Papers ranged in topic from evaluating causes of errors by interviewers in recording errors in CATI surveys to determining causes of breakoffs in web and time diary surveys.
• Arunachalam, H., G. Atkin, A. Eck, D. Wettlaufer, L.-K. Soh, and R. F. Belli (2015). I Know What You Did Next: Predicting Respondent’s Next Activity Using Machine Learning
• Atkin, G., H. Arunachalam, A. Eck, D.Wettlaufer, L.-K. Soh, and R. F. Belli (2015). Using Machine Learning Techniques to Predict Respondent Type from A Priori Demographic Information
• Belli, R. F., L. D. Miller, L.-K. Soh, and T. Al Baghal (2015). Using Data Mining to Examine Interviewr-Respondent Interactions in Calendar Interviews
• Cordova Cazar, A. L., & Belli, R. F. (2015). The Use of Paradata to Evaluate Interview Complexity and Data Quality (in Calendar and Time Diary Surveys).
• Deal, C. E., Kirchner, A., Cordova Cazar, A. L., Ellyne, L., & Belli, R. F. (2015). Changing ‘Who’ or ‘Where’: Implications for Data Quality in the American Time Use Survey
• Kirchner, Antje and Kristen Olson. 2015. Effects of interviewer and respondent behavior on data quality: An investigation of question types and interviewer learning.
• McCutcheon, Allan L. (2015) Survey Informatics: The Future of Survey Methodology and Survey Statistics Training in the Academy?
• McCutcheon, Allan L. (2015) The Role of Device Type and Respondent Characteristics in Internet Panel Survey Breakoff.
• Olson, K. & Smyth, J. D. 2015. Why Do Interviewers Speed Up? An Examination of Chagnes in Interviewer Behaviors over the Course of the Survey Field Period.
• Smyth, J. D. & Olson, K. (2015). Recording What the Respondent Says: Does Question Format Matter?
• Soh, L.-K., A. Eck, and A. L. McCutcheon (2015). Predicting Breakoff Using Sequential Machine Learning Methods
• Wang, Mengyang, Allan L. McCutcheon, and Laura Allen (2015) Grids and Online Panels: A Comparison of Device Type from a Survey Quality Perspective.
• Wettlaufer, D., H. Arunachalam, G. Atkin, A. Eck, L.-K. Soh, R. F. Belli (2015). Determining Potential for Breakoff in Time Diary Survey Using Paradata.