政策调查与问卷数据的数据排除:异常反应与缺失

IF 3.4 Q1 EDUCATION & EDUCATIONAL RESEARCH
Maxwell R. Hong, Matthew F. Carter, Casey Kim, Ying Cheng
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引用次数: 0

摘要

在心理科学中,数据预处理是分析数据之前不可或缺的一步,对其潜在的指导政策具有影响。本文报告了心理学研究者如何处理数据预处理或质量问题,重点关注自我报告测量中的异常反应和缺失数据。从2012年至2018年,从《心理科学》、《人格与社会心理学》、《发展心理学》和《变态心理学》四种期刊中抽取了240篇文章。近一半的研究没有报告任何缺失的数据处理(111/240;46.25%),如果有,最常见的方法是按列表删除(71/240;29.6%)。由于缺失而删除数据的研究平均删除了12%的样本。同样,大多数研究没有报告任何异常反应(1994 /240;80%),但如果他们这样做了,他们将4%的样本归类为可疑样本。大多数研究要么对其数据预处理步骤不够透明,要么可能利用了次优程序。建议可以提高透明度和数据质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Data Exclusion in Policy Survey and Questionnaire Data: Aberrant Responses and Missingness
Data preprocessing is an integral step prior to analyzing data in psychological science, with implications for its potentially guiding policy. This article reports how psychological researchers address data preprocessing or quality concerns, with a focus on aberrant responses and missing data in self-report measures. 240 articles were sampled from four journals: Psychological Science, Journal of Personality and Social Psychology, Developmental Psychology, and Abnormal Psychology from 2012 to 2018. Nearly half of the studies did not report any missing data treatment (111/240; 46.25%), and if they did, the most common approach was listwise deletion (71/240; 29.6%). Studies that remove data due to missingness removed, on average, 12% of the sample. Likewise, most studies do not report any aberrant responses (194/240; 80%), but if they did, they classified 4% of the sample as suspect. Most studies are either not transparent enough about their data preprocessing steps or may be leveraging suboptimal procedures. Recommendations can improve transparency and data quality.
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来源期刊
Policy Insights from the Behavioral and Brain Sciences
Policy Insights from the Behavioral and Brain Sciences Social Sciences-Public Administration
CiteScore
5.30
自引率
0.00%
发文量
24
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