Best practice recommendations for data screening
Best practice recommendations for data screening
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DOI：
10.1002/job.1962
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发表时间：
2015-02-01
影响因子：
6.8
通讯作者：
Desimone, Alice J.
中科院分区：
文献类型：
--
作者：
Desimone, Justin A.;Harms, P. D.;Desimone, Alice J.
调查对象在回答问题时的注意力和努力程度各不相同。研究人员可以使用许多方法来识别未能发挥足够努力的受访者，以增加分析的严谨性和增强研究结果的可信度。筛选技术分为三大类，它们对调查设计和潜在受访者意识的影响不同。讨论了适当使用筛选技术的假设和考虑因素，并对每种技术进行了描述。每种筛选技术的效用都是调查设计和管理的功能。每种技术都有可能识别不同类型的工作不足。提供了一个示例数据集来说明这些差异，并使读者熟悉筛选技术的计算和实现。鼓励研究人员在设计调查时考虑数据筛选，在理论考虑的基础上选择筛选技术（或经验考虑，当试点测试是一种选择），并在使用数据筛选技术之前和之后报告分析结果。版权所有：John Wiley & Sons， Ltd。
Survey respondents differ in their levels of attention and effort when responding to items. There are a number of methods researchers may use to identify respondents who fail to exert sufficient effort in order to increase the rigor of analysis and enhance the trustworthiness of study results. Screening techniques are organized into three general categories, which differ in impact on survey design and potential respondent awareness. Assumptions and considerations regarding appropriate use of screening techniques are discussed along with descriptions of each technique. The utility of each screening technique is a function of survey design and administration. Each technique has the potential to identify different types of insufficient effort. An example dataset is provided to illustrate these differences and familiarize readers with the computation and implementation of the screening techniques. Researchers are encouraged to consider data screening when designing a survey, select screening techniques on the basis of theoretical considerations (or empirical considerations when pilot testing is an option), and report the results of an analysis both before and after employing data screening techniques. Copyright (c) 2014 John Wiley & Sons, Ltd.