What Federal Agencies Need to Know 

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

Issued

December 2008

U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

U S C E N S U S B U R E A U

Helping You Make Informed Decisions

A Compass for Understanding and Using 

American Community Survey Data

What Federal Agencies Need to Know
Acknowledgments Frederick J. Eggers, Former Deputy Assistant Secretary for Economic 

Affairs, U.S. Department of Housing and Urban Development, drafted this 

handbook for the U.S. Census Bureau’s American Community Survey Office. 

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. Griffin, Special Assistant to the Chief of the American 

Community Survey Office, 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 M. 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 

Office.

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 December 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 Affairs

U.S. CENSUS BUREAU

Steve H. Murdock, 

Director

What Federal Agencies Need to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What Federal Agencies 

Need to Know

U.S. Government Printing Office, 

Washington, DC, 

2008.

Economics and Statistics 

Administration

Cynthia A. Glassman,

Under Secretary for Economic Affairs

U.S. CENSUS BUREAU

Steve H. Murdock,

Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Officer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Office

ECONOMICS

AND STATISTICS

ADMINISTRATION
What Federal Agencies Need to Know iii

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

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

1. What Is the American Community Survey (ACS)?......................................1

2. Purpose of This Handbook.......................................................................2

3. How Does the ACS Compare With the Decennial Census Long Form?.......3

4. How to Interpret and Use ACS Data ..........................................................7

Time and ACS Data............................................................................................7

 More Than One Estimate for the Same Measurement.........................................9

5. Technical Issues Involving ACS Data...................................................... 11

Household Population and the Group Quarters (GQ) Population.......................11

Dollar-Valued Data and Adjustments for Inflation ............................................12

Geographic Boundary Changes........................................................................12

Rules Used to Determine Residency ................................................................13

Sample Size and Statistical Precision ...............................................................13

Combining or Comparing ACS Estimates .........................................................14

Nonsampling Error ..........................................................................................15

Sample Size and Suppression of Data ..............................................................15

Issues With Specific Variables ..........................................................................16

Evaluations of the ACS ....................................................................................16

6. Making the Transition to the ACS ........................................................... 17

Statutory Basis for Using ACS Data..................................................................17

 Initial Choices in Using ACS Data for Federal Purposes ...................................17

 Changes in the ACS ........................................................................................18

 Examples of the Use of ACS Data by Federal Agencies....................................18

7. How to Obtain ACS Data ......................................................................... 19

The American FactFinder (AFF)........................................................................19

Summary Files ................................................................................................20

Custom Tabulations ........................................................................................20

Public Use Microdata Sample (PUMS) Files .......................................................20

8. Future of the ACS.................................................................................... 21

 ACS Data Become Available for All Geographies in 2010.................................21

 Future Evolution of the ACS at the Census Bureau...........................................21

 Future Evolution of the ACS Outside the Census Bureau ..................................21

Glossary...................................................................................................... 23

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

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

 Appendix 2. Differences 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 Federal Agencies Need 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 different 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 different 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 differ 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 differences 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 differences 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 finding 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 pathfinder in leading others to share what 

you have learned. 
What Federal Agencies Need to Know 

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

While the first decennial census took place in 1790, 

only since 1940 has the U.S. Census Bureau collected 

data for the decennial census in two ways: (1) a set of 

questions administered to all housing units in order 

to count the population and gather basic demographic 

information, such as age, sex, race, and ethnicity and 

(2) an additional set of questions administered to a 

sample of households and housing units in order to 

obtain more detailed demographic, housing, social, 

and economic information. In recent censuses, the set 

of questions asked of all people has become known 

as the “short form,” while the additional questions 

asked only of a sample have become known as the 

“long form.” What we know about households—their 

incomes, their education, their employment, their 

housing—at the state, county, city, and census tract 

levels—has usually come from the long-form sample.

In 2005, the Census Bureau launched the American 

Community Survey (ACS) to replace the functions 

performed by the long form. Prior to 2005, the Census 

Bureau conducted extensive testing of ACS methods in 

specific site tests and in a series of national tests. The 

ACS collects socioeconomic and housing information 

continuously from a national sample of housing units 

and people living in group quarters, and the Census 

Bureau tabulates these data on a calendar year basis. 

In August and September 2006, the Census Bureau 

released the 2005 ACS data for all states, Puerto Rico, 

the District of Columbia, and for specified geographic 

areas, including counties and cities, with total populations of at least 65,000. Similar data were released 

in August and September 2007 for the 2006 ACS. In 

2010, the Census Bureau will begin releasing ACS data 

annually for areas ranging from states to census tracts 

and block groups. The ACS is an integral component 

of the decennial census program, eliminating the need 

for a long-form sample in the decennial census. There 

will not be a long form in the 2010 Census nor in any 

future decennial census. 

Federal agencies have used decennial census data, particularly the detailed information collected on a sample 

basis, for a variety of purposes. Several examples 

include:

•	 Eligibility determinations. Agencies use census 

sample data to determine the eligibility of places 

for participation in federal programs. For example, 

the Low Income Housing Tax Credit provides extra 

tax credits to selected housing projects in certain 

qualified census tracts. These tracts have been 

identified based on a special tabulation of household income distributions at the census tract level. 

•	 Allocation of funds. Federal agencies use longform data to allocate funds. For example, the 

Department of Agriculture’s Smith Lever Act and 

the Department of Education’s Preschool Grants 

for Children with Disabilities program allocate 

funds to communities using funding formulas that 

rely on decennial census data. 

•	 Program parameters. Federal programs often 

operate with parameters that vary by location. For 

example, the Department of Housing and Urban 

Development (HUD) uses decennial income data in 

its calculations of the income limits that determine 

at the local level the eligibility of households for 

various forms of housing assistance. 

•	 Justification for discretionary grants. In 

reviewing applications for discretionary grants, 

agencies require applicants to justify the need 

for a grant, and applicants have often employed 

decennial census sample data in the justification. 

For instance, the Department of Transportation 

works with states and local governments to obtain 

a special tabulation of journey-to-work data at the 

census tract level for use by applicants in models 

that assess the impact of a proposed grant. 

•	 Policy analysis and evaluation. Agencies rely 

on decennial sample data to guide policy development and facilitate research and evaluation. 

In the past, federal agencies used the data from the 

long-form sample for these functions; from now on, 

agencies will rely on ACS data to carry out these functions. 

1. What Is the American Community Survey (ACS)?
 What Federal Agencies Need to Know

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

This handbook is intended to aid analysts, program 

administrators, and policy makers within federal 

agencies when they use the ACS in carrying out the 

business of their agencies. Because agencies depend 

on the detailed housing and socioeconomic data that 

have been provided by the decennial census longform sample, the advent of the ACS is of great interest 

and possibly some concern to the analysts and policy 

makers in agencies that use these data. Data users will 

want to know: 

•	 Whether the ACS will provide the same information 

as the long form. 

•	 Whether ACS data will be as reliable as long-form 

data. 

•	 How having annual data will affect their programs. 

•	 How program recipients might view the shift from 

the long-form census sample to the ACS. 

This handbook is designed to help users in federal 

agencies understand the ACS and answer these questions. While this handbook will describe the essential 

features of the ACS in detail and will discuss some 

important statistical and practical considerations, the 

bottom line is that federal agencies should be able to 

use ACS data for the same purposes for which they 

used decennial census sample data. Clearly, the more 

frequent updating of information available in the ACS 

changes the statistical framework in which federal 

agencies have been operating and opens new opportunities for agencies to advance their missions and base 

policy and decision-making on more current information.

The remainder of this handbook is organized as follows:

Section 3 compares the information the ACS furnishes 

with the information that agencies obtained in the past 

from the long-form sample of the decennial census. 

Sections 4, 5, and 6 explain the essential characteristics of the ACS, provide some perspective on their 

importance, and suggest ways to deal with problems 

that might arise from specific features. 

•	 Section 4 discusses how to interpret ACS estimates since they are based on data collected over 

periods of 1 year, 3 years, and 5 years.

•	 Section 5 introduces some technical issues that 

users should be aware of. 

•	 Section 6 explores some concerns that may arise 

as federal agencies make the transition from using 

the decennial long-form sample data to using the 

ACS data. 

The final two sections furnish information on how to 

obtain ACS information and what to expect from the 

ACS in the future.

•	 Section 7 explains how federal users can access 

ACS data in ways that facilitate their use in federal 

programs. 

•	 Section 8 offers some insight into the future of the 

ACS.

Throughout this handbook, text boxes will contain 

additional information related to the discussion in the 

main text. A glossary at the back of the handbook provides definitions for key terms, and a set of appendixes 

offers more in-depth explorations of some of the issues 

discussed in the text. 

The goal of this handbook is to give the reader the 

basics needed to access and use ACS data. The technical appendixes furnish more detailed information on 

important subjects. In addition, the Census Bureau 

has produced a number of reports on selected topics 

involving the interpretation and quality of ACS data. 

The handbook cites the technical appendixes and 

Census Bureau documents at appropriate places in the 

text, and Appendix 8 lists the most useful references.

2. Purpose of This Handbook
What Federal Agencies Need to Know 

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

Most agencies will use ACS data in applications that 

have—until recently—used data from the decennial 

census long-form sample. To facilitate the transition 

between these two data sources, this section provides 

an overview comparison of the ACS and the decennial 

sample. In content, design, geographical coverage, 

and products, the ACS closely resembles the decennial sample. The most important differences involve 

sample size, the time between data releases, and the 

relationship between geography and the timing of data 

releases.1

Content. The content of the ACS is similar to the 

content of the Census 2000 long form. In both cases, 

the Census Bureau worked with federal agencies and 

the Office of Management and Budget to ensure that 

agency legislative requirements were provided for and 

that the public was not burdened with any question 

that was not based on a legislative requirement or clear 

programmatic need. Table 1 summarizes the range of 

information collected by the ACS. 

The questionnaires for the ACS and the Census 2000 

long form include many of the same questions using 

very similar language. However, there are some differences. For example, the Census 2000 long form asked 

whether a resident lived in the same unit 5 years ago 

while the ACS asks whether a resident lived in the 

same unit 1 year ago. Also the Census 2000 long form 

asked about income in 1999 (the previous calendar 

year), while the ACS asks about income over the 12 

months preceding the interview date. 

3. How Does the ACS Compare With the 

 Decennial Census Long Form?

1 Appendix 2 provides a more detailed comparison of ACS data and 

decennial census sample data. Users can find a comprehensive 

description of the methods and procedures currently used in the ACS 

in American Community Survey Design and Methodology Technical 

Paper at <http://www.census.gov/acs/www/Downloads/tp67.pdf>.

 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 Benefit

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: Subjects Planned for the 2010 Census and American Community Survey, U.S. Census Bureau.

QUESTIONNAIRES

To understand how similar the two surveys are, 

users should go online and compare the latest 

ACS questionnaire with the questionnaire used 

for the Census 2000 long form. The link to the 

questionnaire being used in the 2008 ACS is 

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

/2008Quest.html> and the link to the Census 

2000 long-form questionnaire is <http://www

.census.gov/dmd/www/2000quest.html>.
 What Federal Agencies Need to Know

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

Survey design. The Census Bureau maintains a 

nationwide list of addresses that it uses for mailing 

questionnaires for the decennial census, the ACS, and 

other surveys. For Census 2000, every housing unit 

on this list received either a short form or a long form. 

Since the long form incorporated all the questions 

from the short form, this meant that all people living 

in every housing unit were asked the questions on the 

short form. Only a sample of housing units received 

the Census 2000 long form. Residents of group quarters were also surveyed, using both the short form and 

the long form. Nearly all the Census 2000 forms were 

mailed or delivered in March 2000. The Census Bureau 

kept track of responses and used enumerators to 

follow-up on all housing units that did not complete 

and mail back their census questionnaire. 

In contrast, using the nationwide address list, the 

Census Bureau mails ACS questionnaires to a sample 

of approximately 250,000 addresses every month 

and tries to obtain completed interviews from these 

addresses within 3 months. In the first month, the 

Census Bureau mails a survey questionnaire to the 

sampled addresses followed by a second questionnaire if the first is not returned. In the second month, 

if the Census Bureau has received no response to these 

questionnaires, it attempts to collect the information 

by telephone. Finally, in the third month, for those 

addresses from which there is still no response, the 

Census Bureau sends a highly trained interviewer to a 

subsample ranging from one-third to one-half of the 

nonresponding addresses in an area. The sampling rate 

for the ACS in-person interviews is higher in census 

tracts with a lower predicted percentage of mail and 

telephone respondents. 

Geographical coverage. Starting in 2010, the Census 

Bureau will publish ACS data for the same geographies 

covered in the published long-form data from Census 

2000. These geographies include, but are not limited 

to, the nation, states, counties, places, American Indian 

reservations, census tracts, and block groups. A listing 

of the major geographies for which the Census Bureau 

will publish ACS data is furnished below in the discussion of the relationship between geography and the 

timing of data release. (See Table 3.) Definitions of 

these geographies are provided in the glossary at the 

end of the handbook.

Time between data releases. A fundamental distinction between ACS data and data from the decennial 

long form is that new ACS data are released every year, 

not just once every 10 years. This basic difference has 

three important implications for federal agencies. 

•	 Agencies will not have to wait 10 years between 

censuses to update key information. They will be 

working with the most current data. 

•	 Using ACS data may require more resources. 

Analysis of long-form data within an agency normally took place once a decade. If agencies choose 

to take advantage of the more frequent availability 

of new ACS data, the agencies will need to analyze 

new data more often, perhaps annually. 

•	 Shortening the time between releases of new data 

will call attention to the reliability of published 

estimates. The ACS and its predecessor, the decennial census long form, are sample surveys; therefore, estimates based on either source are subject 

to sampling error. (See explanation of sampling 

error later in this section.) However, users are 

more likely to blame sampling errors for observed 

differences if the time between observations is 

shorter. For example, a 2 percentage point change 

in the poverty rate may not seem unusual over a 

10-year period, but the same 2 percentage point 

change in a single year may appear questionable 

even though sampling errors may cause both 

changes. For this reason, agencies can expect their 

clients to question changes in estimates more 

frequently than when the estimates were 10 years 

apart. 

Geography and data release. Another important distinction between the ACS and the decennial long-form 

sample is how the Census Bureau reports on geographies of different sizes. As befits a once-a-decade 

survey, the Census Bureau released long-form data collected in 2000 for all levels of geography down to the 

block group. The ACS is a continuous survey, and the 

Census Bureau reports data when it believes that the 

sample for a particular location is sufficient to make 

the published estimates reasonably reliable. Table 2 

summarizes how the Census Bureau will release ACS 

data in three different formats: 1-year estimates, 3-year 

estimates, and 5-year estimates. 

The 1-year estimates began in 2006 with the release of 

ACS data collected in 2005. These data were released 

for geographic areas with populations of 65,000 and 

greater. The release of 3-year estimates began in 2008 

based on ACS data collected from 2005 through 2007. 

