Graham Kalton – författare

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 Panel on Estimates of Poverty for Small Geographic Areas was established by the Committee on National Statistics at the National Research Council in response to the Improving America''s Schools Act of 1994. That act charged the U.S. Census Bureau to produce updated estimates of poor school-age children every two years for the nation''s more than 3,000 counties and 14,000 school districts. The act also charged the panel with determining the appropriateness and reliability of the Bureau''s estimates for use in the allocation of more than $7 billion of Title I funds each year for educationally disadvantaged children.

The panel''s charge was both a major one and one with immovable deadlines. The panel had to evaluate the Census Bureau''s work on a very tight schedule in order to meet legal requirements for allocation of Title I funds. As it turned out, the panel produced three interim reports: the first one evaluated county-level estimates of poor school-age children in 1993, the second one assessed a revised set of 1993 county estimates; and the third one covered both county- and school district-level estimates of poor school-age children in 1995. This volume combines and updates these three reports into a single reference volume.

Recent trends in federal policies for social and economic programs have increased the demand for timely, accurate estimates of income and poverty for states, counties, and even smaller areas. Every year more than $130 billion in federal funds is allocated to states and localities through formulas that use such estimates. These funds support a wide range of programs that include child care, community development, education, job training, nutrition, and public health.

A new program of the U.S. Census Bureau is now providing more timely estimates for these programs than those from the decennial census, which have been used for many years. These new estimates are being used to allocate more than $7 billion annually to school districts, through the Title I program that supports educationally disadvantaged children.

But are these estimates as accurate as possible given the available data? How can the statistical models and data that are used to develop the estimates be improved? What should policy makers consider in selecting particular estimates? This new book from the National Research Council provides guidance for improving the Census Bureau''s program and for policy makers who use such estimates for allocating funds.

The U.S. Department of Education uses estimates of school-age children in poverty to allocate federal funds under Title I of the Elementary and Secondary Education Act for education programs to aid disadvantaged children. Historically, the allocations have been made by a two-stage process: the department''s role has been to allocate Title I funds to counties; the states have then distributed these funds to school districts. Until recently, the department has based the county allocations on the numbers and proportions of poor school-age children in each county from the most recent decennial census. States have used several different data sources, such as the decennial census and the National School Lunch Program, to distribute the department''s county allocations to districts. In 1994 Congress authorized the Bureau of the Census to provide updated estimates of poor school-age children every 2 years, to begin in 1996 with estimates for counties and in 1998 with estimates for school districts. The Department of Education is to use the school district estimates to allocate Title I basic and concentration grants directly to districts for the 1999-2000 and later school years, unless the Secretaries of Education and Commerce determine that they are "inappropriate or unreliable" on the basis of a study by the National Research Council. That study is being carried out by the Committee on National Statistics'' Panel on Estimates of Poverty for Small Geographic Areas.

The Panel on Estimates of Poverty for Small Geographic Areas was established by the Committee on National Statistics at the National Research Council in response to the Improving America''s Schools Act of 1994. That act charged the U.S. Census Bureau to produce updated estimates of poor school-age children every two years for the nation''s more than 3,000 counties and 14,000 school districts. The act also charged the panel with determining the appropriateness and reliability of the Bureau''s estimates for use in the allocation of more than $7 billion of Title I funds each year for educationally disadvantaged children.

The panel''s charge was both a major one and one with immovable deadlines. The panel had to evaluate the Census Bureau''s work on a very tight schedule in order to meet legal requirements for allocation of Title I funds. As it turned out, the panel produced three interim reports: the first one evaluated county-level estimates of poor school-age children in 1993, the second one assessed a revised set of 1993 county estimates; and the third one covered both county- and school district-level estimates of poor school-age children in 1995. This volume combines and updates these three reports into a single reference volume.

Recent trends in federal policies for social and economic programs have increased the demand for timely, accurate estimates of income and poverty for states, counties, and even smaller areas. Every year more than $130 billion in federal funds is allocated to states and localities through formulas that use such estimates. These funds support a wide range of programs that include child care, community development, education, job training, nutrition, and public health.

A new program of the U.S. Census Bureau is now providing more timely estimates for these programs than those from the decennial census, which have been used for many years. These new estimates are being used to allocate more than $7 billion annually to school districts, through the Title I program that supports educationally disadvantaged children.

But are these estimates as accurate as possible given the available data? How can the statistical models and data that are used to develop the estimates be improved? What should policy makers consider in selecting particular estimates? This new book from the National Research Council provides guidance for improving the Census Bureau''s program and for policy makers who use such estimates for allocating funds.

The U.S. Department of Education uses estimates of school-age children in poverty to allocate federal funds under Title I of the Elementary and Secondary Education Act for education programs to aid disadvantaged children. Historically, the allocations have been made by a two-stage process: the department''s role has been to allocate Title I funds to counties; the states have then distributed these funds to school districts. Until recently, the department has based the county allocations on the numbers and proportions of poor school-age children in each county from the most recent decennial census. States have used several different data sources, such as the decennial census and the National School Lunch Program, to distribute the department''s county allocations to districts. In 1994 Congress authorized the Bureau of the Census to provide updated estimates of poor school-age children every 2 years, to begin in 1996 with estimates for counties and in 1998 with estimates for school districts. The Department of Education is to use the school district estimates to allocate Title I basic and concentration grants directly to districts for the 1999-2000 and later school years, unless the Secretaries of Education and Commerce determine that they are "inappropriate or unreliable" on the basis of a study by the National Research Council. That study is being carried out by the Committee on National Statistics'' Panel on Estimates of Poverty for Small Geographic Areas.

This book evaluates changes needed to improve the usefulness and cost-effectiveness of the Survey of Income and Program Participation (SIPP). Conducted by the Census Bureau, SIPP is a major continuing survey that is designed to provide information about the economic well-being of the U.S. population and its need for and participation in government assistance programs (e.g., social security, Medicare, Medicaid, food stamps, AFDC).

This volume considers the goals for the survey, the survey and sample design, data collection and processing systems, publications and other data products, analytical techniques for using the data, the methodological research and evaluation to implement and assess the redesign, and the management of the program at the Census Bureau.

Sample design is key to all surveys, fundamental to data collection, and to the analysis and interpretation of the data. Introduction to Survey Sampling, Second Edition provides an authoritative and accessible source on sample design strategies and procedures that is a required reading for anyone collecting or analyzing survey data. Graham Kalton discusses different types of probability samples, stratification (pre and post), clustering, dual frames, replicates, response, base weights, design effects, and effective sample size. It is a thorough revision and update of the first edition, published more than 35 years ago. Although the concepts of probability sampling are largely the same, there have been important developments in the application of these concepts as research questions have increasingly spanned multiple disciplines, computers have become central to data collection as well as data analysis, and cell phones have become ubiquitous, but response rates have fallen, and public willingness to engage in survey research has waned. While most of the volume focuses on probability samples, there is also a chapter on nonprobability samples, which are becoming increasingly important with the rise of social media and the world wide web.