Handling Item-Level Missing Data
Handling Item-Level Missing Data
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处理项目级缺失数据
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Mike C. Parent
中科院分区：
文献类型：
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作者：
Mike C. Parent
缺失数据的问题越来越受到关注，人们呼吁将处理缺失的先进方法应用于咨询心理学研究。本研究旨在评估是否先进的方法处理项目级缺失数据的执行等同于简单的方法，在设计类似的咨询心理学家通常从事。在本调查中使用的初步初步分析，分析使用真实世界的数据，和一系列的模拟研究的结果。结果表明，可用的病例分析、均值替代和多重插补在低水平缺失数据中具有相似的结果，尽管在具有较高水平缺失数据和其他问题（例如，小样本量或内部可靠性较弱的量表）意味着替代会导致项目之间相关系数的膨胀。目前的研究结果支持使用可用的情况下，分析处理低级别的项目级缺失。
The topic of missing data has been receiving increasing attention, with calls to apply advanced methods of handling missingness to counseling psychology research. The present study sought to assess whether advanced methods of handling item-level missing data performed equivalently to simpler methods in designs similar to those counseling psychologists typically engage in. Results of an initial preliminary analysis, an analysis using real-world data, and a series of simulation studies were used in the present investigation. Results indicated that available case analysis, mean substitution, and multiple imputation had similar results across low levels of missing data, though in data with higher levels of missing data and other problems (e.g., small sample size or scales with weak internal reliability) mean substitution produced inflation of correlation coefficients among items. The present results support the use of available case analysis when dealing with low-level item-level missingness.