Uneingeschränkter Zugang

Investigating Adaptive Nonresponse Follow-up Strategies for Small Businesses through Embedded Experiments

Journal of Official Statistics's Cover Image
Journal of Official Statistics
Special Issue on Responsive and Adaptive Survey Design

Zitieren

Andridge, R.R. and R.J.A. Little. 2011. “Proxy Pattern-Mixture Analysis for Survey Nonresponse.” Journal of Official Statistics 27: 153–180.Search in Google Scholar

Andridge, R.R. and K.J. Thompson. 2015(a). “Using the Fraction of Missing Information to Identify Auxiliary Variables for Imputation Procedures via Proxy Pattern-Mixture Models.” International Statistical Review 83(3): 472–492. Doi: http://dx.doi.org/10.1111/insr.12091.10.1111/insr.12091Search in Google Scholar

Andridge, R.R. and K.J. Thompson. 2015(b). “Assessing Nonresponse Bias in a Business Survey: Proxy Pattern-Mixture Analysis for Skewed Data.” Annals of Applied Statistics 9(4): 2237–2265. Doi: http://dx.doi.org/10.1214/15-AOAS878.10.1214/15-AOAS878Search in Google Scholar

Bavdaž, M. 2010. “The Multidimensional Integral Business Survey Response Model.” Survey Methodology 36: 81–93.Search in Google Scholar

Berthelot, J.M. and M. Latouche. 1993. “Improving the Efficiency of Data Collection: A Generic Respondent Follow-up Strategy for Economic Surveys.” Journal of Business and Economic Statistics 11(4): 417–424. Doi: http://dx.doi.org/10.2307/1391632.10.2307/1391632Search in Google Scholar

Brady, C. 2016. “Respondent Outreach Practices at the U.S. Census Bureau.” In Proceedings of the Fifth Conference on Establishment Surveys (ICES-V), June 6, 2016. Alexandria, VA: American Statistical Association.Search in Google Scholar

Cox, D.R. 1975. “Partial Likelihood.” Biometrika 62: 269–276. Doi: http:dx.doi.org/10.2307/2335362.10.1093/biomet/62.2.269Search in Google Scholar

Dillman, D.A., E. Singer, J.R. Clark, and J.B. Treat. 1996. “Effects of Benefits Appeals, Mandatory Appeals, and Variations in Statements on Confidentiality on Completion Rates for Census Questionnaires.” Public Opinion Quarterly 60(3): 376–389.10.1086/297759Search in Google Scholar

Groves, R.M. and S.G. Heeringa. 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. Doi: http://dx.doi.org/10.1111/j.1467-985X.2006.00423.x.10.1111/j.1467-985X.2006.00423.xSearch in Google Scholar

Federal Register Notice. 2006. OMB Standards and Guidelines for Statistical Surveys. Available at: https://unstats.un.org/unsd/dnss/docs-nqaf/USA_standards_stat_surveys.pdf (accessed July 2017).Search in Google Scholar

Hedlin, D., H. Lindkvist, H. Bäckström, and J. Erikson. 2008. “An Experiment on Perceived Survey Response Burden Among Businesses.” Journal of Official Statistics 24(2): 301–318.Search in Google Scholar

Johnson, R.A. and D.W. Wichern. 1988. Applied Multivariate Statistical Analysis (2nd Edition). New Jersey: Prentice Hall.10.2307/2531616Search in Google Scholar

Kaputa, S.J., L. Bechtel, K.J. Thompson, and D. Whitehead. 2014. “Strategies for Subsampling Nonrespondents for Economic Programs.” In Proceedings of the Section on Survey Research Methods, August 6, 2014. Alexandria, VA: American Statistical Association. Available at: http://ww2.amstat.org/sections/srms/Proceedings/ (accessed February 2017).Search in Google Scholar

Kennedy, J. and P. Phipps. 1995. “Respondent Motivation, Response Burden, and Data Quality in the Survey of Employer-provided training.” In Proceedings of the Annual Meeting of the American Association for Public Opinion Research, May 1995, Ft. Lauderdale, FL. Available at: http://www.bls.gov/osmr/pdf/st950250.pdf (accessed September 2016).Search in Google Scholar

Kirgis, N. and J. Lepkowski. 2013. “Design and Management Strategies for Paradata-Driven Responsive Design: Illustrations for the 2006–2010 National Survey of Family Growth.” In Improving Surveys with Paradata, edited by Frauke Kreuter. New York: Wiley.10.1002/9781118596869.ch6Search in Google Scholar

Knutson, J. and G. Cepluch. 2016. Nonresponse Bias Analysis for the U.S. Census Bureau’s Quarterly Financial Report. In Proceedings of the Section on Government Statistics: American Statistical Association, August 4, 2016. Alexandria, VA: American Statistical Association.Search in Google Scholar

