Quantitative human rights scholarship is increasing. New data sets and methods have helped researchers examine a broad array of research questions concerning the many human rights laid out in the United Nations’ 1948 Universal Declaration of Human Rights and related documents. These innovations have enabled quantitative human rights scholarship to better connect to existing qualitative and theoretical literatures and have improved advocacy efforts. Quantitative scholars have primarily operationalized the concept of human rights through the use of four kinds of data: events data (such as counts of abuses or attacks), standards-based data (such as coded scores), survey data, and socioeconomic statistics (such as maternal mortality or malnutrition rates). Each type of data poses particular challenges and weaknesses for analyses, including the biased undercounts of events data and the potential for human error or biases in survey or standards-based data. The human rights field has also seen a systematic overrepresentation of analyses of physical integrity rights, which have fewer component parts to measure. Furthermore, qualitative scholars have pointed out that it is difficult for quantitative data to capture the process of human rights improvement over time. The creation of new technologies and methodologies has allowed quantitative researchers to lessen the impact of these data weaknesses: Latent variables allow scholars to create aggregate measures from a variety of classes of quantitative data, as well as understandings from qualitative scholars, leading to the creation of new measures for rights other than physical integrity rights. New machine learning techniques and algorithms are giving scholars access to greater amounts of data than ever before, improving event counts. Expert surveys are pulling new voices into the data-generating process and incorporating practitioners into data processes that are too often restricted to academics. Experimental studies are furthering the field’s understanding of the processes underlying advocacy. Drawing on the lessons of past work, future scholars can use quantitative methods to improve the field’s theoretical and practical understandings of human rights.
Amanda M. Murdie and K. Anne Watson
Predictive models, which includes forecasting models, are used to study all types of conflict and political violence, including civil wars, international conflict, terrorism, genocide, and protests. These models are defined as those where the researcher explicitly values predictive performance when building and analyzing the model. This is different from inferential models, where the researcher values the accurate operationalization of a theory, and experimental or quasi-experimental designs where the focus is on the estimation of a causal effect. Researchers employ preditive models to guide policy, to assess the importance of variables, to test and compare theories, and for the development of research methods. In addition to these practical applications, there are more fundamental arguments, rooted in the philosophy of science, as to why these models should be used to advance conflict research. Their use has led to numerous substantive findings. For example, while inferential models largely support the democratic peace hypothesis, predictive models have shown mixed results and have been used to refine the scope of the argument. Among the more robust findings are the presence of nonlinear relationships and the importance of dependencies in all types of conflict data. These findings have implications for how researchers model conflict processes. As predictive models become more common and more integrated into the study of conflict, it is important that researchers understand their underlying components to use them appropriately.
Timothy J. A. Passmore
UN peacekeeping serves as the foremost international tool for conflict intervention and peace management. Since the Cold War, these efforts have almost exclusively targeted conflicts within, rather than between, states. Where traditional peacekeeping missions sought to separate combatants and monitor peace processes across state borders, modern peacekeeping in civil wars involves a range of tasks from intervening directly in active conflicts to rebuilding political institutions and societies after the fighting ends. To accommodate this substantial change, peacekeeping operations have grown in number, size, and scope of mandate. The increasing presence and changing nature of peacekeeping has sparked great interest in understanding when and how peacekeeping is used and how effective it is in delivering and sustaining peace. Significant advances in peacekeeping data collection have allowed for a more rigorous investigation of the phenomenon, including differentiation in the objectives, tasks, and structure of a mission as well as disaggregation of the activities and impact of peacekeepers’ presence across time and space. Researchers are particularly interested in understanding the adaption of peacekeeping to the unique challenges of the civil war setting, such as intervention in active conflicts, the greater involvement and victimization of civilians, the reintegration of rebel fighters into society, and the establishment of durable political, economic, and social institutions after the fighting ends. Additional inquiries consider why the UN deploys peacekeeping to some wars and not others, how and why operations differ from one another, and how the presence of and variation across missions impacts conflict countries before and after the fighting has stopped.
