Inaccurate Estimation of Disparities Due to Mischievous Responders: Several Suggestions to Assess Conclusions
Inaccurate Estimation of Disparities Due to Mischievous Responders: Several Suggestions to Assess Conclusions
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DOI：
10.3102/0013189x14534297
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发表时间：
2014-05-01
影响因子：
8.2
通讯作者：
Robinson-Cimpian, Joseph P.
中科院分区：
文献类型：
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作者：
Robinson-Cimpian, Joseph P.
本文介绍了新颖的敏感性分析程序，用于调查和减少顽皮的回答者（即对多个问题提供极端且可能不真实的回答的青少年）经常在基于自填问卷（SAQ）数据的青少年差异估计中引入的偏见。恶作剧的回应者会影响广泛的差异估计，包括被收养者和非被收养者、性少数群体和非少数群体、以及残疾人和非残疾人之间的差异。因此，这里介绍的程序与研究具有广泛的相关性，并且可以广泛、轻松地实施。敏感性分析程序以 9-12 年级青少年 (N = 11,829) 的 SAQ 数据进行说明，以检查基于性别认同、性别认同和身体残疾的组间差异。敏感性分析表明，用这些数据估计的每个差异对于潜在恶作剧响应者的存在极其敏感。在处理顽皮的反应者的多种方法、不同的结果以及不同的组间比较中，模式是相似的。恶作剧的反应者在使用 SAQ 的青少年研究中无处不在，即使数量很少，也可能导致不准确的结论，从而对有关各种差异的研究、政策和公共讨论产生实质性影响。本文呼吁人们关注这一普遍存在的问题，并提供评估该问题的实用建议，即使数据已经收集完毕。
This article introduces novel sensitivity-analysis procedures for investigating and reducing the bias that mischievous responders (i.e., youths who provide extreme, and potentially untruthful, responses to multiple questions) often introduce in adolescent disparity estimates based on data from self-administered questionnaires (SAQs). Mischievous responders affect a wide range of disparity estimates, including those between adoptees and nonadoptees, sexual minorities and nonminorities, and individuals with and without disabilities. Thus, the procedures introduced here have broad relevance to research and can be widely, and easily, implemented. The sensitivity-analysis procedures are illustrated with SAQ data from youths in Grades 9-12 (N = 11,829) to examine between-group disparities based on sexual identity, gender identity, and physical disability. Sensitivity analyses revealed that each disparity estimated with these data was extremely sensitive to the presence of potentially mischievous responders. Patterns were similar across multiple approaches to dealing with mischievous responders, across various outcomes, and across different between-group comparisons. Mischievous responders are ubiquitous in adolescent research using SAQs and can, even in small numbers, lead to inaccurate conclusions that substantively affect research, policy, and public discourse regarding a variety of disparities. This article calls attention to this widespread problem and provides practical suggestions for assessing it, even when data are already collected.