Survey sampling helps the Census Bureau provide timely and cost efficient estimates of population characteristics. Demographic sample surveys estimate characteristics of people or households such as employment, income, poverty, health, insurance coverage, educational attainment, or crime victimization. Economic sample surveys estimate characteristics of businesses such as payroll, number of employees, production, sales, revenue, or inventory. Survey sampling helps the Census Bureau assess the quality of each decennial census. Estimates are produced by use of design-based estimation techniques or model-based estimation techniques. Methods and topics across the three program areas (Demographic, Economic, and Decennial) include: sample design, estimation and use of auxiliary information (e.g., sampling frame and administrative records), weighting methodology, adjustments for non-response, proper use of population estimates as weighting controls, variance estimation, effects of imputation on variances, coverage measurement sampling and estimation, coverage measurement evaluation, evaluation of census operations, uses of administrative records in census operations, improvement in census processing, and analyses that aid in increasing census response.
Nayak, T.K. (2021). “A Review of Rigorous Randomized Response Methods for Protecting Respondent's Privacy and Data Confidentiality,” in Methodology and Applications of Statistics: A Volume in Honor of C.R. Rao on the Occasion of his 100th Birthday, ed. B.C. Arnold, N. Balakrishnan and C.A. Coelho, New York: Springer, pp. 319-341.
Wright, T. (2021). “From Cauchy-Schwartz to the House of Representatives: Application of Lagrange’s Identity,” Mathematics Magazine, Vol 94, 244-256.
Mulry, M., Bates, N., and Virgile, M. (2021). “Viewing Participation in Censuses and Surveys through the Lens of Lifestyle Segments” (print), Journal of Survey Statistics and Methodology, doi:1093/jssam/smaa006.
Zhai, X., and Nayak, T.K. (2021). “A Post-randomization Method for Rigorous Identification Risk Control in Releasing Microdata,” Journal of Statistical Theory and Practice, 15, Article 8, https://doi.org/10.1007/s42519-020-00143-2.
Trudell, T., Dong, K., Slud, E., and Cheng, Y. (In Press). “Computing Replicated Variance for Stratified Systematic Sampling,” Proceedings of the Survey Research Methods Section of the American Statistical Association.
Wright, T. (2020). “A General Exact Optimal Sample Allocation Algorithm: With Bounded Cost and Bounded Sample Sizes,” Statistics and Probability Letters, Vol 165, Article 108829.
Klein M., Wright, T., and Wieczorek, J. (2020). “A Joint Confidence Region for an Overall Ranking of Population,” Journal of the Royal Statistical Society, Series C, 69, Part 3, 589-606.
Mulry, M., Bates, N., and Virgile, M. (2020). “Viewing Participation in Censuses and Surveys through the Lens of Lifestyle Segments.” Journal of Survey Statistics and Methodology. doi: 10.1093/jssam/smaa006 (which In Press year (2021 above) is correct?)
Franco, C., Little, R., Louis, T., and Slud, E. (2019). “Comparative Study of Confidence Intervals for Proportions in Complex Sample Surveys,” Journal of Survey Statistics and Methodology, 7, 334-364.
Slud, E. and Thibaudeau, Y. (2019). “Multi-Outcome Longitudinal Small Area Estimation, A Case Study,” Statistical Theory and Related Fields. Special Issue on Small Area Estimation, 3, 136-149.
Wright, T., Klein, M., and Wieczorek, J. (2019). “A Primer on Visualizations for Comparing Populations, Including the Issue of Overlapping Confidence Intervals,” The American Statistician, Vol 73, No 2, 165-178.
Chai, J. and Nayak, T. (2018). “A Criterion for Privacy Protection in Data Collection and its Attainment via Randomized Response Procedures,” Electronic Journal of Statistics 12 (2), 4264-4287.
de Oliveira, V., Wang, B., and Slud, E. (2018). “Spatial Modeling of Rainfall Accumulated over Short Periods of Time,” Journal of Multivariate Analysis, 166, 129-149.
Dong, K., Trudell, T., Slud, E., and Cheng, Y. (2018). “Understanding Variance Estimator Bias in Stratified Two-Stage Sampling,” Proceedings of the Survey Research Methods Section of the American Statistical Association.
Klein, M., Wright, T., and Wieczorek, J. (2018). “A Simple Joint Confidence Region for A Ranking of K Populations: Application to American Community Survey’s Travel Time to Work Data,” Research Report Series (Statistics #2018-04), Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, D.C.
