References

Abowd, J. M., Stephens, B. E., Vilhuber, L., Andersson, F., McKinney, K. L., Roemer, M., & Woodcock, S. (2009). The LEHD infrastructure files and the creation of the Quarterly Workforce Indicators. Producer Dynamics: New Evidence from Micro Data, 149–230. University of Chicago Press.

Abowd, J. M., & Vilhuber, L. (2005). The sensitivity of economic statistics to coding errors in personal identifiers. Journal of Business and Economic Statistics, 23(2), 133–152.

Abraham, K. G., Haltiwanger, J. C., Hou, C., Sandusky, K., & Spletzer, J. R. (2021). Reconciling survey and administrative measures of self-employment. Journal of Labor Economics, 39(4), 825–860.

Adeyemo, W., & Batchelder, L. (2021). Advancing equity analysis in tax policy. https://home.treasury.gov/news/featured-stories/advancing-equity-analysis-in-tax-policy

Addington, L. A. (2019). NIBRS as the new normal: What fully incident-based crime data mean for researchers. Handbook on Crime and Deviance, 21–33. Springer.

Addington, L. A., & Lauritsen, J. L. (2021). Using national data to inform our understanding of family and intimate partner violence victimization: A review of a decade of innovation. Feminist Criminology, 16(3), 304–319.

Advisory Committee on Data for Evidence Building. (2022). Advisory Committee on Data for Evidence Building: Year 2 Report. https://www.bea.gov/system/files/2022-10/acdeb-year-2-report.pdf

Ahrens, K. A., Haley, B. A., Rossen, L. M., Lloyd, P. C., & Aoki, Y. (2016). Housing assistance and blood lead levels: Children in the United States, 2005–2012. American Journal of Public Health, 106(11), 2049–2056.

Akee, R. (2022, May 18). Administrative data and statistics for small race groups and other populations. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Akee, R, Jones, M. R., Porter, S. R., & Simeonova, E. (2020). Hispanic and Asian earnings inequality: New workers and immigrants. AEA Papers and Proceedings, 110(May), 442–446.

Akee, R., Jones, M. R., & Porter, S. R. (2019). Race matters: Income shares, income inequality, and income mobility for all US races. Demography, 56(3), 999–1021.

Akee, R., Mykerezi, E., & Todd, R. (2020). Business dynamics on American Indian reservations: Evidence from longitudinal datasets. U.S. Census Bureau Center for Economic Studies Working Paper CES 20-38. https://www.census.gov/library/working-papers/2020/adrm/CES-WP-20-38.html

Ali, B., & Dahlhaus, P. (2022). The role of FAIR data towards sustainable agricultural performance: A systematic literature review. Agriculture, 12(2), 309.

Allen, R. (2008). Agriculture Counts: The Founding and Evolution of the National Agricultural Statistics Service, 1957–2007. U.S. Department of Agriculture. https://www.nass.usda.gov/About_NASS/pdf/agriculture_counts.pdf

Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2016). Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing

Aram, J., Zhang, C., Golden, C., Zelaya, C. E., Cox, C. S., Ye, Y., & Mirel, L. B. (2021). Assessing linkage eligibility bias in the National Health Interview Survey. Vital and Health Statistics, 2(186), 1–28.

Arias, E. (2021). Race crossover in longevity. Encyclopedia of Gerontology and Population Aging, 4119–4128. Springer.

Arias, E., Heron, M. P., & Hakes, J. K. (2016). The validity of race and Hispanic origin reporting on death certificates in the United States: An update. Vital and Health Statistics, 2(172), 1–21.

Arias, E., Xu, J., Curtin, S., Bastian, B., & Tejada-Vera, B. (2021). Mortality profile of the non-Hispanic American Indian or Alaska Native Population, 2019. National Vital Statistics Reports, 70(12), 1–27.

Arora, A. (2022a, May 16). Combining multiple data sources to produce inclusive official statistics. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

———. (2022b). Modernizing government statistics for the 21st Century. Amstat News, 541(July), 24–26.

Ashby, M. P. J. (2019). Studying crime and place with the Crime Open Database. Research Data Journal for the Humanities and Social Sciences, 4(1), 65–80.

———. (2020). Initial evidence on the relationship between the coronavirus pandemic and crime in the United States. Crime Science, 9(1), 1–16.

Asher, J., Resnick, D., Brite, J., Brackbill, R., & Cone, J. (2020). An introduction to probabilistic record linkage with a focus on linkage processing for WTC registries. International Journal of Environmental Research and Public Health, 17(18), 6937.

Banks, D., Planty, M., Couzens, L., Lee, P., Brooks, C., Scott, K. M., & Whyde, A. (2019). Arrest-related deaths program: Pilot study of redesigned survey methodology. Technical Report NCJ 252675. U.S. Bureau of Justice Statistics. https://bjs.ojp.gov/content/pub/pdf/ardppsrsm.pdf

Barnett-Ryan, C., & Berzofsky, M. (2022). Establishing new methods for estimating crime in the US—The transition to incident-based crime reporting through NIBRS. [February 17 Webinar]. US Department of Justice, Office of Justice Programs. https://bjs.ojp.gov/media/video/66496

Barnett-Ryan, C., & Swanson, G. (2017). The role of state programs in NIBRS data quality: A case study of two states. Journal of Contemporary Criminal Justice, 24(1), 18–31.

Beals, M., Jamieson, A., Rogers, J., & Raiha, N. (2021). Our Clients Speak: Results from the Social and Health Services Client Survey. Washington State Department of Social and Health Services. https://www.dshs.wa.gov/sites/default/files/rda/reports/research-11-259.pdf

Bearer-Friend, J. (2019). Should the IRS know your race? The challenge of colorblind tax data. Tax Law Review, 73(1), 1–68.

Beaumont, J.-F. (2020). Are probability surveys bound to disappear for the production of official statistics? Survey Methodology, 46(1), 1–28.

Bee, A., & Rothbaum, J. (2019). The Administrative Income Statistics (AIS) project: Research on the use of administrative records to improve income and resource estimates. U.S. Census Bureau. SEHSD Working Paper 2019-36. https://www.census.gov/library/working-papers/2019/demo/SEHSD-WP2019-36.html

Bee, C. A., & Mitchell, J. (2017). Do older Americans have more income than we think? U.S. Census Bureau SESHD Working Paper 2017-39. https://www.census.gov/library/working-papers/2017/demo/SEHSD-WP2017-39.html

Bee, C. A., Gathright, G. M. R., & Meyer B. D. (2015). Bias from unit non-response in the measurement of income in household surveys. https://harris.uchicago.edu/files/jsm2015_bgm_unit_non-response_in_cps.pdf

Bell, W. R., Basel, W. W., & Maples, J. J. (2016). An overview of the US Census Bureau’s Small Area Income and Poverty Estimates program. Analysis of Poverty Data by Small Area Estimation, 349–378. Wiley.

Benedetto, G., Haltiwanger, J., Lane, J., & McKinney, K. (2007). Using worker flows to measure firm dynamics. Journal of Business and Economic Statistics, 25(3), 299–313.

Benedetto, G., Stinson, M. H., & Abowd, J. M. (2013). The creation and use of the SIPP Synthetic Beta. https://www.census.gov/content/dam/Census/programs-surveys/sipp/methodology/SSBdescribe_nontechnical.pdf

Benzeval, M., Bollinger, C. R., Burton, J., Couper, M. P., Crossley, T. F., & Jäckle, A. (2020). Integrated data: Research potential and data quality. Institute for Social and Economic Research, University of Essex: Understanding Society. Working Paper Series 2020-02. https://www.understandingsociety.ac.uk/research/publications/525974

Berzofsky, M., Liao, D., Couzens, G. L., Smith, E. L., & Barnett-Ryan, C. (2022). Estimation Procedures for Crimes in the United States Based on NIBRS Data. Report NCJ-305108. Bureau of Justice Statistics and Federal Bureau of Investigation. https://www.ojp.gov/ncjrs/virtual-library/abstracts/estimation-procedures-crimes-united-states-based-nibrs-data

Binette, O., & Steorts, R. C. (2022). (Almost) all of entity resolution. Science Advances, 8(12), 1–14. https://www.science.org/doi/abs/10.1126/sciadv.abi8021

Bird, S. M., & King, R. (2018). Multiple systems estimation (or capture-recapture estimation) to inform public policy. Annual Review of Statistics and Its Application, 5(1), 95–118.

Black, D. A., Hsu, Y. C., Sanders, S. G., Schofield, L. S., & Taylor, L. J. (2017). The Methuselah effect: The pernicious impact of unreported deaths on old-age mortality estimates. Demography, 54(6), 2001–2024. https://www.thieme-connect.com/products/ejournals/html/10.1055/s-0039-1693129

Black, M. C., Basile, K. C., Breiding, M. J., Smith, S. G., Walters, M. L., Merrick, M. T., Chen, J., & Stevens, M. R. (2011). The National Intimate Partner and Sexual Violence Survey (NISVS): 2010 Summary Report. National Center for Injury Prevention and Control, Centers for Disease Control and Prevention.

Bohensky, M. A., Jolley, D., Sundararajan, V., Evans, S., Ibrahim, J., & Brand, C. (2011). Development and validation of reporting guidelines for studies involving data linkage. Australian and New Zealand Journal of Public Health, 35(5), 486–489.

Bohensky, M. A., Jolley, D., Sundararajan, V., Evans, S., Pilcher, D. V., Scott, I., & Brand, C. A. (2010). Data linkage: A powerful research tool with potential problems. BMC Health Services Research, 10(1), 1–7.

Bohme, F. G. (1989). 200 Years of Census Taking: Population and Housing Questions, 1790-1990. U.S. Government Printing Office.

Bollinger, C. R., Hirsch, B. T., Hokayem, C. M., & Ziliak, J. P. (2019). Trouble in the tails? What we know about earnings nonresponse thirty years after Lillard, Smith, and Welch. Journal of Political Economy, 127(5), 2143–2185.

Boman, J. H., & Gallupe, O. (2020). Has COVID-19 changed crime? Crime rates in the United States during the pandemic. American Journal of Criminal Justice, 45(4), 537–545.

Bond, B., Brown, J. D., Luque, A., & O’Hara, A. (2014). The nature of the bias when studying only linkable person records: Evidence from the American Community Survey. CARRA Working Paper 2014-08. Center for Administrative Records Research and Applications. https://www.census.gov/content/dam/Census/library/working-papers/2014/adrm/carra-wp-2014-08.pdf

Boruch, R. F. (2011). Administrative Record Quality and Integrated Data Systems. Actionable Intelligence for Social Policy, University of Pennsylvania. https://aisp.upenn.edu/wp-content/uploads/2015/09/0033_12_SP2_Record_Quality_Data_Systems_000.pdf

Boryan, C., Yang, Z., Mueller, R., & Craig, M. (2011). Monitoring US Agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program. Geocarto International, 26(5), 341–358.

Boryan, C. G., & Yang Z. (2021) Geospatial land use and land cover data for improving agricultural area sampling frames. Agro-geoinformatics: Theory and Practice. Springer.

Boudreaux, M., Fenelon, A., & Slopen, N. (2018). Misclassification of rental assistance in the National Health Interview Survey: Evidence and implications. Epidemiology, 29(5), 716–720.

Boudreaux, M., Noon, J. M., Fried, B., & Pascale, J. (2019). Medicaid expansion and the Medicaid undercount in the American Community Survey. Health Services Research, 54(6), 1263–1272.

Brackstone, G. (1999). Managing data quality in a statistical agency. Survey Methodology, 25(2), 139–149.

Bradley, V. C., Kuriwaki, S., Isakov, M., Sejdinovic, D., Meng, X.-L., & Flaxman, S. (2021). Unrepresentative big surveys significantly overestimated US vaccine uptake. Nature, 600(December), 695–700.

Brayne, S. (2017). Big data surveillance: The case of policing. American Sociological Review, 82(5), 977–1008.

Bregger, J. E. (1984). The Current Population Survey: A historical perspective and BLS’ role. Monthly Labor Review, 107(6), 8–14.

Brisbane, J., & Mohl, C. (2014). The potential use of remote sensing to produce field crop statistics at Statistics Canada. Proceedings of the Statistics Canada Symposium 2014. https://www.statcan.gc.ca/eng/conferences/symposium2014/program/14259-eng.pdf

Brown, K. S. (2022, May 18). From data to decision-making: Using data and engagement to advance racial equity. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Brown, K. S., Su, Y., Jagganath, J., Rayfield, J., & Randall, M. (2021). Ethics and Empathy in Using Imputation to Disaggregate Data for Racial Equity: Landscape Scan Findings. Urban Institute. https://www.urban.org/sites/default/files/publication/104678/ethics-and-empathy-in-using-imputation-to-disaggregate-data-for-racial-equity_0.pdf

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the 1st Conference on Fairness, Accountability and Transparency, 81, 77–91. https://proceedings.mlr.press/v81/buolamwini18a.html?mod=article_inline

Cai, Y., Guan, K., Peng, J., Wang, S., Seifert, C., Wardlow, B., & Li, Z. (2018). A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote Sensing of Environment, 210, 35–47.