Three-year estimates are produced for areas with 

populations of 20,000 and greater. The release of 5-

year estimates begins in 2010 derived from ACS data 

collected from 2005 through 2009. These 5-year estimates are published for all geographic entities down to 

census tracts and block groups. 

The 3-year and the 5-year estimates are called “period 

estimates” because of the way that the Census Bureau 

constructs them. Each year the Census Bureau creates 

new multiyear estimates by adding the newest year 

of ACS data and dropping the oldest year of ACS data. 

For example, the 5-year estimates released in 2010 

will be based on data collected in 2005, 2006, 2007, 
What Federal Agencies Need to Know 

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

2008, and 2009. These estimates will be called the 

2005–2009 ACS 5-year estimates. The 5-year estimates 

released in 2011 will be based on data collected in 

2006, 2007, 2008, 2009, and 2010. These estimates 

will be called the 2006–2010 ACS 5-year estimates. 

The difference between the 2005–2009 estimates and 

the 2006–2010 estimates is that the latter will include 

2010 data and exclude 2005 data. Both estimates will 

include the same data from 2006, 2007, 2008, and 

2009.

Table 3 summarizes the types of geographic areas 

receiving only 5-year estimates, 5-year and 3-year estimates, and areas receiving all three data sets. 

Table 3 has several important implications for users of 

ACS data: 

•	 As the number of areas in column 1 makes clear, 

the vast majority of areas will receive only the 5-

year estimates.

•	 Estimates based on a single year of ACS data will 

be available only for geographic areas with the 

largest populations. Nearly all states, congressional districts, and Public Use Microdata Areas 

receive 1-year estimates. 

•	 Smaller geographic areas, including census tracts 

and census block groups, will have data that 

portray conditions in those locations not in a 

specific year but over several years. Census tract 

and block group data are only produced as 5-year 

estimates.

•	 For some levels of geography, the Census Bureau 

will release up to three sets of estimates every 

year. For example, in 2010, the ACS Web site will 

include three sets of the same tables for 

Birmingham, Alabama, (a city with 208,000 residents)—one set using data from 2009, one set 

using data from the 2007–2009 period, and one 

set using data from the 2005–2009 period. 

In Section 4, this handbook will examine each of these 

implications more fully.

Products. Section 7 will explain how users can obtain 

ACS data online. The tables available from the 

American FactFinder (AFF) are similar, but not identical, to what was used for Census 2000 long-form data. 

The Census Bureau has refined these tables over time 

in response to data user needs. In general, ACS tables 

include the same topics as did Census 2000 and these 

tables have similar patterns of rows and columns. The 

tables cover the subjects listed in Table 1.

Sample Size. The ACS (and the long form in Census 

2000 and prior years) obtains its information from a 

sample of housing units and group quarters. The 

Census 2000 long form was distributed to approximately 1 in 6 housing units. The ACS uses an annual 

sample of approximately 1 in 40 housing units but 

combines information from successive years so that 

eventually its published tables will represent approximately 1 in 40, 3 in 40, or 5 in 40 (1 in 8) housing 

units. In the published tables, the Census Bureau transforms the ACS sample information into estimates of 

the number of people, households, and housing units. 

While the tables include numbers that some users interpret as “counts,” it is critical to remember that these 

numbers are estimates, not counts.

Sampling error. Questions regarding the precision 

of estimates apply to both census long form and ACS 

data. However, the small sample sizes in the ACS as 

 Table 2. Collection and Release Dates of Data From the ACS (Pattern Repeats After 2010)

2006 2007 2008 2009 2010 2011 2012

Type of period estimate

1-year period estimate for areas 2005 2006 2007 2008 2009 2010 2011

 with 65,000 or more people

3-year period estimates for 2005– 2006– 2007– 2008– 2009–

 areas with 20,000 or more people 2007 2008 2009 2010 2011

5-year period estimates for all 2005– 2006– 2007–

 areas including those with 2009 2010 2011

 fewer than 20,000 people 

Release year (late summer or fall)

Calendar year(s) of monthly data collection

Source: Citro and Kalton (2007: Table 2-6, page 50).
 What Federal Agencies Need to Know

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

compared with the census long-form sample result 

in more statistical variation in ACS estimates than 

in corresponding long-form estimates. The Census 

Bureau provides users with information (the “margin 

of error”) on the statistical accuracy of every estimate 

in ACS tables.2 For example, the 2006 ACS reported 

that there were 20,153 people 25 years and older in 

Birmingham, Alabama, who have a bachelor’s degree 

and that the margin of sampling error associated with 

this estimate is plus or minus 2,588. Since the margin 

of sampling error provided represents the 90-percent 

confidence level, this means that there is a probability 

of 90 percent or more that the number of people with 

a bachelor’s degree in Birmingham, Alabama, in 2006 is 

between 17,565 (20,153 – 2,588) and 22,741 (20,153 

+ 2,588). 

Relating data to time and place. There are some 

subtle but important differences that users should note 

when comparing ACS data with similar data from the 

decennial long form. The Census Bureau collected 

Census 2000 long-form data in a narrow timeframe, 

mostly in March through June of 2000, whereas it 

collects ACS data continuously throughout every year. 

Timing differences affect the interpretation of variables, even when the questions are worded identically. 

For example, both Census 2000 and the ACS ask about 

whether each household member was employed “last 

week.” The Census 2000 answers were used to calculate an unemployment rate for a period around April 1, 

2000, while the ACS answers are used to calculate an 

unemployment rate representative of the entire time 

period over which the ACS data are or will be collected. 

The continuous nature of data collection in the ACS 

also leads the Census Bureau to adjust for changes 

* 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 differ from the numbers in this table.

 

 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 unified) 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. ACS Publication Plans by Size of Geographic Area

2 Appendix 3 explains various measures of statistical reliability and 

how to interpret them.
What Federal Agencies Need to Know 

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

that take place over time. Prior to publication, the 

Census Bureau adjusts some data reported in dollars to 

account for changes in the real value of money. It also 

adjusts the geography for its data products to agree 

with the most recent changes in the boundaries of the 

area being described. Section 4 discusses these important features of ACS data. 

4. How to Interpret and Use ACS Data

This section and Section 5 discuss a number of issues 

that federal agencies should be aware of when using 

ACS data. In most cases, the issues are generic, that is, 

they will have similar implications for different agencies. 

Time and ACS Data 

The preceding section pointed out several distinctions 

between ACS and census long-form data, among them 

that the ACS’s continuous data collection produces 

estimates with a different interpretation than estimates 

from once-a-decade decennial data. In general, ACS 

data describe conditions over the time period during 

which the Census Bureau collected the data (1, 3, or 5 

years); Census 2000 data described conditions around 

April 1, 2000. Thus, ACS data provide “time-period” 

estimates, while prior long-form data provided approximate “point-in-time” estimates. The time-period nature 

of ACS data has several important consequences.

Interpretation of 1-year estimates. ACS estimates 

represent the “average” of conditions over the time 

period during which the Census Bureau collected the 

data. Saying that ACS estimates are “averages” seems 

simple and straightforward, but an “average” may 

mean different things depending on the variable being 

studied. For example, educational attainment is a population characteristic that changes slowly. In this case, 

the ACS estimate is likely to provide a close approximation to conditions at any one point during the year 

even if the Census Bureau collected the data over the 

course of the year. Unemployment, on the other hand, 

fluctuates throughout the year. The ACS unemployment 

estimate therefore may not be a close approximation 

to the unemployment rate on any given day during the 

year. Users must remember that, for 1-year ACS data, 

the estimates do not represent conditions at any one 

point during a year but do reflect an average over the 

year.3

Interpretation of multiyear estimates. The multiyear ACS estimates have the same interpretation as 

single-year estimates, but, in these cases, the aggregated estimates represent information collected over 3 

or 5 years rather than 1 year.4 The longer data collection 

period means that there is more time for conditions to 

change during the course of measurement. In interpreting multiyear estimates, users should take into account 

the variable, the geographical area, and the length of 

the data collection period. In particular, users should 

not assume that a multiyear estimate represents conditions at the end of the multiyear period. Two metropolitan areas may have the same 5-year average poverty 

rates but the poverty rates of the two metropolitan 

areas may be substantially different on the last day of 

the measurement period. 

Comparison of multiyear estimates. Federal 

agencies and other users will benefit from having 

new information on conditions in counties, cities, and 

census tracts every year. Users will want to compare 

data from different years to see how conditions may 

have changed. Comparisons involving single-year ACS 

estimates are straightforward because they involve 

comparisons of data collected in two independent 

samples. Comparisons involving multiyear ACS data 

are complicated by the fact that the estimates being 

compared may be based on some of the same data. 

It is important to distinguish between comparisons 

involving overlapping and nonoverlapping samples. 

The top panel of Table 4 illustrates how the six consecutive 3-year estimates that will be released between 

2008 and 2013 relate to one another. The 2005–2007 

3-year estimates overlap with the 2006–2008 and 

2007–2009 3-year period estimates because all three 

estimates use data collected in 2007. The 2005–2007 

3-year estimates and the 2008–2010 3-year estimates 

are said to be nonoverlapping because they use none 

of the same data. The bottom panel of Table 4 compares the six consecutive 5-year estimates that will 

be released between 2010 and 2015. The 5-year 

estimates that will be released in 2010 will overlap 

with the 5-year estimates that will be released in 2011 

through 2014. In Table 4, only the 2010 and 2015 

releases will be nonoverlapping. This table shows that 

starting with the data collected in 2009, every ACS 

interview is used to produce nine different sets of 

estimates: one 1-year estimate, three 3-year estimates, 

and five 5-year estimates.

Comparisons involving overlapping estimates depict 

changes only as calendar year data sets are dropped 

and added to the sample. Any changes that may 

have occurred between the beginning and the end of 

the survey period are muted by the use of the same 

3 Appendix 1 discusses in more detail how to interpret 1-year ACS 

estimates.

4 Appendix 1 discusses in more detail how to interpret multiyear ACS 

estimates.
 What Federal Agencies Need to Know

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

information (the overlapping samples) in both estimates. For example, changes between the 2005–2007 

3-year estimates and the 2006–2008 3-year estimates 

are entirely driven by the changes between 2005 and 

2008, because the 2006 and 2007 data are used in 

both estimates.

Overlapping estimates may give users a sense of 

underlying trends but, because the number of observations dropped and added between samples may be 

small, a suspected trend may be the result of the deletion and addition of a relatively small number of atypical cases. Nonoverlapping estimates will give users 

the best indication of whether and how conditions 

may have changed. Thus, rather than comparing estimates from the 2005–2007 3-year estimate with the 

2006–2008 3-year estimate, agencies should compare 

the 2005–2007 3-year estimate with the 2008–2010 

3-year estimate. Data users will need to be patient until 

nonoverlapping data sets are available. Typical statistical tests cannot be applied to overlapping samples 

because the overlap gives a false sense of the size of 

the samples being compared.5

Consecutive 3-year Period Estimates and the Years in Which 

the ACS Data Used in Their Construction Were Collected

2005–2007 

3-year 

estimates

2006–2008 

3-year 

estimates

2007–2009 

3-year 

estimates

2008–2010 

3-year 

estimates

2009–2011 

3-year 

estimates

2010–2012 

3-year 

estimates

2005 collected

2006 collected ➜ reused

2007 collected ➜ reused ➜ reused 

2008 released collected ➜ reused ➜ reused

2009 released collected ➜ reused ➜ reused

2010 released collected ➜ reused ➜ reused

2011 released collected ➜ reused

2012 released collected

2013 released

 Table 4. Overlapping and Nonoverlapping Multiyear Estimates

Consecutive 5-year Period Estimates and the Years in Which 

the ACS Data Used in Their Construction Were Collected

2005–2009 

5-year 

estimates

2006–2010 

5-year 

estimates

2007–2011 

5-year 

estimates

2008–2012 

5-year 

estimates

2009–2013 

5-year 

estimates

2010–2014 

5-year 

estimates

2005 collected

2006 collected ➜ reused

2007 collected ➜ reused ➜ reused 

2008 collected ➜ reused ➜ reused ➜ reused

2009 collected ➜ reused ➜ reused ➜ reused ➜ reused

2010 released collected ➜ reused ➜ reused ➜ reused ➜ reused

2011 released collected ➜ reused ➜ reused ➜ reused

2012 released collected ➜ reused ➜ reused

2013 released collected ➜ reused

2014 released collected

2015 released

 Source: U.S. Census Bureau.

5 Refer to Appendix 4 for guidance on comparing ACS estimates with 

overlapping time periods.
What Federal Agencies Need to Know 

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

Implications. There are four important implications of 

this discussion. 

•	 First, users of ACS data should understand that an 

average is not a precise description of conditions 

at any one point in time, just as users of Census 

2000 long-form data realized that a point-in-time 

estimate was not a precise description of conditions 

over a time period. 

•	 Second, users should keep the distinction between 

time-period and point-in-time estimates in mind 

when comparing ACS data with 2000 or earlier census long-form data. While the tables from the two 

sources may use the same or similar wording, the 

numbers reported refer to different concepts. For 

example, the 2005 1-year ACS unemployment rate 

refers to unemployment during 2005 for an area 

while the 2000 decennial census unemployment 

rate refers to unemployment around April 1, 2000, 

for an area.

•	 Third, once users understand that ACS data represent conditions over specific periods of time, they 

should have no concerns about using ACS data to 

compare conditions across places or across time 

periods because the data are collected and reported 

consistently for all areas and for all survey years.6

•	 Fourth, over-time comparisons that rely on nonoverlapping samples will provide the clearest indications of how conditions may have changed.

More Than One Estimate for the Same 

Measurement

The Census Bureau releases ACS estimates in more 

than one format, depending upon the size of the area 

being reported. This feature presents users in federal 

agencies with choices not available from the decennial 

long form. This subsection discusses these choices.

Choosing Between 1-Year, 3-Year, and 5-Year 

Estimates 

By 2010, analysts and policy makers in federal agencies will have three alternative ACS data sets to choose 

from for states, counties, and places with 65,000 or 

more residents. If, for example, in 2010, an agency 

wants to know the median household size for Salinas, 

California, a city with 150,724 inhabitants in 2000, 

it can use the 1-year estimate based on 2009 data, 

the 3-year estimate based on data from 2007 through 

2009, or the 5-year estimate based on data from 2005 

through 2009. As discussed next, which estimate to 

use depends upon several factors.

6 This presumes that the survey questionnaire remains constant over 

time. Section 8 will discuss how the Census Bureau plans to incorporate modifications to the questionnaire to minimize disruptions in 

intertemporal comparisons.

