What High School Teachers Need to Know i

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

U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

USCENSUSBUREAU

Helping You Make Informed Decisions

Issued

December 2008

A Compass for Understanding and Using 

American Community Survey Data

What High School Teachers Need to Know
Acknowledgments William P. O’Hare, Senior Fellow, The Annie E. Casey Foundation, 

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

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

Population Reference Bureau, 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., 

and Susan P. Love, Consultant, provided initial review of this handbook.

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

Community Survey Offi ce, provided the concept and directed the 

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

Understanding and Using American Community Survey Data. Cheryl V. 

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

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

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

this handbook series.

The American Community Survey program is under the direction of 

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

Weinberg, Assistant Director for the American Community Survey and 

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

Offi ce.

Other individuals who contributed to the review and release of these 

handbooks include Dee Alexander, Herman Alvarado, Mark Asiala,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Zimolzak.

Linda Chen and Amanda Perry of the Administrative and Customer Services 

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

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

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

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

and production management.
A Compass for Understanding and Using 

American Community Survey Data Issued December 2008

U.S. Department of Commerce

Carlos M. Gutierrez, 

Secretary

John J. Sullivan, 

Deputy Secretary

Economics and Statistics Administration

Cynthia A. Glassman, 

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock, 

Director

What High School Teachers Need to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What High School Teachers 

Need to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2008.

Economics and Statistics 

Administration

Cynthia A. Glassman,

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Steve H. Murdock,

Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Offi cer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Offi ce

ECONOMICS

AND STATISTICS

ADMINISTRATION
What High School Teachers Need to Know iii

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

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

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

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

Why the ACS Is Important to High School Teachers and Students ................2

What Information Does the ACS Provide? .....................................................2

How the ACS Works .......................................................................................3

What Geographic Areas Are Available in the ACS?........................................4

Accessing ACS Data ......................................................................................6

Understanding and Interpreting ACS Data ................................................. 12

 What Is a Period Estimate? ..................................................................................12

 Interpreting and Comparing Single-Year and Multiyear Estimates ........................12

 Accuracy of ACS Data .........................................................................................12

How Teachers Can Use ACS Data ................................................................ 13

Using the ACS in Social Studies Courses .................................................... 13

Using the ACS in Geography Courses ......................................................... 17

Using the ACS in Mathematics and Statistics Courses ............................... 21

 Proportions and Ratios .......................................................................................22

 Statistical Inference ............................................................................................22

Other Resources for Working With ACS Data .............................................. 27

Conclusion .................................................................................................. 27

Glossary...................................................................................................... 28

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 High School Teachers 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 High School Teachers Need to Know 1

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

Are you a high school teacher looking for new sources 

of timely information and ways to make your courses 

more engaging and relevant to students? If so, this 

handbook will introduce you to a rich, new source of 

online data: The American Community Survey (ACS). 

The ACS provides a wide array of social, economic, and 

demographic information about the nation, your state, 

and your local communities. These data can be used 

to teach concepts and skills, such as statistical literacy, 

and content areas, including social studies, geography, 

and mathematics. Because the ACS is updated annually, 

it provides fresh, timely data for your students every 

year.

This handbook begins with a brief overview of the ACS: 

what it is, how it came about, and why it is important 

to high school teachers and students. We describe the 

types of information and geographic areas covered by 

the ACS and explain how to understand and correctly 

interpret ACS data. Next, we review how to access 

ACS data online and provide specifi c examples that 

illustrate how you can incorporate ACS data into your 

activities or lesson plans to address a variety of social 

studies, geography, and mathematics standards. At the 

end of the handbook, a glossary and a series of technical appendixes are provided that discuss advanced 

applications and issues with the ACS. Throughout the 

handbook, we will point readers to these appendixes 

or other online resources that provide additional or 

more detailed information about particular topics. 

Introduction

The Importance of the Decennial Census

The decennial census plays a fundamental role in our democracy. The U.S. Constitution stipulates that 

political power should be based on population size and that the federal government should conduct a 

census of the entire population every 10 years to apportion seats in the U.S. House of Representatives. 

This puts the decennial census at the heart of our political process. In the 1960s, the courts determined 

that decennial census data should also be used to draw both federal congressional and state legislative 

districts that are equal in population size to make sure every person’s vote carries the same weight (i.e., 

the “one person-one vote” decision). 

