Handling of missing data in psychological research:: Problems and solutions
Handling of missing data in psychological research:: Problems and solutions
复制标题
DOI：
10.1026/0033-3042.58.2.103
复制
发表时间：
2007-01-01
影响因子：
1.2
通讯作者：
Koeller, Olaf
中科院分区：
文献类型：
--
作者：
Luedtke, Oliver;Robitzsch, Alexander;Koeller, Olaf
缺失数据是经验心理学研究中普遍存在的问题。从方法论的角度来看，传统的方法，如逐例和成对删除，回归插补和平均插补有明显的弱点。然而，在过去的三十年中发展起来的用于分析具有缺失值的数据集的现代统计方法尚未在研究实践中获得重要的立足点。我们开始这篇文章介绍的基本概念和术语的缺失数据，提出了鲁宾（1976年）。然后，我们概述了文献中讨论的处理缺失数据的不同方法，区分了三种类型的程序：传统程序（例如，列表删除）、基于插补的程序（其中缺失值被插补值替换）和基于模型的程序（其中模型被估计，缺失数据在单个步骤中处理）。在文章的实证部分，我们使用来自大规模教育评估的数据集演示了多重插补的应用。研究实践的影响进行了讨论。
Missing data are a pervasive problem in empirical psychological research. From the methodological perspective, traditional procedures such as Casewise and Pairwise Deletion, Regression Imputation, and Mean Imputation have distinct weaknesses. Yet modem statistical methods for the analysis of datasets with missing values that have been developed in the past three decades have not yet gained a significant foothold in research practice. We begin this article by introducing the basic concepts and terminology of missing data, as proposed by Rubin (1976). We then give an overview of the different approaches to handling missing data discussed in the literature, distinguishing between three types of procedures: traditional procedures (e.g., Listwise Deletion), imputation-based procedures, in which missing values are replaced by imputed values, and model-based procedures, in which models are estimated and missing data handled in a single step. In the empirical section of the article, we demonstrate the application of Multiple Imputation using a dataset from a large-scale educational assessment. Implications for research practice are discussed.