Replication Data for: Improving Content Analysis: Tools for Working with Undergraduate Research Assistants

Undergraduate research assistants (URAs) play an important role in many political scientists’ research projects. They serve as co-authors, survey respondents, and data collectors. Despite this, there is relatively little discussion about how best to train and manage URAs working on a common task: content coding. Drawing on insights from psychology, text analysis, and business management, as well as my own experience managing a team of nine URAs, I argue that supervisors ought to train URAs by pushing them to engage with their own mistakes. Via a series of simulation exercises, I also argue that supervisors—especially supervisors of small teams—should be concerned about the effects of errant post-training coding on data quality. As such, I contend that supervisors ought to utilize computational tools to monitor URA reliability in real time. I provide researchers with a new R package, ura, and a web-based application to implement these suggestions.本科研究助理（Undergraduate Research Assistants, URAs）在诸多政治科学学者的研究项目中发挥着关键作用。他们可担任共同作者、调查受访者与数据采集者。尽管如此，学界针对如何最优培训与管理承担共性任务——内容编码——的本科研究助理的相关讨论仍相对匮乏。本文借鉴心理学、文本分析与企业管理领域的研究洞见，结合本人管理九名本科研究助理团队的实操经验，提出：导师应通过推动助理直面自身失误的方式开展培训。此外，通过一系列模拟实验，本文还指出，导师——尤其是小型团队的导师——应警惕培训后编码失误对数据质量造成的影响。有鉴于此，本文认为导师应借助计算工具实时监控本科研究助理的编码可靠性。为此，本文为研究者提供了一款全新的R包`ura`以及一款网页端应用程序，以落地本文提出的相关建议。