U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

USCENSUSBUREAU

Helping You Make Informed Decisions

A Compass for Understanding and Using 

American Community Survey Data

What the Media Need to Know

Issued

November 2008
Acknowledgments William H. Frey, Senior Fellow, The Brookings Institution, Brad Edmondson, 

Consultant, and John P. DeWitt, Senior Associate, Social Science Data 

Analysis Network, University of Michigan, drafted this handbook for the U.S. 

Census Bureau’s American Community Survey Offi ce. Kennon R. Copeland

and John H. Thompson of National Opinion Research Center at the 

University of Chicago drafted the technical appendixes. Edward J. Spar, 

Executive Director, Council of Professional Associations on Federal Statistics, 

Frederick J. Cavanaugh, Executive Business Director, Sabre Systems, Inc., 

Susan P. Love, Consultant, Linda A. Jacobsen, Vice President, Domestic 

Programs, Population Reference Bureau, and Mark Mather, Associate Vice 

President, Domestic Programs, Population Reference Bureau, provided initial 

review of this handbook.

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

Community Survey Offi ce, provided the concept and directed the 

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

Understanding and Using American Community Survey Data. Cheryl V. 

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

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

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

this handbook series.

The American Community Survey program is under the direction of 

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

Weinberg, Assistant Director for the American Community Survey and 

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

Offi ce.

Other individuals who contributed to the review and release of these 

handbooks include Dee Alexander, Herman Alvarado, Mark Asiala,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Zimolzak.

Linda Chen and Amanda Perry of the Administrative and Customer Services 

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

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

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

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

and production management.
A Compass for Understanding and Using 

American Community Survey Data Issued November 2008

U.S. Department of Commerce

Carlos M. Gutierrez, 

Secretary

John J. Sullivan, 

Deputy Secretary

Economics and Statistics Administration

Cynthia A. Glassman, 

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock, 

Director

What the Media Need to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What the Media Need to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2008.

Economics and Statistics 

Administration

Cynthia A. Glassman,

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock,

Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Offi cer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Offi ce

ECONOMICS

AND STATISTICS

ADMINISTRATION
What the Media Need to Know iii

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

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

Introduction ..................................................................................................1

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

Get New Answers ..........................................................................................2

Topics Covered by the ACS ...........................................................................4

Population and Housing Universes ..............................................................5

ACS Geography .............................................................................................7

Coping With Period Estimates and Sampling Error ..................................... 10

Period Estimates .................................................................................................10

Sampling Error ...................................................................................................11

Accessing ACS Data .................................................................................... 12

Aggregate Products ............................................................................................12

Public Use Microdata Sample (PUMS) ..................................................................13

FTP: The First Look ..................................................................................... 14

Alternatives to the ACS ............................................................................... 15

Best Use #1: Rankings ................................................................................ 16

Best Use #2: PUMS ...................................................................................... 17

Best Use #3: Mixing It Up ............................................................................ 18

Resources ................................................................................................... 18

Glossary...................................................................................................... 19

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

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

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

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

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

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

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

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

 Appendix 8. Other ACS Resources ...................................................................A-27
U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data

iv What the Media Need to Know

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

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

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

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

concepts that are fundamentally diff erent from those associated with sample 

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

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

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

The ACS Compass Products. 

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

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

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

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

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

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

they cover major stakeholder groups familiar to the Census Bureau.

General data users Congress 

High school teachers Puerto Rico Community Survey data

 users (in Spanish)

Business community Public Use Microdata Sample (PUMS) data

 users

Researchers Users of data for rural areas

Federal agencies State and local governments

Media Users of data for American Indians and 

 Alaska Natives

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

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

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

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

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

target audience. The Census Bureau developed additional ACS Compass Products

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

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

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

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

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

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

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

well as concerns and questions that will arise. 

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

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

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

you have learned. 
What the Media Need to Know 1

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

chosen to represent an area’s population and housing. 

Based on interviews with this small sample, the 

Census Bureau uses statistical methods to produce 

estimates of the characteristics covered by the survey for a broad set of geographic areas such as the 

nation, all states, congressional districts, counties, and 

more. “It isn’t the census, so it isn’t a headcount,” says 

Kenneth Johnson, senior demographer at the Carsey 

Institute at the University of New Hampshire. “It will do 

a good job of telling you what percentage of Chicago’s 

population is Hispanic or poor, but it cannot tell you 

exactly how many Hispanics or poor people there are 

in Chicago.” 

Reporters need to keep a few rules in mind in order to 

use the ACS properly, and those who are already using 

it say the results are well worth the eff ort. “I love the 

ACS and use it all the time,” says Paula Lavigne, formerly of the Dallas Morning News. “What we like about 

the ACS is that you can stand back and see how things 

are changing,” says Terry Schwadron of The New York 

Times. “If you have a question about America’s demographics, the answer is likely to be in there somewhere,” says Paul Overberg of USA Today. 

This guide shows how reporters have used the ACS to 

write great stories. Most of the time, reporters use this 

source to grab a statistic on a deadline that helps them 

make a specifi c point. But the best uses happen off 

deadline, when you think about the topics you’re covering and then sift through the ACS data to see what 

it can tell you about those topics. This approach gave 

Betsy Hammond of the Portland Oregonian a great 

story about how people in Oregon are less and less 

likely to be married. And it allowed Paula Lavigne to 

write an award-winning investigation of high levels of 

consumer debt in the affl uent suburbs of Dallas, which 

was published a year before the subprime mortgage 

market collapsed. 

Introduction

Finding fresh data for news stories used to be diffi -

cult. Now data are available on a wide range of topics 

whenever you want it. Rather than having to wait for 

the once-a-decade census, the U.S. Census Bureau’s 

American Community Survey (ACS) has emerged as a 

resource that can strengthen many kinds of news coverage and feature stories with annual social, housing, 

and economic data. 

The ACS gives reporters three signifi cant improvements in access to demographic information. First, the 

Census Bureau’s estimates of characteristics for local 

areas are now updated every year. Second, much of 

the survey is available through the Census Bureau’s 

user-friendly Internet site, the American FactFinder. 

Third, the ACS can sharpen your overall understanding 

of what is going on in the towns you cover, especially 

when you learn how to combine it with other sources 

of demographic information.

The purpose of this guide is to teach reporters how to 

use this new tool. The guide begins by describing how 

the ACS is put together and outlining the key concepts 

you need to know to get the most out of it. It explains 

the various ways you can fi nd and access ACS data 

and the best ways for novice and experienced users 

to use the estimates. There are also brief descriptions 

of other sources for local area data, as well as stepby-step stories of how journalists have used the ACS 

to fi nd and improve their published work. A glossary 

provides defi nitions for key concepts, and a series of 

appendixes off ers more specifi c information on technical topics, such as interpreting margins of error.

The ACS asks questions of a relatively small number of people living in both housing units (including 

apartments, single-family homes, mobile homes) and 

group quarters (including prisons, nursing homes, college dormitories). These people have been randomly 

What Is the American Community Survey?

The collection of detailed data about the nation’s population and housing has been a part of the decennial 

census since the fi rst census in 1790. Diff erent samples and methods have been used to collect this important information. In Census 2000, a survey of about 

one-sixth of the U.S. population and their housing was 

taken as a snapshot of the nation on Census Day, 

April 1. It was the nation’s primary source for information on the socioeconomic characteristics of every 

neighborhood in the United States, and it delivered 

numbers on everything from median income and commuting to ancestry, educational attainment, and the 

presence of indoor plumbing. It estimated important 

local characteristics, but it only happened once every 

10 years and was far from perfect. After administering 

the long form in 1990, the Census Bureau decided it 

was time for a change.
2 What the Media Need to Know

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

The 2010 Census will not have a long form. The reason 

for the transition can be summarized in the excerpt 

below from A Compass for Understanding and Using 

American Community Survey Data: What Congress 

Needs to Know. 

“After the 1990 census, Congress raised concerns 

about falling census response rates and rising costs. 

Congress also expressed an interest in having more 

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

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

Bureau to explore alternatives to the long form, with 

the goals of simplifying the census, containing costs, 

and producing more timely information to inform 

policy debates and legislative actions.”

The American Community Survey went through a 

decade of testing and development in a limited number 

of locations and 4 years of national implementation 

before it launched nationwide in January 2005 and 

began surveying monthly samples of about 250,000 

housing units. In 2006, a monthly sample of about 

20,000 people living in group quarters was added to 

the housing unit sample.1

 Both samples were chosen 

based on carefully designed methods of selection to 

provide representation of the entire U.S. population. 

After 1 year of continuous data collection, the ACS 

amasses enough information to release single-year 

estimates of housing and population characteristics for 

all areas that have at least 65,000 residents. The 2005 

ACS results were released in the summer of 2006, and 

new 1-year estimates have been released every year 

since then. After 3 years, the ACS amasses enough data 

to make estimates for any place with at least 20,000 

1

 Group quarters include places such as correctional facilities, college 

dormitories, and nursing homes. 2

 For more information on nonsampling errors, see Appendix 6.

residents. The Census Bureau will release the fi rst of 

these 3-year period estimates in December 2008. The 

ACS will have collected enough data to release 5-year 

estimates in 2010 for areas down to the tract and block 

group level, as the decennial census long form has 

done in past censuses. All the estimates will be 

updated for all geographies every year thereafter. 

By combining more than 1 year of responses, the ACS 

is able to provide estimates for smaller geographic 

areas and increase the precision of its estimates for 

larger areas because they are based on more interviews. All sample surveys (including the decennial 

census long-form sample) have a built-in uncertainty 

factor known as “sampling error.” This means that each 

ACS “number” is actually an “estimate” and that the 

ACS estimates will always be bracketed by margins of 

error. Understanding and relying on margins of error 

will allow you to judge how reliable the numbers are. It 

also means that some estimates of very small population segments, such as individual neighborhoods, 

could be based on too few responses to provide useable estimates in the next few years. The good news 

is that nonsampling error, a problem that plagued the 

census long form in the past, has been reduced in the 

ACS through the use of highly trained interviewers.2

To use the ACS wisely, you need to understand how 

its design shapes its best uses. Because its products 

include reliable demographic estimates for most localities in the United States, you can use it to get new 

answers to basic questions. Because it covers so many 

diff erent topics, you can use the ACS to anchor investigations of a variety of issues and diff erent groups of 

people. And because it publishes a new set of estimates every year, you can use it to track population 

trends and explain how communities are changing. 

Get New Answers

Paul Overberg of USA Today says that extracting data 

is only one step in the long process of writing a story 

about population trends. He says that the process usually begins by asking a general question. “Sometimes I 

will talk to another reporter for several months before 

we come up with a question that the data can answer 

in an interesting way. Once we can do that, we have a 

story.”

Overberg is an expert in computer-assisted reporting. 

He has analyzed a huge variety of databases to write 

front-page stories for USA Today on everything from 

airline food to baseball salaries, with results that are 

imaginative and surprising. Since 1996, Overberg has 

been having a long conversation with fellow reporter 

Haya El Nasser about how new immigrants to the 

United States are assimilating into society. “We’re trying 

to fi gure out the who and where of it,” he says. “We 

have known for years about the traditional immigrant 

gateways—New York, Los Angeles, Miami. But obviously, there’s a lot of moving around once they get into 

the U.S. We knew they were spreading out, but where 

were they going? I fi gured that the American 

Community Survey would have something in it to 

inform that question.”
What the Media Need to Know 3

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

Overberg started looking through the ACS tables on 

migration that are published in the Census Bureau’s 

online data search engine, the American FactFinder. He 

eventually downloaded the detailed table on “Residence 1 Year Ago by Citizenship Status in the United 

States” as an ordinary spreadsheet in the Excel format. 

Refer to Figure 1 to see the version of this detailed 

table that is displayed in American FactFinder. This 

table estimates the number of people who live in the 

same house they lived in 1 year ago, plus the number 

who have moved:

• Within the same county.

• From a diff erent county in the same state.

• From a diff erent state.

• From abroad. 

Each of these categories is reported for native-born and 

foreign-born Americans. The foreign born are further 

reported as either naturalized citizens or not U.S. citizens. 

Overberg wanted as much geographic detail as he 

could get, so he downloaded these 1-year 2006 ACS 

variables for all U.S. counties that had more than 

65,000 residents. Then he used Excel’s delete function 

to get rid of the variables he did not need. He ended 

up with a table showing the total number of residents 

in each county and the number of foreign-born county 

residents who had moved across county lines in the 

last year. “I wanted to fi nd the percentage of a place’s 

total population that was foreign born and had moved 

there from elsewhere in the U.S. in the previous year,” 

he says. 

The limits of survey-based data required Overberg 

to make several judgment calls. Some county-level 

estimates from the ACS are based on a relatively small 

number of respondents and the smaller the number of responses, the larger the margin of error that 

surrounds the estimate. Several of the counties that 

registered large percentages of recent foreign-born 

 Figure 1. Residence, 1 Year Ago by Citizenship Status in the United States

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
4 What the Media Need to Know

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

arrivals had margins of error that were large enough 

to make Overberg and El Nasser question their fundamental accuracy. So they steered away from counties 

with relatively small total populations, and they looked 

at the 2005 ACS to see whether the estimates varied 

wildly from one year to the next. 

