U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

USCENSUSBUREAU

Helping You Make Informed Decisions

Issued

November 2008

A Compass for Understanding and Using 

American Community Survey Data

What Congress Needs to Know
Acknowledgments Terri Ann Lowenthal and Mary Jo Hoeksema, Legislative and Policy 

Consultants, drafted this handbook for the U.S. Census Bureau’s American 

Community Survey Offi ce. Kennon R. Copeland and John H. Thompson

of National Opinion Research Center at the University of Chicago drafted 

the technical appendixes. Edward J. Spar, Executive Director, Council of 

Professional Associations on Federal Statistics, Frederick J. Cavanaugh, 

Executive Business Director, Sabre Systems, Inc., Susan P. Love, Consultant, 

Linda A. Jacobsen, Vice President, Domestic Programs, Population Reference 

Bureau, and Mark Mather, Associate Vice President, Domestic Programs, 

Population Reference Bureau, provided initial review of this handbook.

Deborah H. Griffi n, Special Assistant to the Chief of the American 

Community Survey Offi ce, provided the concept and directed the 

development and release of a series of handbooks entitled A Compass for 

Understanding and Using American Community Survey Data. Cheryl V. 

Chambers, Colleen D. Flannery, Cynthia Davis Hollingsworth, Susan L. 

Hostetter, Pamela D. Klein, Anna M. Owens, Clive R. Richmond, Enid 

Santana, and Nancy K. Torrieri contributed to the planning and review of 

this handbook series.

The American Community Survey program is under the direction of 

Arnold A. Jackson, Associate Director for Decennial Census, Daniel H. 

Weinberg, Assistant Director for the American Community Survey and 

Decennial Census, and Susan Schechter, Chief, American Community Survey 

Offi ce.

Other individuals who contributed to the review and release of these 

handbooks include Dee Alexander, Herman Alvarado, Mark Asiala,

Frank Ambrose, Maryam Asi, Arthur Bakis, Genora Barber, Michael 

Beaghen, Judy Belton, Lisa Blumerman, Scott Boggess, Ellen Jean 

Bradley, Stephen Buckner, Whittona Burrell, Edward Castro, Gary 

Chappell, Michael Cook, Russ Davis, Carrie Dennis, Jason Devine,

Joanne Dickinson, Barbara Downs, Maurice Eleby, Sirius Fuller, Dale 

Garrett, Yvonne Gist, Marjorie Hanson, Greg Harper, William Hazard,

Steve Hefter, Douglas Hillmer, Frank Hobbs, Todd Hughes, Trina 

Jenkins, Nicholas Jones, Anika Juhn, Donald Keathley, Wayne Kei,

Karen King, Debra Klein, Vince Kountz, Ashley Landreth, Steve Laue,

Van Lawrence, Michelle Lowe, Maria Malagon, Hector Maldonado,

Ken Meyer, Louisa Miller, Stanley Moore, Alfredo Navarro, Timothy 

Olson, Dorothy Paugh, Marie Pees, Marc Perry, Greg Pewett, Roberto 

Ramirez, Dameka Reese, Katherine Reeves, Lil Paul Reyes, Patrick 

Rottas, Merarys Rios, J. Gregory Robinson, Anne Ross, Marilyn 

Sanders, Nicole Scanniello, David Sheppard, Joanna Stancil, Michael 

Starsinic, Lynette Swopes, Anthony Tersine, Carrie Werner, Edward 

Welniak, Andre Williams, Steven Wilson, Kai Wu, and Matthew 

Zimolzak.

Linda Chen and Amanda Perry of the Administrative and Customer Services 

Division, Francis Grailand Hall, Chief, provided publications management, 

graphics design and composition, and editorial review for the print and 

electronic media. Claudette E. Bennett, Assistant Division Chief, and 

Wanda Cevis, Chief, Publications Services Branch, provided general direction 

and production management.
A Compass for Understanding and Using 

American Community Survey Data Issued November 2008

U.S. Department of Commerce

Carlos M. Gutierrez, 

Secretary

John J. Sullivan, 

Deputy Secretary

Economics and Statistics Administration

Cynthia A. Glassman, 

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock, 

Director

What Congress Needs to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What Congress Needs to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2008.

Economics and Statistics 

Administration

Cynthia A. Glassman,

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock,

Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Offi cer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Offi ce

ECONOMICS

AND STATISTICS

ADMINISTRATION
What Congress Needs to Know iii

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Contents Foreword...................................................................................................... iv

Overview.......................................................................................................1

Introduction: Who Should Use This Handbook? .....................................................1

What Is the American Community Survey? ............................................................1

Who Receives the ACS? .........................................................................................1

What Topics Are Covered in the ACS? ....................................................................2

Administering the ACS .........................................................................................2

From the Long-Form Sample to the ACS: 

 Collecting Characteristics Data in the Census ....................................................3

Is the ACS Required by Law? .................................................................................4

How Does Congress Use ACS Data? ......................................................................4

Does the ACS Provide Offi cial Population Counts? .................................................4

Data Products ...............................................................................................5

When Are ACS Estimates Available? ......................................................................5

What Data Products Are Available From the ACS? ..................................................6

Legislative Uses of the American Community Survey ................................. 7

Use of ACS in Existing Laws ................................................................................. 7

Congressional Uses of ACS Data .......................................................................... 8

Accessing ACS Data .................................................................................... 10

American FactFinder ...........................................................................................10

Fast Facts for Congress .......................................................................................10

Using ACS Data ........................................................................................... 13

Understanding Survey Error ................................................................................13

Understanding Multiyear ACS Estimates ..............................................................14

When Should I Use 1-Year, 3-Year, and 5-Year ACS Estimates? ..............................14

Comparing ACS Estimates Over Time ..................................................................15

ACS Case Study: Trends in School Enrollment ......................................................15

Comparing the ACS With Other Data Sources ............................................. 17

Reference Periods ...............................................................................................17

Comparing the ACS and the Decennial Census ...................................................17

Intercensal Population and Housing Estimates ....................................................17

Comparing the ACS and Other Census Bureau Surveys .......................................18

Additional Resources About the American Community Survey .................. 18

Web Links for Additional Data Sources ...............................................................18

Printed Publications ...........................................................................................19

Contacting Census Bureau Headquarters and Regional Census Offi ces ...............19

Glossary...................................................................................................... 21

Appendixes ................................................................................................A-1

 Appendix 1. Understanding and Using Single-Year and Multiyear Estimates ...A-1

 Appendix 2. Diff erences Between ACS and Decennial Census Sample Data ....A-8

 Appendix 3. Measures of Sampling Error .....................................................A-11

 Appendix 4. Making Comparisons ...............................................................A-18

 Appendix 5. Using Dollar-Denominated Data ...............................................A-22

 Appendix 6. Measures of Nonsampling Error ...............................................A-24

 Appendix 7. Implications of Population Controls on ACS Estimates ..............A-26

 Appendix 8. Other ACS Resources ...............................................................A-27
iv What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Foreword The American Community Survey (ACS) is a nationwide survey designed to 

provide communities with reliable and timely demographic, social, economic, and 

housing data every year. The U.S. Census Bureau will release data from the ACS in 

the form of both single-year and multiyear estimates. These estimates represent 

concepts that are fundamentally diff erent from those associated with sample 

data from the decennial census long form. In recognition of the need to provide 

guidance on these new concepts and the challenges they bring to users of ACS 

data, the Census Bureau has developed a set of educational handbooks as part of 

The ACS Compass Products. 

We recognize that users of ACS data have varied backgrounds, educations, 

and experiences. They need diff erent kinds of explanations and guidance to 

understand ACS data products. To address this diversity, the Census Bureau 

worked closely with a group of experts to develop a series of handbooks, each of 

which is designed to instruct and provide guidance to a particular audience. The 

audiences that we chose are not expected to cover every type of data user, but 

they cover major stakeholder groups familiar to the Census Bureau.

General data users Congress 

High school teachers Puerto Rico Community Survey data

 users (in Spanish)

Business community Public Use Microdata Sample (PUMS) data

 users

Researchers Users of data for rural areas

Federal agencies State and local governments

Media Users of data for American Indians and 

 Alaska Natives

The handbooks diff er intentionally from each other in language and style. Some 

information, including a set of technical appendixes, is common to all of them. 

However, there are notable diff erences from one handbook to the next in the 

style of the presentation, as well as in some of the topics that are included. We 

hope that these diff erences allow each handbook to speak more directly to its 

target audience. The Census Bureau developed additional ACS Compass Products

materials to complement these handbooks. These materials, like the handbooks, 

are posted on the Census Bureau’s ACS Web site: <www.census.gov/acs/www>.

These handbooks are not expected to cover all aspects of the ACS or to provide 

direction on every issue. They do represent a starting point for an educational 

process in which we hope you will participate. We encourage you to review these 

handbooks and to suggest ways that they can be improved. The Census Bureau 

is committed to updating these handbooks to address emerging user interests as 

well as concerns and questions that will arise. 

A compass can be an important tool for fi nding one’s way. We hope The ACS 

Compass Products give direction and guidance to you in using ACS data and that 

you, in turn, will serve as a scout or pathfi nder in leading others to share what 

you have learned. 
What Congress Needs to Know 1

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Overview

Introduction: Who Should Use This Handbook?

This handbook will help congressional staff who are 

unfamiliar with the American Community Survey (ACS) 

or who need a refresher on its many uses. The handbook can help staff use ACS data to:

• Respond to constituent inquiries.

• Draft fl oor and press statements.

• Conduct legislative research.

• Understand funding formulas.

• Compare data from the ACS with other federal 

statistics. 

• Interpret ACS data. 

Congressional staff members may have diff erent levels 

of interest and technical expertise in using ACS data. 

The appendixes with this handbook include information that is more technical, and staff with greater 

expertise or more sophisticated project goals should 

fi nd them useful. The Census Bureau has developed 

a series of handbooks that may be better suited to 

other staff members’ needs. For example, members 

could refer to the handbook for federal agencies for 

additional guidance and information. The list of ACS 

handbooks appears in the text box on this page. 

Census Bureau headquarters and Regional Offi ce staff 

are available to answer questions about the ACS and 

your particular data needs. The contact information for 

headquarters and Regional Census Bureau Offi ces is 

included in the “Additional Resources” section of this 

handbook. 

What Is the American Community Survey?

The American Community Survey (ACS), part of the 

2010 Decennial Census Program, gathers demographic, social, economic, and housing information

about the nation’s people and communities on a 

continuous basis. As the largest survey in the United 

States, it is the only source of small-area data on a 

wide range of important social and economic 

characteristics for all communities in the country. 

In Puerto Rico the survey is called the Puerto Rico 

 Community Survey (PRCS). 

Research on the ACS began after the 1990 Census, 

starting with fi eld-testing in several counties in 1996 

that expanded to nationwide full implementation of 

the ACS in 2005, following congressional funding. 

User Handbooks for the ACS

A Compass for Understanding and Using 

American Community Survey Data

• What Congress Needs to Know

• What High School Teachers Need to Know

• What State and Local Governments Need to 

Know

• What Federal Agencies Need to Know

• What the Media Need to Know

• What the Business Community Needs to 

Know

• What Researchers Need to Know

• What General Data Users Need to Know

• What Users of Data for Rural Areas Need to 

Know

• What Users of Data for American Indian and 

Alaska Native Populations Need to Know

• What Users of Data From the Puerto Rico 

Community Survey Need to Know (in 

Spanish)

• What Public Use Microdata Sample (PUMS) 

Data Users Need to Know

Who Receives the ACS?

The ACS is sent each month to a sample of roughly 

250,000 addresses in the United States and Puerto 

Rico, or 3 million a year (about 2.5 percent of all 

residential addresses). The sample represents all housing units and group quarters in the United States and 

Puerto Rico. Group quarters include places such as 

college dormitories, prisons, military barracks, and 

nursing homes. The addresses are selected from the 

Census Bureau’s Master Address File (MAF), which is 

also the basis for the decennial census. 

The ACS sample is not spread evenly across all areas 

but includes a larger proportion of addresses in 

sparsely populated rural communities and American 

Indian reservations and a lower proportion in densely 

populated areas. 
2 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Over a 5-year period, the ACS will sample about 15 

million addresses and complete interviews for about 11 

million. This sample is suffi cient to produce estimates 

for small geographic areas, such as neighborhoods and 

sparsely-populated rural counties. By comparison, the 

Census 2000 long-form sample was nearly 20 million 

addresses, resulting in about 18 million enumerations. 

In a 5-year period no address will be selected for the 

ACS more than once, and many addresses will never be 

selected for the survey.

What Topics Are Covered in the ACS?

The ACS collects vital information about the nation’s 

demographic, social, and economic characteristics, as 

well as physical and fi nancial characteristics about the 

nation’s housing stock. The subjects covered in the ACS 

(Table 1) provide Congress with a comprehensive portrait of the American people and communities that can 

be used to make policy and programmatic decisions. 

Federal law (Title 13, U.S. Code, §141(f)) requires the 

Census Bureau to submit to Congress the proposed 

subjects to be covered in the decennial census 3 years 

before Census Day. The law also requires the Census 

Bureau to submit to Congress the actual questions it 

plans to include in the decennial census 2 years before 

Census Day. The ACS is part of the 2010 Decennial 

Census Program.

The subjects planned for the 2010 Census and the 

additional subjects covered on the ACS are included 

because federal law or regulation specifi cally mandates 

their inclusion in the census, the legal system requires 

that the data be collected, or federal law requires it for 

program implementation and the census is the only 

source for the information.

Administering the ACS

The ACS collects information using three methods—

mail, telephone, and personal visits.

• Mail. Addresses selected for the ACS sample 

receive a questionnaire by mail. Residents are 

asked to complete the form and mail it back.

• Telephone. About 6 weeks after the ACS forms 

are mailed, the Census Bureau attempts to contact 

by telephone all addresses in the sample that did 

not mail back a questionnaire and for which the 

Census Bureau has a corresponding telephone 

number. This operation is known as computer 

assisted telephone interviewing or CATI.

• Personal visits. After the telephone phase, the 

Census Bureau selects a subsample of addresses 

from which information still has not been collected. Census fi eld representatives visit these 

addresses and conduct interviews in person. This 

operation is called computer assisted personal 

interviewing or CAPI. A higher percentage of 

households in areas with the lowest mail response 

rates are chosen for CAPI to help ensure the quality of the ACS estimates in all neighborhoods. 

 Table 1. Subjects Included in the American Community Survey

Demographic Characteristics

Age

Sex

Hispanic Origin

Race

Relationship to Householder 

(e.g., spouse)

Economic Characteristics

Income

Food Stamps Benefi t

Labor Force Status

Industry, Occupation, and Class 

of Worker

Place of Work and Journey to 

Work

Work Status Last Year

Vehicles Available

Health Insurance Coverage*

Social Characteristics 

Marital Status and Marital History*

Fertility

Grandparents as Caregivers

Ancestry

Place of Birth, Citizenship, and 

Year of Entry

Language Spoken at Home

Educational Attainment and 

School Enrollment

Residence One Year Ago

Veteran Status, Period of Military 

Service, and VA ServiceConnected Disability Rating*

Disability

Housing Characteristics

Year Structure Built

Units in Structure

Year Moved Into Unit

Rooms

Bedrooms

Kitchen Facilities

Plumbing Facilities

House Heating Fuel

Telephone Service Available

Farm Residence

Financial Characteristics

Tenure (Owner/Renter)

Housing Value

Rent

Selected Monthly Owner Costs

*Marital History, VA Service-Connected Disability Rating, and Health Insurance Coverage are new for 2008.

 Source: U.S. Census Bureau.
What Congress Needs to Know 3

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Evolution of the 

American Community Survey

1990: Congress expresses interest in more timely 

“long-form” data.

1996: Census Bureau begins testing a continuous 

survey in 4 counties, increasing to 36 counties by 

1999.

2000: Census Bureau launches Census 2000 

Supplementary Survey with a national sample to 

test the ACS concept on a large scale.

2005: ACS implemented nationwide, replacing 

census long form in 2010 and beyond.

2006: First ACS estimates (based on 2005 ACS) 

released for areas with populations of 65,000 and 

greater.

The subsampling rates for CAPI range from 33 percent to 50 percent. Field representatives also visit 

all group quarters selected for ACS and interview a 

sample of their residents.

