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Measuring Non-electoral Political Participation: Bi-factor Model as a Tool to Extract Dimensions
Social Indicators Research ( IF 2.8 ) Pub Date : 2021-02-19 , DOI: 10.1007/s11205-021-02637-3
Piotr Koc

Political participation is a mainstay of political behavior research. One of the main dilemmas many researchers face pertains to the number of dimensions of political participation, i.e. whether we should model political participation as a unidimensional or multidimensional latent construct. Over the years, scholars usually have favored the solution with more than one dimension of political participation and they have backed the claim of multiple dimensions with a number of empirical tests. In this paper, I argue that the results from the frequently used testing procedures which rely on the model fit inspection and the Kaiser criterion can be very misleading and may yield in extracting too many dimensions. By employing bi-factor modeling to a European Social Survey dataset, I show that in a majority of countries political participation can be considered an essentially unidimensional latent quantity. I demonstrate that additional dimensions of political participation are very weak and unreliable and that we cannot regress them on external variables nor build composite scores based on them. These findings cast doubt on the conclusions of numerous previous studies where researchers modeled more than one dimension of political participation.



中文翻译:

衡量非选举政治参与:双因素模型作为提取维度的工具

政治参与是政治行为研究的支柱。许多研究人员面临的主要难题之一是政治参与的维度数量,即我们是否应该将政治参与建模为一维或多维潜在构造。多年来,学者们通常在一个以上的政治参与维度上都偏爱该解决方案,并且他们通过许多实证检验来支持多维的主张。在本文中,我认为依赖于模型拟合检验和Kaiser准则的经常使用的测试程序的结果可能会产生很大的误导性,并且可能会导致提取过多维度。通过对欧洲社会调查数据集采用双因素建模,我表明,在大多数国家中,政治参与可以被视为本质上是一维的潜在量。我证明,政治参与的其他方面非常薄弱和不可靠,我们不能根据外部变量对它们进行回归,也不能基于它们建立综合评分。这些发现使人们对以前的许多研究得出的结论表示怀疑,在这些研究中,研究人员对政治参与的多个方面进行了建模。

更新日期:2021-02-19
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