TY - RPRT T1 - Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System? Y1 - 2017 A1 - Weinberg, Daniel A1 - Abowd, John M. A1 - Belli, Robert F. A1 - Cressie, Noel A1 - Folch, David C. A1 - Holan, Scott H. A1 - Levenstein, Margaret C. A1 - Olson, Kristen M. A1 - Reiter, Jerome P. A1 - Shapiro, Matthew D. A1 - Smyth, Jolene A1 - Soh, Leen-Kiat A1 - Spencer, Bruce A1 - Spielman, Seth E. A1 - Vilhuber, Lars A1 - Wikle, Christopher AB -

Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Secure the Future of the Federal Statistical System? Weinberg, Daniel; Abowd, John M.; Belli, Robert F.; Cressie, Noel; Folch, David C.; Holan, Scott H.; Levenstein, Margaret C.; Olson, Kristen M.; Reiter, Jerome P.; Shapiro, Matthew D.; Smyth, Jolene; Soh, Leen-Kiat; Spencer, Bruce; Spielman, Seth E.; Vilhuber, Lars; Wikle, Christopher The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN’s research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives. This paper began as a May 8, 2015 presentation to the National Academies of Science’s Committee on National Statistics by two of the principal investigators of the National Science Foundation-Census Bureau Research Network (NCRN) – John Abowd and the late Steve Fienberg (Carnegie Mellon University). The authors acknowledge the contributions of the other principal investigators of the NCRN who are not co-authors of the paper (William Block, William Eddy, Alan Karr, Charles Manski, Nicholas Nagle, and Rebecca Nugent), the co- principal investigators, and the comments of Patrick Cantwell, Constance Citro, Adam Eck, Brian Harris-Kojetin, and Eloise Parker. We note with sorrow the deaths of Stephen Fienberg and Allan McCutcheon, two of the original NCRN principal investigators. The principal investigators also wish to acknowledge Cheryl Eavey’s sterling grant administration on behalf of the NSF. The conclusions reached in this paper are not the responsibility of the National Science Foundation (NSF), the Census Bureau, or any of the institutions to which the authors belong

PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/52650 ER - TY - JOUR T1 - Releasing synthetic magnitude micro data constrained to fixed marginal totals JF - Statistical Journal of the International Association for Official Statistics Y1 - 2016 A1 - Wei, Lan A1 - Reiter, Jerome P. KW - Confidential KW - Disclosure KW - establishment KW - mixture KW - poisson KW - risk AB - We present approaches to generating synthetic microdata for multivariate data that take on non-negative integer values, such as magnitude data in economic surveys. The basic idea is to estimate a mixture of Poisson distributions to describe the multivariate distribution, and release draws from the posterior predictive distribution of the model. We develop approaches that guarantee the synthetic data sum to marginal totals computed from the original data, as well approaches that do not enforce this equality. For both cases, we present methods for assessing disclosure risks inherent in releasing synthetic magnitude microdata. We illustrate the methodology using economic data from a survey of manufacturing establishments. VL - 32 UR - http://content.iospress.com/download/statistical-journal-of-the-iaos/sji959 IS - 1 ER - TY - JOUR T1 - Synthetic establishment microdata around the world JF - Statistical Journal of the International Association for Official Statistics Y1 - 2016 A1 - Vilhuber, Lars A1 - Abowd, John M. A1 - Reiter, Jerome P. KW - Business data KW - confidentiality KW - differential privacy KW - international comparison KW - Multiple imputation KW - synthetic AB - In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large part, this difficulty in providing access to business microdata is due to the skewed and sparse distributions that characterize business data. Synthetic data are simulated data generated from statistical models. We organized sessions at the 2015 World Statistical Congress and the 2015 Joint Statistical Meetings, highlighting work on synthetic \emph{establishment} microdata. This overview situates those papers, published in this issue, within the broader literature. VL - 32 UR - http://content.iospress.com/download/statistical-journal-of-the-iaos/sji964 IS - 1 ER - TY - JOUR T1 - Bayesian Marked Point Process Modeling for Generating Fully Synthetic Public Use Data with Point-Referenced Geography JF - Spatial Statistics Y1 - 2015 A1 - Quick, Harrison A1 - Holan, Scott H. A1 - Wikle, Christopher K. A1 - Reiter, Jerome P. VL - 14 UR - http://www.sciencedirect.com/science/article/pii/S2211675315000718 ER - TY - RPRT T1 - Synthetic Establishment Microdata Around the World Y1 - 2015 A1 - Vilhuber, Lars A1 - Abowd, John A. A1 - Reiter, Jerome P. AB - Synthetic Establishment Microdata Around the World Vilhuber, Lars; Abowd, John A.; Reiter, Jerome P. In contrast to the many public-use microdata samples available for individual and household data from many statistical agencies around the world, there are virtually no establishment or firm microdata available. In large part, this difficulty in providing access to business micro data is due to the skewed and sparse distributions that characterize business data. Synthetic data are simulated data generated from statistical models. We organized sessions at the 2015 World Statistical Congress and the 2015 Joint Statistical Meetings, highlighting work on synthetic establishment microdata. This overview situates those papers, published in this issue, within the broader literature. PB - Cornell University UR - http://hdl.handle.net/1813/42340 ER - TY - JOUR T1 - Handling Attrition in Longitudinal Studies: The Case for Refreshment Samples JF - Statist. Sci. Y1 - 2013 A1 - Deng, Yiting A1 - Hillygus, D. Sunshine A1 - Reiter, Jerome P. A1 - Si, Yajuan A1 - Zheng, Siyu AB - Panel studies typically suffer from attrition, which reduces sample size and can result in biased inferences. It is impossible to know whether or not the attrition causes bias from the observed panel data alone. Refreshment samples—new, randomly sampled respondents given the questionnaire at the same time as a subsequent wave of the panel—offer information that can be used to diagnose and adjust for bias due to attrition. We review and bolster the case for the use of refreshment samples in panel studies. We include examples of both a fully Bayesian approach for analyzing the concatenated panel and refreshment data, and a multiple imputation approach for analyzing only the original panel. For the latter, we document a positive bias in the usual multiple imputation variance estimator. We present models appropriate for three waves and two refreshment samples, including nonterminal attrition. We illustrate the three-wave analysis using the 2007–2008 Associated Press–Yahoo! News Election Poll. VL - 28 UR - http://dx.doi.org/10.1214/13-STS414 ER -