Missing Data: Five Practical Guidelines
Missing Data: Five Practical Guidelines
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DOI：
10.1177/1094428114548590
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发表时间：
2014-10-01
影响因子：
9.5
通讯作者：
Newman, Daniel A.
中科院分区：
文献类型：
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作者：
Newman, Daniel A.
缺失数据 (a) 存在于三个缺失数据分析级别（项目级、构造级和个人级），(b) 由三种缺失数据机制（完全随机缺失、随机缺失和非随机缺失）产生，范围从完全随机到系统缺失，(c) 可能产生两个缺失数据问题（有偏差的参数估计和不准确的假设检验/不准确的标准误差/低功效），以及 (d) 要求从几种缺失数据处理中进行选择（列表删除、成对删除、单一插补、最大似然和多重插补）。尽管所有缺失数据处理都是不完美的并且植根于特定的统计假设，但平均而言，某些缺失数据处理比其他处理更糟糕（即，它们导致参数估计出现更多偏差，假设检验不太准确）。社会科学家仍然经常选择偏差更大、更容易出错的技术（列表和成对删除），这可能是由于对偏差较小/不易出错的技术（最大似然和多重插补）的不熟悉和误解。当前的用户友好审查提供了五个易于理解的实用指南，旨在减少研究结果报告中的缺失数据偏差和错误。提供了用于关联、多元回归和缺失数据的结构方程建模的语法。
Missing data (a) reside at three missing data levels of analysis (item-, construct-, and person-level), (b) arise fromthree missing datamechanisms(missing completely at random, missing at random, and missing not at random) that range from completely random to systematic missingness, (c) can engender two missing data problems (biased parameter estimates and inaccurate hypothesis tests/inaccurate standard errors/low power), and (d) mandate a choice from among several missing data treatments (listwise deletion, pairwise deletion, single imputation, maximum likelihood, and multiple imputation). Whereas all missing data treatments are imperfect and are rooted in particular statistical assumptions, some missing data treatments are worse than others, on average (i. e., they lead to more bias in parameter estimates and less accurate hypothesis tests). Social scientists still routinely choose the more biased and error-prone techniques (listwise and pairwise deletion), likely due to poor familiarity with and misconceptions about the less biased/less error-prone techniques (maximum likelihood and multiple imputation). The current user-friendly review provides five easy-to-understand practical guidelines, with the goal of reducing missing data bias and error in the reporting of research results. Syntax is provided for correlation, multiple regression, and structural equation modeling with missing data.