SurveyMan：编程和自动测量
SurveyMan: Programming and Automatically Debugging Surveys
摘要：
调查可以被看作是程序，包括逻辑、控制流和错误。措辞的选择或提问的顺序可能会无意中使回答产生偏见。模糊、混乱或侵入性的问题可能会导致受访者放弃调查。调查也可能有运行时错误：不注意的受访者可能会污染结果。当在不受控制的环境中部署调查时，例如在网络上或通过众包平台部署调查时，这种影响尤其成问题。由于调查结果会推动商业决策并为科学结论提供信息，因此确保调查结果的正确性至关重要。我们提出SurveyMan，一个系统的设计，部署和自动调试调查。调查作者使用面向最终用户的轻量级领域特定语言编写调查。SurveyMan静态分析调查，以便在部署之前向调查作者提供反馈。然后，它将调查编译成JavaScript，并将其部署到网络或众包平台。SurveyMan的动态分析可自动查找调查错误，并控制响应的质量。我们评估SurveyMan的算法分析和经验，通过亚马逊的土耳其机械进行的社会科学调查的案例研究，证明其有效性。
英文摘要：
Surveys can be viewed as programs, complete with logic, control flow, and bugs. Word choice or the order in which questions are asked can unintentionally bias responses. Vague, confusing, or intrusive questions can cause respondents to abandon a survey. Surveys can also have runtime errors: inattentive respondents can taint results. This effect is especially problematic when deploying surveys in uncontrolled settings, such as on the web or via crowdsourcing platforms. Because the results of surveys drive business decisions and inform scientific conclusions, it is crucial to make sure they are correct. We present SurveyMan, a system for designing, deploying, and automatically debugging surveys. Survey authors write their surveys in a lightweight domain-specific language aimed at end users. SurveyMan statically analyzes the survey to provide feedback to survey authors before deployment. It then compiles the survey into JavaScript and deploys it either to the web or a crowdsourcing platform. SurveyMan's dynamic analyses automatically find survey bugs, and control for the quality of responses. We evaluate SurveyMan's algorithms analytically and empirically, demonstrating its effectiveness with case studies of social science surveys conducted via Amazon's Mechanical Turk.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