In the end, Overberg came up with 10 places where 

he was confi dent that more than 2 percent of the total 

population was foreign-born people who had arrived 

in the last year from another country. El Nasser went 

to some of the places he listed and found immigrants 

who were happily running their own businesses and 

furnishing comfortable suburban homes. The story, 

“For More Immigrants, Suburbia’s a Nice Fit,” ran on the 

front page of USA Today on March 4, 2008. The article 

published Overberg’s list of 10 places and their percentages in a table, but it did not refer to confi dence 

intervals or the places he had left out. 

“There’s always a line beyond which you have to stop 

explaining how you did something,” says Overberg. In 

other words, the average news customer doesn’t know 

or care about the statistical details. She trusts that 

reporters will make good judgments about which facts 

to put in their stories. 

“I did a lot of spadework with this subject before I 

downloaded that table,” he says. “I was looking around 

on all the stuff the ACS has on migration and what 

kinds of places draw what kinds of people. It was a 

pretty interesting list. Once I could explain to Haya 

what the question was that this particular table was 

answering, I fi gured we had a pretty good story. She 

got on the phone and started calling those places, and 

she became convinced.”

Overberg regularly teaches seminars to reporters on 

how to use the ACS. He says the basic method is simple and the best way to get better at it is to practice. 

“One day when you’re not on deadline, think about 

the topics you’re covering,” he says. “Once you have a 

topic, go into the American FactFinder and ask, “What 

can the data say about this topic?” The American 

FactFinder has a keyword tab in the detailed tables that 

will allow you to choose a topic and quickly fi nd the 

tables that relate to it. And if you get stuck, he says, 

call your State Data Center or a local planner: “They 

work with these data all the time.” Information about 

the State Data Centers can be found at <http://www

.census.gov/sdc/www/>.

County/Place State Metro Area Percentage

Alexandria VA Washington, DC 3.9%

Prince William County VA Washington, DC 3.2%

Henry County GA Atlanta 3.0%

Gwinnett County GA Atlanta 2.6%

Riverside County CA Los Angeles 2.6%

Loudoun County VA Washington, DC 2.4%

Fairfax County VA Washington, DC 2.2%

Kings County CA N/A 2.2%

Osceola County FL Orlando 2.1%

Fort Bend County TX Houston 2.1%

 Table 1. A Replica of Paul Overberg’s Findings on Places With High 

Percentages of the Total Population Who Are Foreign Born and 

Who Arrived in the Last Year From Another Country

Source: Paul Overberg, analysis of the 2006 American Community Survey.

Topics Covered by the ACS

The primary reason for the ACS is to help Congress 

determine funding and policies for a wide variety of 

federal programs. Because of this, the topics covered 

by the ACS are diverse (many are the same or similar 

to those on the Census 2000 long form). They include 

social, economic, housing, and demographic variables, 

and they yield a wealth of information journalists 

can use to write better stories. Table 2 gives a more 

detailed list of the topics covered by the ACS.
What the Media Need to Know 5

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

 Table 2. Subjects Included in the American Community Survey

Demographic Characteristics

Age

Sex

Hispanic Origin

Race

Relationship to Householder 

(e.g., spouse)

Economic Characteristics

Income

Food Stamps Benefi t

Labor Force Status

Industry, Occupation, and Class 

of Worker

Place of Work and Journey to 

Work

Work Status Last Year

Vehicles Available

Health Insurance Coverage*

Social Characteristics 

Marital Status and Marital History*

Fertility

Grandparents as Caregivers

Ancestry

Place of Birth, Citizenship, and 

Year of Entry

Language Spoken at Home

Educational Attainment and 

School Enrollment

Residence One Year Ago

Veteran Status, Period of Military 

Service, and VA ServiceConnected Disability Rating*

Disability

Housing Characteristics

Year Structure Built

Units in Structure

Year Moved Into Unit

Rooms

Bedrooms

Kitchen Facilities

Plumbing Facilities

House Heating Fuel

Telephone Service Available

Farm Residence

Financial Characteristics

Tenure (Owner/Renter)

Housing Value

Rent

Selected Monthly Owner Costs

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

 Source: U.S. Census Bureau.

Several ACS questions obtain information for subgroups of the total U.S. population, such as homeowners or people living in family households. The particular subgroup covered by each question is referred to as 

the “universe.” ACS tables also are based on particular 

universes. It is important to note which population or 

housing universe is included in the tabulation when 

you cite an estimate from the ACS. For example, 

responses related to marital status are tabulated only if 

the individual is at least 15 years of age. So in statistics 

on marriage, the universe is the population 15 years 

and over. Similarly, employment characteristics are 

typically reported only for the population 16 years of 

age and over. 

In the table below, the universe is clearly defi ned as 

the population 15 years and over. Depending on the 

data product you are using, the universe may be given 

either in the individual cells or at the top of the table. 

Figure 2 shows an example of an ACS detailed table; 

the universe is circled. 

Population and Housing Universes

Geography

Universe:

POPULATION 15 

YEARS AND OVER: 

Female

(Estimate)

Universe:

POPULATION 15 

YEARS AND OVER: 

Female

(Margin of Error)

Universe:

POPULATION 15 

YEARS AND OVER: 

Female; Now married

(Estimate)

Universe:

POPULATION 15 

YEARS AND OVER: 

Female; Now married

(Margin of Error)

Alabama 1,924,509 ±3,452 989,309 ±11,521

Alaska 255,676 ±1,636 136,494 ±3,750

 Table 3. Example Cells From an ACS Table Showing the Universe and the Margin of Error

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

Many of these topics contain numerous subtopics. For 

example, “Journey to Work” includes data on means 

of transportation (auto, bus, bicycle, walking), travel 

time (both duration and time departed), and whether 

or not a carpool is used. The best way to learn all the 

details of what is off ered in the ACS is to follow Paul 

Overberg’s advice—log on and look around. 
Knowing about the universe is important to reporting. 

For example, if you want to calculate the percentage of 

the population in an area that is married, you’ll need to 

divide the estimate of the number of married people by 

the population 15 years and over, not the total population. It is good practice to publish the fact that an ACS 

estimate is based on a restricted universe, even if it 

may seem obvious since, after all, you wouldn’t expect 

children to get married or hold a job. 

Some ACS topics based on restricted population 

universes include disability, educational attainment, 

fertility, language, migration, school enrollment, and 

veteran status. ACS topics based on restricted housing 

universes include homeownership (tenure), mortgage 

costs, and vacant units for rent. It is also important to 

note that the 2005 ACS did not include people living 

in group quarters, such as jails, college dorms, and 

nursing homes. However, the 2006 ACS and subsequent years did include samples of the group quarters 

population.

The use of diff erent population and housing universes 

makes ACS data more meaningful by tailoring each statistic to its relevant group. The Census Bureau always 

notes the population universe in each table and map, 

making the universes easy to identify. When deciding 

how to cite ACS data in a published story, always think 

about the specifi c universe covered and remember the 

lack of group quarters data in 2005, which could aff ect 

comparisons with ACS data from 2006 and later years. 

The ACS Web site provides valuable guidance about 

when comparisons are appropriate. Refer to <http://

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

 Figure 2. Example of a Population Universe

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

6 What the Media Need to Know

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
What the Media Need to Know 7

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

The Census Bureau produces ACS tabulations on a 

large array of topics and releases the data for an 

even larger number of geographic areas. The largest 

geographic area described by the Census Bureau is 

the United States as a whole, while the smallest is a 

census block group, which is an area roughly equivalent to several city blocks that contains between 600 

and 3,000 residents. There are about 208,000 census 

block groups in the United States. Between these two 

extremes are a wide variety of types of geographic 

areas of varying sizes. 

Because the ACS cannot survey enough housing units 

in every geographic unit in the United States every 

year, it must base its estimates for smaller geographic 

areas on surveys conducted over more than 1 year. 

Estimates based on a single year of ACS data are available only for areas with total populations of at least 

65,000. The 2006 single-year data estimates were 

released in 2007 for about 7,000 geographic areas. In 

December 2008, estimates based on 3 years of ACS 

data will be available for areas with total populations 

of at least 20,000. This encompasses about 20,000 

geographic areas, including 58 percent of all counties. 

Estimates based on 5 years of ACS data, to be released 

starting in 2010, will be available for all geographic 

areas. Additional data are available for Public Use 

Microdata Areas (PUMAs), which are described in more 

detail later in the section.

ACS Geography

 Table 4. 1-Year, 3-Year, and 5-Year ACS Estimates by Calendar Year of Data Release

The diff erences between single-year and multiyear 

estimates raise several issues that are covered in this 

report. The most important thing to understand about 

these minimum population requirements is that they 

determine the type of data that will be available for the 

geographic areas you’re interested in. Table 5 summarizes the major geographies for which 1-year, 3-year, 

and 5-year ACS estimates are released. 

Most news stories use the standard geographic units 

in the ACS, such as states, counties, and cities. But 

the data are also available for many other geographic 

areas, including school districts, congressional 

districts, metropolitan areas, and “census designated 

places” (CDPs). A CDP is the Census Bureau’s term for 

a city, town, or village that lacks a separate municipal 

government but which otherwise physically resembles 

an incorporated place. 

Several other geographic units created by the Census 

Bureau can also be useful to journalists. A census tract, 

for example, is a small, relatively permanent statistical 

subdivision of a county that usually has between 2,500 

and 8,000 residents. Tracts are designed to follow the 

boundaries of neighborhoods; they describe areas 

that are homogeneous with respect to population 

characteristics, economic status, and living conditions. 

PUMAs are another important geographic option. Further explanations of geographic entities can be found 

on the Census Bureau’s Web site <http://www.census

.gov/acs/www/UseData/geo.htm> and in the glossary 

at the back of this handbook. 

It is important to keep in mind that a small number of 

geographic boundaries change each year. The 

American FactFinder updates geographic boundaries 

when new data products are released. However, data 

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

Source: U.S. Census Bureau.

Year of data release

Data Population 2006 2007 2008 2009 2010 2011 2012 2013

product threshold 

 Year(s) of data collection

 1-year 

 estimates 65,000+ 2005 2006 2007 2008 2009 2010 2011 2012

 3-year 2005– 2006– 2007– 2008– 2009– 2010–

 estimates 20,000+ 2007 2008 2009 2010 2011 2012

 5-year 2005– 2006– 2007– 2008–

 estimates All areas* 2009 2010 2011 2012
8 What the Media Need to Know

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

 

 Type of geographic area 

Total 

number of 

areas

Percent of total areas receiving . . .

1-year, 

3-year, 

& 5-year 

estimates

3-year & 

5-year 

estimates 

only

5-year 

estimates 

only

States and District of Columbia 51 100.0 0.0 0.0

Congressional districts 435 100.0 0.0 0.0

Public Use Microdata Areas* 2,071 99.9 0.1 0.0

Metropolitan statistical areas 363 99.4 0.6 0.0

Micropolitan statistical areas 576 24.3 71.2 4.5

Counties and county equivalents 3,141 25.0 32.8 42.2

Urban areas 3,607 10.4 12.9 76.7

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

American Indian areas, Alaska Native areas, and 

Hawaiian homelands 607 2.5 3.5 94.1

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

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

ZIP Code tabulation areas 32,154 0.0 0.0 100.0

Census tracts 65,442 0.0 0.0 100.0

Census block groups 208,801 0.0 0.0 100.0

 Table 5. Major Geographic Areas and Type of ACS Estimates Received

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

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

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

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

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

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

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

may diff er from the numbers in this table.

products released before or just after local boundary changes will contain data based on the previous 

boundaries, an issue that is particularly common with 

incorporated places and CDPs. It’s important to keep 

this potential glitch in mind when you’re planning to 

compare data over several years.

Citing ACS data properly gets more complex as geographic areas get smaller. In 2006, only one-quarter of 

U.S. counties (783 of 3,141) met or exceeded the population threshold of 65,000 for single-year estimates. 

This means that single-year data won’t always be 

available for the county or counties cited in a particular 

story. The options for smaller counties are to use the 

multiyear estimates, to cite statistics for a larger geographic area that includes the county, or to use another 

source. Demographic data on local areas in the United 

States are available from sources other than the ACS, 

and some of those sources are discussed in the following sections.

For many reporters, the diverse nature of ACS 

geography will not be an obstacle because their news 

organization covers only a single large city or region. 