For more information about the history of the decennial census, see Measuring America: The Decennial 

Censuses from 1790 to 2000, available online at <http://www.census.gov/prod/www/abs/ma.html>. For 

more information about how census data are used for redistricting, see Strength in Numbers: Your Guide to 

Census 2000 Redistricting Data From the U.S. Census Bureau available online at <http://www.census

.gov/rdo/pdf/Guidelines_Censu2000.pdf>.

In addition to apportionment and redistricting, decennial census data have also been used for a variety 

of important functions: government agencies use the information to monitor program effectiveness and 

distribute federal funds; teachers, journalists, and nonprofi t leaders use the data to promote a better 

understanding of our society; social scientists use the data to conduct research; and businesses use the 

information to learn more about potential consumers for their products.

What Is the American Community Survey (ACS)?

As a teacher, you are probably already familiar with 

the decennial census, conducted every 10 years in the 

United States since 1790 (see text box). The census 

provides vital data our country needs to perform governmental functions and to understand the changing 

social fabric of our nation. Recent censuses have used 

a “short form” to collect basic information from the 

entire population and a “long form” to collect detailed 

socioeconomic and housing characteristics from a sample of the population (about 1 in every 6 households). 

Please visit the Census Bureau’s Census in Schools Web 

site at <http://www.census.gov/dmd/www/teachers

.html> where teachers will fi nd lesson plans about the 

decennial census and free materials for use in their 

classrooms.
2 What High School Teachers Need to Know

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

Because the pace of change has accelerated so rapidly in the United States, it is no longer suffi cient to 

get detailed data about our population once every 10 

years. That is why our government recently started a 

new population and housing survey called the ACS. 

The ACS is a continuous, nationwide survey designed 

to provide timely demographic, housing, and socioeconomic data for states and local communities every year. 

The ACS will replace the decennial census long form 

in 2010 and thereafter by collecting detailed socioeconomic and housing information throughout the decade 

rather than only once every 10 years.1 Future decennial censuses will consist of a short form only that will 

continue to be used to collect the basic data needed for 

apportionment and redistricting.

Why the ACS Is Important to High School Teachers 

and Students

Since today’s students will become major users of ACS 

data over the next few decades, it is vital that they 

have a good understanding of how this new resource 

can be used, including its strengths and weaknesses. 

While the ACS provides much of the same data that the 

decennial census formerly provided, there are some 

important diff erences that need to be understood if the 

new data are to be used eff ectively. Exposing students 

to the ACS during high school will help them appreciate the power—and limitations—of this new data 

source, and how it might serve them in postsecondary education and in their careers. Using the ACS in 

the high school classroom may also give students an 

early exposure to statistical ideas and procedures in a 

gradual and less intimidating way. 

The ACS can bring learning to life for students by 

 giving them up-to-date information about their communities. Like most Americans, students are more 

interested in the forces that shape their local situation 

than they are in more abstract and/or national trends 

and patterns. The ACS can be used to examine a variety of social issues at the state and local levels with 

timely data. 

What Information Does the ACS Provide?

Beginning in 2006, ACS data describe the characteristics of the population living in both housing units 

and group quarters facilities such as prisons, nursing 

homes, and college dormitories. Prior to the 2006 ACS, 

only the housing unit population was surveyed. The 

ACS covers a wide range of subjects that can help students better understand the social, economic, housing, 

and demographic characteristics of their communities. 

The ACS questionnaire covers all of the topics listed in 

Table 1. 

Today’s students are accustomed to hands-on, interactive learning. By providing up-to-date information on 

a wide range of subjects through user-friendly online 

products, the ACS provides ample opportunities for 

students to gain fi rst-hand knowledge about important 

socioeconomic trends in their communities. Whether 

the course is focused on current events, geography, 

sociology, mathematics, economics, or political science, the ACS can help put important national issues 

into a state and local context. For example, students 

can compare the racial/ethnic composition of their 

school district with that of the state or country as 

a whole, compare median earnings for women and 

men working full-time in their communities, or use 

education and earnings statistics to learn about the 

economic returns of a college degree. ACS data can 

also be used to teach mathematical and statistical 

concepts such as proportions, ratios, margins of error, 

confi dence intervals, and coeffi cients of variation. 

