Rising costs of survey data collection and researchers' growing concerns about the quality of survey data necessitate the development of innovative approaches to data collection. This research will focus on improving survey data collected from computer-assisted methods. The project will include research related to internet data collection and computer-assisted telephone interviews (CATI) data collection systems, along with the variants of these computer-assisted data collection systems. With an overall goal of improving the quality of data derived from surveys, the research will focus on accomplishing three objectives. First, the study will evaluate the use of four diagnostic tools for identifying measurement errors in computer-assisted, interviewer-administered data collection instruments, and it will use these findings to inform visual redesign of common features of computer-assisted, interviewer-administered data collection instruments. Second, the study will evaluate the use of adaptive/responsive designs in which a dynamic modeling of collected data is used to modify the questionnaire as the data are being collected. Third, the study will evaluate the application of calendar- and time diary-based data collection methods to aid in the accuracy of behavioral self-reports by tailoring questions to the needs of individual respondents. The tasks under these objectives will be integrative, allowing researchers to improve survey designs/instruments and enhance data quality by reducing interviewer and respondent burden. The interdisciplinary project team includes experts in statistics, psychology, sociology, survey research and methodology, and computer science. The team will leverage long-term collaborations and partner with industry leaders -- Gallup and Abt SRBI -- as well as Census to accomplish its goal and objectives.
This research will advance scientific knowledge in survey methodology and related fields. Reducing measurement errors in survey data is critical to accurate inference from survey data, and identifying new tools that minimize or reduce measurement errors in survey data will improve conclusions made from surveys across fields. In addition to advancing discovery and understanding in survey data collection, the project will contribute significantly to the training of future survey research professionals through implementation of an education plan designed to: (1) recruit and retain a diverse methodologically-oriented student pool from related social science and statistical disciplines; (2) integrate research into existing and new curricula at UNL; (3) provide all participating students with hands-on research opportunities; and (4) develop student researchers' professional skills. The results will be broadly relevant and broadly disseminated at multidisciplinary and interdisciplinary conferences and workshops, to the federal statistical agencies, and to researchers in the survey industry. This activity is supported by the NSF-Census Research Network funding opportunity.