缺失的数据机制和可能的解决方案/ Datos ausentes:可能解决方案的机制

H. Bar
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引用次数: 17

摘要

经验研究人员面临的最常见的问题之一是当数据的一部分丢失。我们将回顾三种不同类型的“缺失”,即完全随机缺失,随机缺失和非随机缺失,我们将讨论缺失数据如何影响数据分析。我们回顾了处理缺失数据的方法,包括简单的“完整案例分析”方法,在这种方法中,我们只使用所有数据可用的数据集中的观察结果,以及更复杂的“多重imputation”方法,在这种方法中,我们使用数据集的多个(完整的)副本重复分析,并通过平均所有分析来获得感兴趣的估计。我们将演示如何实现丢失数据的解决方案,并回顾这些方法的局限性。
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Missing data — mechanisms and possible solutions / Datos ausentes: mecanismos y posibles soluciones
Abstract One of the most common problems facing empirical researchers is when a portion of the data is missing. We will review three different types of ‘missingness’, namely missing completely at random, missing at random and missing not at random, and we will discuss how missing data can affect data analysis. We review methods to deal with missing data, including the simple ‘complete-case analysis’ approach, in which we only use the observations in the data set for which all the data is available, and the more sophisticated ‘multiple imputation’ approach, in which we repeat the analysis using multiple (completed) copies of the data set, and obtain the estimates of interest by averaging across all analyses. We will demonstrate how to implement solutions to missing data and review the limitations of the methods.
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