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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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References

Abowd, J., Stinson, M., and Benedetto, G. (2006). Final Report to the Social Security Administration on the SIPP/SSA/IRS Public Use File Project. U.S. Census Bureau Longitudinal Employer-Household Dynamics Program. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/sipp/synth_data.html [September 2014].

Agresti, A., and Coull, B.A. (1998). Approximate is better than exact for interval estimation of binomial proportions. The American Statistician, 52(2), 119-126.

Albright, K.A. (2011). Using Subcounty Population Estimates as Controls in Weighting for the American Community Survey. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2011/2011_Albright_01.pdf [September 2011].

Alexander, C.H. (1993a). A Continuous Measurement Alternative for the U.S. Census. Washington, DC: U.S. Department of Commerce. Available: https://www.amstat.org/sections/srms/Proceedings/papers/1993_079.pdf [September 2014].

Alexander, C.H. (1993b). Three General Prototypes for a Continuous Measurement System. Internal Census Bureau Reports CM-1. Washington, DC: U.S. Department of Commerce.

Alvarez, J.A., and Salvo, J. (2014). Navigating Reliability of Small Area Data Lessons from New York City. Presented at the American Community Survey Data User Group Conference, May 29-30, Washington, DC.

Bauder, M., Luery, D., and Szelepka, S. (2011). Small Area Estimation of Health Insurance Coverage in 2010 and 2011. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/did/www/sahie/methods/files/sahie_tech_2011.pdf [September 2014].

Baumgardner, S.K., Griffin, D.H., and Raglin, D.A. (2014). The Effects of Adding an Internet Response Option to the American Community Survey. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2014/2014_Baumgardner_04.pdf [September 2014].

Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
×

Bell, W., Basel, W., Cruse, C., Dalzell, L., Maples, J., O’Hara, B., and Powers, D. (2007). Use of ACS Data to Produce SAIPE Model-Based Estimates of Poverty for Counties. Washington, DC: U.S. Department of Commerce. Available: https://www.census.gov/did/www/saipe/publications/files/report.pdf [September 2014].

Bond, B., Brown, J.D., Luque, A., and O’Hara, A. (2014). The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/srd/carra/The_Nature_of_the_Bias_When_Studying_Only_Linkable_Person_Records.pdf [September 2014].

Bradley, J.R., Wikle, C.K., and Holan, S.H. (2014). (Submitted). Bayesian Spatial Change of Support for Count—Valued Survey Data. ArXiv preprint:1405.7227.

Brown, L.D., Cai, T.T., and DasGupta, A. (2001). Interval estimation for a binomial proportion. Statistical Science, 16(2), 101-117.

Chestnut, J. (2013). Model-Based Mode of Data Collection Switching from Internet to Mail in the American Community Survey, Washington, DC: U.S. Department of Commerce. Available: https://www.census.gov/acs/www/Downloads/library/2013/2013_Chesnut_01.pdf [September 2014].

Clark, S.L. (2014). American Community Survey Item Nonresponse Rates: Mail versus Internet. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2014/2014_Clark_01.pdf [September 2014].

Conrad, F., Couper, M., Tourangeau, R., and Peytchev, A. (2006). Use and non-use of clarification features in web surveys. Journal of Official Statistics, 22(2), 245-269.

Cresce, A.R. (2012). Evaluation of Gross Vacancy Rates from the 2010 Census versus Current Surveys: Early Findings from Comparisons with the 2010 Census and the 2010 ACS 1-Year Estimates. Paper presented at the meeting of the Federal Committee on Statistical Methodology, January 10-12, Washington, DC. SEHSD Working Paper Number 2012-07. Available: http://www.census.gov/housing/files/FCSM%20paper.pdf [September 2014].

Fay, R.E. (2006). Using administrative records with model-assisted estimation for the American Community Survey. Proceedings of the 2006 Joint Statistical Meetings on CD-ROM, American Statistical Association (pp. 2995-3001).

Fay, R.E., and Train, G. (1995). Aspects of survey and model-based postcensal estimation of income and poverty characteristics for states and counties. Proceedings of the Section on Government Statistics, American Statistical Association (pp. 154-159).

Ghosh, M., and Rao, J.N.K. (1994). Small area estimation: An appraisal. Statistical Science, 9(1), 55-76.

Gilary, A., Maples, J., and Slud, E.V. (2012). Small area confidence bounds on small cell proportions in survey populations. Proceedings of the Survey Research Methods Section, American Statistical Association (pp. 3541-3555).

