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The 'data revolution' offers many new opportunities for research in the social sciences. Increasingly, social and political interactions can be recorded digitally, leading to vast amounts of new data available for research. This poses new challenges for organizing and processing research data. This comprehensive introduction covers the entire range of data management techniques, from flat files to database management systems. It demonstrates how established techniques and technologies from computer science can be applied in social science projects, drawing on a wide range of different applied examples. This book covers simple tools such as spreadsheets and file-based data storage and processing, as well as more powerful data management software like relational databases. It goes on to address advanced topics such as spatial data, text as data, and network data. This book is one of the first to discuss questions of practical data management specifically for social science projects.

Much research in political science relies on datasets produced by human coders. Many variables included in these datasets are not based on observable facts but rather require a considerable level of human judgment. This project studies the extent to which this judgment is affected by availability bias and how it influences the retrospective coding of historic cases. The analysis uses coder-level data from the V-Dem project, one of the few datasets collecting and releasing codings tagged with timestamps when they were produced. The results show that recent dramatic events in a country just prior to the coding have a small, but visible impact on coder ratings, but primarily for those variables that are directly related to the observed events. The magnitude of this effect, however, is small. This alleviates concerns that prominent events in world politics around the time of coding significantly affect the reliability of cross-national indicators.

AbstractGovernmental measures against the spread of the Corona virus have been met with varying levels of opposition in many countries worldwide. Existing research has claimed that some of this opposition is linked to esoteric and anthroposophical beliefs. This research note tests this in an observational study using election results from the 2021 parliamentary election in Germany and new data on the distribution of natural healers, homeopathic doctors and Steiner schools. Results show that counter to common expectations, there is no evidence that esoteric beliefs systematically lead to increased support for the established right-wing AFD. Rather, some indicators for esoteric beliefs – in particular, the presence of homeopathic doctors and Waldorf schools – are related to higher support for the new fringe party dieBasis, a single-issue party campaigning against governmental Corona measures.

Disagreement over governmental measures against the spread of the Corona virus has led to increased societal division and polarization in many countries worldwide. Scholars typically locate the sources of resistance against these measures on the right of the political spectrum. This article argues that this explanation is too simple. Using fine-grained spatial data for Germany, it tests whether opposition to Corona restrictions (proxied with electoral support for a new party against governmental Corona measures) is systematically linked to esoteric and anthroposophical beliefs, which are traditionally found on the political left. Using new data on the distribution of natural healers, homeopathic doctors and Steiner schools, the article presents spatial analyses at the level of electoral districts and municipalities. The latter makes it possible to create matched samples for improved causal inference. Results confirm that both the presence of homeopathic doctors and Steiner schools are related to significantly higher opposition against Corona measures. This shows that resistance to governmental measures against the Corona pandemic originates from different societal groups, and will remain a major challenge for governments to address.

Many accounts of civil war violence assume that a conflict's master cleavage also explains the local occurrence of violence. Some scholars, however, have argued that violence is often the result of local cleavages and feuds, many of which may be unrelated to the conflict's master cleavage. How is local violence related to the conflict's master cleavage? Using a computational model, this paper studies an alliance mechanism proposed by Kalyvas (2006, The Logic of Violence in Civil War, Cambridge University Press), where macro-actors support local ones that fight on their behalf. While these alliances create a principal–agent problem, the model shows that they can raise the overall severity of the conflict and serve the interests of the macro-actor. However, the model also shows that these mechanisms work only under limited conditions. Alliances can increase the level of violence perpetrated in the interest of the macro-actor, but only if (a) the latter supports agents that have in the past fought along the master cleavage and if (b) this happens in rural areas. This emphasizes again the importance of the rural dimension in the study of civil war.

Recent data collections about political violence are frequently based on media reports, which can lead to reporting bias. This is an issue in particular for the emergent literature on communication technology and conflict, since this technology may not only affect violence, but also the reporting about it. Using the effect of cellphones on violence as an example, this article presents a quantitative assessment of reporting bias in a micro‐level analysis. Comparing media‐based event reports and those from military sources, the results show that the purported violence‐increasing effect of cellphone coverage is partly due to higher reporting rates of violence in cellphone‐covered areas. A simple diagnostic procedure for this problem is implemented. Applied to the analysis of cellphones and violence in Africa, it produces a pattern that is consistent with reporting bias driving much of the effect found in the Pierskalla and Hollenbach (2013) study about this topic.

Modern communication technology is emerging rapidly, with tremendous social implications. The key innovations introduced by this technology include the increased pervasiveness and the rich nature of digitally transmitted information, and a new type of network structure over which it is disseminated. The articles in this special issue present theoretical and empirical research on the relationship between communication technology and political conflict and violence. There are different pathways through which this can happen: technology can facilitate collective action, but at the same time give governments the opportunity to censor content and gather intelligence about dissidents. Also, audience effects can be introduced by the rich and instant transmission of information from conflict regions. The contributions to this special issue can be divided into three groups. A first group of articles looks at the effects of 'old' communication technologies with state-of-the-art methods, which is necessary to see if the effects of modern technology really differ. A second category of articles focuses on 'new' communication technologies, and try to assess their effect on conflict both theoretically and empirically. The third and last category reverses this question, and looks at the reflection of war and violence in (traditional and new) media channels.

Empirical researchers of civil war rarely collect data on violence themselves and instead rely on other sources of information. One frequently used source is media reports, which serve as the basis for many ongoing data projects in the discipline. However, news reports rarely cover a conflict comprehensively and objectively and may therefore be prone to various reporting issues. This article provides an analysis of the accuracy of information given in news reports. In particular, if focuses on two types of "hard facts" that event data sets require: the location of an event and its severity. By linking media reports to firsthand accounts from a military database, the article does two things: (1) it analyzes the determinants of inaccuracy and confirms the expectation that events with a low number of observers tend to have higher reporting inaccuracies and (2) it assesses the magnitude of these inaccuracies and the implications for conducting empirical analyses with media-based event data.

The majority of conflict event datasets rely on media reports as their sole source of information. Because of the various difficulties associated with media reports, it is useful to compare conflict events based on them with those obtained from other observers. A paper published in 2010 by O'Loughlin and colleagues makes a first attempt to do this by using (1) a media-based event dataset and (2) military records on Afghanistan. While the authors conclude that the level of agreement between the two datasets is high, my results show that this goes away once we aggregate to finer analytical resolutions – those that are typically used in micro-level conflict analyses. Thus, rather than giving us the 'all-clear' for the accuracy and quality of media-based conflict data, my results once again point to the importance of robustness tests in quantitative conflict research, but also to the need to study the discrepancies in different reporting mechanisms to find out what they can and what they cannot tell us.