Intended use. Comparisons with other places should 

be based on the information derived from the same 

sets of estimates.7 If an agency wants to compare 

Salinas with other large cities, any of the three sets of 

estimates would be appropriate, since all three estimates would be available for every large city. But, if the 

agency wanted to compare Salinas with smaller cities, 

then the 3-year or 5-year estimates should be used 

depending upon the size of the smallest city in the 

comparison. The Census Bureau has recommended that 

users should not compare 1-year data with 3- or 5-year 

data. Small cities with populations under 20,000 would 

have only 5-year estimates, so the agency should 

choose 5-year estimates for all the cities being compared. If the agency wanted to compare Salinas in 2010 

to Salinas in 2011, then the nonoverlapping samples 

provided by the 1-year data would provide the clearest 

comparison. For over-time comparisons involving areas 

that do not have 1-year estimates, agencies should use 

nonoverlapping estimates. This means that the smaller 

cities would have only two or three nonoverlapping 

data points to compare during a decade.

Desired precision. The 5-year estimates are based 

on larger samples than the 3-year estimates, and the 

3-year estimates have larger samples than the 1-year 

estimates. Larger samples lead to smaller margins of 

error and produce more precise estimates. The margins 

of error for the single-year samples are approximately 

1.73 times larger than the margins of error for the 

3-year estimates and approximately 2.24 times larger 

than the margins of error for the 5-year estimates. 

Some population and housing characteristics reported 

by the ACS are found infrequently in the population 

or housing stock; for these characteristics, use of the 

multiyear estimates is strongly recommended because 

of the relatively small number of cases with these 

characteristics in the 1-year estimates. The margins of 

error reported in the ACS give agencies the information needed to assess the precision of the alternative 

estimates. Refer to Appendix 1 for additional information on precision and the use of single-year versus 

multiyear estimates.

Currency of the estimates. If an agency wants 

information on current conditions, then 1-year estimates, if available, would be preferred to 3-year 

estimates and 3-year estimates would be preferred to 

5-year estimates. 

Implication. How an agency chooses between the 

available ACS estimates will depend upon its perception of how to balance currency, precision, and consistency to meet the needs of its programs. 

7 Appendix 4 discusses comparisons between ACS estimates.
10 What Federal Agencies Need to Know

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

Choosing Between ACS Estimates and Alternative 

Federal Estimates

For some important measures of socioeconomic and 

housing conditions, the ACS is not the only source of 

data produced by the federal government. Previously, 

estimates derived from decennial long-form samples 

never competed with information from these other 

sources because the alternative sources could furnish 

updated statistics throughout the decade between censuses. The ACS, on the other hand, can deliver updated 

estimates annually and, therefore, may be a plausible 

alternative to these other series. The choice between 

using the ACS and alternative federal statistical series 

hinges on several factors that agencies must take into 

account in the context of their program needs.

Conceptual accuracy. The ACS, like the previous decennial long form, uses a questionnaire that 

is designed to collect a wide range of demographic, 

economic, and housing information. This breadth of 

coverage limits the depth to which the ACS questionnaire can probe any one issue. Other federal surveys 

contain more questions related directly to their primary 

focus, which in most cases allows them to offer fuller 

descriptions of the phenomenon they are describing 

and to incorporate important conceptual refinements 

into their measurements. The Current Population 

Survey (CPS) is an excellent example. Because the CPS’s 

main mission is to measure conditions in the labor 

market, it contains numerous questions to ascertain 

whether unemployed individuals are actively seeking 

employment. Conceptually, only people who are in the 

labor force—that is, either employed or actively seeking employment—should be included when calculating 

the unemployment rate. Because the long form did not 

have all the questions that the CPS has on labor force 

participation, it historically produced higher estimates 

of the unemployment rate than the estimates from the 

CPS covering the same time period. The ACS is subject 

to the same limitations on depth of coverage as the 

decennial census long form.

Sample size. The ACS initially selects approximately 

3 million housing unit addresses each year, resulting in 

about 2 million interviews—a sample size that dwarfs 

the sample size of any other federal household survey 

and enables the Census Bureau to publish substate 

estimates.8 

Geographic detail. Sample size differences become 

increasingly important at finer levels of geography. 

Few federal surveys create estimates for counties and 

places with populations as low as 65,000, the counties 

and places for which the ACS furnishes 1-year estimates. The precision of the ACS 1-year estimates varies 

by the size of the area and can be easily determined 

OTHER IMPORTANT FEDERAL DATA SERIES

The Bureau of Labor Statistics (BLS) produces monthly estimates of employment, unemployment, and labor force participation at the national, state, and city levels. BLS uses 

the Current Population Survey for its national and state unemployment estimates; it uses 

statistical models to provide estimates of labor force, employment, unemployment, and the 

unemployment rate for metropolitan areas and cities down to 10,000 population. Information 

on the national data can be found at <http://www.bls.gov/cps/>. The estimates of local 

employment conditions can be found at <http://www.bls.gov/lau/>.

The Current Population Survey/Housing Vacancy Survey produces quarterly estimates of 

homeownership and vacancy rates at the national and regional levels. Information on these 

housing series and the most recent and historical data can be found at <http://www.census

.gov/hhes/www/housing/hvs/hvs.html>.

The Department of Housing and Urban Development’s (HUD’s) American Housing Survey 

publishes information on the condition of the housing stock at the national and regional level 

every 2 years and for selected metropolitan areas on a 6-year cycle. The report on the 2007 

national American Housing Survey is available at <http://www.census.gov/prod/2008pubs

/h150-07.pdf>.

The Census Bureau’s annual publications contain detailed information on income and poverty 

at the national level. The 2007 report is available at <http://www.census.gov/hhes/www

/poverty/poverty07.html>.

8 Due to the use of subsampling in the final stage of data collection, 

the final annual sample of completed interviews is about 2 million 

housing units.
What Federal Agencies Need to Know 11

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

from the accuracy information released by the Census 

Bureau with the ACS estimates. 

Timeliness and frequency of reporting. As noted 

earlier, the ACS 1-year estimates measure the condition over the preceding year, and the Census Bureau 

releases these typically in August or September of the 

following year. Other federal surveys provide data on 

unemployment on a monthly basis, usually within a 

month of data collection. They also provide data on 

homeownership and housing vacancies on a quarterly basis, usually within 2 months of the end of the 

quarter. The housing data from the American Housing 

Survey are reported only every other year and normally 

6 months after the year in which they were collected. 

The Census Bureau times the release of the ACS data 

on income and poverty to coincide with the release of 

its report on this topic.

Implication. Agencies will have to balance various 

concerns in deciding whether to use ACS estimates or 

an alternative federal statistical series. The relevant 

concerns will be conceptual accuracy, geographical 

detail, statistical precision at lower levels of geography, 

and the timeliness and frequency of reporting.

9 For a complete listing of types of group quarters, see 2006 ACS 

group quarters type codes and definitions at <http://www.census

.gov/acs/www/UseData/GQ/def.htm>.

How Not To Interpret Inflation-Adjusted Variables

Over the time periods for which ACS data are tabulated (1-year estimates, 3-year estimates, 

and 5-year estimates), variables denominated in dollars change because the individual 

variables are subject to economic trends. Incomes generally increase over time, as do rents 

and housing values. These increases reflect both general inflation and other trends such as 

increases in productivity or the rising cost of land. The Census Bureau’s inflation adjustment 

does not adjust for underlying trends in these variables. Instead, the adjustment is designed 

to put the collected data into dollars of equal purchasing power. 

For example, consider a household surveyed by the ACS in April 2005 and reporting a rent 

of $610. The inflation adjustment for the 3-year ACS estimate covering 2005–2007 would 

calculate how many dollars would be needed in 2007 to purchase the equivalent of general 

goods and services that cost $610 in 2005—in this case, roughly $648 would be needed. In 

this example, $610 is adjusted for general inflation; the adjustment does not consider either 

rental market trends in rents that might arise from increases in the size and quality of rental 

units or other factors.

This section deals with several more technical features 

of ACS data that users in federal agencies should know 

about. 

Household Population and the Group 

Quarters (GQ) Population

Like the Census 2000 long-form sample, the ACS 

distinguishes between people living in housing units 

(households) and people living in group quarters (GQ). 

Group quarters include university housing, nursing 

homes, psychiatric hospitals, military barracks or 

ships, religious housing, and correctional facilities.9 

Homeless people who sleep in shelters are included 

in the GQ population surveyed by the ACS, but the 

“street” homeless are not. Neither the Census 2000 

long-form sample nor the ACS furnishes information 

on the street homeless. The ACS began collecting data 

from samples of the GQ population in 2006; only the 

household population was surveyed for the 2005 ACS. 

The ACS typically includes both the household and GQ 

populations in tables that describe the characteristics 

of the total population but includes only the household 

population in tables that describe the characteristics of 

households or housing units. Currently, only nationallevel ACS estimates are released by major GQ type. 

PUMS files, discussed in Section 7, allow users to tabulate the characteristics of the GQ population separately 

from those of the household population at the PUMA 

level. While such tabulations are possible, users need 

5. Technical Issues Involving ACS Data
12 What Federal Agencies Need to Know

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

to consider sampling error and other factors when they 

produce GQ tabulations from the PUMS files.

Dollar-Valued Data and Adjustments for 

Inflation

A number of ACS variables are reported as dollar 

values; these variables include income and its various 

components, rent, and property value. For its multiyear 

estimates, the Census Bureau will adjust many, but not 

all, of the variables that are measured in dollar terms. 

Income and its components are the only variables 

adjusted for inflation in both 1-year and multiyear estimates. Users of ACS data should consult the “Subject 

Definitions” document available on the ACS Web site 

for the appropriate year of the ACS survey to determine 

whether the ACS made an adjustment for inflation.10

Whenever the Census Bureau makes an adjustment for 

inflation, it uses the Consumer Price Index Research 

Series (CPI-U-RS) published by the Bureau of Labor 

Statistics to transform the estimates into real dollars as 

of the reference year—which is the year that interviews 

were conducted or the most current year for multiyear 

estimates. Refer to Appendix 5 for more information 

about inflation adjustments.

The ACS sometimes combines or otherwise manipulates the information provided by respondents to 

create useful measures; these measures are called 

composite or derived variables. For example, the 

ACS reports the estimated number of individuals and 

families living in poverty; it also provides estimates of 

renter households by the ratio of gross rent to income. 

The classification of individuals, families, and households for these tables are made based on the data 

received without adjustment for inflation. For example, 

if a completed questionnaire is received in July 2007, 

the Census Bureau will calculate the ratio of gross rent 

to income by adding the components of gross rent as 

they were reported on the questionnaire and dividing by the income reported on the questionnaire. This 

10 The most recent “Subject Definitions” can be found at 

<http://www.census.gov/acs/www/UseData/Def.htm>.

procedure defines the composite variable at the time of 

data collection, an approach consistent with the correct 

interpretation of the composite variables. 

Geographic Boundary Changes

The Census Bureau organizes and reports ACS data 

by geography; it releases tables for the nation, states, 

counties, places, and other entities with physical 

boundaries. Since the boundaries of places and other 

entities can and do change over the period in which 

the Census Bureau collects ACS data, a consistent rule 

is needed on how to report data when boundaries 

change. 

The Census Bureau always uses the legal boundary in 

effect on January 1 of the year in which it tabulates 

the data. Table 5 illustrates how the Census Bureau 

would handle ACS data for a city that annexed territory in April of 2006. In 2008, the Census Bureau will 

tabulate 1-year estimates for 2007 and 3-year estimates based on data from 2005, 2006, and 2007 using 

the new boundaries that were in effect on January 1, 

2007, which would reflect this annexation. However, 

the Census Bureau will not update the 2005 and 2006 

1-year estimates that were released previously and had 

used the old boundaries. Therefore, for the city in this 

example, the 2007 1-year estimates will be based on 

different boundaries than those used in 2005 and 2006 

1-year estimates and the 3-year estimates released in 

2008 would not be equivalent to an aggregation of the 

published 2005, 2006, and 2007 1-year estimates. 

Table 5 points out an important feature of how the 

Census Bureau processes ACS data that is broader than 

the issue of boundary changes. In creating its 3-year 

and 5-year estimates, the Census Bureau combines the 

data from all the relevant years and calculates population, household, and household characteristics for the 

relevant geography. The data from previous years are 

retabulated together with the new data from the most 

recent year. 

 Table 5. How a Boundary Change in 2006 Will Affect ACS Estimates

 Source: U.S. Census Bureau.

ACS 1-year estimates

Year data 

collected

Year tabulated 

and released

Boundaries 

as of

2005 ACS estimates 2005 2006 January 2005

2006 ACS estimates 2006 2007 January 2006

2007 ACS estimates 2007 2008 January 2007

2005–2007 ACS estimates 2005, 2006, 2007 2008 January 2007
What Federal Agencies Need to Know 13

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

Rules Used to Determine Residency

An important technical difference between the ACS 

and the decennial census involves the rules used by 

the Census Bureau to determine where to consider a 

person a resident if that person lives or stays in different places at different times during a year. The Census 

Bureau refers to these rules as “residence rules.” See 

Appendix 2 for more information about the census and 

ACS residence rules.

Household members who split their time between two 

or more housing units may be considered residents of 

different places in the ACS than they would have under 

the census residence rule. Thus, the ACS will base its 

characterization of the households and people in 

a given area on a slightly different “population” 

of households than either Census 2000 or the 

2010 Census would use.11 This difference in residency 

rules is unlikely to result in any measurable difference 

in the characterization of the local population except 

perhaps in highly seasonal areas where a sizable portion of the housing stock consists of second homes 

occupied for more than 2 consecutive months at a 

time. Resort communities in which families rent units 

for 1 or 2 weeks would not be affected by the difference in residence rules.

The second consequence involves the definition of a 

“vacant” unit. The ACS would classify fewer units as 

vacant in seasonal areas than the decennial census 

would. Users should look on this consequence as 

the result of a different concept of what constitutes 

vacancy rather than as a measurement error.

Sample Size and Statistical Precision

In 2005, there were approximately 124 million housing 

units in the United States.12 The ACS annual sample of 

close to 3 million housing unit addresses represents 

an overall sampling rate of approximately 2.4 percent. 

The 3-year ACS estimates rely on a sample of approximately 7.2 percent and the 5-year ACS estimates rely 

on a sample of approximately 12 percent. Due to the 

use of subsampling in the final stage of data collection, 

the final sampling rates are lower than this. By contrast, the Census 2000 long form involved an overall 

sample of 16.7 percent, although the actual sampling 

rate varied by location based on population size, as 

does the ACS sample. In general, Census 2000 had 

a larger sample and therefore provided more precise 

estimates than should be anticipated from any of the 

ACS estimates. 

