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调查或特定问题的数据时，便会产生无应答现象。本文比较了纵向研究中无应答偏差分析（NRBA）的多种方法，并以2010-11学年幼儿园班级早期儿童纵向研究（ECLS-K:2011）为例进行说明。由样本流失导致的轮次无应答通常会产生单调缺失模式，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模式的加权法、多重插补法（MI）、不完全数据建模法以及贝叶斯方法在NRBA中的应用。当构建的权重与所关注的调查结果相关时，加权调整尤为有效。MI允许将存在缺失值的变量纳入插补模型，从而可能降低估计偏差并提高估计效率。采用最大似然估计的多水平模型以及使用广义估计方程估计的边缘模型也可处理不完全纵向数据。贝叶斯方法引入先验信息，有助于稳定模型估计。我们在MAR结果中加入偏移量进行敏感性分析，以评估MNAR偏离情况。我们对描述性统计结果和分析模型估计值进行了NRBA，发现在ECLS-K:2011应用案例中，NRBA仅对实质性结论产生微小影响。NRBA的证据力度取决于无应答调整中使用的特征与关键调查结果之间关系的强度，因此，成功的NRBA关键在于纳入强预测变量。