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 State and Local Governments Need to Know

Issued

February 2009
Acknowledgments Joseph J. Salvo, Arun Peter Lobo, and Joel A. Alvarez, Population 

Division, New York City Department of City Planning, 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 M. Klein, Anna M. Owens, Clive R. Richmond, Enid 

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

this handbook series.

The American Community Survey program is under the direction of 

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

Weinberg, Assistant Director for the American Community Survey and 

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

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 February 2009

U.S. Department of Commerce

Vacant, 

Secretary

Vacant, 

Deputy Secretary

Economics and Statistics Administration

Vacant, 

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg, 

Acting Director

What State and Local Governments Need to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What State and Local Governments 

Need to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2009.

Economics and Statistics 

Administration

Vacant,

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg,

Acting 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 State and Local Governments Need to Know iii

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

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

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

The Role of Data in the Delivery of Government Services ............................1

Establishing Priorities Through a Needs Assessment ............................................1

Developing a General Plan ....................................................................................1

Implementing the Selected Plan ............................................................................1

Important Points to Consider When Using the ACS ......................................2

Working With ACS Data .........................................................................................2

ACS Data Products ................................................................................................3

Working With Multiyear Estimates .........................................................................4

Choosing a Current Estimate.............................................................................4

Choosing an Estimate From the Recent Past ......................................................6

Case Studies .................................................................................................8

Distribution of Funds to Senior Centers ................................................................8

Overview...........................................................................................................8

Strategy and Data Sources ................................................................................8

Analysis and Findings .....................................................................................10

Summary: What Have We Learned? .................................................................11

Fire Prevention Education for Those With Limited English Profi ciency .................11

Overview.........................................................................................................11

Strategy and Data Sources ..............................................................................12

Analysis and Findings .....................................................................................13

Summary: What Have We Learned? .................................................................14

Examining Growth in the Foreign-Born Population ..............................................16

Overview.........................................................................................................16

Strategy and Data Sources ..............................................................................16

Analysis and Findings .....................................................................................17

Summary: What Have We Learned? .................................................................19

Conclusion .................................................................................................. 19

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

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

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

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

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

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

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

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

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

 Appendix 8. Other ACS Resources ...............................................................A-27
iv What State and Local Governments Need to Know

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

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

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

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

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

concepts that are fundamentally 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 State and Local Governments Need to Know 1

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

A primary mission of state and local governments is 

to deliver effi cient services and enact policies that 

advance public safety and economic growth. For 

decades, data from the decennial census long form 

have provided invaluable information that helped 

frame these issues in the proper context, enabling governments to create proposals, develop budgets, and 

execute strategies to address well-documented needs. 

With the elimination of the long form, state and local 

governments must avail themselves of the U.S. Census 

Bureau’s American Community Survey (ACS), which will 

now provide detailed information about the population 

and housing attributes of states, counties, and municipalities, large and small. In addition, the ACS will off er 

state and local governments a more dynamic picture of 

their communities.

Unlike the decennial censuses, the ACS is a continuous national survey. Rather than collect data as of a 

single decennial reference point, the ACS collects data 

nearly every day and summarizes it over 1-, 3-, and 

5-year periods. Much like the census, the ACS provides a picture of the social, economic, and housing 

characteristics of the population, but it has the added 

advantage of providing these data on a yearly basis. 

The provision of more current data is not without its 

tradeoff s, which include smaller samples and therefore 

higher levels of variability. In fact, ACS data users will 

be faced with compromises between the provision of 

current data and the reliability of estimates. The most 

signifi cant of these choices will involve the use of estimates for small areas involving multiple years of data.

While diff erences between the decennial census and 

the ACS may be subtle, they frequently necessitate 

a new analytical approach. The major goal of this 

handbook is to illustrate how ACS data can be 

used to address typical issues faced by state 

and local governments and, in the process, provide information that can facilitate an eff ective 

transition to ACS data.

A glossary and a series of technical appendixes—for 

those interested in more advanced ACS applications—

are included at the back of this handbook.

Introduction

The Role of Data in the Delivery of 

Government Services

In the delivery of government services, data are often 

used to help establish priorities through a needs 

assessment, to develop general plans, and to implement selected plans. We briefl y discuss how data are 

used in each of these steps. Often, this is not a linear 

process; rather these steps often feed into and inform 

each other.

Establishing Priorities Through a Needs 

Assessment

Given competing demands and limited resources at 

their disposal, governments need to carefully ascertain 

appropriate funding levels for their initiatives. ACS 

data can be analyzed to assess the level of need and to 

prioritize funding levels for proposed initiatives.

Governments also receive requests for help from 

myriad community groups and civic organizations that 

need to be assessed. ACS data could be extremely useful in evaluating the overall needs of the community 

and the size of local populations in need of various 

services in order to prioritize requests for assistance. 

Developing a General Plan

Once a government decides on its priorities, it needs 

to come up with an eff ective plan. This often calls for 

examining various alternatives. If, for example, a local 

government decides to make the alleviation of poverty 

a priority, it needs to examine where exactly to apply 

its resources. Should the alleviation of child poverty be 

a priority or should the focus be on the elderly poor, 

or on the elderly poor who are living alone? Or, should 

resources be applied in some proportion to each of 

these groups? Examination of ACS data could be instrumental in formulating plans and actions to guide the 

distribution of resources. 

Implementing the Selected Plan

Once a plan is decided on, it must be implemented. 

If, for example, a local government decides to focus 

primarily on the elderly poor, ACS data can be used 

to target neighborhoods for the delivery of funds and 

other resources. A specifi c program could be developed for neighborhoods with the largest number of 

elderly poor. 
2 What State and Local Governments Need to Know

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

The ACS is based on a questionnaire that is sent each 

month to a sample of about 250,000 addresses in 

the United States.1 Each calendar year, these data are 

pooled and estimates are produced for about 60 different social, economic, and housing characteristics. 

Since the size of a geographic area largely determines the size of the sample, only larger areas—

those with 65,000 or more people—receive 

1-year estimates. For smaller places, estimates 

are created for multiyear periods: for areas with 

populations between 20,000 and 65,000, 3 years 

of data are needed; and for areas with fewer than 

20,000 people, 5 years of data need to be 

collected in order to provide estimates.

2 All of this 

diff ers from the decennial census, where the data are 

pegged to an April 1 reference point (even though data 

collection actually occurs into August of the census 

year).

The use of a continuous data collection operation 

means that the ACS can provide local governments 

with updates more than once a decade. Another innovation is that the ACS collects information for everyone 

who is living or staying in a sampled ACS unit for more 

than 2 months, even if they have another residence. 

(This is not the case with the decennial census, since 

“where you live most of the time” determines where 

you are enumerated.) For example, if a couple lives 

most of the year in Florida, but lives in New York City 

for 4 consecutive months each year, that couple may 

be included in the New York City ACS sample if their 

New York home is sampled and interviewed while 

they are residing there. Compared with the decennial 

census, the ACS will better account for seasonal migration patterns that occur in some communities and will 

measure the characteristics of the population actually 

present at various times of the year. For more information about how the ACS compares with the decennial 

census, see Appendix 2.

Estimates based on information collected over 1, 3, 

and 5 years are referred to as “period” estimates, meaning that numbers represent an area’s characteristics 

for the specifi ed period of time. Period estimates may 

be thought of as averages and represent the biggest 

conceptual change from the decennial census. Working with period estimates for 1, 3, and 5 years requires 

a diff erent approach to data analysis, one that is best 

defi ned through the use of illustrations provided later 

in this handbook. Appendix 1 provides additional background on period estimates.

It is important to keep in mind that the main purpose 

of the ACS is to estimate the demographic, social, economic, and housing characteristics of the population. 

ACS estimates of the overall number of people and 

housing units at the county level are obtained from the 

Census Bureau’s Population Estimates Program. Each 

year, the Census Bureau creates independent population estimates for all counties in the United States by 

evaluating change using a variety of primarily administrative data sources. Members of the Federal State 

Cooperative for Population Estimates (FSCPE) participate in the review and development of these estimates. 

For more information about the Census Bureau’s Population Estimates Program, see their Web site at 

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

These detailed population estimates are used as survey 

controls to make ACS estimates of characteristics more 

reliable over time and to correct for defi cits in the 

sample, i.e., diff erences in the level of coverage by age, 

sex, race, and Hispanic origin. Thus, while the ACS is 

the primary source of estimates on social, economic, 

and housing characteristics, overall population and 

housing estimates at the county level are independently determined by the Population Estimates Program 

and adopted by the ACS. Appendix 7 provides more 

information about the implications of population 

controls for ACS estimates. 

Finally, like the decennial census long form, all ACS 

estimates for demographic, social, economic, and 

housing characteristics come from a sample. This 

means that a certain level of variability will be associated with ACS estimates. This variability is referred 

to as “sampling error” and is expressed as a band or 

“margin of error” (MOE) around the estimate. Understanding the basic methods of statistical sampling and 

the ramifi cations of working with sample data are key 

to using the ACS successfully. A detailed discussion of 

how to apply concepts such as MOEs is included later 

in this handbook and in Appendix 3.