To illustrate how the ACS compares with Census 2000 

with respect to statistical accuracy, Table 6 contains an 

example using estimates of the number of noncitizens 

in five cities of various sizes. Recall that the margins 

of error reported in Table 6 represent the amount that 

a user would have to add to and subtract from the 

estimate to obtain a range about which the user could 

be 90 percent confident that the actual number of noncitizens would fall into that range. For example, a user 

would be 90 percent confident that the number of noncitizens in Dallas around April 1, 2000, was between 

232,106 (234,829 – 2,723) and 237,552 (234,829 + 

2,723).

11 In this sentence, “population” is used in the statistical sense as the 

collection of items from which a sample is drawn for the purpose of 

describing that collection of items.

12 American Housing Survey for the United States: 2005, Table 1-A1, 

issued August 2006.

 Table 6. Comparison of Estimated Margins of Error on Census 2000 Long-Form Estimates and ACS 

 Estimates for Five Cities

 Source: U.S. Census Bureau.

2000 Census long form 2006 ACS ACS margin of error

1 2 3 4 5 6 7 8

Total 2000 

population Noncitizens Margin of error (est.)

Total 2006 

population Noncitizens 1 year 3 year (est.)

5 year 

(est.)

Dallas, TX 1,188,204 234,829 2,723 1,192,538 259,182 11,894 6,867 5,319

Portland, OR 529,025 44,359 1,190 539,950 44,248 4,719 2,725 2,110

Newark, NJ 273,546 44,645 1,212 266,736 51,730 6,511 3,759 2,912

Gainesville, FL 95,605 5,488 451 105,714 6,461 1,656 961 745

Youngstown, OH 82,026 559 113 70,459 951 322 186 144
14 What Federal Agencies Need to Know

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

The margins of error for Census 2000 noncitizen 

population counts in column 3 were estimated using 

procedures recommended by the Census Bureau.13 The 

margins of error for the 2006 ACS 1-year data, 

reported in column 6, were obtained from the published ACS data. The estimated margins of error for 

the 3-year and 5-year ACS estimates in columns 7 and 

8 were derived by assuming that the estimated counts 

of noncitizens would remain the same and that the 

samples would be 3 and 5 times larger respectively.14

Several important observations can be drawn from 

Table 6 that apply to most examples comparing data 

from Census 2000 and the ACS and within the ACS for 

1-year, 3-year, and 5-year estimates:

•	 Census 2000 estimates have associated margins 

of error even though the Census Bureau did not 

publish them with the long-form data. In some 

cases, for example, Youngstown in Table 5, the 

margin of error is large relative to the estimate.

•	 As a general rule, margins of error are smaller 

for the larger areas. Looking at the long-form 

data, the margin of error is roughly 1 percent 

of the estimate for Dallas and 20 percent for 

Youngstown. Statistical accuracy depends upon 

the number of observations in the sample and not 

on the sampling rate. The long-form sampling rate 

of 12.8 percent for Dallas resulted in a sample 

of over 150,000, while the sampling rate of 15.5 

percent for Youngstown produced a sample of less 

than 13,000. 

•	 As expected, the ACS margins of error are larger 

than the corresponding margins of error associated with the long-form data because of the difference in sample size.

•	 The ACS margins of error are smallest for the 

5-year estimates and largest for the 1-year estimates.

For Census 2000 data, most data users treated the 

long-form data as “counts” and did not consider 

sampling error. If one needed a count of noncitizens 

in Youngstown, one used 559 and ignored the possible margin of error. To correct this misconception, 

the Census Bureau has intentionally included margins 

of error with ACS data and much of its information for 

users has attempted to improve understanding of data 

reliability, quality, and associated measurement error. 

For many areas, ACS users will eventually be able to 

choose between 1-year, 3-year, and 5-year estimates. 

The 1-year estimates are the least precise but the most 

current, while the 5-estimates are the most precise but 

the least current. Users will have to balance currency 

against precision. The Census Bureau’s decision to publish the associated margins of error will enable federal 

users to make this choice with a clear understanding of 

the precision of each of the choices.

Combining or Comparing ACS Estimates

Users will often want to combine or compare ACS estimates. With ACS data, users can easily add estimates, 

Examples of Combinations and Comparisons 

Using ACS Data

(The numbers preceded by a B or C are the numbers 

of the detailed tables available on 

the American FactFinder—see Section 7.)

• Combining two ACS estimates within the same 

table for the same geography. From the table 

on educational attainment (C15002), one might 

want to combine the four rows containing 

estimates of males with bachelor’s degrees 

and with graduate or professional degrees 

and females with bachelor’s degrees and with 

graduate or professional degrees. 

• Combining ACS estimates from two different 

geographies. One might want to estimate the 

number of owner-occupied homes valued at 

$1 million or more in DuPage and Lake 

Counties, Illinois, using Table C25075 for both 

counties. 

• Forming ratios or proportions using ACS 

estimates. One could use the data in Table 

C18008 to calculate the proportion of the male 

population between 16 and 64 years old who 

have an employment disability.

• Comparing ACS estimates from two different 

areas. One might compare median household 

income in Denver, Colorado, in 2006 with 

median household income in Salt Lake City, 

Utah, in 2006 using Table B19013.

• Comparing an ACS estimate for two different 

years or periods for the same geography. One 

could compare median household income 

in Denver, Colorado, in 2005 with median 

household income in Denver, Colorado, in 2006 

(Table B19013).

13 Accuracy of the Data: Census 2000 Summary File 3 (SF 3)—Sample 

Data, Chapter 8, which can be found at <http://www.census.gov

/prod/cen2000/doc/sf3.pdf#page=933>. 

14 Margins of error are inversely proportional to the square root of the 

sample sizes. The example is simplified because (a) the ACS 1-year, 

3-year, and 5-year estimates would differ from one another as they 

would be based on different (if overlapping) samples and (b) because 

the sizes of the multiyear samples would not be exactly 3 and 5 times 

as large as the 1-year sample. Note that generalized variances were 

used to compute the margins of error for the Census 2000 sample 

estimates.
What Federal Agencies Need to Know 15

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

take ratios or proportions, or compare estimates from 

different places or time periods. However, correct data 

usage requires that users consider the statistical precision of the resulting combination or comparison. Fortunately, calculating the required measures of statistical 

precision using the information furnished in the ACS 

tables is straightforward, if somewhat complex. Appendixes 7 and 8 explain how to calculate the correct 

measures of precision for each of the five examples 

listed in the text box titled “Examples of Combinations 

and Comparisons Using ACS Data.”

Nonsampling Error

The imprecision that results from sampling is just one 

of the reasons that a statistic can differ from the true 

value of what it is supposedly measuring. All the other 

reasons are grouped together under the term 

“nonsampling error.” Nonsampling errors include 

incomplete coverage in sample design or execution, 

the inability to obtain any information for selected 

units (unit nonresponse), failure of respondents to 

answer specific questions (item nonresponse), wrong 

answers, and errors in processing the data.15

The ACS has some important advantages over Census 

2000 with respect to nonsampling error. The ACS has a 

three stage data collection procedure: 1) self-response 

mail questionnaires, 2) computer-assisted telephone 

follow-up for nonresponse, and 3) computer-assisted 

personal visit interviews on a subsample of the remaining nonresponding sample addresses. Unlike the nonresponse follow-up operations conducted during Census 

2000—operations that relied on temporary enumerators hired for the once-a-decade workload—the Census 

Bureau carries out ACS nonresponse data collection 

with a permanent telephone and field interviewing staff 

thoroughly familiar with the survey and its content. 

Studies have indicated that the ACS procedures produce lower unit nonresponse and item nonresponse 

than experienced by the decennial long form.16 While 

the impact of sampling variation can be easily translated into measures such as the margins of error in 

Table 5, it is difficult to actually measure how improvements in unit and item nonresponse translate into 

more accurate data. Survey experts generally believe 

that nonsampling error is the largest source of error in 

large-scale, modern surveys. 17 

Sample Size and Suppression of Data

Rigorous provisions to protect the confidentiality of 

information supplied by respondents govern both the 

ACS and the decennial census. The Census Bureau has 

developed a number of safeguards to maintain confidentiality. Generally the safeguards applied to the ACS 

are the same as those applied to the long-form sample.18 Because the application of a specific safeguard is 

usually triggered by the small number of observations 

used in a table or in a specific cell within a table, ACS 

tables are more likely to require safeguarding because 

its sample sizes are smaller.

An additional restriction is applied to ACS tables with 

1-year estimates and 3-year estimates. Two versions of 

many of the detailed tables are produced, one with the 

full detail and a second with less detail—a “collapsed” 

version. Both are released as long as they pass a reliability standard. If the range of variation for the estimates in the detailed table is too high, the table is not 

released.19 If a collapsed version is available that does 

meet the standard, the collapsed table will be released. 

However, if a collapsed version has not been specified, 

no version of the table will be available for that area. 

This safeguard was imposed to prevent users from 

using unreliable estimates; it was not used in tables 

from the 2000 long-form sample and will not be used 

in tables based on the 5-year ACS estimates. 20

The safeguards listed above apply to published tables. 

The Census Bureau allows federal agencies to purchase 

customized tables but applies additional safeguards 

to these special products. The additional safeguards 

involve both minimal sample size rules and rounding 

rules.21 

Public Use Microdata Sample (PUMS) files are also 

available. In PUMS, all personal identifiers and address 

information are stripped from the sample of individual 

ACS response records. Some variables are also coded 

to protect confidentiality. The geographic location of 

the respondents represented in these PUMS file records 

is no more precise than the defined Public Use 

Microdata Areas (PUMAs), geographical units containing an average of about 100,000 people.

15 Appendix 6 discusses each of these sources of nonsampling 

errors.

16 Susan P. Love and Deborah H. Griffin, A Closer Look at the Quality of Small Area Estimates From the American Community Survey, 

paper presented at the 2003 Joint Statistical Meetings in San 

Francisco, California, August 3–7, 2003.

17 For example, on page D-1 of American Housing Survey for the 

United States: 2005, the Census Bureau concludes with respect to 

that survey: “Incomplete data and wrong answers are usually the 

largest source of errors, larger than sampling errors.”

18 See American Community Survey Design and Methodology Technical 

Paper at <http://www.census.gov/acs/www/Downloads/tp67.pdf>.

19 The ratio of the standard deviation to the mean is called the coefficient of variation. If the median coefficient of variation for a table 

exceeds 0.61, the Census Bureau will not publish the table.

20 To see how this safeguard affects ACS data, the reader can compare 

Tables B25014 and C25014 (persons per room by tenure) for Atlanta, 

Georgia, and Columbia, South Carolina, in the 2006 ACS detailed 

tables. Table B25014 has 10 independent cells and is available only for 

Atlanta. Table C25014 collapses the 10 independent cells into 4 independent cells and is available for both Atlanta and Columbia.

21 Tables by area generally require at least three cases and a mean cell 

sample size of three. The rounding rules are: 0 remains 0, 1–7 rounds 

to 4, and 8 or greater rounds to nearest multiple of 5. 
16 What Federal Agencies Need to Know

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

Issues With Specific Variables

Disability. The Census 2000 estimates of disability, 

as well as estimates of disability in the ACS demonstrations prior to 2003, were unreliable. The Census 

Bureau redesigned this question in 2003 and the 

change produced a substantial downward shift in the 

percentage of people with disabilities between the 

Census 2000 long-form sample and the 2005 ACS. 

Comparisons between ACS years are not affected, but 

comparisons with the Census 2000 long-form sample 

and with the pre-2005 demonstrations of the ACS are 

not advised.22

Migration. As noted previously, the ACS asks a different question on mobility than the long form. The 

Census 2000 long form asked whether a resident lived 

in the same unit 5 years previous, while the ACS asks 

whether a resident lived in the same unit last year. 

Comparisons across ACS years are not affected but 

comparisons with the Census 2000 long form are not 

appropriate.

Vacancy. Because the ACS uses a “current residence” 

rule rather than the “usual residence” rule used by the 

decennial census, the ACS has a different concept of 

housing vacancy than the long form. This difference in 

concept makes it inappropriate to compare ACS 

vacancy rates with vacancy rates from the decennial 

census. However, the conceptual differences would 

result in only minor differences in the measurement 

of housing vacancy. More importantly, the ACS has 

a recognized downward bias in measuring vacancy 

because of the design of its data collection; this bias is 

unrelated to the differences in concept.23

Evaluations of the ACS

The Census Bureau tested the concept of continuous 

measurement for many years before the first full ACS 

in 2005, beginning with four sites in 1996. In 1999, 

the Census Bureau expanded its experimental ACS 

data collection to 31 sites (in 36 counties) across the 

country to test the questionnaire and data collection 

techniques. A nationwide sample of about 800,000 

addresses—called the Census 2000 Supplementary 

Survey (C2SS)—was conducted in 2000, and the 

Census Bureau began releasing data for the nation, 

large states, and large areas in 2001. 

The test sites and demonstration period allowed the 

Census Bureau to study the ACS concept in depth, and 

the analysis is available in a series of papers that can 

be found on the ACS Web site. In particular, the Census 

Bureau was able to make direct comparisons between 

data from the Census 2000 long form and ACS data 

from the combined 1999, 2000, and 2001 surveys 

conducted in the 36 ACS test areas. This evaluation 

compared measures of both sampling and nonsampling 

error between ACS and decennial census data down to 

the tract level. 

22 For a discussion of the changes in the disability question, see: Disability Data From the American Community Survey: A Brief Examination of the Effects of a Question Redesign in 2003, Sharon Stern and 

Matthew Brault, U.S. Census Bureau, Housing and Household Economic 

Statistics Division, January 28, 2005, available at <http://www.census

.gov/acs/www/AdvMeth/Papers/Papers1.htm>. 

Census Bureau Evaluations of the ACS

Users who are interested in the Census Bureau’s analyses of the strengths and weaknesses 

of the ACS should visit the ACS Web site, particularly the section on “About the Data.” This 

section contains:

• A comparison of the 1999–2001 ACS data with 2000 decennial data in the 36 test 

counties, <http://www.census.gov/acs/www/AdvMeth/acs_census/index.htm>. 

• Eleven evaluation reports, <http://www.census.gov/acs/www/AdvMeth/Reports.htm>.

• A number of other methodology papers, <http://www.census.gov/acs/www/admeth

/papers/papers1.htm>.

23 A unit in the ACS sample is not considered vacant until an interviewer visits the unit and determines it is unoccupied. The 3-month 

period between the mailing of the questionnaire and the in-person 

interview provides time for a vacant unit to become occupied prior to 

the interview. This procedure produces a downward bias in measuring 

vacancy. 
What Federal Agencies Need to Know 17

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

Statutory Basis for Using ACS Data

This section identifies issues that federal users may 

experience as they begin to use ACS data. The section 

focuses on programmatic uses, such as the allocation 

of funds by formula, but some of the same issues may 

arise in agency use of ACS data for research, evaluation, or policy development.