Calderwood, L., & Lessof, C. (2009). Enhancing longitudinal surveys by linking to administrative data. Methodology of Longitudinal Surveys, 55–72. John Wiley & Sons.

Carfagna, E., & Carfagna, A. (2015). Combining list frames with different kinds of area frames. Proceedings of the 60th World Statistics Congress of the International Statistical Institute. https://2015.isiproceedings.org/Files/STS006-P4-S.pdf

Carletto, C., Dillon, A., & Zezza, A. (2021). Agricultural data collection to minimize measurement error and maximize coverage. Handbook of Agricultural Economics, 5, 4407–4480. Elsevier.

Carson, E. A. (2015). Linking administrative BJS data: Better understanding of prisoners’ personal histories by linking the National Corrections Reporting Program (NCRP) and CARRA data. Proceedings of the 2015 Federal Committee on Statistical Methodology (FCSM) Research Conference. https://nces.ed.gov/FCSM/pdf/A1_Carson_2015FCSM.pdf

Catalano, S. (2016). Interviewing conditions in the National Crime Victimization Survey, 1993–2013. NCJ-249682, Bureau of Justice Statistics. https://www.bjs.gov/content/pub/pdf/icncvs9313.pdf

Celhay, P. A., Meyer, B. A., & Mittag, N. (2021). Errors in reporting and imputation of government benefits and their implications. National Bureau of Economic Research Working Paper 29184. http://www.nber.org/papers/w29184

Chen, H. (2022, May 16). Promoting global data equity: The work of the Inter-Secretariat Working Group on Household Surveys. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Chen, J. T. (2015). Merging survey data with aggregate data from other sources: Opportunities and challenges. Health Survey Methods, 717–754. John Wiley & Sons.

Chen, L., & Nandram, B. (2022). A hierarchical Bayesian beta-binomial model for sub-areas. Applied Statistical Methods. ISGES 2020. Springer Proceedings in Mathematics & Statistics, 380. Springer. https://doi.org/10.1007/978-981-16-7932-2_2

Chen, L., Cruze, N., & Young, L. (2021). Transitioning to model-based official statistics: The case of crops county estimates. Presentation to the Sixth International Conference on Establishment Statistics.

Chen, L., Nandram, B., & Cruze, N. B. (2022). Hierarchical Bayesian model with inequality constraints for US county estimates. Journal of Official Statistics, 38(3), 709–732.

Chen, S., & Haziza, D. (2019). Recent developments in dealing with item non-response in surveys: A critical review. International Statistical Review, 87(S1), S192–S218.

Chow, M., Fort, T. C., Goetz, C., Goldschlag, N., Lawrence, J., Perlman, E. R., Stinson, M., & White, T. K. (2021). Redesigning the Longitudinal Business Database. U.S. Census Bureau Center for Economic Studies Working Paper 21-08. https://www.census.gov/library/working-papers/2021/adrm/CES-WP-21-08.html

Christen, P. (2019). Data linkage: The big picture. Harvard Data Science Review, 1(2). https://doi.org/10.1162/99608f92.84deb5c4

Chromy, J., & Wilson, D. (2012). Multiple frame approaches to identify and survey victims of rape and sexual assault. Paper commissioned by the National Research Council Panel on Measuring Rape and Sexual Assault in the Bureau of Justice Statistics Household Surveys, Washington, DC. https://sites.nationalacademies.org/cs/groups/dbassesite/documents/webpage/dbasse_080064.pdf

Citro, C. F. (2014). From multiple modes for surveys to multiple data sources for estimates. Survey Methodology, 40(2), 137–161.

Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy, 40(1), 79–96.

Comenetz, J. (2016). Frequently occurring surnames in the 2010 census. U.S. Census Bureau. https://www2.census.gov/topics/genealogy/2010surnames/surnames.pdf

Compson, M. (2022). Improving county-level earnings estimates with a new methodology for assessing geographic and demographic information to U.S. workers. Social Security Bulletin, 82(1), 11–28.

Cook, S. L., Gidycz, C. A., Koss, M. P., & Murphy, M. (2011). Emerging issues in the measurement of rape victimization. Violence Against Women, 17(2), 201–218.

Cooper, C. R., & Getter, D. E. (2020). Consumer Credit Reporting, Credit Bureaus, Credit Scoring, and Related Policy Issues. Congressional Research Service.

Couper, M. P. (2013). Is the sky falling? New technology, changing media, and the future of surveys. Survey Research Methods, 7(3), 145–156.

Craig, M. (2010). A history of the Cropland Data Layer at NASS. https://pdi.scinet.usda.gov/portal/sharing/rest/content/items/5fb11fe081744dab8d470fdd9b472771/data

Crosby, A. E., Mercy, J. A., and Houry, D. (2016). The National Violent Death Reporting System. American Journal of Preventive Medicine, 51(5), S169–S172.

Cruze, N. B., Erciulescu, A. L., Nandram, B., Barboza, W. J., & Young, L. J. (2019). Producing official county-level agricultural estimates in the United States: Needs and challenges. Statistical Science, 34(2), 301–316.

Culhane, D. P., Fantuzzo, J., Rouse, H. L., Tam, V., & Lukens, J. (2010). Connecting the dots: The promise of integrated data systems for policy analysis and systems reform. Intelligence for Social Policy, University of Pennsylvania. https://aisp.upenn.edu/resource-article/connecting-the-dots-the-promise-of-integrated-data-systems-for-policy-analysis-and-systems-reform/

Cummings, J. (1918). Statistical work of the federal government in the United States. The History of Statistics, 573–689. Burt Franklin.

Cunningham, C., Foster, L., Grim, C., Haltiwanger, J., Pabilonia, S. W., & Stewart, J. (2021). Productivity dispersion, entry, and growth in US manufacturing industries. U.S. Census Bureau Center for Economic Studies Working Paper 21-21.

Cunningham, J. (2021). Uniting our data to inform our states. Presented at the Value of Science: Data, Products, & Use conference. https://coleridgeinitiative.org/wp-content/uploads/2021/06/Cunningham-KYSTATS.pdf

Cunningham, J, Hui, A., Lane, J., & Putnam, G. (2022). A value-driven approach to building data infrastructures: The example of the MidWest Collaborative. Harvard Data Science Review, 4(1), 1–9. https://hdsr.mitpress.mit.edu/pub/mfhpwpxq/release/2

Czajka, J. L., & Beyler, A. (2016). Declining response rates in federal surveys: Trends and implications. Mathematica Policy Research. https://mathematica.org/publications/declining-response-rates-in-federal-surveys-trends-and-implications-background-paper

Czajka, J. L., & Denmead, G. (2008). Income data for policy analysis: A comparative assessment of eight surveys: Final report. Mathematica Policy Research, Inc. Report 6302-601. https://mathematica.org/publications/income-data-for-policy-analysis-a-comparative-assessment-of-eight-surveys

Czajka, J. L., & Stange, M. (2018). Transparency in the Reporting of Quality for Integrated Data: A Review of International Standards and Guidelines. Mathematica Policy Research.

Daas, P. J., Ossen, S., Vis-Visschers, R., & Arends-Tóth, J. (2009). Checklist for the Quality Evaluation of Administrative Data Sources. The Hague: Statistics Netherlands.

Dalton, J. (2007). SOI approaches first century in the twenty-first. Internal Revenue Service Statistics of Income Bulletin, 27(1), 4–5.

Data Foundation and AGree Initiative. (2022). Modernizing Data Infrastructure to Improve Economic and Ecological Outcomes. Data Foundation. https://www.datafoundation.org/modernizing-agriculture-data-infrastructure-to-improve-economic-and-ecological-outcomes-2022

Davern, M., Call, K. T., Ziegenfuss, J., Davidson, G., Beebe, T. J., & Blewett, L. (2008). Validating health insurance coverage survey estimates: A comparison of self-reported coverage and administrative data records. Public Opinion Quarterly, 72(2), 241–259.

Davern, M., Klerman, J. A., Baugh, D. K., Call, K. T., & Greenberg, G. D. (2009). An examination of the Medicaid undercount in the Current Population Survey: Preliminary results from record linking. Health Services Research, 44(3), 965–987.

Davern, M., Roemer, M., & Thomas, W. (2014). Merging survey data with administrative data for health research purposes. Handbook of Health Survey Methods, 1651–1698. John Wiley & Sons.

Davies, C. (2009). Area frame design for agricultural surveys. National Agricultural Statistics Service RDD Research Report RDD-09-xx. U.S. Department of Agriculture. https://www.nass.usda.gov/Publications/Methodology_and_Data_Quality/Advanced_Topics/AREA%20FRAME%20DESIGN.pdf

Davis, S. J., Haltiwanger, J. C., & Schuh, S. (1998). Job Creation and Destruction. MIT Press.

Day, H. R., & Parker, J. D. (2013). Self-report of diabetes and claims-based identification of diabetes among Medicare beneficiaries. National Health Statistics Reports 69. NCHS. https://www.cdc.gov/nchs/data/nhsr/nhsr069.pdf

Decker, S. L., Doshi, J. A., Knaup, A. E., & Polsky, D. (2012). Health service use among the previously uninsured: Is subsidized health insurance enough? Health Economics, 21(10), 1155–1168.

Deming, W. E. (1950). Some Theory of Sampling. John Wiley & Sons.

Doidge, J. C., & Harron, K. L. (2019). Reflections on modern methods: Linkage error bias. International Journal of Epidemiology, 48(6), 2050–2060.

Dunn, M., Haugen S. E., & Kang, J.-L. (2018). The current population survey—Tracking unemployment in the United States for over 75 years. Monthly Labor Review, 141(1), 1–22.

Dushi, I., & Trenkamp, B. (2021). Improving the measurement of retirement income of the aged population. Social Security Administration Office of Research, Evaluation, and Statistics Working Paper 116.

Dutwin, D., & Buskirk, T. D. (2021). Telephone sample surveys: Dearly beloved or nearly departed? Trends in survey errors in the era of declining response rates. Journal of Survey Statistics and Methodology, 9(3), 353–380.

The Economist. (2022). The pulse of the people. The Economist (Technology Quarterly), 443(9295), 11–12. https://www.economist.com/technology-quarterly/2022/05/02/data-from-wearable-devices-are-changing-disease-surveillance-and-medical-research

Egan, K. B., Cornwell, C. R., Courtney, J. G., & Ettinger, A. S. (2021). Blood lead levels in US children ages 1–11 years, 1976–2016. Environmental Health Perspectives, 129(3-037003), 1–11.

Eggleston, J., Klee, M. A., & Munk, R. (2022). Self-employment status: Imputations, implications, and improvements. U.S. Census Bureau SIPP Working Paper #303; SEHSD Working Paper 2022-06. https://www.census.gov/content/dam/Census/library/workingpapers/2022/demo/sehsd-wp2022-06.pdf

Elliott, M. N., Morrison, P. A., Fremont, A., McCaffrey, D. F., Pantoja, P., & Lurie, N. (2009). Using the Census Bureau’s surname list to improve estimates of race/ethnicity and associated disparities. Health Services and Outcomes Research Methodology, 9(2), 69–83.

Elliott, M. R., & Valliant, R. (2017). Inference for nonprobability samples. Statistical Science, 32(2), 249–264.

Elliott, M. R., Raghunathan, T. E., & Schenker, N. (2018). Combining estimates from multiple surveys. Wiley StatsRef: Statistics Reference Online, 1–10. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat08079

Engelhardt, G. V., & Kumar, A. (2007). Employer matching and 401(k) saving: Evidence from the Health and Retirement Study. Journal of Public Economics, 91(10), 1920–1943.

Equitable Data Working Group. (2022). A Vision for Equitable Data: Recommendations from the Equitable Data Working Group. Office of the President. https://www.whitehouse.gov/wp-content/uploads/2022/04/eo13985-vision-for-equitable-data.pdf

Erciulescu, A. L., Cruze, N. B., & Nandram, B. (2018). Benchmarking a triplet of official estimates. Environmental and Ecological Statistics, 25(November), 523–547.

———. (2019). Model-based county level crop estimates incorporating auxiliary sources of information. Journal of the Royal Statistical Society: Series A, 182(1), 283–303.

———. (2020). Statistical challenges in combining survey and auxiliary data to produce official statistics. Journal of Official Statistics, 36(1), 63–88.

Eurostat. (2021). European Statistical System Handbook for Quality and Methods Reports—Re-edition 2021. Publications Office of the European Union.

Executive Order 13985. (2021). Executive order on advancing racial equity and support for underserved communities through the federal government. Federal Register, 86(14), 7009–7013.