Working With ACS Data

If ACS data are deemed appropriate, one has to decide 

on the right ACS product. Depending on the geographic area being examined, decisions need to be 

made as to whether a 1-year average would be more 

appropriate than a multiyear average. The availability 

Important Points to Consider When Using the ACS

1 About 3,000 addresses are also sampled each month for the Puerto 

Rico Community Survey. Similar methods are used to collect, process, 

and publish data for Puerto Rico.

2 Each year a geographic area is evaluated to see if data for the area 

will be published in either or all of the 1-, 3-, and 5-year data products. 

An area is eligible for estimates as soon as it meets the total population threshold and for each year thereafter, as long as it does not fall 5 

percent below the threshold. The release of estimates is always determined by the total population estimate for the latest year. An area that 

reaches 65,000 people in the latest year will receive a 1-, 3- and 5-year 

estimate, regardless of its population size in the prior years. For more 

information, please see Appendix 1.
What State and Local Governments Need to Know 3

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

of these options requires data users to clearly defi ne 

the objectives of their analyses, which is the single 

most important step to eff ective use of the ACS.

Table 1 summarizes, by type of geographic area, the 

proportion of the geographic area type that will receive 

ACS data in the form of 1-year, 3-year, and 5-year estimates; 3-year and 5-year estimates only; and those that 

will only receive 5-year estimates.

ACS Data Products

Data from the ACS are available in several forms, 

including data profi les, detailed tables, and Public Use 

Microdata Sample (PUMS) fi les, all accessible via the 

Census Bureau’s Web site. The type of data 

needed is closely related to the question or issue being 

addressed. For many of the most basic applications, 

where single numbers (e.g., people below the poverty 

line) or summary statistics (median family income) are 

needed, data profi les are useful tools and are easily 

accessible. When data are needed for more detailed 

questions, such as the ratio of income to the poverty line or a distribution of income for households, 

detailed tables are frequently required. There are two 

levels of detailed tables, those that are abbreviated 

(these table numbers begin with a “C”) and those 

with more detailed categories (table numbers beginning with a “B”). It is important to keep in mind that 

increased detail always comes at the expense of data 

reliability; the margins of error are generally larger for 

estimates in tables with the more detailed characteristics. Moreover, due to sample size constraints and 

the greater detail demanded, data may be too sparsely 

distributed in the cells of a detailed table, which may 

result in some tables being “blanked-out” or suppressed. Optimal data utilization occurs when there is 

 

 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 1. Major Geographic Areas and Type of ACS Estimates Published

* 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.
4 What State and Local Governments Need to Know

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

a good balance between the level of detail examined 

for a particular variable and the reliability of those estimates, as explained later in this handbook.

The most fl exible data product is the PUMS. (See the 

text box titled “What Is the Public Use Microdata Sample?”) Since microdata are actual ACS response records, 

these data can provide the most detailed tabulations, 

limited only by the number of records in the fi le. In 

exchange for this fl exibility, the geographic codes provided for individual records are limited to states and 

specifi c areas of at least 100,000 population (called 

Public Use Microdata Areas or PUMAs). Aggregation 

of these records cannot occur for smaller geographic 

units, such as counties or census tracts.3

Microdata products require more knowledge of the 

form and content of ACS data than other ACS data 

products and a clear idea of the purpose of the analysis; “browsing” table categories is not possible because 

there are no preset tabulations, as with the summary 

tables. Instead, the records need to be downloaded 

from the ACS Web site into software that can aggregate records using the variables in the fi le. Statistical 

software, such as SAS (Statistical Analysis System), 

SPSS (Statistical Package for the Social Sciences), or 

relational database software, such as MS ACCESS, can 

all be used to aggregate records to create tabulations 

or other output. Data users who do not have statistical 

software can elect to work with the PUMS fi les using 

an online data tool called DataFerrett. DataFerrett is a 

data analysis tool that provides access to PUMS fi les 

via the Internet. It can be installed as a stand-alone 

application or used through an Internet browser. The 

DataFerrett Web site also includes an online tutorial on 

how to access and use microdata fi les. While accessing 

PUMS requires additional work, data users can customize tables so that the results specifi cally address their 

needs.

Working With Multiyear Estimates

For data users who are working with places of at least 

65,000 people, one by-product of the increased complexity of the ACS is the array of options that multiyear averages provide. A classic case in point is when 

a local government needs a number that represents 

the best “current” assessment of an attribute for its 

jurisdiction, or an estimate from the recent past. Let’s 

assume the attribute of interest is public assistance.

There are multiple ways of measuring levels of public 

assistance using ACS data, ways that vary depending 

on the size of the geographic area being examined. 

Beginning in 2010, following the release of the fi rst 5-

year estimates, areas with 65,000 people or more will 

have the option of using 1-, 3-, or 5-year averages. The 

examples provided in the following sections are based 

on research from ACS test counties for which 1-, 3-, 

and 5-year data are currently available. 

Choosing a Current Estimate

If one needs to examine the most current estimate of 

an attribute, one has to “anchor” the 1-, 3-, and 5-year 

estimates on the most recent year for which data are 

available. If the most current estimate of households 

receiving public assistance income is required, it would 

be ideal to use just the latest 1-year estimate. However, 

the 1-year estimate is derived from a smaller sample 

and is less reliable than 3- or 5-year estimates. So how 

does one decide on which estimate to use? 

What Is the Public Use Microdata Sample?

Researchers who need tabulations that are 

more detailed than the Census Bureau’s 

ready-made tables often use the American 

Community Survey’s Public Use Microdata 

Sample (PUMS) fi le. Microdata fi les contain 

a sample of actual survey response records, 

minus any identifying information. This allows 

data users to create cross-tabulations using a 

host of variables. For example, a criminologist 

studying delinquency might want to know the 

number of young adults between the ages 

of 16 and 21 who are not enrolled in school, 

who have not graduated from high school, 

and who do not have a job. Exploring the 

American FactFinder, it is possible to get tables 

on this population only for those between 

the ages of 16 and 19. If the criminologist 

felt 20- and 21-year olds had higher rates 

of delinquency, he or she might want to use 

the PUMS to create a table for 16- to 21-year 

olds. There are tradeoff s, however. In order to 

preserve respondent confi dentiality in these 

fi les, geographic identifi cation is limited to 

states and a set of areas of 100,000 or more 

population, called Public Use Microdata Areas 

or PUMAs. Also, there are limits to the level of 

subject detail that PUMS can provide, based 

upon the number of people or housing units 

in the sample. More information on PUMS is 

available in the handbook for PUMS data users.

3 PUMAs are combinations of contiguous census tracts. For more 

information about PUMAs and the Census Bureau’s Public Use Microdata Sample (PUMS) fi les see the PUMS overview Web page at <http://

www.census.gov/acs/www/Products/PUMS/index.htm> or the handbook in this series for PUMS data users.
What State and Local Governments Need to Know 5

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

Along with estimates, the ACS provides MOEs, which 

allow users to calculate a standard error. When a 

standard error is divided by the estimate, the resulting 

statistic is known as the coeffi cient of variation or CV. 

(See the text box titled “What Are Margins of Error and 

Coeffi cients of Variation?”) For example, if the number of people below the poverty line is 1,000 and the 

standard error is 80, the CV equals 8 percent, which 

indicates that the standard error equals 8 percent of 

the estimate. CVs are a standardized indicator of the 

reliability of an estimate. While there is no hard-andfast rule, for the purposes of this handbook, estimates 

with CVs of more than 15 percent are considered cause 

for caution when interpreting patterns in the data. 

The choice of a CV level threshold that distinguishes a 

reliable estimate from an unreliable estimate will vary 

by application. While CVs for 3- and 5-year estimates 

would be generally lower than that of the 1-year 

estimate, one could go with the most recent 1-year 

estimate if its CV were 15 percent or lower. The illustrations that follow will highlight the computation and use 

of CVs. 

What Are Margins of Error and Coeffi cients of Variation?

Since the estimates of characteristics from the ACS are based on a sample, data are published with 

margins of error (MOEs) for every estimate. ACS MOEs are based on a 90-percent confi dence level. 

MOEs give users an idea of how reliable, or precise, estimates actually are. For example, there is an ACS 

estimate of 43,527 kindergarteners in Utah, with an MOE of plus or minus 2,834. The MOE tells us if 

we had the time and the dollars to create the same estimate of kindergartners several thousand times, 

from several thousand samples, that 90 percent of the estimates of kindergarteners in Utah would be 

between 40,693 (43,527–2,834) and 46,361 (43,527+2,834), a fairly precise estimate.

