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：纵向研究中，当个体未能提供整轮或特定问题的调查数据时，会产生无应答问题。本文比较了纵向研究中无应答偏差分析（NRBA）的方法，并将其应用于早期儿童纵向研究——2010-11年幼儿园队列（ECLS-K:2011）。伴随样本流失的轮次无应答通常产生单调缺失模式，其缺失机制可能为随机缺失（MAR）或非随机缺失（MNAR）。我们讨论了针对单调模式的加权法、多重插补（MI）、不完整数据建模及贝叶斯方法。当构建的权重与目标调查结果相关时，加权调整效果显著；多重插补允许将含缺失值的变量纳入插补模型，从而可能降低偏差并提高估计效率；采用最大似然估计的多层模型及通过广义估计方程估计的边缘模型也可处理不完整纵向数据；贝叶斯方法引入先验信息并可能稳定模型估计。我们在MAR结果中增加偏移量以进行敏感性分析，评估MNAR偏差。我们对描述性总结与分析模型估计开展NRBA，发现ECLS-K:2011应用中，NRBA对实质结论的影响较小。NRBA的证据强度取决于无应答调整特征与关键调查结果之间的关联强度，因此成功开展NRBA的关键在于纳入强预测变量。