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  - <p>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</p>
PB  - NCRN Coordinating Office
UR  - http://hdl.handle.net/1813/52650
ER  - 

TY  - JOUR
T1  - Spatial Variation in the Quality of American Community Survey Estimates
JF  - Demography
Y1  - 2016
A1  - Folch, David C.
A1  - Arribas-Bel, Daniel
A1  - Koschinsky, Julia
A1  - Spielman, Seth E.
VL  - 53
ER  - 

TY  - RPRT
T1  - Uncertain Uncertainty: Spatial Variation in the Quality of American Community Survey Estimates
Y1  - 2014
A1  - Folch, David C.
A1  - Arribas-Bel, Daniel
A1  - Koschinsky, Julia
A1  - Spielman, Seth E.
AB  - Uncertain Uncertainty: Spatial Variation in the Quality of American Community Survey Estimates Folch, David C.; Arribas-Bel, Daniel; Koschinsky, Julia; Spielman, Seth E. The U.S. Census Bureau's American Community Survey (ACS) is the foundation of social science research, much federal resource allocation and the development of public policy and private sector decisions. However, the high uncertainty associated with some of the ACS's most frequently used estimates can jeopardize the accuracy of inferences based on these data. While there is high level understanding in the research community that problems exist in the data, the sources and implications of these problems have been largely overlooked. Using 2006-2010 ACS median household income at the census tract scale as the test case (where a third of small-area estimates have higher than recommend errors), we explore the patterns in the uncertainty of ACS data. We consider various potential sources of uncertainty in the data, ranging from response level to geographic location to characteristics of the place. We find that there exist systematic patterns in the uncertainty in both the spatial and attribute dimensions. Using a regression framework, we identify the factors that are most frequently correlated with the error at national, regional and metropolitan area scales, and find these correlates are not consistent across the various locations tested. The implication is that data quality varies in different places, making cross-sectional analysis both within and across regions less reliable. We also present general advice for data users and potential solutions to the challenges identified.
PB  - University of Colorado at Boulder / University of Tennessee
UR  - http://hdl.handle.net/1813/38122
ER  -