Faul, J., & Levy, H. (2022, May 18). Overview of HRS Administrative Data Linkages. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Fay, R. E. (2021). Constructing and Disseminating Small-Area Estimates from the National Crime Victimization Survey, 2007-2018. Report NCJ-300603. Bureau of Justice Statistics. https://www.ojp.gov/library/publications/constructing-and-disseminating-small-area-estimates-national-crime

Federal Committee on Statistical Methodology. (2005). Report on Statistical Disclosure Limitations Methodology. Statistical Policy Working Paper 22. https://www.hhs.gov/sites/default/files/spwp22.pdf

———. (2018). Transparent Quality Reporting in the Integration of Multiple Data Sources: A Progress Report, 2017–2018. https://nces.ed.gov/FCSM/pdf/Quality_Integrated_Data.pdf

———. (2020). A Framework for Data Quality. https://nces.ed.gov/FCSM/pdf/FCSM.20.04_A_Framework_for_Data_Quality.pdf

Feldman, J. M., Gruskin, S., Coull, B. A., & Krieger, N. (2017). Quantifying underreporting of law-enforcement-related deaths in United States vital statistics and news-media-based data sources: A capture-recapture analysis. PLoS Medicine, 14(10), e1002399.

Ferguson, A. G. (2017). The Rise of Big Data Policing. New York University Press.

Fernandez, L., Ennis, S., Porter, S. R., & Carson, E. (2022). Mortality in a multi-state cohort of former state prisoners, 2010–2015. NCJ-304203. Bureau of Justice Statistics. https://bjs.ojp.gov/library/publications/mortality-multi-state-cohort-former-state-prisoners-2010-2015

Fernandez, L., Shattuck, R., & Noon, J. (2018). The use of administrative records and the American Community Survey to study characteristics of undercounted young children in the 2010 census. Center for Administrative Records Research and Applications Working Paper 2018-05. U.S. Census Bureau. https://www.census.gov/content/dam/Census/library/working-papers/2018/adrm/carra-wp-2018-05.pdf

Finlay, K., Mueller-Smith, M., & Papp, J. (2022). The criminal justice administrative records system: A next-generation research data platform. Scientific Data, 9(562), 1–11.

Fischer, R. L., Richter, F. G.-C., Anthony, E., Lalich, E., & Coulton, C. (2019). Leveraging administrative data to better serve children and families. Public Administration Review, 79(5), 675–683.

Fisher, G. G., & Ryan, L. H. (2018). Overview of the Health and Retirement Study and introduction to the special issue. Work, Aging and Retirement, 4(1), 1–9.

Fitzpatrick, T. B. (1988). The validity and practicality of sun-reactive skin types I through VI. Archives of Dermatology, 124(6), 869–871.

Flegal, K. M, Graubard, B. I., Williamson, D. F., & Gail, M. H. (2005). Excess deaths associated with underweight, overweight, and obesity. Journal of the American Medical Association, 293(15), 1861–1867.

Fouch, D., & Martin, K. (2022, February 15). Shifting the crime reporting paradigm—Lessons learned from the FBI’s transition to NIBRS [Webinar]. U.S. Department of Justice, Office of Justice Programs. https://bjs.ojp.gov/media/video/66506

Fox, L., & Burns, K. (2021). The Supplemental Poverty Measure: 2020. U.S. Census Bureau Current Population Report P60-275. https://www.census.gov/content/dam/Census/library/publications/2021/demo/p60-275.pdf

Fox, L., Rothbaum, J., & Shantz, K. (2022). Fixing errors in a SNAP: Addressing SNAP underreporting to evaluate poverty. American Economic Association Papers and Proceedings, 112(May), 330–334. https://doi.org/10.1257/pandp.20221040

Fritz, S., See, L., Bayas, J. K. L., Waldner, F., Jacques, D., Becker-Reshef, I., Whitcraft, A., Baruth, B., Bonifacio, R., Crutchfield, J., Rembold, F., Rojas, O., Schucknecht, A., Van der Velde, M., Verdin, J., Wu, B., Yan, N., You, L., Gilliams, S., Mücher, S., Tetrault, R., Moorthy, I., & McCallum, I. (2019). A comparison of global agricultural monitoring systems and current gaps. Agricultural Systems, 168(January), 258–272.

Gallego J., Carfagna E., & Baruth B. (2010). Accuracy, objectivity and efficiency of remote sensing for agricultural statistics. Agricultural Survey Methods, 193–211. Wiley.

Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327(5967), 828–831.

Genadek, K. R., & Alexander, J. T. (2019). The Decennial Census Digitization and Linkage Project. U.S. Census Bureau ADEP Working Paper 2019-01. https://www.census.gov/library/working-papers/2019/econ/adep-wp-dc-digitization-linkage.html

Gennetian, L. A., Seshadri, R., Hess, N. D., Winn, A. N., & Goerge, R. M. (2016). Supplemental Nutrition Assistance Program (SNAP) benefit cycles and student disciplinary infractions. Social Service Review, 90(3), 403–433.

Giest, S., & Samuels, A. (2020). “For good measure”: Data gaps in a big data world. Policy Sciences, 53, 559–569.

Gilbert, R., Lafferty, R., Hagger-Johnson, G., Harron, K., Zhang, L. C., Smith, P., Dibben, C., & Goldstein, H. (2018). GUILD: Guidance for information about linking data sets. Journal of Public Health, 40(1), 191–198.

Gindi, R., & Cohen, R. A. (2012). Assessing measurement error in Medicare coverage from the National Health Interview Survey. Medicare Care & Medicaid Research Review, 2(2), E1–E15.

Global Strategy to Improve Agricultural and Rural Statistics. (2017). Handbook on Remote Sensing for Agricultural Statistics. GSARS Handbook.

Goerge, R. M., & Lee, B. J. (2002). Matching and cleaning administrative data. Chapter 7 in National Academies of Sciences, Engineering, and Medicine, Studies of Welfare Populations: Data Collection and Research Issues. The National Academies Press.

Goerge, R. M., & Wiegand, E. R. (2019). Understanding vulnerable families in multiple service systems. RSF: The Russell Sage Foundation Journal of the Social Sciences, 5(2), 86–104.

Goerge, R. M., Harden, A., & Lee, B. J. (2008). Consequences of teen childbearing for child abuse, neglect, and foster care placement. Kids Having Kids: Economic Costs and Social Consequences of Teen Pregnancy (2nd ed.), 257–288. The Urban Institute.

Goerge, R. M., Harris, A., Bilaver, L. M., Franzetta, K., Reidy, M., Schexnayder, D., Schroeder, D., Staveley, J., Kreader, J. L., Obenski, S., Prevost, R. C., Berning, M. E., & Resnick, D. M. (2009). Employment outcomes for low-income families receiving child care subsidies in Illinois, Maryland, and Texas. Final Report to U.S. Department of Health and Human Services, Administration for Children and Families, Office of Planning, Research, and Evaluation under Grant 90YE0070.

Goerge, R. M., van Voorhis, J., & Lee, B. J. (1994). Illinois’s longitudinal and relational child and family research database. Social Science Computer Review, 12(3), 351–365.

Goetz, C., & Stinson, M. (2021). The Business Dynamics Statistics: Describing the evolution of the US economy from 1978–2019. US Census Bureau Center for Economic Studies Working Paper 21-33. https://www.census.gov/library/working-papers/2021/adrm/CES-WP-21-33.html

Golden, C., & Mirel, L. B. (2021). Enhancement of health surveys with data linkage. Administrative Records for Survey Methodology. John Wiley & Sons.

Golden, C., Driscoll, A. K., Simon, A. E., Judson, D. H., Miller, E. A., & Parker, J. D. (2015). Linkage of NCHS population health surveys to administrative records from Social Security Administration and Centers for Medicare & Medicaid Services. Vital and Health Statistics, 1(58), 1–53.

Goodchild, M. (2022, May 16). Discussion: Improving agriculture statistics with new data sources. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Goodchild, M., & Li, L. (2012). Assuring the quality of volunteered geographic information. Spatial Statistics, 1, 110–120.

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

———. (2022, May 18). Lessons learned and next steps: Discussion and wrap-up session. Comments at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Groves, R. M., & Lyberg, L. (2010). Total survey error: Past, present, and future. Public Opinion Quarterly, 74(5), 849–879.

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2011). Survey Methodology. (2nd ed.). John Wiley & Sons.

Gustman, A. L., & Steinmeier, T. L. (2005). Imperfect knowledge of Social Security and pensions. Industrial Relations, 44(2), 373–397.

Haas, A., Elliott, M. N., Dembosky, J. W., Adams, J. L., Wilson Frederick, S. M., Mallett, J. S., Gaillot, S., Haffer, S. C., & Haviland, A. M. (2019). Imputation of race/ethnicity to enable measurement of HEDIS performance by race/ethnicity. Health Services Research 54(1), 13–23.

Hamilton, A. (1791). Report on duties arising on tonnage, for the year ending September 30, 1790. American State Papers: Commerce and Navigation, 7, 6–8. Gales and Seaton.

Hand, D. J. (2020). Dark Data: Why What You Don’t Know Matters. A Practical Guide to Making Good Decisions in a World of Missing Data. Princeton University Press.

Handley, K., Kamal, F., & Ouyang, W. (2021). A long view of employment growth and firm dynamics in the United States: Importers vs. exporters vs. non-traders. U.S. Census Bureau Center for Economic Studies Working Paper 21-38. https://www.census.gov/library/working-papers/2021/adrm/CES-WP-21-38.html

Hansen, M. H., Hurwitz, W. N., & Madow, W. G. (1953). Sample Survey Methods and Theory, Vol. 1. John Wiley & Sons.

Hanson, E. J. (2021). The National Incident-Based Reporting System: Benefits and Issues. Congressional Research Service.

Harcourt, B. E. (2015). Risk as a proxy for race: The dangers of risk assessment. Federal Sentencing Reporter, 27(4), 237–243.

Harrell, E. (2021). Crimes against persons with disabilities, 2009–2019—Statistical tables. NCJ 301367. Bureau of Justice Statistics. https://bjs.ojp.gov/content/pub/pdf/capd0919st.pdf

Harron, K., Goldstein, H., & Dibben, C. (Eds.). (2016). Methodological Developments in Data Linkage. John Wiley & Sons.

Hart, N., & Yohannes, M. (Eds.). (2019). Evidence Works: Cases Where Evidence Meaningfully Informed Policy. Bipartisan Policy Center. https://bipartisanpolicy.org/report/evidenceworks/

Haziza, D. (2009). Imputation and inference in the presence of missing data. Sample Surveys: Design, Methods, and Applications. Handbook of Statistics, 29A, 215–246. North-Holland.

Hedegaard, H., & Warner, M. (2021). Evaluating the cause-of-death information needed for estimating the burden of injury mortality: United States, 2019. National Vital Statistics Reports, 70(13), 1–22.

Helms, V. E., King, B. A., & Ashley, P. J. (2017). Cigarette smoking and adverse health outcomes among adults receiving federal housing assistance. Preventive Medicine, 99, 171–177.

Herz, D. C., Dierkhising, C. B., Raithel, J., Schretzman, M., Guiltinan, S., Goerge, R. M., Cho, Y., Coulton, C., & Abbott, S. (2019). Dual system youth and their pathways: A comparison of incidence, characteristics and system experiences using linked administrative data. Journal of Youth and Adolescence, 48(12), 2432–2450.

Hetzel, A. M. (1997). History and Organization of the Vital Statistics System. Hyattsville, MD: National Center for Health Statistics.

Hill, C., Helm, K., Hong, J., & Phan, N. (2022). Census coverage estimates for people in the United States by state and Census operations. 2020 Post-Enumeration Survey Estimation Report, PES20-G-02RV. U.S. Government Printing Office. https://www2.census.gov/programs-surveys/decennial/coverage-measurement/pes/census-coverage-estimates-for-people-in-the-united-states-by-state-and-census-operations.pdf

Himelein-Wachowiak, M., Giorgi, S., Devoto, A., Rahman, M., Ungar, L., Schwartz, H. A., Epstein, D. H., Leggio, L., & Curtis, B. (2021). Bots and misinformation spread on social media: Implications for COVID-19. Journal of Medical Internet Research, 23(5). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8139392/

Hokayem, C., Bollinger, C., & Ziliak, J. P. (2015). The role of CPS nonresponse in the measurement of poverty. Journal of the American Statistical Association, 110(511), 935–945.

Hokayem, C., Raghunathan, T., & Rothbaum, J. (2022). Match bias or nonignorable nonresponse? Improved imputation and administrative data in the CPS ASEC. Journal of Survey Statistics and Methodology, 10(1), 81–114.

Honeycutt, A. A., Segel, J. E., Zhuo, X., Hoerger, T. J., Imai, K., & Williams, D. (2013). Medical costs of CKD in the Medicare population. Journal of the American Society of Nephrology, 24(9), 1478–1483.

Horst, M., & Marion, A. (2019). Racial, ethnic and gender inequities in farmland ownership and farming in the US. Agriculture and Human Values, 36(1), 1–16.