James D. Morrow
Theory shapes how data is collected and analyzed in at least three ways. Theoretical concepts inform how we collect data because data attempt to capture and reflect those concepts. Theory provides testable hypotheses that direct our research. Theory also helps us draw conclusions from the results of empirical research. Meanwhile, research using quantitative methods seeks to be rigorous and reproducible. Mathematical models develop the logic of a theory carefully, while statistical methods help us judge whether the evidence matches the expectations of our theories. Quantitative scholars tend to specialize in one approach or the other. The interaction of theory and data for them thus concerns how models and statistical analysis draw on and respond to one another. In the abstract, they work together seamlessly to advance scientific understanding. In practice, however, there are many places and ways this abstract process can stumble. These difficulties are not unique to rigorous methods; they confront any attempt to reconcile causal arguments with reality. Rigorous methods help by making the issues clear and forcing us to confront them. Furthermore, these methods do not ensure arguments or empirical judgments are correct; they only make it easier for us to agree among ourselves when they do.
The scientific study of international processes (SSIP) has made substantial progress over the past twenty years, establishing itself as the mainstream research community in the field of international relations (IR) and attracting more and more attention from other disciplines. This was due to the convergence of several revolutions that have taken place in the field, including the data revolution, the formal modeling revolution, the methods revolution, the substantive revolution, and the epistemological revolution. In addition to the dramatic increase in the number of the community of scholars who use scientific logic, systematic methods, and empirical data to study IR, there was a significant improvement in the quality of research. This research has yielded important contributions to our understanding of international processes. Some of these contributions went far beyond the field; they have attracted the attention of policy makers as well as quite a few scholars from other disciplines. Some of the key findings that emerged from this research have become—correctly or incorrectly—a key component of the discourse of political leaders. Growing data availability, increased methodological sophistication, and greater scientific discipline within the profession have converged to open new research frontiers, but important challenges remain, such as the disconnect between theory and empirical tests that exists in many cases, and the almost exclusive reliance on the dyadic level of analysis. It is important to make our understanding of international processes translated into broader policy implications.
Paul R. Hensel
The International Studies Association’s (ISA) Scientific Study of International Processes (SSIP) section is dedicated to the systematic analysis of empirical data covering the entire range of international political questions. Drawing on the canons of scientific inquiry, SSIP seeks to support and promote replicable research in terms of the clarity of a theoretical argument and/or the testing of hypotheses. Journals that have been most likely to publish SSIP-related research include the top three general journals in the field of political science: the American Political Science Review, American Journal of Political Science, and Journal of Politics. A number of more specialized journals frequently publish research of interest to the SSIP community, such as Conflict Management and Peace Science, International Interactions, International Organization, International Studies Quarterly, Journal of Conflict Resolution, and Journal of Peace Research. Together, these journals published a total of 1,024 qualifying articles between 2003 and 2010. These articles cover a wide range of topics, from armed conflict and conflict management to terrorism, international political economy, economic development or growth, monetary policy, foreign aid, sanctions, human rights and repression, international law, international organizations/institutions, and foreign policy attitudes and beliefs. Data users who are interested in conducting their own research must: choose the most appropriate data set(s), become familiar with what the data set includes and how its central concepts are measured, multipurpose data sources, investigate missing data, and assess robustness across multiple data sets.
Kristian Skrede Gleditsch, Kyle Beardsley, and Sara M. T. Polo
Conflict data sets can shed light on how different ways of measuring conflict (or any other international relations phenomenon) result in different conclusions. Data collection procedures affect our efforts to answer key descriptive questions about war and peace in the world and their relationship to other features of interest. Moreover, empirical data or information can answer some pointed questions about world politics, such as, “Has there been a decline in conflict in the international system?” The development of data on characteristics relevant to the study of international relations has undeniably allowed a great deal of progress to be made on many research questions. However, trying to answer seemingly simple descriptive questions about international relations often shows how data rarely speak entirely for themselves. The specific ways in which we pose questions or try to reach answers will often influence our conclusions. Likewise, the specific manner in which the data have been collected will often have implications for our inferences. In turn, proposed answers to descriptive questions are often contested by other researchers. Many empirical debates in the study of international relations, upon closer inspection, often hinge on assumptions and criteria that are not made fully explicit in studies based on empirical data.