Data analysts and policy makers in agencies should 

work with their legal offices to ensure that all relevant staff knows that the long-form sample has been 

replaced with the ACS. The Census Bureau considers 

the ACS to be a component of the decennial census; 

all the statutory language in Census Bureau legislation 

that applies to the decennial census also applies to 

the ACS. As part of the decennial census, respondents 

are required by law to respond to the ACS questionnaire, and the Census Bureau is bound to protect their 

responses in the same way that it protects responses 

to the long form. While the Census Bureau considers 

the ACS to be part of the decennial census, it is up to 

each agency to interpret the agency’s legislation and 

to decide how ACS data should be used. While it is 

unlikely, it is not inconceivable that agencies may need 

to work with their congressional committees to adjust 

legislative language to encompass the characteristics 

of the ACS. 

Initial Choices in Using ACS Data for Federal 

Purposes

When to start using ACS data? “When” boils down 

to the choice between 1-year, 3-year, and 5-year estimates. If an agency’s requirements include data for 

areas with populations below 20,000 (i.e., small jurisdictions, school districts, census tracts), then the 

agency will probably have to wait until 2010 when 

the first 5-year data become available. The 3-year 

estimates for areas with at least 20,000 inhabitants 

become available in 2008. Even if 1-year data would 

be appropriate for the planned use, an agency may 

elect to wait for 3-year or 5-year data to take advantage of the greater reliability offered by the multiyear 

estimates.

How often to “refresh” ACS data? This choice 

involves statistical, policy, and workload concerns. 

•	 Agencies that plan to use ACS data in allocation 

formulas for program funds may decide to use 

5-year estimates for their greater precision and to 

update the 5-year estimates every year so that the 

allocations change smoothly. While this approach 

ensures that formula allocations evolve slowly, it 

runs the risk of picking up turning points in local 

conditions slowly, 2 to 4 years after conditions 

change. 

•	 Agencies that plan to use ACS data to determine 

the eligibility of entities, such as counties or cities, 

for participation in federal programs may decide 

to use 5-year data for their greater precision 

and to update the eligibility determination only 

when nonoverlapping 5-year estimates become 

available. Waiting for nonoverlapping estimates 

ensures that entities do not experience “on-again/

off-again” determinations every time new ACS data 

become available. 

The decision on how frequently to replace one set of 

ACS data with a newer set depends upon the unique 

features of a particular agency use. Maximizing precision and minimizing the impact on the entities with 

which the agency deals are important factors, but 

agencies may choose to emphasize other considerations such as currency.

Workload is an issue that federal agencies need to 

focus on early in the transition to ACS data. In the 

past, federal agencies received new long-form data 

only every 10 years and usually no special provision 

was made for processing the new data; staff simply 

worked around this once-a-decade spike in their workload. With the potential of having to process new data 

6. Making the Transition to the ACS

Example of a Statutory Constraint

The legislation for most active federal programs 

was drafted long before the ACS was developed 

and was written with the decennial census in 

mind, often without distinguishing between 

the 100 percent and sample data. In most 

cases, the language that was drafted with the 

decennial census in mind should not present 

any problems or create any limitations in using 

ACS data. But this will not always be true. For 

example, the Department of Housing and Urban 

Development (HUD) uses decennial census data 

to allocate funds among states and jurisdictions 

for its Community Development Block Grant 

(CDBG) program. The CDBG legislation mandates 

that HUD use data “referable to the same point 

or period in time.” It would appear that HUD 

would have to use the ACS multiyear data for all 

recipients even through ACS 1-year data would 

be available for states that receive CDBG funds 

separately from municipalities. While HUD may 

prefer using the multiyear estimates in allocations 

to both states and municipalities, the legislation 

eliminates any alternatives when using ACS data. 
18 What Federal Agencies Need to Know

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

annually, agencies may have to add staff to handle the 

additional workload associated with the ACS. 

How to prepare their institutional customers for 

ACS data? The ACS will be new to the entities, such as 

states, counties, cities, and planning consortiums, with 

which an agency normally deals. They may not be prepared for the changes that will accompany having new 

data every year. If agencies use formulas to allocate 

program funds, in the past the amounts received by an 

entity would vary from one year to the next only as the 

overall program budget changed—except for a oncea-decade change when new long-form sample data 

became available. With the ACS, an entity’s allocation 

may change every year even if the program budget 

remains the same. Similarly an entity’s eligibility to 

participate in a program may change more frequently 

than every decade.

Agencies will have to educate their institutional customers about the ACS and how it will affect the way 

the agency interacts with them. In addition, as the next 

two items will explain, agencies will have to prepare to 

deal with challenges from clients based on two features unique to the ACS, explicit recognition of statistical precision and multiple sets of estimates.

Statistical precision. Consider the following situation: an agency receives new ACS data, 

processes it, and informs an entity that it is no 

longer eligible for program benefits based on 

the new data. The client challenges the agency’s 

determination because the threshold for participation is within the published margin of error of the 

variable used to make the determination for that 

entity.

Multiple estimates. An agency chooses to use 

5-year ACS estimates in an allocation formula for 

program funds. One customer, New York City, 

claims that the 1-year estimates have sufficient 

precision, that the 5-year estimates have failed 

to pick up deterioration in its economy, and that 

using 1-year estimates would give it the higher 

funding that it deserves. 

Agencies will not be able to rely on precedent to deal 

with these kinds of situations. The long-form sample 

produced only one set of estimates. Moreover, challenges based on statistical accuracy almost never 

occurred with past long-form sample data. The absence 

of published margins of error and the 10-year span 

between releases of new data created the sense that 

any measured change between censuses was a true 

reflection of actual changes. The publication of margins of errors and the short time period between data 

releases is likely to highlight questions of accuracy. 

Federal agencies should decide in advance how to 

handle problems like these and should consider incorporating how they use ACS data into formal rulemaking, which often provides the specific procedures for 

implementing statutory language. Going through the 

federal regulation process has several advantages. 

Because of requirements to publish proposed and final 

rulemaking in the Federal Register, rulemaking can 

help educate clients about the features of ACS data. It 

can solicit input on how ACS data are proposed to be 

used, input that may help agencies identify unexpected 

problems. Rulemaking can provide an agency with a 

stronger rationale for the procedures that it adopts.

Changes in the ACS

The ACS itself is going through some start-up changes 

that agency users should be aware of. These changes 

will have little or no effect on future ACS data but do 

affect the comparisons involving the first few years of 

data. 

Data on group quarters. The ACS began collecting 

data on group quarters in 2006. The 2005 ACS population estimates include the household population only 

and are not comparable with total population estimates 

in the 2006 ACS. Data on group quarters are included 

in the 2006 1-year estimates and will be included in all 

future ACS 1-year estimates. The first 3-year estimates 

released in 2008 contain information on group quarters, as the Census Bureau adjusted the 2006 and 2007 

group quarters data to represent all 3 years covered in 

the 3-year estimates released in 2008. 

Household estimates. The Census Bureau changed 

the method of weighting for housing units and persons 

after the 2005 ACS resulting in a lower estimate of 

occupied housing units. That change impacts the estimate of households since the ACS estimate of households is equal to the estimate of occupied housing 

units. As a result, the household estimates are generally smaller in 2006 than in 2005. The same change 

causes the ACS estimate of total housing units to be 

slightly higher than the Census Bureau’s official estimate of housing units in 2006. All future ACS releases 

will use the methods adopted in 2006. In the first 

3-year estimate, released in 2008, the Census Bureau 

reweighted the 2005 data using the new method. 

Examples of the Use of ACS Data by Federal 

Agencies

Federal agencies have already begun to use ACS data. 

Some agencies have been very proactive in making the 

transition to the ACS. For example, the Department 

of Transportation is working with its state and local 

partners to develop new specifications for the Census 

Transportation Planning Package (CTPP), a special tabulation of small area data to help local planning agencies assess their transportation needs. The CTPP was 
What Federal Agencies Need to Know 19

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

formerly based on the decennial long-form sample; the 

next tabulation will use the ACS. Here are two examples of how agencies are using ACS data:

•	 Veteran Affairs (VA). The VA Office of Policy 

and Preparedness is responsible for the Veteran 

Population Model that reports veteran population 

at the state level by count, period of service, age, 

and gender annually. Formerly this was done by 

taking veteran counts from the decennial census, 

adding discharges as reported by the Department 

of Defense, subtracting estimated deaths, and 

adjusting for migration. When new decennial longform sample data became available, adjustments 

were made for the previous 10 years based on the 

new decennial census counts. The ACS provides 

estimates of the number of veterans at the state 

level by age and gender and by period of service. 

These estimates are based on a uniform methodology that does not require use of any data other 

than ACS data.

•	 Housing and Urban Development. Every year 

HUD estimates for every metropolitan area and 

every nonmetropolitan county the income limits 

that determine the eligibility of households for 

federal housing assistance. The Department of 

Agriculture, the Internal Revenue Service, and 

the Department of Veteran Affairs also use HUD’s 

income limits. Formerly HUD derived income limits 

using a special tabulation of the long-form sample 

data and updated the estimates annually using 

wage data from the Bureau of Labor Statistics (BLS). 

Now HUD uses a weighted average of ACS and BLS 

data to update areas with populations of 65,000 

or more. For areas with fewer than 65,000, HUD 

uses the ACS data to update at the state level and 

then uses BLS data to transform the state updates 

to smaller areas. When ACS releases multiyear 

estimates, HUD plans to make further changes in 

its procedures for calculating income limits.

7. How to Obtain ACS Data

This section explains the most common ways in which 

federal users can access the ACS data that they will 

need to carry out agency business. This includes an 

overview of how to access tables via the American 

FactFinder, Summary Files by File Transfer Protocol, 

and Public Use Microdata Sample files. Custom tabulations are also discussed.

The American FactFinder (AFF)

The discussion starts with the American FactFinder 

(AFF) because this data dissemination tool is the simplest way to obtain specific ACS tables and because 

the AFF provides a convenient introduction to ACS 

products. 

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

 Figure 1. Data Presentation Options Available on the American FactFinder
20 What Federal Agencies Need to Know

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

One can access the AFF directly from the Census 

Bureau’s home page, from the ACS home page, or 

simply by using the link for AFF at <http://factfinder

.census.gov>. After accessing the AFF’s home page, 

highlighting “data sets” on the left margin allows one 

to select decennial census data, ACS data, or another 

Census Bureau data series. Clicking on “American Community Survey” provides access to the most recently 

released ACS data, as well as data from earlier full 

implementation releases. 

For example, clicking the radio button next to “2007 

American Community Survey” reveals a series of 

options on the right-hand side of the Web page. (See 

Figure 1.) The first seven options are different types 

of prepared ACS products. Users should examine each 

of the products to see what types of tables are readily 

available. We will focus on the “detailed tables.” Users 

should review the list of “detailed tables” because the 

detailed tables are the most thorough and complete 

tables published by the Census Bureau using ACS data; 

they are also the building blocks used to construct 

other products available on the AFF.

Clicking on “detailed tables” leads to a new page where 

the user can select the geography for the desired data. 

One can choose multiple geographies, including geographies of different types, such as, Texas, the HoustonSugarland-Baytown metropolitan area, and Houston 

city. After selecting the appropriate geography, the 

AFF asks the user to select from among approximately 

1,300 tables. The tables are organized by keywords 

to help users find specific topics. The user can select 

multiple tables. After the selection of both geographies 

and tables, the AFF displays the desired tables for the 

selected geographies. The tables can be printed or 

downloaded in various formats, including a Microsoft 

Excel workbook. 

Summary Files

Federal users will most likely want a number of 

detailed tables for a large number of geographies. For 

example, an agency that uses ACS data to allocate 

funds at the state level would want data on each of 

the ACS variables used in the allocation formula for all 

states and the District of Columbia. The Census Bureau 

packages the detailed tables into a series of summary 

files that can be downloaded. 

Users can find summary files at the Census Bureau’s 

FTP (File Transfer Protocol) site, <http://www2.census

.gov/acs2006/Final_Summaryfile/>. This site provides 

summary files for the United States and for each state 

and also includes documentation and instruction files. 

In particular, users should see the “Revised_ACS_2006

_SF_Tech_Doc.pdf” file for information on how to read 

the summary files. The summary files are designed to 

be easily accessible through statistical programs such 

as SAS.

Custom Tabulations

Federal agencies can request and purchase custom 

tabulations of ACS data from the Census Bureau. 

Custom tabulations allow agencies to specify crosstabulations of variables not available in the detailed 

tables or tabulations of data for unique geographies or 

for specialized subsets of the population. All custom 

tabulations must be approved by the Census Bureau’s 

Disclosure Review Board to assure that the information 

conforms to the confidentiality requirements imposed 

by the statutes governing the decennial census. All 

data in custom tabulations are rounded and there are 

restrictions on the minimum number of sample cases 

for a given table and for each cell within a table. Custom tabulations are produced after the Census Bureau 

completes processing the regular products for each 

annual ACS data release cycle. 

Agency staff interested in purchasing custom tabulations should go to <http://www.census.gov/acs

/www/Products/spec_tabs/index.htm> or call 301-

763-INFO to identify the ACS contact within the agency 

who will arrange for a meeting with appropriate staff at 

the Census Bureau. 

Public Use Microdata Sample (PUMS) Files 

All regular ACS products involve aggregations of 

responses from sampled housing units and group 

quarters within the geography being tabulated. The 

Census Bureau never releases completed individual 

questionnaires. It does, however, take a sample of 

interview records, remove all identifying information, 

and assign the response records to specially defined 

geographical areas called Public Use Microdata Areas 

(PUMAs). PUMAs are a set of contiguous census tracts 

that subdivide a state into a set of nonoverlapping 

areas each with a minimum population of 100,000. The 

Census Bureau releases the modified data in PUMS files. 

A complete description of the PUMS can be found at 

<http://www.census.gov/acs/www/Products/PUMS/>. 

Federal users would most likely take advantage of the 

PUMS files for policy research purposes. The ability to 

look at individual records allows the user to construct 

cross-tabulations not available in the detailed tables 

and to perform regression analyses. The geographic 

areas are not often convenient for programmatic purposes. 

Users can download the PUMS data into data processing programs such as SPSS or SAS and create their 

own tabulations, or they can use the Census Bureau’s 

DataFerrett tool to analyze the PUMS data. Information 

about DataFerrett can be found at <http://dataferrett

.census.gov/>.
What Federal Agencies Need to Know 21

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

ACS Data Become Available for All 

Geographies in 2010

Table 3 explains what ACS products—1-year estimates, 

3-year estimates, and 5-year estimates—will be available for different geographies. The 1-year estimates 

were first released in 2006, the first 3-year estimates 

were released in 2008, and the first 5-year estimates 

will be released in 2010. Beginning in 2010, the 

Census Bureau will issue a new set of 1-year estimates, 

3-year estimates, and 5-year estimates every year for 

geographies entitled to these estimates. 

Future Evolution of the ACS at the Census 

Bureau

The long form evolved over time and so will the ACS. 

