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The Success of Protest Groups: Multivariate Analyses

The author tested these models by means of a regression approach to time-series analysis and modeled joint effects by introducing a series of interactive terms in the regressions. In this paper we replicate his study using another method to see whether we come to the same conclusions. Specifically, we test the two main hypotheses that can be drawn from what the author has called the joint-effect model of social movement outcomes: (1) the policy impact of social movements is conditioned by the presence of powerful allies within the institutional arenas, the presence of a favorable public opinion, or both factors at the same time; and (2) the policy impact of social movements is more likely when the latter address issues and policy domains with a low degree of saliency.4 In addition, we consider a further aspect which is present in Giugni’s (2004) analysis, but which has not been the object of a clear hypothesis: the comparison of the policy impact of social movements across countries. This allows us to go beyond the original analysis, which was geared towards assessing the role of public opinion, political alliances, and issue salience on the policy impact of social movements.5 The method we use is qualitative comparative analysis (QCA), which is particularly suited to model joint (that is, interactive and conditional) effects.6 A few works have made use of QCA as a technique to study the impact of protest activities and social movements. For example, Amenta and colleagues (1992; see also Amenta et al. 1994, 2005) examine the conditions under which the Townsend movement succeeded or failed at the state level. Similarly, Cress and Snow (2000) utilize QCA to illuminate how framing played a role in social movement outcomes. Here we apply this method to test the findings against those obtained through time-series analysis of the same data. Our aim is to assess whether and to what extent the reliance on different techniques yields similar or contradictory results on the same data.. We do not repeat the time-series analysis, but only summarize the main finding