In more technical terms, MOEs provide an idea of how much variability (i.e., sampling error) is associated with the estimate. As an MOE gets larger, relative to the size of an estimate, the estimate becomes 

less reliable. A measure called the “coeffi cient of variation” (CV) can also be used to discern the level of 

reliability of an estimate. This measure is constructed in two steps:

 a) Calculate the standard error: SE = MOE/1.645

 b) Calculate the Coeffi cient of Variation: CV = SE/Estimate * 100

With respect to the above example, the standard error associated with the estimate for kindergarteners 

in Utah is 2,834/1.645 or 1,723. The CV equals 1,723/43,527 * 100, or 4.0 percent. This means that 

one standard error is 4 percent of the estimate, a fairly low level of variability that indicates that the 

estimate is reliable. For some applications, CVs that are in excess of 15 percent should be a cause for 

concern.

With respect to tables provided by the ACS, when the Census Bureau deems that CVs are too high for 

a tabulation, the entire table may be blanked-out or suppressed. This usually occurs because the data 

are too sparsely distributed in the cells of a detailed table, which will cause some estimates in a data 

profi le and entire detailed and subject tables to be suppressed. In general, tables based on 5-year estimates are not subject to suppression. 

For more information on CVs and MOEs, see Appendix 3. 

In Figure 1, households receiving public assistance 

income are shown using 1-, 3-, and 5-year estimates. 

The period of time represented by each estimate is 

shown by the dotted lines. While all three estimates 

share the same fi nal year of data collection, they diff er 

in other important ways. The 1-year (2005) estimate 

is based on interviews conducted throughout calendar 

year 2005. The 3-year (2003–2005) estimate is based 

on the same sample interviews used to produce the 

three most current 1-year estimates (2003, 2004, and 

2005). The same can be said of the 5-year estimates 

(2001–2005), which include the sample interviews 

used to produce the fi ve 1-year estimates in 2001, 

2002, 2003, 2004, and 2005. This is why the 1-year 

estimate is preferred when attempting to get a “current” estimate, as it does not draw in data from earlier 

years. Still, if one looks at the CVs shown at the bottom 

of Figure 1, it is clear that the 1-year estimate has a 

higher CV than either the 3- or 5-year estimates. Nevertheless, since the CV for the 1-year estimate from 2005 

is under the 15 percent cutoff , we choose to go with 

that estimate. 
6 What State and Local Governments Need to Know

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

While 15 percent is a good cut-off , it is important 

to consider the application of the data in order to 

determine whether a specifi c CV is reasonable. If, for 

example, a data user is using public assistance data to 

produce a very general portrait of an area, estimates 

with CVs beyond 15 percent may be acceptable. If, 

however, the data are being used to apply for a federal 

grant, where estimates must be precise, then estimates with lower CVs may be needed. If the data user 

is working with geographies of under 20,000 people, 

then there is no recourse but to use the most current 

5-year estimate to represent the attributes of an area.

In all cases, it is important for data users who want the 

most current data to “anchor” estimates on the fi nal 

time point and then if the option exists, to determine 

which option best optimizes the trade-off between data 

that are current and data that are reliable. For those 

seeking to use the most current estimates, anchoring on the fi nal year makes sense for several reasons. 

First, the release of estimates is “pinned” to the population threshold of an area at the latest time point, thus 

providing the most current data. Second, such comparisons are appropriate because when an area changes its 

geographic boundaries, the geography for the estimate 

will be current as of the fi nal year of the estimate. 

Finally, any economic items will be adjusted according 

to diff erences with the Consumer Price Index for the 

last year of the estimate. 

Choosing an Estimate From the Recent Past

While data users usually want the most current information available, sometimes data from the more recent 

past may be required. A new political administration, 

for example, may want to know the number of public 

assistance recipients at the start of the prior administration, say in 2002. 

Once again, assume that 1-, 3-, and 5-year estimates 

are available (Figure 2). In this example the 1-year 

estimate is for 2002, and the appropriate multiyear 

estimates are now shown as “centered” on 2002, the 

year of interest. Thus, the 3-year period estimate 

would be for the years 2001–2003, while the 5-year 

estimate would be for the years 2000–2004.4 While the 

multiyear estimates are not designed to be estimates 

of the middle year of the series, such comparisons can 

be useful to see if all available data are telling you the 

same story. This “centering” on 2002 stands in contrast 

to “current” estimates in the earlier section that were 

anchored on the fi nal year. 

The ideal estimate would be the 1-year estimate, since 

2002 is the year of interest. But given its CV of nearly 

4 It is important to note that multiyear estimates were only available 

for selected test counties prior to 2008. Beginning in December 2008, 

3-year estimates will become available for a much broader set of specifi c areas throughout the United States, followed by 5-year estimates 

in 2010.

4,000

4,500

5,000

5,500

6,000

6,500

2001

3-Year Estimate

1-Year Estimate

5-Year Estimate

2002 2003 2005 2004

Figure 1. Households on Public Assistance, Using 1-Year, 

 3-Year, and 5-Year ACS Data Anchored on 2005

Source: U.S. Census Bureau, American Community Survey. Multiyear Estimates Study.

Estimates for 2005 Year of Interest

Estimate MOE CV

5 Year (2001–2005) 5,091 564 6.7

3 Year (2003–2005) 4,454 760 10.4

1 Year (2005) 5,023 1,174 14.2
What State and Local Governments Need to Know 7

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

16 percent, one needs to consider the 3- and 5-year 

estimates. Both the 5-year estimate (5,356) and the 

3-year estimate (5,029) shown in Figure 2 have CVs 

below the 15 percent cutoff . It would be preferable to 

use the 3-year estimate because it adds only 2 years of 

additional data to the 1-year estimate. For more information on single- and multiyear estimates and choosing between them, see Appendix 1.

The sections that follow provide three case studies that 

address a composite of issues faced by state and local 

governments. The fi rst two case studies use a single 

set of ACS estimates to characterize an area, while the 

third case study examines two sets of estimates to 

gauge change over time. The data analysis involved 

in each case study off ers principles and guideposts 

for using the ACS. Each case study embodies diff erent 

aspects of ACS use, with an emphasis on applications 

that are most pertinent for data users in state and 

local government. What these case studies show is 

that eff ective use of the ACS often requires trade-off s 

involving the timeliness of the data, the sampling variability, and the geographic fi t. The goal was to achieve 

the best fi t between the questions at hand and the 

available data. Hopefully, an examination of this process will allow data users to develop a sense of what 

responsible use of the ACS entails.

4,000

4,500

5,000

5,500

6,000

6,500

2000

3-Year Estimate

1-Year Estimate

5-Year Estimate

2001 2002 2004 2003

Figure 2. Households on Public Assistance, Using 1-Year, 

 3-Year, and 5-Year ACS Data Centered on 2002

Source: U.S. Census Bureau, American Community Survey. Multiyear Estimates Study.

Estimates for 2002 Year of Interest

Estimate MOE CV

5 Year (2000–2004) 5,356 638 7.2

3 Year (2001–2003) 5,029 779 9.4

1 Year (2002) 6,642 1,742 15.9
8 What State and Local Governments Need to Know

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

Distribution of Funds to Senior Centers

Overview

The Department for the Aging (DFTA) in New York 

County, New York, is working to enhance the quality of 

life of the elderly in a cost-eff ective manner. It maintains a network of neighborhood-based senior centers 

across the county that provide meals and recreational 

activities, as well as education, health, housing, and 

social services under the auspices of federal-, state-, 

and county-sponsored programs. A utilization study 

recently revealed that use of the centers’ resources was 

unbalanced, with some centers, especially in wealthier 

areas, experiencing chronic underutilization. A policy 

decision then followed to tilt the allocation of the 

annual budget to a few centers located in communities with larger numbers of the elderly who are poor or 

near poor. This move was seen as a precursor to future 

allocation eff orts, since the local planning department 

has projected a huge increase in the elderly population 

over the next 20 years, when cost-eff ective allocation 

of limited resources will be even more of a challenge.

DFTA sought to put a program in place that would 

identify those communities that contained the largest 

numbers of seniors who are poor or near poor. While 

the numbers for the entire county were important, this 

initiative required good data for subcounty areas. The 

analyst in charge of this initiative was familiar with 

issues aff ecting the elderly and had some exposure to 

census data, mostly via the Census Bureau Web site 

and through contact with the State Data Center.5 After 

talking with the Data Center and searching the Census 

Bureau’s Web site, she concluded that the best source 

of data for the task at hand was the 2006 ACS. DFTA 

administrators, however, were a bit reluctant, having 

heard that the ACS was derived from a small sample, 

compared with the size of the decennial census sample. While they were eager to get an up-to-date look 

at a rapidly aging population, they wondered whether 

they should not just turn to data from the Census 2000 

long form. The agency’s administrators requested the 

analyst to present a case for the ACS that conclusively 

demonstrated how the allocation of resources was best 

served using these data.