Hughes, A. G., McCabe, S. D., Hobbs, W. R., Remy, E., Shah, S., & Lazer, D. M. (2021). Using administrative records and survey data to construct samples of Tweeters and Tweets. Public Opinion Quarterly, 85(S1), 323–346.

Humes, K., & Hogan, H. (2009). Measurement of race and ethnicity in a changing, multicultural America. Race and Social Problems, 1, 111–131.

Hurd, M. D., Martorell, P., Delavande, A., Mullen, K. J., & Langa, K. M. (2013). Monetary costs of dementia in the United States. New England Journal of Medicine, 368(14), 1326–1334.

Hurst, B. (2016). Testimony in Big Data and Agriculture: Innovation and Implications. Hearing before the Committee on Agriculture, House of Representatives, 114th Congress, October 28, 2015, Serial No. 114-32, 6–15. U.S. Government Publishing Office. https://www.govinfo.gov/content/pkg/CHRG-114hhrg97412/pdf/CHRG-114hhrg97412.pdf

Hyman, M., Sartore, L., & Young, L. J. (2022). Capture-recapture estimation of characteristics of US local food farms using a web-scraped list frame. Journal of Survey Statistics and Methodology, 10(4), 979–1004.

Iachan, R., & Dennis, M. L. (1993). A multiple frame approach to sampling the homeless and transient population. Journal of Official Statistics, 9(4), 747–764.

Institute of Medicine. (2009). Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. The National Academies Press.

Interagency Technical Working Group on Evaluating Alternative Measures of Poverty. (2021). Final report of the interagency technical working group on evaluating alternative measures of poverty. https://www.bls.gov/cex/itwg-report.pdf

International Association of Chiefs of Police. (1929). Uniform Crime Reporting: A Complete Manual for Police. Report of the Committee on Uniform Crime Records. J.J. Little and Ives Company.

International Society of Precision Agriculture. (2019). Monthly newsletter (July). https://ispag.org/site/newsletter/?id=90

Iwashyna, T. J., Ely, E. W., Smith, D. M., & Langa, K. M. (2010). Long-term cognitive impairment and functional disability among survivors of severe sepsis. Journal of the American Medical Association, 304(16), 1787–1794.

Iwig, W., Berning, M., Marck, P., & Prell, M. (2013). Data quality assessment tool for administrative data. Prepared for the Statistical Uses of Administrative Records subcommittee of the Federal Committee on Statistical Methodology. https://nces.ed.gov/FCSM/pdf/DataQualityAssessmentTool.pdf

Jäckle, A., Beninger, K., Burton, J., & Couper, M. P. (2021a). Understanding data linkage consent in longitudinal surveys. Advances in Longitudinal Survey Methodology, 122–150. John Wiley & Sons.

Jäckle, A., Burton, J., Couper, M. P., Crossley, T. F., & Walzenbach, S. (2021b). Understanding and improving data linkage consent in surveys. Institute for Social and Economic Research, University of Essex: Understanding Society Working Paper Series 2021-01.

Jagadish, H. V., Stoyanovich, J., & Howe, B. (2021a). COVID-19 brings data equity challenges to the fore. Digital Government: Research and Practice, 2(2), 1–7.

———. (2021b). The many facets of data equity. Central Europe Workshop Proceedings, 2841. http://ceur-ws.org/Vol-2841/PIE+Q_6.pdf

James, N., & Council, L. R. (2008). How Crime in the United States Is Measured. Congressional Research Service. https://fas.org/sgp/crs/misc/RL34309.pdf

Jarmin, R. S., & Miranda, J. (2002). The Longitudinal Business Database. Center for Economic Studies Working Paper 02-17. https://www2.vrdc.cornell.edu/news/3/20050812JarminMiranda2002.pdf

Jarvis, J. P. (2015). Examining National Incident-Based Reporting System (NIBRS) data: Perspectives from a quarter century of analysis efforts. Justice Research and Policy, 16(2), 195–210.

Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., Wang, S., Ying, Y., & Lin, T. (2019). A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global Change Biology, 26(December), 1754–1766.

Johansson, R., Effland, A., & Coble, K. (2017). Falling response rates to USDA crop surveys: Why it matters. farmdoc daily, 7(January), 9.

Jones, M. R., & Ziliak, J. P. (2022). The antipoverty impact of the EITC: New estimates from survey and administrative tax records. National Tax Journal, 75(3), 451–479.

Jones, N., Marks, R., Ramirez, R., & Ríos-Vargas, M. (2021). 2020 census illuminates racial and ethnic composition of the country. https://www.census.gov/library/stories/2021/08/improved-race-ethnicity-measures-reveal-united-states-population-much-more-multiracial.html

Judson, D. (2000). The Statistical Administrative Records System: System design, successes, and challenges. Paper presented at the 2000 NISS/Telcordia Data Quality Conference. NISS. https://www.niss.org/events/niss-affiliates-workshop-data-quality-challenges-computer-science-and-statistics

Judson, D., & Popoff, C. (2005). Administrative records research. Encyclopedia of Social Measurement, 17–27. Elsevier.

Kalton, G. (2020). Introduction to Survey Sampling (2nd ed.). Sage.

Keisler-Starkey, K., & Bunch, L. N. (2022). Health Insurance Coverage in the United States: 2021. Current Population Reports P60-278. U.S. Census Bureau. https://www.census.gov/content/dam/Census/library/publications/2022/demo/p60-278.pdf

Keller, S., Prewitt, K., Thompson, J., Jost, S., Barrett, C., Nusser, S., Salvo, J., & Shipp, S. (2022). A 21st Century Census Curated Data Enterprise: A Bold New Approach to Create Official Statistics. University of Virginia Biocomplexity Institute. https://libraopen.lib.virginia.edu/public_view/zw12z549f

Kennedy, C. (2022). Exploring the assumption that online opt-in respondents are answering in good faith. Paper presented at the 2022 Morris Hansen Lecture, March 1 [slides]. https://washstat.org/hansen/2022Kennedy.pdf

Kennedy, C., Hatley, N., Lau, A., Mercer, A., Keeter, S., Ferno, J., & Asare-Marfo, D. (2021). Strategies for detecting insincere respondents in online polling. Public Opinion Quarterly, 85(4), 1050–1075.

Kennel, T. (2021). The Post-Enumeration Survey: Measuring coverage error. https://www.census.gov/newsroom/blogs/random-samplings/2021/12/post-enumeration-measuring-coverage-error.html

Keyes, K. M., Rutherford, C., Popham, F., Martins, S. S., & Gray, L. (2018). How healthy are survey respondents compared with the general population?: Using survey-linked death records to compare mortality outcomes. Epidemiology, 29(2), 299–307.

Khubba, S., Heim, K., & Hong, J. (2022). National Census Coverage Estimates for People in the United States by Demographic Characteristics. 2020 Post-Enumeration Survey Estimation Report PES20-G-01. U.S. Government Publishing Office. https://www2.census.gov/programs-surveys/decennial/coverage-measurement/pes/national-census-coverage-estimates-by-demographic-characteristics.pdf

Kilss, B., & Alvey, W. (1984). Statistical Uses of Administrative Data: Recent Research and Present Projects, Vol. 1. Department of the Treasury.

Kilss, B., & Scheuren, F. J. (1978). The 1973 CPS-IRS-SSA Exact Match Study. Social Security Bulletin, 41(10), 14–22.

Kim, D. Y., & Phillips, S. W. (2021). When COVID-19 and guns meet: A rise in shootings. Journal of Criminal Justice, 73, 101783.

Kinney, S. K., Reiter, J. P., Reznek, A. P., Miranda, J., Jarmin, R. S., & Abowd, J. M. (2011). Towards unrestricted public use business microdata: The Synthetic Longitudinal Business Database. International Statistical Review, 79(3), 362–384.

Kitzmiller, E. M. (2013). IDS Case Study, Chapin Hall: Leveraging Chapin Hall’s Mission to Enhance Child Well-Being. University of Pennsylvania, Actionable Intelligence for Social Policy. http://www.aisp.upenn.edu/wp-content/uploads/2015/08/ChapinHall_CaseStudy.pdf

Kohler, U., Kreuter, F., & Stuart, E. A. (2019). Nonprobability sampling and causal analysis. Annual Review of Statistics and Its Application, 6(March), 149–172.

Konny, C. G., Williams, B. K., & Friedman, D. M. (2022). Big data in the US Consumer Price Index: Experiences and plans. Big Data for Twenty-First-Century Economic Statistics, 69–98. University of Chicago Press.

Krebs, C. (2014). Measuring sexual victimization: On what fronts is the jury still out and do we need it to come in? Trauma, Violence, & Abuse, 15(3), 170–180.

Kreuter, F. (2022, May 18). Diversity in data: How data collection decisions help and harm the outcome. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Kreuter, F., Ghani, R., & Lane, J. (2019). Change through data: A data analytics training program for government employees. Harvard Data Science Review, 1(2). https://hdsr.mitpress.mit.edu/pub/0mb0zzlc/release/7

Kuehn, D. (2022a). Better Data for Better Policy: Lessons Learned from across the Coleridge Initiative’s Partnerships. Urban Institute. https://www.urban.org/sites/default/files/2022-05/Better%20Data%20for%20Better%20Policy.pdf

———. (2022b). Better Data for Better Policy: The Coleridge Initiative in Ohio: Steady Progress and Productive Collaboration. Urban Institute. https://www.urban.org/sites/default/files/2022-05/Better%20Data%20for%20Better%20Policy%20-%20the%20Coleridge%20Initiative%20in%20Ohio.pdf

Lariscy, J. T. (2011). Differential record linkage by Hispanic ethnicity and age in linked mortality studies: Implications for the epidemiologic paradox. Journal of Aging and Health, 23(8), 1263–1284.

———. (2017). Black-white disparities in adult mortality: Implications of differential record linkage for understanding the mortality crossover. Population Research and Policy Review, 36(1), 137–156.

Larrimore, J., Mortenson, J., & Splinter, D. (2021). Household incomes in tax data: Using addresses to move from tax-unit to household income distributions. Journal of Human Resources, 56(July), 600–631.

Lauritsen, J. L. (2022a, May 18). Crime measurement in the United States. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

———. (2022b). Victimization in different types of areas in the United States: Subnational findings from the National Crime Victimization Survey, 2010–2015. NCJ-252630. Bureau of Justice Statistics. https://bjs.ojp.gov/library/publications/victimization-different-types-areas-united-states-subnational-findings

Leach, M. A., Van Hook, J., & Bachmeier, J. D. (2018). Using Linked data to investigate true intergenerational change: Three generations over seven decades. U.S. Census Bureau CARRA Working Paper 2018-09.

Leary, K. (2022, May 18). Fireside chat on data equity. Discussion at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Lee, S., Nishimura, R., Burton, P., & McCammon, R. (2021). HRS 2016 Sampling Weights. University of Michigan Institute of Social Research. https://hrs.isr.umich.edu/sites/default/files/biblio/HRS2016SamplingWeights.pdf

Lehnen, R. G., & Skogan, W. (Eds.). (1981). The National Crime Survey: Working Papers, 1: Current and Historical Perspectives. NCJ-75374. Bureau of Justice Statistics.

Leroux, A., Di, J., Smirnova, E., Mcguffey, E. J., Cao, Q., Bayatmokhtari, E., Tabacu, L., Zipunnikov, V., Urbanek, J. K., & Crainiceanu, C. (2019). Organizing and analyzing the activity data in NHANES. Statistics in Biosciences, 11(2), 262–287.

LeRoy, L., Wasserman, M., Rezaee, M., & White, A. (2013). Understanding Disparities in Persons with Multiple Chronic Conditions: Research Approaches and Datasets. Abt Associates. https://aspe.hhs.gov/reports/understanding-disparities-persons-multiple-chronic-conditions-research-approaches-datasets-0

Lesiv, M., Bayas, J. C. L., See, L., Duerauer, M., Dahlia, D., Durando, N., Hazarika, R., Sahariah, P. K., Vakolyuk, M., Blyshchyk, V., Bilous, A., Perez-Hoyos, A., Gengler, S., Prestele, R., Bilous, S., Akhtar, I. U. H., Singha, K., Choudhury, S. B., Chetri, T., Malek, Z., Bungnamei, K., Saikia, A., Sahariah, D., Narzary, W., Danylo, O., Sturn, T., Karner, M., McCallum, I., Schepaschenko, D., Moltchanova, E., Fraisl, D., Moorthy, I., & Fritz, S. (2019). Estimating the global distribution of field size using crowdsourcing. Global Change Biology, 25, 174–186.

Leslie, D., Katell, M., Aitken, M., Singh, J., Briggs, M., Powell, R., Rincón, C., Perini, A., & Javadeva, S. (2022). Data Justice in Practice: A Guide for Impacted Communities. The Alan Turing Institute in collaboration with The Global Partnership on AI.

Levenstein, M. (2022, May 18). Discussion on “Issues in data equity”. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Lewis, K., Ellwood, M., & Czajka, J. (1998). Counting the Uninsured: A Review of the Literature. Urban Institute.

