人格测量中缺失项目得分的发生率及简单项目得分的归算

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
J. V. van Ginkel, K. Sijtsma, L. A. van der Ark, J. Vermunt
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引用次数: 54

摘要

本研究的重点是人格研究中不同类型的数据缺失问题的发生率及其处理方法。在三个主要的人格杂志上发表的800多篇文章中,大约有一半报告了数据缺失问题。在这些文章中,单位无反应、损耗和计划缺失被区分出来,但缺失项目得分在特质测量中被报道得最多。列表删除是处理所有丢失数据问题最常用的方法。已知列表删除会降低参数估计的准确性和统计检验的能力,并经常产生有偏差的统计分析结果。本研究提出了一种简单的替代方法来处理缺失的项目得分,称为双向imputation,它使样本量保持不变,并已被证明可以产生基于多项目问卷数据的几乎无偏的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Incidence of Missing Item Scores in Personality Measurement, and Simple Item-Score Imputation
The focus of this study was the incidence of different kinds of missing-data problems in personality research and the handling of these problems. Missing-data problems were reported in approximately half of more than 800 articles published in three leading personality journals. In these articles, unit nonresponse, attrition, and planned missingness were distinguished but missing item scores in trait measurement were reported most frequently. Listwise deletion was the most frequently used method for handling all missing-data problems. Listwise deletion is known to reduce the accuracy of parameter estimates and the power of statistical tests and often to produce biased statistical analysis results. This study proposes a simple alternative method for handling missing item scores, known as two-way imputation, which leaves the sample size intact and has been shown to produce almost unbiased results based on multi-item questionnaire data.
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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