Handling missing data in self-report measures
Handling missing data in self-report measures
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DOI：
10.1002/nur.20100
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发表时间：
2005-12-01
影响因子：
2
通讯作者：
El-Masri, MM
中科院分区：
文献类型：
--
作者：
Fox-Wasylyshyn, SM;El-Masri, MM
自我报告测量在护理研究中被广泛使用。从这类报告中得出的数据可能会受到数据缺失问题的影响。为了帮助确保准确的参数估计和有效的研究结果，需要适当地解决数据缺失的问题。然而，对护理研究文献的回顾显示，诸如缺失的程度和模式，以及用于处理缺失数据的方法等问题很少被报道。这篇文章的目的是为研究人员提供与缺失数据相关的问题的概念性概述，确定缺失模式所使用的程序，以及处理缺失数据的技术。本文还强调了这些技术的优点和缺点，并区分了在项级别和变量级别丢失的数据。本文涉及的缺失数据处理技术包括删除法、均值替换法、基于回归的推理法、热板式推销法、多重推销法和最大似然推销法。(C)2005年威利期刊公司。
Self-report measures are extensively used in nursing research. Data derived from such reports can be compromised by the Problem of missing data. To help ensure accurate parameter estimates and valid research results, the problem of missing data needs to be appropriately addressed. However, a review of nursing research literature revealed that issues such as the extent and pattern of missingness, and the approach used to handle missing data are seldom reported. The purpose of this article is to provide researchers with a conceptual overview of the issues associated with missing data, procedures used in determining the pattern of missingness, and techniques for handling missing data. The article also highlights the advantages and disadvantages of these techniques, and makes distinctions between data that are missing at the item versus variable levels. Missing data handling techniques addressed in this article include deletion approaches, mean substitution, regression-based imputation, hot-deck imputation, multiple imputation, and maximum likelihood imputation. (c) 2005 Wiley Periodicals, Inc.