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Since its origin 23 years ago as a pilot test conducted in four U.S. counties, the U.S. Census Bureau's American Community Survey (ACS) has been the focus of continuous research, development, and refinement. The survey cleared critical milestones 14 years ago when it began full-scale operations, including comprehensive nationwide coverage, and 5 years later when the ACS replaced a long-form sample questionnaire in the 2010 census as a source of detailed demographic and socioeconomic information. Throughout that existence and continuing today, ACS research and testing has worked to improve the survey's conduct in the face of challenges ranging from detailed and procedural to the broad and existential. This publication summarizes the presentations and discussion at the September 26â€"27, 2018, Workshop on Improving the American Community Survey (ACS), sponsored by the U.S. Census Bureau. Workshop participants explored uses of administrative records and third-party data to improve ACS operations and potential for boosting respondent participation through improved communication.
In the early 1990s, the Census Bureau proposed a program of continuous measurement as a possible alternative to the gathering of detailed social, economic, and housing data from a sample of the U.S. population as part of the decennial census. The American Community Survey (ACS) became a reality in 2005, and has included group quarters (GQ)-such places as correctional facilities for adults, student housing, nursing facilities, inpatient hospice facilities, and military barracks-since 2006, primarily to more closely replicate the design and data products of the census long-form sample. The decision to include group quarters in the ACS enables the Census Bureau to provide a comprehensive benchmark of the total U.S. population (not just those living in households). However, the fact that the ACS must rely on a sample of what is a small and very diverse population, combined with limited funding available for survey operations, makes the ACS GQ sampling, data collection, weighting, and estimation procedures more complex and the estimates more susceptible to problems stemming from these limitations. The concerns are magnified in small areas, particularly in terms of detrimental effects on the total population estimates produced for small areas. Small Populations, Large Effects provides an in-depth review of the statistical methodology for measuring the GQ population in the ACS. This report addresses difficulties associated with measuring the GQ population and the rationale for including GQs in the ACS. Considering user needs for ACS data and of operational feasibility and compatibility with the treatment of the household population in the ACS, the report recommends alternatives to the survey design and other methodological features that can make the ACS more useful for users of small-area data.
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"Census microdata are the confidential records of specific individuals and housing units from whom Decennial Census or American Community Survey responses have been obtained. The U.S. Census Bureau also draws a sample from the full set of microdata and makes these sampled records available in the Public Use Microdata Sample (PUMS) data products, so that users can develop their own tabulations. These data are being used by state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) for studies, such as analyses of the commuting characteristics of population subgroups, and for supporting travel demand model and land use models."--Preface
The American Community Survey (ACS) is a major new initiative from the U.S. Census Bureau designed to provide continuously updated information on the numbers and characteristics of the nation's people and housing. It replaces the "long form" of the decennial census. Using the American Community Survey covers the basics of how the ACS design and operations differ from the long-form sample; using the ACS for such applications as formula allocation of federal and state funds, transportation planning, and public information; and challenges in working with ACS estimates that cover periods of 12, 36, or 60 months depending on the population size of an area. This book also recommends priority areas for continued research and development by the U.S. Census Bureau to guide the evolution of the ACS, and provides detailed, comprehensive analysis and guidance for users in federal, state, and local government agencies, academia, and media.
The National Science Foundation (NSF) has long collected information on the number and characteristics of individuals with education or employment in science and engineering and related fields in the United States. An important motivation for this effort is to fulfill a congressional mandate to monitor the status of women and minorities in the science and engineering workforce. Consequently, many statistics are calculated by race or ethnicity, gender, and disability status. For more than 25 years, NSF obtained a sample frame for identifying the target population for information it gathered from the list of respondents to the decennial census long-form who indicated that they had earned a bachelors or higher degree. The probability that an individual was sampled from this list was dependent on both demographic and employment characteristics. But, the source for the sample frame will no longer be available because the census long-form is being replaced as of the 2010 census with the continuous collection of detailed demographic and other information in the new American Community Survey (ACS). At the request of NSF's Science Resources Statistics Division, the Committee on National Statistics of the National Research Council formed a panel to conduct a workshop and study the issues involved in replacing the decennial census long-form sample with a sample from the ACS to serve as the frame for the information the NSF gathers. The workshop had the specific objective of identifying issues for the collection of field of degree information on the ACS with regard to goals, content, statistical methodology, data quality, and data products.