment co-variates that are related to treatment (Ho et al. 2007; Rubin 2008). In pathway analysis, however, the goal differs. By definition, pathway analysis assumes that the causal effect has been identified in previous scholarship and matching is used as a means to identify cases that are appropriate for exploring the mechanisms underlying the previously identified relationship.
Social scientists have identified a need to move beyond the analysis of correlation among variables to the study of causal mechanisms that link them. Nicholas Weller and Jeb Barnes propose that a solution lies in 'pathway analysis', the use of case studies to explore the causal links between related variables. This book focuses on how the small-N component of multi-method research can meaningfully contribute and add value to the study of causal mechanisms. The authors present both an extended rationale for the unique role that case studies can play in causal mechanism research, and a detailed view of the types of knowledge that case studies should try to generate and how to leverage existing large-N data to guide the case selection process. The authors explain how to use their approach both to select cases and to provide context on previously studied cases.
In the last chapter, we applied our method to several simple textbook examples, including a hypothetical that posited a linear X1/Y relationship with equifinality (that is, multiple pathways between X1 and Y). In this chapter, we turn to a discussion of case selection in a more complex empirical setting: one that features a non-linear relationship between X1 and Y and the relationship is embedded in a longer causal chain that involves dynamic processes. Under these conditions, researchers must decide where in the causal process to investigate and how to deal with temporal dynamics in their large- and small-N studies. This chapter grapples with these issues by using an example drawn from the policy diffusion literature. As we make clear, in the context of pathway analysis, conceptualizing what is the key explanatory variable and what is the mechanism is not determined by the labels used in prior research or deep philosophical concerns, but rather by practical concerns regarding the relevant research question, how variables are used within the existing empirical research, and data availability. Once the relevant literature is properly understood, our method can be applied in a relatively straightforward manner.
International policy diffusion
The literature on international policy diffusion is both immense and contested. For purposes of this chapter, therefore, we distinguish between two very different types of studies. One type focuses on the threshold question of whether policy choices spread across political boundaries; that is, whether policy adoption is purely a function of domestic pressures (Berry and Berry 1990; Gray 1973, 1994; Simmons et al. 2008; cf. Volden et al. 2008). The second type of study essentially assumes that policies diffuse across borders, so its focus is on how and under what conditions policy decisions of one country spill over into others. In general, the literature that assumes the existence of diffusion and focuses on how policy diffusion works is more interesting for our purposes, as the search for mechanisms lies at its center (Simmons et al. 2008).