More options for applying the ACS in the classroom are 

described later in this handbook in the section titled, 

“How Teachers Can Use ACS Data.” 

1 See Appendix 8 for Web site links to additional background information about the ACS.
What High School Teachers Need to Know 3

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

 Table 1. Subjects Included in the American Community Survey

Demographic Characteristics

Age

Sex

Hispanic Origin

Race

Relationship to Householder 

(e.g., spouse)

Economic Characteristics

Income

Food Stamps Benefi t

Labor Force Status

Industry, Occupation, and Class 

of Worker

Place of Work and Journey to 

Work

Work Status Last Year

Vehicles Available

Health Insurance Coverage*

Social Characteristics 

Marital Status and Marital History*

Fertility

Grandparents as Caregivers

Ancestry

Place of Birth, Citizenship, and 

Year of Entry

Language Spoken at Home

Educational Attainment and 

School Enrollment

Residence One Year Ago

Veteran Status, Period of Military 

Service, and VA ServiceConnected Disability Rating*

Disability

Housing Characteristics

Year Structure Built

Units in Structure

Year Moved Into Unit

Rooms

Bedrooms

Kitchen Facilities

Plumbing Facilities

House Heating Fuel

Telephone Service Available

Farm Residence

Financial Characteristics

Tenure (Owner/Renter)

Housing Value

Rent

Selected Monthly Owner Costs

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

 Source: U.S. Census Bureau.

In 2005, the ACS began sampling nearly 3 million 

addresses each year, resulting in about 2 million completed interviews from which the ACS estimates are 

based.2 This annual ACS sample is smaller than recent 

decennial census long-form samples. For example, the 

Census 2000 long-form sample included about 18 million housing units. The Census 2000 long-form sample 

was designed to provide reliable estimates for small 

populations and for small areas such as census tracts 

and places. The ACS must combine population and 

housing data from multiple years to produce reliable 

estimates for geographic areas with populations below 

65,000. As a result, the ACS will provide 1-, 3-, and 5-

year estimates of data.

ACS data are very timely because they are released in 

the year immediately following the year in which they 

are collected. ACS data collected from 2000 through 

2004, and published from 2001 through 2005, are 

available for geographic areas with 250,000 people 

or more, including all states, the District of Columbia, 

and many large counties and cities. In 2005, the ACS 

sample size increased substantially. As a result, starting in 2006, ACS information has been published for 

areas with populations of 65,000 or more (see Table 2). 

In December 2008, the fi rst 3-year estimates will be 

released based on combined data from the 2005, 2006, 

and 2007 surveys. These estimates will be available 

for all geographic areas with at least 20,000 people. 

Three-year estimates will then be updated annually by 

removing the earliest year and replacing it with the 

latest year. For example, in 2009, the 3-year estimates 

will be based on combined data from the 2006, 2007, 

and 2008 ACS surveys.

By the end of 2009, the ACS will have sampled approximately 15 million addresses, resulting in a total sample 

large enough to provide data for geographic areas as 

small as census tracts and block groups. In 2010, the 

ACS will provide the fi rst 5-year estimates of demographic, housing, social, and economic data for all geographic areas, including those with fewer than 20,000 

people.3 These estimates will be based on combined 

data from the 2005, 2006, 2007, 2008, and 2009 surveys. Like the 3-year estimates, these 5-year estimates 

will also be updated annually by removing the earliest 

year and replacing it with the latest one (see Table 2). 

Thus, in 2011 the 5-year estimates will be based on 

combined data from the 2006, 2007, 2008, 2009, and 

2010 surveys. 

How the ACS Works

2 The ACS was not fully implemented until 2005. Prior to 2005 the ACS 

sampled about 800,000 addresses each year. Information on the initial 

sample size and the fi nal number of completed interviews in the 2007 

ACS are available at <http://www.census.gov/acs/www/SBasics

/SSizes/SSizes07.htm>.

3 In both the decennial census and the ACS, all information provided 

by respondents is confi dential. No personal information that could be 

used to identify individuals or households is ever released or shared. 