Liao, D., Zimmer, S., & Berzofsky, M. (2021). Small Area Estimation for the National Crime Victimization Survey: A Guide for Data Processing and Estimation Procedures. NCJ 300580. Bureau of Justice Statistics. https://www.ojp.gov/library/publications/small-area-estimation-national-crime-victimization-survey-guide-data

Liebler, C. A., Porter, S. R., Fernandez, L. E., Noon, J. M., & Ennis, S. R. (2017). America’s churning races: Race and ethnicity response changes between census 2000 and the 2010 census. Demography, 54(1), 259–284.

Lin, P. J., Daly, A. T., Olchanski. N., Cohen J. T., Neumann, P. J., Faul, J. D., Fillit, H. M., & Freund, K. M. (2021). Dementia diagnosis disparities by race and ethnicity. Medical Care, 59(8), 679–686.

Lloyd, P. C., Helms, V. E., Simon, A. E., Golden, C., Brittain, J., Call, E., Mirel, L. B., Steffen, B. L., Sperling, J., Rudd, E. C., Parker, J. D., & Star, C. S. (2017). Linkage of 1999–2012 National Health Interview Survey and National Health and Nutrition Examination Survey data to US Department of Housing and Urban Development administrative records. Vital and Health Statistics, 1(60), 1–31.

Lohr, S. L. (2019). Measuring Crime: Behind the Statistics. CRC Press.

———. (2021). Multiple-frame surveys for a multiple-data-source world. Survey Methodology, 47(2), 229–263.

———. (2022). Sampling: Design and Analysis (3rd ed.). CRC Press.

Lohr, S. L., & Raghunathan, T. E. (2017). Combining survey data with other data sources. Statistical Science, 32(2), 293–312.

Lukens, G., & Sharer, B. (2021). Closing Medicaid Coverage Gap Would Help Diverse Group and Narrow Racial Disparities. Center on Budget and Policy Priorities. https://www.cbpp.org/sites/default/files/6-10-21health.pdf

Lynch, J. (2018). Not even our own facts: Criminology in the era of big data. Criminology, 56(3), 437–454.

Lyu, F., Yang, Z., Xiao, Z., Diao, C., Park, J., & Wang, S. (2022). CyberGIS for scalable remote sensing data fusion. Proceedings of Practice and Experience in Advanced Research Computing (PEARC’22). Association for Computing Machinery.

Mahajan, P. (2021). Immigration and local business dynamics: Evidence from US firms. U.S. Census Bureau Center for Economic Studies Working Paper 21-18.

Mancuso, D., & Huber, A. (2021). Integrated Client Databases. Washington State Health and Human Services Research and Data Analysis Division. https://www.dshs.wa.gov/sites/default/files/rda/reports/research-11-205.pdf

Mapes, B. M., Foster, C. S., Kusnoor, S. V., Epelbaum, M. I., AuYoung, M., Jenkins, G., Lopex-Class, M., Richardson-Heron, D., Elmi, A., Surkan, K., Cronin, R. M., Wilkins, C. H., Pérez-Stable, E., Dishman, E., Denny, J. C., & Rutter, J. L. (2020). Diversity and inclusion for the All of Us research program: A scoping review. PLoS One, 15(7), e0234962.

Marra, E., & Kennel, T. (2022). Source and Accuracy of the 2020 Post-Enumeration Survey Person Estimates. 2020 Post-Enumeration Survey Methodology Report PES20-J-01. U.S. Government Publishing Office. https://www2.census.gov/programs-surveys/decennial/coverage-measurement/pes/2020-source-and-accuracy-pes-estimates.pdf

Martin, K. (2021). Sexual assaults recorded by law enforcement, 2019. NCJ-301236. Bureau of Justice Statistics. https://bjs.ojp.gov/nibrs/reports/sarble/sarble19

Martínez, R. (2015). Latino Homicide: Immigration, Violence, and Community (2nd ed.). Routledge.

———. (2022, May 16). Panel Discussion: Measuring Crime in the 21st Century. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Mathews, K., Phelan, J., Jones, N. A., Konya, S., Marks, R., Pratt, B. M., Coombs, J., & Bentley, M. (2017). 2015 National Content Test Race and Ethnicity Analysis Report. U.S. Census Bureau. https://www2.census.gov/programs-surveys/decennial/2020/program-management/final-analysis-reports/2015nct-race-ethnicity-analysis.pdf

McClure, D., Santos, R., & Kooragayala, S. (2017). Administrative records in the 2020 US Census: Civil rights considerations and opportunities. Urban Institute Research Report. https://www.urban.org/research/publication/administrative-records-2020-us-census

McGrath-Lone, L., Libuy, N., Etoori, E., Blackburn, R., Gilbert, R., & Harron, K. (2021). Ethnic bias in data linkage. The Lancet Digital Health, 3(6), e339.

Medalia, C., Meyer, B., O’Hara, A., & Wu, D. (2019). Linking survey and administrative data to measure income, inequality, and mobility. International Journal of Population Data Science, 4(1), 1–8.

Meitinger, K. M., & Johnson, T. P. (2020). Power, culture and item nonresponse in social surveys. Understanding Survey Methodology, 169–191. Springer.

Mendez-Costabel, M. (2022, May 16). Layers and beyond: Modern use of connected spatial and nonspatial datasets to unlock insights in R&D. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Meng, X. L. (2018). Statistical paradises and paradoxes in big data (I): Law of large populations, big data paradox, and the 2016 US presidential election. The Annals of Applied Statistics, 12(2), 685–726.

Mercer, A. W., Kreuter, F., Keeter, S., & Stuart, E. A. (2017). Theory and practice in nonprobability surveys: Parallels between causal inference and survey inference, Public Opinion Quarterly, 81(S1), 250–271. https://doi.org/10.1093/poq/nfw060

Mercer, A. W., Lau, A., and Kennedy, C. (2018). For Weighting Online Opt-In Samples, What Matters Most? Pew Research. https://www.census.gov/library/working-papers/2011/adrm/ces-wp-11-14.html

Meyer, B. D., & Mittag, N. (2019). Using linked survey and administrative data to better measure income: Implications for poverty, program effectiveness, and holes in the safety net. American Economic Journal: Applied Economics, 11(2), 176–204.

Meyer, B. D., & Mittag, N. (2021). Combining administrative and survey data to improve income measurement. Administrative Records for Survey Methodology, 297–322. Wiley.

Meyer, B. D., Mok, W. K. C., & Sullivan, J. X. (2015). Household surveys in crisis. Journal of Economic Perspectives, 29(4), 199–226.

Meyer, B. D., Wyse, A., Grunwaldt, A., Medalia, C., & Wu, D. (2021a). Learning about homelessness using linked survey and administrative data. National Bureau of Economic Research Working Paper 28861. https://www.nber.org/papers/w28861

Meyer, B. D., Wu, D., Mooers, V., & Medalia, C. (2021b). The use and misuse of income data and extreme poverty in the United States. Journal of Labor Economics, 39(S1), S5–S58.

Miller, E. A., McCarty, F. A., & Parker, J. D. (2017). Racial and ethnic differences in a linkage with the National Death Index. Ethnicity & Disease, 27(2), 77.

Mirel, L. B. (2022, May 18). Realizing the power of health data through linkages. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Mirel, L. B., Arispe, I., Helms, V., & Cox, C. (2019a). Assessing children’s health in public and assisted housing. Evidence Works: Cases Where Evidence Meaningfully Informed Policy. Policy Center.

Mirel, L. B., Golden, C., Keralis, J. M., Ye, Y., Lloyd, P. C., & Weeks, J. D. (2019b). Evaluating survey report of social security disability benefit receipt using linked National Health Interview Survey and Social Security Administration Data. National Health Statistics Reports 131. https://www.cdc.gov/nchs/data/nhsr/nhsr131-508.pdf

Mirel, L. B., Simon, A. E., Golden, C., Duran, C. R., & Schoendorf, K. C. (2014). Concordance between survey report of Medicaid enrollment and linked Medicaid administrative records in two national studies. National Health Statistics Reports 72. https://www.cdc.gov/nchs/data/nhsr/nhsr072.pdf

Mirel, L. B., Wheatcroft, G., Parker, J. D., & Makuc, D. M. (2012). Health Characteristics of Medicare Traditional Fee-for-Service and Medicare Advantage Enrollees: 1999–2004 National Health and Nutrition Examination Survey Linked to 2007 Medicare Data. National Health Statistics Reports 53. National Center for Health Statistics.

Morgan, R. E., & Thompson, A. (2021). Criminal victimization, 2020. Report NCJ 301775. Bureau of Justice Statistics. https://bjs.ojp.gov/library/publications/criminal-victimization-2020

———. (2022). The nation’s two crime measures, 2011–2020. Report NCJ 303385. Bureau of Justice Statistics. https://bjs.ojp.gov/library/publications/nations-two-crime-measures-2011-2020

Moyer, B. (2021). National Center for Health Statistics initiatives to use private sector data. Presentation to the National Academy of Sciences, Engineering, and Medicine Panel on the Scope, Components, and Key Characteristics of a 21st Century Data Infrastructure. December 16, 2021. https://www.nationalacademies.org/event/12-16-2021/the-scope-components-and-key-characteristics-of-a-21st-century-data-infrastructure-workshop-1b

Mule, T. (2021). Administrative records and the 2020 Census. https://www.census.gov/newsroom/blogs/random-samplings/2021/04/administrative_recor.html

Murphy, S. L., Xu, J., Kochanek, K. D., Curtin, S. C., & Arias, E. (2017). Deaths: Final data for 2015. National Vital Statistics Reports, 66(6), 1–73. https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_06.pdf

Nandram, B., Cruze, N. B., Erciulescu, A. L., & Chen, L. (2022). Bayesian small area models under inequality constraints with benchmarking and double shrinkage. RDD Research Report Number RDD-22-02. U.S. Department of Agriculture.

Narayanan, A., Stern, A., & Macdonald, G. (2021). Spatial Equity Data Tool Technical Appendix. Urban Institute.

National Academies of Science, Engineering, and Medicine (NASEM). (2016a). Modernizing Crime Statistics: Report 1: Defining and Classifying Crime. The National Academies Press.

———. (2016b). Reducing Response Burden in the American Community Survey: Proceedings of a Workshop. The National Academies Press. https://doi.org/10.17226/23639

———. (2017a). Federal Statistics, Multiple Data Sources, and Privacy Protection: Next Steps. The National Academies Press.

———. (2017b). Improving Crop Estimates by Integrating Multiple Data Sources. The National Academies Press.

———. (2017c). Innovations in Federal Statistics: Combining Data Sources While Protecting Privacy. The National Academies Press.

———. (2018). Modernizing Crime Statistics: Report 2: New Systems for Measuring Crime. The National Academies Press.

———. (2020). 2020 Census Data Products: Data Needs and Privacy Considerations: Proceedings of a Workshop. The National Academies Press. https://doi.org/10.17226/25978

———. (2021a). A Satellite Account to Measure the Retail Transformation: Organizational, Conceptual, and Data Foundations. The National Academies Press.

———. (2021b). Principles and Practices for a Federal Statistical Agency: Seventh Edition. The National Academies Press.

———. (2022a). A Vision and Roadmap for Education Statistics. The National Academies Press.

———. (2022b). Improving Consent and Response in Longitudinal Studies of Aging: Proceedings of a Workshop. The National Academies Press.

———. (2022c). Measuring Sex, Gender Identity, and Sexual Orientation. The National Academies Press.

———. (2022d). Modernizing the Consumer Price Index for the 21st Century. The National Academies Press.

———. (2022e). Transparency in Statistical Information for the National Center for Science and Engineering Statistics and All Federal Statistical Agencies. The National Academies Press.

———. (2023). Toward a 21st Century National Data Infrastructure: Mobilizing Information for the Common Good. National Academies Press.

National Archive of Criminal Justice Data. (2022). Resource Guide: Uniform Crime Reporting Program. University of Michigan. https://www.icpsr.umich.edu/web/pages/NACJD/guides/ucr.html

National Research Council. (1995). Modernizing the US Census. The National Academies Press.

———. (2000). Small-Area Estimates of School-Age Children in Poverty: Evaluation of Current Methodology. The National Academies Press.

———. (2003). Planning the 2010 Census: Second Interim Report. The National Academies Press.

———. (2009). Vital Statistics: Summary of a Workshop. The National Academies Press.

Naudé, W., & Vinuesa, R. (2021). Data deprivations, data gaps and digital divides: Lessons from the COVID-19 pandemic. Big Data & Society, 8(2), 1–12.

