2022, Ruhe, Constantin, Schneider, Gerald, Spilker, Gabriele

Dieses Kapitel gibt einen Überblick über die Verwendung quantitativer Methoden in den Internationalen Beziehungen. Nach einer kurzen Diskussion der verschiedenen Probleme, die sich in einer quantitativen Untersuchung ergeben können, präsentieren wir im zweiten Teil die mannigfachen Herausforderungen, die beim ersten Schritt jeglicher empirischer Untersuchung – dem Messen der theoretischen Konstrukte – entstehen können. Danach beschreiben wir, wie sich zwei unterschiedliche Datentypen – Experimental- und Beobachtungsdaten – analysieren lassen. In diesem Zusammenhang diskutieren wir ausführlich anhand einiger prominenter Beispiele zentrale Schwierigkeiten bei der Durchführung einer Regressionsanalyse: die Wahl eines passenden Modellierungsverfahrens, die Drittvariablenkontrolle sowie das Problem der Stichprobenverzerrung. Der Aufsatz endet mit einer Schlussbetrachtung und einem Überblick über einige neuere Trends in der Verwendung von quantitativen Methoden in den Internationalen Beziehungen.

2017, Ruhe, Constantin, Schneider, Gerald, Spilker, Gabriele

Dieses Kapitel gibt einen Überblick über die Verwendung quantitativer Methoden in den Internationalen Beziehungen. Nach einer kurzen Diskussion der verschiedenen Probleme, durch die eine quantitative Untersuchung scheitern kann, beschreiben wir im zweiten Teil des Kapitels die Analyse von experimentellen sowie Beobachtungsdaten. Im dritten Teil dieses Kapitels illustrieren wir dann mithilfe eines der Literatur entnommenen Beispiels mögliche Schwierigkeiten bei der Durchführung einer quantitativen Analyse. Das Kapitel endet mit einer Schlussbetrachtung und einem Überblick über einige neuere Trends in der Verwendung von quantitativen Methoden in den Internationalen Beziehungen.

2015, Ruhe, Constantin

Research on mediation has shown that mediation can be an effective conflict management tool to contain intrastate conflicts, prevent escalation of low intensity conflicts, and foster de-escalation. But can ripe moments for conflict prevention effectively be anticipated? This article argues that the short-term conflict history provides a good predictor of the probability of mediation onset in low-intensity conflicts. It builds on an expected utility theory of mediation and states that conflict intensity is a primary indicator of whether a window of opportunity for mediation exists. Thereby, the article asserts that the direction of the effect is conditional on the respective probability of victory of each conflict party. The theory postulates that high conflict intensity only increases the probability of mediation onset when neither side is likely to prevail militarily. If one of the conflict parties has a high chance of a military success, then it will not regard conflict intensity as costly, since it can expect to impose these costs on its opponent. Under these circumstances the conflict parties will not be willing to engage in mediation. The article presents empirical support for this proposition. It uses temporally disaggregated data of low-intensity African conflicts from 1993 to 2004 and demonstrates that the theoretically motivated model predicts mediation onset with high accuracy. The results show that conflict dynamics are highly relevant covariates in predicting mediation. This selection process should be considered when the impact of mediation is evaluated.

2020-11-06, Ruhe, Constantin, Leib, Julia, Weidmann, Nils B., Bussmann, Margit

2016, Ruhe, Constantin

In many applications of the Cox model, the proportional-hazards assumption is implausible. In these cases, the solution to nonproportional hazards usually consists of modeling the effect of the variable of interest and its interaction effect with some function of time. Although Stata provides a command to implement this interaction in stcox, it does not allow the typical visualizations using stcurve if stcox was estimated with the tvc() option. In this article, I provide a short workaround that estimates the survival function after stcox with timedependent coefficients. I introduce and describe the scurve_tvc command, which automates this procedure and allows users to easily visualize survival functions for models with time-varying effects.

2012, Schneider, Gerald, Bussmann, Margit, Ruhe, Constantin

Many observers contend that wartime civilian victimization is an instrument of political leaders to achieve a particular goal. This article examines whether retaliation for similar acts by the other side, the developments on the battlefield, or the behavior of international actors accounts for the ups and downs of this so-called one-sided violence. Using information from the Konstanz One-Sided Violence Event Dataset and other sources, we evaluate the empirical relevance of these complementary models statistically. Time series analyses of the weekly number of killed and harmed Muslims (Bosniacs) and Serbs during the Bosnian civil war support the military and the massacre logic. We show that the Serbian side decreased one-sided violence following a territorial conquest, but that its one-sided violence was not a reciprocal response to the Bosniac targeting of civilians. Conversely, the Bosniac side resorted to violence during times of increasing Serbian atrocities and when the fighting was particularly intense. The analysis reveals that most international interventions did not reduce the carnage, but that the Serbs responded to Russian moves.

2018-01-29, Ruhe, Constantin

Duration analyses in political science often model nonproportional hazards through interactions with analysis time. To facilitate their interpretation, methodologists have proposed methods to visualize time-varying coefficients or hazard ratios. While these techniques are a useful, initial postestimation step, I argue that they are insufficient to identify the overall impact of a time-varying effect and may lead to faulty inference when a coefficient changes its sign. I show how even significant changes of a coefficient’s sign do not imply that the overall effect is reversed over time. In order to enable a correct interpretation of time-varying effects in this context, researchers should visualize their results with survivor functions. I outline how survivor functions are calculated for models with time-varying effects and demonstrate the need for such a nuanced interpretation using the prominent finding of a time-varying effect of mediation on interstate conflict. The reanalysis of the data using the proposed visualization methods indicates that the conclusions of earlier mediation research are misleading. The example highlights how survivor functions are an essential tool to clarify the ambiguity inherent in time-varying coefficients in event history models.

2015, Ruhe, Constantin