As people increasingly communicate via asynchronous non-spoken modes on mobile devices, particularly text messaging (e.g., short message service (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. Demographic variables include participants' gender, race, education level, and household income.

As people increasingly communicate via asynchronous non-spoken modes on mobile devices, particularly text messaging (e.g., short message service (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. Demographic variables include participants' gender, race, education level, and household income.

Now that people on mobile devices can easily choose their mode of communication (e.g., voice, text, video) survey designers can allow respondents to answer questions in whatever mode they find momentarily convenient given their circumstances or that they chronically prefer. Investigators conducted an experiment to explore how mode choice affects response quality, participation, and satisfaction in smartphone interviews. Respondents were interviewed on their iPhone in one of four modes: Human Voice, Human Text, Automated Voice, and Automated Text. Respondents were either assigned the mode of their interview (Assigned Mode), in which case the contact and interviewing mode were the same, or they were required to choose the mode of their interview (Mode Choice) after being contacted in one of the four modes. 634 respondents completed the interview and a post-interview online debriefing questionnaire in the Assigned Mode group and 626 respondents completed the interview and online debriefing in the Assigned Mode group. This dataset contains 2691 cases, the 1,260 respondents who completed the interview and debriefing, as well as 1,431 cases that were invited to participate but ended their participation somewhere shy of the last debriefing question (either they did not choose a mode, did not answer the first question, started but did not finish the interview, or finished the interview but did not complete the debriefing). All respondents (who completed the interview) answered 32 questions from US social surveys. 13 interviewers from the University of Michigan Survey Research Center administered voice and text interviews (five administered interviews in both experimental conditions, three conducted only Assigned Mode interviews, and five conducted interviews in just the Mode Choice condition). Automated systems launched parallel text and voice interviews at the same time as the human interviews. Respondents who chose their interview modes provided more conscientious (fewer rounded and non-differentiated) answers, and they reported greater satisfaction with the interview. Although fewer respondents started the interview when given a choice of mode, a higher percentage of Mode Choice respondents who started the interview completed it. For certain mode transitions (e.g., from automated interview modes) there was no reduction in participation. The results demonstrate clear benefits and relatively few drawbacks resulting from mode choice, at least among these modes and with this sample of iPhone users, suggesting that further exploration of mode choice and the logistics of its implementation is warranted. Demographic variables include participants' gender, race, education level, and household income.

Now that people on mobile devices can easily choose their mode of communication (e.g., voice, text, video) survey designers can allow respondents to answer questions in whatever mode they find momentarily convenient given their circumstances or that they chronically prefer. Investigators conducted an experiment to explore how mode choice affects response quality, participation, and satisfaction in smartphone interviews. Respondents were interviewed on their iPhone in one of four modes: Human Voice, Human Text, Automated Voice, and Automated Text. Respondents were either assigned the mode of their interview (Assigned Mode), in which case the contact and interviewing mode were the same, or they were required to choose the mode of their interview (Mode Choice) after being contacted in one of the four modes. 634 respondents completed the interview and a post-interview online debriefing questionnaire in the Assigned Mode group and 626 respondents completed the interview and online debriefing in the Assigned Mode group. This dataset contains 2691 cases, the 1,260 respondents who completed the interview and debriefing, as well as 1,431 cases that were invited to participate but ended their participation somewhere shy of the last debriefing question (either they did not choose a mode, did not answer the first question, started but did not finish the interview, or finished the interview but did not complete the debriefing). All respondents (who completed the interview) answered 32 questions from US social surveys. 13 interviewers from the University of Michigan Survey Research Center administered voice and text interviews (five administered interviews in both experimental conditions, three conducted only Assigned Mode interviews, and five conducted interviews in just the Mode Choice condition). Automated systems launched parallel text and voice interviews at the same time as the human interviews. Respondents who chose their interview modes provided more conscientious (fewer rounded and non-differentiated) answers, and they reported greater satisfaction with the interview. Although fewer respondents started the interview when given a choice of mode, a higher percentage of Mode Choice respondents who started the interview completed it. For certain mode transitions (e.g., from automated interview modes) there was no reduction in participation. The results demonstrate clear benefits and relatively few drawbacks resulting from mode choice, at least among these modes and with this sample of iPhone users, suggesting that further exploration of mode choice and the logistics of its implementation is warranted. Demographic variables include participants' gender, race, education level, and household income.