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Folch, David C., Daniel Arribas-Bel, Julia Koschinsky, and Seth E. Spielman. Uncertain Uncertainty: Spatial Variation in the Quality of American Community Survey Estimates. University of Colorado at Boulder / University of Tennessee Preprint 1813:38122, 2014, available at http://hdl.handle.net/1813/38122.
Gelman, Michael. "Understanding Household Consumption and Saving Behavior using Account Data." (forthcoming).
H. Shaefer, Luke, Kathryn Edin, and E. Talbert. "Understanding the Dynamics of $2-a-Day Poverty in the United States." The Russell Sage Foundation Journal of the Social Sciences 1, no. Severe Deprivation (2015).
Eck, A., S. Leen-Kiat, A. L. McCutcheon, J.D. Smyth, and R.F. Belli. "Understanding the Human Condition through Survey Informatics." IEEE Computer 48, no. 11 (2015): 112-116. DOI: 10.1109/MC.2015.327.
Chen, B., A. Shrivastava, and R. C. Steorts. Unique Entity Estimation with Application to the Syrian Conflict, arXiv. 1710.02690, 2017, available at https://arxiv.org/abs/1710.02690.
McCutcheon, Allan L., K. Rao, and O. Kaminska. "The Untold Story of Multi-Mode (Online and Mail) Consumer Panels: From Optimal Recruitment to Retention and Attrition." In Online Panel Surveys: An Interdisciplinary Approach, edited by M. Callegaro, R. Baker, J. Bethlehem, A. Göritz, J. Krosnick and P. Lavrakas. Wiley, 2014.
Kimberlin, Sara, Jiyoun Kim, and Luke Shaefer. "An updated method for calculating income and payroll taxes from PSID data using the NBER’s TAXSIM, for PSID survey years 1999 through 2011." Unpublished manuscript, University of Michigan. Accessed May 6 (2014): 2016.
Cordova-Cazar, A.L., and R.F. Belli. "The use of paradata (in time use surveys) to better evaluate data quality." In American Association for Public Opinion Research 2014 Annual Conference. Anaheim, CA, 2014, available at http://www.aapor.org/AAPORKentico/Conference/Recent-Conferences.aspx.
Cordova-Cazar, A.L., and R.F. Belli. "The Use of Paradata to Evaluate Interview Complexity and Data Quality (in Calendar and Time Diary Surveys)." In 70th Annual Conference of the American Association for Public Opinion Research (AAPOR). Hollywood, Florida, 2015, available at http://www.aapor.org/AAPORKentico/Conference/Recent-Conferences.aspx.
Stuart, Leonard Cleve. User Modeling via Machine Learning and Rule-based Reasoning to Understand and Predict Errors in Survey Systems. University of Nebraska-Lincoln Masters, 2013, available at http://digitalcommons.unl.edu/computerscidiss/70/.
Lee, Jinyoung, Ben Seloske, and Robert F. Belli. Using audit trails to evaluate an event history calendar survey instrument.
Belli, R. F.. Using behavior coding to understand respondent retrieval strategies that inform the structure of autobiographical knowledge.
Belli, R.F., L.D. Miller, L.-K. Soh, and T. Al Baghal. "Using Data Mining to Examine Interviewer-Respondent Interactions in Calendar Interviews." In 70th Annual Conference of the American Association for Public Opinion Research (AAPOR). Hollywood, Florida, 2015, available at http://www.aapor.org/AAPORKentico/Conference/Recent-Conferences.aspx.
Belli, Robert F., Dee L. Miller, Tarek Al Baghal, and Leen-Kiat Soh. "Using Data Mining to Predict the Occurrence of Respondent Retrieval Strategies in Calendar Interviewing: The Quality of Retrospective Reports." Journal of Official Statistics 32, no. 3 (2016): 579-600. DOI: https://doi.org/10.1515/jos-2016-0030.
Spielman, S. E., and J. Logan. "Using High Resolution Population Data to Identify Neighborhoods and Determine their Boundaries." Annals of the Association of American Geographers 103 (2013): 67-84. DOI: 10.1080/00045608.2012.685049, available at http://www.tandfonline.com/doi/abs/10.1080/00045608.2012.685049.
Atkin, G., H. Arunachalam, A. Eck, D. Wettlaufer, L.-K. Soh, and R.F. Belli. "Using Machine Learning Techniques to Predict Respondent Type from A Priori Demographic Information." In 70th Annual Conference of the American Association for Public Opinion Research (AAPOR). Hollywood, Florida, 2015, available at http://www.aapor.org/AAPORKentico/Conference/Recent-Conferences.aspx.
Miranda, J., and L. Vilhuber. "Using Partially Synthetic Data to Replace Suppression in the Business Dynamics Statistics: Early Results." Privacy in Statistical Databases (2014): 232-242. DOI: 10.1007/978-3-319-11257-2_18, available at http://dx.doi.org/10.1007/978-3-319-11257-2_18.
Miranda, Javier, and Lars Vilhuber. Using partially synthetic data to replace suppression in the Business Dynamics Statistics: early results. Cornell University Preprint 1813:40852, 2014, available at http://hdl.handle.net/1813/40852.
Vilhuber, Lars, and Javier Miranda. Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics. Cornell University Preprint 1813:42339, 2015, available at http://hdl.handle.net/1813/42339.
Miranda, Javier, and Lars Vilhuber. "Using partially synthetic microdata to protect sensitive cells in business statistics." Statistical Journal of the International Association for Official Statistics 32, no. 1 (2016): 69-80. DOI: 10.3233/SJI-160963, available at http://content.iospress.com/download/statistical-journal-of-the-iaos/sji963.
Wilson, C. R.. Using Satellite Imagery to Evaluate and Analyze Socioeconomic Changes Observed with Census Data. Ph.D., 2013.
Antenucci, Dolan, Michael J. Cafarella, Margaret C. Levenstein, Christopher Ré, and Matthew Shapiro. Using Social Media to Measure Labor Market Flows. Mimeo, 2014, available at http://www-personal.umich.edu/~shapiro/papers/LaborFlowsSocialMedia.pdf.
Haney, Samuel, Ashwin Machanavajjhala, John M. Abowd, Matthew Graham, Mark Kutzbach, and Lars Vilhuber. Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics. Cornell University Preprint 1813:49652, 2017, available at http://hdl.handle.net/1813/49652.
Haney, Samuel, Ashwin Machanavajjhala, John M. Abowd, Matthew Graham, and Mark Kutzbach. "Utility Cost of Formal Privacy for Releasing National Employer-Employee Statistics." Proceedings of the 2017 ACM International Conference on Management of Data (2017). DOI: 10.1145/3035918.3035940, available at http://dl.acm.org/citation.cfm?doid=3035918.3035940.