Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics
Multiple Imputation with Massive Data: An Application to the Panel Study of Income Dynamics
复制标题
海量数据多重插补：收入动态面板研究的应用
DOI：
10.1093/jssam/smab038
复制
发表时间：
2021
影响因子：
2.1
通讯作者：
Raghunathan, Trivellore
中科院分区：
文献类型：
--
作者：
Si, Yajuan;Heeringa, Steve;Johnson, David;Little, Roderick J;Liu, Wenshuo;Pfeffer, Fabian;Raghunathan, Trivellore
多重插补（MI）是处理多元数据集缺失数据的一种流行和成熟的方法，但其在海量和复杂数据集上的实用性受到质疑。其中一个数据集是收入动态小组研究（PSID），这是对美国家庭收入和财富的长期广泛调查。由于实施简单，目前使用传统的hot deck方法处理本次调查的缺失数据;然而，单变量hot deck会导致较大的随机财富波动。管理信息系统是有效的，但面临着业务挑战。我们使用序贯回归/链式方程方法，使用软件IVEware，对2013年PSID中的横截面财富数据进行多重插补，并将所得插补数据与当前热甲板方法的分析进行比较。实际困难，如非正态分布的变量，跳跃模式，分类变量与许多水平，多重共线性，与我们的方法来克服它们一起描述。我们评估插补的质量和有效性与内部诊断和外部基准数据。多元智能有助于保持相关结构，如PSID财富组成部分之间的关联以及家庭净资产与社会人口因素之间的关系，从而改进了现有的热甲板方法，并促进了具有一般目的的完整数据分析。MI将高度预测性的协变量纳入插补模型并提高效率。我们建议MI的实际实施，并期望更大的收益时，丢失的信息的分数是大的。
Multiple imputation (MI) is a popular and well-established method for handling missing data in multivariate data sets, but its practicality for use in massive and complex data sets has been questioned. One such data set is the Panel Study of Income Dynamics (PSID), a longstanding and extensive survey of household income and wealth in the United States. Missing data for this survey are currently handled using traditional hot deck methods because of the simple implementation; however, the univariate hot deck results in large random wealth fluctuations. MI is effective but faced with operational challenges. We use a sequential regression/chained-equation approach, using the software IVEware, to multiply impute cross-sectional wealth data in the 2013 PSID, and compare analyses of the resulting imputed data with those from the current hot deck approach. Practical difficulties, such as non-normally distributed variables, skip patterns, categorical variables with many levels, and multicollinearity, are described together with our approaches to overcoming them. We evaluate the imputation quality and validity with internal diagnostics and external benchmarking data. MI produces improvements over the existing hot deck approach by helping preserve correlation structures, such as the associations between PSID wealth components and the relationships between the household net worth and sociodemographic factors, and facilitates completed data analyses with general purposes. MI incorporates highly predictive covariates into imputation models and increases efficiency. We recommend the practical implementation of MI and expect greater gains when the fraction of missing information is large.