In addition, data will not be published for small geographic areas or 

groups of people if it would be possible to identify a particular individual.
4 What High School Teachers Need to Know

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

 Table 2. Collection and Release Dates of Data From the ACS 

(Pattern repeats after 2012)

 2006 2007 2008 2009 2010 2011 2012

Type of period estimate

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

 with 65,000 or more people

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

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

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

 areas including those with 2009 2010 2011

 fewer than 20,000 people 

Source: Citro and Kalton (2007: Table 2-6, page 50).

What Geographic Areas Are Available in the ACS?

The ACS data are tabulated and published for a wide 

variety of geographic areas. These diff erent types of 

geographic areas range in size from broad geographic 

regions (Northeast, Midwest, South, and West) to small 

towns to census tracts, and even to clusters of city 

blocks. The population size of an area determines if 

the ACS data are provided as 1-year, 3-year, or 5-year 

estimates. Table 3 lists the major types of geographic 

areas published in the ACS, as well as the type of ACS 

estimates each area will receive. (Note that Table 3 

does not include estimates for Puerto Rico.) For example, ACS data collected in 2006 are currently available 

for geographic areas with at least 65,000 people, 

including regions, divisions, states, the District of 

Columbia, Puerto Rico, congressional districts, Public 

Use Microdata Areas, and many large counties, metropolitan areas, cities, school districts, and American 

Indian areas. For a defi nition and description of these 

diff erent types of geographic areas, see the glossary at 

the back of the handbook.

Release year (late summer or fall)

Calendar year(s) of monthly data collection
What High School Teachers Need to Know 5

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

 

 Type of geographic area 

Total 

number of 

areas

Percent of total areas receiving . . .

1-year, 

3-year, 

& 5-year 

estimates

3-year & 

5-year 

estimates 

only

5-year 

estimates 

only

States and District of Columbia 51 100.0 0.0 0.0

Congressional districts 435 100.0 0.0 0.0

Public Use Microdata Areas* 2,071 99.9 0.1 0.0

Metropolitan statistical areas 363 99.4 0.6 0.0

Micropolitan statistical areas 576 24.3 71.2 4.5

Counties and county equivalents 3,141 25.0 32.8 42.2

Urban areas 3,607 10.4 12.9 76.7

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

American Indian areas, Alaska Native areas, and 

Hawaiian homelands 607 2.5 3.5 94.1

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

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

ZIP Code tabulation areas 32,154 0.0 0.0 100.0

Census tracts 65,442 0.0 0.0 100.0

Census block groups 208,801 0.0 0.0 100.0

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

* 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.
6 What High School Teachers Need to Know

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

ACS data are available through a series of user-friendly 

products, all of which are available through the Census 

Bureau’s American FactFinder Web site. To get to the 

American FactFinder Web site, fi rst go to the Census 

Bureau’s Home Page at <http://www.census.gov> and 

then click on American FactFinder, circled in red in the 

screen shot in Figure 1.

Accessing ACS Data

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

 Figure 1. Census Bureau Home Page
What High School Teachers Need to Know 7

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

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

 Figure 2. American FactFinder Home Page

From the American FactFinder home page, click on 

Data Sets, and then choose the American Community 

Survey from the list of options in the drop down menu,

as shown in Figure 2. (Note: A tutorial for the 

American FactFinder Web site is available online at 

<http://factfi nder.census.gov/home/en/epss/sitetour

/3_01.html>.)

This takes you to a Web page listing all of the data sets 

available in the American FactFinder for the American 

Community Survey, from the most recent year back to 

2005. Data for the 2000–2004 ACS are archived and 

only available from the FTP site. The survey for the 

most recent year (2007 ACS) is automatically selected, 

along with the available data products, as shown in 

Figure 3. ACS data products range from one-page summary tables to detailed cross-tabulations of data. 

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

 Figure 3. ACS Data Products Available Through American FactFinder
8 What High School Teachers Need to Know

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

A brief description of each type of ACS data product 

and the geographic areas it covers is provided in Table 

4 below. More detailed information about each data 

product is also available on the Census Bureau Web site 

by clicking on the link 2007 Quick Guide.

Data product

Geographic areas 

covered Description

Data profi les All* Provide broad social, economic, housing, and demographic profi les.

Narrative profi les All* Summarize the information in the data profi les using 

concise, nontechnical text.

Selected population 

 profi les

All* Provide broad social, economic, and housing profi les for 

a large number of race, ethnic, and ancestry groups.