Nelson, A. H., Jenkins, D., Zanti, S., Katz, M., Berkowitz, E., Burnett, T. C., & Culhane, D. (2020). A Toolkit for Centering Racial Equity Throughout Data Integration. Actionable Intelligence for Social Policy, University of Pennsylvania. https://aisp.upenn.edu/wp-content/uploads/2022/07/AISP-Toolkit_5.27.20.pdf

Newman, C., & Scherpf, E. (2013). Supplemental Nutrition Assistance Program (SNAP) Access at the State and County Levels: Evidence from Texas SNAP Administrative Records and the American Community Survey. Economic Research Report 156. U.S. Department of Agriculture, Economic Research Service.

Nixon, M., Thomas, S. D., Daffern, M., & Ogloff, J. R. (2017). Estimating the risk of crime and victimisation in people with intellectual disability: A data-linkage study. Social Psychiatry and Psychiatric Epidemiology, 52(5), 617–626.

Nkwimi-Tchahou, H., Mohl, C., Reichert, G., & Bédard, F. (2022, May 16). The evolution of the use of satellite and administrative data in estimating mid-season field crop yields at Statistics Canada. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Noon, J. M., Fernandez, L. E., & Porter, S. R. (2019). Response error and the Medicaid undercount in the Current Population Survey. Health Services Research, 54(1), 34–43.

Nwaoha-Brown, F., Eliason, J., Sundukchi, M., Farber, J., & Mattingly, T. (2021). Source and Accuracy Statement for Calendar Year 2020 Data Collection of the Survey of Income and Program Participation (SIPP), Version 1.0. U.S. Census Bureau. https://www2.census.gov/programs-surveys/sipp/tech-documentation/source-accuracy-statements/2020/sipp-2020-SA-02-DEC21.pdf

O’Hara, A., Bee, A., & Mitchell, J. (2016). Preliminary research for replacing or supplementing the income question on the American Community Survey with administrative records. U.S. Census Bureau Center for Administrative Records Research and Applications Memorandum Series 16-7.

Office for Victims of Crime. (2013). Vision 21: Transforming victim services final report. NCJ-239957. Office of Justice Programs, U.S. Department of Justice. https://ovc.ojp.gov/library/publications/vision-21-transforming-victim-services-final-report

Organisation for Economic Co-operation and Development. (2012). Quality framework and guidelines for OECD statistical activities version 2011/1. Publication STD/QFS(2011)1. Paris: OECD. http://www.oecd.org/officialdocuments/displaydocumentpdf/?cote=std/qfs(2011)1&doclanguage=en

Oronce, C. I. A., Scannell, C. A., Kawachi, I., & Tsugawa, Y. (2020). Association between state-level income inequality and COVID-19 cases and mortality in the USA. Journal of General Internal Medicine, 35(9), 2791–2793.

Orvis, K. (2022). Reviewing and revising standards for maintaining, collecting, and presenting data on race and ethnicity. https://www.whitehouse.gov/omb/briefing-room/2022/06/15/reviewing-and-revising-standards-for-maintaining-collecting-and-presenting-federal-data-on-race-and-ethnicity/

Parker, J. D., Kravets, N., & Vaidyanathan, A. (2018). Particulate matter air pollution exposure and heart disease mortality risks by race and ethnicity in the United States: 1997–2009 National Health Interview Survey with mortality follow-up through 2011. Circulation, 137(16), 1688–1697.

Parrish, J. W., Shanahan, M. E., Schnitzer, P. G., Lanier, P., Daniels, J. L., & Marshall, S. W. (2017). Quantifying sources of bias in longitudinal data linkage studies of child abuse and neglect: Measuring impact of outcome specification, linkage error, and partial cohort follow-up. Injury Epidemiology, 4(1), 1–13.

Pattavina, A., Hirschel, D., & Scearbo, M. (2013). Reliability in NIBRS reporting of substance use in incidents of intimate partner violence. Justice Research and Policy, 15(2), 21–42.

Pedace, R., & Bates, N. (2000). Using administrative records to assess earnings reporting error in the Survey of Income and Program Participation. Journal of Economic and Social Measurement, 26(3-4), 173–192.

Peressini, T., McDonald, L., & Hulchanski, J. D. (2010). Towards a strategy for counting the homeless. Finding Home: Policy Options for Addressing Homelessness in Canada, 728–751. Cities Centre Press. https://www.homelesshub.ca/resource/finding-home-policy-options-addressing-homelessness-canada

Perlman, J. (1951). The Continuous Work-History Sample: The first 12 years. Social Security Bulletin, 14(4), 3–10.

Peterson, S., & Will, I. (2021). Source and accuracy statement for the 2020 National Crime Victimization Survey, Version 1.0. National Crime Victimization Survey, [United States], 2020 (ICPSR 38090), 1–24. Inter-university Consortium for Political and Social Research. https://www.icpsr.umich.edu/web/ICPSR/studies/38090

Peterson, S., Toribio, N., Farber, J., & Hornick, D. (2021). Nonresponse Bias Report for the 2020 Household Pulse Survey, Version 1.0. U.S. Census Bureau. https://www2.census.gov/programs-surveys/demo/technical-documentation/hhp/2020_HPS_NR_Bias_Report-final.pdf

Pew Research. (2017). Methods 101: Random Sampling [Video]. https://www.pewresearch.org/methods/2017/05/12/methods-101-video-random-sampling/

Piquero, A. R., Scott, K., Smith, E., & Abraham, E. (2022, October 4). Generating National Estimates of Crime Using NIBRS Data: Understanding the Transition. [Webinar]. https://bjs.ojp.gov/media/video/68371

Planty, M., Banks, D., & Goree, S. (2018). Web-scraping data for official statistics: Examining the periodicity and quality of indicators of crime from law enforcement web sites. Paper presented at the annual conference of the American Association of Public Opinion Research.

Porter, S. R., Liebler, C. A., & Noon, J. M. (2016). An outside view: What observers say about others’ races and Hispanic origins. American Behavioral Scientist, 60(4), 465–497.

President’s Council of Advisors on Science and Technology. (2014). Big Data and Privacy: A Technological Perspective. Executive Office of the President. https://obamawhitehouse.archives.gov/sites/default/files/microsites/ostp/PCAST/pcast_big_data_and_privacy_-_may_2014.pdf

President’s Task Force on Environmental Health Risks and Safety Risks to Children. (2016). Key Federal Programs to Reduce Childhood Lead Exposure and Eliminate Associated Health Impacts. https://ptfceh.niehs.nih.gov/features/assets/files/key_federal_programs_to_reduce_childhood_lead_exposures_and_eliminate_associated_health_impactspresidents_508.pdf

Prevost, R., & Leggieri, C. (1999). Expansion of administrative records uses at the Census Bureau: A long-range research plan. Paper presented at the November 1999 meeting of the Federal Committee on Statistical Methodology.

Pujol, D., & Machanavajjhala, A. (2021). Equity and privacy: More than just a tradeoff. IEEE Security & Privacy, 19(6), 93–97.

Rabin, R. (1989). Warnings unheeded: A history of childhood lead poisoning. American Journal of Public Health, 79(12), 1668–1674.

Radin, J. M., Wineinger, N. E., Topol, E. J., & Steinhubl, S. R. (2020). Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: A population-based study. The Lancet Digital Health, 2(2), e85–e93.

Raghunathan, T., Ghosh, K., Rosen, A., Imbriano, P., Stewart, S., Bondarenko, I., Messer, K., Berglund, P., Shaffer, J., & Cutler, D. (2021). Combining information from multiple data sources to assess population health. Journal of Survey Statistics and Methodology, 9(3), 598–625.

Ramirez, R., & Borman, C. (2021). How we complete the census when demographic and housing characteristics are missing. https://www.census.gov/newsroom/blogs/random-samplings/2021/08/census-when-demographic-and-housing-characteristics-are-missing.html

Randall, M., Stern, A., & Su, Y. (2021). Five Ethical Risks to Consider Before Filling Missing Race and Ethnicity Data. Urban Institute.

Randall, S., Brown, A., Boyd, J., Schnell, R., Borgs, C., & Ferrante, A. (2018). Sociodemographic differences in linkage error: An examination of four large-scale datasets. BMC Health Services Research, 18(1), 1–9.

Rao, J. N. K. (2021). On making valid inferences by combining data from surveys and other sources. Sankhyā B: The Indian Journal of Statistics, 83(1), 242–272.

Rao, J. N. K., & Molina, I. (2015). Small Area Estimation. John Wiley & Sons.

Rässler, S., & Riphahn, R.T. (2006). Survey item nonresponse and its treatment. Allgemeines Statistisches Archiv, 90(1), 217–232.

Ratcliffe, M. (2021a). Frames Program Overview. Presentation to the Census Bureau Scientific Advisory Committee. U.S. Census Bureau. https://www2.census.gov/about/partners/cac/sac/meetings/2021-03/presentation-frames-project.pdf

———. (2021b). Frames Program Update. Presentation to the Census Bureau Scientific Advisory Committee. U.S. Census Bureau. https://www2.census.gov/about/partners/cac/sac/meetings/2021-09/presentation-frames-program-update.pdf

Regoeczi, W. C., & Banks, D. (2014). The nation’s two measures of homicide. Report NCJ 247060, Bureau of Justice Statistics. https://www.bjs.gov/content/pub/pdf/ntmh.pdf

Reichert, G., Bédard, F., Mohl, C., Benjamin, W., Jiongo, V. D., Chipanshi, A., & Zhang, Y. (2016). Canada—crop yield modelling using remote sensing, agroclimatic data, and statistical survey data. Proceedings of the Seventh International Conference on Agricultural Statistics (ICAS-VII). Food and Agriculture Organization. https://www.istat.it/storage/icas2016/g43-reichert.pdf

Reiter, J. P. (2021). Assessing uncertainty when using linked administrative records. Administrative Records for Survey Methodology, 139–153. John Wiley & Sons.

Roberts, T., & Hernandez, K. (2021). Open Data for Agriculture and Nutrition: A Literature Review and Proposed Conceptual Framework. Institute of Development Studies Working Paper 545. https://ieeexplore.ieee.org/document/9592826

Robinson, S., & Willyard, K. A. (2021). Small Area Health Insurance Estimates: 2019. Current Population Reports P30-09. U.S. Census Bureau. https://www.census.gov/content/dam/Census/library/publications/2021/demo/p30-09.pdf

Rogers, R. G., Hummer, R. A., & Everett, B. G. (2013). Educational differentials in US adult mortality: An examination of mediating factors. Social Science Research, 42(2), 465–481.

Rose Li & Associates. (2016). Expert Meeting on the Potential Value of Centers for Medicare and Medicaid Services Data as a Resource for National Institute on Aging Studies. https://www.nia.nih.gov/sites/default/files/d7/2016-05-04_nas_cms_meeting_summary_rla.pdf

———. (2019). Expert Meeting on the Demography of the Older Residential Care Population: Research Questions and Data Gaps. https://www.nia.nih.gov/sites/default/files/2019-12/Expert-Mtg-Demography-Older-Res-Care-Final-508.pdf

Rosenfeld, R. (2022). Was crime up or down in 2021? Why the FBI can’t tell us. The Crime Report (October 11). https://thecrimereport.org/2022/10/11/was-crime-up-or-down-in-2021-why-the-fbi-cant-tell-us/

Rosenfeld, R., & Lopez, E. (2022). Pandemic, Social Unrest, and Crime in US Cities: 2021 year-end Update. Council on Criminal Justice. https://counciloncj.org/crime-trends-year-end-2021-update/

Rothbard, A. (2013). Quality Issues in the Use of Administrative Data Records. Actionable Intelligence for Social Policy, University of Pennsylvania. https://aisp.upenn.edu/wp-content/uploads/2015/06/Data-Quality-Paper_Final.pdf

Rothbaum, J. (2019). Using Administrative Records to Improve Income and Resource Estimates. Presentation to the Interagency Technical Working Group on Evaluating Alternative Measures of Poverty. Unpublished manuscript.

———. (2022, May 18). National Experimental Wellbeing Statistics (NEWS). Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Rothbaum, J., & Bee, A. (2021). Addressing nonresponse bias in household surveys using linked administrative data. https://www.aeaweb.org/conference/2022/preliminary/paper/H3e9AQh9

Rothbaum, J., Eggleston, J., Bee, A., Klee, M., & Mendez-Smith B. (2021). Addressing nonresponse bias in the American Community Survey during the pandemic using administrative data. U.S. Census Bureau Working Paper SEHSD-2021-24. https://www.census.gov/library/working-papers/2021/acs/2021_Rothbaum_01.html

Rothwell, C. J., Freedman, M. A., & Weed, J. A. (2014). The National Vital Statistics System. Public Health Informatics and Information Systems, 309–327. Springer.

Ryan, M. (2019). Ethics of using AI and big data in agriculture: The case of a large agriculture multinational. The ORBIT Journal, 2(2), 1–27.

Sakshaug, J. W., & Antoni, M. (2019). Evaluating the utility of indirectly linked federal administrative records for nonresponse bias adjustment. Journal of Survey Statistics and Methodology, 7(2), 227–249.

