Praise forEnvisioning the Survey Interview of the Future "This book is an excellent introduction to some brave new technologies . . . and their possible impacts on the way surveys might be conducted. Anyone interested in the future of survey methodology should read this book." -Norman M. Bradburn, PhD, National Opinion Research Center, University of Chicago "Envisioning the Survey Interview of the Future gathers some of the brightest minds in alternative methods of gathering self-report data, with an eye toward the future self-report sample survey. Conrad and Schober, by assembling a group of talented survey researchers and creative inventors of new software-based tools to gather information from human subjects, have created a volume of importance to all interested in imagining future ways of interviewing." -Robert M. Groves, PhD, Survey Research Center, University of Michigan This collaboration provides extensive insight into the impact of communication technology on survey research As previously unimaginable communication technologies rapidly become commonplace, survey researchers are presented with both opportunities and obstacles when collecting and interpreting data based on human response. Envisioning the Survey Interview of the Future explores the increasing influence of emerging technologies on the data collection process and, in particular, self-report data collection in interviews, providing the key principles for using these new modes of communication. With contributions written by leading researchers in the fields of survey methodology and communication technology, this compilation integrates the use of modern technological developments with established social science theory. The book familiarizes readers with these new modes of communication by discussing the challenges to accuracy, legitimacy, and confidentiality that researchers must anticipate while collecting data, and it also provides tools for adopting new technologies in order to obtain high-quality results with minimal error or bias. Envisioning the Survey Interview of the Future addresses questions that researchers in survey methodology and communication technology must consider, such as: How and when should new communication technology be adopted in the interview process? What are the principles that extend beyond particular technologies? Why do respondents answer questions from a computer differently than questions from a human interviewer? How can systems adapt to respondents' thinking and feeling? What new ethical concerns about privacy and confidentiality are raised from using new communication technologies? With its multidisciplinary approach, extensive discussion of existing and future technologies, and practical guidelines for adopting new technology, Envisioning the Survey Interview of the Future is an essential resource for survey methodologists, questionnaire designers, and communication technologists in any field that conducts survey research. It also serves as an excellent supplement for courses in research methods at the upper-undergraduate or graduate level.

Powerful and economic sensors such as high definition cameras and corresponding recognition software have become readily available, e.g. for face and motion recognition. However, designing user interfaces for robots, phones and computers that facilitate a seamless, intuitive, and apparently effortless communication as between humans is still highly challenging. This has shifted the focus from developing ever faster and higher resolution sensors to interpreting available sensor data for understanding social signals and recognising users' intentions. Psychologists, Ethnologists, Linguists and Sociologists have investigated social behaviour in human-human interaction. But their findings are rarely applied in the human-robot interaction domain. Instead, robot designers tend to rely on either proof-of-concept or machine learning based methods. In proving the concept, developers effectively demonstrate that users are able to adapt to robots deployed in the public space. Typically, an initial period of collecting human-robot interaction data is used for identifying frequently occurring problems. These are then addressed by adjusting the interaction policies on the basis of the collected data. However, the updated policies are strongly biased by the initial design of the robot and might not reflect natural, spontaneous user behaviour. In the machine learning approach, learning algorithms are used for finding a mapping between the sensor data space and a hypothesised or estimated set of intentions. However, this brute-force approach ignores the possibility that some signals or modalities are superfluous or even disruptive in intention recognition. Furthermore, this method is very sensitive to peculiarities of the training data. In sum, both methods cannot reliably support natural interaction as they crucially depend on an accurate model of human intention recognition. Therefore, approaches to social robotics from engineers and computer scientists urgently have to be informed by studies of intention recognition in natural human-human communication. Combining the investigation of natural human behaviour and the design of computer and robot interfaces can significantly improve the usability of modern technology. For example, robots will be easier to use by a broad public if they can interpret the social signals that users spontaneously produce for conveying their intentions anyway. By correctly identifying and even anticipating the user's intention, the user will perceive that the system truly understands her/his needs. Vice versa, if a robot produces socially appropriate signals, it will be easier for its users to understand the robot's intentions. Furthermore, studying natural behaviour as a basis for controlling robots and other devices results in greater robustness, responsiveness and approachability. Thus, we welcome submissions that (a) investigate how relevant social signals can be identified in human behaviour, (b) investigate the meaning of social signals in a specific context or task, (c) identify the minimal set of intentions for describing a context or task, (d) demonstrate how insights from the analysis of social behaviour can improve a robot's capabilities, or (e) demonstrate how a robot can make itself more understandable to the user by producing more human-like social signals.