Lu, B. and Ashmead, R. (2018). “Propensity Score Matching Analysis for Causal Effects with MNAR Covariates,” Statistica Sinica, 28, 2005-2025.
Mulry, M.H, Kaputa, S., and Thompson, K. (2018). “Initial M-estimation Parameter Settings for Detection and Treatment of Influential Values,” Journal of Official Statistics, 34(2). 483–501. http://dx.doi.org/10.2478/JOS-2018-0022
Nayak, T., Zhang, C., and You, J. (2018). “Measuring Identification Risk in Microdata Release and Its Control by Post‐randomisation,” International Statistical Review, 86 (2), 300-321.
Slud, E., Vonta, I., and Kagan, A. (2018). “Combining Estimators of a Common Parameter across Samples,” Statistical Theory and Related Fields, 2, 158-171.
Wright, T. (2018). “No Calculation When Observation Can Be Made,” in A.K. Chattopadhyay and G. Chattopadhyay (Eds), Statistics and Its Applications, Springer Singapore, 139-154.
Ashmead, R., Slud, E., and Hughes, T. (2017). “Adaptive Intervention Methodology for Reduction of Respondent Contact Burden in the American Community Survey,” Journal of Official Statistics, 33(4), 901-919.
Ashmead, R. and Slud, E. (2017). “Small Area Model Diagnostics and Validation with Applications to the Voting Rights Act Section 203,” Proceedings of Survey Research Methods Section, American Statistical Association, Alexandria, VA.
Mulry, M.H. and Keller, A. (2017). “Comparison of 2010 Census Nonresponse Follow-up Proxy Responses with Administrative Records Using Census Coverage Measurement Results,” Journal of Official Statistics, 33(2), 455–475. DOI: https://doi.org/10.1515/jos-2017-0022
Mulry, M.H., Nichols, E. M., and Hunter Childs, J. (2017). “Using Administrative Records Data at the U.S. Census Bureau: Lessons Learned from Two Research Projects Evaluating Survey Data.” In Biemer, P.P, Eckman, S., Edwards, B., Lyberg, L., Tucker, C., de Leeuw, E., Kreuter, F., and West, B.T. Total Survey Error in Practice. Wiley. New York. 467-473.
Slud, E. and Ashmead, R. (2017). “Hybrid BRR and Parametric-Bootstrap Variance Estimates for Small Domains in Large Surveys,” Proceedings of Survey Research Methods Section, American Statistical Association, Alexandria, VA.
Thibaudeau, Y., Slud, E., and Gottschalck, A. (2017). “Modeling Log-linear Conditional Probabilities for Estimation in Surveys,” Annals of Applied Statistics, 11 (2), 680-697.
Wieczorek, J. (2017). “Ranking Project: The Ranking Project: Visualizations for Comparing Populations,” R package version 0.1.1. URL: https://cran.r-project.org/package=RankingProject.
Wright, T. (2017). “Exact Optimal Sample Allocation: More Efficient Than Neyman,” Statistics and Probability Letters, 129, 50-57.
Mulry, M. H., Nichols, E. M., and Childs, J. Hunter (2016). “A Case Study of Error in Survey Reports of Move Month Using the U.S. Postal Service Change of Address Records,” Survey Methods: Insights from the Field. Retrieved from http://surveyinsights.org/?p=7794
Mulry, M.H., Oliver, B., Kaputa, S., and Thompson, K. J. (2016). “Cautionary Note on Clark Winsorization.” Survey Methodology 42 (2), 297-305. http://www.statcan.gc.ca/pub/12-001-x/2016002/article/14676-eng.pdf
Nayak, T. and Adeshiyan, S. (2016). “On Invariant Post‐randomization for Statistical Disclosure Control,” International Statistical Review, 84 (1), 26-42.
Nayak, T., Adeshiyan, S. and Zhang, C. (2016). “A Concise Theory of Randomized Response Techniques for Privacy and Confidentiality Protection,” Handbook of Statistics, 34, 273-286.
Wright, T. (2016). “Two Optimal Exact Sample Allocation Algorithms: Sampling Variance Decomposition Is Key,” Research Report Series (Statistics #2016-03), Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, D.C.
Nagaraja, C. and McElroy, T. (2015). “On the Interpretation of Multi-Year Estimates of the American Community Survey as Period Estimates.” Published online, Journal of the International Association of Official Statistics.
