Agley Jon, Tidd David, Jun Mikyoung, Eldridge Lori, Xiao Yunyu, Sussman Steve, Jayawardene Wasantha, Agley Daniel, Gassman Ruth, Dickinson Stephanie L
Prevention Insights, Department of Applied Health Science, School of Public Health Bloomington, Indiana University Bloomington, Bloomington, IN, USA.
Silver School of Social Work, New York University, New York, NY, USA.
Educ Psychol Meas. 2021 Feb;81(1):90-109. doi: 10.1177/0013164420938457. Epub 2020 Jul 8.
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.
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。在学校环境中，对于低风险和/或可能有益的干预措施或调查，在使用免除家长同意的情况下，数据质量和道德标准可能都更强——但这样做通常需要使用匿名数据收集方法。解决这个问题的标准方法是使用自行生成的识别码。然而，这样的代码通常包含个性化元素（例如出生月份、中间名首字母），即使符合匿名的技术标准，也可能引起青少年参与者及其父母的担忧，可能会改变参与意愿、回答质量或引发愤怒。因此，开发一种自行生成的识别码和匹配方法可能是有价值的，这种方法不仅在技术上是匿名的，而且对于没有研究经验的人来说也看起来是匿名的。本文为一种新型的基于学校的纵向数据收集匹配方法提供了概念验证，该方法有可能实现这一目标。