From the Long-Form Sample to the ACS: 

Collecting Characteristics Data in the Census

After years of planning, development, and a demonstration period, the ACS began nationwide full implementation in 2005. Before the ACS, the Census Bureau 

collected characteristics about the nation’s people and 

communities once every 10 years during the decennial 

census. 

Past decennial censuses have included two types of 

questionnaires: 

• A short form on which most people were enumerated and that counted the population and asked 

only a few basic questions.

• A long form on which a sample of people, approximately 1 in 6 in Census 2000, were enumerated. 

The long form gathered a wide range of information about demographic, economic, social, and 

housing characteristics and also included all of the 

questions asked on the short form.

Together, the basic demographic information collected 

from everyone established the count of the population 

used primarily to apportion seats in the U.S. House of 

Representatives and to facilitate legislative redistricting.

After the 1990 Census, Congress raised concerns 

about falling census response rates and rising costs. 

Congress also expressed an interest in having more 

timely long-form sample data for policy purposes, noting that decennial census long-form data were out of 

date not long after their release and became less useful as the years went by. Congress asked the Census 

Bureau to explore alternatives to the long form, with 

the goals of simplifying the census, containing costs, 

and producing more timely information to inform 

policy debates and legislative actions.

In 1996, the Census Bureau began testing a continuous 

survey to collect the information previously gathered 

once every 10 years on the long form. Testing of the 

ACS concept began on a small scale, in four counties, 

and by 1999 had expanded to 36 counties around 

the country. Starting in 2000, the Census Bureau also 

fi elded a national sample of about 800,000 households (called the Census 2000 Supplementary Survey) 

to evaluate ACS operations on a larger scale and to 

compare ACS estimates with information collected in 

Census 2000. These supplementary surveys continued 

through 2004. 

The ACS, as part of the decennial census, is a mandatory survey. That is, response to the ACS is required 

by law. During the demonstration period, Congress 

directed the Census Bureau to test the eff ect that making the ACS a voluntary survey would have on survey 

costs. The results showed that a voluntary ACS would 

be considerably more expensive than a mandatory 

ACS. The next section discusses the mandatory nature 

of the ACS in greater detail.

In addition, all federal agencies documented that their 

respective questions on the ACS were required by law 

or regulation and were still needed to carry out their 

missions. Congress closely monitored development of 

the survey and provided funding for full implementation of the survey nationwide starting in 2005. 

Congress appropriated funds to include group quarters, such as college dormitories, military barracks, 

prisons, and nursing homes, starting in 2006.

The text box on this page summarizes the evolution of 

the American Community Survey.
4 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Is the ACS Required by Law?

The ACS is part of the 2010 Decennial Census Program. 

The U.S. Constitution (Article I, Section 2) requires a 

census every 10 years. The census is authorized under 

Title 13, U.S. Code (the Census Act). Numerous federal laws also require information about the nation’s 

population and housing to allocate formula grants and 

to establish eligibility for programs. The census is the 

primary source of data for many of these programmatic 

requirements.

In a 2002 opinion, the Government Accountability 

Offi ce (GAO) found that Sections 141 and 193 of the 

Census Act authorized the Census Bureau to develop 

and administer the ACS and that no additional legal 

authority for the survey was required. (Legal Authority for American Community Survey, B-289852, April 

4, 2002.) Therefore, you will not fi nd a specifi c vote 

authorizing the ACS. See the text box below for the 

specifi c legal authority for the census and the ACS.

Legal Authority for the Census and the 

American Community Survey

Constitution of the United States, Article 

I, Sec. 2 (in relevant part). Representatives 

and direct Taxes shall be apportioned among 

the several States which may be included 

within this Union, according to their respective 

Numbers . . . The actual Enumeration shall be 

made within three Years after the fi rst Meeting 

of the Congress of the United States, and 

within every subsequent Term of ten Years, in 

such Manner as they shall by law direct.

Title 13, U.S. Code, §141(a). The Secretary 

shall, in the year 1980 and every 10 years 

thereafter, take a decennial census of 

population as of the fi rst day of April of 

such year, which date shall be known as the 

“decennial census date”, in such form and 

content as he may determine, including the use 

of sampling procedures and special surveys. In 

connection with any such census, the Secretary 

is authorized to obtain such other census 

information as necessary.

Title 13, U.S. Code, §193. In advance of, 

in conjunction with, or after the taking of 

each census provided for by this chapter, the 

Secretary may make surveys and collect such 

preliminary and supplementary statistics 

related to the main topic of the census as 

are necessary to the initiation, taking, and 

completion thereof.

How Does Congress Use ACS Data?

The questions on the ACS supply the raw data needed 

for a range of programs affecting education, veterans, 

employment, housing and community development, 

public health care, commuting, services for the elderly 

and disabled, and assistance programs for low-income 

families and children. About $400 billion in federal program funds are distributed each year based, in whole 

or in part, on census and ACS data.

The ACS produces updated information every year, 

providing Congress with timely data for allocating formula grants and establishing eligibility for programs, 

evaluating community needs, and deciding how and 

where to invest funds in the future.

For example:

• Information on veteran status and period of 

military service is used to allocate funds to states 

and local areas for employment and job training 

programs for veterans. 

• Information on educational attainment and school 

enrollment is used to allocate funds to provide 

classes in basic skills for adults who have not 

completed high school and who are not functionally literate. Employers also use the information to 

locate new jobs and stimulate economic growth.

• Income data from the ACS are used to determine 

poverty status, measure economic well-being, 

and assess the need for assistance. Many federal 

programs, including Low-Income Home Energy 

Assistance, Community Development Block Grant, 

Older Americans Act, WIC, Head Start, and No 

Child Left Behind Act, use income data to allocate 

formula grants.

Does the ACS Provide Offi cial Population Counts?

No. The ACS is not designed to provide offi cial counts 

of the nation’s population. The decennial census of 

population, conducted once every 10 years (2000, 

2010, etc.), is the offi cial source of population counts 

for every jurisdiction in the United States. Between 

censuses, the Census Bureau provides updated offi cial 

population estimates by race, Hispanic origin, age, and 

gender and estimates of total housing units for states, 

counties, and cities through its Population Estimates 

Program.
What Congress Needs to Know 5

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Congressional staff can fi nd ACS data in a wide variety of formats, including tables, profi les, and maps, 

through the Census Bureau’s Web site and in written 

reports. Once the ACS completes collection of its fi rst 

5 years of data (2005–09), the Census Bureau will publish updated estimates for large and small geographic 

areas and population groups every year.

When Are ACS Estimates Available?

Annually, the ACS produces updated, single-year 

estimates of demographic, housing, social, and economic characteristics for all states, as well as for larger 

counties, cities, metropolitan and urban areas, and 

congressional districts. Geographic areas must have a 

minimum population of 65,000 to qualify for estimates 

based on a single year’s sample. Every congressional 

district meets this threshold and therefore new singleyear estimates are released each year for every congressional district. Some school districts, townships, 

and American Indian and Alaska Native areas also meet 

this population threshold.

For areas with populations of at least 20,000, the 

Census Bureau will produce estimates using data collected over 3 years. For rural areas and city neighborhoods (including census tracts and block groups) with 

fewer than 20,000 people, the Census Bureau plans to 

produce estimates using data collected over 5 years. 

The Census Bureau plans to update these multiyear 

estimates every year. Appendix 1 provides a more complete explanation of how the Census Bureau produces 

multiyear estimates and what they represent.

Data Products

ACS data are released every year, about 8 months 

after the end of each calendar year of data collection. 

Table 2 shows release dates for 1-, 3-, and 5-year ACS 

estimates. In particular, Table 2 shows how the population size of an area defi nes when an area can expect to 

receive data from the ACS and the types of estimates 

those areas will receive. 

For some geographic areas—including three-quarters 

of all counties, most school districts, and most cities, 

towns, and American Indian reservations—only 3-year 

or 5-year estimates will be available because of their 

population size. Because some federal grant programs 

allocate funds directly to these areas, Congress can 

use the 3- and 5-year estimates to evaluate needs at 

the relevant geographic level, compare characteristics 

between areas within and among states, and analyze 

how various formulas distribute funds. Many funding 

formulas are complex and, in addition to using census 

data, may include caps and ‘hold harmless’ provisions 

that also aff ect funding allocations. Table 3 summarizes the major types of geographic areas and the ACS 

estimates that will be published for these areas. The 

Census Bureau Web site posts defi nitions of these geographic areas at <http://www.census.gov/acs/www

/UseData/geo.htm>. As the number of areas in column 

1 makes clear, the vast majority of areas will receive 

only 5-year estimates.

In partnership with the states, the Census Bureau 

created Public Use Microdata Areas (PUMAs), which are 

special, nonoverlapping areas within a state, each with 

a population of about 100,000. 

 Table 2. Availability of ACS Estimates

*Five-year estimates will be available for areas as small as census tracts and block groups. 

Source: U.S. Census Bureau.

Population of geographic area

Year data 

available

Type of 

estimates

65,000 or more 

(receive 1-year, 

3-year, and 5-year 

estimates)

20,000–65,000

(receive only 

3-year and 5-year 

estimates)

Less than 20,000

(receive only 

5-year estimates)

2006 1 year only ✔

2007 1 year only ✔

2008 1 year and 3 year ✔ ✔

2009 1 year and 3 year ✔ ✔

2010 & 

beyond

1 year, 3 year, and 

5 year

✔✔ ✔
6 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

While designed specifi cally for the presentation of 

microdata, ACS data users in rural areas can use PUMA 

geography to obtain substate level 1-year ACS estimates that are not available for most other substate 

levels of geography. Because rural areas are sparsely 

populated, the ACS sample is not large enough to 

produce reliable single-year estimates for these communities. For example, in West Virginia, only 7 of its 55 

counties have populations of at least 65,000, the size 

necessary to receive single-year ACS estimates. The 

counties with at least 20,000 population will receive 

3-year estimates, and all areas will receive 5-year estimates. In the 2006 ACS, social, economic, housing, and 

demographic characteristics data were available for 

2,068 PUMAs nationwide. The Census Bureau also will 

produce multiyear estimates for PUMAs. 

What Data Products Are Available From the ACS? 

The Census Bureau publishes a number of data products that will help Congress use ACS estimates for a 

range of important purposes.

• Fast Facts for Congress provides social, economic, and housing data on every congressional 

district.

• Detailed tables provide the most detailed data on 

all topics and geographic areas and are the foundation on which other ACS data products are built. 

• Data profi les summarize key social, economic, 

housing, and demographic characteristics. 

 

 Type of geographic area 

Total 

number of 

areas

Percent of total areas receiving . . .

1-year, 

3-year, 

& 5-year 

estimates

3-year & 

5-year 

estimates 

only

5-year 

estimates 

only

States and District of Columbia 51 100.0 0.0 0.0

Congressional districts 435 100.0 0.0 0.0

Public Use Microdata Areas* 2,071 99.9 0.1 0.0

Metropolitan statistical areas 363 99.4 0.6 0.0

Micropolitan statistical areas 576 24.3 71.2 4.5

Counties and county equivalents 3,141 25.0 32.8 42.2

Urban areas 3,607 10.4 12.9 76.7

School districts (elementary, secondary, and unifi ed) 14,120 6.6 17.0 76.4

American Indian areas, Alaska Native areas, and 

Hawaiian homelands 607 2.5 3.5 94.1

Places (cities, towns, and census designated places) 25,081 2.0 6.2 91.8

Townships and villages (minor civil divisions) 21,171 0.9 3.8 95.3

ZIP Code tabulation areas 32,154 0.0 0.0 100.0

Census tracts 65,442 0.0 0.0 100.0

Census block groups 208,801 0.0 0.0 100.0

 Table 3. Major Geographic Areas and Type of ACS Estimates Received

* When originally designed, each PUMA contained a population of about 100,000. Over time, some of these PUMAs have gained or lost 

population. However, due to the population displacement in the greater New Orleans areas caused by Hurricane Katrina in 2005, Louisiana 

PUMAs 1801, 1802, and 1805 no longer meet the 65,000-population threshold for 1-year estimates. With reference to Public Use Microdata 

Sample (PUMS) data, records for these PUMAs were combined to ensure ACS PUMS data for Louisiana remain complete and additive.

Source: U.S. Census Bureau, 2008. This tabulation is restricted to geographic areas in the United States. It was based on the population sizes of 

geographic areas from the July 1, 2007, Census Bureau Population Estimates and geographic boundaries as of January 1, 2007. Because of the 

potential for changes in population size and geographic boundaries, the actual number of areas receiving 1-year, 3-year, and 5-year estimates 

may diff er from the numbers in this table.
What Congress Needs to Know 7

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

• Narrative profi les provide information based on 

the data profi les in a user-friendly, text-and-graphic 

format that puts various topics into words for the 

general user. 

• Comparison profi les make it easy to compare 

data for two sequential years. They are new in 

2007 and will present 2006 ACS and 2007 ACS 

estimates and display the results of statistical testing to assess if the diff erences in these 2 years are 

statistically diff erent.

• Subject tables provide more detail than the data 

profi les and present summarized topic-specifi c 

tables (for example on education, employment, 

income, and children). 

• Selected population profi les provide population 

profi les for various race, Hispanic origin, ancestry, and place of birth groups. Selected population 

profi les are only released for geographic areas 

with populations of 500,000 or greater, including 

all congressional districts. More than 200 diff erent 

population groups are represented in the selected 

population profi les. 

• State ranking tables compare various characteristics for the United States, all states, the District of 

Columbia, and Puerto Rico. 

• Thematic maps present information in the state 

ranking tables on a map to show geographic relationships for these population characteristics. 

• Geographic comparison tables complement 

state ranking tables by showing key population 

characteristics for geographic areas beyond the 

state level. 

• Public Use Microdata Samples provide data fi les 

of individual response records for a sample of all 

housing unit and group quarters residents who 

responded to the survey. All personally identifi -

able information is removed from these records to 

protect confi dentiality. The only geographic codes 

available on these fi les are state and PUMA.

• Summary fi les contain the complete set of ACS 

detailed tables released each year in a computer 

fi le format.

Legislative Uses of the American Community Survey

This section covers two major areas:

1. Uses of ACS data to implement existing laws.

2. Uses of ACS data to carry out the daily responsibilities of congressional staff .

Use of ACS in Existing Laws

Many laws require census data as the basis for allocating funds, creating program and grant eligibility 

requirements, and establishing geographic designations (e.g., metropolitan areas, urban areas, nonurban 

areas). Census data are also used to monitor compliance with federal laws, such as the Age Discrimination 

and Employment Act.

The text box on this page gives an example of a wellknown federal program that relies on data from the 

ACS to allocate funds.

Following are additional examples of signifi cant federal 

laws and programs that rely on ACS data for implementation, along with a list of ACS questions that produce 

the needed data. The Census Bureau has prepared 

a report for Congress describing the history of each 

question on the ACS and showing specifi c legislative 

uses for the data. The full report is available at <http://

www.census.gov/acs/www/Downloads/Final_2010

_Census_and_American_Community_Survey_Subjects

_Notebook.pdf>.

Low-Income Home Energy Assistance 

Program (LIHEAP)

[42 U.S. Code, 8629(a) & (b)(2), and 

8622(11)]

Enacted as part of the Omnibus Budget Reconciliation 

Act of 1981, the Low Income Home Energy 

Assistance Program (LIHEAP) is a mandatory block 

grant program to the states. The mission of LIHEAP is 

to help low-income households pay their home energy 

bills. 

Congress established the law’s block grant distribution 

formula based on each state’s weather and lowincome population. The income question on the ACS 

is essential to determining the low-income population 

in each state. Implementation of the LIHEAP program 

also relies on data from other ACS questions, 

including age, gender, Hispanic origin, relationship, 

disability, units in structure, and occupants per room. 

