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.” AIDS Care 15(6): 807–820. Doi: http://dx.doi.org/10.1080/09540120310001618658 . Bethlehem, J., C. Fannie, and B. Schouten. 2011. Handbook of Nonresponse in Household Surveys . New Jersey: Wiley. Biemer, P.P., P. Chen, and K. Wang. 2013. “Using Level-of-Effort Paradata in Non-Response Adjustments with Application to Field Surveys.” Journal of Royal Statistical Society: Serie A 176: 147–168. Doi: http://dx.doi.org/10.2307/23355181 . Bozdogan, H. 1987. “Model Selection and Akaike’s Information Criterion (AIC): the General Theory and its Analytical Extensions

References Biemer, P.P., P. Chen, and K. Wang. 2013. “Using Level-of-Effort Paradata in Non-Response Adjustments with Application to Field Surveys.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 176(1): 147-168. Doi: http://dx.doi.org/10.1111/j.1467-985X.2012.01058.x. Bienias, J.L., E.M. Sweet, and C.H. Alexander. 1990. A Model for Simulating Interviewer Travel Costs for Different Cluster Sizes. Proceedings of the Survey Research Methods Section, American Statistical Association, 20-27. Anaheim, CA. Available at: https://ww2.amstat

5. References Bates, N., J. Dahlhamer, P. Phipps, A. Safir, and L. Tan. 2010. “Assessing Contact History Paradata Quality Across Several Federal Surveys.” In JSM Proceedings, Survey Research Methods Section, American Statistical Association, Vancouver, BC, July 31-August 5, 2010. Alexandria, VA: American Statistical Association. 91–105. Available at: http://ww2.amstat.org/sections/SRMS/Proceedings/y2010/Files/306005_55654.pdf (accessed February 2017). Bates, N., J. Dahlhamer, and E. Singer. 2008. “Privacy Concerns, Too Busy, or Just Not Interested: Using

7. References Beullens, K., G. Loosveldt, K. Denies, and C. Vandenplas. 2016. “Quality Matrix for the European Social Survey, Round 7.” Leuven, Belgium: Centre for Sociological Research, KU Leuven. Biemer, P.P., P. Chen, and K. Wang. 2013. “Using Level-of-Effort Paradata in Non-Response Adjustments with Application to Field Surveys.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 176(1): 147 – 168. DOI: https://doi.org/10.1111/j.1467-985X.2012.01058.x . Blom, A.G. 2012. “Explaining Cross-Country Differences in Survey Contact Rates

Nonresponse Over the Past Quarter Century. Public Opinion Quarterly, 69, 87-98. de Heer, W. (1999). International Response Trends: Results of an International Survey. Journal of Official Statistics, 15, 129-142. Groves, R. and Heeringa, S. (2006). Responsive Design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs. Journal of the Royal Statistical Society Series A: Statistics in Society, 169, 439-457. Hunter, D.C, Mitchell, S., Carley-Baxter, L., and Keating, M. (2012). Using Paradata to Monitor Survey Production, Cost, and Quality within an

2010. “A Response Propensity Modeling Navigator for Paradata.” Proceedings of the Survey Research Methods Section of the American Statistical Association, Joint Statistical Meetings, Vancouver, Canada, 356–369. http://ww2.amstat.org/sections/SRMS/Proceedings/y2010/Files/306125_55196.pdf . Chun, A.Y., B. Schouten, and J. Wagner. 2017. JOS Special Issue on Responsive and Adaptive Design: “Looking Back to See Forward – Editorial.” In A.Y. Chun, B. Schouten, J. Wagner (Eds), JOS Special Issue on Responsive and Adaptive Design, Journal of Official Statistics , 33

: Proceedings of the Section on Survey Research Methods, American Statistical Association. Cunningham-Hunter, D., Mitchell, S., Carley-Baxter, L., and Keating, M. (2012). Using Paradata to Monitor Survey Production, Cost, and Quality within an Adaptive Total Design Framework. Paper presented at the 2012 Federal Conference on Survey Methodology, Washington, DC. Available at: http://www.fcsm.gov/12papers/Hunter_2012FCSM_V-C.pdf. (accessed February 12, 2013). Dunnet, G. (2007). The BmTS: Creating a new business model for a national statistical office of the 21st century. Invited

Household Surveys: Tools for Actively Controlling Survey Errors and Costs.” Journal of the Royal Statistical Society. Series A: Statistics in Society 169(3): 439–457. Doi: http://dx.doi.org/10.1111/j.1467-985X.2006.00423.x . Jans, M., R. Sirkis, and D. Morgan. 2013. “Managing Data Quality Indicators with Paradata based Statistical Quality Control Tools: the Keys to Survey Performance.” In Improving Surveys with Paradata. Analytic Uses of Process Information , edited by Frauke Kreuter, 191–229. Hoboken, New Jersey: John Wiley & Sons. Juran, J.M. and F.M. Gryna. 1988

Abstract

We propose an adaptive data collection procedure for call prioritization in the context of computer-assisted telephone interview surveys. Our procedure is adaptive in the sense that the effort assigned to a sample unit may vary from one unit to another and may also vary during data collection. The goal of an adaptive procedure is usually to increase quality for a given cost or, alternatively, to reduce cost for a given quality. The quality criterion often considered in the literature is the nonresponse bias of an estimator that is not adjusted for nonresponse. Although the reduction of the nonresponse bias is a desirable goal, we argue that it is not a useful criterion to use at the data collection stage of a survey because the bias that can be removed at this stage through an adaptive collection procedure can also be removed at the estimation stage through appropriate nonresponse weight adjustments. Instead, we develop a procedure of call prioritization that, given the selected sample, attempts to minimize the conditional variance of a nonresponse-adjusted estimator subject to an overall budget constraint. We evaluate the performance of our procedure in a simulation study.