small-N Analysis Research Articles

Explaining the US Health Paradox

Abstract Despite its high health spending, the US shows below-average life expectancy when compared to other OECD-countries. To find explanatory factors for this paradox, this article employs a Nested Analysis combining Large N Analysis (LNA) and Small N Analysis (SNA). First, it examines how far US life expectancy deviates from OLS model predictions which include a comprehensive set of health determinants. The results show that the established determinants clearly fail to explain the US underperformance. Second, a comparative analysis between the US and Canada identifies three idiosyncratic factors that explain the US deviation: its health care system particularities (1), the opioid crisis and drug-related deaths (2), and the particularly severe impact of the COVID-19 pandemic (3).

The “added benefit” – Female peacekeepers in UN peacekeeping missions

Since the 1990s, the United Nations (UN) has advocated for an increased presence of female peacekeepers in peacekeeping missions to mitigate incidents of sexual violence against civilian populations. The rationale behind this initiative posits that women possess a unique capability to recognise and address the specific needs of fellow females while exerting a positive influence on their male colleagues. However, this dual role, encompassing both tending to local populations' needs and regulating the behaviour of male peacekeepers, places female peacekeepers under a double burden.This article evaluates the possible relationship between the proportion of female military personnel in peacekeeping missions and reported sexual violence cases. Through a comparative analysis of two missions, it explores the impact of female peacekeepers on local populations and trust in peacekeeping operations. This research addresses a significant gap in academic discourse, as the influence of women in peacekeeping is infrequently discussed, and scholarly opinions on their actual impact differ. The study employs a hybrid approach, combining qualitative small-N analysis and quantitative data. Despite observing a potential positive influence on reported sexual violence cases in the selected peacekeeping mission, the study underscores the limited actual impact of female peacekeepers. Importantly, the author contends that the current UN approach—simply deploying women to peacekeeping missions—fails to address the underlying issues rooted in military deployment, signalling a need for more comprehensive strategies to effect lasting change.

‘A veto for the government’: the Money Message as a foil to new legislation

ABSTRACT Veto players hold the capacity to decline a choice being made, making them powerful actors within legislative systems. During 2016–2020, Ireland was governed by a minority government, reliant on confidence and supply votes from other parliamentarians, thus it did not hold a veto during parliamentary votes. However, if the independent Bills Office and Ceann Comhairle deemed a ‘Money Message’ would be necessary for a Private Members’ Bill (PMB), even if it had achieved majority support in the Dáil, then the government could refuse to grant one, returning veto status to the minority government. To date, research has not provided a comprehensive overview of the PMBs blocked, nor examined in detail the cases that were high-profile. Via a nested analysis, we code 79 PMBs and complement our analysis through interviews with parliamentarians whose bills were blocked. We identify eleven policy themes in the PMBs that were blocked, demonstrating that vetoing via the Money Message was a widespread phenomenon. Next, we conduct a small-n analysis of the application of Money Messages across a variety of bills. Our analysis provides the first comprehensive examination of a procedure that holds lasting implications for the legislative process, perceptions of democracy, and executive dominance over minor parties in Ireland.

Multilevel Research Designs: Case Selection, Levels of Analysis, and Scope Conditions

Subnational research in comparative politics has been growing steadily in the last two decades. However, methodological advances have been rather limited. This article builds upon Snyder’s (Stud Comp Int Dev 36(1):93–110, 2001) subnational comparative method and extends its logic to the comparison of subnational units from different countries. It proposes a novel typology of multilevel research designs that focuses particularly on cross-national small-N analysis (CSNA). This research design offers three different logics of qualitative case selection to achieve a sound trade-off between internal and external validity. This article analyzes the advantages and limitations of the underlying logics of CSNA and illustrates their use with recent empirical research from Latin American countries. It concludes by highlighting its versatility and offers a series of best practices in order to produce more generalizable findings than the majority of single-country subnational comparisons.

Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data

Mixed-methods designs, especially those where cases selected for small-N analysis (SNA) are nested within a large-N analysis (LNA), have become increasingly popular. Yet, since the LNA in this approach assumes that units are independently distributed, such designs are unable to account for spatial dependence, and dependence becomes a threat to inference, rather than an issue for empirical or theoretical investigation. This is unfortunate, since research in political science has recently drawn attention to diffusion and interconnectedness more broadly. In this paper we develop a framework for mixed-methods research with spatially dependent data—a framework we label “geo-nested analysis”—where insights gleaned at each step of the research process set the agenda for the next phase and where case selection for SNA is based on diagnostics of a spatial-econometric analysis. We illustrate our framework using data from a seminal study of homicides in the United States.