LIHEAP program administrators also use data from 

the ACS question on selected monthly owner costs 

to analyze current residential energy supply and 

consumption and to forecast future energy needs.
8 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Civil Rights Act

42 U.S. Code, 2000, 42 U.S. Code, 1975c 

• Age

• Gender

• Hispanic origin

• Race

• Labor force status

• Work status last year

Older Americans Act

42 U.S. Code, 3002(28)–(30)

• Age

• Gender

• Hispanic origin

• Race

• Relationship

• Marital status

• Ancestry

• Language spoken at home

• Educational attainment/school enrollment

• Residence one year ago

• Disability 

• Income

• Work status last year

• Plumbing facilities

• Telephone services available

• Selected monthly owner costs

• Marital history

• VA service-connected disability rating

Community Services Block Grant Act

42 U.S. Code, 9902(2)

• Labor force status

• Income

• Year structure built

• Number of occupants per room

• Number of bedrooms

• Kitchen facilities

• Plumbing facilities

• Telephone service available

• Tenure

• Rent

• Selected monthly owner costs

National Aff ordable Housing Act 

42 U.S. Code, 12705 (b)(1); 24 CFR 91.205 

(a) 42 U.S. Code 12747

• Gender

• Hispanic origin

• Race

• Relationship

• Units in structure

• Year moved into unit

• Kitchen facilities

• Plumbing facilities

• Tenure

• Rent

• Selected monthly owner costs

Congressional Uses of ACS Data

Data from the ACS and the decennial census are helpful to members of Congress and their staff s in many 

ways. ACS data can be used in drafting congressional 

testimony, press releases, statements, and constituent 

correspondence; conducting legislative research; and 

developing grant formulas for proposed programs. 

Census Bureau staff in the 12 regional offi ces and 

national headquarters are available to answer questions about the ACS and your particular data needs. 

See the “Additional Resources” section of this handbook for complete contact information for Census 

Bureau regional offi ces and headquarters. Figure 1 has 

some examples of how congressional staff can use ACS 

data to carry out their responsibilities. 

Cosponsoring Legislation

Your member receives a Dear Colleague letter requesting that he or she cosponsor a bill to expand benefi ts 

for veterans. First, you want to know how many veterans live in your congressional district. 

You can access information on the district’s civilian 

veteran population based on the latest ACS data by 

using Fast Facts for Congress at <http://fastfacts

.census.gov>. After entering any ZIP Code from the district on the Fast Facts home page, the database links 

you to a table of key demographic, social, housing, 

and economic data for the entire district. Information 

about the civilian veteran population appears in a table 

entitled “Social Characteristics.” See Figure 1 for an 

example based on Congressional District 11 in Virginia.
What Congress Needs to Know 9

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Uses at <http://www.census.gov/Press-Release

/www/2007/subjects_notebook.pdf> includes detailed 

information on the subjects covered in the ACS.

Analyzing Legislative Proposals

During a meeting with an advocacy group, your member receives a proposal for reforming the social security system. Using the latest ACS data, you download 

information on labor force participation and disability 

to evaluate how many people would be aff ected by this 

proposal. You can access these data from the 2007 ACS 

data products home page at <http://www.census

.gov/acs/www/Products/>. 

Conducting Legislative Research

The Senator wants you to draft a housing assistance 

bill that targets people who are spending 30 percent or 

more of their income on housing and utilities. The bill 

would create a formula grant for the states to distribute funds authorized in the bill. Under “Demographic 

Links” on the Fast Facts for Congress home page, click 

on “Housing,” then on “Financial Characteristics” to 

access tables and maps displaying data for all of the 

states on homeowners and renters who spend 30 percent or more of their income on housing and utilities. 

Data are available to download into a variety of formats. You then go to the American FactFinder Guide to 

Data Products at <http://www.census.gov/acs/www

/Products/users_guide/> to access more detailed information about the characteristics of homeowners and 

renters within each state.

 Figure 1. Example of Social Characteristics Data on Fast Facts for 

Congress

Source: U.S. Census Bureau, Fast Facts for Congress, accessed at <http://fastfacts.census.gov>.

Drafting a Floor Statement

In less than an hour your member wants to make a 

speech about the economy. You need quick information 

on how many families in the district are living below 

the poverty level and how that fi gure compares with 

the poverty level for the nation as a whole. 

A summary of key economic characteristics for all congressional districts is available through Fast Facts for 

Congress at <http://fastfacts.census.gov>. After entering any ZIP Code from the district on the Fast Facts

home page, the database links you to a table of key 

demographic, social, housing, and economic data. The 

information, based on the latest ACS data, summarizes 

selected economic characteristics, including the number of families and individuals living below the poverty 

level, to the nation as a whole. These data are located 

in a table entitled “Economic Characteristics.” Figure 2 

displays the economic characteristics in the fact sheet 

for Congressional District 11 in Virginia.

Responding to a Constituent Inquiry

Your offi ce receives a letter from a constituent who 

wants to know why he was selected to participate in 

the ACS. You need to understand how addresses are 

selected, how personal information is protected, what 

questions are asked on the ACS, and how Congress 

uses the responses to write legislation and assist constituents. 

The Census Bureau publication Questions and Answers: 

American Community Survey at <http://www.census

.gov/acs/www/Downloads/ACSQandA.pdf> can help 

you respond to your constituent’s letter. Subjects 

Planned for the 2010 Census and American 

Community Survey: Federal Legislative and Program 
10 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The Census Bureau’s Web site is the most accessible 

source of ACS data. The Census Bureau has developed 

several data dissemination tools to meet the needs of 

congressional staff . A description of these tools and 

examples of how congressional staff ers can use them 

are provided on the following pages. 

When deciding which product to use, you should 

consider how much data you need to accomplish your 

task. Fast Facts for Congress often will meet your 

needs if you are looking for quick socioeconomic and 

demographic information about a specifi c congressional district or state. Fast Facts for Congress also 

includes a Frequently Asked Questions and Answers 

page to answer the most common questions about the 

ACS and other census programs.

If you need a greater level of technical information 

about the data, the detailed tables and statistics in the 

American FactFinder often are more useful. 

American FactFinder 

The American FactFinder, at <http://factfi nder.census

.gov>, is an electronic system for disseminating Census 

Bureau data, including ACS estimates, on the Internet. To access the American FactFinder (AFF) from the 

 Census Bureau home page: 

• Go to <http://www.census.gov/>.

• Select “American FactFinder” from the left side bar. 

(See link circled in Figure 3.)

From the American FactFinder page you can access a 

variety of Census Bureau products. If, for example, you 

wanted to access information on commuting patterns 

within a congressional district, you could use the “Fast 

Access to Information” box, shown in Figure 4. Entering a ZIP Code will get you a fact sheet for that area. 

Scrolling down the fact sheet you will fi nd information 

on the mean travel time to work. If you want additional details, click on “show more.” Using the links in 

the left side bar you will also be able to access other 

summaries that may be available for your needs. For 

example, clicking on “People” and then “Employment” 

reaches a screen with a list of products including tables 

on “Commuting to Work.”

Here are some uses of American FactFinder data: 

• To access detailed tables of demographic, social, 

economic, and housing data for diff erent geographic areas, including states, counties, school 

districts, and regions.

• To access narrative profi les (text and bar charts) 

highlighting data from specifi c geographic areas. 

You can use these charts to accompany congressional testimony or fl oor statements.

Fast Facts for Congress

Fast Facts for Congress is a Web-based dissemination 

system that provides demographic, social, economic, 

and housing data for congressional districts (see Figure 

5). The site was specially designed to meet the needs 

of congressional staff members who need quick access 

to succinct data.

To obtain a quick profi le of your congressional district: 

• Go to <http://fastfacts.census.gov/home/cws

/main.html>.

 Figure 2. Example of Economic Characteristics Data on Fast Facts for 

Congress

Source: U.S. Census Bureau, Fast Facts for Congress, accessed at <http://fastfacts.census.gov>.

Accessing ACS Data
What Congress Needs to Know 11

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

• Enter an address or ZIP Code, or select a state, 

county, or congressional district and select GO.

• A table will appear that includes social, economic, 

housing, and demographic estimates about the 

congressional district, using the most recent ACS 

data (see Figure 6).

Here are some ways to use data from Fast Facts for 

Congress:

• To determine the percentage of college graduates 

in a congressional district compared with the rest 

of the United States.

• To determine the median age and racial composition of a congressional district.

• To determine the percentage of homeowners and 

renters in a congressional district. 

 Figure 3. How to Access the American FactFinder From the Census Bureau Home Page

Source: U.S. Census Bureau, accessed at <http://www.census.gov>.
12 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Figure 4. American FactFinder Web Site

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.

 Figure 5. Fast Facts for Congress Web Site

Source: U.S. Census Bureau, Fast Facts for Congress, accessed at <http://fastfacts.census.gov>.
What Congress Needs to Know 13

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Figure 6. Example of a Fact Sheet on Fast Facts for Congress Web Site

Source: U.S. Census Bureau, Fast Facts for Congress, accessed at <http://fastfacts.census.gov>.

ACS estimates off er Congress a rich resource for up-todate information about population and housing characteristics in large and small communities across the 

country. Updated every year, ACS estimates are useful 

for legislators seeking to evaluate and address national 

and local needs in a timely way. Understanding how to 

use the data will help members of Congress and their 

staff s realize the full benefi t of the ACS.

There is a notable diff erence between data from the 

ACS and data collected on the census long form in 

2000 and previous decades. The ACS produces data 

that approximate conditions over specifi c periods of 

time consisting of 1, 3, or 5 calendar years. These 

types of estimates are called period estimates. The 

census long-form sample produced data that approximated characteristics as of a single day, Census Day. 

These types of estimate are called point-in-time estimates. For more information about diff erences between 

the ACS and the decennial census, see Appendix 2.

Understanding Survey Error

As with any survey results, ACS estimates refl ect some 

level of error. When the primary sources of error are 

understood, ACS estimates can provide accurate information to assist Congress in carrying out its legislative 

and oversight functions.

Sampling Error

The sample of addresses that receive the ACS is 

selected from the Census Bureau’s Master Address File, 

also used for the decennial census. Sampling error 

occurs when information is collected from a subset of 

the people or addresses instead of from all units being 

measured. For a more detailed discussion of sampling 

error, see Appendix 3.

A larger sample increases the likelihood that an 

address or person will be included and lowers the 

uncertainty in the fi nal data, because there is greater 

confi dence that homes and people in the sample will 

represent all homes and people. This level of confi -

dence is expressed in the margins of error that are 

published with all ACS estimates. These margins of 

error are based on a 90-percent level of confi dence. 

Adding and subtracting the margin of error to the 

estimate produces a range around the estimate, called 

a confi dence interval, which means that there is a 90 

percent chance that the estimate for the population 

falls within the range.

Figure 7 shows how margins of error are presented in 

the Census Bureau’s American FactFinder.

Using ACS Data
14 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Figure 7. Example of Margins of Error on the American FactFinder Web Site

Source: U.S. Census Bureau, 2006 American Community Survey, Table S0201 (Selected Population Profi le in the United States), accessed at 

<http://factfi nder.census.gov>.

Other Sources of Error

In addition to sampling error, the quality of ACS estimates can be aff ected by other factors related to diffi -

culties in gathering or processing the data, collectively 

referred to as nonsampling error. Nonresponse is the 

most well-known source of nonsampling error. Some 

households refuse to answer the ACS (even though 

response is required by law); sometimes interviewers 

cannot fi nd anyone at home, even after several tries; 

and sometimes a household will participate in the ACS 

but fail to answer all of the questions. Other factors 

may occur during the data collection and processing 

phases that aff ect the quality of the fi nal estimates. 

See Appendix 6 for more information on nonsampling 

error.

Understanding Multiyear ACS Estimates

The ACS produces period estimates and is designed to 

provide estimates that describe characteristics over the 

full time period. In the case of multiyear ACS estimates, 

the period is either 3 or 5 calendar years (e.g., 

January 2005 through December 2007 or January 2006 

through December 2010). 

While one may think of these estimates as representing 

average characteristics over the 36 or 60 months of 

the reference period, it must be remembered that the 

estimates are not calculated as an average. Rather, the 

ACS collects survey information continuously nearly 

every day of every year and then aggregates the results 

over the entire time period. 

Appendix 1 provides greater detail on interpretation of 

ACS single-year and multiyear estimates. 

When Should I Use 1-Year, 3-Year, and 5-Year ACS 

Estimates? 

Starting in 2010, there will be three sets of estimates 

available from the ACS. Refer to Table 2 for a summary 

of timing of the release of each set of estimates. 

• For areas with populations below 20,000, each 

year the Census Bureau will produce a single set of 

multiyear estimates based on 5 years of data collection. 

• For areas with populations between 20,000 and 

65,000, each year the Census Bureau will produce two diff erent sets of estimates. One will be 

based on data collected over a 3-year period; the 

other will be based on data collected over a 5-year 

period. 

• For areas with populations of at least 65,000, the 

Census Bureau plans to release three diff erent 

sets of estimates each year: single-year estimates 

(based on a single calendar year’s data collection) 

and two sets of multiyear estimates (based on data 

collected over 3-year and 5-year periods).
What Congress Needs to Know 15

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

TIP: If you need the most current data, use the ACS 

estimates that are based on the fewest years of 

data collection. For example:

 If you are interested in tracking trends in language spoken at home for a county with a population of 250,000, the 1-year estimates for that 

county will provide the most current data from 

year to year. For areas of this size, the 1-year 

estimates should be suffi ciently reliable for major 

language groups. For a relatively rare language, 

even in a very large county, the 3-year estimates 

may be a better source of reliable data.

 If a county in your district has a population of 

only 30,000 and you are interested in analyzing 

county-level data on language spoken at home, 

then you must use 3-year estimates for that 

county, which will be available starting in 2008 

and every year thereafter. Estimates based on 

1 year of data collection will not be published for 

a county of this size.

TIP: Do not compare data from diff erent sets of estimates. For example:

 You want to compare the number of children 

living on New York City’s Lower East Side whose 

primary household language is Spanish with 

Spanish-speaking children in the city as a whole 

and in New York State. Use 5-year ACS estimates 

for all three areas of comparison, even though 

1-year estimates are available for the city and the 

state, because only 5-year estimates are available 

for census tracts that make up the Lower East 

Side.

TIP: Educational materials developed by the Census 

Bureau can help you decide when to use 1-year, 

3-year, or 5-year estimates. For example:

 Appendix 1 off ers guidelines for making these 

decisions and provides examples. Appendix 4 

provides a wealth of information about the 

mechanics of comparing ACS estimates from one 

year to the next. The ACS Web site provides links 

to other valuable references. The Congressional 

Aff airs Offi ce at the Census Bureau can direct you 

to the materials you need.

Comparing ACS Estimates Over Time

The key to making useful comparisons of ACS estimates over time is to focus on trends in the data. 

Because ACS estimates are derived from a sample 

survey, some level of uncertainty is associated with the 

data. The level of uncertainty is expressed by a 

confi dence interval, which represents a range within 

which the true estimate lies. The margin of error, provided for each estimate that the ACS releases, is used 

to determine this range.

The estimates for a certain characteristic might appear 

to move up or down from year to year, but the data 

user should note whether the amount of change is 

greater than the margin of error associated with that 

estimate. Figure 8, showing school enrollment estimates for Dallas County, Texas, should help you understand the basic numbers in ACS tables.

ACS Case Study: Trends in School Enrollment

You are interested in knowing whether the number of 

children 3 years and over enrolled in school in Dallas 

County has changed from 2006 to 2007.

First, fi nd the 2007 ACS school enrollment estimate 

for the county from the American FactFinder’s detailed 

tables (see Figure 8). The point estimate for the county 

is 618,967 children, with a margin of error of +/-7,683. 

That means the number of children enrolled in school 

is likely to be (with 90-percent confi dence) between 

611,284 and 626,650.

Now fi nd the 2006 ACS school enrollment estimate 

for the county from the American FactFinder’s detailed 

tables (see Figure 9). In 2006, the point estimate 

for the county is 612,863 children, with a margin of 

error of +/-6,530. That means the number of children 

enrolled in school is likely to be (with 90-percent confi -

dence) between 606,333 and 619,393. A quick look at 

the two intervals tells you that they overlap, which is a 

signal that these two estimates are not likely to be statistically diff erent. But testing should be done to make 

a true determination.

To evaluate whether the enrollment fi gure has changed 

in a meaningful way, calculate the diff erence between 

the point estimates for 2007 and 2006. The diff erence is 6,104. Using the margins of error for these two 

estimates you can test to see if this is a statistically 

signifi cant diff erence. Appendix 3 gives you step-bystep instructions. In this example we could not conclude that the estimates were statistically diff erent and 

therefore could not conclude that school enrollment for 

this area has changed. 

Because Congress generally is interested in trends 

over a longer period of time (for example, the 5-year 

period covered by a program authorization), instead of 

year-to-year conditions, the ACS off ers a unique way to 

track whether school enrollment is going up or down. 

