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Using Imputation Techniques To Evaluate Stopping Rules In Adaptive Survey Design

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  • Thais Paiva
  • Jerry Reiter

Abstract

Adaptive Design methods for social surveys utilize the information from the data as it is collected to make decisions about the sampling design. In some cases, the decision is either to continue or stop the data collection. We evaluate this decision by proposing measures to compare the collected data with follow-up samples. The options are assessed by imputation of the nonrespondents under different missingness scenarios, including Missing Not at Random. The variation in the utility measures is compared to the cost induced by the follow-up sample sizes. We apply the proposed method to the 2007 U.S. Census of Manufacturers.

Suggested Citation

  • Thais Paiva & Jerry Reiter, 2014. "Using Imputation Techniques To Evaluate Stopping Rules In Adaptive Survey Design," Working Papers 14-40, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:14-40
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    References listed on IDEAS

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    1. Schouten, Barry & Shlomo, Natalie & Skinner, Chris J., 2011. "Indicators for monitoring and improving representativeness of response," LSE Research Online Documents on Economics 39121, London School of Economics and Political Science, LSE Library.
    2. Robert M. Groves & Steven G. Heeringa, 2006. "Responsive design for household surveys: tools for actively controlling survey errors and costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 439-457, July.
    3. Hang J. Kim & Jerome P. Reiter & Quanli Wang & Lawrence H. Cox & Alan F. Karr, 2014. "Multiple Imputation of Missing or Faulty Values Under Linear Constraints," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(3), pages 375-386, July.
    4. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    5. Ishwaran H. & James L. F, 2001. "Gibbs Sampling Methods for Stick Breaking Priors," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 161-173, March.
    6. Barry Schouten & Jelke Bethlehem & Koen Beullens & Øyvin Kleven & Geert Loosveldt & Annemieke Luiten & Katja Rutar & Natalie Shlomo & Chris Skinner, 2012. "Evaluating, Comparing, Monitoring, and Improving Representativeness of Survey Response Through R-Indicators and Partial R-Indicators," International Statistical Review, International Statistical Institute, vol. 80(3), pages 382-399, December.
    7. Chris Fraley & Adrian E. Raftery, 2007. "Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 155-181, September.
    8. Shlomo, Natalie & Skinner, Chris J. & Schouten, Barry, 2012. "Estimation of an indicator of the representativeness of survey response," LSE Research Online Documents on Economics 39124, London School of Economics and Political Science, LSE Library.
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