Geo-Nested Analysis: Mixed-Methods Research with Spatially Dependent Data

Mixed-methods designs have become increasingly popular. Lieberman (2005), for instance, advocates “nested analysis”, where cases for small-N analysis (SNA) are selected based on a large-N analysis (LNA). Yet, since the LNA in this approach assumes that units are independently distributed, such designs are unable to account for spatial dependence, and dependence becomes a threat to inference, rather than an issue for empirical or theoretical investigation. This is unfortunate, since research in political science has recently drawn attention to diffusion and interconnectedness more broadly. Within traditional nested analysis, if process tracing during the SNA discovers diffusion as a causal mechanism, the LNA is disconnected from the SNA. Extending Lieberman’s nested analysis to spatially dependent data, we outline a framework for “geo-nested analysis” – where case selection for SNA is based on diagnostics of a spatial-econometric analysis. We illustrate this strategy using data from a seminal study of homicides in the United States.

Why do ethnopolitical organisations turn to crime?

This paper empirically analyses the involvement of ethnopolitical organisations in criminal behaviour across time in two regions of the world – the Middle East and Eastern Europe. Drawing on the data from the Minorities at Risk Organizational Behavior data set, it contributes important insights to a literature on organisational crime that is dominated by case studies and small-N analysis, as well as reinvigorating the study of ethnopolitical organisations as actors in the analysis of organised crime. Our findings reveal that in both regions groups that engage in violence of some form were significantly more likely to engage in many kinds of criminal activity. Our analysis also finds that ideological orientation has a marginal impact, while economic grievances and diaspora connections were significant predictors of criminal activity for groups in the Middle East (but not Eastern Europe). In sum, this analysis suggests that the decision to engage in criminal activity is primarily dependent on the organisation’s internal attributes and external influences.

Party Size and Portfolio Payoffs: The Proportional Allocation of Ministerial Posts in Coalition Governments

Over 30 years ago, Eric Browne and Mark Franklin demonstrated that parties in a coalition tend to receive portfolio payoffs in almost perfect proportionality to their seat share. Even though this result has been confirmed in several studies, few researchers have asked what the underlying mechanism is that explains why parties receive a proportional payoff. The aim of this paper is to investigate the causal mechanism linking party size and portfolio payoffs. To fulfil this aim, a small-n analysis is performed. By analysing the predictions from a statistical analysis of all post-war coalition governments in 14 Western European countries, two predicted cases are selected, the coalitions that formed after the 1976 Swedish election and the 1994 German election. In these case studies two hypotheses are evaluated: that the proportional distribution of ministerial posts is the result of a social norm, and that parties obtain payoffs according to their bargaining strength. The results give no support to the social norm hypothesis. Instead, it is suggested that proportionality serves as a bargaining convention for the actors involved, thus rendering proportional payoffs more likely.

Strategies of Causal Inference in Small-N Analysis

Much debate concerning small-N analysis has centered on the question of whether this research tradition has powerful tools for assessing causality. Yet, recent contributions make it clear that scholars are not in consensus with regard to the more basic issue of which procedures and underlying logic are in fact used in small-N causal assessment. Focusing on the field of comparative-historical analysis, this article attempts to clarify these procedures and logic. Methods associated with three major strategies of small-N causal inference are examined: nominal comparison, ordinal comparison, and within-case analysis. The article argues that the use of these three strategies within particular small-N studies has led scholars to reach radically divergent conclusions about the logic of causal analysis in small-N research. One implication of this argument is that methodologists must sort out the interrelationship between strategies of causal inference before arriving at conclusions about the overall strengths and limitations of small-N analysis.

Thickening Thin Concepts and Theories: Combining Large N and Small in Comparative Politics

In recent controversies about comparative politics, it often seems as though there are only two approaches: the systematic logical deduction of universalistic theory and the more traditional case studies and small-N comparisons leading more inductively to middle range theory.1 The purpose of this article is to situate large-N quantitative analysis in this controversy. Quantitative analysis has its weaknesses, but they could be counterbalanced by some real strengths in small-N analysis. And quantitative analysis has certain methodological advantages that help compensate for some of the weaknesses of small-N analysis. On the one hand, small-N analysis tends to develop (complex or multidimensional) concepts and theories that are well-suited for description and for making inferences about simple causation on a small scale or in a few cases, but thick concepts and theories are unwieldy in generalizing or rigorously testing complex hypotheses. On the other hand, quantitative analysis is justifiably criticized for its (reductionist or simplistic) concepts and theories, but it is the best method available for testing generalizations, especially generalizations about complex causal relationships. Quantitative analysis has hardly begun to exploit its full potential in assimilating complex concepts and testing complex theories, largely due to data limitations. In order to realize its potential, scholars need to answer two key questions that arise at the intersection of small-N and quantitative analysis. Can thick concepts be translated into the thin format of quantitative data? And can the nuanced, conditional, complex, and contextualized hypotheses of small-N analysis be translated into quantitative models? I argue that the answer to both questions is yes in principle, but that in order to make these approaches complementary in practice we must collect different data and more data and do it more systematically and rigorously.