A more detailed analysis of ACS data would allow 

Congress to look at how any gains or losses aff ected 

the characteristics of the student population, such as 

median family income, language spoken at home, and 

race.
16 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Figure 8. 2007 School Enrollment Data From the American FactFinder

Source: U.S. Census Bureau, 2007 American Community Survey, Table B14001 (SCHOOL ENROLLMENT BY LEVEL OF SCHOOL FOR THE 

 POPULATION 3 YEARS AND OVER), accessed at <http://factfi nder.census.gov>.

 Figure 9. 2006 School Enrollment Data From the American FactFinder

Source: U.S. Census Bureau, 2006 American Community Survey, Table B14001 (SCHOOL ENROLLMENT BY LEVEL OF SCHOOL FOR THE 

 POPULATION 3 YEARS AND OVER), accessed at <http://factfi nder.census.gov>.
What Congress Needs to Know 17

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Comparing the ACS With Other Data Sources

The ACS is the largest household survey in the country, 

off ering comprehensive data on demographic, social, 

economic, and housing characteristics, including 

poverty, income, race, and disability, for almost all U.S. 

geographic areas. By 2010, the survey will generate 

estimates for all areas, using 5-year averages, down to 

the community level: census tracts and block groups.

Because of its large representative sample (about 3 million addresses annually), the ACS is considered a major 

source of information on the nation’s socioeconomic, 

housing, and demographic characteristics. However, 

other sources of data on these characteristics exist 

both inside and outside the Census Bureau. In some 

cases, other federal agencies contract with the Census 

Bureau to conduct surveys on their behalf. 

Congress can compare data from the ACS with data 

from other federal sources, including the decennial 

census. In doing so, the user should understand how 

diff erences in design and methodology for collecting 

data aff ect the meaning and reliability of the data. 

Census Bureau staff can help you make appropriate 

comparisons. 

Reference Periods

This section provides information on reference periods for selected ACS topics, such as employment, 

education, and income. A reference period helps you 

understand the period of time that the data refl ect. 

The unique design and methodology of each census 

and survey the Census Bureau and other federal agencies conduct require some diff erences in defi nitions, 

reference periods, and other factors that bear on the 

interpretation and use of data. Appendix 2 discusses 

some of these factors in greater detail. Congressional 

staff ers are encouraged to contact the Census Bureau’s 

Congressional Aff airs Offi ce for more guidance on 

interpreting and using specifi c data sets.

Understanding the reference period for questions on 

the ACS will help congressional staff correctly interpret 

the characteristics of their congressional district, state, 

or other areas of interest. Questions on income, education, and previous residence are examples of topics 

that have rolling reference points, instead of reference 

points fi xed in time, in order to produce information 

that refl ects conditions in communities over a year’s 

time. 

Comparing the ACS and the Decennial Census

The Census Bureau provides detailed guidance on how 

best to compare estimates from the ACS and Census 

2000. The guidance is divided into three major categories: compare, compare with caution, and do not 

compare. The complete table is located at <http://

www.census.gov/acs/www/UseData/compACS.htm>. 

It is important to understand when it is appropriate 

to make comparisons between ACS and Census 2000 

data. 

Appendix 2 discusses diff erences between data from 

the ACS and Census 2000 that might aff ect interpretation of the estimates. Some key factors to bear in mind 

when comparing the ACS and the census include:

• Several topics included on the ACS were not 

included in Census 2000, although they may have 

been included on previous censuses.

• Some questions on the ACS that also were asked 

in Census 2000 have been modifi ed to improve 

the quality of the data. An important example of 

a modifi ed question is disability. The ACS asks a 

diff erent series of questions than were asked in 

Census 2000 to better identify disabilities that 

aff ect a person’s day-to-day activities. Several of 

the ACS questions related to housing characteristics have been modifi ed from Census 2000, based 

on extensive research and testing.

• The ACS views communities over a period of 

time—instead of on a specifi c date, like the census. 

People come and go from households over time, 

of course, so how does the Census Bureau decide 

who should be considered a resident of a sample 

address? The answer is determined by a residence 

rule. The ACS uses a “current” residence rule, 

defi ned as anyone living or staying for more than 

2 months in the residence at the time the data are 

collected. By contrast, the decennial census uses a 

“usual residence” rule, defi ned as the place where a 

person lives or stays most of the time as of Census 

Day (April 1).

Intercensal Population and Housing Estimates

While the ACS produces demographic and housing unit 

estimates between decennial censuses, the Population 

Estimates Program produces and disseminates the offi -

cial estimates of the population for the nation, states, 

counties, cities, and towns, and estimates of housing 

units for states and counties. These estimates are often 

referred to as annual population estimates or intercensal estimates. The Population Estimates Program also 

releases estimates of the population by age, sex, race, 

and Hispanic origin for the nation, states, and counties.

Congress uses intercensal population and housing 

estimates in formulas for allocating federal funds and 
18 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Additional Resources About the American 

Community Survey

to monitor demographic changes and trends. Section 

183 of the Census Act (Title 13, U.S. Code) requires 

federal agencies to use the most recent population 

estimates to determine funding allocations to states, 

counties, and localities, whenever population and basic 

population characteristics are called for in the funding 

formula, unless the law specifi cally requires the use of 

decennial census data.

Intercensal estimates are updated every year as of July 1. 

As new data become available, estimates are revised 

for years dating back to the most recent decennial 

census.

The offi cial population estimates of sex, age, race, and 

Hispanic origin, along with total housing units are used 

in the fi nal stage of weighting to improve data consistency. The ACS produces estimates of the population 

and housing for geographic areas (such as congressional districts) for which intercensal estimates are not 

available. For more information about the implications 

of population controls on ACS estimates, see Appendix 7.

Comparing the ACS and Other Census Bureau 

Surveys

Three of the best-known surveys the Census Bureau 

conducts are the Current Population Survey (CPS), the 

Survey of Income and Program Participation (SIPP), and 

the American Housing Survey (AHS). A review of factors 

that make these surveys unique and distinguish them 

from the ACS will help you to select the survey that 

best meets your data needs. 

The CPS is conducted for the Labor Department’s 

Bureau of Labor Statistics and is the primary source 

of information on labor force characteristics. The 

survey generates the nation’s offi cial monthly 

employment and unemployment rates, as well 

as annual poverty rates. Supplemental questions 

produce information on other useful topics such as 

income, employee benefi ts, school enrollment, and 

work schedules. Compared with the ACS, the CPS 

has a smaller sample that produces direct estimates 

for the nation as a whole and serves as part of 

model-based estimates for individual states and 

other geographic areas.

The SIPP is a longitudinal survey, meaning it follows the same respondents over a period of time. 

The survey, which the Census Bureau sponsors, is 

designed to track changes in income over time for 

the same households, regardless of employment 

status. The survey collects data on taxes, assets, 

liabilities, and participation in government transfer 

programs. SIPP data allow the government to evaluate the eff ectiveness of federal, state, and local 

programs; to estimate future costs and coverage for 

government programs such as food stamps; and to 

provide improved statistics on the distribution of 

income and measures of economic well-being in the 

country. 

The AHS is conducted under contract with the 

Department of Housing and Urban Development. 

AHS collects data on the nation’s housing stock, 

including apartments, single-family homes, mobile 

homes, vacant housing units, household characteristics, income, housing and neighborhood quality, 

housing costs, equipment and fuels, size of housing 

unit, and recent movers. A national survey of about 

55,000 housing units is conducted every 2 years 

to produce reliable housing information for major 

metropolitan areas. A metropolitan area survey is 

conducted every 2 years with metropolitan areas in 

sample on a rotating basis. Each area is surveyed 

every 6 years. 

Several nonprofi t organizations that use ACS data 

extensively in their research have prepared written 

and visual resources that can help congressional staff 

use ACS data eff ectively. We are pleased to recommend 

the following publications as additional resources for 

congressional data users.

Population Bulletin: The American Community Survey

<http://www.prb.org/pdf05/60.3The_American

_Community.pdf>

Event: Tracking Who We Are and Where We Are Going: 

Using the American Community Survey (video)

<http://www.brookings.edu/events/2005

/0628community-development.aspx>

Web Links for Additional Data Sources

Intercensal Population Estimates

<http://www.census.gov/popest/estimates.php>

Survey of Income and Program Participation

<http://www.sipp.census.gov/sipp/overview.html>
What Congress Needs to Know 19

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

American Housing Survey

<http://www.census.gov/hhes/www/housing/ahs/ahs

.html>

Economic Census

<http://www.census.gov/econ/census07/>

Current Population Survey

<http://www.census.gov/cps/>

Printed Publications

The Census Bureau publishes a number of resources to 

help people use ACS data eff ectively and appropriately. 

These documents can be downloaded from the Census 

Bureau’s Web site and printed. 

Questions and Answers: American Community Survey

<http://www.census.gov/acs/www/Downloads

/ACSQandA.pdf>

Subjects Planned for the 2010 Census and American 

Community Survey: Federal Legislative and Program 

Uses (3/31/07)

<http://www.census.gov/acs/www/Downloads/Final

_2010_Census_and_American_Community_Survey

_Subjects_Notebook.pdf>

Questions Planned for the 2010 Census and American 

Community Survey

<http://www.census.gov/acs/www/Downloads

/Questions_Planned_for_the_2010_Census_and

_American_Community_Survey.pdf>

2006 ACS Data Users Handbook

<http://www.census.gov/acs/www/Downloads

/Handbook2006.pdf>

Facts for Features: Collections of statistics from the 

Census Bureau’s demographic and economic subject 

areas that commemorate anniversaries and observances (such as Women’s History Month and 

St. Patrick’s Day) or provide background information 

for topics in the news (such as a state holding a presidential primary)

<http://www.census.gov/Press-Release/www/releases

/archives/facts_for_features_special_editions/index

.html>

For a list of additional ACS resources, see Appendix 8.

Contacting Census Bureau Headquarters and 

Regional Census Offi ces

Census Bureau’s Congressional Aff airs Offi ce

Phone: 301-763-6100

E-mail: <cao@census.gov>

American Community Survey

American Community Survey

U.S. Census Bureau

4600 Silver Hill Rd. 

Room 3K276

Washington, DC 20233-7500

Phone: 301-763-INFO (4636)

E-mail: <cmo.acs@census.gov>

Internet: <www.census.gov/acs/www>

Atlanta, GA

U.S. Census Bureau

Atlanta Regional Offi ce

101 Marietta St., Suite 3200

Atlanta, GA 30303-2700 

Toll-free: 1-800-424-6974

Phone: 404-730-3832

Fax: 404-730-3835

TDD: 404-730-3963

E-mail: <atlanta.regional.offi ce@census.gov>

Boston, MA 

U.S. Census Bureau

Boston Regional Offi ce

4 Copley Place, Suite 301

P.O. Box 9108

Boston, MA 02117-9108 

Toll-free: 1-800-562-5721

Phone: 617-424-4501

Fax: 617-424-0547

TDD: 617-424-0565

E-mail: <boston.regional.offi ce@census.gov> 

Charlotte, NC 

U.S. Census Bureau

Charlotte Regional Offi ce

901 Center Park Drive, Suite 106

Charlotte, NC 28217-2935

Toll-free: 1-800-331-7360

Phone: 704-424-6400

Fax: 704-424-6944

TDD: 704-424-6963 

E-mail: <charlotte_regional_offi ce@census.gov>
20 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Chicago, IL

U.S. Census Bureau

Chicago Regional Offi ce

1111 W. 22nd Street, Suite 400

Oak Brook, IL 60523-1918

Toll-free: 1-800-865-6384

Phone: 630-288-9200 

Fax: 630-288-9288

TDD: 708-562-1791

E-mail: <chicago.regional.offi ce@census.gov>

Dallas, TX 

U.S. Census Bureau

Dallas Regional Offi ce

8585 N. Stemmons Freeway

Suite 800 S

Dallas, TX 75247-3836 

Toll-free: 1-800-835-9752

Phone: 214-253-4400

Fax: 214-655-5362

TDD: 214-655-5363

E-mail: <daro.census@census.gov>

Denver, CO

U.S. Census Bureau 

Denver Regional Offi ce

6900 West Jeff erson Avenue, Suite 100

Lakewood, CO 80235-2032 

Toll-free: 1-800-852-6159

Phone: 303-264-0202

Fax: 303-969-6777

TDD: 303-969-6767

E-mail: <Denver_Regional_Offi ce@census.gov> 

Detroit, MI

U.S. Census Bureau 

Detroit Regional Offi ce

1395 Brewery Park Blvd.

Detroit, MI 48207 

Toll-free: 1-800-432-1495

Phone: 313-259-1158

Fax: 313-259-5045

TDD: 313-259-5169

E-mail: <detroit.internet.mail@census.gov> 

Kansas City, KS 

U.S. Census Bureau

Kansas City Regional Offi ce

1211 North 8th Street

Kansas City, KS 66101-2129 

Kansas City, KS—Con.

Toll-free: 1-800-728-4748

Phone: 913-551-6728

Fax: 913-551-6789

TDD: 913-551-5839

E-mail: <KC.Regional.Offi ce@census.gov> 

Los Angeles, CA 

U.S. Census Bureau

Los Angeles Regional Offi ce

15350 Sherman Way, Suite 400 

Van Nuys, CA 91406-4224

Toll-free: 1-800-992-3530

Phone: 818-267-1700

Fax: 818-267-1711

TDD: 818-904-6429

E-mail: <la.regional.offi ce@census.gov>

New York, NY 

U.S. Census Bureau

New York Regional Offi ce

395 Hudson Street, Suite 800

New York, NY 10014

Toll-free: 1-800-991-2520

Phone: 212-584-3400

Fax: 212-478-4800

TDD: 212-478-4793

E-mail: <new.york.regional.offi ce@census.gov>

Philadelphia, PA 

U.S. Census Bureau

Philadelphia Regional Offi ce

833 Chestnut Street

5th Floor, Suite 504

Philadelphia, PA 19107-4405

Toll-free: 1-800-262-4236

Phone: 215-717-1800

Fax: 215-717-0755

TDD: 215-717-0894

E-mail: <Philadelphia.Regional.Offi ce@census. gov>

Seattle, WA 

U.S. Census Bureau

Seattle Regional Offi ce

601 Union Street, Suite 3800

Seattle, WA 98101-1074

Toll-free: 1-800-233-3308

Phone: 206-381-6200

Fax: 206-381-6310

TDD: 206-381-6318

E-mail: <seattle.regional.offi ce@census.gov> 
Glossary

Accuracy. One of four key dimensions of survey 

quality. Accuracy refers to the diff erence between 

the survey estimate and the true (unknown) value. 

Attributes are measured in terms of sources of error 

(for example, coverage, sampling, nonresponse, 

measurement, and processing). 

American Community Survey Alert. This periodic 

electronic newsletter informs data users and other 

interested parties about news, events, data releases, 

congressional actions, and other developments 

associated with the ACS. See <http://www.census

.gov/acs/www/Special/Alerts/Latest.htm>.

American FactFinder (AFF). An electronic system 

for access to and dissemination of Census Bureau 

data on the Internet. AFF off ers prepackaged data 

products and user-selected data tables and maps 

from Census 2000, the 1990 Census of Population 

and Housing, the 1997 and 2002 Economic 

Censuses, the Population Estimates Program, annual 

economic surveys, and the ACS. 

Block group. A subdivision of a census tract (or, 

prior to 2000, a block numbering area), a block 

group is a cluster of blocks having the same fi rst 

digit of their four-digit identifying number within 

a census tract. 

Census geography. A collective term referring 

to the types of geographic areas used by the 

Census Bureau in its data collection and tabulation 

operations, including their structure, designations, 

and relationships to one another. See <http://www

.census.gov/geo/www/index.html>.

Census tract. A small, relatively permanent 

statistical subdivision of a county delineated by a local 

committee of census data users for the purpose of 

presenting data. Census tract boundaries normally 

follow visible features, but may follow governmental 

unit boundaries and other nonvisible features; they 

always nest within counties. Designed to be relatively 

homogeneous units with respect to population 

characteristics, economic status, and living conditions 

at the time of establishment, census tracts average 

about 4,000 inhabitants. 

Coeffi cient of variation (CV). The ratio of the 

standard error (square root of the variance) to the 

value being estimated, usually expressed in terms 

of a percentage (also known as the relative standard 

deviation). The lower the CV, the higher the relative 

reliability of the estimate. 