Strategy and Data Sources

The analyst decided that to identify the poor or near 

poor, the focus should be on poverty estimates, as 

they are determined by using both income and family/

household size. In the American Factfi nder (AFF), the 

analyst located Table C17024 that shows the ratio of 

income to poverty level for broad age groups.6 A portion of this table is shown in Figure 3. Refer to the text 

box for step-by-step instructions of how the analyst 

found this information.

Case Studies

5 The State Data Center program is a partnership program with the 

Census Bureau. For more information, see <http://www.census.gov

/sdc/www/>.

6 For more information on the Census Bureau’s American FactFinder, go 

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

Step-by-Step Instructions for Case Study 1

Start at <www.census.gov>.

1. Select American FactFinder.

2. Under “Getting Detailed Data,” select 

American Community Survey—get data.

3. Choose your data set. From the list of 

data sets, select the 2006 American 

Community Survey.

4. Choose your data product. From the list 

of data products on the right, select detailed 

tables. The detailed tables contain the greatest variety of information for many topics 

so it makes sense to start here to search for 

information on poverty.

5. Choose your geography of interest.

From a series of drop-down lists, select the 

geographic area type, the state, and then 

the specifi c area. For this example select 

county, New York, and then the specifi c 

county within New York—New York County. 

Click “Add” then “Next.” 

6. Choose your tables. To search for information on poverty use the keyword selection 

method and enter “poverty.” Reviewing the 

list of possible poverty tables, there are 

two versions of a table on “Age by Ratio 

of Income to Poverty Level in the Past 12 

Months”—B17024 and C17024. Keep in 

mind that the “B” version has greater detail. 

It’s best to start with the C version—if it 

has the detail you need, select it. For this 

example, we are interested in seniors and 

the age categories in the C table are perfect. 

Select C17024, click on “Add” then “Show 

result.”

7. Consider additional geographies. To 

look at additional subcounty data, select 

another geographic area type (Public Use 

Microdata Area), the state (New York), and 

then select specifi c PUMAs (PUMAs 03801, 

03802, 03803 . . . 03810 are the 10 PUMAs 

in New York County). Click on “Add” then 

“Next.” 
What State and Local Governments Need to Know 9

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

Using Table C17024 with its age groupings, poverty 

ratio groupings, estimates, and MOEs for the county 

and its PUMAs, it was possible to carry out a detailed 

analysis of the distribution of economically distressed 

elderly people. This table has major advantages. First, 

the data are available for diff erent age groups, which 

make it possible to separate out the elderly population based on the agency’s defi nition of the elderly as 

people 65 years and over. Second, the various ratios 

provide a great deal of fl exibility in identifying the poor 

and near poor. Third, since this is a large county, 1-year 

data are available for the county overall and for its 10 

PUMAs, making these data well suited for the application. As mentioned earlier, PUMAs are areas with at 

least 100,000 residents in contiguous census tracts. 

The tables also include MOEs for each estimate, which 

permit the analyst to calculate coeffi cients of variation 

and provide a detailed assessment of data reliability. 

(See the text box titled “What Are Margins of Error and 

Coeffi cients of Variation?”)

The analyst used options available in the AFF to download Table C17024 into a spreadsheet and was able to 

isolate the specifi c data of interest—the data for people 

aged 65 and over. Examining the poverty categories 

available in Table C17024, the analyst determined that 

a ratio of income to poverty level of less than 1.0 identifi es the population below the poverty level. Persons 

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

 Figure 3. Example of Poverty Data for PUMAs in New York County, New York
10 What State and Local Governments Need to Know

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

Individual and Merged PUMAs

Persons

Estimate MOE CV per Acre

Selected County 81,877 5,061 3.8 5.6

PUMA 3801 10,651 2,127 12.1 5.4

PUMA 3802 9,687 1,802 11.3 11.3

PUMA 3803 7,439 1,291 10.5 8.0

PUMA 3804 8,920 1,750 11.9 6.0

PUMA 3805 & 3806 14,185 2,248 9.6 4.1

PUMA 3808 4,761 1,068 13.6 4.5

PUMA 3809 15,579 2,326 9.1 14.0

PUMA 3807 & 3810 10,655 2,046 11.7 2.8

with a ratio of less than 2.0 are persons living below 

twice the poverty level. The analyst decided that for 

her analysis elderly people with incomes below twice 

the poverty level would be considered poor or near 

poor. To obtain data for those below twice the poverty 

level, the estimates of people in four income-to-poverty 

ratios had to be summed: under 0.50, 0.50 to 0.99, 

1.00 to 1.24, and 1.25 to 1.99. These combined estimates are shown in Table 2. 

Analysis and Findings 

While summing estimates of poverty across four 

income-to-poverty ratios gives one the estimate for the 

combined category, summing MOEs does not produce 

the correct MOE. The analyst found the formula that 

she needed to produce the right MOE for a derived 

estimate in Appendix 3. 

Using this formula for each PUMA, the analyst calculated the MOE for the combined category of people age 

65 and over with an income under twice the poverty 

level. Using the data in this table, she had to determine 

whether the estimates were reliable enough to satisfy 

DFTA administrators. The MOEs, however, seemed to 

vary with the estimates, with large estimates producing 

large MOEs. With further sleuthing, the analyst decided 

to calculate CVs, which are standardized indicators 

of reliability, thus allowing comparisons of reliability 

across estimates. (See the text box titled “What Are 

Margins of Error and Coeffi cients of Variation?”) While 

thresholds for acceptable CVs have always been a 

subject of debate, she decided to use a threshold of 15 

percent; any estimate with a CV over 15 percent would 

be considered unreliable. 

Two out of the 10 PUMAs—PUMA 3805 and PUMA 

3810—had CVs that were over the 15 percent threshold. But with so many reliable estimates, the analyst 

did not want to abandon the current ACS data for outdated decennial data. So, the analyst decided to merge 

each of the PUMAs with a CV over the threshold with 

an adjacent PUMA. PUMA 3805 was combined with the 

adjacent PUMA 3806, while PUMA 3810 was combined 

with neighboring PUMA 3807. This geographic aggregation increased the sample size in the merged PUMAs, 

resulting in a decline in their CVs to under the 15 percent threshold (right half of Table 2). This assured DFTA 

administrators that the subcounty estimates of the 

elderly population at or near poverty were reliable. But 

the overall number of geographic units available for 

analysis declined from the original 10 units to 8 units 

(from 10 PUMAs to 6 PUMAs plus 2 areas, each with 2 

merged PUMAs).

It became clear that the county was far from uniform in 

its distribution of the elderly poor and near poor. One 

PUMA (3809) had more than three times the number 

of another PUMA (3808). The administrators wanted to 

know whether these estimates could be rank ordered 

by the size of the target population. The analyst 

warned the administrators not to focus on strict rankSource: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.

 Table 2. Persons 65 and Over Who Are Below Twice the Poverty Level in a Selected County and 

 Its Component PUMAs, 2006

Individual PUMAs

Estimate MOE CV

Selected County 81,877 5,061 3.8

PUMA 3801 10,651 2,127 12.1

PUMA 3802 9,687 1,802 11.3

PUMA 3803 7,439 1,291 10.5

PUMA 3804 8.920 1,750 11.9

PUMA 3805 5,026 1,244 15.1

PUMA 3806 9,159 1,873 12.4

PUMA 3807 5,819 1,334 13.9

PUMA 3808 4,761 1,068 13.6

PUMA 3809 15,579 2,326 9.1

PUMA 3810 4,836 1,551 19.5
What State and Local Governments Need to Know 11

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

ings or small diff erences between the PUMA estimates 

since the sample was not capable of making such fi ne 

distinctions. To help avoid “over-analysis” of fi ne diff erences between two estimates, the analyst suggested a 

rule of thumb: If the fi rst estimate +/– its MOE overlapped the second estimate +/– its MOE, the diff erence 

between the two estimates would not be considered to 

be statistically signifi cant. Using this rule, the size of 

the elderly poor and near poor in PUMA 3809 (15,579 

+/– 2,326 = 13,253 to 17,905) was not signifi cantly 

diff erent from the merged PUMAs 3805/3806 (14,185 

+/– 2,248 = 11,937 to 16,433). Similarly, the estimate 

of the elderly poor for PUMA 3801 was not statistically 

diff erent from PUMAs 3807/3810 or PUMA 3802. Thus, 

there were many PUMAs that had large target populations that were similar in size. While this rule of thumb 

is useful, it is not a defi nitive test of statistical signifi -

cance.7

The analyst was also aware that the merged PUMAs 

resulted in a larger target population. So for each 

PUMA, as well as for the merged PUMAs, she calculated 

the population density of the targeted group, defi ned 

as the population per acre of the poor and near poor. 

This helped her gauge the numerical strength of the 

elderly poor and near poor in the context of the size of 

the PUMAs, especially the merged PUMAs. 

