Every 10 years since 1790, Congress has authorized funds to conduct a national census of the U.S. population, as required by the U.S. Constitution. In the past, a majority of households received a short-form questionnaire, while one in six households received a long form that contained additional questions and provided more detailed socioeconomic information about the population.
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South Carolina 2010 Profile
Total Population: 4,625,364
Population Density: 153.9 people per square mile
Numeric Change in Population, 2000-2010: +613,352
State Rank Based on Numeric Increase in Population, 2000-2010: 12
Percent Increase in Population, 2000-2010: +15.3%
State Rank Based on Percent Increase in Population, 2000-2010: 10
Beginning in 2010, the decennial census changed to a short-form only census that counts all residents living in the United States. This form asks for name, sex, age, date of birth, race, ethnicity, relationship and housing tenure - taking just minutes to complete, while long form surveys became reserved for a more rotational distribution.
This detailed socioeconomic data gathering method became the American Community Survey. The survey provides current data about your community every year, rather than once every 10 years. It is sent to a small percentage of the population on a rotating basis throughout the decade. No household will receive the survey more often than once every five years.
The American Community Survey, a relatively new survey conducted by the Census Bureau, is ushering in the most substantial change in the decennial census in more than 60 years. The ACS is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, economic, and financial data every year. The ACS puts this up-to-date information about important social issues at the fingertips of people who need it, including policymakers, researchers, businesses and nongovernmental organizations, journalists, teachers, students, and the public.
Much of the ACS data provided on the Census Bureau’s Web site are available separately by age group, race, Hispanic origin, and sex. For example, data users can compare the poverty status of children and the elderly, college enrollment rates for men and women, or housing costs for African Americans and non-Hispanic Whites. No other resource provides such a wealth of social, economic, and housing information about American society.
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Federal Agencies
The federal government uses ACS information to evaluate the need for federal programs and to run those programs effectively.
The U.S. Department of Veterans Affairs uses ACS data on the characteristics of veterans to evaluate the need for educational, employment, and health care programs to assist those who have served in the military. The Special Supplemental Food Program for Women, Infants, and Children (WIC) uses income data from the ACS to determine the potential demand for food assistance across states and counties.
For more information about federal uses of ACS data, see U.S. Census Bureau, Subjects Planned for the 2010 Census and American Community Survey: Federal Legislative and Program Uses.
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State and Local Governments
State and local governments are using ACS information to keep track of year-to-year changes in their jurisdictions
Information from the ACS is critical for state and local policymakers and planners who need up-to-date information about their communities to evaluate the need for new roads, hospitals, schools, senior centers, and other basic services. For example, the Council on Virginia’s Future, which advises the Governor and the Virginia General Assembly, relies on ACS data to monitor annual trends in the travel time to work.
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Non-governmental Organizations
Non-governmental organizations use the ACS in a variety of ways to monitor trends among important subgroups of the population, often at the state level.
The Annie E. Casey Foundation uses ACS data to track annual changes in the well-being of children across the 50 states and the District of Columbia, including measures of child poverty, educational attainment, school enrollment, and employment status of parents. The Migration Policy Institute uses ACS data to present detailed, state-level information about the 37.5 million current U.S. residents who were born outside the United States. And the Population Reference Bureau has recently used ACS data to produce a datasheet on the U.S. labor force, including state-level estimates of people working in high-tech and other science and engineering jobs.
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Journalists
Journalists use ACS data to report on new or emerging social trends, or to put a piece of anecdotal evidence into a broader context.
A recent article on commuting by CNN used ACS data to report that among large cities, New York has the highest share of workers using public transportation; Portland has the highest proportion of people who bike to work; and Boston leads large cities in the proportion of people who walk to work.*
Les Christie, "New Yorkers are top transit users: More than half ride subway or bus to work," CNNMoney.Com, June 29, 2007.
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Understanding Single-Year and Multi-Year Estimates
The ACS provides several advantages over the information that has been collected in the past through the decennial census long-form samples.
The main benefits of the ACS are timeliness and access to annual data for states and local areas with larger populations, by using single-year estimates. Multi-year estimates are used to provide data for areas with smaller populations.
Single-year estimates are published for areas with 65,000 or more residents, and five-year estimates are published for areas with fewer than 20,000 residents. While single-year estimates include information collected over a 12-month period, 5-year estimate include data collected over a 60month period. The primary advantage of using multiyear estimates is the increased statistical reliability of the data for less populated areas and small population subgroups. For these less populated areas and groups, the level of precision improves dramatically with five-year estimates.
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Understanding Margin of Error
All data that are based on samples, such as the ACS and the census long-form samples include a range of uncertainty. Two broad types of error can occur: sampling error and non-sampling error. Non-sampling errors can result from mistakes in how the data are reported or coded, problems in the sampling frame or survey questionnaires, or problems related to non-response or interviewer bias. The Census Bureau tries to minimize non-sampling errors by using trained interviewers and by carefully reviewing the survey’s sampling methods, data processing techniques, and questionnaire design.
Sampling error occurs when data are based on a sample of a population rather than the full population. Sampling error is easier to measure than non-sampling error and can be used to assess the statistical reliability of survey data. For any given area, the larger the sample and the more months included in the data, the greater the confidence in the estimate. The Census Bureau reported the 90-percent confidence interval on all ACS estimates produced for 2005 and earlier. Since the release of the 2007 ACS data, margins of error have been provided for every ACS estimate. Ninety-percent confidence intervals define a range expected to contain the true value of an estimate with a level of confidence of 90 percent. Margins of error are easily converted into these confidence ranges.
For example, the 2007 ACS Data Profile for South Carolina, shown below, shows that 828,713 married-couple families resided in the state in 2007.
By adding and subtracting the margin of error from the point estimate, we can calculate the 90-percent confidence interval for that estimate:
828,713 – 11,898 = 816,815 = Lower-bound interval
828,713 + 11,898 = 840,611 = Upper-bound interval
Therefore, we can be 90 percent confident that the true number of married-couple families in South Carolina in 2007 falls somewhere between 816,815 and 840,611.
The margin of error around an estimate is important because it helps you draw conclusions about the data. Small differences between two estimates may not be statistically significant if the confidence intervals of those estimates overlap. However, the Census Bureau cautions data users not to rely on overlapping confidence intervals as a test for statistical significance, because this method will not always produce accurate results. Instead, the Census Bureau recommends conducting statistical significance tests.
In some cases, data users will need to construct custom ACS estimates by combining data across multiple geographic areas or population subgroups or it may be necessary to derive a new percentage, proportion, or ratio from published ACS data. In such cases, additional calculations are needed to produce confidence intervals and margins of error for the derived estimates. Note that these error measures do not tell us about the magnitude of non-sampling errors.