Precision and Disclosure in Text and Voice Interviews on Smartphones: 2012 [United States]

As people increasingly communicate via asynchronous non-spoken modes on mobile devices, particularly text messaging (e.g., SMS), longstanding assumptions and practices of social measurement via telephone survey interviewing are being challenged. This dataset contains 1,282 cases, 634 cases that completed an interview and 648 cases that were invited to participate, but did not start or complete an interview on their iPhone. Participants were randomly assigned to answer 32 questions from US social surveys via text messaging or speech, administered either by a human interviewer or by an automated interviewing system. 10 interviewers from the University of Michigan Survey Research Center administered voice and text interviews; automated systems launched parallel text and voice interviews at the same time as the human interviews were launched. The key question was how the interview mode affected the quality of the response data, in particular the precision of numerical answers (how many were not rounded), variation in answers to multiple questions with the same response scale (differentiation), and disclosure of socially undesirable information. Texting led to higher quality data—fewer rounded numerical answers, more differentiated answers to a battery of questions, and more disclosure of sensitive information—than voice interviews, both with human and automated interviewers. Text respondents also reported a strong preference for future interviews by text. The findings suggest that people interviewed on mobile devices at a time and place that is convenient for them, even when they are multitasking, can give more trustworthy and accurate answers than those in more traditional spoken interviews. The findings also suggest that answers from text interviews, when aggregated across a sample, can tell a different story about a population than answers from voice interviews, potentially altering the policy implications from a survey.随着人们在移动设备上愈发依赖异步非语音方式进行沟通，尤其是短信（SMS），长期以来依托电话调查访谈开展社会测量的假设与实践正受到挑战。本数据集共包含1282个案例，其中634例完成了访谈，另有648例受邀参与但未在其iPhone设备上启动或完成访谈。参与者被随机分配通过短信或语音的方式作答美国社会调查中的32道问题，访谈可由人类访员或自动化访谈系统执行。密歇根大学调查研究中心的10名访员负责开展语音与短信访谈；自动化访谈系统则在人类访谈启动的同时，同步发起平行的短信与语音访谈。本研究的核心问题为访谈模式如何影响应答数据质量，具体涵盖数值型答案的精准度（即未被四舍五入的答案占比）、使用相同应答量表的多道问题的答案变异度（区分度），以及社会禁忌信息的披露情况。相较于人类或自动化访员开展的语音访谈，短信访谈可产出更高质量的数据：数值答案被四舍五入的比例更低，针对一系列问题的答案区分度更高，且敏感信息披露率更高。参与短信访谈的受访者还明确表示更倾向于未来采用短信方式开展访谈。研究结果表明，即便受访者处于多任务处理状态，只要在其方便的时间与地点使用移动设备接受访谈，其给出的答案也比传统语音访谈更为可信与准确。此外，研究还发现，若对样本数据进行汇总分析，短信访谈所得结果与语音访谈的结果可能呈现出不同的群体画像，进而可能改变基于该调查得出的政策启示。