Taking into account current utilization of senior centers in PUMAs and the size and density of the elderly 

poor and near poor population, the analyst decided 

to recommend focused funding for senior centers in 

two PUMAs. The fi rst PUMA recommended was PUMA 

3809, since it had both the largest target population 

and highest population density. She decided against 

recommending the merged PUMAs 3805/3806 and 

3807/3810 though each had a large population of 

elderly poor and near poor residents, since the density 

of this population was among the lowest in the county 

and utilization of senior centers was below average. 

Instead, the second PUMA recommended was PUMA 

3802, with nearly 9,700 elderly poor and near poor 

residents and the second highest population density in 

the county.

One other option the analyst could have considered to 

make estimates more reliable was to employ estimates 

using data at the PUMA level for 3-year and/or 5-year 

periods. The analyst was able to use 1-year estimates 

because the county of interest was very large. In fact, 

for most small governments (whose areas have fewer 

than 65,000 people), only estimates for 3 and/or 5 

years will be available. In general, considerable increases in reliability over 1-year estimates can be achieved 

by using the 3- and 5-year period estimates, but this 

comes at a cost. As each additional year is added to 

the estimate, it becomes less current, covering a larger 

period of time. Along with the use of 5-year estimates 

at the PUMA level, the analyst could also have looked 

at 5-year census tract data to get a better sense of the 

population density of the elderly poor and near poor in 

the “catchment” area of each senior center.

Summary: What Have We Learned?

Eff ective use of the ACS can be said to represent a 

“healthy tension” between the data required to answer 

a question and the statistical limits of those data. 

There is always more to learn through the use of 

more detailed table categories, but this comes 

with a compromise in the form of lower levels 

of reliability. All ACS applications require a balance 

between content detail and reliability. The use of standard errors, as seen in the MOEs, is essential information that enables a data user to optimize this relationship. Using the decision-making guidelines presented 

in the previous application, it is possible for data users 

to make the most of the data, while still maintaining 

a sense that the numbers are within an acceptable 

level of reliability. Decisions become diffi cult when 

they require a loss of content, as with the decision to 

combine PUMAs, whose level of reliability was questionable. 

Collapsing of geographic areas is one of several ways 

of enhancing reliability; collapsing data categories is 

another. Both techniques were adopted here, though 

the collapsing of the income-to-poverty ratios was 

required conceptually, rather than being employed as 

a means to improve reliability. Once the 3- and 5-year 

averages become available, it will be possible to obtain 

more reliable data for individual PUMAs.

Finally, the most important point to understand 

is that the path chosen is not solely a statistical 

decision but one also based on the question at 

hand and the purpose of the application. A fi rm 

idea of what one is trying to accomplish with the data 

is the critical prerequisite for using the ACS.

Fire Prevention Education for Those With Limited 

English Profi ciency

Overview

In March of 2007, a nighttime fi re swept through a 

small apartment building in a New York City neighborhood, killing one adult and nine children from the 

African country of Mali. Of the factors that contributed 

to the lethal nature of the fi re, the absence of batteries in the building’s two smoke alarms, was the most 

troubling for local offi cials. This tragedy highlighted 

the fact that more needed to be done to promote fi re 

safety awareness among recent immigrants, especially 

those who spoke little or no English. 7 For exact methods of signifi cance testing see Appendix 4, “Making 

Comparisons.”
12 What State and Local Governments Need to Know

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

Subsequent to this tragedy, a task force was setup 

in Queens County to initiate a fi re safety campaign 

to raise public awareness about the importance of 

maintaining functioning smoke alarms. This campaign 

was to be targeted to residents who were not fl uent 

in English. The campaign would consist of a series of 

television, radio, print, and Internet ads in the major 

languages spoken by those not profi cient in English. 

Strategy and Data Sources

The fi rst order of business was to fi nd out the number 

of people who had limited English-speaking skills and 

the languages they spoke. These data would be needed 

for the overall county and, for the purposes of grassroots communication eff orts, for subcounty areas.

Using the Census Bureau’s AFF, 2006 ACS data can be 

accessed to fi nd the latest information on languages 

spoken and English-speaking ability. Starting with the 

data profi les for Queens County, New York, the 2006 

ACS turned up a generalized list of languages and 

English language ability for the population 5 years and 

over, but the only specifi c language not subsumed into 

a larger linguistic grouping was Spanish. A portion of 

this data profi le is shown in Figure 4. Further research 

using detailed tables for the same geographic area 

revealed Table B16001, which appeared to be a perfect 

fi t. Part of this table is shown in Figure 5. It shows language spoken at home by ability to speak English for 

the population 5 years and over. Also, the table is very 

detailed, with 38 languages or language groupings. 

Figure 6 provides a general summary of these data. It 

shows that the estimate of the population in Queens 

County reporting that they speak English less than 

“very well” numbered over 620,000, and Spanish 

speakers were the number one group, accounting for 

over 43 percent of the total. Chinese (17 percent) and 

Korean (7 percent) rounded out the top three languages, which together accounted for about two-thirds 

of the population in the county with limited Englishspeaking abilities.

As these data compilation eff orts were underway, funding for the task force’s outreach eff orts was cut and 

the task force decided to limit its outreach to the most 

hard-to-reach segment of this population—residents 

who had both limited English-speaking skills and less 

than a high school education. Since a table that crossed 

language abilities by educational attainment was not 

available in the AFF, the task force turned to the 2006 

ACS PUMS fi le, which allows a user to create detailed 

tabulations down to the PUMA level. (See the text box 

titled “What Is the Public Use Microdata Sample?”) The 

tabulations one can derive from the PUMS are limited 

only by the number of records in the fi le that meet 

the criteria desired. For details on how to obtain PUMS 

records, refer to the handbook in this series for PUMS 

data users.

PUMS records for the county were fi rst identifi ed, followed by the records for people not profi cient in 

English. These were defi ned as those who spoke 

English less than “very well.”8 The data were further 

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

 Figure 4. Information Available on Language Spoken at Home From ACS Data Profi le

8 The Census Bureau uses four categories of English language ability for 

households that report speaking a language other than English at home: 

those that speak English “very well,” “well,” “not well,” and “not at all.” 

Those who speak English well, not well, or not at all are considered to 

have limited English profi ciency.
What State and Local Governments Need to Know 13

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

limited to people 25 years and over who had less than 

a high school education. The age cutoff was important 

since most people have completed their high school 

education by age 25. Once the population was limited to those 25 years and over without a high school 

diploma and not profi cient in English, it was possible 

to ascertain the languages they spoke at home. 

Analysis and Findings 

While the estimate of the total population in Queens 

County with limited English-speaking abilities was 

about 620,000, Figure 7 shows that the estimate of the 

population 25 years and over without a high school 

diploma with limited English-speaking abilities was 

only about 185,000. Adding educational attainment 

successfully limited the task force target population. 

The subset that spoke Spanish numbered over 98,000, 

accounting for about 53 percent. (It’s worth noting that 

Spanish speakers are overrepresented in this subgroup, 

as they accounted for 43 percent of the total population with limited English-speaking abilities.) Chinese 

speakers accounted for about 16 percent, while Italian 

speakers accounted for about 5 percent. Thus, the top 

3 languages for the population without a high school 

diploma were slightly diff erent than for the total population. These three groups accounted for nearly threequarters of the population 25 years and over without a 

high school diploma and not profi cient in English. If it 

were important to be sure which language groups were 

most prevalent, it would have been best to include the 

margins of error or to conduct statistical testing. For 

this initial assessment, the task force was only interested in a broad picture of the language needs.

Next, similar estimates were produced for each PUMA 

in Queens County. Table 3 displays these estimates 

along with the associated MOEs and CVs. As described 

in the fi rst case study, the MOEs are provided with the 

tables but the CVs must be calculated using the simple 

formula provided in Appendix 3. The task force chose 

to apply a 15 percent CV threshold to identify reliable 

estimates for targeting programs. Using this defi nition, 

6 of the 14 PUMAs had reliable estimates of the 

population age 25 and over with limited Englishspeaking abilities and less than a high school 

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

 Figure 5. Information Available on Language Spoken at Home From ACS Detailed 

 Table, Queens County, New York, 2006 American Community Survey
14 What State and Local Governments Need to Know

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

education. For the proportion that were Spanish 

speaking, 6 of the 14 PUMAs had reliable estimates 

that could be used for targeting purposes, while just 

1 PUMA for Chinese speakers had a reliable estimate. 

The countywide fi gures for the population that spoke 

Italian, Greek, and Korean were not reliable and neither 

were the PUMA estimates. Nevertheless, there was 

enough information by county and PUMA for the total 

limited English-speaking, less than high school diploma 

population, as well as for the subset of Spanish speakers, to guide the taskforce in its targeting eff orts.

Summary: What Have We Learned?