Comparison profi le. Comparison profi les are 

available from the American Community Survey for 

1-year estimates beginning in 2007. These tables 

are available for the United States, the 50 states, the 

District of Columbia, and geographic areas with a 

population of more than 65,000.

Confi dence interval. The sample estimate and its 

standard error permit the construction of a confi dence 

interval that represents the degree of uncertainty about 

the estimate. A 90-percent confi dence interval can be 

interpreted roughly as providing 90 percent certainty 

that the interval defi ned by the upper and lower 

bounds contains the true value of the characteristic. 

Confi dentiality. The guarantee made by law 

(Title 13, U.S. Code) to individuals who provide 

census information, regarding nondisclosure of that 

information to others. 

Consumer Price Index (CPI). The CPI program of 

the Bureau of Labor Statistics produces monthly data 

on changes in the prices paid by urban consumers for 

a representative basket of goods and services.

Controlled. During the ACS weighting process, the 

intercensal population and housing estimates are used 

as survey controls. Weights are adjusted so that ACS 

estimates conform to these controls. 

Current Population Survey (CPS). The CPS is 

a monthly survey of about 50,000 households 

conducted by the Census Bureau for the Bureau of 

Labor Statistics. The CPS is the primary source of 

information on the labor force characteristics of the 

U.S. population.

Current residence. The concept used in the ACS to 

determine who should be considered a resident of a 

sample address. Everyone who is currently living or 

staying at a sample address is considered a resident of 

that address, except people staying there for 2 months 

or less. People who have established residence at the 

sample unit and are away for only a short period of 

time are also considered to be current residents. 

Custom tabulations. The Census Bureau off ers a 

wide variety of general purpose data products from the 

ACS. These products are designed to meet the needs 

of the majority of data users and contain predefi ned 

What Congress Needs to Know 21

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
22 What Congress Needs to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

sets of data for standard census geographic areas, 

including both political and statistical geography. 

These products are available on the American 

FactFinder and the ACS Web site.

For users with data needs not met through the general 

purpose products, the Census Bureau off ers “custom” 

tabulations on a cost-reimbursable basis, with the 

American Community Survey Custom Tabulation 

program. Custom tabulations are created by tabulating 

data from ACS microdata fi les. They vary in size, 

complexity, and cost depending on the needs of the 

sponsoring client.

Data profi les. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profi le that should be used if offi cial 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 diff erent 

tables that contain basic distributions of 

characteristics. These tables provide the most detailed 

data and are the basis for other ACS products.

Disclosure avoidance (DA). Statistical methods 

used in the tabulation of data prior to releasing data 

products to ensure the confi dentiality of responses. 

See Confi dentiality. 

Estimates. Numerical values obtained from a 

statistical sample and assigned to a population 

parameter. Data produced from the ACS interviews are 

collected from samples of housing units. These data 

are used to produce estimates of the actual fi gures that 

would have been obtained by interviewing the entire 

population using the same methodology. 

File Transfer Protocol (FTP) site. A Web site that 

allows data fi les to be downloaded from the Census 

Bureau Web site.

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates refl ect the characteristics 

of a geographic area over the entire 5-year period and 

will be published for all geographic areas down to the 

census block group level. 

Geographic comparison tables. More than 80 

single-variable tables comparing key indicators for 

geographies other than states.

Geographic summary level. A geographic summary 

level specifi es the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifi es the state-county 

hierarchy. Thus, both the state code and the county 

code are required to uniquely identify a county in the 

United States or Puerto Rico. 

Group quarters (GQ) facilities. A GQ facility is a 

place where people live or stay that is normally owned 

or managed by an entity or organization providing 

housing and/or services for the residents. These 

services may include custodial or medical care, as well 

as other types of assistance. Residency is commonly 

restricted to those receiving these services. People 

living in GQ facilities are usually not related to each 

other. The ACS collects data from people living in both 

housing units and GQ facilities.

Group quarters (GQ) population. The number of 

persons residing in GQ facilities. 

Item allocation rates. Allocation is a method 

of imputation used when values for missing or 

inconsistent items cannot be derived from the existing 

response record. In these cases, the imputation 

must be based on other techniques such as using 

answers from other people in the household, other 

responding housing units, or people believed to have 

similar characteristics. Such donors are refl ected in a 

table referred to as an allocation matrix. The rate is 

percentage of times this method is used.

Margin of error (MOE). Some ACS products provide 

an MOE instead of confi dence intervals. An MOE is the 

diff erence between an estimate and its upper or lower 

confi dence bounds. Confi dence bounds can be created 

by adding the margin of error to the estimate (for the 

upper bound) and subtracting the margin of error from 

the estimate (for the lower bound). All published ACS 

margins of error are based on a 90-percent confi dence 

level. 

Multiyear estimates. Three- and fi ve-year estimates 

based on multiple years of ACS data. Three-year 

estimates will be published for geographic areas with 

a population of 20,000 or more. Five-year estimates 

will be published for all geographic areas down to the 

census block group level. 

Narrative profi le. A data product that includes easyto-read descriptions for a particular geography. 

Nonsampling error. Total survey error can be 

classifi ed into two categories—sampling error and 

nonsampling error. Nonsampling error includes 

measurement errors due to interviewers, respondents, 

instruments, and mode; nonresponse error; coverage 

error; and processing error.
What Congress Needs to Know 23

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Period estimates. An estimate based on information 

collected over a period of time. For ACS the period is 

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population Estimates Program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

release: 1) initial sample size and fi nal interviews; 

2) coverage rates; 3) response rates, and; 4) item 

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

refer to the day of the interview; others refer to “the 

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

(if anyone) is considered to be a resident of a sample 

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

or ethnic group (for example, Alaska Natives) or other 

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

likely to be from random chance (sampling error) alone. 

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

percent level” if their diff erence is large enough to infer 

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

of ACS data. These estimates are meant to refl ect the 

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

geographic areas with a population of 20,000 or more.

Appendix A-1

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

an area over a specifi c time period. In the case of ACS 

single-year estimates, the period is the calendar year 

(e.g., the 2007 ACS covers January through December 

2007). In the case of ACS multiyear estimates, the 

period is either 3 or 5 calendar years (e.g., the 2005–

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

the 1-year estimates are not calculated as an average of 

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

the average of 3 or 5 single-year estimates. Rather, the 

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

over a specifi c time period—1 year, 3 years, or 5 years. 

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

for areas over an entire time frame, ACS single-year 

and multiyear estimates contrast with “point-in-time” 

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

upon data collected around April 1, 2000, and thus its 

data refl ect a narrower time frame than ACS data. The 

monthly CPS collects data for an even narrower time 

frame, the week containing the 12th of each month.

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

that would be obtained from a “point-in-time” survey 

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

(even for a single-year) may noticeably diff er from 

“point-in-time” estimates. The impact will be more 

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

area. This logic can be extended to better interpret 3-

year and 5-year estimates where the periods involved 

are much longer. If, over the full period of time (for 

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

could diff er from a “point-in-time” estimate within the 

year is provided in Table 1. Imagine a town on the Gulf 

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

(similar in concept to a period estimate), a “point-intime” estimate for any particular month would yield 

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

Source: U.S. Census Bureau, Artifi cial Data.

Month

Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sept. Oct. Nov. Dec.

20 20 40 60 60 60 60 60 60 50 30 20
A-2 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

any specifi c day or period within the year, while ACS 

multiyear estimates describe the population and 

characteristics of an area for the full 3- or 5-year 

period, not for any specifi c day, period, or year within 

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

populations of 20,000 or more. Beginning in 2010, 

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

that fi nal set of estimates can be released. This means 

that in 2008 only 1-year and 3-year estimates are 

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

the 2006–2008 ACS estimates) and consecutive 5-year 

estimates contain 4 years of overlapping coverage. 

 Table 2. Sets of Sample Cases Used in Producing ACS Multiyear Estimates

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

 estimates 2005–2007 2006–2008 2007–2009 2008–2010 2009–2011

5-year

 estimates Not Available Not Available 2005–2009 2006–2010 2007–2011

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

Single-year estimates provide more current information about areas that have changing population and/or 

housing characteristics because they are based on the 

most current data—data from the past year. In contrast, 

multiyear estimates provide less current information 

because they are based on both data from the previous 

year and data that are 2 and 3 years old. As noted earlier, for many areas with minimal change taking place, 

using the “less current” sample used to produce the 

multiyear estimates may not have a substantial infl uence on the estimates. However, in areas experiencing 

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

same because they are based on data from two different time periods. This will be true even if the ACS 

single year is the midyear of the ACS multiyear period 

(e.g., 2007 single year, 2006–2008 multiyear).

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

shows a hypothetical set of 1-year and 3-year estimates. Comparing data by release year shows that for 

an area such as this with steady growth, the 3-year 

estimates for a period are seen to lag behind the estimates for the individual years. 

Reliability

Multiyear estimates are based on larger sample sizes 

and will therefore be more reliable. The 3-year estimates are based on three times as many sample cases 

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

Source: U.S. Census Bureau.
Appendix A-3

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Table 3. Example of Diff erences in Single- and Multiyear Estimates—Percent of Population

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

and multiyear ACS estimates, should be taken into consideration when deciding which set of estimates to use.

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

only option. For the very smallest of these areas (less 

than 20,000 population), the only option will be to 

use the 5-year ACS estimates. Users have a choice of 

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

20,000. Both 3-year and 5-year ACS estimates will be 

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

they also have higher sampling variability. One measure, the coeffi cient of variation (CV) can help you 

determine the fi tness for use of a single-year estimate 

in order to assess if you should opt instead to use the 

multiyear estimate (or if you should use a 5-year estimate rather than a 3-year estimate). The CV is calculated as the ratio of the standard error of the estimate 

to the estimate, times 100. A single-year estimate with 

a small CV is usually preferable to a multiyear estimate 

as it is more up to date. However, multiyear estimates 

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

than 65,000. Users may think that they only need to 

use multiyear estimates when they are working with 

small areas, but this isn’t the case. Estimates for large 

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

as building blocks to produce estimates for meaningful higher levels of geography. These aggregations will 

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

Three primary uses of ACS estimates are to understand the characteristics of the population of an area 

for local planning needs, make comparisons across 

areas, and assess change over time in an area. Local 

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

Source: U.S. Census Bureau, Artifi cial Data.
A-4 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

where Spanish is spoken at home for ACS test counties Broward, Florida, and Lake, Illinois. The standard 

errors and CVs associated with those estimates are also 

shown.

In this illustration, the CV for the single-year estimate 

in Broward County is 1.0 percent (0.2/19.9) and in 

Lake County is 1.3 percent (0.2/15.9). Both are suffi ciently small to allow use of the more current singleyear estimates.

Single-year estimates for small subpopulations (e.g., 

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

larger CVs. In general, multiyear estimates are preferable to single-year estimates when looking at estimates 

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

had an estimated population of 76,632 in 2004 according to the Population Estimates Program. This population is larger than the Census Bureau’s 65,000-

population requirement for publishing 1-year estimates. However, many subpopulations within this 

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

but only 1,883 families with a female householder, no 

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

precision using the 3-year or 5-year estimate. In this 

example, the 5-year estimate of 40.2 percent has an 

SE of 4.9 percentage points that yields a CV of 12.2 

percent (4.9/40.2), and the 3-year estimate of 40.4 percent has an SE of 6.8 percentage points which yields a 

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

estimate as a way to assess “fi tness for use.” The CV 

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

broader categories) can allow you to improve the reliability of the aggregate estimate, bringing the CVs to a 

more acceptable level.

 Table 4. Example of How to Assess the Reliability of Estimates—Percent of Population 

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

 Table 5. Percent in Poverty by Family Type for Sevier County, TN

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

 With related children under 18 years 9,067 15.3 1.5 16.5 2.4 17.8 4.5

Married-couple families 17,320 5.8 0.7 5.4 0.9 7.9 2.0

 With related children under 18 years 6,633 7.7 1.2 7.3 1.7 12.1 3.9

Families with female householder, no husband 3,433 27.2 3.0 26.7 4.8 19.0 7.2

 With related children under 18 years 1,883 40.2 4.9 40.4 6.8 38.3 13.0

Source: U.S. Census Bureau, Multiyear Estimates Study data.

Source: U.S. Census Bureau, Multiyear Estimates Study data.
Appendix A-5

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

will do much of the statistical work to identify statistically signifi cant diff erences between the 2007 ACS and 

the 2006 ACS. 

As noted earlier, caution is needed when using multiyear estimates for estimating year-to-year change 

in a particular characteristic. This is because roughly 

two-thirds of the data in a 3-year estimate overlap with 

the data in the next year’s 3-year estimate (the overlap is roughly four-fi fths for 5-year estimates). Thus, 

as shown in Figure 1, when comparing 2006–2008 

3-year estimates with 2007–2009 3-year estimates, 

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

diff erence in these two estimates describes the diff erence between 2009 and 2006. While the interpretation 

of this diff erence is diffi cult, these comparisons can be 

made with caution. Users who are interested in comparing overlapping multiyear period estimates should 

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

be in the form of rankings or of specifi c pairs of comparisons. Whenever you want to make a comparison 

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

type. That is, 1-year estimates should only be compared with other 1-year estimates, 3-year estimates 

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

100,000 but you should use the data set that they have 

in common. In this example you could use the 3-year 

or the 5-year estimates because they are available for 

areas of 30,000 and areas of 100,000.

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

Source: U.S. Census Bureau.
A-6 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

 Figure 2. Civilian Veterans, County X Single-Year, Multiyear Estimates

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

(near the 65,000-publication threshold) and small subgroups within even large areas may limit the ability to 

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

be used to assess the “lagging eff ect” in the multiyear 

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

the period. However, this could be the case under some 

specifi c conditions, as is the case when an area is experiencing growth in a linear trend.

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

Source: U.S. Census Bureau. Based on data from the Multiyear Estimates Study.
Appendix A-7

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

single-year estimates have large CVs or when the precision of the estimates is more important than the currency of the data. Multiyear estimates should also be 

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

Single-year estimates should, in general, be used for 

larger geographies and populations when currency is 

more important than the precision of the estimates. 

Single-year estimates should be used to examine yearto-year change for estimates with small CVs. Given the 

availability of a single-year estimate, calculating the CV 

provides useful information to determine if the singleyear estimate should be used. For areas believed to be 

experiencing rapid changes in a characteristic, singleyear estimates should generally be used rather than 

multiyear estimates as long as the CV for the singleyear estimate is reasonable for the specifi c usage.

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

when they fi rst occur. Single-year estimates, if associated with suffi ciently small CVs, can be very valuable 

in identifying and studying such phenomena. Graphing trends for such areas using single-year, 3-year, and 

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

estimates could be graphed together to better understand local area variations.

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

data used for single-year estimates will be used in the 

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

 Figure 3. Proportion of Population With Bachelor’s Degree or Higher, City X Single-Year, 

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

Source: U.S. Census Bureau. Based on data from the Multiyear Estimates Study.
A-8 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

of the population were collected using a “long-form” 

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

most questions are identical or nearly identical. Differences in the design and implementation of the two 

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

characteristics for the same time frame as the decennial census estimates, which are interpreted to be a 

snapshot of April 1 of the census year. Additional differences are described below. 

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

the census, and their timing, led to important diff erences in the choice of data collection methods. For 

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

household membership. Defi ning the rules in a dissimilar way can aff ect those two very important estimates. 

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

principle of “usual residence” on April 1, 2000, in keeping with the focus of the census on the requirements 

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

concept since data are collected continuously throughout the entire year with responses provided relative 

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

The ACS yearly samples, spread over 12 months, collect information that is anchored to the day on which 

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

“last week” or “the last 12 months” all begin the reference period as of this interview date. Even the information on types and amounts of income refers to the 12 

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

period. The 1-year estimates refl ect the full calendar 

year; 3-year and 5-year estimates refl ect the full 36- or 

60-month period. 

Most decennial census sample estimates are anchored 

in this same way to the date of enumeration. The most 

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

the time period used to collect data for each singleyear ACS estimate. But a more important diff erence is 

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

Census Bureau analysts have compared sample estimates from Census 2000 with 1-year ACS estimates 

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

fi ndings and can be found at <http://www.census

.gov/acs/www/AdvMeth/Reports.htm>. In general, 

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

Residence rules determine which individuals are considered to be residents of a particular housing unit or group 

quarters. While many people have defi nite ties to a single 

housing unit or group quarters, some people may stay 

in diff erent places for signifi cant periods of time over the 

course of the year. For example, migrant workers move 

with crop seasons and do not live in any one location for 

the entire year. Diff erences in treatment of these populations in the census and ACS can lead to diff erences in 

estimates of the characteristics of some areas. 