Reporters who are writing stories that compare 

several localities statewide or nationally will need to be 

a little more careful, but the Census Bureau has created 

a shortcut that makes large-scale comparisons a lot 

easier. The American FactFinder interface (described in 

the “Accessing ACS Data” section), simplifi es the process by allowing users to select “geo within geo.” When 

selecting the geography in the detailed tables section 

of the American FactFinder, click the “geo within geo” 

tab. You will be directed to identify the subunits (e.g., 

counties) followed by the main unit of which each subunit is a member (e.g., nation or state). Figure 3 shows 

you an example of this feature. 
What the Media Need to Know 9

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

Public Use Microdata Areas (PUMAs) are nonoverlapping 

regions that partition a state and contain approximately 100,000 people each. Each state government 

defi ned its PUMA boundaries for use with the 

Census 2000 Public Use Microdata Sample (PUMS), so 

they refl ect local knowledge about the boundaries of 

regions. With the exception of three units in Louisiana 

aff ected by Hurricane Katrina, all PUMAs are included in 

the annual releases of the ACS single-year estimates.

The value of using PUMA geography becomes apparent when looking at a state such as West Virginia. The 

2006 ACS includes data for only seven of West 

Virginia’s 55 counties, but it also released data for 12 

West Virginia PUMAs covering the entire state (shown 

in Figure 4).

PUMAs are also useful for examining densely populated 

areas in depth. Journalists who want to investigate 

single-year estimates for neighborhoods in large urban 

areas can do so by using PUMA-level data to obtain 

detailed descriptions of the areas. For example, the 

ACS releases estimates for the City of Chicago (in Cook 

County, IL), but Chicago is also divided into 19 PUMAs, 

each of which can be used to make separate estimates.

 Figure 3. Example of the “geo within geo” Feature in American FactFinder

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
10 What the Media Need to Know

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

 Figure 4. PUMAs for West Virginia, 2000

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

The decennial census is a snapshot of the population 

taken once every 10 years on Census Day, April 1. But 

the ACS collects data continuously throughout the year, 

creating what is known as a period estimate.3

 Areas 

that have a consistent population throughout the year 

will not see major diff erences between a period estimate and the old “snapshot” number. But the estimated 

numbers may change a lot for areas with populations 

that fl uctuate considerably between seasons, such as 

college towns and seasonal retirement areas.4

 This is 

one more reason for using caution when comparing 

ACS fi gures with those from point-in-time estimates 

such as Census 2000.

Period Estimates 

ACS statistics for small geographic areas also pose special problems because they are created by pooling survey results collected over 3 or 5 years. “The multiyear 

estimates are a challenge to everyone who uses the 

ACS,” says Ken Hodges of Nielsen Claritas. “But they 

could be especially challenging for journalists, who 

really can’t devote much space to nuances in the data.” 

He states that, “A 1-year income estimate for 2006 is 

clear enough, but a 5-year estimate that covers 2005 

through 2009 is a new kind of animal.” Demographers 

would refer to that as a “5-year period estimate.” 

The complications of using multiyear period estimates 

make single-year estimates easier to describe, but the 

single-year estimate isn’t always going to be the best 

choice.5

 The trade-off is between accuracy and currency. 

For many statistics, margins of error for single-year 

Coping With Period Estimates and Sampling Error

3

 See Appendix 2 for a description of diff erences between the ACS and 

decennial censuses.

4 See Appendix 1 for more information on period estimates. 5

 See Appendix 1 for further guidance on use of single-year versus 

multiyear estimates.
What the Media Need to Know 11

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

estimates will be much larger than they are for multiyear estimates. So if accuracy is important, look closely 

at the multiyear estimate. 

In general, trends over time should be examined 

using nonoverlapping multiyear estimates.6

 These are 

multiyear estimates that are either 3 or 5 years apart, 

depending on which multiyear estimate was used. 

For example, to show how a segment of the population is changing using 3-year estimates, you could use 

2005–2007, 2008–2010, and 2011–2013 estimates. 

The resulting estimates weren’t produced from interviews in common because they don’t have overlapping 

years. Unfortunately, this tactic will not be possible for 

the ACS until the second round of 3-year estimates are 

available in 2011. 

Overlapping multiyear estimates, which will be available beginning in 2009, can still be useful in a more 

limited way. When you use them in combination with 

single-year estimates, they can provide insight into 

areas undergoing rapid change.7

 In general though, 

multiyear estimates should not be used to describe 

change over a single year. The appendixes at the end 

of this handbook explain the issues and provide examples of how to use overlapping multiyear estimates.

Sampling Error8

Statistical error is a reality that is diffi cult for reporters and readers to understand. The decennial census 

sample had error in its estimates of local areas, but 

most people used the estimates from the long form 

as exact numbers describing the population. Error in 

the census long-form data included nonsampling error, 

which is diffi cult to measure precisely.9

 So the long 

form’s reputation for great accuracy might not have 

been deserved in every case, but it endured because 

statisticians couldn’t tell how inaccurate it was, and 

the Census Bureau did not provide measures of sampling error for these estimates. The good news is that 

nonsampling error in the ACS is reduced relative to the 

long form. 

All surveys have sampling error. The main diff erence 

between the census and the ACS is that with the ACS, 

it’s easy to tell which numbers are good and which 

aren’t. Margins of error are included with all ACS data 

products (see Figures 1 and 2) and can be used to 

assess the quality of the estimates.10 If an estimate is 

deemed insuffi ciently reliable, then consider using a 

multiyear estimate instead. 

How do you decide when the margin of error for a local 

estimate is so large that the number should not be 

used? Paul Overberg compared county-level estimates 

of foreign-born newcomers for several years to see if 

they were similar and threw out those that weren’t. 

Paula Lavigne doesn’t use an estimate if the margin of 

error is more than 10 percent of the total estimate, and 

she sometimes throws out other estimates with large 

margins of error if the local area’s total population is 

small. Betsy Hammond of the Portland Oregonian says 

that if the year-to-year change in the characteristic 

she’s measuring is smaller than the margin of error, she 

won’t use it. 

“What we’re really talking about is what reporters have 

to do all the time. We use our judgment and only go 

with things that satisfy a certain comfort level,” says 

Terry Schwadron, information and technology editor at 

the New York Times. “The stories should not be about 

the statistics. They should be about a broader subject, 

and the numbers should work for the story.”

One of the most common uses of ACS estimates is to 

make comparisons over time or across geographies. 

Appendix 4 off ers assistance for these tasks, particularly when comparing ACS data with that of other 

sources such as the decennial censuses. Appendix 3 

also provides guidance on calculating measures of 

sampling error when aggregating estimates (e.g., combining estimates for a three-county area or for fi ve age 

groups).

6

 See Appendix 4 for trend analysis using nonoverlapping estimates.

7

 See Appendix 1.

8

 See Appendix 3 for information and methods for calculating measures 

of sampling error.

9

 See Appendix 6 for more information on nonsampling error.

10 See Appendix 3 for information on margins of error and confi dence 

intervals. The Census Bureau provides the margins of error at a 

90-percent confi dence level with ACS products. To use other confi -

dence levels, see Appendix 3.
12 What the Media Need to Know

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

The Census Bureau delivers the ACS estimates in standard products that can be categorized as aggregate 

fi les or microdata (PUMS). Your story’s specifi c focus 

and subject matter will dictate which of these products 

is the best fi t.

Aggregate Products 

Aggregate products are the most commonly used 

data products available for the ACS. These include the 

tables and maps in the American FactFinder <http://

factfi nder.census.gov>, which describe the distributions for basic and detailed population and housing 

queries. These products are referred to as “aggregate” 

products because the Census Bureau has aggregated 

the responses from the survey samples into defi ned 

categories and computed the corresponding estimates, 

thereby summarizing the data. These products diff er 

from reports because they off er a variety of options 

and allow you to work with the data, so you can get 

the estimate you need. They are most easily found 

using the American FactFinder, although in a few cases 

it may be necessary to access them by downloading 

the whole data set through the Census Bureau’s File 

Transfer Protocol (FTP) site <www2.census.gov>. 

The aggregate products available for ACS data include 

the following:

• Detailed tables. These are where experienced 

journalists go fi rst to fi nd the estimates they need. 

In the American FactFinder, these tables are known 

as the detailed and custom tables and include the 

most descriptive and detailed data. These tables 

feature simple frequency estimates for individual 

variables and estimates for combinations (such as 

poverty status by sex and age). Many variables in 

the detailed tables, such as age, are subdivided 

into several categories (such as ages 0–17, 18–64, 

65 and older, and so on). Detailed tables can also 

be obtained through FTP.

• Subject tables. These are summarized, topicspecifi c tables based on data from the detailed 

tables. They are easier to navigate and can be a 

better choice if you just need a quick overview, or 

if you’re new to the ACS. Subject tables provide 

data for some of the most popular topics, such as 

fi nances, households, and occupational characteristics for a single geography. If your question is 

simple, subject tables may provide the data you 

need with a minimum of fuss.

• Ranking tables. These tables compare the 50 

states, the District of Columbia, and Puerto Rico 

according to various characteristics and rank them 

from highest to lowest. Ranking tables present 

state data for nearly 100 diff erent characteristics. 

These tables can also be viewed as charts, using 

a link on the page. The charts show the 90-percent 

confi dence limits around each estimate as an 

indication of which rankings may be statistically 

diff erent (meaning that two estimates probably are 

truly diff erent).

• Geographic comparison tables. These are 

similar to ranking tables but are available for geographical levels that extend below the state level. 

Unlike ranking tables, they provide margins of 

error for the estimates but do not tell you whether 

or not the diff erences in rankings are statistically 

signifi cant. 

• Data profi les. This product off ers tables that provide summaries of several basic social, economic, 

housing, and demographic characteristics for each 

geographic unit. While they are less sophisticated 

than detailed tables, data profi les do a good job of 

describing the broad characteristics of a geographic area. 

• Narrative profi les. Accessible through data profi les are narrative profi les, which present the data 

in plain language and use graphics, similar to a 

news article. These products contain data that are 

automatically inserted into a preformatted text. 

• Selected population profi les. Population 

profi les are ready-made tabulations for specifi c 

groups of interest, such as a specifi c ancestry or 

race. While other ACS profi les provide general 

information for a geographic area, the selected 

population profi les use a similar format to provide 

basic information for a specifi c segment of the 

population. 

• Thematic maps. The maps include two important features in addition to their categorical color 

schemes. First, users can quickly identify which 

other geographic units have a signifi cant statistical 

diff erence from the selected unit for a particular 

characteristic. Also, tabular interfaces to the data 

are readily available as a link on the left of the 

page.

• Comparison profi les. The comparison profi les 

show data side-by-side from the 2006 ACS and 

the 2007 ACS, indicating where there is a statistically signifi cant diff erence between the two sets of 

estimates. Comparison profi les are only available 

for 1-year estimates.

Accessing ACS Data
What the Media Need to Know 13

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

• Summary fi les. The ACS summary fi le includes all 

detailed tables for all published geographic areas. 

Summary fi les are accessed from the FTP site.

Public Use Microdata Sample (PUMS)

Accessible through the American FactFinder Web site, 

microdata fi les are individual response records with 

all identifying information removed to protect the 

respondent’s confi dentiality. Aggregate fi les are tables 

of totals, while microdata fi les are a sample of the individual survey response records used to arrive at those 

totals. Microdata fi les are harder to use than the aggregate fi les, but have unlimited possibilities because you 

can create your own tables with the variables of your 

choosing. 

In general, the PUMS fi les are more diffi cult to work 

with than the aggregate products described above 

since you have to use a statistical package such as SAS 

<http://www.sas.com>. Also, the responsibility for 

producing estimates from PUMS and judging their statistical signifi cance is up to the user. But once you learn 

how to work with PUMS—the Census Bureau publishes 

a handbook for PUMS users for those who are interested—the story possibilities are endless. The smallest 

geographic area on these fi les is the PUMA (see the 

“ACS Geography” section), which has a minimum population of 100,000.

PUMS can be used to produce specially tailored tables 

from the most current ACS. Additionally, many analysts 

fi nd the PUMS fi les helpful when doing trend analyses 

to compare PUMS data for each year. An example of 

using the current PUMS on its own can be found later in 

“Best Use #2.” The two examples below show how you 

can compare the current ACS PUMS data with historical 

decennial census PUMS data.

David Peterson of the Minneapolis Star Tribune used 

PUMS to fi nd out the facts about a group that is often 

part of a standard punch line for jokes about 

Minnesotans. With the help of a research center at the 

University of Minnesota called IPUMS, Peterson wrote 

a query that asked the computer to fi nd the response 

records of unmarried men of Norwegian ancestry 

employed as farmers who had been enumerated in the 

census. The search covered every census from 1930 to 

2000, plus the ACS. It revealed that there were tens of 

thousands of Norwegian bachelor farmers as recently 

as the 1930s and that half of them were in Minnesota. 

It also showed that their numbers have been declining 

steadily since the 1930s and that there weren’t many 

left in 2005.

The Integrated Public Use Microdata Series or IPUMS 

<http://usa.ipums.org/usa/> is a 100-person research 

center that works with social scientists to crunch 

microdata from a variety of sources, including the 

ACS. Most of its work is of a far more serious nature. 

For example, David Knox of the Akron Beacon Journal

used IPUMS to look at how the wages of Ohioans have 

changed at diff erent ages between 1949 and 2006. 