This example demonstrates how to use three diff erent 

data products to gather information about language 

spoken at home and English-speaking ability. The data 

profi les provided the least detailed data, which can be 

suffi cient for some needs but were not useful for this 

application. The detailed tables provided greater detail, 

but not the optimal information. The PUMS fi le off ered 

a useful alternative for local government data users 

when the detail needed to successfully implement a 

program is beyond what is available in the AFF. This 

fl exibility comes at a cost in the form of smaller samples from which to derive estimates. For large counties, 

PUMS data provide rich opportunities to explore myriad 

tabulations that may be extremely useful in setting up 

and carrying out programs. The desire for additional 

detail in the tabulations needs to be balanced against 

the decrease in reliability, especially at the PUMA level. 

The reliability of the estimates shown in Table 3 are 

reduced due to the partitioning of the population into 

increasingly smaller subgroups—the population age 

25 and over that reported speaking a specifi c language 

at home, reported speaking English less than “very 

well,” and reported not having a high school diploma. 

Reliability, however, can be enhanced by using 3- and 

5-year PUMS fi les, when they eventually become available.

Spanish

43.4%

Chinese

17.3%

Korean

7.0%

Other Indic 

languages

5.4%

Russian

4.0%

Other

13.2%

Italian

2.4%

Greek

2.2%

Polish

2.0%

Tagalog

1.6%

French Creole

1.6%

Total = 620,238

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

 Figure 6. Top Languages Spoken at Home in a Selected County for the Population 5 

 Years and Over Who Are Limited English Profi cient, 2006
What State and Local Governments Need to Know 15

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

Spanish

53.4%

Chinese

15.9%

Other Indic 

languages

4.5%

Russian

1.0% Other

10.9%

Italian

4.6%

Greek

3.8%

Korean

2.7%

Arabic

1.0%

Hebrew

0.9%

Total = 184,538

French Creole

1.4%

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

 Figure 7. Top Languages Spoken at Home in a Selected County for the Population 

 25 Years and Over Who Are Limited English Profi cient and Have Less Than 

 a High School Education, 2006

Estimate MOE CV Estimate MOE CV Estimate MOE CV Estimate MOE CV Estimate MOE CV Estimate MOE CV

Selected County 12,127 4.0 98,487 184,538 6,314 3.9 29,317 4,626 9.6 8,467 2,648 19.0 6,937 2,407 21.1 4,942 2,043 25.1

PUMA 4101 21,779 4,083 11.4 12,348 2,154 10.6 400 584 88.8 1,665 1,155 42.2 2,847 1,466 31.3 - - - 

PUMA 4102 28,272 4,559 9.8 24,543 1,676 4.2 2,654 1,445 33.1 108 306 172.2 193 408 128.5 - - - 

PUMA 4103 22,632 4,152 11.2 3,837 1,663 26.3 11,406 2,216 11.8 1,248 1,012 49.3 1,566 1,125 43.7 2,047 1,271 37.7

PUMA 4104 7,629 2,520 20.1 2,036 1,138 34.0 2,157 1,159 32.7 605 695 69.8 856 812 57.7 932 843 55.0

PUMA 4105 5,789 2,206 23.2 2,237 1,091 29.6 - - - 144 349 147.3 - - - - - - 

PUMA 4106 7,904 2,563 19.7 2,699 1,242 28.0 2,404 1,205 30.5 175 385 133.7 418 586 85.2 269 475 107.3

PUMA 4107 25,422 4,362 10.4 16,949 2,214 7.9 4,295 1,760 24.9 612 720 71.5 154 364 143.7 1,027 925 54.8

PUMA 4108 6,351 2,307 22.1 1,092 886 49.3 1,879 1,072 34.7 - - - - - - - - - 

PUMA 4109 15,283 3,488 13.9 8,219 1,816 13.4 2,150 1,266 35.8 125 328 159.5 644 732 69.1 464 625 81.9

PUMA 4110 13,300 3,273 15.0 6,596 1,699 15.7 751 784 63.5 2,851 1,394 29.7 - - - - - - 

PUMA 4111 9,293 2,768 18.1 5,856 1,371 14.2 607 702 70.3 72 249 210.2 - - - - - - 

PUMA 4112 8,128 2,597 19.4 5,314 1,264 14.5 336 529 95.7 88 275 190.0 - - - 72 249 210.2

PUMA 4113 5,644 2,179 23.5 3,168 1,098 21.1 141 345 148.7 774 761 59.8 160 367 139.4 60 227 230.0

PUMA 4114 7,112 2,436 20.8 3,593 1,242 21.0 137 342 151.8 - - - 99 291 178.7 71 247 211.5

Total Spanish Chinese* Italian Greek Korean

 Table 3. Top Languages Spoken at Home in a Selected County and Its PUMAs for the Population 

 25 Years and Over Who Are Limited English Profi cient and Have Less Than a High School 

 Education, 2006

 

* Chinese combines the PUMS languages “Chinese,” “Mandarin,” and “Cantonese.”

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
16 What State and Local Governments Need to Know

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

Examing Growth in the Foreign-Born Population

Overview

Neighborhood advocacy groups in a city of about 

50,000 have been vocal about the eff ects of a growing foreign-born population on their community. They 

have deluged the city government with requests for 

programs to help recent immigrants acclimate to their 

new environment. With the budget already strained and 

competing requests from other groups for city 

services, government leaders need to make tough 

decisions. In an eff ort to get a handle on the situation, 

the city manager has asked a data analyst to establish 

whether there has indeed been an increase in foreignborn residents in this community.

Strategy and Data Sources

This fi ctitious community that we will call Bedford, 

is not a political/administrative entity that would be 

separately recognized by the Census Bureau in its 

tabulations, but is an established neighborhood that is 

recognized by the city. Since the data must be “current,” decennial census long-form data for 1990 and 

2000 are not useful for this application. The data analyst turns to the ACS for a look at data in the post-2000 

period.

Since Bedford is much smaller than the city in which 

it is contained, city-level data cannot be used as a 

basis for analysis. The city manager has identifi ed 

“geographic fi t” as the most important dimension of 

his request, so the application must provide data for a 

geographic area that would appropriately encompass 

Bedford. The only relevant data available are 5-year 

period estimates from the AFF for each of the 10 census tracts that roughly correspond to Bedford (Figure 

8). Since the city is located in an ACS test county, three 

5-year census tract estimates are available: 1999–2003, 

2000–2004, and 2001–2005. Figure 9 displays the time 

periods covered by each of these three period estimates. 

These three 5-year estimates allow three possible 

comparisons: 1999–2003 vs. 2000–2004, 2000–2004 

vs. 2001–2005, and 1999–2003 vs. 2001–2005. The 

fi rst thing the data analyst has to decide is which 

comparison best establishes whether there has been an 

increase in immigrants in Bedford.

The fi rst two comparisons cover a 6-year period with 4 

years of overlap (Table 4). For example, when comparing 1999–2003 with 2000–2004, the four overlapping 

years are 2000, 2001, 2002, and 2003, and change 

is solely a function of the diff erence between year 1 

419

425

421

431

423

413

415

411

429.01

429.02

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

 Figure 8. Census Tracts Comprising Bedford
What State and Local Governments Need to Know 17

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

1999

2001 to 2005

2000 to 2004

1999 to 2003

2000 2001 2003 2004 2005 2002

 Figure 9. 5-Year Tract Estimates Available for 1999–2005

Source: U.S. Census Bureau, American Community Survey.

(1999) and year 6 (2004). When comparing 2000–2004 

with 2001–2005, the 4 overlapping years are 2001, 

2002, 2003, and 2004, and change is solely a function 

of the diff erence between 2000 and 2005. The 4 years 

of overlap result in a situation where change in the two 

5-year period estimates is likely to be statistically signifi cant only if the magnitude of change between year 

1 (1999) and year 6 (2004), in the case of the 1999–

2003 and 2000–2004 comparison, is relatively large. 

The third comparison, 1999–2003 versus 2001–2005, 

covers 7 years with three overlapping years: 2001, 

2002, and 2003. This means that the diff erence 

between the 1999–2003 and 2001–2005 estimates is 

a function of the change between the two nonoverlapping time periods: 1999–2000 and 2004–2005. The 

data analyst realizes this comparison is likely to prove 

more useful than the others due to the greater length 

of the entire comparison period (7 years) and the 

smaller degree of overlap (3 years). Eventually, as more 

of these 5-year period estimates become available, it 

will be possible to compare data for periods with less 

overlap and, ultimately, two 5-year estimates with no 

overlap.

Analysis and Findings

Table 5 shows the city, neighborhood, and tract-level 

estimates for the foreign-born population for the 

periods 1999–2003 and 2001–2005 and displays the 

estimates of change between these two time periods. 

To increase reliability, the data analyst aggregated the 

10 census tracts and created an estimate for the entire 

neighborhood of Bedford. Appendix 3 explains how to 

calculate margins of error for a derived estimate such 

as this neighborhood aggregation. 

