Journal•ISSN: 0738-8942
Conflict Management and Peace Science
SAGE Publishing
About: Conflict Management and Peace Science is an academic journal published by SAGE Publishing. The journal publishes majorly in the area(s): Politics & Poison control. It has an ISSN identifier of 0738-8942. Over the lifetime, 734 publications have been published receiving 21034 citations. The journal is also known as: CMPS.
Topics: Politics, Poison control, Terrorism, Civil Conflict, Ethnic group
Papers published on a yearly basis
Papers
More filters
TL;DR: Militarized interstate disputes are cases of conflict in which the threat, display or use of military force short of war by one member state is explicitly directed towards another member state as discussed by the authors.
Abstract: Militarized interstate disputes are united historical cases of conflict in which the threat, display or use of military force short of war by one member state is explicitly directed towards the gov...
879 citations
TL;DR: The data-collection process for the Militarized Interstate Dispute (MID) data is outlined, two new data sets emerging from the project are introduced, and statistics indicate that the MID3 data are remarkably similar to the MID2.1 version.
Abstract: Dealing with questions of war and peace and understanding the causes of interstate conflict is a primary goal of the field of international relations. In order to study interstate conflict in a rigorous manner, scholars have relied on established rules and procedures for gathering information into coherent data sets. Among those data sets is the Militarized Interstate Dispute (MID) data. In this paper we first outline the data-collection process for the MID3 data. Second, we introduce two new data sets emerging from the project, “MID-I” and “MID-IP.” Third, we present relatively small changes in coding rules for the new MID3 data and some descriptive statistics. The statistics indicate that the MID3 data are remarkably similar to the MID2.1 version, varying in some minor and predictable ways.
704 citations
TL;DR: A typology of case studies based on their purposes is constructed, including idiographic, hypothesis-generating, hypotheses-testing, and plausibility probe case studies, and the issue of selection bias and the “single logic” debate is addressed.
Abstract: I focus on the role of case studies in developing causal explanations. I distinguish between the theoretical purposes of case studies and the case selection strategies or research designs used to advance those objectives. I construct a typology of case studies based on their purposes: idiographic (inductive and theory-guided), hypothesis-generating, hypothesis-testing, and plausibility probe case studies. I then examine different case study research designs, including comparable cases, most and least likely cases, deviant cases, and process tracing, with attention to their different purposes and logics of inference. I address the issue of selection bias and the “single logic” debate, and I emphasize the utility of multi-method research.
674 citations
TL;DR: The shifting nature of international conflict has prompted a rethinking of the Correlates of War Project's classification of wars and a new expanded war typology and the resultant three war data sets as discussed by the authors.
Abstract: The shifting nature of international conflict has prompted a rethinking of the Correlates of War Project's classification of wars. This research note describes the new expanded war typology and the resultant three war data sets. Lists of the qualifying wars in the inter-state, extra-state, and intra-state categories during the 1816-1997 period are appended.
515 citations
TL;DR: In this article, it was shown that the inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation.
Abstract: Quantitative political science is awash in control variables. The justification for these bloated specifications is usually the fear of omitted variable bias. A key underlying assumption is that the danger posed by omitted variable bias can be ameliorated by the inclusion of relevant control variables. Unfortunately, as this article demonstrates, there is nothing in the mathematics of regression analysis that supports this conclusion. The inclusion of additional control variables may increase or decrease the bias, and we cannot know for sure which is the case in any particular situation. A brief discussion of alternative strategies for achieving experimental control follows the main result.
467 citations