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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.

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.

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.

There has been a dramatic increase in research on terrorism and counterterrorism since the terrorist attacks of September 11, 2001. Given its prominence, many scholars have assessed the advancement of the field in terms of publication output and research questions. However, there has also been a significant growth in data collection efforts. Datasets on terrorism and counterterrorism have been developed and revised across a number of levels: the event level, organizational level, and individual level. At the event level, datasets offer cross-national, regional, and subnational coverage of individual terrorist events and their characteristics, such as lethality, targets, tactics, and perpetrators. Organizational-level datasets unveil important characteristics of terrorist organizations—including ideology, capabilities, duration, social service provision, and networks—over time and space. Individual-level datasets contain information on global jihad, online activity, terrorist leaders, and terrorism in the United States. While more limited on coverage, data on counterterrorism focus on hard-power counterterrorism, targeted counterterrorism (e.g., drone strikes and leadership decapitation), and soft-power counterterrorism, which encompasses strategies aimed at raising the perceived benefits of abstaining from terrorism. Many datasets and integration techniques have also been developed to study the practice of terrorism in various contexts, such as civil war and ethnic conflict. Data integration expands and deepens our understanding of the causes, dynamics, and consequences of terrorism in various contexts and sheds light on the relationship between terrorism and other violent and nonviolent tactics. The growth of data collection efforts is beneficial for researchers in the field of terrorism and beyond as well as for policy makers and practitioners.

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.