His series “The Incredible Shrinking Middle Class” was 

prompted by “agreement on all sides that the middle 

class in Ohio was in trouble,” he says. After 6 months 

of work with IPUMS and an analysis of 51 million 

microdata records, Knox was able to show readers just 

how bad things were. 

“I expected to fi nd that younger workers these days 

were doing just as well as their parents had done 

when they were starting out,” he said. “But I found 

that younger workers in Ohio in 2006 had an average income that was about 25 percent lower than the 

average income of a young Ohioan in 1970.” Using 

PUMS, Knox also found that incomes of mid-career 

workers had gone down dramatically in the last several 

decades, although the oldest Ohioans were doing comparatively well. 

Knox says that the biggest advantage of using PUMS to 

look at long-term trends in income is its ability to track 

how conditions change for diff erent groups. The series 

began on September 20, 2007, and continued into 

2008 with candid interviews with anonymous Ohioans 

on debt, bankruptcy, and their hopes for their children. 
14 What the Media Need to Know

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

File Transfer Protocol (FTP) is a common way of sending large data fi les from one computer to another 

across the Internet. FTP has several advantages over 

the familiar protocols used to view Web pages. Many 

FTP programs, including the latest versions of Internet 

Explorer, allow users to view fi les on remote computers 

as if they were on their own machine. You can quickly 

navigate through several folders and see the contents 

much faster than you could if you waited for a Web 

page to load. FTP programs also make it easy to download multiple fi les. 

The Census Bureau maintains an FTP server, which can 

be found at <www.census.gov/acs/www/Special

/acsftp.html>. But don’t go there until you’ve done 

some homework. Despite its many benefi ts, getting 

and using FTP downloads can be challenging.11

It is almost always easier to use the American 

FactFinder than it is to download ACS data from the 

FTP site. “The American Community Survey is not particularly friendly to database downloading,” says 

Overberg. “There is an awful lot of formatting in their 

raw data that you have to strip out before you can 

open it in your own database. Just getting a spreadsheet is usually easier.” Since the detailed tables in the 

American FactFinder contain every variable in the ACS, 

every variable you can get in the aggregate fi les via 

FTP is also accessible in the American FactFinder.

There are two cases where an FTP download might 

be your best option. First, if your story is extremely 

complex—if it uses multiple data sources including 

the ACS, and it also uses a lot of ACS data—it might 

be worthwhile to work with the ACS data so you can 

include the whole database in your analysis. Most 

reporters will never need to do this. 

Second, FTP is used to provide journalists with embargoed data when the new year’s ACS data are rolled out. 

To allow journalists to report on data as soon as they 

are released, the Census Bureau gives members of the 

media access to new data sets a few days before their 

offi cial release. To get access during this “embargo” 

period, journalists fi ll out a form on a registration 

page that is available as a link on the Census Bureau’s 

homepage. Once registered, you’ll be informed of and 

provided access to data (via FTP) before it is released to 

the public. This is also the place to register your e-mail 

so you can get a steady stream of press releases from 

the Census Bureau. Stories using embargoed data cannot be published until the release date, but newsrooms 

might benefi t from downloading ACS data from the 

FTP site once a year. When the data are in an embargo 

period, FTP is the only way to get it.

Betsy Hammond, a reporter at the Portland Oregonian, 

used the FTP site to get brand-new data into a story 

about statewide trends in marriage. A colleague had 

been tracking the eff orts of evangelical churches to 

encourage people to tie the knot. The churches had 

been running their programs for several years, and the 

reporters wondered if their eff orts had had any eff ect. 

What percentage of Oregonians was married, and how 

did that number compare with 6 years ago? 

“I said let’s look at the ACS,” says Hammond. “Maybe 

it will give this story a peg.” It was August 2007 and 

Hammond, who subscribes to the Census Bureau’s 

news service, had noticed that 2006 data from the 

American Community Survey had come out that week. 

She went to the American FactFinder and found that 

the data she needed weren’t posted yet. “I needed the 

number married in each county in Oregon for diff erent 

age groups,” she says. “We were being picky, but it was 

because experts had told us that people are delaying 

marriage. We needed the age data to see whether this 

was happening in Oregon. We eventually found that 

people are marrying later and also that they are less 

likely to be married in general.”

When she started working with ACS data through the 

FTP site, Hammond was baffl ed by the way the data 

were organized. “It was unbelievably hard to understand,” she says. “We had to correlate the table numbers to the data points to see what the statistics were 

measuring. It was a real chore to fi nd the tables we 

needed. Then we had to do it again to locate individual 

counties in Oregon. I had covered the census fulltime for a few years, so I knew what I was doing on 

FTP sites, but this was a real challenge. I showed two 

diff erent people how I did it and they both laughed at 

how complicated it was. But once I fi nally was able to 

extract what we needed, it was beautiful.” 

The story, “Marriage Today: Fewer ‘I do’s’ and more 

just ‘I’s,’” ran on September 23, 2007. It showed that 

38 percent of Oregonians ages 20 to 34 were married 

in 2006, a share about equal to the national average. 

It also showed that the share of Oregonians who were 

married had declined sharply between 2000 and 2006 

in the 15 Oregon counties and 5 cities for which the 

ACS published data. In some rural counties the decline 

over 6 years had been 8 percentage points, which was 

twice as fast as the 4-point national decline.

FTP: The First Look

11 Paul Overberg suggests that the problems some have experienced 

may be solved with the release of a new ACS-SF product 

<http://www.census.gov/acs/www/UseData/sf/tech_doc.htm>. 
What the Media Need to Know 15

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

Comparing Census 2000 with the ACS is not always 

possible because of diff erences in the way the data are 

collected. However, Hammond says that the Census 

Bureau’s tables came with clear instructions on which 

numbers were comparable and which were not. “They 

wrote that in plain English and put it in a place where 

you would defi nitely fi nd it,” she says. Small sample 

sizes for some counties could also have been a concern, but Hammond protected herself by citing percentages rather than the actual numbers. If the change 

in the number married between 2000 and 2006 was 

smaller than the margin of error in the 2006 number, 

she didn’t use it. 

The best way to tackle the ACS as a database is to take 

a class before you’re on deadline. Several organizations off er training in the use of ACS and other federal 

databases. Probably the best known is The National 

Institute for Computer Assisted Reporting (NICAR), 

which is a joint program of Investigative Reporters and 

Editors (IRE) and the Missouri School of Journalism. 

Since 1989, NICAR has trained thousands of journalists in the practical skills of fi nding, prying loose, and 

analyzing all kinds of electronic information. It off ers 

regular workshops to help reporters navigate the ACS 

and other federal databases.

Alternatives to the ACS

The Census Bureau runs several surveys and programs 

besides the ACS that provide high-quality local area 

data, and your stories may benefi t if you know about 

these alternative sources. The Census Bureau’s annual 

Population Estimates Program and the upcoming 

2010 Census may each be a better source for information they have in common with the ACS, specifi cally 

total population, sex, age, and race/ethnicity for states 

and counties. For example, the numbers released 

annually from the Population Estimates Program are 

the offi cial population totals until the next decennial 

census. They also indicate the components of population change (births, deaths, and migration), which are 

not found elsewhere. 

The Current Population Survey (CPS) has collected 

occupational and economic data monthly since 1947. 

More recently, an Annual Social and Economic 

Supplement has been added to the CPS to collect more 

social information in the survey from about 100,000 

households annually. The CPS is often the best source 

for long-term analyses of economic conditions, 

because the data are consistent and comparable back 

to 1947. The CPS does not provide data for counties 

or metropolitan areas. While it does provide state-level 

data, they are less statistically reliable than those from 

the ACS due to a much smaller sample size. 

The American Housing Survey (AHS) is a longitudinal 

survey that collects housing-related data from the 

same housing units over time. It extends back to the 

1970s, collects data at the national level every other 

year, and for 47 metropolitan areas, about every 6 

years. The AHS is ideal for analyzing the change in 

households and the quality of housing.

Other federal agencies provide related data, some of 

which may help support your specifi c story better than 

the ACS can. However, each source has its drawbacks. 

For example, the Internal Revenue Service releases 

estimates of migration between all U.S. counties every 

year. This is a great resource with lots of potential for 

journalists, but the only people measured are those 

who fi le tax returns. So it isn’t a good source if your 

story is about the migration of low-income groups, for 

example. 

The best demographics reporters use the ACS as one 

arrow in a quiver full of statistical sources. “What we 

tell reporters is, you can always get an answer,” says 

Terry Schwadron. “You have to look at the answer 

in terms of what the question really is. What we like 

about the ACS is that you can stand back and see how 

things are changing. You might not use it to get the 

numbers on a specifi c neighborhood, but you would 

use it to get a sense of how things are changing in that 

neighborhood.” 

The New York Times retains Queens College demographer Andrew Beveridge to help reporters use sources 

well. “We can ask him our questions and get his advice 

on how those questions might best be answered,” says 

Schwadron. “He has access to the databases, and he 

can advise us on the most appropriate data to use to 

answer the question. We’re looking for guidance to 

make sure we understand the question in context. We 

don’t want to just pull numbers out of the air.”

Of course, most of us can’t aff ord to keep a private 

demographer on call. Professional and academic 

demographers often are happy to give advice for free. 

City planning departments, school district planning 

offi ces, reference librarians, and college sociology 

departments are all good places to look for advice. 

Each state maintains an offi cial State Data Center with 

knowledgeable staff ers to answer your questions. 

There are also numerous Census Information Centers 

spread out across the country that can provide valuable assistance and advice. 
16 What the Media Need to Know

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

Finally, while reporters wait for ACS estimates to 

become reliable for neighborhoods, small towns, and 

small rural counties, they should be aware of alternatives beyond the federal government. Some private 

sector companies off er unoffi cial demographic estimates at the level of census tracts and ZIP Codes that 

use statistical models to “age” data from the decennial 

census and other sources. In many cases, these companies will provide these data on request from reporters 

for free, in exchange for a credit. However, many such 

vendors do not have offi cial data and release their data 

using proprietary models based on assumptions that 

are not always verifi able.

You should look at databases in the same way that you 

judge human sources. First, identify the data sets that 

could be most informative for your topic. Then judge 

their reliability and also ask yourself if the data are 

appropriate for the topic of your story. The ACS will 

often turn out to be the best choice because of its great 

breadth and fl exibility, but there will be times when 

you will need a diff erent source to get the job done. If 

you have questions about which source to use, contact 

the State Data Center or a professional demographer. 

They work with the data on a regular basis, so they can 

point you in the right direction.

Here are a few of the best ways reporters use the ACS, 

told in their own words.

Best Use #1: Rankings

“The biggest advantage of the ACS is the annual 

updates,” says Cheryl Russell, who has spent the last 

30 years writing about demographic change. “It is 

incredible that these statistics are fi nally being updated 

every year. But getting to the statistics is like peeling 

an onion. It takes a long time to get what you want, 

and it can be tedious. You need patience.”

Russell, the editor-in-chief of New Strategist 

Publications and former top editor at American Demographics magazine, writes an e-mail newsletter and 

blog that specialize in checking conventional wisdom 

against the facts, see <http://www.newstrategist.com>. 

A few months ago, she noticed pundits saying that 

the mortgage crisis was due to Americans using their 

homes as ATM machines by taking out home equity 

loans and second mortgages. “The sources they quoted 

were macroeconomic statistics,” she says. “It is easy to 

be sold by those kinds of statistics. But with the ACS 

it’s just as easy to look at the behavior of individual 

households.” 

Russell went to the American FactFinder and found 

two subject tables that put the home equity story in 

context. Subject Table #S2503, “Financial Characteristics,” gave her the big picture: there were an estimated 

75,086,485 owner-occupied housing units in the 

United States in 2006, give or take a few thousand 

(remember, the numbers are more accurate as the 

sample gets larger). Then she went to Subject Table 

#S2506, “Financial Characteristics for Housing Units 

With a Mortgage,” and found that an estimated 

51,234,170 owner-occupied housing units were carrying a mortgage. In other words, about one-third of 

homeowners don’t have any mortgage debt at all. 

Going further with Subject Table #S2506, Russell saw 

that an estimated 25.4 percent of homeowners who 

had a mortgage also had a second mortgage or a home 

equity loan, but not both: 19.3 percent had a home 

equity loan only, and 6.1 percent had a second mortgage. Only 1.1 percent had both. Simple multiplication 

revealed the estimate that 9,888,195 homeowners 

with a mortgage also had a home equity loan. That 

is 13.1 percent of all homeowners, hardly universal 

behavior.

To fi nd out if over-mortgaged America is more of a 

problem in some places than others, it is easy to use 

the ACS to fi nd debt hot spots.

• Choose the “Detailed Tables” option within the 

most recent ACS in the American FactFinder.

• Select the geography you’d like to analyze, such 

as all metropolitan areas within a state or metropolitan and “micropolitan” areas in the United 

States.

• Using the “subject” tab, fi nd all the detailed tables 

that match the terms “owner-occupied” and “mortgage.”

• Detailed Table #B25081, “Mortgage Status – Universe: Owner-Occupied Housing Units,” shows 

the estimated number of owner-occupied housing units in each area, the estimated number that 

have any kind of mortgage, the estimated number 

with second mortgages, and the estimated number with home equity loans.

