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.

翻译：当个体未能提供整个波次或特定调查问题的数据时，纵向研究会面临无应答问题。本文比较了纵向研究中无应答偏差分析（NRBA）的方法，并以早期儿童纵向研究（2010-11学年幼儿园班级队列，ECLS-K:2011）为例进行说明。伴随样本流失的波次无应答通常产生单调缺失模式，其缺失机制可能是随机缺失（MAR）或非随机缺失（MNAR）。我们讨论了针对单调缺失模式的加权法、多重插补（MI）、不完全数据建模及贝叶斯方法。当构建的权重与所关注的调查结果相关时，加权调整效果显著；MI允许将含有缺失值的变量纳入插补模型，从而可能获得偏差更小、效率更高的估计；使用最大似然估计的多层模型和基于广义估计方程的边际模型也可处理不完全纵向数据；贝叶斯方法引入先验信息并可能稳定模型估计。我们在MAR结果中附加偏移量进行敏感性分析以评估MNAR偏差。针对描述性汇总和解析模型估计开展NRBA后发现，在ECLS-K:2011应用中，NRBA对实质性结论的影响较小。NRBA证据的强度取决于无应答调整中的特征与关键调查结果之间关系的强弱，因此成功开展NRBA的关键在于纳入强预测变量。