Determination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report
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- Chung H. & Loken E. & Schafer J.L., 2004. "Difficulties in Drawing Inferences With Finite-Mixture Models: A Simple Example With a Simple Solution," The American Statistician, American Statistical Association, vol. 58, pages 152-158, May.
- Zsuzsa Bakk & Jouni Kuha, 2018. "Two-Step Estimation of Models Between Latent Classes and External Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 871-892, December.
- Bakk, Zsuzsa & Kuha, Jouni, 2018. "Two-step estimation of models between latent classes and external variables," LSE Research Online Documents on Economics 85161, London School of Economics and Political Science, LSE Library.
- Frauke Kreuter & Ting Yan & Roger Tourangeau, 2008. "Good item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 723-738, June.
- Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
- Paul P. Biemer & Christopher Wiesen, 2002. "Measurement error evaluation of self‐reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 97-119, February.
- Mary Layne & Deborah Wagner & Cynthia Rothhaas, 2014. "Estimating Record Linkage False Match Rate for the Person Identification Validation System," CARRA Working Papers 2014-02, Center for Economic Studies, U.S. Census Bureau.
- Laura McKenna, 2018. "Disclosure Avoidance Techniques Used for the 1970 through 2010 Decennial Censuses of Population and Housing," Working Papers 18-47, Center for Economic Studies, U.S. Census Bureau.
- J. David Brown & Misty L. Heggeness & Suzanne M. Dorinski & Lawrence Warren & Moises Yi, 2018. "Understanding the Quality of Alternative Citizenship Data Sources for the 2020 Census," Working Papers 18-38, Center for Economic Studies, U.S. Census Bureau.
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Cited by:
- Meyer, Bruce D. & Wyse, Angela & Corinth, Kevin, 2023. "The size and Census coverage of the U.S. homeless population," Journal of Urban Economics, Elsevier, vol. 136(C).
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More about this item
Keywords
citizenship; administrative records; voting-age population; big data;All these keywords.
JEL classification:
- J1 - Labor and Demographic Economics - - Demographic Economics
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
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