Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children’s influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).

中文翻译：

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处理密集纵向数据建模中的缺失数据


文献中存在多种处理缺失数据的方法。然而，很少有研究调查这些方法在使用密集纵向数据时的可行性和实用性。在本研究中，我们比较并说明了两种多重插补（MI）方法，用于处理不同缺失数据机制下拟合多元时间序列模型的缺失问题。它们包括完整的 MI 方法（其中所有因变量和协变量同时估算）和部分 MI 方法（其中缺失的协变量用 MI 估算），而因变量的缺失则通过全信息最大似然估计来处理。我们发现，在正确指定的模型下，部分 MI 会产生最佳的总体估计结果。我们讨论了两种 MI 方法的优点和局限性，并通过经验数据集展示了它们的使用，其中儿童对父母冲突的影响被建模为 15 天过程中的协变量（Schermerhorn、Chow 和 Cummings，2010）。