As society changes and as Congress adds or changes 

programmatic requirements, some questions will 

need to be reworded, other questions will need to be 

dropped, and new questions will need to be added. 

Users will demand new products or modifications to 

existing products. The Census Bureau anticipates that 

change will be needed and welcomes suggestions on 

products and content. As agencies gain experience 

working with ACS data, they should let the Census 

Bureau know what works well and where improvements could be made. 

With respect to changes in content, a major content 

test was conducted in 2006 and a substantial revision 

of the questionnaire was implemented in January 2008. 

The most significant change was the addition of questions related to health insurance, marital history, and 

VA service-connected disability ratings. Agencies can 

always discuss additional changes or possible needs 

for new questions with the Census Bureau. However, 

change will be infrequent due to the complexity of 

implementing changes and the need for data to be 

consistent over a 5-year period. 

Agencies should also take into consideration that the 

purpose of all ACS questions is to obtain information 

to carry out a legislative directive or critical programmatic need. As noted in Section 3, all the questions 

on the ACS questionnaire have been carefully vetted 

to ensure that they are fully justified and that the data 

are needed at small areas of geography that cannot be 

obtained through other federal surveys. Proposed new 

questions will receive the same review.

Future Evolution of the ACS Outside 

the Census Bureau

The ACS, like the decennial long form before it, is part 

of the nation’s economic infrastructure. Federal agencies, state and local governments, businesses, and 

private citizens have used the output from past decennial long-form samples for a wide variety of purposes. 

Initially users will most likely plug ACS data into 

applications that formerly used long-form data but, as 

users become accustomed to receiving new data every 

year, they will undoubtedly find creative new applications for ACS data.

Once federal agencies understand the ACS and have 

mastered its features, they should contemplate how to 

take advantage of its unique characteristics. More frequent updating of program parameters and eligibility 

determinations are obvious possibilities. Other ideas 

include combining ACS data with data from smaller 

but more focused surveys to create efficient statistical models for small area estimation and using ACS 

responses to design highly targeted specialty surveys 

that can obtain important information at lower cost 

and with less burden on respondents. The ACS provides federal agencies with opportunities not available 

with the decennial long form; agencies should prepare 

to take advantage of these opportunities. 

8. Future of the ACS
22 What Federal Agencies Need to Know

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

Glossary

Accuracy. One of four key dimensions of survey 

quality. Accuracy refers to the difference 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 offers 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 first 

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. 

Coefficient 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 profile. Comparison profiles 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.

Confidence interval. The sample estimate and its 

standard error permit the construction of a confidence 

interval that represents the degree of uncertainty about 

the estimate. A 90-percent confidence interval can be 

interpreted roughly as providing 90 percent certainty 

that the interval defined by the upper and lower 

bounds contains the true value of the characteristic. 

Confidentiality. 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 offers 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 predefined 
What Federal Agencies Need to Know 23

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 offers “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 files. They vary in size, 

complexity, and cost depending on the needs of the 

sponsoring client.

Data profiles. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profile that should be used if official 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 different 

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 confidentiality of responses. 

See Confidentiality. 

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 figures 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 files to be downloaded from the Census 

Bureau Web site.

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates reflect 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 specifies the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifies 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 reflected 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 confidence intervals. An MOE is the 

difference between an estimate and its upper or lower 

confidence bounds. Confidence 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 confidence 

level. 

Multiyear estimates. Three- and five-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 profile. A data product that includes easyto-read descriptions for a particular geography. 

Nonsampling error. Total survey error can be 

classified 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.
24 What Federal Agencies Need to Know

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. Official Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also official Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defines the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) files. 

Computerized files 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 different quality measures with the annual data 

release: 1) initial sample size and final 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 define 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, differences 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 profiles. An ACS data product 

that provides certain characteristics for a specific 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 significance. The determination of 

whether the difference 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 “significantly different at the 90 

percent level” if their difference is large enough to infer 

that there was a less than 10 percent chance that the 

difference 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 files. 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 reflect 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-

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 specific 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 differences in time periods between single-year 

and multiyear ACS estimates affect 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 specific 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 reflect 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 different 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 differ 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 differ markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

could differ 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, Artificial 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- 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 specific 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 specific 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 final 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

Differences 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 influence on the estimates. However, in areas experiencing 

major changes over a given time period, the multiyear 

estimates may be quite different 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-

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

 Table 3. Example of Differences 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 differences 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 offer more current estimates, 

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

determine the fitness 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 benefit 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 benefit 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 suffered 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, Artificial Data.
A- 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 sufficiently 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 “fitness 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 Coefficient 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-

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. Specific 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 profile 

will do much of the statistical work to identify statistically significant differences 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-fifths for 5-year estimates). Thus, 

as shown in Figure 1, when comparing 2006–2008 

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

the differences in overlapping multiyear estimates are 

driven by differences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those differences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

of this difference is difficult, 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 specific pairs of comparisons. Whenever you want to make a comparison 

between two different 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- 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 effect 

of the multiyear weighting methodology. It also can 

be used to assess the “lagging effect” 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 

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

As Figure 2 shows, while the single-year estimates 

fluctuate 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-

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 specific 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 first occur. Single-year estimates, if associated with sufficiently 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- 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 differences exist in the specific 

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 differences 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 

Definitions

The fundamentally different purposes of the ACS and 

the census, and their timing, led to important differences 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. Defining the rules in a dissimilar way can affect 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 specific 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 reflect 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 specific time 

period. The 1-year estimates reflect the full calendar 

year; 3-year and 5-year estimates reflect 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 difference 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 difference 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 

findings 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 definite ties to a single 

housing unit or group quarters, some people may stay 

in different places for significant 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. Differences in treatment of these populations in the census and ACS can lead to differences in 

estimates of the characteristics of some areas. 

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

Differences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-

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 differences in the ACS and census data as a consequence of the different 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 different 

residence decisions. Appreciable differences 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 differ 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 reflect that date, regardless of whether the enumeration is conducted by mail in March or by a field 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 reflect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data reflecting the entire 

period of decennial data collection, and are greatly 

influenced 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 reflect 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 different time, and reference period than 

the census. 

Some specific differences in reference periods between 

the ACS and the decennial census are described below. 

Users should consider the potential impact these different 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 specific 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 inflation-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—differ between Census 2000 and the 

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

asks for the utility costs in the previous month. 

Definitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly different definitions 

that could affect 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 differences discussed above were a part of the 

census and survey design objectives, other differences 

observed between ACS and census results were not 

by design, but due to nonsampling error—differences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience different levels and 

types of coverage error, different levels and treatment 

of unit and item nonresponse, and different 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 differ 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 different 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, confidence 

interval, and coefficient 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 differ 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 differences between the estimates can be said to be statistically significant.

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 confidence. The confidence 

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

(the MOE) from the population value. Confidence 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 confidence 

intervals and test for statistical significance.

The Census Bureau statistical standard for published 

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

MOEs published with the ACS estimates correspond 

to a 90-percent confidence level. However, users may 

want to use other confidence levels, such as 

95 percent or 99 percent. The choice of confidence 

level is usually a matter of preference, balancing risk 

for the specific application, as a 90-percent confidence 

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

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

incorrect conclusion is substantial, the user should 

consider increasing the confidence 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 find differences 

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 confidence level would imply that 10 of the 

comparisons would be expected to be found significant even if no differences actually existed. Using a 

99-percent confidence 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 

Confidence Intervals

A confidence 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 

confidence” or “confidence 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 Confidence Interval From a Margin of 

Error

To construct a CI at the 90-percent confidence 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 confidence 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 confidence 

level, the appropriate MOE must first 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 confidence level.

The CI can thus be expressed as the range

3

Calculating Margins of Error for Alternative Confidence 

Levels

If you want to use an MOE corresponding to a confidence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confidence level is 95 percent, then the factor is equal to 

1.960/1.645.1 If the desired confidence 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 

different confidence 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 confidence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of significance (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 

confidence 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 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

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

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the derived SE for the ACS estimate 

100 0.1%

771,782

7,512 CV u 

( )

18,355 20,000 21,645

Factors Associated With Margins of

Error for Commonly Used Confidence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

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

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

( )

18,355 20,000 21,645

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

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

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CILU 

1

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

where MOEACS is the positive value of the ACS 

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range CICL 

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the der

100 0.1%

771,782

7,512 CV u 

 

( 

18,355 20,000 

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percen

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

where MOEACS is the positive value of the AC

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range CICL 

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the de

7512 

( 

18,355 20,000 

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

where MOEACS is the positive value of the ACS published MOE for the 

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100  u

SE CV

( )

18,355 20,000 21,645

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

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

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

SE 

( )

18,355 20,000 21,645

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

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

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the derived SE for the ACS estimate 

( )

18,355 20,000 21,645

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

where MOEACS is the positive value of the ACS published MOE for the 

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the derived SE for the ACS est

100 0.1%

771,782

7,512 CV u 

 

( )

18,355 20,000 21,645

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

where MOEACS is the positive value of the ACS published MO

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

( )

18,355 20,000 21,645

MOE MOEACS 1.645

1.960

95 

MOE MOEACS 1.645

2.576

99 

where MOEACS is the ACS published 90-percent MOE for the estimate. 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

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

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the derived SE for the ACS estimate 

100 0.1%

771,782

7,512 CV u 

( )

18,355 20,000 21,645
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 

confidence 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 confidence 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 confidence 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

Coefficients of Variation

A coefficient 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 confident 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 

7,512

1.645

SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the derived SE for the ACS estimate 

100 0.1%

771,782

7,512 CV u 

 

( )

18,355 20,000 21,645

1.645

1.645

MOEACS 

 

e MOEACS is the positive value of the ACS published MOE for the estimate 

7,512

1.645

12,357 

 

 X  MOECL ˆ

 X  MOECL ˆ

CI can thus be expressed as the range   CICL LCL UCL , .

1

 771,782 14,723 757,059

 771,782 14,723 786,505

100 ˆ u

X

SE 

e Xˆ is the ACS estimate and SE is the derived SE for the ACS estimate 

100 0.1%

771,782

7,512 

 u 

( )

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 Coefficients 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.

UCL X  MOECL 

The CI can thus be expressed as the range CICL 

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the de

100 0.1%

771,782

7,512 CV u 

 

( 

18,355 20,000 

where MOEACS is the ACS published 90-percent MOE for the 

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

where MOEACS is the positive value of the ACS published MO

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range   CICL LCL UCL , .

1

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the derived SE for th

100 0.1%

771,782

7,512 CV u 

 

( )

18,355 20,000 21,645

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the range  

CICL L 

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE is the derive

100 0.1%

771,782

7,512 CV u 

 

( 

18,355 20,000 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confidence Levels

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the th

c component estimate 

52,354 19,464 

Xˆ Xˆ F

if

 Xˆ A

lit

 Xˆ Al

di

   

Margin of Error (MOE) for Alternate Confidence L

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the th

c component estim

52354 1946

Xˆ Xˆ  Xˆ  Xˆ   

Margin of Error (MOE) for Alternate Confiden

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component est

52,354 19

Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria  

Margin of Error (MOE) for Alternate Confid

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

LCL X  MOECL ˆ

UCL X  MOECL ˆ

The CI can thus be expressed as the ra

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

100 ˆ u

X

SE CV

where Xˆ is the ACS estimate and SE 

100 0.1%

771,782

7,512 CV u 

 

( 

18,355 

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 benefits 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 confidence intervals (pre-2005) or margins 

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

confidence 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 

differences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

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

confidence level other than 90 percent is desired, the 

user should first calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confidence 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)

 r ¦ 17,190 +1,854

c

MOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 

Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexan

Calculate the MOE for the aggregate estimated as the squ

squared MOEs. 

(3,303) (2,011) (1,854) 18,39

2 2 2 MOEagg r   r 

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the deriv

X den

ˆ is the estimate used as the denominator of the der

Confidence Interval (CI)

Coefficient of Variation (CV)

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estim

52,354 19,4

Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria  

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOE

Calculate the MOE for the aggregate estimated as th

squared MOEs. 

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

2 2 2 MOEagg r   r 

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the 

X den

ˆ is the estimate used as the denominator of th

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 17,19

Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria 

Calculate the MOE for the aggregate estimated as the square ro

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 

2 2 2 MOEagg r   r 

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived pr

X den

ˆ is the estimate used as the denominator of the derived p

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

1.645

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 17,190 89,008 Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria r1,854

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

Margin of Error (MOE) for Alternate Confidence Levels

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 17,190 89,008 Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria r1,854

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

Margin of Error (MOE) for Alternate Confidence Levels

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 17,190 89,008 Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria r1,854

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

Margin of Error (MOE) for Alternate Confidence Levels

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 17,190 89,008 Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria r1,854

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the th

c component estimate 

52,354 19,464 17,190 89,008 Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria r1,854

Calculate the MOE for the aggregate estimated as the square root of the su

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

ACS

1.645

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 17,190 89,008 Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria r1,854

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV

 r ¦cMOEagg MOEc

2

where MOEc is the MOE of the thc component estimate 

52,354 19,464 17,190 89,008 Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria   

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , MOEArlington r2,011, MOEAlexandria r1,854

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

Standard Error (SE)

Confidence Interval (CI)

Coefficient of Variation (CV)

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

CI CL Xˆ  MOE CL , Xˆ  MOE CL 

100 ˆ u

X

SE CV
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

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years of age and older in Fairfax 

County with a graduate degree and X BF ˆ 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). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared proportion and subtract the 

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

0.1461

31,713

4,634

ˆ

BF

gradBF

X

X

radBF 

 is the ACS estimate of Black females 25 years of age and older in Fairfax 

with a graduate degree and X BF ˆ is the ACS estimate of Black females 25 years 

d older in Fairfax County. 

OEs of the numerator (number of Black females 25 years of age and older in 

ounty with a graduate degree) and denominator (number of Black females 25 

age and older in Fairfax County). 

 r989 

um , 601 MOEden r

the squared MOE of the denominator by the squared proportion and subtract the 

m the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,121 7,712.3 970,408.7 2 2 2 2 2 2 

um  p MOEden   

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years of age and older in Fairfax 

County with a graduate degree and X BF ˆ 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). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared proportion and subtract the 

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

fg

Calculate the MOE for the aggregate estimated as the square root of the sum of the 

squared MOEs. 

(3,303) (2,011) (1,854) 18,391,246 4,289 2 2 2 MOEagg r   r r

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

p ˆ

ˆ ˆ is the derived proportion 

X num

ˆ is the estimate used as the numerator of the derived proportion 

X den

ˆ is the estimate used as the denominator of the derived proportion 

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years of age and older in Fairfax 

County with a graduate degree and X BF ˆ 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). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared proportion and subtract the 

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

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.

County with a graduate degree and X BF ˆ is the ACS estimate o

of age and older in Fairfax County. 

