Social movement, civil society, and world polity scholars use counts of nongovernmental organizations (NGOs) to evaluate important theoretical and empirical claims. To construct these measures, researchers often classify NGOs by their goals and/or domains. However, over time, the ways organizations describe and orient themselves change, blurring boundaries between organizations and complicating measurement. In this research note, we identify methodological challenges of organizational classification in the context of our work constructing a longitudinal dataset of transnational social movement organizations. We draw attention to an understudied cause of measurement error: overcounting of organizations. We suggest that as automated methods for classifying data become widespread, devising strategies for dealing with these challenges becomes even more pressing.

中文翻译：

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组织分类的挑战：研究笔记

社会运动，公民社会和世界政治学者使用非政府组织（NGO）的数量来评估重要的理论和经验主张。为了制定这些措施，研究人员经常按其目标和/或领域对非政府组织进行分类。但是，随着时间的流逝，组织描述和定位自身的方式发生了变化，模糊了组织之间的界限，并使度量变得复杂。在本研究笔记中，我们在构建跨国社会运动组织纵向数据集的背景下，确定了组织分类的方法论挑战。我们提请您注意未充分衡量的度量误差原因：组织计数过高。我们建议随着自动的数据分类方法逐渐普及，