Slud, Eric. (2015). “Impact of Mode-based Imputation on ACS Estimates,” American Community Survey Research and Evaluation Memorandum, #ACS-RER-O7.
Franco, C., Little, R., Louis, T., and Slud, E. (2014). “Coverage Properties of Confidence Intervals for Proportions in Complex Sample Surveys,” Proceedings of Survey Research Methods Section, American Statistical Association, Alexandria, VA.
Griffin, D., Slud, E., and Erdman, C. (2014). “Reducing Respondent Burden in the American Community Survey's Computer Assisted Personal Visit Interviewing Operation - Phase 3 Results,” ACS Research and Evaluation Memorandum #ACS 14- RER-28.
Hogan, H. and Mulry, M. H. (2014). “Assessing Accuracy of Postcensal Estimates: Statistical Properties of Different Measures,” in N. Hogue (Ed.), Emerging Techniques in Applied Demography. Springer. New York.
Hunley, Pat. (2014). “Proof of Equivalence of Webster’s Method and Willcox’s Method of Major Fractions,” Research Report Series (Statistics #2014-04), Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, D.C.
Joyce, P., Malec, D., Little, R., Gilary, A., Navarro, A., and Asiala, M. (2014). “Statistical Modeling Methodology for the Voting Rights Act Section 203 Language Assistance Determinations,” Journal of American Statistical Association, 109 (505), 36-47.
Mulry, M. H. (2014). “Measuring Undercounts in Hard-to-Survey Groups,” in R. Tourangeau, N. Bates, B. Edwards, T. Johnson, and K. Wolter (Eds.), Hard-to-Survey Populations. Cambridge University Press, Cambridge, England.
Mulry, M. H., Oliver, B. E., and Kaputa, S. J. (2014) “Detecting and Treating Verified Influential Values in a Monthly Retail Trade Survey.” Journal of Official Statistics, 30(4), 1–28.
Shao, J., Slud, E., Cheng, Y., Wang, S., and Hogue, C. (2014). “Theoretical and Empirical Properties of Model Assisted Decision- Based Regression Estimators,” Survey Methodology 40(1), 81-104.
Tang, M., Slud, E., and Pfeiffer, R. (2014). “Goodness of Fit Tests for Linear Mixed Models,” Journal of Multivariate Analysis, 130, 176-193.
Wright, T. (2014). “A Simple Method of Exact Optimal Sample Allocation under Stratification with Any Mixed Constraint Patterns,” Research Report Series (Statistics #2014-07), Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, D.C.
Wright, T. (2014). “Lagrange’s Identity and Congressional Apportionment,” The American Mathematical Monthly, 121, 523-528.
Slud, E., Grieves, C., and Rottach, R. (2013). “Single Stage Generalized Raking Weight Adjustment in the Current Population Survey,” Proceedings of Survey Research Methods Section, American Statistical Association, Alexandria, VA.
Wright, T. (2013). “A Visual Proof, a Test, and an Extension of a Simple Tool for Comparing Competing Estimates,” Research Report Series (Statistics #2013-05), Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, D.C.
Wright, T., Klein, M., and Wieczorek, J. (2013). “An Overview of Some Concepts for Potential Use in Ranking Populations Based on Sample Survey Data,” 2013 Proceedings of the World Congress of Statistics (Hong Kong), International Statistical Institute.
Ikeda, M., Tsay, J., and Weidman, L. (2012). “Exploratory Analysis of the Differences in American Community Survey Respondent Characteristics between the Mandatory and Voluntary Response Methods,” Research Report Series (Statistics #2012-01), Center for Statistical Research & Methodology, U.S. Census Bureau, Wash. D.C.
Wright, T. (2012). “The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives,” The American Statistician, 66 (4), 217-224.
Klein, M. and Wright, T. (2011). “Ranking Procedures for Several Normal Populations: An Empirical Investigation,” International Journal of Statistical Sciences, Volume 11 (P.C. Mahalanobis Memorial Special Issue), 37-58.
Slud, E. and Thibaudeau,Y. (2010). “Simultaneous Calibration and Nonresponse Adjustment,” Research Report Series (Statistics#2010-03), Statistical Research Division, U.S. Census Bureau, Washington, D.C.
Eric Slud, Mary Mulry, Michael Ikeda, Patrick Joyce, Tapan Nayak, Edward H. Porter, Tommy Wright
0331 – Working Capital Fund / General Research Project
Various Decennial, Demographic, and Economic Projects