Ranking tables States, DC, 

Puerto Rico

Provide state rankings of estimates across 86 key 

variables.

Subject tables All* Similar to data profi les but include more detailed ACS 

data, classifi ed by subject.

Detailed tables All* Provide access to the most detailed ACS data and crosstabulations of ACS variables.

Geographic comparison

 tables

All* Compare geographic areas other than states (e.g., counties or congressional districts) for key variables.

Thematic maps All* Interactive, online maps that can be used to display ACS 

data.

Custom tables All* Allow the user to extract specifi c rows of data from the 

ACS detailed tables.

Summary fi les All* 

Provide access to the detailed tables through a series of 

comma-delimited text fi les on the Census Bureau’s FTP 

site <www2.census.gov>.

Public Use Microdata

 Sample fi les

States, DC, 

Puerto Rico, PUMAs

Provide access to ACS microdata for data users with SAS 

and SPSS software experience.

*Note: Data will be available for local areas and small population subgroups with the release of 3-year and 5-year estimates.

 Table 4. Summary of ACS Data Products

Of the diff erent data products, the Data Profi les are 

perhaps the best place to start for high school students because they provide broad social, economic, 

housing, and demographic profi les of states and local 

areas. Data Profi les provide information ranging from 

mean travel time to work, to educational attainment, to 

homeownership rates. After you click on Data Profi les

(shown in Figure 3), the screen shown in Figure 4 will 

appear. From the drop-down menus, select the geographic type, the state, and the specifi c geographic 

area you want. In this example, the geography we 

have selected is the Unifi ed School District for Fairfax 

County, Virginia. After making geographic selections, 

click on Show Result, circled in red in Figure 4. 

The data shown in Figure 5 will then appear. The Social 

Data Profi le pops up as the default profi le. To access 

Economic, Housing, or Demographic Profi les, simply 

click on the links provided, circled in red in Figure 5.
What High School Teachers Need to Know 9

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

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

 Figure 4. Selecting Geographics in American FactFinder

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

 Figure 5. Sample Data Profi le for School District
10 What High School Teachers Need to Know

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

For those who are just learning how to interpret statistics, Narrative Profi le summarizes the data in concise, 

nontechnical terms as illustrated in Figure 6. To access 

the Narrative Profi le, just click on the link provided 

below the link for the Demographic Profi le, or use the 

navigation tool in the left sidebar. Both options are 

circled in red in Figure 5. 

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

 Figure 6. Sample Narrative Profi le for School District

The other ACS data products described earlier can also 

be accessed through the links provided on the ACS 

Data Sets Web page, as shown in Figure 3. 

Ranking Tables provide comparisons of estimates 

across a set of key variables from the ACS (e.g., median 

family income or the percentage of people born in 

Asia). The data can be displayed in either tabular or 

chart format and are available for the 50 states, the 

District of Columbia, and Puerto Rico. 

The Geographic Comparison Tables are similar to 

the Ranking Tables but compare geographic areas other 

than states (e.g., counties or congressional districts) for 

key variables. The Thematic Maps include the same 

set of variables, but the data are presented in a series 

of maps that students can produce through an interactive menu.

Subject Tables are similar to the Data Profi les but 

include more detailed information, classifi ed by 

subject. For example, students interested in people 

born outside of the United States can select the table 

 “Selected Characteristics of the Native and ForeignBorn Populations” or another summary table on that 

topic (see Figure 7). The Selected Population Profi les allow the data user to access summary tables for 

a large number of race, ethnic, and ancestry groups 

and beginning with the 2007 ACS, summary tables 

based on country of birth.
What High School Teachers Need to Know 11

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

As the name implies, the Detailed Tables include 

the most detailed ACS data and provide the foundation for other types of data products. Detailed 

Tables are available through the American FactFinder 

or through the ACS Summary File on the Census 

Bureau’s FTP site <www.census.gov/acs/www/Special 

/acsftp.html>. These Detailed Tables may be useful 

for Advanced Placement students or those working 

on longer-term projects, but they are generally more 

useful to researchers. The same is true of the Public 

Use Microdata Sample (PUMS) fi les, which contain a 

sample of individual records of people and households 

that responded to the survey. Note that these records 

do not contain any personally identifi able information. 