Obtain MOEs of the numerator (number of Black females 25 y

Fairfax County with a graduate degree) and denominator (num

years of age and older in Fairfax County). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared 

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978

2 2 2 2 2 2 MOEnum  p MOEden  

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

Thus, the derived estimate of the proportion of Black females 2

with a graduate degree in Fairfax County, Virginia, is 0.1461, 

estimate is +0.0311.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ra

X den

ˆ is the estimate used as the denominator of the derived 

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOEnum r989, MOEden r1,328

*   989 0.7200 *1,328 97

ˆ 2 2 2 2 2 2 MOEnum  R MOEden  

Calculate the MOE by dividing the square root of the prior resu

0.2135

6,440

1,375.2

6,440

1,891,259.1 r

r r MOER 

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years o

County with a graduate degree and X BF ˆ is the ACS estimate o

of age and older in Fairfax County. 

Obtain MOEs of the numerator (number of Black females 25 y

Fairfax County with a graduate degree) and denominator (num

years of age and older in Fairfax County). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared p

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,

2 2 2 2 2 2 MOEnum  p MOEden  

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

Thus, the derived estimate of the proportion of Black females 2

with a graduate degree in Fairfax County, Virginia, is 0.1461, a

estimate is +0.0311.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ra

X den

ˆ is the estimate used as the denominator of the derived 

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOEnum r989, MOEden r1,328

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years of a

County with a graduate degree and X BF ˆ is the ACS estimate of B

of age and older in Fairfax County. 

Obtain MOEs of the numerator (number of Black females 25 yea

Fairfax County with a graduate degree) and denominator (numbe

years of age and older in Fairfax County). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared pro

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,12

2 2 2 2 2 2 MOEnum  p MOEden  

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

Thus, the derived estimate of the proportion of Black females 25 

with a graduate degree in Fairfax County, Virginia, is 0.1461, and 

estimate is +0.0311.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ratio 

X den

ˆ is the estimate used as the denominator of the derived rat

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOEnum r989, MOEden r1,328

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years of a

County with a graduate degree and X BF ˆ is the ACS estimate of B

of age and older in Fairfax County. 

Obtain MOEs of the numerator (number of Black females 25 yea

Fairfax County with a graduate degree) and denominator (numbe

years of age and older in Fairfax County). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared pro

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,12

2 2 2 2 2 2 MOEnum  p MOEden  

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

Thus, the derived estimate of the proportion of Black females 25 

with a graduate degree in Fairfax County, Virginia, is 0.1461, and 

estimate is +0.0311.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ratio 

X den

ˆ is the estimate used as the denominator of the derived rat

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOE

 r989

MOE r1328

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years of a

County with a graduate degree and X BF ˆ is the ACS estimate of B

of age and older in Fairfax County. 

Obtain MOEs of the numerator (number of Black females 25 yea

Fairfax County with a graduate degree) and denominator (numbe

years of age and older in Fairfax County). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared pro

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,12

2 2 2 2 2 2 MOEnum  p MOEden  

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

Thus, the derived estimate of the proportion of Black females 25 

with a graduate degree in Fairfax County, Virginia, is 0.1461, and 

estimate is +0.0311.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ratio 

X den

ˆ is the estimate used as the denominator of the derived rat

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOEnum r989, MOEden r1,328

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

where X gradBF ˆ is the ACS estimate of Black females 25 years of a

County with a graduate degree and X BF ˆ is the ACS estimate of B

of age and older in Fairfax County. 

Obtain MOEs of the numerator (number of Black females 25 yea

Fairfax County with a graduate degree) and denominator (numbe

years of age and older in Fairfax County). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared pro

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,12

2 2 2 2 2 2 MOEnum  p MOEden  

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

Thus, the derived estimate of the proportion of Black females 25 

with a graduate degree in Fairfax County, Virginia, is 0.1461, an

estimate is +0.0311.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ratio 

X den

ˆ is the estimate used as the denominator of the derived ra

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOE

r989

MOEr1328

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601

2 2 2 2 2 

MOEnum  p MOEden  

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,32

ˆ ( 2 2 2 2 2 

MOEnum  R MOEden  

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

762,

802,210 

100%* ˆ

ˆ ˆ 100%*

1

2 1 

 ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 

Xˆ 

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

* )   22,866 [1.0521 *22,6

ˆ ( 2 2 2 

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived 

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970

2 2 2 2 2 2 MOEnum  p MOEden   

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 

ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 

2 2 2 2 2 2 MOEnum  p MOEden  

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 

ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden  

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

762475

802,210 762

100%* ˆ

ˆ ˆ 100%*

2 1 

 ̈

©

§   X

X X Percent Change
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 first squared estimate by the second estimate’s squared MOE.

4) Multiply the second squared estimate by the 

first 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 first and second estimates, 

respectively.

 is the MOE of the first 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.

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

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.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ratio 

X den

ˆ is the estimate used as the denominator of the derived ratio 

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOEnum r989, MOEden r1,328

*   989 0.7200 *1,328 978,121 913,318.1 1,891,259.1 ˆ 2 2 2 2 2 2 MOEnum  R MOEden   

Calculate the MOE by dividing the square root of the prior result by the denominator. 

0.2135

6,440

1,375.2

6,440

1,891,259.1 r

r r MOER 

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). 

 r989 MOEnum , 601 MOEden r

Multiply the squared MOE of the denominator by the squared proportion and subtract the 

result from the squared MOE of the numerator. 

ˆ *   989 0.1461 *601 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

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.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ratio 

X den

ˆ is the estimate used as the denominator of the derived ratio 

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOEnum r989, MOEden r1,328

*   989 0.7200 *1,328 978,121 913,318.1 1,891,259.1 ˆ 2 2 2 2 2 2 MOEnum  R MOEden   

Calculate the MOE by dividing the square root of the prior result by the denominator. 

0.2135

6,440

1,375.2

6,440

1,891,259.1 r

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

 over (first estimate) 50,624 +2,423

Percent of Black workers 16 

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

2 2 13.4%2 +2

2.7% MOEAuB r A u MOEB  B u MOEA

MOEA is the MOE of the first estimate 

MOEB is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 MOEAuB r u  u r

 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   

100%

ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

1

1

2

1

2 1

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

 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

31,37331,373

p 

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.

den

num den

R

X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

where MOEnum is the MOE of the numerator 

MOEden is the MOE of the denominator 

den

num

X

X

R ˆ

ˆ ˆ is the derived ratio 

X num

ˆ is the estimate used as the numerator of the derived ratio 

X den

ˆ is the estimate used as the denominator of the derived ratio 

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

MOEnum r989, MOEden r1,328

*   989 0.7200 *1,328 978,121 913,318.1 1,891,259.1 ˆ 2 2 2 2 2 2 MOEnum  R MOEden   

Calculate the MOE by dividing the square root of the prior result by the denominator. 

0.2135

6,440

1,375.2

6,440

1,891,259.1 r

r r MOER 

2 2 2 2 MOEAuB r A u MOEB  B u MOEA

MOEA is the MOE of the first estimate 

MOEB is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 MOEAuB r u  u r

 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   

100%

ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

1

1

2

1

2 1

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate the M

should be used here to obtain the MOE of the percent change. 

The percent change is: 

762,475

802,210 762,47

100%* ˆ

ˆ ˆ 100%*

1

2 1 

 ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOEnum = +/-22,866, MOEden = +/-22,666 

2 2 2 2 MOEAuB r A u MOEB  B u MOEA

MOEA is the MOE of the first estimate 

MOEB is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 MOEAuB r u  u r

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   

ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate the MOE of 

should be used here to obtain the MOE of the percent change. 

The percent change is: 

5

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOE

= +/-22866MOEd= +/-22666 

2 2 2 2 MOEAuB r A u MOEB  B u MOEA

MOEA is the MOE of the first estimate 

MOEB is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 MOEAuB r u  u r

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   

ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate the MOE of 

should be used here to obtain the MOE of the percent change. 

The percent change is: 

5

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOE

/22866MOE/22666 

2 2 2 2 MOEAuB r A u MOEB  B u MOEA

MOEA is the MOE of the first estimate 

MOEB is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 MOEAuB r u  u r

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§   1 ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

2

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X Percent Change

 ̧

 ̧

·

 ̈

 ̈

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

2

1

2

1

2

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X MOE

X

X MOE

X

X MOEPercent Change MOE

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

den

num den

X

MOE p MOE

ˆ

( ˆ * ) 2 2 2 

 

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 

2 2 2 2 2 

p MOEden   

den

num den

X

MOE R MOE

ˆ

* ) ˆ ( 2 2 2  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 

2 2 2 2 2 

R MOEden   

1

2 1

ˆ

ˆ ˆ 100%*

X

X X 

hange  

s not a subset of 1 Xˆ ,

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X 

ge

 the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ 

2 2 2 2

1

2 

R MOE  

61 *601 ] 978,121 7,712.3 970,408.7 

2 2 

  

00 *1,328 ] 978,121 913,318.1 1,891,259.1 

2 2 

  

5.21%

762,475

802,210 762,475 

 ̧ 

1

· 

©

§  

minator ( 1 Xˆ ) are: 

21 *22,666 ] 1,091,528,529 

2 2

 
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 Difference”

The “percent change” or “percent difference” between 

two estimates (for example, the same estimates in two 

different 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 different 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 

 different house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 different house in the U.S. 

 1 year ago, 2005

762,475 +22,666

2 2 2 2 MOEAuB r A u MOEB  B u MOEA

MOEA is the MOE of the first estimate 

MOEB is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 MOEAuB r u  u r

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§   1 ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

2

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate the MOE of a ratio above 

should be used here to obtain the MOE of the percent change. 

The percent change is: 

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOEnum = +/-22,866, MOEden = +/-22,666 

Add the squared MOE of the numerator (MOE2) to the product of the squared ratio and 

the squared MOE of the denominator (MOE1):

*   22,866 1.0521 *22,666 1,091,528,529 ˆ 2 2 2 2 2 2 MOEnum  R MOEden  

Calculate the MOE by dividing the square root of the prior result by the denominator 

( 1 Xˆ )

0.0433

762,475

33,038.3

762,475

1,091,528,529 r

r r MOER 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§   1 ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

2

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate the MOE of a ratio above 

should be used here to obtain the MOE of the percent change. 

The percent change is: 

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOEnum = +/-22,866, MOEden = +/-22,666 

Add the squared MOE of the numerator (MOE2) to the product of the squared ratio and 

the squared MOE of the denominator (MOE1):

*   22,866 1.0521 *22,666 1,091,528,529 ˆ 2 2 2 2 2 2 MOEnum  R MOEden  

Calculate the MOE by dividing the square root of the prior result by the denominator 

( 1 Xˆ )

0.0433

762,475

33,038.3

762,475

1,091,528,529 r

r r MOER 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§   1 ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

2

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate the MOE of a ratio above 

should be used here to obtain the MOE of the percent change. 

The percent change is: 

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOEnum = +/-22,866, MOEden = +/-22,666 

Add the squared MOE of the numerator (MOE2) to the product of the squared ratio and 

the squared MOE of the denominator (MOE1):

*   22,866 1.0521 *22,666 1,091,528,529 ˆ 2 2 2 2 2 2 MOEnum  R MOEden  

Calculate the MOE by dividing the square root of the prior result by the denominator 

( 1 Xˆ )

0.0433

762,475

33,038.3

762,475

1,091,528,529 r

r r MOER 

2 2 2 2 MOEAuB r A u MOEB  B u MOEA

MOEA is the MOE of the first estimate 

MOEB is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 MOEAuB r u  u r

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§   1 ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

2

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X Percent Change

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X MOEPercent Change MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate the MOE of a ratio above 

should be used here to obtain the MOE of the percent change. 

The percent change is: 

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOEnum = +/-22,866, MOEden = +/-22,666 

2 2 2 2 

B r A u MOEB  B u MOEA

 is the MOE of the first estimate 

 is the MOE of the second estimate 

50,624 0.027 0.134 2,423 1,405 2 2 2 2 r u  u r

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§   1 ˆ

ˆ 100%* ˆ

ˆ

ˆ

ˆ 100%* ˆ

ˆ ˆ 100%*

1

2

1

1

1

2

1

2 1

X

X

X

X

X

X

X

X X 

e

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

 ̧

 ̧

1

·

 ̈

 ̈

©

§   ̧

 ̧

1

·

 ̈

 ̈

©

§

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

2

1

2

1

2

ˆ

ˆ 100%*

1 ˆ

ˆ 1 100%* ˆ

ˆ 100%*

X

X MOE

X

X MOE

X

X 

MOE

 a subset of 1 Xˆ , the procedure to calculate the MOE of a ratio above 

e to obtain the MOE of the percent change. 

e is: 

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X 

e

 formula, the ratio of the two estimates is: 

1.0521

475

210

numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

 ̧

 ̧

1

·

 ̈

 ̈

©

§ 

1

©

1

2

ˆ

ˆ 100%*

X

X MOE

Because 2 Xˆ is not a subset of 1 Xˆ , the procedure to calculate th

should be used here to obtain the MOE of the percent change. 

