Opinion surveys can contain closed questions to which respondents can give multiple answers. We propose to model these data as networks in which vertices are eligible items and arcs are respondents. This representation opens up the possibility of using complex networks methodologies to retrieve information and most prominently, the possibility of using clustering/community detection techniques to reduce data complexity. We will take advantage of the implicit null hypothesis of the modularity function, namely, that items are chosen without any preferential pairing, to show how the hypothesis can be tested through the usual calculation of p-values. We illustrate the methodology applying it to Eurobarometer data. There, a question about national concerns can receive up to two selections. We will show that community clustering groups together concerns that can be interpreted in consistent way and in general terms, such as Economy, Security and Welfare issues. Moreover, we will show that in this way cleavages between social sectors can be determined.
翻译:意见调查可以包含被调查者可以提供多种答案的封闭性问题。我们建议将这些数据作为数据模型,作为脊椎是合格项目,弧是答复者;这种表示为利用复杂的网络方法检索信息开辟了可能性,最突出的是,利用集群/社区探测技术减少数据复杂性的可能性;我们将利用模块功能的隐含的无效假设,即项目选择没有优惠配对,以显示如何通过通常的p值计算来测试假设。我们用它来说明对Eurowrabowm数据应用的方法。关于国家关切的问题最多可以接受两个选择。我们将显示,社区集群群集的关切可以一致和笼统地解释,例如经济、安全和福利问题。此外,我们将表明,通过这种方式,可以确定社会部门之间的分界线。