在一项针对美国和加拿大青年的调查中,减少估算性取向差异时的无效数据偏差。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Jorge Andrés Delgado-Ron, Thiyaana Jeyabalan, Sarah Watt, Travis Salway
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引用次数: 0

摘要

本评论探讨了 "减少无效和恶作剧调查回答 "中所述方法的适用性:约瑟夫-辛皮安博士及其同事撰写的 "减轻无效和调皮的调查回答:审查异性恋和女同性恋、男同性恋、双性恋或问题青年之间风险差异的登记报告 "中所述方法的适用性,以探讨非概率样本中已有数据的性取向差异。Understanding Affirming Communities, Relationships, and Networks(理解肯定的社区、关系和网络)"是一项针对美国和加拿大 9674 名 15-29 岁性取向和性别轻微化青年的研究,其中大多数为白人(77.4%)。研究评估了无效数据对四种健康结果流行率的影响。所采用的方法产生了与原始论文类似的效果。准确性随结果流行率的变化而变化,并对模型的错误规范具有稳健性。因此,这种方法适用于已有数据似乎是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mitigating invalid data bias in the estimation of sexual orientation disparities in a survey of youth in US and Canada

The current commentary explored the applicability of the methods described in “Mitigating invalid and mischievous survey responses: A registered report examining risk disparities between heterosexual and lesbian, gay, bisexual, or questioning youth” by Dr. Joseph Cimpian and colleagues to explore sexual orientation disparities in preexisting data from a nonprobability sample. Understanding Affirming Communities, Relationships, and Networks was a study of mostly White (77.4%) 9674 sexual and gender-minoritized youth aged 15–29 from the US and Canada. The influence of invalid data on the prevalence ratios of four health outcomes was assessed. The methods yielded similar effects to the original paper. The accuracy varied by outcome prevalence and was robust to misspecification of the model. Therefore, the applicability of this method to preexisting data seems feasible.

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CiteScore
7.20
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