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

 Table 4. Comparisons of Overlapping 5-Year Estimates, 1999–2005

 ACS Years

Estimate Period 1999 2000 2001 2002 2003 2004 2005

4 years of overlap

1999–2003 XXXXX

2000–2004 XXXXX

2000–2004 XXXXX

2001–2005 XXXXX

3 years of overlap

1999–2003 XXXXX

2001–2005 XXXXX

X – Overlapping Year X – Unique Year (Nonoverlapping Year)
18 What State and Local Governments Need to Know

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

This handbook has focused on the margin of error 

(MOE) and coeffi cient of variation (CV) as tools to evaluate the reliability of ACS estimates. The use of MOEs 

for assessing the reliability of change over time is more 

complicated, especially when change is being examined using multiyear estimates. From a technical 

standpoint, change over time is best evaluated 

with multiyear estimates that do not overlap. At 

the same time, many local governments whose only 

source of data will be 5-year estimates will not want to 

wait until 2015 to evaluate change (i.e., compare estimates for 2005–2009 with those for 2010–2014). Also, 

there may be instances where 5-year nonoverlapping 

estimates are available but a small local government 

may not want to go back 10 years to examine change, 

so it chooses a later starting point and some level of 

overlap (e.g., 2007–2011 and 2010–2014).

Change between these two 5-year estimates involves 

an evaluation of change in the nonoverlapping years 

(1999 and 2004, 2000 and 2005). Statistical testing 

was conducted and results of signifi cant diff erences are 

fl agged in Table 5. Calculating the diff erence between 

these two period estimates is simple, but calculating 

the MOEs associated with this diff erence requires the 

recognition that the estimates include common sample 

interviews. Refer to Appendix 1 and Appendix 4 for 

more guidance on making such comparisons, interpreting such comparisons, and calculating MOEs for diff erences derived from such comparisons. 

Walking through the statistical testing for the neighborhood of Bedford, the standard errors (SEs) can be 

derived directly from the published MOEs. For the 

1999–2003 estimate the SE is 967, for the 2001–2005 

the SE is 1,083. The value of C is determined as the 

fraction of overlapping years, here 3 out of 5 or 0.6. 

Appendix 4 instructs us to approximate the SE of this 

diff erence as:

Thus the SE of the diff erence value of 2,663 is 918. 

Appendix 4 explains that the ratio of the estimate 

of the diff erence to the SE of that diff erence gives 

you a test value that, when compared with a critical 

value (1.645 for example for a confi dence level of 90 

percent) allows you to determine if that diff erence is 

statistically signifi cant. This means that the user can be 

certain to a specifi ed degree that the observed diff erence in the two estimates was not due to chance. In 

this example the ratio is 2.9. Since it is greater than 

1.645 we can be confi dent (at 90 percent) that the difference of 2,663 is statistically signifi cant. 

A look at change over time reveals what looks like substantial increases in several census tracts, but only one 

tract increase is statistically signifi cant. Change at the 

neighborhood level shows that the number of foreignborn people has indeed increased by a substantial margin, from 19,161 for the 1999–2003 period to 21,824 

for 2001–2005, a statistically signifi cant increase of 

nearly 2,700 people, or about 14 percent. This compares with the citywide increase of about 8 percent. 

1999–2003 2001–2005 Change, 1999–2003 to 2001–2005

Estimate Estimate Number Percent

Selected City 48,600 52,454 3,854* 7.9*

 Bedford 19,161 21,824 2,663* 13.9*

Census Tract 041100 916 1,034 118 12.9

Census Tract 041300 2,778 3,553 775 27.9

Census Tract 041500 2,211 3,104 893* 40.4*

Census Tract 041900 1,718 2,094 376 21.9

Census Tract 042100 2,213 2,335 122 5.5

Census Tract 042300 1,161 1,400 239 20.6

Census Tract 042500 2,331 2,470 139 6.0

Census Tract 042901 902 859 –43 –4.8

Census Tract 042902 1,492 1,771 279 18.7

Census Tract 043100 3,439 3,204 –235 –6.8

*Signifi cant at the 0.10 level.

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

 Table 5. Change in the Foreign-Born Population of a Selected City, and the Neighborhood of 

 Bedford and Its Component Census Tracts, 1999–2003 and 2001–2005

  2

2

2

21 1 1) ˆˆ ( XXSE #  SESEC

  2 2 SE #  967083,16.01)161,19824,21(
What State and Local Governments Need to Know 19

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

The data analyst concludes that Bedford has indeed 

experienced a substantial increase in its foreign-born 

population over this period. 

Summary: What Have We Learned?

While medium-sized cities, like the one in this case 

study, could have 1-, 3-, and 5-year estimates, these 

data may not refl ect what is going on in various 

neighborhoods. Small areas, such as neighborhoods, 

that are not political/administrative units will have to 

rely on aggregating census tract data, which are only 

available as 5-year estimates. When working with these 

estimates, it is best to minimize the overlap between 

periods being compared. In general, estimates with 4 

years of overlap should be avoided altogether, in that 

diff erences are entirely a function of change between 

just two single nonoverlapping years. A comparison of 

estimates with 3 years of overlap is an improvement, 

but ultimately, as the ACS program continues to move 

forward, 5-year estimates with little or no overlap 

should be employed.

Combining census tracts is a good way to produce 

reliable estimates, as was evidenced by the combination of the set of census tracts that comprised 

Bedford. However, the data analyst could have elected 

to combine just a few census tracts within Bedford to 

see how growth in the immigrant population was distributed within the neighborhood. Indeed, combining 

census tracts 041300, 041500, and 041900 accounted 

for about three-quarters of the foreign-born increase 

in Bedford, which is statistically signifi cant. Such an 

observation can help local offi cials target services provided by local groups to specifi c areas experiencing the 

largest increases in immigrants.

This case study demonstrates the power of using 

5-year estimates as building blocks to create neighborhood statistics, but similar approaches could be used 

to combine 3-year estimates for areas with populations 

over 20,000 into larger geographic areas of particular 

interest. Such aggregations improve the reliability of 

the estimates. 

Conclusion

The American Community Survey (ACS) represents 

new terrain for data users. Its greatest strength is 

that it will off er data on an annual basis, but this also 

results in an array of options that will aff ect how state 

and local governments use the data. This handbook 

illustrates some of the ways that the ACS can be used 

to achieve its full potential to address issues that are 

pertinent to state and local governments.

Understanding multiyear period estimates is critical 

to the proper use of these new data. The concept of a 

multiyear estimate is, in itself, a big leap for most data 

users who are used to the April 1 reference point for 

decennial census data. However, multiyear estimates 

will be the primary data source for most governments. 

Of the approximately 53,000 states, counties, cities, 

urban areas, towns, townships, villages, other minor 

civil divisions, and census designated places, well 

over 90 percent will rely on multiyear estimates, with 

most using 5-year period estimates exclusively. This is 

because most local governments are small, covering 

geographic areas with fewer than 20,000 people. Table 

1 provides information on the availability of estimates 

by type of geographic area.

Still, for some geographic areas, such as counties, 

there will be considerable fl exibility in the choice of 

estimates. One-quarter of all counties—those with at 

least 65,000 people—will have the full choice of 1-, 3-, 

and 5-year estimates. These counties account for more 

than 80 percent of the nation’s population. For these 

areas, and other governmental jurisdictions in the 

same size class, their choices involve a more complex 

series of decisions regarding diff erent sets of estimates. For example, the presence of multiple multiyear 

estimates increases the opportunities for measuring 

change but adds the burden of deciding which set of 

estimates makes the most sense to use. In the end, 

what makes the most sense is a matter of judgment 

regarding the balance between the period of time covered by an estimate and its level of reliability. The key 

is to strive to use only reliable estimates, where the 

time period covered best suits the question at hand.

Good judgment also entails increased attention to a 

number of data features, some of which have always 

been with us but have heretofore been largely confi ned to footnotes and appendixes. The most salient is 

the heightened importance of sampling variability on 

whether data are useful “straight out of the box,” or 

whether some of the strategies described herein (e.g., 

combining geographic areas or data categories) are 

required to make the data reliable.

Finally, there is the issue of how to use multiyear characterizations of an area to measure change over time. 

As the ACS moves forward, a series of multiyear estimates for various time intervals will become available. 

Once successive sets of 3-year and 5-year averages 

become available with little or no overlap, the value of 

ACS data will become even more evident. Moreover, as 

a “feedback loop” develops between the Census Bureau 

and the data user community, new perspectives and 

solutions regarding the use of multiyear averages are 

likely to emerge. 

What State and Local Governments Need to Know 21

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 

ACS. These products are designed to meet the needs 

of the majority of data users and contain predefi ned 
22 What State and Local Governments Need to Know

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

sets of data for standard census geographic areas, 

including both political and statistical geography. 

These products are available on the American 

FactFinder and the ACS Web site.

For users with data needs not met through the general 

purpose products, the Census Bureau off ers “custom” 

tabulations on a cost-reimbursable basis, with the 

American Community Survey Custom Tabulation 

program. Custom tabulations are created by tabulating 

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

complexity, and cost depending on the needs of the 

sponsoring client.