For the past several censuses, decennial census residence rules were designed to produce an accurate 

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

count of the population as of Census Day, April 1, 

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

units, the decennial census uses a “usual residence” rule 

to enumerate people at the place where they live or stay 

most of the time as of April 1. The ACS uses a “current 

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

it is visited by an ACS interviewer is eligible to be sampled and interviewed for the survey. Further information 

on residence rules can be found at <http://www.census 

.gov/acs/www/AdvMeth/CollProc/CollProc1.htm>.

The diff erences in the ACS and census data as a consequence of the diff erent residence rules are most likely 

minimal for most areas and most characteristics. However, for certain segments of the population the usual 

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

lake, or mountain vacation areas may diff er appreciably between the census and the ACS if populations live 

there for more than 2 months.

More on Reference Periods

The decennial census centers its count and its age distributions on a reference date of April 1, the assumption 

being that the remaining basic demographic questions 

also refl ect that date, regardless of whether the enumeration is conducted by mail in March or by a fi eld followup in July. However, nearly all questions are anchored to 

the date the interview is provided. Questions with their 

own reference periods, such as “last week,” are referring 

to the week prior to the interview date. The idea that 

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

are, in many ways, similar to those in the census in that 

they refl ect the circumstances on the day the data are 

collected and the individual reference periods of questions relative to that date. However, the ACS estimates 

represent the average characteristics over a full year (or 

sets of years), a diff erent time, and reference period than 

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

Users should consider the potential impact these diff erent reference periods could have on distributions when 

comparing ACS estimates with Census 2000. 

Those who are interested in more information about differences in reference periods should refer to the Census 

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

Census 2000 with those used in the ACS. See <http://

www.census.gov/acs/www/UseData/compACS.htm>. 

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

prior to the interview date as the reference period. Thus, 

while Census 2000 collected income information for 

calendar year 1999, the ACS collects income information for the 12 months preceding the interview date. The 

responses are a mixture of 12 reference periods ranging 

from, in the case of the 2006 ACS single-year estimates, 

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

person had “at any time in the last 3 months attended 

a school or college.” A consistent 3-month reference 

period is used for all interviews. In contrast, 

Census 2000 asked if a person had “at any time since 

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

short as about 6 weeks or as long as 7 months. 

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

ACS. The census asked for annual costs, while the ACS 

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

home. Census 2000 included installment loan costs in 
A-10 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

the total annual costs but the ACS does not. In this 

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

Census 2000. Please see <http://www.census.gov/acs

/www/AdvMeth/Reports.htm> for detailed comparisons of these measures of survey quality. 
Appendix A-11

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

interval, and coeffi cient of variation. As the ACS estimates are based on a sample survey of the U.S. population, information about the sampling error associated 

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

they diff er and how each should be used. It is intended 

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

A standard error (SE) measures the variability of an estimate due to sampling. Estimates derived from a sample 

(such as estimates from the ACS or the decennial 

census long form) will generally not equal the population value, as not all members of the population were 

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

from the sample survey can be expected to deviate from this population value. It is the foundational 

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

to determine whether the diff erences between the estimates can be said to be statistically signifi cant.

A very basic example of the standard error is a population of three units, with values of 1, 2, and 3. The average value for this population is 2. If a simple random 

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

with values of 1 and 2 selected), 2 (units with values 

of 1 and 3 selected), or 2.5 (units with values of 2 and 

3 selected). In this simple example, two of the three 

samples yield estimates that do not equal the population value (although the average of the estimates 

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

the sample size used for the survey. In general, the 

larger the sample size, the smaller the standard error 

of the estimates produced from the sample. This relationship between sample size and SE is the reason ACS 

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

A margin of error (MOE) describes the precision of the 

estimate at a given level of confi dence. The confi dence 

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

(the MOE) from the population value. Confi dence levels 

of 90 percent, 95 percent, and 99 percent are commonly used in practice to lessen the risk associated 

with an incorrect inference. The MOE provides a concise measure of the precision of the sample estimate 

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

data is to use a 90-percent confi dence level. Thus, the 

MOEs published with the ACS estimates correspond 

to a 90-percent confi dence level. However, users may 

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

for the specifi c application, as a 90-percent confi dence 

level implies a 10 percent chance of an incorrect inference, in contrast with a 1 percent chance if using a 

99-percent confi dence level. Thus, if the impact of an 

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

incomes for each of 100 counties in a state, using a 

90-percent confi dence level would imply that 10 of the 

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

99-percent confi dence level would reduce the likelihood of this kind of false inference.

Measures of Sampling Error

Appendix 3.
A-12 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

where is the positive value of the ACS published MOE for the estimate.

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

from the 2006 ACS is +12,357. The SE for the estimate 

would be derived as 

Confi dence Intervals

A confi dence interval (CI) is a range that is expected 

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

confi dence” or “confi dence level.” CIs are useful when 

graphing estimates to display their sampling variabilites. The sample estimate and its MOE are used to 

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

To construct a CI at the 90-percent confi dence level, 

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

For example, if an estimate of 20,000 had an MOE 

at the 90-percent confi dence level of +1,645, the CI 

would range from 18,355 (20,000 – 1,645) to 21,645 

(20,000 + 1,645).

For CIs at the 95-percent or 99-percent confi dence 

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

 is the positive value of the MOE for the estimate at the desired confi dence level.

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

If you want to use an MOE corresponding to a confi -

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

dence level is 95 percent, then the factor is equal to 

1.960/1.645.1

 If the desired confi dence level is 99 

percent, then the factor is equal to 2.576/1.645. 

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

Virginia is +12,357. The MOE corresponding to a 95-

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

an estimate, the SEs of the estimates are needed. To 

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

confi dence intervals from the margins of error. For example, a small 

population estimate may have a calculated lower bound less than zero. 

A negative number of persons doesn’t make sense, so the lower bound 

should be set to zero instead.

1

 The value 1.65 must be used for ACS single-year estimates for 2005 

or earlier, as that was the value used to derive the published margin of 

error from the standard error in those years.

2

 If working with ACS 1-year estimates for 2005 or earlier, use the 

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 645.1

960.1

95 

Factors Associated With Margins of

Error for Commonly Used Confi dence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

  723,14357,12 645.1

960.1 MOE95 r r 

645.1

MOEACS SE 

512,7 645.1

357,12 SE 

CL XL  MOECL ˆ

CL XU  MOECL ˆ

MOECL

  CL ULCI CLCL , .

MOEACS

MOEACS

Xˆ

MOE MOEACS 645.1

576.2

99 
Appendix A-13

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

For example, to construct a CI at the 95-percent 

confi dence level for the number of civilian veterans in 

the state of Virginia in 2006, one would use the 2006 

estimate (771,782) and the corresponding MOE at the 

95-percent confi dence level derived above (+14,723).

The 95-percent CI can thus be expressed as the range 

757,059 to 786,505.

The CI is also useful when graphing estimates, to show 

the extent of sampling error present in the estimates, 

and for visually comparing estimates. For example, 

given the MOE at the 90-percent confi dence level used 

in constructing the CI above, the user could be 90 

percent certain that the value for the population was 

between 18,355 and 21,645. This CI can be represented visually as

Coeffi cients of Variation

A coeffi cient of variation (CV) provides a measure of 

the relative amount of sampling error that is associated with a sample estimate. The CV is calculated as 

the ratio of the SE for an estimate to the estimate itself 

and is usually expressed as a percent. It is a useful 

barometer of the stability, and thus the usability of a 

sample estimate. It can also help a user decide whether 

a single-year or multiyear estimate should be used for 

analysis. The method for obtaining the SE for an estimate was described earlier.

The CV is a function of the overall sample size and the 

size of the population of interest. In general, as the 

estimation period increases, the sample size increases 

and therefore the size of the CV decreases. A small CV 

indicates that the sampling error is small relative to the 

estimate, and thus the user can be more confi dent that 

the estimate is close to the population value. In some 

applications a small CV for an estimate is desirable and 

use of a multiyear estimate will therefore be preferable 

to the use of a 1-year estimate that doesn’t meet this 

desired level of precision.

For example, if an estimate of 20,000 had an SE of 

1,000, then the CV for the estimate would be 5 percent ([1,000 /20,000] x 100). In terms of usability, 

the estimate is very reliable. If the CV was noticeably 

larger, the usability of the estimate could be greatly 

diminished. 

While it is true that estimates with high CVs have 

important limitations, they can still be valuable as 

L95  059,757723,14782,771

U95  505,786723,14782,771

( )

18,355 20,000 21,645

building blocks to develop estimates for higher levels 

of aggregation. Combining estimates across geographic areas or collapsing characteristic detail can 

improve the reliability of those estimates as evidenced 

by reductions in the CVs.

Calculating Coeffi cients of Variation From Standard 

Errors

The CV can be expressed as

where is the ACS estimate and is the derived SE 

for the ACS estimate.

For example, to determine the CV for the estimated 

number of civilian veterans in the state of Virginia in 

2006, one would use the 2006 estimate (771,782), 

and the SE derived previously (7,512).

This means that the amount of sampling error present 

in the estimate is only one-tenth of 1 percent the size 

of the estimate.

The text box below summarizes the formulas used 

when deriving alternative sampling error measures 

from the margin or error published with ACS estimates.

100 ˆ u 

X

SE CV

Xˆ

%1.0100

782,771

512,7 CV u 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confi dence Levels

Standard Error (SE)

Confi dence Interval (CI)

Coeffi cient of Variation (CV)

MOE MOE ACS

.1 645

.1 960 95 

MOE MOE ACS

.1 645

.2 576 99 

.1 645

MOE ACS SE 

100 ˆ u 

X

SE CV

SE

CI X MOE X MOE   CL CL CL    , 
A-14 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Calculating Margins of Error for Derived 

Estimates

One of the benefi ts of being familiar with ACS data is 

the ability to develop unique estimates called derived 

estimates. These derived estimates are usually based 

on aggregating estimates across geographic areas or 

population subgroups for which combined estimates 

are not published in American FactFinder (AFF) tables 

(e.g., aggregate estimates for a three-county area or for 

four age groups not collapsed).

ACS tabulations provided through AFF contain the 

associated confi dence intervals (pre-2005) or margins 

of error (MOEs) (2005 and later) at the 90-percent 

confi dence level. However, when derived estimates are 

generated (e.g., aggregated estimates, proportions, 

or ratios not available in AFF), the user must calculate 

the MOE for these derived estimates. The MOE helps 

protect against misinterpreting small or nonexistent 

diff erences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

at the 90-percent confi dence level. If an MOE with a 

confi dence level other than 90 percent is desired, the 

user should fi rst calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confi dence level as described earlier in this appendix.

Calculating MOEs for Aggregated Count Data

To calculate the MOE for aggregated count data:

1) Obtain the MOE of each component estimate.

2) Square the MOE of each component estimate.

3) Sum the squared MOEs.

4) Take the square root of the sum of the squared 

 MOEs.

The result is the MOE for the aggregated count. Algebraically, the MOE for the aggregated count is calculated as:

where is the MOE of the component estimate.

The example below shows how to calculate the MOE 

for the estimated total number of females living alone 

in the three Virginia counties/independent cities that 

border Washington, DC (Fairfax and Arlington counties, 

Alexandria city) from the 2006 ACS.

The aggregate estimate is:

Obtain MOEs of the component estimates: 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs.

Thus, the derived estimate of the number of females 

living alone in the three Virginia counties/independent 

cities that border Washington, DC, is 89,008, and the 

MOE for the estimate is +4,289.

Calculating MOEs for Derived Proportions

The numerator of a proportion is a subset of the 

denominator (e.g., the proportion of single person 

households that are female). To calculate the MOE for 

derived proportions, do the following:

1) Obtain the MOE for the numerator and the MOE 

for the denominator of the proportion.

2) Square the derived proportion.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator by 

the squared proportion.

6) Subtract the result of (5) from the squared MOE of 

the numerator.

7) Take the square root of the result of (6).

8) Divide the result of (7) by the denominator of the 

proportion.

 Table 1. Data for Example 1 

Characteristic Estimate MOE

Females living alone in 

 Fairfax County 

 (Component 1)

52,354 +3,303

Females living alone in 

 Arlington County

 (Component 2)

19,464 +2,011

Females living alone in 

 Alexandria city

 (Component 3)

17,190 +1,854

Fairfax Arlington  XXXX ˆˆˆˆ Alexandria 

MOEFairfax r 303,3 , 

)854,1()011,2()303,3( 2 2 2 MOEagg r 

r ¦cMOEagg MOEc

2

MOEc

th

c

  008,89190,17464,19354,52

 011 MOEArlington r ,2 , 

r r 289,4246,391,18

MOEAlexandria r ,1854
Appendix A-15

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The result is the MOE for the derived proportion. Algebraically, the MOE for the derived proportion is calculated as:

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived proportion.

 is the estimate used as the numerator of the 

derived proportion.

 is the estimate used as the denominator of the 

derived proportion.

There are rare instances where this formula will fail—

the value under the square root will be negative. If that 

happens, use the formula for derived ratios in the next 

section which will provide a conservative estimate of 

the MOE.

The example below shows how to derive the MOE for 

the estimated proportion of Black females 25 years of 

age and older in Fairfax County, Virginia, with a graduate degree based on the 2006 ACS.

 The estimated proportion is:

where is the ACS estimate of Black females 25 

years of age and older in Fairfax County with a graduate degree and is the ACS estimate of Black 

females 25 years of age and older in Fairfax County.

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older in Fairfax County 

with a graduate degree) and denominator (number 

of Black females 25 years of age and older in Fairfax 

County).

 Table 2. Data for Example 2

Characteristic Estimate MOE

Black females 25 years 

 and older with a graduate 

 degree (numerator)

4,634 +989

Black females 25 years 

 and older

 (denominator)

31,713 +601

1461.0

713,31

634,4

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

r 989 MOEnum , 601 MOEden r 

MOEnum

MOEden den

den

num

X

X

p ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ

X gradBF ˆ

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

Thus, the derived estimate of the proportion of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County, Virginia, is 0.1461, and the 

MOE for the estimate is +0.0311. 

Calculating MOEs for Derived Ratios

The numerator of a ratio is not a subset (e.g., the ratio 

of females living alone to males living alone). To calculate the MOE for derived ratios:

1) Obtain the MOE for the numerator and the MOE 

 for the denominator of the ratio.

2) Square the derived ratio.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator 

 by the squared ratio.

6) Add the result of (5) to the squared MOE of the 

 numerator.

7) Take the square root of the result of (6).

8) Divide the result of (7) by the denominator of 

 the ratio.

The result is the MOE for the derived ratio. Algebraically, the MOE for the derived ratio is calculated as:

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived ratio.

 is the estimate used as the numerator of the 

derived ratio.

 is the estimate used as the denominator of the 

derived ratio.

0311.0

373,31

1.985

373,31

7.408,970 r 

r r MOEp 

MOEnum

MOEden

den

num

X

X

R ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ i

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ )* 2 2 2 r  ( ˆ )* 2 2 2 MOEnum  p MOEden 

     ]601*1461.0[989 2 2 2  

  7.408,9703.712,7121,978

den

num den

R

X

MOE R MOE

MOE ˆ

)* ˆ ( 2 2 2 r  
A-16 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

The example below shows how to derive the MOE for 

the estimated ratio of Black females 25 years of age 

and older in Fairfax County, Virginia, with a graduate 

degree to Black males 25 years and older in Fairfax 

County with a graduate degree, based on the 2006 

ACS.

The estimated ratio is:

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County) and denominator (number 

of Black males 25 years of age and older in Fairfax 

County with a graduate degree).

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator.

Thus, the derived estimate of the ratio of the number 

of Black females 25 years of age and older in Fairfax 

County, Virginia, with a graduate degree to the number of Black males 25 years of age and older in Fairfax 

County, Virginia, with a graduate degree is 0.7200, and 

the MOE for the estimate is +0.2135.