Salvo, J. (2022, May 16). Better data for local decisions: The promise of the curated data enterprise. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Santos, R. (2022, May 16). Census Bureau modernization: A new vision for an enterprise approach to statistical data. Keynote address at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Saralioglu, E., & Gungor, O. (2020). Crowdsourcing in remote sensing: A review of applications and future directions. IEEE Geoscience and Remote Sensing Magazine, 8(4), 89–110. https://doi.org/10.1109/MGRS.2020.2975132

Schleimer, J. P., Pear, V. A., McCort, C. D., Shev, A. B., De Biasi, A., Tomsich, E., Buggs, S., Laqueur, H. S., & Wintemute, G. J. (2022). Unemployment and crime in US cities during the coronavirus pandemic. Journal of Urban Health, 99(1), 82–91.

Schnepf, R. (2017). NASS and US Crop Production Forecasts: Methods and Issues. Congressional Research Service Report 44814. Congressional Research Service.

Schor, E. L., & Johnson, K. (2021). Child health inequities among state Medicaid programs. JAMA Pediatrics, 175(8), 775–776.

Seeskin, Z. H., Ugarte, G., & Datta, A. R. (2019). Constructing a toolkit to evaluate quality of state and local administrative data. International Journal of Population Data Science, 4(1).

Sehra, S. T., George, M., Wiebe, D. J., Fundin, S., & Baker, J. F. (2020). Cell phone activity in categories of places and associations with growth in cases of COVID-19 in the US. Journal of the American Medical Association Internal Medicine, 180(12), 1614–1620.

Shantz, K., & Fox, L. E. (2018). Precision in measurement: Using state-level Supplemental Nutrition Assistance Program and Temporary Assistance for Needy Families administrative records and the Transfer Income Model (TRIM3) to evaluate poverty measurement. U.S. Census Bureau SEHSD Working Paper 2018-30.

Shapiro, M. (2021). Panel Discussion: Lessons learned, opportunities, challenges, and next steps. Presentation to the National Academy of Sciences, Engineering, and Medicine Panel on the Scope, Components, and Key Characteristics of a 21st Century Data Infrastructure. December 9, 2021. https://www.nationalacademies.org/event/12-09-2021/the-scope-components-and-key-characteristics-of-a-21st-century-data-infrastructure-workshop-1a

Shrider, E. A., Kollar, M., Chen, F., & Semega, J. (2021). Income and Poverty in the United States: 2020. U.S. Census Bureau Current Population Reports P60-273. U.S. Government Publishing Office. https://www.census.gov/content/dam/Census/library/publications/2021/demo/p60-273.pdf

Sikstrom, L., Maslej, M. M., Hui, K., Findlay, Z., Buchman, D. Z., & Hill, S. L. (2022). Conceptualising fairness: Three pillars for medical algorithms and health equity. BMJ Health & Care Informatics, 29(100459), 1–11. https://doi.org/10.1136/bmjhci-2021-100459

Simon, A. E., Fenelon, A., Helms, V., Lloyd, P. C., & Rossen, L. M. (2017). HUD housing assistance associated with lower uninsurance rates and unmet medical need. Health Affairs, 36(6), 1016–1023.

Singh, L., Traugott, M., Bode, L., Budak, C., Davis-Kean, P. E., Guha, R., Ladd, J., Mneimneh, Z., Nguyen, Q., Pasek, J., Raghunathan, T., Ryan, R., Soroka, S., & Wahedi, L. (2020). Data Blending: Haven’t We Been Doing This for Years? Georgetown University Massive Data Institute. https://www.jonathanmladd.com/uploads/5/3/6/6/5366295/mdi_data_blending_white_paper_-_april2020.pdf

Skinner, C., & Wakefield, J. (2017). Introduction to the design and analysis of complex survey data. Statistical Science, 32(2), 165–175.

Smith, C. (1989). The Social Security Administration’s continuous work history sample. Social Security Bulletin, 52(10), 20–28.

Smith, E. (2017). Estimating costs for transitioning to the National Incident-Based Reporting System (NIBRS): Guidance for local law-enforcement agencies. https://bjs.ojp.gov/sites/g/files/xyckuh236/files/media/document/local_agency_-_estimating_cost_for_transitioning_to_nibrs_01232017.pdf

———. (2022, May 16). Panel Discussion: Measuring crime in the 21st century. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Smith, E., Martin, K., Barrick, K., & Richardson, N. (2018). Research in brief: Leveraging NIBRS to better understand sexual violence. Police Chief Magazine. https://www.policechiefmagazine.org/rib-leveraging-nibrs-sexual-violence/?ref=7430fa6083fd228a6e33ef329bada5

SNACC. (2007). Phase I Research Results: Overview of National Medicare and Medicaid Files. State Health Access Data Assistance Center. https://www.census.gov/library/working-papers/2007/adrm/snacc-phase-1.html

———. (2010). Phase V Research Results: Extending the Phase II Analysis of Discrepancies between the National Medicaid Statistical Information System (MSIS) and the Current Population Survey (CPS) Annual Social and Economic Supplement (ASEC) from Calendar Years 2000–2001 to Calendar Years 2002–2005. Minneapolis: State Health Access Data Assistance Center. https://www.census.gov/library/working-papers/2010/adrm/snacc-phase-5.html

Sonnega, A. (2017). Aging in the 21st Century: Challenges and Opportunities for Americans. University of Michigan. https://hrsonline.isr.umich.edu/sitedocs/databook/inc/pdf/HRS-Aging-in-the-21St-Century.pdf

Sonnega, A., Faul, J. D., Ofstedal, M. B., Langa, K. M., Phillips, J. W., & Weir, D. R. (2014). Cohort profile: The Health and Retirement Study (HRS). International Journal of Epidemiology, 43(2), 576–585.

Sourav, A. I., & Emanuel, A. W. R. (2021). Recent trends of big data in precision agriculture: A review. IOP Conference Series: Materials Science and Engineering, 1096, 012081.

Statistics Canada. (2019). Statistics Canada Quality Guidelines (6th ed.). https://www150.statcan.gc.ca/n1/pub/12-539-x/12-539-x2019001-eng.htm

———. (2020a). An Integrated Crop Yield Model Using Remote Sensing, Agroclimatic Data and Crop Insurance Data. https://www.statcan.gc.ca/en/statistical-programs/document/3401_D2_V1

———. (2020b). Integrated Crop Yield Modelling Using Remote Sensing, Agroclimatic Data and Survey Data. https://www.statcan.gc.ca/en/statistical-programs/document/5225_D1_T9_V1

———. (2022). Development of a Composite Quality Indicator for Statistical Products Derived from Administrative Sources. https://www150.statcan.gc.ca/n1/pub/46-280001/2022001/article/00001-eng.htm

Stock, R., & Gardezi, M. (2022). Arrays and algorithms: Emerging regimes of dispossession at the frontiers of agrarian technological governance. Earth System Governance, 12(April), 100137. https://www.sciencedirect.com/science/article/pii/S2589811622000064#!

Stubbs, M. (2016). Big Data in US Agriculture. Congressional Research Service Report R-44331. https://fas.org/sgp/crs/misc/R44331.pdf

Stucky, T. D., Payton, S. B., & Ottensmann, J. R. (2016). Intra- and inter-neighborhood income inequality and crime. Journal of Crime and Justice, 39(3), 345–362.

Studds, S. (2021, December 9). Blended data in the Census Bureau’s Monthly State Retail Sales Data Product. Presentation to the National Academy of Sciences, Engineering, and Medicine Panel on the Scope, Components, and Key Characteristics of a 21st Century Data Infrastructure. https://www.nationalacademies.org/event/12-09-2021/the-scope-components-and-key-characteristics-of-a-21st-century-data-infrastructure-workshop-1a

Swaine, J., & McCarthy, C. (2016, December 15). Killings by US police logged at twice the previous rate under new federal program. The Guardian. https://www.theguardian.com/us-news/2016/dec/15/us-police-killings-department-of-justice-program

———. (2017, January 8). Young black men again faced highest rate of US police killings in 2016. The Guardian. https://www.theguardian.com/us-news/2017/jan/08/the-counted-police-killings-2016-young-black-men

Tran, H. N., Gerling, M. W., Mitchell, M., & O’Connor, T. P. (2010). Data tables: Reasons for nonresponse in the 2009 June Area Survey. National Agricultural Research Service RDD Research Report RDD-10-04A. U.S. Department of Agriculture.

Truman, J. L., & Brotsos, H. (2022). Update on the NCVS instrument redesign. Report NCJ 304055. Bureau of Justice Statistics. https://bjs.ojp.gov/content/pub/pdf/uncvsir_sum.pdf

Truman, J. L., & Morgan, R. E. (2022). Violent victimization by sexual orientation and gender identity, 2017–2020. Report NCJ 304277. Bureau of Justice Statistics. https://bjs.ojp.gov/library/publications/violent-victimization-sexual-orientation-and-gender-identity-2017-2020

Turner, A. G. (1983). Research on dual-frame sampling. The National Crime Survey: Working Papers, Vol. 2: Methodological Studies. NCJ-90307, 120. Bureau of Justice Statistics. https://www.ojp.gov/pdffiles1/nij/90307.pdf

Uhl, S. (2011). Building and maintaining the Master Address File. https://www2.census.gov/geo/pdfs/education/Uhl_CAS_2011.pdf

United Kingdom Statistics Authority. (2019). Administrative Data Quality Assurance Toolkit. https://osr.statisticsauthority.gov.uk/wp-content/uploads/2019/02/qualityassurancetoolkitupdatedFeb192.pdf

United Nations. (2019). United Nations National Quality Assurance Frameworks Manual for Official Statistics. United Nations. https://desapublications.un.org/publications/united-nations-quality-assurance-frameworks-manual

United Nations Economic and Social Council. (2019). In-depth Review of Satellite Imagery/Earth Observation Technology in Official Statistics. United Nations. https://unece.org/DAM/stats/documents/ece/ces/2019/ECE_CES_2019_16-1906490E.pdf

United Nations Inter-Secretariat Working Group on Household Surveys. (2022). Positioning Household Surveys for the Next Decade. United Nations Statistical Division. https://unstats.un.org/unsd/statcom/53rd-session/documents/BG-3a-Position-Household-Survey-for-Next-Decade-E.pdf

U.S. Bureau of the Census. (1947a). Estimated population of the United States, by regions, divisions, and states: July 1, 1946. Current Population Reports, Series P-25, No. 2.

———. (1947b). Suggested procedures for estimating the current population of counties. Population–Special Reports, Series P-47, No. 4.

———. (1967). Estimates of the population of standard metropolitan statistical areas, July 1, 1965. Current Population Reports, Series P-25, No. 371.

———. (1968). Estimates of the population of counties, July 1, 1966. Current Population Reports, Series P-25, No. 401.

———. (1979). The Standard Statistical Establishment List Program. Technical Paper 44.

U.S. Bureau of Justice Statistics (BJS). (2021a). National Crime Statistics Exchange (NCS-X). https://bjs.ojp.gov/programs/national-crime-statistics-exchange

———. (2021b). National Crime Victimization Survey, [United States], 2020. ICPSR 30890. Inter-university Consortium for Political and Social Research.

———. (2022a). NCVS Subnational Estimates. https://bjs.ojp.gov/subnational-estimates-program

———. (2022b). NIBRS Estimation Summary. Report NCJ 305107. https://bjs.ojp.gov/library/publications/nibrs-estimation-summary.

U.S. Census Bureau. (2002). Measuring America: The Decennial Censuses From 1790 to 2000. Report POL/02-MA. U.S. Department of Commerce. https://www.census.gov/library/publications/2002/dec/pol_02-ma.html

———. (2014). American Community Survey Design and Methodology. https://www.census.gov/programs-surveys/acs/methodology/design-and-methodology.html

———. (2019). Current Population Survey Design and Methodology. Technical Paper 77. https://www.census.gov/programs-surveys/cps/technical-documentation/complete.html

———. (2020a). Your Guide to the 2020 Census: How to Respond to the 2020 Census Paper Questionnaire. https://www2.census.gov/programs-surveys/decennial/2020/resources/language-materials/guides/English-Guide.pdf

———. (2020b). 2020 Census: Our Mission to Count Everyone. https://www.test.census.gov/content/dam/Census/library/factsheets/2020/dec/mission-count-everyone/mission-count-everyone.pdf

———. (2021a). 2010–2020 County-Level Estimation Details. https://www.census.gov/programs-surveys/saipe/technical-documentation/methodology.html

———. (2021b). 2020 Survey of Income and Program Participation Users’ Guide. https://www2.census.gov/programs-surveys/sipp/tech-documentation/methodology/2020_SIPP_Users_Guide_OCT21.pdf

———. (2021c). Disclosure Avoidance for the 2020 Census: An Introduction. https://www2.census.gov/library/publications/decennial/2020/2020-census-disclosure-avoidance-handbook.pdf

———. (2021d). Monthly State Retail Sales Technical Documentation. https://www.census.gov/retail/mrts/www/statedata/msrs_technical_documentation.pdf

———. (2021e). US Decennial Census Measurement of Race and Ethnicity Across the Decades: 1790-2020 [Infographic]. https://www.census.gov/library/visualizations/interactive/decennial-census-measurement-of-race-and-ethnicity-across-the-decades-1790-2020.html

———. (2022a). Research to improve data on race and ethnicity. https://www.census.gov/about/our-research/race-ethnicity.html

———. (2022b). Source of the Data and Accuracy of the Estimates for the Household Pulse Survey–Phase 3.5. https://www.census.gov/programs-surveys/household-pulse-survey/technical-documentation.html

U.S. Centers for Disease Control and Prevention (CDC). (2021a, December 22). Data Modernization Initiative Strategic Implementation Plan. https://www.cdc.gov/surveillance/pdfs/FINAL-DMI-Implementation-Strategic-Plan-12-22-21.pdf

———. (2021b). US influenza surveillance: Purpose and methods. https://www.cdc.gov/flu/weekly/overview.htm

———. (2022). National Violent Death Reporting System (NVDRS). https://www.cdc.gov/violenceprevention/datasources/nvdrs/

U.S. Commission on Evidence-Based Policymaking. (2017). The Promise of Evidence-Based Policymaking.