When survey respondents are consciously or unconsciously influenced by the characteristics of the interviewer, bias in the survey estimates may result. The effects of interviewer characteristics such as gender and race on survey estimates have been considered previously, but isolating and experimentally manipulating a single interviewer characteristic has been infeasible. With the advent of online virtual worlds, it is now possible to conduct experiments focusing on individual physical interviewer characteristics. We conducted an exploratory study, surveying 60 individuals in Second Life, an online virtual-world community in which the respondent and interviewer were both represented as avatars—three-dimensional representations of real-life individuals. To explore the effect of interviewer appearance on reported health behaviors and attitudes, we randomly assigned half of the survey respondents to a “thin” interviewer and half to a “heavy” interviewer. The data suggest that those who reported to the heavy interviewer were less likely to say their own avatar was attractive, reported less frequent real-life exercise, and reported a higher real-life body mass index, although because of the small number of participants, we did not detect statistically significant differences. The findings suggest that interviewer appearance may have a biasing effect on reports in virtual-world surveys—and perhaps in real-world surveys. Despite the lack of statistically significant findings, the study illustrates the future potential, benefits, and challenges to surveying and conducting methodological research in a virtual world.

"This book provides an overview of online research methods in urban and planning studies, exploring and discussing new digital tools and Web-based research methods, as well as the scholarly, legal, and ethical challenges associated with their use"--Provided by publisher.

Interviewer Effects from a Total Survey Error Perspective presents a comprehensive collection of state-of-the-art research on interviewer-administered survey data collection. Interviewers play an essential role in the collection of the high-quality survey data used to learn about our society and improve the human condition. Although many surveys are conducted using self-administered modes, interviewer-administered modes continue to be optimal for surveys that require high levels of participation, include difficult-to-survey populations, and collect biophysical data. Survey interviewing is complex, multifaceted, and challenging. Interviewers are responsible for locating sampled units, contacting sampled individuals and convincing them to cooperate, asking questions on a variety of topics, collecting other kinds of data, and providing data about respondents and the interview environment. Careful attention to the methodology that underlies survey interviewing is essential for interviewer-administered data collections to succeed. In 2019, survey methodologists, survey practitioners, and survey operations specialists participated in an international workshop at the University of Nebraska-Lincoln to identify best practices for surveys employing interviewers and outline an agenda for future methodological research. This book features 23 chapters on survey interviewing by these worldwide leaders in the theory and practice of survey interviewing. Chapters include: The legacy of Dr. Charles F. Cannell’s groundbreaking research on training survey interviewers and the theory of survey interviewing Best practices for training survey interviewers Interviewer management and monitoring during data collection The complex effects of interviewers on survey nonresponse Collecting survey measures and survey paradata in different modes Designing studies to estimate and evaluate interviewer effects Best practices for analyzing interviewer effects Key gaps in the research literature, including an agenda for future methodological research Chapter appendices available to download from https://digitalcommons.unl.edu/sociw/ Written for managers of survey interviewers, survey methodologists, and students interested in the survey data collection process, this unique reference uses the Total Survey Error framework to examine optimal approaches to survey interviewing, presenting state-of-the-art methodological research on all stages of the survey process involving interviewers. Acknowledging the important history of survey interviewing while looking to the future, this one-of-a-kind reference provides researchers and practitioners with a roadmap for maximizing data quality in interviewer-administered surveys.

The new edition of this landmark volume emphasizes the dynamic, interactional, and reflexive dimensions of the research interview. Contributors highlight the myriad dimensions of complexity that are emerging as researchers increasingly frame the interview as a communicative opportunity as much as a data-gathering format. The book begins with the history and conceptual transformations of the interview, which is followed by chapters that discuss the main components of interview practice. Taken together, the contributions to The SAGE Handbook of Interview Research: The Complexity of the Craft encourage readers simultaneously to learn the frameworks and technologies of interviewing and to reflect on the epistemological foundations of the interview craft.

This handbook is a comprehensive reference guide for researchers, funding agencies and organizations engaged in survey research. Drawing on research from a world-class team of experts, this collection addresses the challenges facing survey-based data collection today as well as the potential opportunities presented by new approaches to survey research, including in the development of policy. It examines innovations in survey methodology and how survey scholars and practitioners should think about survey data in the context of the explosion of new digital sources of data. The Handbook is divided into four key sections: the challenges faced in conventional survey research; opportunities to expand data collection; methods of linking survey data with external sources; and, improving research transparency and data dissemination, with a focus on data curation, evaluating the usability of survey project websites, and the credibility of survey-based social science. Chapter 23 of this book is open access under a CC BY 4.0 license at link.springer.com.