Improving Data Quality Awareness in the United States Federal Statistical Agencies

1994 ◽  
Vol 48 (1) ◽  
pp. 12
Author(s):  
Maria Elena Gonzalez
Keyword(s):  
United States ◽  
Data Quality ◽  
The United States ◽  
Federal Statistical ◽  
Statistical Agencies ◽  
Quality Awareness

New Technology in Survey Research: Computer-Assisted Personal Interviewing (CAPI)

1992 ◽  
Vol 10 (2) ◽  
pp. 145-157 ◽  
Author(s):  
Reginald P. Baker
Keyword(s):  
United States ◽  
Data Quality ◽  
Survey Research ◽  
New Technology ◽  
The United States ◽  
Computer Assisted ◽  
Government Agencies ◽  
Research Organizations ◽  
Statistical Agencies ◽  
Areas Of Concern

This paper summarizes what is currently known about computer-assisted personal interviewing (CAPI). It gives an overview of research on CAPI within U.S. Government agencies, at a number of European statistical agencies, and at university-based research organizations in the United States. The paper focuses on four areas of concern among CAPI developers that have slowed deployment of this technology on a broader scale: respondent acceptance, interviewer acceptance, impact on data quality, and costs. It argues that experience to date relative to all of these issues has been encouraging, although we are more certain at this stage about acceptance by respondents and interviewers than we are about the precise impact on data quality or the likely costs of CAPI.

scholarly journals Data Quality Evaluation of Sentinel-1 and GF-3 SAR for Wind Field Inversion

2021 ◽  
Vol 13 (18) ◽  
pp. 3723
Author(s):  
Yong Wan ◽  
Sheng Guo ◽  
Ligang Li ◽  
Xiaojun Qu ◽  
Yongshou Dai
Keyword(s):  
United States ◽  
Data Quality ◽  
Wind Field ◽  
The United States ◽  
Band Model ◽  
East And West ◽  
Sar Data ◽  
Better Than ◽  
Field Inversion

Synthetic aperture radar (SAR) is an important means to observe the sea surface wind field. Sentinel-1 and GF-3 are located on orbit SAR satellites, but the SAR data quality of these two satellites has not been evaluated and compared at present. This paper mainly studies the data quality of Sentinel-1 and GF-3 SAR satellites used in wind field inversion. In this study, Sentinel-1 SAR data and GF-3 SAR data located in Malacca Strait, Hormuz Strait and the east and west coasts of the United States are selected to invert wind fields using the C-band model 5.N (CMOD5.N). Compared with reanalysis data called ERA5, the root mean squared error (RMSE) of the Sentinel-1 inversion results is 1.66 m/s, 1.37 m/s and 1.49 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively; the RMSE of GF-3 inversion results is 1.63 m/s, 1.45 m/s and 1.87 m/s in three intervals of 0~5 m/s, 5~10 m/s and above 10 m/s, respectively. Based on the data of Sentinel-1 and GF-3 located on the east and west coasts of the United States, CMOD5.N is used to invert the wind field. Compared with the buoy data, the RMSE of the Sentinel-1 inversion results is 1.20 m/s, and the RMSE of the GF-3 inversion results is 1.48 m/s. The results show that both Sentinel-1 SAR data and GF-3 SAR data are suitable for wind field inversion, but the wind field inverted by Sentinel-1 SAR data is slightly better than GF-3 SAR data. When applied to wind field inversion, the data quality of Sentinel-1 SAR is slightly better than the data quality of GF-3 SAR. The SAR data quality of GF-3 has achieved a world-leading level.

scholarly journals Population-based cancer survival in the United States: Data, quality control, and statistical methods

Cancer ◽  
2017 ◽  
Vol 123 ◽  
pp. 4982-4993 ◽  
Author(s):  
Claudia Allemani ◽  
Rhea Harewood ◽  
Christopher J. Johnson ◽  
Helena Carreira ◽  
Devon Spika ◽  
...  
Keyword(s):  
United States ◽  
Quality Control ◽  
Data Quality ◽  
Statistical Methods ◽  
Cancer Survival ◽  
The United States ◽  
Population Based ◽  
Data Quality Control ◽  
States Data

scholarly journals Air Quality Index and air quality awareness among adults in the United States

2020 ◽  
Vol 183 ◽  
pp. 109185 ◽  
Author(s):  
Maria C. Mirabelli ◽  
Stefanie Ebelt ◽  
Scott A. Damon
Keyword(s):  
United States ◽  
Air Quality ◽  
Quality Index ◽  
The United States ◽  
Air Quality Index ◽  
Quality Awareness

scholarly journals Innovative Quality-Assurance Strategies for Tuberculosis Surveillance in the United States

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Lilia Ponce Manangan ◽  
Cheryl Tryon ◽  
Elvin Magee ◽  
Roque Miramontes
Keyword(s):  
United States ◽  
Quality Assurance ◽  
Data Quality ◽  
The United States ◽  
Surveillance Systems ◽  
Web Based ◽  
High Data ◽  
National Tuberculosis ◽  
Control And Prevention ◽  
Reporting Discrepancies

Introduction. The Centers for Disease Control and Prevention (CDC)’s National Tuberculosis Surveillance System (NTSS) is the national repository of tuberculosis (TB) data in the United States. Jurisdictions report to NTSS through the Report of Verified Case of Tuberculosis (RVCT) form that transitioned to a web-based system in 2009.Materials and Methods. To improve RVCT data quality, CDC conducted a quality assurance (QA) needs assessment to develop QA strategies. These include QA components (case detection, data accuracy, completeness, timeliness, data security, and confidentiality); sample tools such as National TB Indicators Project (NTIP) to identify TB case reporting discrepancies; comprehensive training course; resource guide and toolkit.Results and Discussion. During July–September 2011, 73 staff from 34 (57%) of 60 reporting jurisdictions participated in QA training. Participants stated usefulness of sharing jurisdictions’ QA methods; 66 (93%) wrote that the QA tools will be effective for their activities. Several jurisdictions reported implementation of QA tools pertinent to their programs. Data showed >8% increase in NTSS and NTIP enrollment through Secure Access Management Services, which monitors system usage, from August 2011–February 2012.Conclusions. Despite challenges imposed by web-based surveillance systems, QA strategies can be developed with innovation and collaboration. These strategies can also be used by other disease programs to ensure high data quality.

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