• Download this table as a spreadsheet fi le. When 

you open it, you’ll notice that the data are spread 

out horizontally instead of vertically, a problem 

you must correct yourself. You’ll also notice that 

each number is followed by an estimate of its 

margin of error. The margins of error may seem 

unnecessary and deleting them will make your 

table smaller. But, it’s important to study them 

before you make any conclusions, especially 

comparisons. 
What the Media Need to Know 17

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

• The resulting table allows you to calculate the 

percentage of all owner-occupied housing units 

in various places that have two mortgages, or a 

mortgage and a home-equity loan, or no mortgage 

debt at all.

Now you’re cooking with facts. This exercise reveals 

that in 2006, there were seven metropolitan areas in 

the United States where about half of all homeowners 

did not have any kind of mortgage debt at all. Four of 

them are in Texas (Odessa, Beaumont, McAllen, and 

Brownsville) two are in West Virginia (Wheeling and 

Charleston), and the seventh is Johnstown, 

Pennsylvania. There are also 25 metropolitan areas 

where at least one-quarter of homeowners carry a second mortgage or home equity loan. Eighteen of these 

are in the West, and of those, fi ve are in Colorado, six 

are in California. Only two (Washington, DC, and 

Bridgeport, Conneticut) are in the Northeast. This list 

is loaded with glamorous towns like Santa Cruz and 

Boulder, and it is evidence that many people are living 

high by leveraging themselves to the hilt. 

“The ACS is the homework that every reporter needs 

to do,” says Russell. “You can’t really explain the world 

unless you are literate about statistics, and the ACS is a 

great place to learn.”

Best Use #2: PUMS

Sacramento is a hot spot for the home mortgage crisis. 

Investigations have suggested that one of the reasons 

for the crisis is that lenders allowed mortgage applicants to infl ate their stated incomes far above what 

they really made. “Behind the Meltdown,” which ran in 

the Sacramento Bee on Sunday, November 18, 2007, 

put a local spin on this aspect of the story. Using a 

variety of sources anchored by the Public Use Microdata Sample of the ACS, the story detailed large gaps 

between the incomes listed on 2006 homeowner’s loan 

applications in the Sacramento area, and the same 

incomes as estimated by the Census Bureau. 

Sacramento Bee reporter Phil Reese used a database 

from the Federal Home Mortgage Disclosure Act to fi nd 

the listed incomes of individual loan applicants who 

originated fi rst lien loans for home purchases in the 

Sacramento metropolitan area. The query was designed 

to exclude investors. “With that I had the declared 

income of everyone who bought a home they planned 

to use as their residence in 2006,” he says. These data 

were for individual loan applicants, but they contained 

ZIP Codes that allowed Reese to aggregate them into 

median incomes at the county level.

Next, Reese used the ACS to fi nd the median income of 

owner-occupied households with a mortgage who had 

moved within the last year (2005–2006). “This number gave me everyone who had used a mortgage to 

purchase a home in 2006 that they planned to live in,” 

he says. “There were slight diff erences in how the two 

sources measured income, but they were small enough 

that we felt we could ignore them.”

Reese needed to use a PUMS extract instead of aggregate data because the American FactFinder did not 

allow him to specify owner-occupied households with a 

mortgage who had moved in the last year. “The slice of 

the population I needed had one too many conditions,” 

he says. “But I had used PUMS before with Census 

2000, and the ACS PUMS is actually easier to use than 

the Census 2000 data set. It takes a little getting used 

to, but there are tons of stories in PUMS.”

Reese learned to use PUMS by going to a NICAR workshop. “When I fi rst started working with the ACS PUMS, 

it took an hour or so to fi gure out where things were 

and to build queries. The site has a specialized query 

function, and the documentation has to be long, so you 

have to fi gure out exactly where all the codes should 

be. But if you are familiar with database management 

software, it should be no problem.”

The Sacramento Bee’s Web site drove the story home 

with interactive maps that showed the data and income 

gaps for each county. “Once I got the two data sources 

to line up at the county level, the mapping part was 

easy,” says Reese. In the Sacramento Metropolitan Area, 

PUMAs lined up with the county boundaries that he 

needed to present his results. This is not always the 

case, particularly in some New England states.
18 What the Media Need to Know

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

There is power in the mix. Paula Lavigne is a reporter 

for the Dallas Morning News who trained herself in 

computer-assisted reporting by joining the e-mail 

list serve of NICAR. “In 2004, I noticed stories about 

layoff s and foreclosures that were coming out of Collin 

County, which contains the wealthiest parts of the 

Dallas metro,” she says. “Social service agencies in 

Collin County were actually getting requests from 

people to help pay for their kids’ private schools or 

their Mercedes-Benz. These were affl uent people, but 

they were living way beyond their means. The pursuit 

of stuff had taken on a life of its own.” 

Lavigne went to a press offi cer for Claritas and, in 

exchange for attribution, got the company’s estimates 

of personal debt for households in diff erent parts of 

Collin County. She also got the number of personal 

bankruptcies and foreclosures over the last several 

years from private and federal sources. “But I wouldn’t 

have had a story without the ACS,” she says. “I used 

it to fi nd the counties in the U.S. that had the highest 

estimated median household incomes, home values, 

household size, and number of children. Then I compared the fi nancial statistics for those counties against 

the stats for Collin.” For example, Lavigne got the 

estimated number of owner-occupied housing units in 

various counties from the ACS, and then compared that 

number against the number of foreclosures and bankruptcies in each county to get a rate.

“I found that Collin County really did stand out among 

affl uent counties for the share of people who are in 

economic trouble,” she said. “The ACS helped put the 

Collin County numbers in context.”

Once she knew there was a story, Lavigne kept using 

the ACS to assist her reporting. “I looked at where 

people really diverged in their spending,” she said. 

“The ACS told me the places that had the highest 

median household incomes, so I went to those places 

and looked for signs of foreclosure. I also used the ACS 

to make a profi le of the typical Collin County family, 

and then I went out and tried to fi nd people who fell 

into that description.” 

Lavigne’s series, “The Price of Prosperity,” debuted on 

the front page of the Sunday edition of the Dallas 

Morning News on August 14, 2005. It drew record 

numbers of readers and made Lavigne a fi nalist for a 

2006 Livingston Award for excellence, given to journalists under the age of 35. A year later, as the magnitude of the debt and foreclosure story was becoming 

more evident, Lavigne revisited her sources and ran 

an update. She found that most Collin residents were 

even deeper in debt, but focused most of the story on 

residents who were fi nding a way out.

Best Use #3: Mixing It Up

Resources

Investigative Reporters and Editors (IRE): <http://www

.ire.org> 

National Institute for Computer-Assisted Reporting, a 

program of IRE: <http://www.nicar.org> 

IPUMS USA: <http://usa.ipums.org> 
What the Media Need to Know 19

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 diff erence between 

the survey estimate and the true (unknown) value. 

Attributes are measured in terms of sources of error 

(for example, coverage, sampling, nonresponse, 

measurement, and processing). 

American Community Survey Alert. This periodic 

electronic newsletter informs data users and other 

interested parties about news, events, data releases, 

congressional actions, and other developments 

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

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

American FactFinder (AFF). An electronic system 

for access to and dissemination of Census Bureau 

data on the Internet. AFF off ers prepackaged data 

products and user-selected data tables and maps 

from Census 2000, the 1990 Census of Population 

and Housing, the 1997 and 2002 Economic Censuses, 

the Population Estimates Program, annual economic 

surveys, and the ACS. 

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

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

group is a cluster of blocks having the same fi rst 

digit of their four-digit identifying number within a 

census tract. 

Census geography. A collective term referring 

to the types of geographic areas used by the 

Census Bureau in its data collection and tabulation 

operations, including their structure, designations, 

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

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

Census tract. A small, relatively permanent statistical 

subdivision of a county delineated by a local committee 

of census data users for the purpose of presenting 

data. Census tract boundaries normally follow visible 

features, but may follow governmental unit boundaries 

and other nonvisible features; they always nest within 

counties. Designed to be relatively homogeneous 

units with respect to population characteristics, 

economic status, and living conditions at the time 

of establishment, census tracts average about 4,000 

inhabitants. 

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

standard error (square root of the variance) to the 

value being estimated, usually expressed in terms 

of a percentage (also known as the relative standard 

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

reliability of the estimate. 

Comparison profi le. Comparison profi les are 

available from the American Community Survey for 

1-year estimates beginning in 2007. These tables 

are available for the U.S., the 50 states, the District of 

Columbia, and geographic areas with a population of 

more than 65,000.

Confi dence interval. The sample estimate and its 

standard error permit the construction of a confi dence 

interval that represents the degree of uncertainty about 

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

interpreted roughly as providing 90 percent certainty 

that the interval defi ned by the upper and lower 

bounds contains the true value of the characteristic. 

Confi dentiality. The guarantee made by law (Title 

13, United States Code) to individuals who provide 

census information, regarding nondisclosure of that 

information to others. 

Consumer Price Index (CPI). The CPI program of the 

Bureau of Labor Statistics produces monthly data on 

changes in the prices paid by urban consumers for a 

representative basket of goods and services.

Controlled. During the ACS weighting process, the 

intercensal population and housing estimates are used 

as survey controls. Weights are adjusted so that ACS 

estimates conform to these controls. 

Current Population Survey (CPS). The CPS is a 

monthly survey of about 50,000 households conducted 

by the Census Bureau for the Bureau of Labor Statistics. 

The CPS is the primary source of information on the 

labor force characteristics of the U.S. population.

Current residence. The concept used in the ACS to 

determine who should be considered a resident of a 

sample address. Everyone who is currently living or 

staying at a sample address is considered a resident of 

that address, except people staying there for 2 months 

or less. People who have established residence at the 

sample unit and are away for only a short period of 

time are also considered to be current residents. 

Custom tabulations. The Census Bureau off ers a 

wide variety of general purpose data products from the 

American Community Survey (ACS). These products 

are designed to meet the needs of the majority of data 
20 What the Media Need to Know

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

users and contain predefi ned sets of data for standard 

census geographic areas, including both political and 

statistical geography. These products are available on 

the American FactFinder and the ACS Web site.

For users with data needs not met through the general 

purpose products, the Census Bureau off ers “custom” 

tabulations on a cost-reimbursable basis, with the 

American Community Survey Custom Tabulation 

program. Custom tabulations are created by tabulating 

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

complexity, and cost depending on the needs of the 

sponsoring client.

Data profi les. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profi le that should be used if offi cial 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 diff erent 

tables that contain basic distributions of 

characteristics. These tables provide the most detailed 

data and are the basis for other ACS products.

Disclosure avoidance (DA). Statistical methods 

used in the tabulation of data prior to releasing data 

products to ensure the confi dentiality of responses. 

See Confi dentiality. 

Estimates. Numerical values obtained from a 

statistical sample and assigned to a population 

parameter. Data produced from the ACS interviews are 

collected from samples of housing units. These data 

are used to produce estimates of the actual fi gures that 

would have been obtained by interviewing the entire 

population using the same methodology. 

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

allows data fi les to be downloaded from the Census 

Bureau Web site.

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates refl ect the characteristics 

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

will be published for all geographic areas down to the 

census block group level. 

Geographic comparison tables. More than 80 

single-variable tables comparing key indicators for 

geographies other than states.

Geographic summary level. A geographic summary 

level specifi es the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifi es the state-county 

hierarchy. Thus, both the state code and the county 

code are required to uniquely identify a county in the 

United States or Puerto Rico. 

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

place where people live or stay that is normally owned 

or managed by an entity or organization providing 

housing and/or services for the residents. These 

services may include custodial or medical care, as well 

as other types of assistance. Residency is commonly 

restricted to those receiving these services. People 

living in GQ facilities are usually not related to each 

other. The ACS collects data from people living in both 

housing units and GQ facilities.

Group quarters (GQ) population. The number of 

persons residing in GQ facilities. 

Item allocation rates. Allocation is a method 

of imputation used when values for missing or 

inconsistent items cannot be derived from the existing 

response record. In these cases, the imputation must 

be based on other techniques such as using answers 

from other people in the household, other responding 

housing units, or people believed to have similar 

characteristics. Such donors are refl ected in a table 

referred to as an allocation matrix. The rate is the 

percentage of times this method is used.

Margin of error (MOE). Some ACS products provide 

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

diff erence between an estimate and its upper or lower 

confi dence bounds. Confi dence bounds can be created 

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

upper bound) and subtracting the margin of error from 

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

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

level. 

Multiyear estimates. Three- and fi ve-year estimates 

based on multiple years of ACS data. Three-year 

estimates will be published for geographic areas with 

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

will be published for all geographic areas down to the 

census block group level. 

