Inconvenient Samples: Modeling Biases Related to Parental Consent by Coupling Observational and Experimental Results
Inconvenient Samples: Modeling Biases Related to Parental Consent by Coupling Observational and Experimental Results
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DOI：
10.1162/opmi_a_00031
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Yue Yu;Patrick Shafto;E. Bonawitz
中科院分区：
文献类型：
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作者：
Yue Yu;Patrick Shafto;E. Bonawitz
在涉及人类受试者的研究中，自愿参与可能导致抽样偏倚，从而限制了研究结果的普遍性。这种影响在发展研究中可能特别明显，父母既是主要的环境输入者，也是孩子是否参加研究的决策者。我们提出了一种新的经验和建模方法来估计父母的同意可能会导致儿童行为的偏差测量。具体来说，我们将公共空间中亲子互动的自然观察与儿童行为测试相结合，并使用建模方法来估算未参与的儿童的行为。结果显示，家长使用问题教学的倾向与孩子在测试中的行为和家长的参与倾向有关。利用基于模型的多重插补和倾向评分匹配程序来利用这些关联，我们估计参与组和未参与组的平均值在测试测量值上的差异可能高达0.23个标准差，并且标准差本身可能被低估。这些结果表明，忽略与同意相关的因素可能会导致系统性偏差时，推广超出实验室样本，和拟议的一般方法提供了一种方法来估计这些偏见在未来的研究。
In studies involving human subjects, voluntary participation may lead to sampling bias, thus limiting the generalizability of findings. This effect may be especially pronounced in developmental studies, where parents serve as both the primary environmental input and decision maker of whether their child participates in a study. We present a novel empirical and modeling approach to estimate how parental consent may bias measurements of children’s behavior. Specifically, we coupled naturalistic observations of parent–child interactions in public spaces with a behavioral test with children, and used modeling methods to impute the behavior of children who did not participate. Results showed that parents’ tendency to use questions to teach was associated with both children’s behavior in the test and parents’ tendency to participate. Exploiting these associations with a model-based multiple imputation and a propensity score–matching procedure, we estimated that the means of the participating and not-participating groups could differ as much as 0.23 standard deviations for the test measurements, and standard deviations themselves are likely underestimated. These results suggest that ignoring factors associated with consent may lead to systematic biases when generalizing beyond lab samples, and the proposed general approach provides a way to estimate these biases in future research.