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Best practice recommendations for data screening

机译:数据筛选的最佳做法建议

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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.
机译:受访者在回答项目时的关注程度和努力程度有所不同。研究人员可以使用多种方法来识别未能尽力的受访者,以增加分析的严格性并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的受访者意识的影响不同。讨论了有关适当使用筛选技术的假设和注意事项,以及每种技术的说明。每种筛选技术的实用性是调查设计和管理的功能。每种技术都有可能识别出不足类型的不同类型。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应考虑数据筛选,应根据理论考虑因素(或选择试点测试时的经验考虑因素)选择筛选技术,并在采用数据筛选技术之前和之后报告分析结果。

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