Students with access to statistical software packages 

such as SAS, SPSS, and STATA can use the PUMS fi les to 

create custom tabulations of ACS data.

Students may fi nd that some information is not available for some of the more detailed data products, 

especially for small geographic areas. In some cases, 

statistics will be missing from a table because there are 

too few cases to produce a reliable estimate. In other 

cases, the Census Bureau has suppressed entire tables, 

because there are too few people in the statistical universe to allow the release or presentation of any data 

(e.g., characteristics of Navajo Indians living in Maine). 

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

 Figure 7. Sample Subject Table
12 What High School Teachers Need to Know

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

What Is a Period Estimate? 

Because ACS data are collected continuously throughout the year, the ACS provides “period” estimates 

rather than the “point-in-time” estimates provided by 

the decennial census. This means that ACS estimates 

describe the characteristics of the population over a 

period of time: 12 months for the 1-year estimates; 

36 months for the 3-year estimates; and 60 months 

for the 5-year estimates, whereas Census 2000 estimates approximate a description of the population as 

of a point in time—April 1, 2000. For most areas with 

consistent population characteristics over the calendar 

year, ACS estimates will likely be very similar to those 

that would be obtained from a point-in-time survey like 

the decennial census. However, in areas with seasonal 

populations, such as resort communities, or areas that 

experience rapid population change during a calendar 

year, the ACS estimates might be noticeably diff erent 

from those that would be obtained at one time of the 

year. ACS estimates also diff er from 2000 long-form 

estimates in several other ways that are described in 

detail in Appendix 2 of this handbook. 

Interpreting and Comparing Single-Year and 

Multiyear Estimates

While single-year ACS estimates are similar to point-intime estimates, multiyear estimates are more complex 

and harder to interpret. For example, it is tempting 

to describe a 3-year estimate based on data from 

2005–2007 as a “2006 average,” since 2006 is the 

middle year of the period. However, ACS 3-year estimates are not produced by averaging each of the three 

single-year estimates included in the time period. That 

is, a 2005–2007 estimate of the percent of children 

in poverty is not produced by averaging the percents 

from 2005, 2006, and 2007. Instead, the 2005–2007 

estimate is produced by fi rst combining the data 

records for all 3 years, and then calculating the percent 

of children in poverty for the 36-month period. The 

resulting estimate should be interpreted as the “percent of children in poverty in 2005–2007.” Similarly, a 

5-year period estimate for 2005–2009 should not be 

interpreted as a 2007 average. For more information 

on understanding single-year and multiyear estimates, 

see Appendix 1 of this handbook.

ACS data users need to be careful not to compare different geographic areas using estimates from diff ering 

period lengths. A 1-year estimate is not comparable 

to a 3-year estimate or a 5-year estimate because the 

time periods are inconsistent. To compare geographic 

areas, it is important to use a consistent time frame—

all 1-year estimates, all 3-year estimates, or all 5-year 

estimates. For example, suppose that students wanted 

to compare estimates for Boston with estimates for 

Nantucket, a small island off the coast of 

Massachusetts. Even though the ACS publishes 1-year 

estimates for Boston, only 5-year estimates will be 

published for Nantucket. Therefore, in 2010, when 

5-year estimates for smaller geographic areas become 

available, students should compare 2005–2009 estimates for Nantucket with 2005–2009 estimates for 

Boston, even though more recent single-year estimates are available for Boston. In 2010 and thereafter, 

the availability of 5-year estimates for every level of 

geography down to the census tract will allow users to 

compare data across all geographic areas. Additional 

guidance on the use and comparison of ACS single- and 

multiyear estimates is provided in Appendix 1 of this 

handbook.

ACS data can also be used to look at trends over time. 

While comparison of single-year estimates for diff erent 

years is straightforward, comparison of multiyear estimates can be tricky. The Census Bureau recommends 

comparing estimates for periods that do not overlap, 

such as comparing 2005–2007 estimates with 2008–

2010 estimates. Comparisons of 3-year estimates from 

2005 to 2007 and 2006 to 2008 are unlikely to show 

much diff erence because two of the years overlap 

(both include 2006 and 2007 data). The same is true 

of 5-year estimates with overlapping periods. Finally, 

as noted earlier, the ACS sample prior to 2006 did 

not include the group quarters population. Data users 

should make comparisons of 2006 ACS data with ACS 

data from prior years only when the geographic area 

under consideration does not include a substantial 

group quarters population. Students can also conduct 

formal tests for statistical signifi cance as described in 

Appendix 4. 