Calculating MOEs for the Product of Two Estimates

To calculate the MOE for the product of two estimates, 

do the following:

1) Obtain the MOEs for the two estimates being 

multiplied together.

2) Square the estimates and their MOEs.

3) Multiply the fi rst squared estimate by the second estimate’s squared MOE.

4) Multiply the second squared estimate by the 

fi rst estimate’s squared MOE.

5) Add the results from (3) and (4).

6) Take the square root of (5).

The result is the MOE for the product. Algebraically, the 

MOE for the product is calculated as:

where A and B are the fi rst and second estimates, 

respectively.

 is the MOE of the fi rst estimate.

 is the MOE of the second estimate.

The example below shows how to derive the MOE for 

the estimated number of Black workers 16 years and 

over in Fairfax County, Virginia, who used public transportation to commute to work, based on the 2006 ACS.

To apply the method, the proportion (0.134) needs to 

be used instead of the percent (13.4). The estimated 

product is 50,624 × 0.134 = 6,784. The MOE is calculated by:

Thus, the derived estimate of Black workers 16 years 

and over who commute by public transportation is 

6,784, and the MOE of the estimate is ±1,405.

MOEnum r 989, 328 MOEden r ,1

2135.0

440,6

2.375,1

440,6

1.259,891,1 r 

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

 over (fi rst estimate) 50,624 +2,423

Percent of Black workers 16 

years and over who commute by public transportation (second estimate)

13.4% +2.7%

423,2134.0027.0624,50 2 2 2 2 MOE uBA r uu

 Table 3. Data for Example 3 

Characteristic Estimate MOE

Black females 25 years and 

 older with a graduate 

 degree (numerator)

4,634 +989

Black males 25 years and

 older with a graduate degree 

 (denominator)

6,440 +1,328

7200.0

440,6

634,4

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

2 22 2 MOE uBA A MOEB B uur MOEA

MOEA

MOEB

r 405,1

)* ˆ ( 2 2 2 MOEnum  R MOEden 

     ]328,1*7200.0[989 2 2 2  

 1.259,891,11.318,913121,978
Appendix A-17

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Calculate the MOE by dividing the square root of the 

prior result by the denominator ( ). 

Finally, the MOE of the percent change is the MOE of 

the ratio, multiplied by 100 percent, or 4.33 percent.

The text box below summarizes the formulas used to 

calculate the margin of error for several derived estimates.

Calculating MOEs for Estimates of “Percent Change” or 

“Percent Diff erence”

The “percent change” or “percent diff erence” between 

two estimates (for example, the same estimates in two 

diff erent years) is commonly calculated as

Because is not a subset of , the procedure 

to calculate the MOE of a ratio discussed previously 

should be used here to obtain the MOE of the percent 

change.

The example below shows how to calculate the margin of error of the percent change using the 2006 and 

2005 estimates of the number of persons in Maryland 

who lived in a diff erent house in the U.S. 1 year ago.

The percent change is:

For use in the ratio formula, the ratio of the two estimates is:

The MOEs for the numerator ( ) and denominator 

( ) are:

Add the squared MOE of the numerator (MOE2) to the 

product of the squared ratio and the squared MOE of 

the denominator (MOE1):

 Table 5. Data for Example 5

Characteristic Estimate MOE

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2005

762,475 +22,666

0521.1

475,762

210,802

ˆ

ˆ ˆ

1

2 X

X

R

2 Xˆ 1 Xˆ

2 Xˆ

1 Xˆ

1 Xˆ

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

0433.0

475,762

3.038,33

475,762

529,528,091,1 r 

r r MOER 

r ¦cMOE agg MOE c

2

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

1

12

ˆ

ˆˆ *%100

X

XX

Percent Change  

ˆ

ˆˆ *%100

1

12  X

XX

Percent Change

%21.5

475,762

475,762210,802 *%100  ̧ 

1

·  ̈

©

§ 

MOE2 = +/-22,866, MOE1= +/-22,666 

)* ˆ ( 2

1

22 MOE2  R MOE 

     ]666,22*0521.1[866,22 2 2 2

 

529,528,091,1

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  
A-18 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

One of the most important uses of the ACS estimates is 

to make comparisons between estimates. Several key 

types of comparisons are of general interest to users: 

1) comparisons of estimates from diff erent geographic 

areas within the same time period (e.g., comparing the 

proportion of people below the poverty level in two 

counties); 2) comparisons of estimates for the same 

geographic area across time periods (e.g., comparing 

the proportion of people below the poverty level in a 

county for 2006 and 2007); and 3) comparisons of ACS 

estimates with the corresponding estimates from past 

decennial census samples (e.g., comparing the proportion of people below the poverty level in a county for 

2006 and 2000).

A number of conditions must be met when comparing survey estimates. Of primary importance is that 

the comparison takes into account the sampling error 

associated with each estimate, thus determining 

whether the observed diff erences between estimates 

are statistically signifi cant. Statistical signifi cance 

means that there is statistical evidence that a true 

diff erence exists within the full population, and that 

the observed diff erence is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical signifi cance when making comparisons is 

presented in the next section. Considerations associated with the various types of comparisons that could 

be made are also discussed.

Determining Statistical Signifi cance

When comparing two estimates, one should use the 

test for signifi cance described below. This approach 

will allow the user to ascertain whether the observed 

diff erence is likely due to chance (and thus is not statistically signifi cant) or likely represents a true diff erence that exists in the population as a whole (and thus 

is statistically signifi cant). 

The test for signifi cance can be carried out by making 

several computations using the estimates and their 

corresponding standard errors (SEs). When working 

with ACS data, these computations are simple given 

the data provided in tables in the American FactFinder.

1) Determine the SE for each estimate (for ACS 

data, SE is defi ned by the positive value of the 

margin of error (MOE) divided by 1.645).4

2) Square the resulting SE for each estimate.

3) Sum the squared SEs.

4) Calculate the square root of the sum of the 

squared SEs.

5) Calculate the diff erence between the two estimates.

6) Divide (5) by (4).

7) Compare the absolute value of the result of (6) 

with the critical value for the desired level of 

confi dence (1.645 for 90 percent, 1.960 for 95 

percent, 2.576 for 99 percent).

8) If the absolute value of the result of (6) is greater than the critical value, then the diff erence 

between the two estimates can be considered 

statistically signifi cant at the level of confi dence 

corresponding to the critical value used in (7).

Algebraically, the signifi cance test can be expressed as 

follows:

If , then the diff erence 

between estimates and is statistically signifi cant 

at the specifi ed confi dence level, CL

where is estimate i (=1,2)

 is the SE for the estimate i (=1,2)

 is the critical value for the desired confi dence 

level (=1.645 for 90 percent, 1.960 for 95 percent, 

2.576 for 99 percent).

The example below shows how to determine if the 

diff erence in the estimated percentage of households 

in 2006 with one or more people of age 65 and older 

between State A (estimated percentage =22.0, SE=0.12) 

and State B (estimated percentage =21.5, SE=0.12) is 

statistically signifi cant. Using the formula above:

Since the test value (2.90) is greater than the critical 

value for a confi dence level of 99 percent (2.576), the 

diff erence in the percentages is statistically signifi cant 

at a 99-percent confi dence level. This is also referred 

to as statistically signifi cant at the alpha = 0.01 level. 

A rough interpretation of the result is that the user can 

be 99 percent certain that a diff erence exists between 

the percentages of households with one or more 

people aged 65 and older between State A and State B.

Making Comparisons

Appendix 4.

4

 NOTE: If working with ACS single-year estimates for 2005 or earlier, 

use the value 1.65 rather than 1.645. 

ZCL

SESE

XX

!





2

2

2

1

21

ˆˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   12.012.0

5.210.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

90.2

173.0

5.0

03.0

5.0

015.0015.0

5.0 

Ei
Appendix A-19

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

By contrast, if the corresponding estimates for State C 

and State D were 22.1 and 22.5, respectively, with standard errors of 0.20 and 0.25, respectively, the formula 

would yield

Since the test value (1.25) is less than the critical value 

for a confi dence level of 90 percent (1.645), the difference in percentages is not statistically signifi cant. 

A rough interpretation of the result is that the user 

cannot be certain to any suffi cient degree that the 

observed diff erence in the estimates was not due to 

chance.

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same 

time period (e.g., from the same year or the same 

3-year period) are straightforward and can be carried 

out as described in the previous section. There is, 

however, one statistical aspect related to the test for 

statistical signifi cance that users should be aware 

of. When comparing estimates within the same time 

period, the areas or groups will generally be nonoverlapping (e.g., comparing estimates for two diff erent 

counties). In this case, the two estimates are independent, and the formula for testing diff erences is statistically correct.

In some cases, the comparison may involve a large 

area or group and a subset of the area or group (e.g., 

comparing an estimate for a state with the corresponding estimate for a county within the state or comparing an estimate for all females with the corresponding 

estimate for Black females). In these cases, the two 

estimates are not independent. The estimate for the 

large area is partially dependent on the estimate for the 

subset and, strictly speaking, the formula for testing 

diff erences should account for this partial dependence. 

However, unless the user has reason to believe that the 

two estimates are strongly correlated, it is acceptable 

to ignore the partial dependence and use the formula 

for testing diff erences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a fi nding of statistical signifi cance will still 

be correct, but a fi nding of a lack of statistical signifi -

cance based on the formula may be incorrect. If it is 

important to obtain a more exact test of signifi cance, 

the user should consult with a statistician about 

approaches for accounting for the correlation in performing the statistical test of signifi cance.

   25.020.0

1.225.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

25.1

320.0

4.0

1025.0

4.0

0625.004.0

4.0 

Comparisons Across Time Periods

Comparisons of estimates from diff erent time periods 

may involve diff erent single-year periods or diff erent 

multiyear periods of the same length within the same 

area. Comparisons across time periods should be made 

only with comparable time period estimates. Users are 

advised against comparing single-year estimates with 

multiyear estimates (e.g., comparing 2006 with 2007–

2009) and against comparing multiyear estimates of 

diff ering lengths (e.g., comparing 2006–2008 with 

2009–2014), as they are measuring the characteristics 

of the population in two diff erent ways, so diff erences 

between such estimates are diffi cult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

When comparing estimates from two diff erent singleyear periods, one prior to 2006 and the other 2006 or 

later (e.g., comparing estimates from 2005 and 2007), 

the user should recognize that from 2006 on the ACS 

sample includes the population living in group quarters (GQ) as well as the population living in housing 

units. Many types of GQ populations have demographic, 

social, or economic characteristics that are very different from the household population. As a result, 

comparisons between 2005 and 2006 and later ACS 

estimates could be aff ected. This is particularly true 

for areas with a substantial GQ population. For most 

population characteristics, the Census Bureau suggests 

users make comparisons across these time periods 

only if the geographic area of interest does not include 

a substantial GQ population. For housing characteristics or characteristics published only for the household 

population, this is obviously not an issue. 

Comparisons Based on Overlapping Periods

When comparing estimates from two multiyear periods, ideally comparisons should be based on nonoverlapping periods (e.g., comparing estimates from 

2006–2008 with estimates from 2009–2011). The comparison of two estimates for diff erent, but overlapping 

periods is challenging since the diff erence is driven by 

the nonoverlapping years. For example, when comparing the 2005–2007 ACS with the 2006–2008 ACS, data 

for 2006 and 2007 are included in both estimates. 

Their contribution is subtracted out when the estimate 

of diff erences is calculated. While the interpretation 

of this diff erence is diffi cult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of diff erence should not be interpreted as a 

refl ection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

over time is complicated when change is being evaluated using multiyear estimates. From a technical standpoint, change over time is best evaluated with multiyear estimates that do not overlap. At the same time, 
A-20 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

many areas whose only source of data will be 5-year 

estimates will not want to wait until 2015 to evaluate 

change (i.e., comparing 2005–2009 with 2010–2014). 

When comparing two 3-year estimates or two 5-year 

estimates of the same geography that overlap in 

sample years one must account for this sample overlap. 

Thus to calculate the standard error of this diff erence 

use the following approximation to the standard error:

where C is the fraction of overlapping years. For example, the periods 2005–2009 and 2007–2011 overlap for 

3 out of 5 years, so C=3/5=0.6. If the periods do not 

overlap, such as 2005–2007 and 2008–2010, then C=0.

With this SE one can test for the statistical signifi cance 

of the diff erence between the two estimates using the 

method outlined in the previous section with one modifi cation; substitute for 

 in the denominator of the formula for 

the signifi cance test.

Comparisons With Census 2000 Data

In Appendix 2, major diff erences between ACS data and 

decennial census sample data are discussed. Factors 

such as diff erences in residence rules, universes, and 

reference periods, while not discussed in detail in this 

appendix, should be considered when comparing ACS 

estimates with decennial census estimates. For example, given the reference period diff erences, seasonality 

may aff ect comparisons between decennial census and 

ACS estimates when looking at data for areas such as 

college towns and resort areas. 

The Census Bureau subject matter specialists have 

reviewed the factors that could aff ect diff erences 

between ACS and decennial census estimates and they 

have determined that ACS estimates are similar to 

those obtained from past decennial census sample data 

for most areas and characteristics. The user should 

consider whether a particular analysis involves an area 

or characteristic that might be aff ected by these diff erences.5

When comparing ACS and decennial census sample 

estimates, the user must remember that the decennial 

census sample estimates have sampling error associated with them and that the standard errors for both 

ACS and census estimates must be incorporated when 

performing tests of statistical signifi cance. Appendix 

3 provides the calculations necessary for determining 

statistical signifi cance of a diff erence between two 

estimates. To derive the SEs of census sample estimates, use the method described in Chapter 8 of either 

the Census 2000 Summary File 3 Technical Documentation <http://www.census.gov/prod/cen2000/doc/sf3

.pdf> or the Census 2000 Summary File 4 Technical 

Documentation <http://www.census.gov/prod

/cen2000/doc/sf4.pdf>.

A conservative approach to testing for statistical signifi -

cance when comparing ACS and Census 2000 

estimates that avoids deriving the SE for the 

Census 2000 estimate would be to assume the SE for 

the Census 2000 estimate is the same as that determined for the ACS estimate. The result of this approach 

would be that a fi nding of statistical signifi cance can be 

assumed to be accurate (as the SE for the Census 2000 

estimate would be expected to be less than that for the 

ACS estimate), but a fi nding of no statistical signifi -

cance could be incorrect. In this case the user should 

calculate the census long-form standard error and follow the steps to conduct the statistical test.

Comparisons With 2010 Census Data

Looking ahead to the 2010 decennial census, data 

users need to remember that the socioeconomic data 

previously collected on the long form during the 

census will not be available for comparison with ACS 

estimates. The only common variables for the ACS and 

2010 Census are sex, age, race, ethnicity, household 

relationship, housing tenure, and vacancy status.

The critical factor that must be considered when comparing ACS estimates encompassing 2010 with the 

2010 Census is the potential impact of housing and 

population controls used for the ACS. As the housing 

and population controls used for 2010 ACS data will 

be based on the Population Estimates Program where 

the estimates are benchmarked on the Census 2000 

counts, they will not agree with the 2010 Census 

population counts for that year. The 2010 population 

estimates may diff er from the 2010 Census counts 

for two major reasons—the true change from 2000 to 

2010 is not accurately captured by the estimates and 

the completeness of coverage in the 2010 Census is 

diff erent than coverage of Census 2000. The impact of 

this diff erence will likely aff ect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large diff erences between the 

population controls and the 2010 Census population 

counts is greater.

Comparisons With Other Surveys

Comparisons of ACS estimates with estimates from 

other national surveys, such as the Current Population

Survey, may be of interest to some users. A major consideration in making such comparisons will be that ACS 

5

 Further information concerning areas and characteristics that do not 

fi t the general pattern of comparability can be found on the ACS Web 

site at <http://www.census.gov/acs/www/UseData/compACS.htm>.

  2

2

2

21 1 1) ˆˆ ( XXSE #  SESEC

  2

2

2

1 1  SESEC

2

2

2

1  SESE
Appendix A-21

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

estimates include data for populations in both institutional and noninstitutional group quarters, and estimates from most national surveys do not include institutional populations. Another potential for large eff ects 

when comparing data from the ACS with data from 

other national surveys is the use of diff erent questions 

for measuring the same or similar information.

Sampling error and its impact on the estimates from 

the other survey should be considered if comparisons 

and statements of statistical diff erence are to be made, 

as described in Appendix 3. The standard errors on 

estimates from other surveys should be derived 

according to technical documentation provided for 

those individual surveys.

