论文标题
纵向研究中的无响应偏差分析：对幼儿纵向研究的应用的比较综述
Nonresponse Bias Analysis in Longitudinal Studies: A Comparative Review with an Application to the Early Childhood Longitudinal Study
论文作者
论文摘要
当个人无法为整个波浪或调查的特定问题提供数据时，纵向研究会遭受无响应。我们比较了纵向研究中无响应偏见分析（NRBA）的方法，并在2010-11幼儿园班级幼儿园纵向研究中对其进行了说明（ECLS-K：2011）。波浪无响应通常会产生单调失踪模式，并且可能在随机（MAR）中丢失缺失机制，也可以不随机（MNAR）失踪。我们讨论了加权，多个插补（MI），不完整的数据建模以及NRBA单调模式的贝叶斯方法。当构造的权重与感兴趣的调查结果相关时，加权调整是有效的。 MI允许将缺少值的变量包括在插补模型中，从而产生潜在的偏见和更有效的估计。具有最大似然估计的多级模型和使用通用估计方程估算的边际模型也可以处理不完整的纵向数据。贝叶斯方法介绍了先前的信息并可能稳定模型估计。我们在MAR结果中增加了偏移，以提供敏感性分析以评估MNAR偏差。我们对NRBA进行描述性摘要和分析模型估计，并发现在ECLS-K：2011应用中，NRBA对实质性结论产生了微小的变化。关于NRBA的证据的强度取决于未响应调整中特征与关键调查结果之间关系的强度，因此成功NRBA的关键是包括强有力的预测因子。
Longitudinal studies are subject to nonresponse when individuals fail to provide data for entire waves or particular questions of the survey. We compare approaches to nonresponse bias analysis (NRBA) in longitudinal studies and illustrate them on the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011). Wave nonresponse with attrition often yields a monotone missingness pattern, and the missingness mechanism can be missing at random (MAR) or missing not at random (MNAR). We discuss weighting, multiple imputation (MI), incomplete data modeling, and Bayesian approaches to NRBA for monotone patterns. Weighting adjustments are effective when the constructed weights are correlated to the survey outcome of interest. MI allows for variables with missing values to be included in the imputation model, yielding potentially less biased and more efficient estimates. Multilevel models with maximum likelihood estimation and marginal models estimated using generalized estimating equations can also handle incomplete longitudinal data. Bayesian methods introduce prior information and potentially stabilize model estimation. We add offsets in the MAR results to provide sensitivity analyses to assess MNAR deviations. We conduct NRBA for descriptive summaries and analytic model estimates and find that in the ECLS-K:2011 application, NRBA yields minor changes to the substantive conclusions. The strength of evidence about our NRBA depends on the strength of the relationship between the characteristics in the nonresponse adjustment and the key survey outcomes, so the key to a successful NRBA is to include strong predictors.