Lineback, J.L. and E. Fink. 2012. Recent Developments in Assessing and Mitigating Nonresponse Bias. In Proceedings of the Fourth International Conference on Establishment Surveys (ICES-IV). June 13, 2012. Alexandria, VA: American Statistical Association. Available at: www.amstat.org/meetings/ices/2012/papers/302146.pdf (accessed February 2017).Search in Google Scholar

Little, R.J.A. and D.B. Rubin. 2002. Statistical Analysis with Missing Data (2nd Edition). New York: Wiley.10.1002/9781119013563Search in Google Scholar

Marquette, E., M. Kornbau, and J. Toribio. 2015. “Testing Contact Strategies to Improve Response in the 2012 Economic Census.” In Proceedings of the Section on Government Statistics: American Statistical Association, August 10, 2015. Alexandria, VA: American Statistical Association.Search in Google Scholar

Ouwehand, P. and B. Schouten. 2014. “Measuring Representativeness of Short-Term Business Statistics.” Journal of Official Statistics 30(4): 623–649. Doi: http://dx.doi.org/10.2478/jos-2014-0041.10.2478/jos-2014-0041Search in Google Scholar

Rao, J.N.K. and A.J. Scott. 1987. “On Simple Adjustments to Chi-Square Tests with Sample Survey Data.” The Annals of Statistics 15(1): 385–397.10.1214/aos/1176350273Search in Google Scholar

Särndal, C. and P. Lundquist. 2014. “Accuracy in Estimation with Nonresponse: A Function of Degree of Imbalance and Degree of Explanation.” Journal of Survey Statistics and Methodology 2(4): 361–387. Doi: https://doi.org/10.1093/jssam/smu014.10.1093/jssam/smu014Search in Google Scholar

“SAS/STAT(R) 9.3 User’s Guide”. SAS/STAT(R) 9.3 User’s Guide. N.p., n.d. Web. 09 Oct. 2015.Search in Google Scholar

Schouten, B., M. Calinescu, and A. Luiten. 2013. “Optimizing Quality of Response through Adaptive Survey Designs.” Survey Methodology 39(2): 29–58.Search in Google Scholar

Schouten, B., F. Cobben, and J. Bethlehem. 2009. “Indicators of Representativeness of Survey Nonresponse.” Survey Methodology 35: 101–113.Search in Google Scholar

Snijkers, G., G. Haraldsen, J. Jones, and D.K. Willimack. 2013. Designing and Conducting Business Surveys. New York: Wiley.10.1002/9781118447895Search in Google Scholar

Thompson, K.J. and B.E. Oliver. 2012. “Response Rates in Business Surveys: Going Beyond the Usual Performance Measure.” Journal of Official Statistics 28: 221–237.Search in Google Scholar

Thompson, K.J., B.E. Oliver, and J. Beck. 2015. “An Analysis of the Mixed Collection Modes for Two Business Surveys Conducted by the US Census Bureau.” Public Opinion Quarterly 79(3): 769–789. Doi: http://dx.doi.org/10.1093/poq/nfv013.10.1093/poq/nfv013Search in Google Scholar

Thompson, K.J. and K.T. Washington. 2013. “Challenges in the Treatment of Unit Nonresponse for Selected Business Surveys: A Case Study.” Survey Methods: Insights from the Field. Available at: http://surveyinsights.org/?p=2991.Search in Google Scholar

Torres van Grinsven, V., I. Bolko, and M. Bavdaž. 2014. “In Search of Motivation for the Business Survey Response Task.” Journal of Official Statistics 30(4): 579–606. Doi: https://doi.org/10.2478/jos-2014-0039.10.2478/jos-2014-0039Search in Google Scholar

Tulp, D.R., Jr., C.E. Hoy, G.L. Kusch, and S.L. Cole. 1991. “Nonresponse under Mandatory versus Voluntary Reporting in the 1989 Survey of Pollution Abatement Costs and Expenditures.” In Proceedings of the Section on Survey Research Methods, August 1991. Alexandria, VA: American Statistical Association. Available at: http://ww2.amstat.org/sections/srms/Proceedings/ (accessed February 2017).Search in Google Scholar

Wagner, J. 2010. “The Fraction of Missing Information as a Tool for Monitoring the Quality of Survey Data.” Public Opinion Quarterly 74: 223–243. Doi: http://dx.doi.org/10.1093/poq/nfq007.10.1093/poq/nfq007Search in Google Scholar

Wagner, J. 2012. “A Comparison of Alternative Indicators for the Risk of Nonresponse Bias.” Public Opinion Quarterly 76(3): 555–575. Doi: http://dx.doi.org/10.1093/poq/nfs032.10.1093/poq/nfs032627697530538342Search in Google Scholar

Willimack, D. and E. Nichols. 2010. “A Hybrid Response Process Model for Business Surveys.” Journal of Official Statistics 26: 3–24.Search in Google Scholar

eISSN:
2001-7367
Sprache:
Englisch
Zeitrahmen der Veröffentlichung:
4 Hefte pro Jahr
Fachgebiete der Zeitschrift:
Mathematik, Wahrscheinlichkeitstheorie und Statistik