Finally, the user wishing to compare ACS estimates 

with estimates from other national surveys should 

consider the potential impact of other factors, such 

as target population, sample design and size, survey 

period, reference period, residence rules, and interview 

modes on estimates from the two sources.
A-22 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

where is the All Items CPI-U-RS Annual Average 

for the earlier year (Y1).

 is the All Items CPI-U-RS Annual Average for the 

more recent year (Y2).

 is the published ACS estimate for the earlier year 

(Y1).

The example below compares the national median 

value for owner-occupied mobile homes in 2005 

($37,700) and 2006 ($41,000). First adjust the 2005 

median value using the 2005 All Items CPI-U-RS Annual 

Average (286.7) and the 2006 All Items CPI-U-RS Annual 

Average (296.1) as follows:

Thus, the comparison of the national median value for 

owner-occupied mobile homes in 2005 and 2006, in 

2006 dollars, would be $38,936 (2005 infl ationadjusted to 2006 dollars) versus $41,000 

(2006 dollars).

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost 

values are created with infl ation adjustments. The 

Census Bureau uses the All Items CPI-U-RS Annual Averages for each year in the multiyear time period to calculate a set of infl ation adjustment factors. Adjustment 

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from its most recent year to 

the CPI-U-RS Annual Averages from each of its earlier 

years. The ACS values for each of the earlier years in 

the multiyear period are multiplied by the appropriate 

infl ation adjustment factors to produce the infl ationadjusted values. These values are then used to create 

the multiyear estimates. 

As an illustration, consider the time period 2004–2006, 

which consisted of individual reference-year income 

values of $30,000 for 2006, $20,000 for 2005, and 

$10,000 for 2004. The multiyear income components 

are created from infl ation-adjusted reference period 

income values using factors based on the All Items 

CPI-U-RS Annual Averages of 277.4 (for 2004), 286.7 

(for 2005), and 296.1 (for 2006). The adjusted 2005 

value is the ratio of 296.1 to 286.7 applied to $20,000, 

which equals $20,656. Similarly, the 2004 value is 

the ratio of 296.1 to 277.4 applied to $10,000, which 

equals $10,674.

Using Dollar-Denominated Data

Appendix 5.

Dollar-denominated data refer to any characteristics 

for which infl ation adjustments are used when producing annual estimates. For example, income, rent, home 

value, and energy costs are all dollar-denominated 

data.

Infl ation will aff ect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using infl ation factors based on the Consumer Price Index (CPI).

Given the potential impact of infl ation on observed 

diff erences of dollar-denominated data across time 

periods, users should adjust for the eff ects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for infl ation. In making adjustments, the 

Census Bureau recommends using factors based on 

the All Items CPI-U-RS (CPI research series). The Bureau 

of Labor Statistics CPI indexes through 2006 are found 

at <http://www.bls.gov/cpi/cpiurs1978_2006.pdf>. 

Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount 

of income received during the 12 months preceding 

the interview month. This is the income reference 

period. Since there are 12 diff erent income reference 

periods throughout an interview year, 12 diff erent 

income infl ation adjustments are made. Monthly CPIU-RSs are used to infl ation-adjust the 12 reference 

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no infl ation adjustments for single-year estimates of rent, home value, or energy cost values.

Adjusting Single-Year Estimates Over Time

When comparing single-year income, rent, home value, 

and energy cost value estimates from two diff erent 

years, adjustment should be made as follows: 

1) Obtain the All Items CPI-U-RS Annual Averages for 

the 2 years being compared.

2) Calculate the infl ation adjustment factor as the ratio 

of the CPI-U-RS from the more recent year to the 

CPI-U-RS from the earlier year.

3) Multiply the dollar-denominated data estimated for 

the earlier year by the infl ation adjustment factor.

The infl ation-adjusted estimate for the earlier year can 

be expressed as:

936,38$700,37$ 7.286

1.296 ˆ

X ,2005 Adj u 

1

1

2

,1 ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

CPIY1

CPIY 2

1 ˆ

XY
Appendix A-23

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

As an illustration, consider ACS multiyear estimates for 

the two time periods of 2001–2003 and 2004–2006. 

To compare the national median value for owneroccupied mobile homes in 2001–2003 ($32,000) and 

2004–2006 ($39,000), fi rst adjust the 2001–2003 

median value using the 2003 All Items CPI-U-RS Annual 

Averages (270.1) and the 2006 All Items CPI-U-RS 

Annual Averages (296.1) as follows:

Thus, the comparison of the national median value 

for owner-occupied mobile homes in 2001–2003 

and 2004–2006, in 2006 dollars, would be $35,080 

(2001–2003 infl ation-adjusted to 2006 dollars) versus 

$39,000 (2004–2006, already in 2006 dollars).

Issues Associated With Infl ation Adjustment

The recommended infl ation adjustment uses a national 

level CPI and thus will not refl ect infl ation diff erences 

that may exist across geographies. In addition, since 

the infl ation adjustment uses the All Items CPI, it will 

not refl ect diff erences that may exist across characteristics such as energy and housing costs.

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as 

follows:

1) Obtain the All Items CPI-U-RS Annual Average for 

the most current year in each of the time periods 

being compared.

2) Calculate the infl ation adjustment factor as the 

ratio of the CPI-U-RS Annual Average in (1) from 

the most recent year to the CPI-U-RS in (1) from 

the earlier years.

3) Multiply the dollar-denominated estimate for the 

earlier time period by the infl ation adjustment 

factor.

The infl ation-adjusted estimate for the earlier years can 

be expressed as:

where CPI P1 is the All Items CPI-U-RS Annual Average 

for the last year in the earlier time period (P1). 

 is the All Items CPI-U-RS Annual Average for the 

last year in the most recent time period (P2).

 is the published ACS estimate for the earlier time 

period (P1).

080,35$000,32$ 1.270

1.296 ˆ

X  ,20032001 Adj u 

1

1

2

,1 ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

CPI P2

1 ˆ

X P
All survey estimates are subject to both sampling and 

nonsampling error. In Appendix 3, the topic of sampling error and the various measures available for 

understanding the uncertainty in the estimates due to 

their being derived from a sample, rather than from an 

entire population, are discussed. The margins of error 

published with ACS estimates measure only the eff ect 

of sampling error. Other errors that aff ect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any error 

aff ecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error.

Types of Nonsampling Errors

Coverage error occurs when a housing unit or person 

does not have a chance of selection in the sample 

(undercoverage), or when a housing unit or person has 

more than one chance of selection in the sample, or is 

included in the sample when they should not have been 

(overcoverage). For example, if the frame used for the 

ACS did not allow the selection of newly constructed 

housing units, the estimates would suff er from errors 

due to housing undercoverage.

The fi nal ACS estimates are adjusted for under- and 

overcoverage by controlling county-level estimates to 

independent total housing unit controls and to independent population controls by sex, age, race, and 

Hispanic origin (more information is provided on the 

coverage error defi nition page of the “ACS Quality Measures” Web site at <http://www.census.gov/acs/www

/UseData/sse/cov/cov_def.htm>). However, it is important to measure the extent of coverage adjustment by 

comparing the precontrolled ACS estimates to the fi nal 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that diff erences in characteristics of undercovered or overcovered 

housing units or individuals diff er from those eligible to 

be selected. When this occurs, the ACS may not provide 

an accurate picture of the population prior to the coverage adjustment, and the population controls may not 

eliminate or minimize that coverage error.

Unit nonresponse is the failure to obtain the minimum required information from a housing unit or a resident of a group quarter in order for it to be considered 

a completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

Appendix 6.

or person. For example, if no one in a sampled housing unit is available to be interviewed during the time 

frame for data collection, unit nonresponse will result.

It is important to measure unit nonresponse because 

it has a direct eff ect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the fi nal survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

unit nonresponse bias. This will happen if the characteristics of nonresponding units diff er from the characteristics of responding units.

Item nonresponse occurs when a respondent fails to 

provide an answer to a required question or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question diff er 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias.

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation 

of the fi nal data fi les. For example, errors may occur if 

data entry of questionnaire information is incomplete 

Measures of Nonsampling Error

A-24 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
Appendix A-25

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

or inaccurate. Coding of responses incorrectly also 

results in processing error. Critical reviews of edits and 

tabulations by subject matter experts are conducted to 

keep errors of this kind to a minimum.

Nonsampling error can result in random errors and 

systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there is 

an error in the questionnaire design that negatively 

aff ects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the fi nal results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

ACS Quality Measures

Nonsampling error is extremely diffi cult, if not 

impossible, to measure directly. However, the Census 

Bureau has developed a number of indirect measures of 

nonsampling error to help inform users of the quality 

of the ACS estimates: sample size, coverage rates, unit 

response rates and nonresponse rates by reason, and 

item allocation rates. Starting with the 2007 ACS, these 

measures are available in the B98 series of detailed 

tables on AFF. Quality measures for previous years are 

available on the “ACS Quality Measures” Web site at 

<http:/www.census.gov/acs/www/UseData/sse/>.

Sample size measures for the ACS summarize information for the housing unit and GQ samples. The measures available at the state level are:6

Housing units

Number of initial addresses selected

Number of fi nal survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of fi nal survey interviews

Sample size measures may be useful in special circumstances when determining whether to use single-year 

or multiyear estimates in conjunction with estimates of 

the population of interest. While the coeffi cient of variation (CV) should typically be used to determine 

usability, as explained in Appendix 3, there may be 

some situations where the CV is small but the user 

has reason to believe the sample size for a subgroup 

is very small and the robustness of the estimate is in 

question. 

For example, the Asian-alone population makes up 

roughly 1 percent (8,418/656,700) of the population 

in Jeff erson County, Alabama. Given that the number of 

successful housing unit interviews in Jeff erson County 

for the 2006 ACS were 4,072 and assuming roughly 2.5 

persons per household (or roughly 12,500 completed 

person interviews), one could estimate that the 2006 

ACS data for Asians in Jeff erson County are based on 

roughly 150 completed person interviews.

Coverage rates are available for housing units, and 

total population by sex at both the state and national 

level. Coverage rates for total population by six race/

ethnicity categories and the GQ population are also 

available at the national level. These coverage rates are 

a measure of the extent of adjustment to the survey 

weights required during the component of the estimation methodology that adjusts to population controls. 

Low coverage rates are an indication of greater potential for coverage error in the estimates. 

Unit response and nonresponse rates for housing 

units are available at the county, state, and national 

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, other, and data insuffi cient to be considered 

an interview. Rates are also provided separately for persons in group quarters at the national and state levels.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. For example, 

the 2006 housing unit response rates are at least 94 

percent for all states. The response rate for the District 

of Columbia in 2006 was 91 percent.

Item allocation rates are determined by the content 

edits performed on the individual raw responses and 

closely correspond to item nonresponse rates. Overall 

housing unit and person characteristic allocation rates 

are available at the state and national levels, which 

combine many diff erent characteristics. Allocation rates 

for individual items may be calculated from the B99 

series of imputation detailed tables available in AFF. 

Item allocation rates do vary by state, so users are 

advised to examine the allocation rates for 

characteristics of interest before drawing conclusions 

from the published estimates.

6

 The sample size measures for housing units (number of initial addresses 

selected and number of fi nal survey interviews) and for group quarters 

people cannot be used to calculate response rates. For the housing unit 

sample, the number of initial addresses selected includes addresses 

that were determined not to identify housing units, as well as initial 

addresses that are subsequently subsampled out in preparation for personal visit nonresponse follow-up. Similarly, the initial sample of people 

in group quarters represents the expected sample size within selected 

group quarters prior to visiting and sampling of residents. 
A-26 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Implications of Population Controls on ACS Estimates

Appendix 7.

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defi ned by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s offi cial estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the offi cial count 

of the population comes from the previous census, 

updated by the Population Estimates Program.

In the case of the ACS, the total housing unit estimates 

and the total population estimates by age, sex, race 

and Hispanic origin are controlled at the county (or 

groups of counties) level. The group quarters total 

population is controlled at the state level by major type 

of group quarters. Such adjustments are important to 

correct the survey data for nonsampling and sampling 

errors. An important source of nonsampling error is 

the potential under-representation of hard-toenumerate demographic groups. The use of the 

population controls results in ACS estimates that more 

closely refl ect the level of coverage achieved for those 

groups in the preceding census. The use of the population estimates as controls partially corrects demographically implausible results from the ACS due to 

the ACS data being based on a sample of the population rather than a full count. For example, the use of 

the population controls “smooths out” demographic 

irregularities in the age structure of the population that 

result from random sampling variability in the ACS. 

When the controls are applied to a group of counties 

rather than a single county, the ACS estimates and the 

offi cial population estimates for the individual counties 

may not agree. There also may not be agreement 

between the ACS estimates and the population estimates for levels of geography such as subcounty areas 

where the population controls are not applied.

The use of population and housing unit controls also 

reduces random variability in the estimates from year 

to year. Without the controls, the sampling variability 

in the ACS could cause the population estimates to 

increase in one year and decrease in the next (especially for smaller areas or demographic groups), when 

the underlying trend is more stable. This reduction in 

variability on a time series basis is important since 

results from the ACS may be used to monitor trends 

over time. As more current data become available, the 

time series of estimates from the Population Estimates 

Program are revised back to the preceding census while 

the ACS estimates in previous years are not. Therefore, 

some diff erences in the ACS estimates across time may 

be due to changes in the population estimates. 

For single-year ACS estimates, the population and total 

housing unit estimates for July 1 of the survey year 

are used as controls. For multiyear ACS estimates, the 

controls are the average of the individual year population estimates.
Appendix A-27

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

Background and Overview Information

American Community Survey Web Page Site Map: 

<http://www.census.gov/acs/www/Site_Map.html>

This link is the site map for the ACS Web page. It provides an overview of the links and materials that are 

available online, including numerous reference documents.

What Is the ACS? <http://www.census.gov/acs/www

/SBasics/What/What1.htm> This Web page includes 

basic information about the ACS and has links to additional information including background materials.

ACS Design, Methodology, Operations

American Community Survey Design and Methodology 

Technical Paper: <http://www.census.gov/acs/www

/Downloads/tp67.pdf> This document describes the 

basic design of the 2005 ACS and details the full set 

of methods and procedures that were used in 2005. 

Please watch our Web site as a revised version will be 

released in the fall of 2008, detailing methods and 

procedures used in 2006 and 2007. 

About the Data (Methodology: <http://www.census

.gov/acs/www/AdvMeth/> This Web page contains 

links to information on ACS data collection and processing, evaluation reports, multiyear estimates study, 

and related topics.

ACS Quality

Accuracy of the Data (2007): <http://www.census.gov

/acs/www/Downloads/ACS/accuracy2007.pdf> This 

document provides data users with a basic understanding of the sample design, estimation methodology, and 

accuracy of the 2007 ACS data.

ACS Sample Size: <http://www.census.gov/acs/www

/SBasics/SSizes/SSizes06.htm> This link provides 

sample size information for the counties that were

published in the 2006 ACS. The initial sample size 

and the fi nal completed interviews are provided. The 

sample sizes for all published counties and county 

equivalents starting with the 2007 ACS will only be 

available in the B98 series of detailed tables on American FactFinder.

ACS Quality Measures: <http://www.census.gov/acs

/www/UseData/sse/> This Web page includes information about the steps taken by the Census Bureau to 

improve the accuracy of ACS data. Four indicators of 

survey quality are described and measures are provided at the national and state level.

Guidance on Data Products and Using the Data

How to Use the Data: <http://www.census.gov/acs

/www/UseData/> This Web page includes links to 

many documents and materials that explain the ACS 

data products. 

Comparing ACS Data to other sources: <http://www

.census.gov/acs/www/UseData/compACS.htm> Tables 

are provided with guidance on comparing the 2007 

ACS data products to 2006 ACS data and Census 2000 

data. 

Fact Sheet on Using Diff erent Sources of Data for 

Income and Poverty: <http://www.census.gov/hhes

/www/income/factsheet.html> This fact sheet highlights the sources that should be used for data on 

income and poverty, focusing on comparing the ACS 

and the Current Population Survey (CPS).

Public Use Microdata Sample (PUMS): <http://www

.census.gov/acs/www/Products/PUMS/> This Web 

page provides guidance in accessing ACS microdata.

Other ACS Resources

Appendix 8.