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

Nonsampling error. Total survey error can be 

classifi ed into two categories—sampling error and 

nonsampling error. Nonsampling error includes 

measurement errors due to interviewers, respondents, 

instruments, and mode; nonresponse error; coverage 

error; and processing error.
What the Media Need to Know 21

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

Period estimates. An estimate based on information 

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

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population Estimates Program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

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

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

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

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

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

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

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

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

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

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

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

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

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

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

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

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

Appendix A-1

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

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

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

single-year estimates, the period is the calendar year 

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

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

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

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

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

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

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

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

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

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

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

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

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

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

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

monthly CPS collects data for an even narrower time 

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

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

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

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

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

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

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

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

year and 5-year estimates where the periods involved 

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

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

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

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

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

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

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

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

Month

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

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

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

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

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

multiyear estimates describe the population and 

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

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

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

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

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

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

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

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

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

estimates contain 4 years of overlapping coverage. 

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

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

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

5-year

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

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

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

housing characteristics because they are based on the 

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

multiyear estimates provide less current information 

because they are based on both data from the previous 

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

using the “less current” sample used to produce the 

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

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

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

single year is the midyear of the ACS multiyear period 

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

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

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

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

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

Reliability

Multiyear estimates are based on larger sample sizes 

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

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

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

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

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

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

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

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

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

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

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

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

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

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

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

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

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

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

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

a small CV is usually preferable to a multiyear estimate 

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

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

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

use multiyear estimates when they are working with 

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

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

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

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

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

for local planning needs, make comparisons across 

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

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

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

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

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

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

errors and CVs associated with those estimates are also 

shown.

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

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

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

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

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

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

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

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

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

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

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

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

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

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

SE of 4.9 percentage points that yields a CV of 12.2 

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

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

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

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

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

more acceptable level.

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

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

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

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

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

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

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

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

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

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

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

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

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

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

the 2006 ACS. 

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

in a particular characteristic. This is because roughly 

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

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

as shown in Figure 1, when comparing 2006–2008 

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

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

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

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

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

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

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

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

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

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

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

or the 5-year estimates because they are available for 

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

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

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

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

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

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

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

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

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

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

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

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

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

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

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

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

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

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

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

larger geographies and populations when currency is 

more important than the precision of the estimates. 

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

availability of a single-year estimate, calculating the CV 

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

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

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

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

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

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

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

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

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

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

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

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

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

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

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

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

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

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

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

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

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

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

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

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

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

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

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

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

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

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

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

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

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

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

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

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

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

60-month period. 

Most decennial census sample estimates are anchored 

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

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

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

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

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

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

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

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

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

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

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

housing unit or group quarters, some people may stay 

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

course of the year. For example, migrant workers move 

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

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

estimates of the characteristics of some areas. 

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

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

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

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

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

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

to enumerate people at the place where they live or stay 

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

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

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

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

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

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

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

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

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

there for more than 2 months.

More on Reference Periods

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

being that the remaining basic demographic questions 

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

the date the interview is provided. Questions with their 

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

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

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

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

they refl ect the circumstances on the day the data are 

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

represent the average characteristics over a full year (or 

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

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

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

comparing ACS estimates with Census 2000. 

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

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

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

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

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

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

while Census 2000 collected income information for 

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

responses are a mixture of 12 reference periods ranging 

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

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

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

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

period is used for all interviews. In contrast, 

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

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

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

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

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

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

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

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

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

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

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

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

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

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

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

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

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

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

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

(such as estimates from the ACS or the decennial 

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

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

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

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

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

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

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

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

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

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

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

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

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

larger the sample size, the smaller the standard error 

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

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

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

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

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

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

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

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

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

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

MOEs published with the ACS estimates correspond 

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

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

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

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

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

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

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

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

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

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

Measures of Sampling Error

Appendix 3.
A-12 Appendix

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

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

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

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

would be derived as 

Confi dence Intervals

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

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

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

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

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

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

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

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

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

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

(20,000 + 1,645).

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

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

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

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

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

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

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

1.960/1.645.1

 If the desired confi dence level is 99 

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

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

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

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

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

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

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

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

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

should be set to zero instead.

1

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

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

error from the standard error in those years.

2

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

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 645.1

960.1

95 

Factors Associated With Margins of

Error for Commonly Used Confi dence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

  723,14357,12 645.1

960.1 MOE95 r r 

645.1

MOEACS SE 

512,7 645.1

357,12 SE 

CL XL  MOECL ˆ

CL XU  MOECL ˆ

MOECL

  CL ULCI CLCL , .

MOEACS

MOEACS

Xˆ

MOE MOEACS 645.1

576.2

99 
Appendix A-13

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

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

confi dence level for the number of civilian veterans in 

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

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

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

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

757,059 to 786,505.

The CI is also useful when graphing estimates, to show 

the extent of sampling error present in the estimates, 

and for visually comparing estimates. For example, 

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

in constructing the CI above, the user could be 90 

percent certain that the value for the population was 

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

Coeffi cients of Variation

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

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

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

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

barometer of the stability, and thus the usability of a 

sample estimate. It can also help a user decide whether 

a single-year or multiyear estimate should be used for 

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

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

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

estimation period increases, the sample size increases 

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

indicates that the sampling error is small relative to the 

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

the estimate is close to the population value. In some 

applications a small CV for an estimate is desirable and 

use of a multiyear estimate will therefore be preferable 

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

desired level of precision.

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

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

the estimate is very reliable. If the CV was noticeably 

larger, the usability of the estimate could be greatly 

diminished. 

While it is true that estimates with high CVs have 

important limitations, they can still be valuable as 

L95  059,757723,14782,771

U95  505,786723,14782,771

( )

18,355 20,000 21,645

building blocks to develop estimates for higher levels 

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

improve the reliability of those estimates as evidenced 

by reductions in the CVs.

Calculating Coeffi cients of Variation From Standard 

Errors

The CV can be expressed as

where is the ACS estimate and is the derived SE 

for the ACS estimate.

For example, to determine the CV for the estimated 

number of civilian veterans in the state of Virginia in 

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

and the SE derived previously (7,512).

This means that the amount of sampling error present 

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

of the estimate.

The text box below summarizes the formulas used 

when deriving alternative sampling error measures 

from the margin or error published with ACS estimates.

100 ˆ u 

X

SE CV

Xˆ

%1.0100

782,771

512,7 CV u 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confi dence Levels

Standard Error (SE)

Confi dence Interval (CI)

Coeffi cient of Variation (CV)

MOE MOE ACS

.1 645

.1 960 95 

MOE MOE ACS

.1 645

.2 576 99 

.1 645

MOE ACS SE 

100 ˆ u 

X

SE CV

SE

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

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

Calculating Margins of Error for Derived 

Estimates

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

the ability to develop unique estimates called derived 

estimates. These derived estimates are usually based 

on aggregating estimates across geographic areas or 

population subgroups for which combined estimates 

are not published in American FactFinder (AFF) tables 

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

four age groups not collapsed).

ACS tabulations provided through AFF contain the 

associated confi dence intervals (pre-2005) or margins 

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

confi dence level. However, when derived estimates are 

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

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

the MOE for these derived estimates. The MOE helps 

protect against misinterpreting small or nonexistent 

diff erences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

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

confi dence level other than 90 percent is desired, the 

user should fi rst calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confi dence level as described earlier in this appendix.

Calculating MOEs for Aggregated Count Data

To calculate the MOE for aggregated count data:

1) Obtain the MOE of each component estimate.

2) Square the MOE of each component estimate.

3) Sum the squared MOEs.

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

 MOEs.

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

where is the MOE of the component estimate.

The example below shows how to calculate the MOE 

for the estimated total number of females living alone 

in the three Virginia counties/independent cities that 

border Washington, DC (Fairfax and Arlington counties, 

Alexandria city) from the 2006 ACS.

The aggregate estimate is:

Obtain MOEs of the component estimates: 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs.

Thus, the derived estimate of the number of females 

living alone in the three Virginia counties/independent 

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

MOE for the estimate is +4,289.

Calculating MOEs for Derived Proportions

The numerator of a proportion is a subset of the 

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

households that are female). To calculate the MOE for 

derived proportions, do the following:

1) Obtain the MOE for the numerator and the MOE 

for the denominator of the proportion.

2) Square the derived proportion.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator by 

the squared proportion.

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

the numerator.

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

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

proportion.

 Table 1. Data for Example 1 

Characteristic Estimate MOE

Females living alone in 

 Fairfax County 

 (Component 1)

52,354 +3,303

Females living alone in 

 Arlington County

 (Component 2)

19,464 +2,011

Females living alone in 

 Alexandria city

 (Component 3)

17,190 +1,854

Fairfax Arlington  XXXX ˆˆˆˆ Alexandria 

MOEFairfax r 303,3 , 

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

r ¦cMOEagg MOEc

2

MOEc

th

c

  008,89190,17464,19354,52

 011 MOEArlington r ,2 , 

r r 289,4246,391,18

MOEAlexandria r ,1854
Appendix A-15

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

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived proportion.

 is the estimate used as the numerator of the 

derived proportion.

 is the estimate used as the denominator of the 

derived proportion.

There are rare instances where this formula will fail—

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

happens, use the formula for derived ratios in the next 

section which will provide a conservative estimate of 

the MOE.

The example below shows how to derive the MOE for 

the estimated proportion of Black females 25 years of 

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

 The estimated proportion is:

where is the ACS estimate of Black females 25 

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

females 25 years of age and older in Fairfax County.

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older in Fairfax County 

with a graduate degree) and denominator (number 

of Black females 25 years of age and older in Fairfax 

County).

 Table 2. Data for Example 2

Characteristic Estimate MOE

Black females 25 years 

 and older with a graduate 

 degree (numerator)

4,634 +989

Black females 25 years 

 and older

 (denominator)

31,713 +601

1461.0

713,31

634,4

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

r 989 MOEnum , 601 MOEden r 

MOEnum

MOEden den

den

num

X

X

p ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ

X gradBF ˆ

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

Thus, the derived estimate of the proportion of Black 

females 25 years of age and older with a graduate 

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

MOE for the estimate is +0.0311. 

Calculating MOEs for Derived Ratios

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

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

1) Obtain the MOE for the numerator and the MOE 

 for the denominator of the ratio.

2) Square the derived ratio.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator 

 by the squared ratio.

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

 numerator.

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

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

 the ratio.

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived ratio.

 is the estimate used as the numerator of the 

derived ratio.

 is the estimate used as the denominator of the 

derived ratio.

0311.0

373,31

1.985

373,31

7.408,970 r 

r r MOEp 

MOEnum

MOEden

den

num

X

X

R ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ i

den

num den

p

X

MOE p MOE

MOE ˆ

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

     ]601*1461.0[989 2 2 2  

  7.408,9703.712,7121,978

den

num den

R

X

MOE R MOE

MOE ˆ

)* ˆ ( 2 2 2 r  
A-16 Appendix

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

The example below shows how to derive the MOE for 

the estimated ratio of Black females 25 years of age 

and older in Fairfax County, Virginia, with a graduate 

degree to Black males 25 years and older in Fairfax 

County with a graduate degree, based on the 2006 

ACS.

The estimated ratio is:

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County) and denominator (number 

of Black males 25 years of age and older in Fairfax 

County with a graduate degree).

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator.

Thus, the derived estimate of the ratio of the number 

of Black females 25 years of age and older in Fairfax 

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

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

the MOE for the estimate is +0.2135.

Calculating MOEs for the Product of Two Estimates

To calculate the MOE for the product of two estimates, 

do the following:

1) Obtain the MOEs for the two estimates being 

multiplied together.

2) Square the estimates and their MOEs.

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

4) Multiply the second squared estimate by the 

fi rst estimate’s squared MOE.

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

6) Take the square root of (5).

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

MOE for the product is calculated as:

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

respectively.

 is the MOE of the fi rst estimate.

 is the MOE of the second estimate.

The example below shows how to derive the MOE for 

the estimated number of Black workers 16 years and 

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

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

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

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

Thus, the derived estimate of Black workers 16 years 

and over who commute by public transportation is 

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

MOEnum r 989, 328 MOEden r ,1

2135.0

440,6

2.375,1

440,6

1.259,891,1 r 

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

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

Percent of Black workers 16 

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

13.4% +2.7%

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

 Table 3. Data for Example 3 

Characteristic Estimate MOE

Black females 25 years and 

 older with a graduate 

 degree (numerator)

4,634 +989

Black males 25 years and

 older with a graduate degree 

 (denominator)

6,440 +1,328

7200.0

440,6

634,4

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

2 22 2 MOE uBA A MOEB B uur MOEA

MOEA

MOEB

r 405,1

)* ˆ ( 2 2 2 MOEnum  R MOEden 

     ]328,1*7200.0[989 2 2 2  

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

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

Calculate the MOE by dividing the square root of the 

prior result by the denominator ( ). 

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

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

The text box below summarizes the formulas used to 

calculate the margin of error for several derived estimates.

Calculating MOEs for Estimates of “Percent Change” or 

“Percent Diff erence”

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

two estimates (for example, the same estimates in two 

diff erent years) is commonly calculated as

Because is not a subset of , the procedure 

to calculate the MOE of a ratio discussed previously 

should be used here to obtain the MOE of the percent 

change.