U.S. Congress (1845). Public Statutes at Large of the United States of America from the Organization of Government in 1789, to March 3, 1845. https://tile.loc.gov/storage-services/service/ll/llsl/llsl-c1/llsl-c1.pdf

———. (1917). The Statutes at Large of the United States of America from December, 1915, to March, 1917. https://www.loc.gov/item/llsl-v39/

———. (1992). The Residential Lead-Based Paint Hazard Reduction Act of 1992. https://www.congress.gov/102/statute/STATUTE-106/STATUTE-106-Pg3672.pdf

———. (1994). Census Address List Improvement Act of 1994. https://www.congress.gov/103/statute/STATUTE-108/STATUTE-108-Pg4393.pdf

———. (2016). Evidence-Based Policymaking Commission Act of 2016. https://www.congress.gov/114/statute/STATUTE-130/STATUTE-130-Pg317.pdf

———. (2019). Foundations for Evidence-Based Policy Making Act of 2018. P.L. 115-435. https://www.congress.gov/115/plaws/publ435/PLAW-115publ435.pdf

U.S. Department of Agriculture. (1969). The story of US agricultural estimates. Statistical Reporting Service Miscellaneous Publication No. 1088. https://www.nass.usda.gov/About_NASS/pdf/The%20Story%20of%20US%20Agricultural%20Estimates.pdf

———. (2019). 2017 Census of Agriculture: Appendix A. https://www.nass.usda.gov/Publications/AgCensus/2017/Full_Report/Volume_1,_Chapter_1_US/usappxa.pdf

U.S. Department of Housing and Urban Development (HUD). (2021). American Healthy Homes Survey II Lead Findings. https://www.hud.gov/sites/dfiles/HH/documents/AHHS_II_Lead_Findings_Report_Final_29oct21.pdf

U.S. Environmental Protection Agency (EPA). (2022). Biomonitoring—Lead, in America’s Children and the Environment. https://www.epa.gov/americaschildrenenvironment/biomonitoring-lead

U.S. Federal Bureau of Investigation (FBI). (2013). Summary Reporting System (SRS) User Manual, Version 1.0. U.S. Department of Justice. https://le.fbi.gov/file-repository/summary-reporting-system-user-manual.pdf/view

———. (2021). 2021.1 National Incident-Based Reporting System User Manual. April 15, 2021 version. https://www.fbi.gov/file-repository/ucr/ucr-2019-1-nibrs-user-manua-093020.pdf

———. (2022a). NIBRS Estimation FAQs. https://cde.ucr.cjis.gov

———. (2022b). The Transition to the National Incident-Based Reporting System (NIBRS): A Comparison of 2020 and 2021 NIBRS Estimates.

U.S. Federal Trade Commission. (2022). Consumer Sentinel Network Data Book 2021. https://www.ftc.gov/reports/consumer-sentinel-network-data-book-2021

U.S. Government Accountability Office. (2016). Sexual Violence Data: Actions Needed to Improve Clarity and Address Differences Across Federal Data Collection Efforts. GAO-16-546. https://www.gao.gov/assets/680/678511.pdf

———. (2020). 2020 Census: The Bureau Concluded Field Work but Uncertainty about Data Quality, Accuracy, and Protection Remains. GAO Report 21-206R. https://www.gao.gov/assets/gao-21-206r.pdf

U.S. Internal Revenue Service (IRS). (2019). Federal Tax Compliance Research: Tax Gap Estimates for Tax Years 2011–2013. Publication 1415 (Rev. 9-2019). https://www.irs.gov/pub/irs-pdf/p1415.pdf

U.S. National Agricultural Statistics Service (NASS). (2012). The Yield Forecasting Program of NASS. Statistical Methods Branch Staff Report Number SMB 12-01. https://www.nass.usda.gov/Publications/Methodology_and_Data_Quality/Advanced_Topics/Yield%20Forecasting%20Program%20of%20NASS.pdf

———. (2022). Annual Crop Production Methodology and Quality Measures. https://www.nass.usda.gov/Publications/Methodology_and_Data_Quality/Crop_Production/01_2022/cranqm22.pdf

US National Center for Health Statistics (NCHS). (2009). The National Health Interview Survey (1986-2004) Linked Mortality Files, Mortality Follow-up through 2006: Matching Methodology. https://www.cdc.gov/nchs/data/datalinkage/matching_methodology_nhis_final.pdf

———. (2016). The Linkage of National Center for Health Statistics Surveys to Medicare Enrollment and Claims Data (1999–2013)—Methodology and Analytic Considerations. https://www.cdc.gov/nchs/data-linkage/cms/nchs_medicare_linkage_methodology_and_analytic_considerations.pdf

———. (2020a). National Health and Nutrition Examination Survey. NCHS Fact Sheet. https://www.cdc.gov/nchs/data/factsheets/factsheet_nhanes.pdf

———. (2020b). National Health Interview Survey. NCHS Fact Sheet. https://www.cdc.gov/nchs/data/factsheets/factsheet_nhis.pdf

———. (2021a). National Vital Statistics System. NCHS Fact Sheet. https://www.cdc.gov/nchs/data/factsheets/factsheet_NVSS.pdf

———. (2021b). National Vital Statistics System Improvements. https://www.cdc.gov/nchs/data/factsheets/2020-NVSS-improvement-factsheet-508.pdf

———. (2021c). The Linkage of National Center for Health Statistics Survey Data to Medicare Enrollment, Claims/Encounters and Assessment data (2014-2018): Linkage Methodology and Analytic Considerations. https://www.cdc.gov/nchs/data-linkage/cms/nchs_medicare14_18_linkage_methodology_and_analytic_considerations.pdf

———. (2022a). About the National Death Index. https://www.cdc.gov/nchs/ndi/about.htm

———. (2022b). National Health Interview Survey, 2021: Survey Description. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2021/srvydesc-508.pdf

———. (2022c). The Linkage of the National Center for Health Statistics (NCHS) Survey Data to US Department of Housing and Urban Development (HUD) Administrative Data: Linkage Methodology and Analytic Considerations. https://www.cdc.gov/nchs/data/datalinkage/NCHS-HUD-Linked-Data-Methodology-and-Analytic-Considerations.pdf

———. (2022d). The Linkage of National Center for Health Statistics Survey Data to the National Death Index — 2019 Linked Mortality File (LMF): Linkage Methodology and Analytic Considerations. https://www.cdc.gov/nchs/data/datalinkage/2019ndi-linkage-methods-and-analytic-considerations-508.pdf

U.S. Office of Management and Budget (OMB). (1997). Revisions to the standards for the classification of federal data on race and ethnicity. Federal Register, 62(210), 58782–58790. https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-28653.pdf

———. (2002). Guidelines for ensuring and maximizing the quality, objectivity, utility, and integrity of information disseminated by federal agencies. Federal Register, 67(36), 8452–8460. https://www.govinfo.gov/content/pkg/FR-2002-02-22/pdf/R2-59.pdf

———. (2006). Standards and Guidelines for Statistical Surveys. https://obamawhitehouse.archives.gov/sites/default/files/omb/inforeg/statpolicy/standards_stat_surveys.pdf

———. (2014). Statistical Policy Directive No. 1: Fundamental Responsibilities of Federal Statistical Agencies and Recognized Statistical Units. Federal Register, 79(231), 71610–71616. https://www.govinfo.gov/content/pkg/FR-2014-12-02/pdf/2014-28326.pdf

———. (2016). Standards for maintaining, collecting, and presenting federal data on race and ethnicity. Federal Register, 81(190), 67398–67401. https://www.govinfo.gov/content/pkg/FR-2016-09-30/pdf/2016-23672.pdf

———. (2019a). Federal Data Strategy—A Framework for Consistency. OMB Memorandum M-19-18. https://www.whitehouse.gov/wp-content/uploads/2019/06/M-19-18.pdf

———. (2019b). Improving Implementation of the Information Quality Act. OMB Memorandum M-19-15. https://www.cdo.gov/assets/documents/OMB-Improving-Implementation-of-Info-Quality-Act-M-19-15.pdf

———. (2021). Evidence-Based Policymaking: Learning Agendas and Annual Evaluation Plans. OMB Memorandum M-21-27. https://www.whitehouse.gov/wp-content/uploads/2021/06/M-21-27.pdf

Urban Institute. (2021a). Introducing the Spatial Equity Data Tool Version 2. https://urban-institute.medium.com/introducing-the-spatial-equity-data-tool-version-2-f2a8e900f84

———. (2021b). Spatial Equity Data Tool. https://apps.urban.org/features/equity-data-tool/

Van Buuren, S. (2018). Flexible Imputation of Missing Data. Boca Raton, FL: CRC Press.

Veitenheimer, D. (2022, May 16). Panel Discussion: Measuring crime in the 21st century. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Wadsworth, T., & Roberts, J. M. (2008). When missing data are not missing: A new approach to evaluating Supplemental Homicide Report imputation strategies. Criminology, 46(4), 841–870.

Wagner, D., & Layne, M. (2014). The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software. U.S. Census Bureau CARRA Working Paper 2014-01. https://www.census.gov/content/dam/Census/library/working-papers/2014/adrm/carra-wp-2014-01.pdf

Wang, S. (2016). CyberGIS and spatial data science. GeoJournal, 81(6), 965–968.

Wang, S., & Goodchild, M. F. (2019). CyberGIS for Geospatial Innovation and Discovery. Springer.

Wardell, C. (2022, May 18). Fireside chat on data equity. National Academies of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Weir, D., Faul, J., & Langa, K. (2011). Proxy interviews and bias in the distribution of cognitive abilities due to non-response in longitudinal studies: A comparison of HRS and ELSA. Longitudinal and Life Course Studies, 2(2), 170–184.

Williams, B. A., Brooks, C. F., & Shmargad, Y. (2018). How algorithms discriminate based on data they lack: Challenges, solutions, and policy implications. Journal of Information Policy, 8(March), 78–115.

Williams, D., & Brick, J. M. (2018). Trends in US face-to-face household surveys and level of effort. Journal of Survey Statistics and Methodology, 6(2), 186–211.

Wu, C. (2022). Statistical inference with non-probability survey samples. Survey Methodology, 48(2), 283–311.

Xu, J., Murphy, S. L., Kochanek, K., & Arias, E. (2021). Deaths: Final Data for 2019. National Center for Health Statistics. https://stacks.cdc.gov/view/cdc/106058

Young, L. J. (2022, May 16). The Crops County Estimates program: Developing official statistics based on available data. Presentation at the National Academy of Sciences, Engineering, and Medicine Workshop on the Implications of Using Multiple Data Sources for Major Survey Programs. https://www.nationalacademies.org/event/05-16-2022/the-implications-of-using-multiple-data-sources-for-major-survey-programs-workshop

Young, L. J., & Chen, L. (2022). Using small area estimation to produce official statistics. Stats 5, 881–897.

Young L. J., Hyman, M., & Rater, B. R. (2018). Exploring a big data approach to building a list frame for urban agriculture: A pilot study in the City of Baltimore. Journal of Official Statistics, 34(2), 323–340.

Young, L. J., & Jacobsen, M. (2022). Sample design and estimation when using a web-scraped list frame and capture-recapture methods. Journal of Agricultural, Biological and Environmental Statistics, 27, 261–279.

Zablotsky, B., & Black, L. I. (2019). Concordance between survey reported childhood asthma and linked Medicaid administrative records. Journal of Asthma, 56(3), 285–295.

Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23(100224), 1–11.

Zook, M., Barocas, S., Boyd, D., Crawford, K., Keller, E., Gangadharan, S. P., Goodman, A., Hollander, R., Koenig, B. A., Metcalf, J., Narayanan, A., Nelson, A., & Pasquale, F. (2017). Ten simple rules for responsible big data research. PLoS Computational Biology, 13(3), e1005399.