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使用安全问题将纵向数据收集的参与者联系起来。

Using Security Questions to Link Participants in Longitudinal Data Collection.

机构信息

Department of Biostatistics, New York University, 715 Broadway, 10th Floor, New York, NY, 10010, USA.

Columbia University, New York, NY, USA.

出版信息

Prev Sci. 2020 Feb;21(2):194-202. doi: 10.1007/s11121-019-01080-8.

Abstract

Anonymous data collection systems are often necessary when assessing sensitive behaviors but can pose challenges to researchers seeking to link participants over time. To assist researchers in anonymously linking participants, we outlined and tested a novel security question linking (security question linking; SEEK) method. The SEEK method includes four steps: (1) data management and standardization, (2) many-to-many matching, (3) fuzzy matching, and (4) rematching and verification. The method is demonstrated in SAS with two samples from a longitudinal study of adolescent dating violence. After an initial assessment during a laboratory visit, participants were asked to complete an online assessment either (a) once, 3 months later (Sample 1, n = 60), or (b) three times at 1-month intervals (Sample 2, n = 140). Demographics, eye color, and responses to nine security questions were used as key variables to link responses from the laboratory and online follow-up assessments. The rates of matched cases were 100% in Sample 1 and from 94.3 to 98.3% in Sample 2. To quantify the confidence in the data quality of successfully matched pairs, we reported the means and standard deviations of the number of matched security questions. In addition, we reported the rank order and counts of the mismatched components in key variables. Results indicate that the SEEK method provides a feasible and reliable solution to link responses in longitudinal studies with sensitive questions.

摘要

匿名数据收集系统在评估敏感行为时通常是必要的,但对于希望随着时间的推移将参与者联系起来的研究人员来说,可能会带来挑战。为了帮助研究人员匿名地将参与者联系起来,我们概述并测试了一种新的安全问题链接(security question linking; SEEK)方法。SEEEK 方法包括四个步骤:(1)数据管理和标准化,(2)多对多匹配,(3)模糊匹配,以及(4)重新匹配和验证。该方法在 SAS 中用来自青少年约会暴力纵向研究的两个样本进行了演示。在实验室访问期间进行初步评估后,参与者被要求在线完成评估,要么(a)仅一次,3 个月后(样本 1,n=60),要么(b)每隔一个月进行三次(样本 2,n=140)。人口统计学信息、眼睛颜色以及对九个安全问题的回答被用作将实验室和在线随访评估中的回答联系起来的关键变量。在样本 1 中,匹配病例的比例为 100%,在样本 2 中为 94.3%至 98.3%。为了量化成功匹配对数据质量的置信度,我们报告了匹配安全问题数量的平均值和标准差。此外,我们还报告了关键变量中不匹配组件的排序和计数。结果表明,SEEEK 方法为在具有敏感问题的纵向研究中链接响应提供了一种可行且可靠的解决方案。

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