Prospective longitudinal data collection is an important way for researchers and evaluators to assess change. In school-based settings, for low-risk and/or likely-beneficial interventions or surveys, data quality and ethical standards are both arguably stronger when using a waiver of parental consent—but doing so often requires the use of anonymous data collection methods. The standard solution to this problem has been the use of a self-generated identification code. However, such codes often incorporate personalized elements (e.g., birth month, middle initial) that, even when meeting the technical standard for anonymity, may raise concerns among both youth participants and their parents, potentially altering willingness to participate, response quality, or generating outrage. There may be value, therefore, in developing a self-generated identification code and matching approach that not only is technically anonymous but also appears anonymous to a research-naive individual. This article provides a proof of concept for a novel matching approach for school-based longitudinal data collection that potentially accomplishes this goal.

中文翻译：

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开发和验证一种新颖的匿名方法来匹配纵向学校数据

前瞻性纵向数据收集是研究人员和评估人员评估变化的重要方式。在以学校为基础的环境中，对于低风险和/或可能有益的干预措施或调查，在放弃父母同意的情况下，数据质量和道德标准可以说都更强，但这样做通常需要使用匿名数据收集方法。该问题的标准解决方案是使用自行生成的识别码。然而，此类代码通常包含个性化元素（例如出生月份、中间名首字母），即使满足匿名的技术标准，也可能会引起青少年参与者及其父母的担忧，从而可能改变参与意愿、响应质量或产生影响。暴行。因此，开发一种自我生成的识别码和匹配方法可能是有价值的，这种识别码和匹配方法不仅在技术上是匿名的，而且对于未经研究的个人来说也是匿名的。本文为基于学校的纵向数据收集的新颖匹配方法提供了概念证明，该方法有可能实现这一目标。