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

2005 estimates of the number of persons in Maryland 

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

The percent change is:

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

The MOEs for the numerator ( ) and denominator 

( ) are:

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

product of the squared ratio and the squared MOE of 

the denominator (MOE1):

 Table 5. Data for Example 5

Characteristic Estimate MOE

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2005

762,475 +22,666

0521.1

475,762

210,802

ˆ

ˆ ˆ

1

2 X

X

R

2 Xˆ 1 Xˆ

2 Xˆ

1 Xˆ

1 Xˆ

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

0433.0

475,762

3.038,33

475,762

529,528,091,1 r 

r r MOER 

r ¦cMOE agg MOE c

2

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

1

12

ˆ

ˆˆ *%100

X

XX

Percent Change  

ˆ

ˆˆ *%100

1

12  X

XX

Percent Change

%21.5

475,762

475,762210,802 *%100  ̧ 

1

·  ̈

©

§ 

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

)* ˆ ( 2

1

22 MOE2  R MOE 

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

 

529,528,091,1

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  
A-18 Appendix

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

One of the most important uses of the ACS estimates is 

to make comparisons between estimates. Several key 

types of comparisons are of general interest to users: 

1) comparisons of estimates from diff erent geographic 

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

proportion of people below the poverty level in two 

counties); 2) comparisons of estimates for the same 

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

the proportion of people below the poverty level in a 

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

estimates with the corresponding estimates from past 

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

2006 and 2000).

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

the comparison takes into account the sampling error 

associated with each estimate, thus determining 

whether the observed diff erences between estimates 

are statistically signifi cant. Statistical signifi cance 

means that there is statistical evidence that a true 

diff erence exists within the full population, and that 

the observed diff erence is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical signifi cance when making comparisons is 

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

be made are also discussed.

Determining Statistical Signifi cance

When comparing two estimates, one should use the 

test for signifi cance described below. This approach 

will allow the user to ascertain whether the observed 

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

is statistically signifi cant). 

The test for signifi cance can be carried out by making 

several computations using the estimates and their 

corresponding standard errors (SEs). When working 

with ACS data, these computations are simple given 

the data provided in tables in the American FactFinder.

1) Determine the SE for each estimate (for ACS 

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

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

2) Square the resulting SE for each estimate.

3) Sum the squared SEs.

4) Calculate the square root of the sum of the 

squared SEs.

5) Calculate the diff erence between the two estimates.

6) Divide (5) by (4).

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

with the critical value for the desired level of 

confi dence (1.645 for 90 percent, 1.960 for 95 

percent, 2.576 for 99 percent).

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

between the two estimates can be considered 

statistically signifi cant at the level of confi dence 

corresponding to the critical value used in (7).

Algebraically, the signifi cance test can be expressed as 

follows:

If , then the diff erence 

between estimates and is statistically signifi cant 

at the specifi ed confi dence level, CL

where is estimate i (=1,2)

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

 is the critical value for the desired confi dence 

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

2.576 for 99 percent).

The example below shows how to determine if the 

diff erence in the estimated percentage of households 

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

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

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

statistically signifi cant. Using the formula above:

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

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

diff erence in the percentages is statistically signifi cant 

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

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

A rough interpretation of the result is that the user can 

be 99 percent certain that a diff erence exists between 

the percentages of households with one or more 

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

Making Comparisons

Appendix 4.

4

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

use the value 1.65 rather than 1.645. 

ZCL

SESE

XX

!





2

2

2

1

21

ˆˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   12.012.0

5.210.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

90.2

173.0

5.0

03.0

5.0

015.0015.0

5.0 

Ei
Appendix A-19

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

By contrast, if the corresponding estimates for State C 

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

would yield

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

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

A rough interpretation of the result is that the user 

cannot be certain to any suffi cient degree that the 

observed diff erence in the estimates was not due to 

chance.

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same 

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

3-year period) are straightforward and can be carried 

out as described in the previous section. There is, 

however, one statistical aspect related to the test for 

statistical signifi cance that users should be aware 

of. When comparing estimates within the same time 

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

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

In some cases, the comparison may involve a large 

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

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

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

estimates are not independent. The estimate for the 

large area is partially dependent on the estimate for the 

subset and, strictly speaking, the formula for testing 

diff erences should account for this partial dependence. 

However, unless the user has reason to believe that the 

two estimates are strongly correlated, it is acceptable 

to ignore the partial dependence and use the formula 

for testing diff erences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a fi nding of statistical signifi cance will still 

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

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

important to obtain a more exact test of signifi cance, 

the user should consult with a statistician about 

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

   25.020.0

1.225.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

25.1

320.0

4.0

1025.0

4.0

0625.004.0

4.0 

Comparisons Across Time Periods

Comparisons of estimates from diff erent time periods 

may involve diff erent single-year periods or diff erent 

multiyear periods of the same length within the same 

area. Comparisons across time periods should be made 

only with comparable time period estimates. Users are 

advised against comparing single-year estimates with 

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

2009) and against comparing multiyear estimates of 

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

2009–2014), as they are measuring the characteristics 

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

between such estimates are diffi cult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

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

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

the user should recognize that from 2006 on the ACS 

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

units. Many types of GQ populations have demographic, 

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

comparisons between 2005 and 2006 and later ACS 

estimates could be aff ected. This is particularly true 

for areas with a substantial GQ population. For most 

population characteristics, the Census Bureau suggests 

users make comparisons across these time periods 

only if the geographic area of interest does not include 

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

population, this is obviously not an issue. 

Comparisons Based on Overlapping Periods

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

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

periods is challenging since the diff erence is driven by 

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

for 2006 and 2007 are included in both estimates. 

Their contribution is subtracted out when the estimate 

of diff erences is calculated. While the interpretation 

of this diff erence is diffi cult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of diff erence should not be interpreted as a 

refl ection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

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

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

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

estimates will not want to wait until 2015 to evaluate 

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

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

estimates of the same geography that overlap in 

sample years one must account for this sample overlap. 

Thus to calculate the standard error of this diff erence 

use the following approximation to the standard error:

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

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

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

With this SE one can test for the statistical signifi cance 

of the diff erence between the two estimates using the 

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

 in the denominator of the formula for 

the signifi cance test.

Comparisons With Census 2000 Data

In Appendix 2, major diff erences between ACS data and 

decennial census sample data are discussed. Factors 

such as diff erences in residence rules, universes, and 

reference periods, while not discussed in detail in this 

appendix, should be considered when comparing ACS 

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

may aff ect comparisons between decennial census and 

ACS estimates when looking at data for areas such as 

college towns and resort areas. 

The Census Bureau subject matter specialists have 

reviewed the factors that could aff ect diff erences 

between ACS and decennial census estimates and they 

have determined that ACS estimates are similar to 

those obtained from past decennial census sample data 

for most areas and characteristics. The user should 

consider whether a particular analysis involves an area 

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

When comparing ACS and decennial census sample 

estimates, the user must remember that the decennial 

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

ACS and census estimates must be incorporated when 

performing tests of statistical signifi cance. Appendix 

3 provides the calculations necessary for determining 

statistical signifi cance of a diff erence between two 

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

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

.pdf> or the Census 2000 Summary File 4 Technical 

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

/cen2000/doc/sf4.pdf>.

A conservative approach to testing for statistical signifi -

cance when comparing ACS and Census 2000 

estimates that avoids deriving the SE for the 

Census 2000 estimate would be to assume the SE for 

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

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

assumed to be accurate (as the SE for the Census 2000 

estimate would be expected to be less than that for the 

ACS estimate), but a fi nding of no statistical signifi -

cance could be incorrect. In this case the user should 

calculate the census long-form standard error and follow the steps to conduct the statistical test.

Comparisons With 2010 Census Data

Looking ahead to the 2010 decennial census, data 

users need to remember that the socioeconomic data 

previously collected on the long form during the 

census will not be available for comparison with ACS 

estimates. The only common variables for the ACS and 

2010 Census are sex, age, race, ethnicity, household 

relationship, housing tenure, and vacancy status.

The critical factor that must be considered when comparing ACS estimates encompassing 2010 with the 

2010 Census is the potential impact of housing and 

population controls used for the ACS. As the housing 

and population controls used for 2010 ACS data will 

be based on the Population Estimates Program where 

the estimates are benchmarked on the Census 2000 

counts, they will not agree with the 2010 Census 

population counts for that year. The 2010 population 

estimates may diff er from the 2010 Census counts 

for two major reasons—the true change from 2000 to 

2010 is not accurately captured by the estimates and 

the completeness of coverage in the 2010 Census is 

diff erent than coverage of Census 2000. The impact of 

this diff erence will likely aff ect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large diff erences between the 

population controls and the 2010 Census population 

counts is greater.

Comparisons With Other Surveys

Comparisons of ACS estimates with estimates from 

other national surveys, such as the Current Population 

Survey, may be of interest to some users. A major consideration in making such comparisons will be that ACS 

5

 Further information concerning areas and characteristics that do not 

fi t the general pattern of comparability can be found on the ACS Web 

site at <http://www.census.gov/acs/www/UseData/compACS.htm>.

  2

2

2

21 1 1) ˆˆ ( XXSE #  SESEC

  2

2

2

1 1  SESEC

2

2

2

1  SESE
Appendix A-21

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

estimates include data for populations in both institutional and noninstitutional group quarters, and estimates from most national surveys do not include institutional populations. Another potential for large eff ects 

when comparing data from the ACS with data from 

other national surveys is the use of diff erent questions 

for measuring the same or similar information.

Sampling error and its impact on the estimates from 

the other survey should be considered if comparisons 

and statements of statistical diff erence are to be made, 

as described in Appendix 3. The standard errors on 

estimates from other surveys should be derived 

according to technical documentation provided for 

those individual surveys.

Finally, the user wishing to compare ACS estimates 

with estimates from other national surveys should 

consider the potential impact of other factors, such 

as target population, sample design and size, survey 

period, reference period, residence rules, and interview 

modes on estimates from the two sources.
A-22 Appendix

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

where is the All Items CPI-U-RS Annual Average 

for the earlier year (Y1).

 is the All Items CPI-U-RS Annual Average for the 

more recent year (Y2).

 is the published ACS estimate for the earlier year 

(Y1).

The example below compares the national median 

value for owner-occupied mobile homes in 2005 

($37,700) and 2006 ($41,000). First adjust the 2005 

median value using the 2005 All Items CPI-U-RS Annual 

Average (286.7) and the 2006 All Items CPI-U-RS Annual 

Average (296.1) as follows:

Thus, the comparison of the national median value for 

owner-occupied mobile homes in 2005 and 2006, in 

2006 dollars, would be $38,936 (2005 infl ationadjusted to 2006 dollars) versus $41,000 

(2006 dollars).

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost 

values are created with infl ation adjustments. The 

Census Bureau uses the All Items CPI-U-RS Annual Averages for each year in the multiyear time period to calculate a set of infl ation adjustment factors. Adjustment 

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from its most recent year to 

the CPI-U-RS Annual Averages from each of its earlier 

years. The ACS values for each of the earlier years in 

the multiyear period are multiplied by the appropriate 

infl ation adjustment factors to produce the infl ationadjusted values. These values are then used to create 

the multiyear estimates. 

As an illustration, consider the time period 2004–2006, 

which consisted of individual reference-year income 

values of $30,000 for 2006, $20,000 for 2005, and 

$10,000 for 2004. The multiyear income components 

are created from infl ation-adjusted reference period 

income values using factors based on the All Items 

CPI-U-RS Annual Averages of 277.4 (for 2004), 286.7 

(for 2005), and 296.1 (for 2006). The adjusted 2005 

value is the ratio of 296.1 to 286.7 applied to $20,000, 

which equals $20,656. Similarly, the 2004 value is 

the ratio of 296.1 to 277.4 applied to $10,000, which 

equals $10,674.

Using Dollar-Denominated Data

Appendix 5.

Dollar-denominated data refer to any characteristics 

for which infl ation adjustments are used when producing annual estimates. For example, income, rent, home 

value, and energy costs are all dollar-denominated 

data.

Infl ation will aff ect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using infl ation factors based on the Consumer Price Index (CPI).

Given the potential impact of infl ation on observed 

diff erences of dollar-denominated data across time 

periods, users should adjust for the eff ects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for infl ation. In making adjustments, the 

Census Bureau recommends using factors based on 

the All Items CPI-U-RS (CPI research series). The Bureau 

of Labor Statistics CPI indexes through 2006 are found 

at <http://www.bls.gov/cpi/cpiurs1978_2006.pdf>. 

Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount 

of income received during the 12 months preceding 

the interview month. This is the income reference 

period. Since there are 12 diff erent income reference 

periods throughout an interview year, 12 diff erent 

income infl ation adjustments are made. Monthly CPIU-RSs are used to infl ation-adjust the 12 reference 

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no infl ation adjustments for single-year estimates of rent, home value, or energy cost values.