The percent change is: 

762,475

802,210 76

100%* ˆ

ˆ ˆ 100%*

1

2 1 

 ̈

©

§   X

X X Percent Change

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

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X R

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

MOEnum = +/-22,866, MOEden = +/-22,666 

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

the squared MOE of the denominator (MOE1):

*   22,866 1.0521 *22,666 

ˆ 2 2 2 2 2 2 MOEnum  R MOEden  

Calculate the MOE by dividing the square root of the prior resu

( 1 Xˆ )

0.0433

762,475

33,038.3

762,475

1,091,528,529 r

r r MOER 

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

the squared MOE of the denominator (MOE1):

*   22,866 1.0521 *22,666 1

ˆ 2 2 2 2 2 2 MOEnum  R MOEden  

Calculate the MOE by dividing the square root of the prior result b

( 1 Xˆ )

0.0433

762,475

33,038.3

762,475

1,091,528,529 r

r r MOER 

Calculating Margins of Error for Derived Est

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE

MOE ˆ

ˆ * 2 2 2 r  

den

num den

R X

MOE R MOE

MOE ˆ

* ˆ 2 2 2 r  

 r ¦cMOE agg MOE c

2

17

762,

802,210 

100%* ˆ

ˆ ˆ 100%*

1

2 1 

 ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 

Xˆ 

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

* )   22,866 [1.0521 *22,6

ˆ ( 2 2 2 

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived 

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2

den

num den

X

E p MOE

ˆ

( ˆ * ) 2 2 2 

 

)   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 

2 2 2 2 

MOEden   

den

num den

X

E R MOE

ˆ

* ) ˆ ( 2 2 2  

)   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 

2 2 2 2 

MOEden   

1

2 1

ˆ

ˆ ˆ 100%*

X

X X  

 subset of 1 Xˆ ,

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 

00%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X 

umerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

6, MOE1= +/-22,666 

)   22,866 [1.0521 *22,666 ] 1,091,528,529 

2 2 2 2

1

OE  

ting Margins of Error for Derived Estimates

 Data

ns

den

um den

X

p MOE ) 

ˆ

(ˆ * 2 2 2 

 

MOE c

2

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

MOE p MOE ) 

(ˆ * 2 2 2 r 

 r ¦c

MOE agg MOE c

2

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 2 2 2 2 2 2 MOEnum  p MOEden   

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 2 2 2 2 2 2 MOEnum  R MOEden   

P

1

2 1

ˆ

ˆ ˆ 100%*

X

X X Percent Change  

Because 2 Xˆ is not a subset of 1 Xˆ ,

17

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

 r ¦c

MOE agg MOE c

2

den

num den

X

MOE p MOE

ˆ

( ˆ * ) 2 2 2 r  

( ˆ * )   989 [0.1461 *601 ] 978,121 7,712.3 970,408.7 

2 2 2 2 2 

m  p MOEden   

den

num den

X

MOE R MOE

ˆ

* ) ˆ ( 2 2 2 r  

* )   989 [0.7200 *1,328 ] 978,121 913,318.1 1,891,259.1 ˆ ( 

2 2 2 2 2 

m  R MOEden   

1

2 1

ˆ

ˆ ˆ 100%*

X

X X 

t Change  

2 Xˆ is not a subset of 1 Xˆ ,

5.21%

762,475

802,210 762,475 100%* ˆ

ˆ ˆ 100%*

1

2 1  ̧ 

1

·  ̈

©

§   X

X X 

Change

s for the numerator ( 2 Xˆ ) and denominator ( 1 Xˆ ) are: 

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

* )   22,866 [1.0521 *22,666 ] 1,091,528,529 ˆ ( 2 2 2 2

1

2 

 R MOE  

Calculating Margins of Error for Derived Estimates

ed Count Data

 r ¦

MOE 

2

61 *601 ] 978,121 7,712.3 970,408.7 

2 2 

  

00 *1,328 ] 978,121 913,318.1 1,891,259.1 

2 2 

  

5.21%

762,475

802,210 762,475 

 ̧ 

1

· 

©

§  

minator ( 1 Xˆ ) are: 

21 *22,666 ] 1,091,528,529 

2 2

 

r Derived Estimates

17

762,

802,210 

100%* ˆ

ˆ ˆ 100%*

1

2 1 

 ̈

©

§   X

X X Percent Change

The MOEs for the numerator ( 2 Xˆ ) and denominator ( 

Xˆ 

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

* )   22,866 [1.0521 *22,6

ˆ ( 2 2 2 

1

2 2 MOE2  R MOE  

Calculating Margins of Error for Derived 

Aggregated Count Data

Derived Proportions

Derived Ratios

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  

 r ¦c

MOE agg MOE c

2
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 different 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 differences between estimates 

are statistically significant. Statistical significance 

means that there is statistical evidence that a true 

difference exists within the full population, and that 

the observed difference is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical significance 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 Significance

When comparing two estimates, one should use the 

test for significance described below. This approach 

will allow the user to ascertain whether the observed 

difference is likely due to chance (and thus is not statistically significant) or likely represents a true difference that exists in the population as a whole (and thus 

is statistically significant). 

The test for significance 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 defined 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 difference 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 

confidence (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 difference 

between the two estimates can be considered 

statistically significant at the level of confidence 

corresponding to the critical value used in (7).

Algebraically, the significance test can be expressed as 

follows:

If , then the difference 

between estimates and is statistically significant 

at the specified confidence level, CL

where is estimate i (=1,2)

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

 is the critical value for the desired confidence 

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 

difference 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 significant. Using the formula above:

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

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

difference in the percentages is statistically significant 

at a 99-percent confidence level. This is also referred 

to as statistically significant at the alpha = 0.01 level. 

A rough interpretation of the result is that the user can 

be 99 percent certain that a difference 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. 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimates 

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level (=

for 95 percent, 2.576 for 99 percent). 

   

0.

0.

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

0.

0

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for t

CPIY 2 is the All Items CPI-U-RS Annual Average for the m

1 ˆ

XY is the published ACS estimate for the earlier year (Y1)

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average for t

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimates 

Xˆ 

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level (=1.6

for 95 percent, 2.576 for 99 percent). 

   

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

0.10

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the 

CPIY 2 is the All Items CPI-U-RS Annual Average for the mo

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

2

1, ˆ ˆ P

P

P Adj X

CPI

CPI X 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimates 1 Xˆ and 2 Xˆ is statistically 

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level (=1.645 for 90 percent, 1.960 

for 95 percent, 2.576 for 99 percent). 

   

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

CPIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

2

1

ˆ ˆ P

P

P Adj X

CPI X 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimates 1 Xˆ and 2 Xˆ is statistically 

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level (=1.645 for 90 percent, 1.960 

for 95 percent, 2.576 for 99 percent). 

   

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

CPIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

2

1

ˆ ˆ P

P

P Adj X

CPI X 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimates 1 Xˆ and 2 Xˆ is statistically 

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level (=1.645 for 90 percent, 1.960 

for 95 percent, 2.576 for 99 percent). 

   

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

CPIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estim

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence leve

for 95 percent, 2.576 for 99 percent). 

   

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average 

CPIY 2 is the All Items CPI-U-RS Annual Average for 

1 ˆ

XY is the published ACS estimate for the earlier year 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimates 1 Xˆ and 2 Xˆ is statistically 

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level (=1.645 for 90 percent, 1.960 

for 95 percent, 2.576 for 99 percent). 

   

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimat

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level 

for 95 percent, 2.576 for 99 percent). 

   

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average fo

CPIY 2 is the All Items CPI-U-RS Annual Average for th

1 ˆ

XY is the published ACS estimate for the earlier year (Y

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average fo
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 confidence level of 90 percent (1.645), the difference in percentages is not statistically significant. 

A rough interpretation of the result is that the user 

cannot be certain to any sufficient degree that the 

observed difference 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 significance 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 different 

counties). In this case, the two estimates are independent, and the formula for testing differences 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 

differences 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 differences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a finding of statistical significance will still 

be correct, but a finding of a lack of statistical significance based on the formula may be incorrect. If it is 

important to obtain a more exact test of significance, 

the user should consult with a statistician about 

approaches for accounting for the correlation in performing the statistical test of significance.

CL 

s e ccvue oe desed codece eve(.65 o90 pece, .960 

for 95 percent, 2.576 for 99 percent). 

   

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

CPIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average for the last year in the earlier 

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for the last year in the most current 

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time period (P1). 

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

  1,2

e estimate i 1,2

alue for the desired confidence level (=1.645 for 90 percent, 1.960 

576 for 99 percent). 

  

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

2 0.12

.0 21.5 

2 2

  



 

  

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0 0.25

.5 22.1 

2 2

  



 

+ SE2)/2 * k]

Items CPI-U-RS Annual Average for the earlier year (Y1); 

ms CPI-U-RS Annual Average for the more recent year (Y2); and 

 ACS estimate for the earlier year (Y1). 

700 $38,936 

 Items CPI-U-RS Annual Average for the last year in the earlier 

 

ms CPI-U-RS Annual Average for the last year in the most current 

 and 

CS estimate for the earlier time period (P1). 

$32,000 $35,080 

Comparisons Across Time Periods

Comparisons of estimates from different time periods 

may involve different single-year periods or different 

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 

differing lengths (e.g., comparing 2006–2008 with 

2009–2014), as they are measuring the characteristics 

of the population in two different ways, so differences 

between such estimates are difficult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

When comparing estimates from two different 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 affected. 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 different, but overlapping 

periods is challenging since the difference 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 differences is calculated. While the interpretation 

of this difference is difficult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of difference should not be interpreted as a 

reflection 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 difference 

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 significance 

of the difference between the two estimates using the 

method outlined in the previous section with one modification; substitute for 

 in the denominator of the formula for 

the significance test.

Comparisons With Census 2000 Data

In Appendix 2, major differences between ACS data and 

decennial census sample data are discussed. Factors 

such as differences 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 differences, seasonality 

may affect 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 affect differences 

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 affected by these differences.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 significance. Appendix 

3 provides the calculations necessary for determining 

statistical significance of a difference 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 significance 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 finding of statistical significance 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 finding of no statistical significance 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 differ 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 

different than coverage of Census 2000. The impact of 

this difference will likely affect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large differences 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 

fit the general pattern of comparability can be found on the ACS Web 

site at <http://www.census.gov/acs/www/UseData/compACS.htm>.

When comparing two three-year estimates or two five-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 difference use the following approximation to the 

standard error:

  2

2

2

1 2 1 ) 1 ˆ ˆ SE(X  X #  C SE  SE

where C is the fraction of overlapping years. For example, the periods 2005-2009 and 

2007-2011 overlap for three out of five 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 significance of the difference between the two 

estimates using the method outlined in the previous section with one modification; 

substitute   2

2

2

1 1 C SE  SE for 22

2

1 SE  SE in the denominator of the formula 

for the significance test. 

When comparing two three-year estimates or two five-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 difference use the following approximation to the 

standard error:

  2

2

2

1 2 1 ) 1 ˆ ˆ SE(X  X #  C SE  SE

where C is the fraction of overlapping years. For example, the periods 2005-2009 and 

2007-2011 overlap for three out of five 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 significance of the difference between the two 

estimates using the method outlined in the previous section with one modification; 

substitute   2

2

2

1 1 C SE  SE for 22

2

1 SE  SE in the denominator of the formula 

for the significance test. 

ee-year estimates or two five-year estimates of the same 

 sample years one must account for this sample overlap. Thus 

rror of this difference use the following approximation to the 

  2

2

2

1 

1  C SE  SE

 overlapping years. For example, the periods 2005-2009 and 

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

07 and 2008-2010, then C = 0.

for the statistical significance of the difference between the two 

d outlined in the previous section with one modification; 

2

2

 SE for 2

2

2

1 SE  SE in the denominator of the formula 
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 effects 

when comparing data from the ACS with data from 

other national surveys is the use of different 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 difference 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 inflationadjusted 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 inflation 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 inflation 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 

inflation adjustment factors to produce the inflationadjusted 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 inflation-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 inflation adjustments are used when producing annual estimates. For example, income, rent, home 

value, and energy costs are all dollar-denominated 

data.

Inflation will affect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using inflation factors based on the Consumer Price Index (CPI).

Given the potential impact of inflation on observed 

differences of dollar-denominated data across time 

periods, users should adjust for the effects of inflation. 

Such an adjustment will provide comparable estimates 

accounting for inflation. 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 different income reference 

periods throughout an interview year, 12 different 

income inflation adjustments are made. Monthly CPIU-RSs are used to inflation-adjust the 12 reference 

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no inflation 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 different 

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 inflation 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 inflation adjustment factor.

The inflation-adjusted estimate for the earlier year can 

be expressed as:

   

0.015 0.015

0.5

0.12 0.12

22.0 21.5 

2 2 2

2

2

1

1 2  



 



SE SE

X X

   

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average 

CPIY 2 is the All Items CPI-U-RS Annual Average for 

1 ˆ

XY is the published ACS estimate for the earlier year 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average 

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for 

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time pe

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , then the difference between estimates 1 Xˆ and 2 Xˆ is statistically 

significant at the specified confidence level, CL 

where Xi

ˆ is estimate i  1,2

SEi is the SE for the estimate i 1,2

ZCL is the critical value for the desired confidence level (=1.645 for 90 percent, 1.960 

for 95 percent, 2.576 for 99 percent). 

   

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

CPIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

SE Difference  [(SE1 + SE2)/2 

 k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for th

CPIY 2 is the All Items CPI-U-RS Annual Average for the m

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average for th

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for the la

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time period (

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the 

CPIY 2 is the All Items CPI-U-RS Annual Average for the mor

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average for the 

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for the last 

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time period (P1

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

   

0.20 0.25

21



SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the 

CPIY 2 is the All Items CPI-U-RS Annual Average for the mo

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average for the 

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for the last 

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time period (P1

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 
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), first 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 inflation-adjusted to 2006 dollars) versus 

$39,000 (2004–2006, already in 2006 dollars).

Issues Associated With Inflation Adjustment

The recommended inflation adjustment uses a national 

level CPI and thus will not reflect inflation differences 

that may exist across geographies. In addition, since 

the inflation adjustment uses the All Items CPI, it will 

not reflect differences 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 inflation 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 inflation adjustment 

factor.

The inflation-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).

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average f

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for t

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time peri

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

CPIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average for the last year in the earlier 

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for the last year in the most current 

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time period (P1). 

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2  



 





SE SE

X X

SE Difference = [(SE1 + SE2)/2 * k]

App5

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

where CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

CPIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 ˆ

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

where CPI P1 is the All Items CPI-U-RS Annual Average for the last year in the earlier 

time period (P1); 

CPI P2 is the All Items CPI-U-RS Annual Average for the last year in the most current 

time period (P2); and 

1 ˆ

X P is the published ACS estimate for the earlier time period (P1). 

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

for 95 percent, 2.576 for 99 percent). 

   

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5

0.12 0.12

22.0 21.5 

ˆ

2 2 2

2

2

1

1 2  



 





E SE

X

   

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4

0.20 0.25

22.5 22.1 

ˆ

2 2 2

2

2

1

1 2  



 





E SE

X

ifference = [(SE1 + SE2)/2 * k]

5

1

1

2

ˆ

Y

Y

Y

Adj X

CPI

CPI 

 

e CPIY1 is the All Items CPI-U-RS Annual Average for the earlier year (Y1); 

PIY 2 is the All Items CPI-U-RS Annual Average for the more recent year (Y2); and 

1 

XY is the published ACS estimate for the earlier year (Y1). 

$37,700 $38,936

286.7

296.1 

5,Adj u 

1

1

2

ˆ P

P

P

Adj X

CPI

CPI 

 

e CPI P1 is the All Items CPI-U-RS Annual Average for the last year in the earlier 

time period (P1); 

PI P2 is the All Items CPI-U-RS Annual Average for the last year in the most current 

time period (P2); and 

is the published ACS estimate for the earlier time period (P1). 

$32,000 $35,080

270.1

296.1 

2003,Adj u 
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 effect 

of sampling error. Other errors that affect 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 

affecting 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 suffer from errors 

due to housing undercoverage.

The final 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 definition 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 final 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that differences in characteristics of undercovered or overcovered 

housing units or individuals differ 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 effect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the final 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 differ 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 differ 

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 final data files. 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 

affects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the final 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 difficult, 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 final survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of final 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 coefficient 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 Jefferson County, Alabama. Given that the number of 

successful housing unit interviews in Jefferson 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 Jefferson 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 insufficient 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 different 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 final 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 

defined by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s official estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the official 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 reflect 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 

official 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 differences 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 final 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 Different 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.