Griffin, D.H. (2013). Effect of Changing Call Parameters in the American Community Survey’s Computer-Assisted Telephone Interviewing Operation. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2013/2013_Griffin_03.pdf [September 2014].

Griffin, D.H., and Hughes, T. (2012). Projected 2013 Costs of a Voluntary American Community Survey. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2012/2012_Griffin_03.pdf [September 2014].

Griffin, D.H., and Raglin, D. (2011). Quality Measures Associated with a Voluntary American Community Survey. Available: http://www.census.gov/acs/www/Downloads/library/2011/2011_Griffin_02.pdf [September 2014].

Groves, R. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646-675.

Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
×

Groves, R.M., and Peytcheva, E. (2008). The impact of nonresponse rates on nonresponse bias. Public Opinion Quarterly, 72, 167-189.

Hernandez-Viver, A., and Starsinic, M. (2013). Assessing ACS Data Products: Meeting the Census Bureau’s Statistical Quality Standards. Presentation prepared for the Panel on Addressing Priority Technical Issues for the Next Decade of the American Community Survey, January 17, Washington, DC.

Horwitz, R., Tancreto, J.G., Zelenak, M.F., and Davis, M.C. (2012). Data Quality Assessment of the American Community Survey Internet Response Data. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2012/2012_Horwitz_02.pdf [September 2014].

Horwitz, R., Tancreto, J.G., Zelenak, M.F., and Davis, M.C. (2013). Use of Paradata to Assess the Quality and Functionality of the American Community Survey Internet Instrument. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2013/2013_Horwitz_01.pdf [September 2014].

Interagency Council on Statistical Policy Subcommittee for the American Community Survey. (2012). Charter of the Interagency Council on Statistical Policy Subcommittee on the American Community Survey. Washington, DC: U.S. Department of Commerce. Available: https://www.census.gov/acs/www/Downloads/operations_admin/ICSP_Charter.pdf [September 2014].

Janicki, R., and Malec, D. (2013). A Small Sample Evaluation of Design-Adjusted Likelihoods Using Bernoulli Outcomes. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/srd/papers/pdf/rrs2014-05.pdf [September 2014].

Jiang, J., and Lahiri, P. (2006). Mixed model prediction and small area estimation. Test, 15(1), 1-96.

Joyce, P.M., Malec, D., Little, R.J.A., Gilary, A., Navarro, A., and Asiala, M.E. (2014). Statistical modeling methodology for the Voting Rights Act section 203 language assistance determinations. Journal of the American Statistical Association, 109(505), 36-47.

Judkins, D.R. (1990). Fay’s method for variance estimation. Journal of Official Statistics, 6(3), 223-239.

Kim, J.K., and Rao, J.N.K. (2012). Combining data from two independent surveys: A model-assisted approach. Biometrika, 99, 85-100.

Kingkade, W. (2013). Self-Assessed Housing Values in the American Community Survey: An Exploratory Evaluation Using Linked Real Estate Records. Paper presented at the 2013 Joint Statistical Meetings, Montreal, Canada.

Kinney, S.K., Reiter, J.P., Reznek, A.P., Miranda, J., Jarmin, R.S., and Abowd, J.M. (2011). Towards unrestricted public use business microdata: The synthetic longitudinal business database. International Statistical Review, 79(3), 362-384.

Kish, L. (1981). Using Cumulated Rolling Samples to Integrate Census and Survey Operations of the Census Bureau: An Analysis, Review, and Response. Washington, DC: U.S. Government Printing Office.

Liu, Y., and Kott, P. (2009). Evaluating alternative one-sided coverage intervals for a proportion. Journal of Official Statistics, 25, 569-588.

Luque, A., and Bhaskar, R. (2013). 2010 American Community Survey Match Study. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/srd/carra/2010_American_Community_Survey_Match_Study.pdf [September 2014].

Maples, J.J., and Brault, M. (2013). Improving small area estimates of disability: Combining the American Community Survey with the Survey of Income and Program Participation. Proceedings of the Joint Statistical Meetings, American Statistical Association (pp. 2076-2086).

Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
×

National Research Council. (2000a). Small-Area Estimates of School-Age Children in Poverty: Evaluation of Current Methodology. Panel on Estimates of Poverty for Small Geographic Areas. C.F. Citro and G. Kalton (Eds.). Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

National Research Council. (2000b). Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Panel on Estimates of Poverty for Small Geographic Areas. C.F. Citro and G. Kalton (Eds.). Committee on National Statistics, Commission on Behavioral and Social Sciences and Education. Washington, DC: National Academy Press.

National Research Council. (2005). Expanding Access to Research Data: Reconciling Risks and Opportunities. Panel on Data Access for Research Purposes. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2007). Using the American Community Survey: Benefits and Challenges. C.F. Citro and G. Kalton (Eds.). Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2012). Small Populations, Large Effects: Improving the Measurement of the Group Quarters Population in the American Community Survey. P.R. Voss and K. Marton (Eds.). Panel on Measuring the Group Quarters Population in the American Community Survey. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

National Research Council. (2013). Benefits, Burdens, and Prospects of the American Community Survey: Summary of a Workshop. D.L. Cork, Rapporteur. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.

Nugent, C., and Hawala, S. (2012). Research and Development for Methods of Estimating Poverty for School-Age Children. Washington, DC: U.S. Department of Commerce. Available: https://www.census.gov/did/www/saipe/publications/files/nugenthawalajsm2012.pdf [September 2014].

O’Malley, A.J., and Zaslavsky, A. (2007). Optimal survey design when nonrespondents are subsampled for followup. ASA Proceedings of the Joint Statistical Meetings (pp. 3268-3274).

Pfeffermann, D. (2002). Small area estimation—New developments and directions. International Statistical Review, 70(1), 125-143.

Pfeffermann, D. (2013). New important developments in small area estimation. Statistical Science, 28(1), 40-68.

Porter, A.T., Wikle, C.K., and Holan, S.H. (2014a). Small area estimation via multivariate Fay-Herriot models with latent spatial dependence. Australian and New Zealand Journal of Statistics.

Porter, A.T., Holan, S.H., Wikle, C.K., and Cressie, N. (2014b). Spatial Fay-Herriot models for small area estimation with functional covariates. Spatial Statistics, 10, 27-42.

Raghunathan, T.E., Reiter, J.P., and Rubin, D.B. (2003). Multiple imputation for statistical disclosure limitation. Journal of Official Statistics, 19, 1-16.

Rao, J.N.K. (2003). Small Area Estimation. Hoboken, NJ: John Wiley & Sons.

Reamer, A. (2010). Surveying for Dollars: The Role of the American Community Survey in the Geographic Distribution of Federal Funds. Washington, DC: The Brookings Institution. Available: http://www.brookings.edu/~/media/Files/rc/reports/2010/0726_acs_reamer/0726_acs_reamer.pdf [September 2014].

Reiter, J.P. (2005). Releasing multiply-imputed, synthetic public use microdata: An illustration and empirical study. Journal of the Royal Statistical Society, Series A(168), 185-205.

Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Rubin, D.B. (1987). Multiple Imputation for Nonresponse in Surveys. Hoboken, NJ: John Wiley & Sons.

Salvo, J. (2014). Using Small Area Data from the ACS: Overcoming Reliability Issues. Presented at the Annual Meeting of the Population Association of America, May 1-3, Boston, MA.

Schirm, A., and Zaslavsky, A. (2002). Reweighting a national database to improve the accuracy of state estimates. Proceedings of the Joint Statistical Meetings, American Statistical Association (pp. 3101-3106).

Slud, E. (2012). Assessment of zeroes in survey-estimated tables via small-area confidence bounds. Journal of the Indian Society of Agricultural Statistics, 66(2), 157-169.

Sommers, D., and Hefter, S. (2014). Evaluating the Impact of the 2011 Sample Reallocation for the American Community Survey. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2014/2014_Sommers_01.pdf [September 2014].

Starsinic, M. (2005). American Community Survey: Improving reliability for small area estimates. Proceedings of the 2005 Joint Statistical Meetings on CD-ROM, American Statistical Association (pp. 3592-3599).

Starsinic, M., and Tersine, A., Jr. (2007). Analysis of Variance Estimates from American Community Survey Estimates. Prepared for presentation at the Joint Statistical Meetings, July 29-August 2, Salt Lake City, UT.

U.S. Census Bureau. (2006). U.S. Census Bureau Policy on New Content. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/operations_admin/ACS_Content_Policy.pdf [September 2014].