Data profi les. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profi le that should be used if offi cial 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 diff erent 

tables that contain basic distributions of 

characteristics. These tables provide the most detailed 

data and are the basis for other ACS products.

Disclosure avoidance (DA). Statistical methods 

used in the tabulation of data prior to releasing data 

products to ensure the confi dentiality of responses. 

See Confi dentiality. 

Estimates. Numerical values obtained from a 

statistical sample and assigned to a population 

parameter. Data produced from the ACS interviews are 

collected from samples of housing units. These data 

are used to produce estimates of the actual fi gures that 

would have been obtained by interviewing the entire 

population using the same methodology. 

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

allows data fi les to be downloaded from the Census 

Bureau Web site.

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates refl ect the characteristics 

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

will be published for all geographic areas down to the 

census block group level.

Geographic comparison tables. More than 80 

single-variable tables comparing key indicators for 

geographies other than states.

Geographic summary level. A geographic summary 

level specifi es the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifi es the state-county 

hierarchy. Thus, both the state code and the county 

code are required to uniquely identify a county in the 

United States or Puerto Rico. 

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

place where people live or stay that is normally owned 

or managed by an entity or organization providing 

housing and/or services for the residents. These 

services may include custodial or medical care, as well 

as other types of assistance. Residency is commonly 

restricted to those receiving these services. People 

living in GQ facilities are usually not related to each 

other. The ACS collects data from people living in both 

housing units and GQ facilities.

Group quarters (GQ) population. The number of 

persons residing in GQ facilities. 

Item allocation rates. Allocation is a method 

of imputation used when values for missing or 

inconsistent items cannot be derived from the existing 

response record. In these cases, the imputation 

must be based on other techniques such as using 

answers from other people in the household, other 

responding housing units, or people believed to have 

similar characteristics. Such donors are refl ected in a 

table referred to as an allocation matrix. The rate is 

percentage of times this method is used.

Margin of error (MOE). Some ACS products provide 

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

diff erence between an estimate and its upper or lower 

confi dence bounds. Confi dence bounds can be created 

by adding the MOE to the estimate (for the upper 

bound) and subtracting the MOE from the estimate (for 

the lower bound). All published ACS MOE 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 State and Local Governments Need to Know 23

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

Period estimates. An estimate based on information 

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

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population Estimates Program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

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

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

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

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

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

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

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

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

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

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

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

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

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

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

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

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

Appendix A-1

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

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

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

single-year estimates, the period is the calendar year 

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

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

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

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

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

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

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

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

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

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

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

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

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

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

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

monthly CPS collects data for an even narrower time 

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

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

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

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

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

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

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

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

year and 5-year estimates where the periods involved 

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

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

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

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

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

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

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

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

Month

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

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

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

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

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

multiyear estimates describe the population and 

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

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

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

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

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

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

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

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

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

estimates contain 4 years of overlapping coverage. 

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

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

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

5-year

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

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

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

housing characteristics because they are based on the 

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

multiyear estimates provide less current information 

because they are based on both data from the previous 

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

using the “less current” sample used to produce the 

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

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

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

single year is the midyear of the ACS multiyear period 

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

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

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

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

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

Reliability

Multiyear estimates are based on larger sample sizes 

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

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

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

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

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

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

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

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

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

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

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

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

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

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

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

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

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

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

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

a small CV is usually preferable to a multiyear estimate 

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

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

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

use multiyear estimates when they are working with 

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

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

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

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

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

for local planning needs, make comparisons across 

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

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

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

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

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

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

errors and CVs associated with those estimates are also 

shown.

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

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

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

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

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

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

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

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

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

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

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

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

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

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

SE of 4.9 percentage points that yields a CV of 12.2 

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

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

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

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

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

more acceptable level.

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

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

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

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

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

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

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

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

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

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

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

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

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

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

the 2006 ACS. 

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

in a particular characteristic. This is because roughly 

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

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

as shown in Figure 1, when comparing 2006–2008 

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

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

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

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

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

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

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

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

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

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

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

or the 5-year estimates because they are available for 

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

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

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

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

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

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

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

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

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

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

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

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

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

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

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

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

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

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

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

larger geographies and populations when currency is 

more important than the precision of the estimates. 

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

availability of a single-year estimate, calculating the CV 

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

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

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

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

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

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

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

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

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

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

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

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

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

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

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

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

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

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

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

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

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

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

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

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

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

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

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

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

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

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

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

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

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

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

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

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

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

60-month period. 

Most decennial census sample estimates are anchored 

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

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

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

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

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

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

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

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

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

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

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

housing unit or group quarters, some people may stay 

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

course of the year. For example, migrant workers move 

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

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

estimates of the characteristics of some areas. 

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

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

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

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

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

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

to enumerate people at the place where they live or stay 

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

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

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

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

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

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

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

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

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

there for more than 2 months.

More on Reference Periods

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

being that the remaining basic demographic questions 

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

the date the interview is provided. Questions with their 

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

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

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

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

they refl ect the circumstances on the day the data are 

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

represent the average characteristics over a full year (or 

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

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

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

comparing ACS estimates with Census 2000. 

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

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

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

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

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

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

while Census 2000 collected income information for 

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

responses are a mixture of 12 reference periods ranging 

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

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

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

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

period is used for all interviews. In contrast, 

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

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

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

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

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

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

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

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

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

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

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

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

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

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

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

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

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

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

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

(such as estimates from the ACS or the decennial 

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

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

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

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

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

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

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

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

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

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

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

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

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

larger the sample size, the smaller the standard error 

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

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

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

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

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

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

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

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

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

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

MOEs published with the ACS estimates correspond 

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

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

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

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

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

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

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

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

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

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

Measures of Sampling Error

Appendix 3.
A-12 Appendix

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

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

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

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

would be derived as 

Confi dence Intervals

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

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

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

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

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

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

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

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

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

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

(20,000 + 1,645).

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

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

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

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

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

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

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

1.960/1.645.1

 If the desired confi dence level is 99 

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

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

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

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

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

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

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

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

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

should be set to zero instead.

1

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

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

error from the standard error in those years.

2

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

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 1.645

1.960

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.

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

MOECL

  CICL LCL UCL , .

MOEACS 

MOEACS 

Xˆ

MOE MOEACS 1.645

2.576

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 771,782 14,723 757,059

U95 771,782 14,723 786,505

( )

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

100 0.1%

771,782

7,512 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

Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria 

MOEFairfax r3,303 , 

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

 r ¦cMOEagg MOEc

2

MOEc

th

c

52,354 19,464 17,190 89,008 

 011 r2, MOEArlington , 

r 18,391,246 r4,289

MOEAlexandria r1,854
Appendix A-15

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

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived proportion.

 is the estimate used as the numerator of the 

derived proportion.

 is the estimate used as the denominator of the 

derived proportion.

There are rare instances where this formula will fail—

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

happens, use the formula for derived ratios in the next 

section which will provide a conservative estimate of 

the MOE.

The example below shows how to derive the MOE for 

the estimated proportion of Black females 25 years of 

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

 The estimated proportion is:

where is the ACS estimate of Black females 25 

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

females 25 years of age and older in Fairfax County.

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older in Fairfax County 

with a graduate degree) and denominator (number 

of Black females 25 years of age and older in Fairfax 

County).

 Table 2. Data for Example 2

Characteristic Estimate MOE

Black females 25 years 

 and older with a graduate 

 degree (numerator)

4,634 +989

Black females 25 years 

 and older

 (denominator)

31,713 +601

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

 r989 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.

0.0311

31,373

985.1

31,373

970,408.7 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 

     989 [ 0.1461 * 601 ] 2 2 2  

978,121 7,712.3 970,408.7

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 r989, 328 r1, MOEden

0.2135

6,440

1,375.2

6,440

1,891,259.1 r

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

 over (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%

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

 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

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

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

MOEA

MOEB

 r1,405

* ) ˆ ( 2 2 2 MOEnum  R MOEden 

     989 [ 0.7200 * 1,328 ] 2 2 2  

978,121 913,318.1 1,891,259.1
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

1.0521

762,475

802,210

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

0.0433

762,475

33,038.3

762,475

1,091,528,529 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

2 1

ˆ

ˆ ˆ 100%*

X

X X

Percent Change  

ˆ

ˆ ˆ 100%*

1

2 1  X

X X

Percent Change

5.21%

762,475

802,210 762,475 100%*  ̧ 

1

·  ̈

©

§ 

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

* ) ˆ ( 2

1

2 2 MOE2  R MOE 

     22,866 [ 1.0521 * 22,666 ] 2 2 2

 

1,091,528,529

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

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2 



 





SE SE

X X

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5 

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.

   0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2 



 





SE SE

X X

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4 

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

1 2 1 ) 1 ˆ ˆ SE(X  X #  C SE  SE

  2

2

2

1 1 C SE  SE

2

2

2

1 SE  SE
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:

$37,700 $38,936

286.7

296.1 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).

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,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.