Adjusting Single-Year Estimates Over Time

When comparing single-year income, rent, home value, 

and energy cost value estimates from two diff erent 

years, adjustment should be made as follows: 

1) Obtain the All Items CPI-U-RS Annual Averages for 

the 2 years being compared.

2) Calculate the infl ation adjustment factor as the ratio 

of the CPI-U-RS from the more recent year to the 

CPI-U-RS from the earlier year.

3) Multiply the dollar-denominated data estimated for 

the earlier year by the infl ation adjustment factor.

The infl ation-adjusted estimate for the earlier year can 

be expressed as:

936,38$700,37$ 7.286

1.296 ˆ

X ,2005 Adj u 

1

1

2

,1 ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

CPIY1

CPIY 2

1 ˆ

XY
Appendix A-23

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

As an illustration, consider ACS multiyear estimates for 

the two time periods of 2001–2003 and 2004–2006. 

To compare the national median value for owneroccupied mobile homes in 2001–2003 ($32,000) and 

2004–2006 ($39,000), fi rst adjust the 2001–2003 

median value using the 2003 All Items CPI-U-RS Annual 

Averages (270.1) and the 2006 All Items CPI-U-RS 

Annual Averages (296.1) as follows:

Thus, the comparison of the national median value 

for owner-occupied mobile homes in 2001–2003 

and 2004–2006, in 2006 dollars, would be $35,080 

(2001–2003 infl ation-adjusted to 2006 dollars) versus 

$39,000 (2004–2006, already in 2006 dollars).

Issues Associated With Infl ation Adjustment

The recommended infl ation adjustment uses a national 

level CPI and thus will not refl ect infl ation diff erences 

that may exist across geographies. In addition, since 

the infl ation adjustment uses the All Items CPI, it will 

not refl ect diff erences that may exist across characteristics such as energy and housing costs.

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as 

follows:

1) Obtain the All Items CPI-U-RS Annual Average for 

the most current year in each of the time periods 

being compared.

2) Calculate the infl ation adjustment factor as the 

ratio of the CPI-U-RS Annual Average in (1) from 

the most recent year to the CPI-U-RS in (1) from 

the earlier years.

3) Multiply the dollar-denominated estimate for the 

earlier time period by the infl ation adjustment 

factor.

The infl ation-adjusted estimate for the earlier years can 

be expressed as:

where CPI P1 is the All Items CPI-U-RS Annual Average 

for the last year in the earlier time period (P1). 

 is the All Items CPI-U-RS Annual Average for the 

last year in the most recent time period (P2).

 is the published ACS estimate for the earlier time 

period (P1).

080,35$000,32$ 1.270

1.296 ˆ

X  ,20032001 Adj u 

1

1

2

,1 ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

CPI P2

1 ˆ

X P
All survey estimates are subject to both sampling and 

nonsampling error. In Appendix 3, the topic of sampling error and the various measures available for 

understanding the uncertainty in the estimates due to 

their being derived from a sample, rather than from an 

entire population, are discussed. The margins of error 

published with ACS estimates measure only the eff ect 

of sampling error. Other errors that aff ect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any error 

aff ecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error.

Types of Nonsampling Errors

Coverage error occurs when a housing unit or person 

does not have a chance of selection in the sample 

(undercoverage), or when a housing unit or person has 

more than one chance of selection in the sample, or is 

included in the sample when they should not have been 

(overcoverage). For example, if the frame used for the 

ACS did not allow the selection of newly constructed 

housing units, the estimates would suff er from errors 

due to housing undercoverage.

The fi nal ACS estimates are adjusted for under- and 

overcoverage by controlling county-level estimates to 

independent total housing unit controls and to independent population controls by sex, age, race, and 

Hispanic origin (more information is provided on the 

coverage error defi nition page of the “ACS Quality Measures” Web site at <http://www.census.gov/acs/www

/UseData/sse/cov/cov_def.htm>). However, it is important to measure the extent of coverage adjustment by 

comparing the precontrolled ACS estimates to the fi nal 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that diff erences in characteristics of undercovered or overcovered 

housing units or individuals diff er from those eligible to 

be selected. When this occurs, the ACS may not provide 

an accurate picture of the population prior to the coverage adjustment, and the population controls may not 

eliminate or minimize that coverage error.

Unit nonresponse is the failure to obtain the minimum required information from a housing unit or a resident of a group quarter in order for it to be considered 

a completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

Appendix 6.

or person. For example, if no one in a sampled housing unit is available to be interviewed during the time 

frame for data collection, unit nonresponse will result.

It is important to measure unit nonresponse because 

it has a direct eff ect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the fi nal survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

unit nonresponse bias. This will happen if the characteristics of nonresponding units diff er from the characteristics of responding units.

Item nonresponse occurs when a respondent fails to 

provide an answer to a required question or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question diff er 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias.

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation 

of the fi nal data fi les. For example, errors may occur if 

data entry of questionnaire information is incomplete 

Measures of Nonsampling Error

A-24 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
Appendix A-25

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

or inaccurate. Coding of responses incorrectly also 

results in processing error. Critical reviews of edits and 

tabulations by subject matter experts are conducted to 

keep errors of this kind to a minimum.

Nonsampling error can result in random errors and 

systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there is 

an error in the questionnaire design that negatively 

aff ects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the fi nal results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

ACS Quality Measures

Nonsampling error is extremely diffi cult, if not 

impossible, to measure directly. However, the Census 

Bureau has developed a number of indirect measures of 

nonsampling error to help inform users of the quality 

of the ACS estimates: sample size, coverage rates, unit 

response rates and nonresponse rates by reason, and 

item allocation rates. Starting with the 2007 ACS, these 

measures are available in the B98 series of detailed 

tables on AFF. Quality measures for previous years are 

available on the “ACS Quality Measures” Web site at 

<http:/www.census.gov/acs/www/UseData/sse/>.

Sample size measures for the ACS summarize information for the housing unit and GQ samples. The measures available at the state level are:6

Housing units

Number of initial addresses selected

Number of fi nal survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of fi nal survey interviews

Sample size measures may be useful in special circumstances when determining whether to use single-year 

or multiyear estimates in conjunction with estimates of 

the population of interest. While the coeffi cient of variation (CV) should typically be used to determine 

usability, as explained in Appendix 3, there may be 

some situations where the CV is small but the user 

has reason to believe the sample size for a subgroup 

is very small and the robustness of the estimate is in 

question. 

For example, the Asian-alone population makes up 

roughly 1 percent (8,418/656,700) of the population 

in Jeff erson County, Alabama. Given that the number of 

successful housing unit interviews in Jeff erson County 

for the 2006 ACS were 4,072 and assuming roughly 2.5 

persons per household (or roughly 12,500 completed 

person interviews), one could estimate that the 2006 

ACS data for Asians in Jeff erson County are based on 

roughly 150 completed person interviews.

Coverage rates are available for housing units, and 

total population by sex at both the state and national 

level. Coverage rates for total population by six race/

ethnicity categories and the GQ population are also 

available at the national level. These coverage rates are 

a measure of the extent of adjustment to the survey 

weights required during the component of the estimation methodology that adjusts to population controls. 

Low coverage rates are an indication of greater potential for coverage error in the estimates. 

Unit response and nonresponse rates for housing 

units are available at the county, state, and national 

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, other, and data insuffi cient to be considered 

an interview. Rates are also provided separately for persons in group quarters at the national and state levels.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. For example, 

the 2006 housing unit response rates are at least 94 

percent for all states. The response rate for the District 

of Columbia in 2006 was 91 percent.

Item allocation rates are determined by the content 

edits performed on the individual raw responses and 

closely correspond to item nonresponse rates. Overall 

housing unit and person characteristic allocation rates 

are available at the state and national levels, which 

combine many diff erent characteristics. Allocation rates 

for individual items may be calculated from the B99 

series of imputation detailed tables available in AFF. 

Item allocation rates do vary by state, so users are 

advised to examine the allocation rates for 

characteristics of interest before drawing conclusions 

from the published estimates.

6

 The sample size measures for housing units (number of initial addresses 

selected and number of fi nal survey interviews) and for group quarters 

people cannot be used to calculate response rates. For the housing unit 

sample, the number of initial addresses selected includes addresses 

that were determined not to identify housing units, as well as initial 

addresses that are subsequently subsampled out in preparation for personal visit nonresponse follow-up. Similarly, the initial sample of people 

in group quarters represents the expected sample size within selected 

group quarters prior to visiting and sampling of residents. 
A-26 Appendix

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

Implications of Population Controls on ACS Estimates

Appendix 7.

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defi ned by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s offi cial estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the offi cial count 

of the population comes from the previous census, 

updated by the Population Estimates Program.

In the case of the ACS, the total housing unit estimates 

and the total population estimates by age, sex, race 

and Hispanic origin are controlled at the county (or 

groups of counties) level. The group quarters total 

population is controlled at the state level by major type 

of group quarters. Such adjustments are important to 

correct the survey data for nonsampling and sampling 

errors. An important source of nonsampling error is 

the potential under-representation of hard-toenumerate demographic groups. The use of the 

population controls results in ACS estimates that more 

closely refl ect the level of coverage achieved for those 

groups in the preceding census. The use of the population estimates as controls partially corrects demographically implausible results from the ACS due to 

the ACS data being based on a sample of the population rather than a full count. For example, the use of 

the population controls “smooths out” demographic 

irregularities in the age structure of the population that 

result from random sampling variability in the ACS. 

When the controls are applied to a group of counties 

rather than a single county, the ACS estimates and the 

offi cial population estimates for the individual counties 

may not agree. There also may not be agreement 

between the ACS estimates and the population estimates for levels of geography such as subcounty areas 

where the population controls are not applied.

The use of population and housing unit controls also 

reduces random variability in the estimates from year 

to year. Without the controls, the sampling variability 

in the ACS could cause the population estimates to 

increase in one year and decrease in the next (especially for smaller areas or demographic groups), when 

the underlying trend is more stable. This reduction in 

variability on a time series basis is important since 

results from the ACS may be used to monitor trends 

over time. As more current data become available, the 

time series of estimates from the Population Estimates 

Program are revised back to the preceding census while 

the ACS estimates in previous years are not. Therefore, 

some diff erences in the ACS estimates across time may 

be due to changes in the population estimates. 

For single-year ACS estimates, the population and total 

housing unit estimates for July 1 of the survey year 

are used as controls. For multiyear ACS estimates, the 

controls are the average of the individual year population estimates.
Appendix A-27

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

Background and Overview Information

American Community Survey Web Page Site Map: 

<http://www.census.gov/acs/www/Site_Map.html>

This link is the site map for the ACS Web page. It provides an overview of the links and materials that are 

available online, including numerous reference documents.

What Is the ACS? <http://www.census.gov/acs/www

/SBasics/What/What1.htm> This Web page includes 

basic information about the ACS and has links to additional information including background materials.

ACS Design, Methodology, Operations

American Community Survey Design and Methodology 

Technical Paper: <http://www.census.gov/acs/www

/Downloads/tp67.pdf> This document describes the 

basic design of the 2005 ACS and details the full set 

of methods and procedures that were used in 2005. 

Please watch our Web site as a revised version will be 

released in the fall of 2008, detailing methods and 

procedures used in 2006 and 2007. 

About the Data (Methodology: <http://www.census

.gov/acs/www/AdvMeth/> This Web page contains 

links to information on ACS data collection and processing, evaluation reports, multiyear estimates study, 

and related topics.

ACS Quality

Accuracy of the Data (2007): <http://www.census.gov

/acs/www/Downloads/ACS/accuracy2007.pdf> This 

document provides data users with a basic understanding of the sample design, estimation methodology, and 

accuracy of the 2007 ACS data.

ACS Sample Size: <http://www.census.gov/acs/www

/SBasics/SSizes/SSizes06.htm> This link provides 

sample size information for the counties that were

published in the 2006 ACS. The initial sample size 

and the fi nal completed interviews are provided. The 

sample sizes for all published counties and county 

equivalents starting with the 2007 ACS will only be 

available in the B98 series of detailed tables on American FactFinder.

ACS Quality Measures: <http://www.census.gov/acs

/www/UseData/sse/> This Web page includes information about the steps taken by the Census Bureau to 

improve the accuracy of ACS data. Four indicators of 

survey quality are described and measures are provided at the national and state level.

Guidance on Data Products and Using the Data

How to Use the Data: <http://www.census.gov/acs

/www/UseData/> This Web page includes links to 

many documents and materials that explain the ACS 

data products. 

Comparing ACS Data to other sources: <http://www

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

are provided with guidance on comparing the 2007 

ACS data products to 2006 ACS data and Census 2000 

data. 

Fact Sheet on Using Diff erent Sources of Data for 

Income and Poverty: <http://www.census.gov/hhes

/www/income/factsheet.html> This fact sheet highlights the sources that should be used for data on 

income and poverty, focusing on comparing the ACS 

and the Current Population Survey (CPS).

Public Use Microdata Sample (PUMS): <http://www

.census.gov/acs/www/Products/PUMS/> This Web 

page provides guidance in accessing ACS microdata.

Other ACS Resources

Appendix 8.