U.S. Census Bureau. (2009). A Compass for Understanding and Using American Community Survey Data: What Researchers Need to Know. Washington, DC: U.S. Government Printing Office.

U.S. Census Bureau. (2013). American Community Survey Data Suppression. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/data_documentation/data_suppression/ACSO_Data_Suppression.pdf [September 2014].

U.S. Census Bureau. (2014a). American Community Survey Design and Methodology. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/methodology/methodology_main/ [September 2014].

U.S. Census Bureau. (2014b). American Community Survey Multiyear Accuracy of the Data (3-Year 2010-2012 and 5-Year 2008-2012). Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/data_documentation/Accuracy/MultiyearACSAccuracyofData2012.pdf [September 2014].

U.S. Census Bureau. (2014c). Evaluating the Impact of the 2011 Sample Reallocation for the American Community Survey. Washington, DC: U.S. Department of Commerce.

U.S. Census Bureau. (2014d). Overview of the American Community Survey (ACS) Content Review. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/operations_admin/2014_content_review/ACS_Content_Review_Overview.pdf [September 2014].

U.S. Office of Management and Budget. (2014). Guidance for Providing and Using Administrative Data for Statistical Purposes. Washington, DC: Executive Office of the President. Available: http://www.whitehouse.gov/sites/default/files/omb/memoranda/2014/m-14-06.pdf [September 2014].

Weisberg, H.F. (2005). The Total Survey Error Approach. Chicago, IL: University of Chicago Press.

Wieczorek, J., Nugent, C., and Hawala, S. (2012). A Bayesian zero-one inflated beta model for small area shrinkage estimation. Proceedings of the Joint Statistical Meeting, American Statistical Association (pp. 3896-3910).

Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
×

Wilson, E. (1927). Probable inference, the law of the succession, and statistical inference. Journal of the American Statistical Association, 22(158), 209-212.

Wolter, K.M. (1984). An investigation of some estimators of variance for systematic sampling. Journal of the American Statistical Association, 79(388), 781-790.

Yovell, T., and Devine, J. (2013). Evaluating Current and Alternative Methods to Produce 2010 County Population Estimates. Washington, DC: Executive Office of the President. Available: https://www.census.gov/content/dam/Census/library/working-papers/2013/demo/POP-twps0100.pdf [September 2014].

Zaslavsky, A.M. (2004). Representing the census undercount by multiple imputation of households. In A. Gelman and X.L. Meng (Eds.), Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives (pp. 129-140). Hoboken, NJ: John Wiley & Sons.

Zelenak, M.F. (2013). Impact of Multiple Contacts by Computer-Assisted Telephone Interview and Computer-Assisted Personal Interview on Final Interview Outcome in the American Community Survey. Washington, DC: U.S. Department of Commerce. Available: http://www.census.gov/acs/www/Downloads/library/2013/2013_Zelenak_01.pdf [September 2014].

Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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Suggested Citation:"References." National Research Council. 2015. Realizing the Potential of the American Community Survey: Challenges, Tradeoffs, and Opportunities. Washington, DC: The National Academies Press. doi: 10.17226/21653.
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The American Community Survey (ACS) was conceptualized as a replacement to the census long form, which collected detailed population and housing data from a sample of the U.S. population, once a decade, as part of the decennial census operations. The long form was traditionally the main source of socio-economic information for areas below the national level. The data provided for small areas, such as counties, municipalities, and neighborhoods is what made the long form unique, and what makes the ACS unique today. Since the successful transition from the decennial long form in 2005, the ACS has become an invaluable resource for many stakeholders, particularly for meeting national and state level data needs. However, due to inadequate sample sizes, a major challenge for the survey is producing reliable estimates for smaller geographic areas, which is a concern because of the unique role fulfilled by the long form, and now the ACS, of providing data with a geographic granularity that no other federal survey could provide. In addition to the primary challenge associated with the reliability of the estimates, this is also a good time to assess other aspects of the survey in order to identify opportunities for refinement based on the experience of the first few years.

Realizing the Potential of the American Community Survey provides input on ways of improving the ACS, focusing on two priority areas: identifying methods that could improve the quality of the data available for small areas, and suggesting changes that would increase the survey's efficiency in responding to new data needs. This report considers changes that the ACS office should consider over the course of the next few years in order to further improve the ACS data. The recommendations of Realizing the Potential of the American Community Survey will help the Census Bureau improve performance in several areas, which may ultimately lead to improved data products as the survey enters its next decade.

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