Accuracy of ACS Data

All estimates from sample surveys, including the ACS 

and the census long-form samples, are subject to error. 

There are two broad types of error that can occur: sampling error and nonsampling error. Nonsampling errors 

can result from mistakes in how the data are reported 

or coded, problems in the sampling frame or survey 

questionnaires, or problems related to nonresponse 

or interviewer bias. The Census Bureau tries to minimize nonsampling errors in the ACS by using trained 

interviewers and by carefully reviewing the survey’s 

sampling methods, data processing techniques, and 

questionnaire design. Appendix 6 provides a more 

detailed description of diff erent types of errors in the 

ACS and other measures of ACS quality.

Understanding and Interpreting ACS Data
What High School Teachers Need to Know 13

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

Sampling error occurs when data are based on a 

sample of a population rather than the full population. 

Sampling error is easier to measure than nonsampling 

error and can be used to assess the statistical reliability of survey data. For any given area, the larger the 

sample and the more months included in the data, the 

greater the confi dence in the estimate. The Census 

Bureau reported the 90-percent confi dence intervals 

for every ACS estimate produced for the 2005 ACS and 

earlier. Confi dence intervals allow data users to assess 

the level of sampling error, or statistical reliability, of 

an estimate. A 90-percent confi dence interval defi nes a 

range around an estimate expected to contain the true

value with a level of confi dence of 90 percent. Beginning with the release of the 2006 ACS data, margins of 

error are provided for every ACS estimate. Margins of 

error (MOEs) can be easily converted into confi dence 

intervals. For example, according to 2006 ACS data, 

18.3 percent of children under age 18 in the United 

States live in poverty with a margin of error of plus or 

minus 0.2 percentage points. By adding and subtracting the MOE from the estimate, we can calculate the 

90-percent confi dence interval for this estimate. Therefore, we can be 90 percent confi dent that the true child 

poverty rate in the United States in 2006 falls somewhere between 18.1 percent and 18.5 percent.

18.3% (estimate) – 0.2 percentage points (MOE) = 18.1% 

(lower confi dence limit) 

18.3% (estimate) + 0.2 percentage points (MOE) = 18.5% 

(upper confi dence limit) 

Detailed information about sampling error and instructions for calculating confi dence intervals and margins 

of error are included in Appendix 3. A classroom application is illustrated later in this handbook. 

Now that we have reviewed the types of information 

the ACS provides and how students can access ACS 

data on the Census Bureau’s Web site, the next several 

sections of this handbook provide specifi c examples 

to illustrate how teachers in social studies, geography, 

mathematics, and statistics courses can use ACS in the 

classroom.

How Teachers Can Use ACS Data

We recognize that there is considerable state-by-state 

variation in the specifi c standards and curriculum 

teachers are required to follow in core subject areas 

such as mathematics, science, and social studies. 

Although the United States does not have nationally 

mandated standards, several professional organizations have developed standards in social studies, 

geography, and mathematics.4 We have drawn on 

these standards to illustrate how the ACS can be used 

in social studies, geography, mathematics, and statistics courses. 

Using the ACS in Social Studies Courses

4 These include “Geography for Life: The National Geography Standards” from the Geography Education Standards Project; “Expectations 

of Excellence: Curriculum Standards for Social Studies” from the 

National Council for the Social Studies; and “Principles and Standards 

for School Mathematics” from the National Council of Teachers of Mathematics.

The information gathered in the ACS makes it especially 

useful for social studies teachers. Many of the topics 

examined in the ACS can be used to shed light on fundamental institutions and social processes in American 

society (such as families, fertility, and economic stratifi cation) and a host of social issues (such as poverty, 

racial equality, and housing). 

What makes the ACS particularly appealing for use 

in the high school classroom is the ability to provide 

 timely information about the communities where students live. For example, the ACS can be used to study 

the living arrangements of children, the number of 

elderly people in a community, or the employment situation at the national, state, or local level. The ACS provides a rich set of information about workers, including 

employment status (working, not working, not in the 

labor force) as well as the full-time or part-time status, 

occupation, and industry of workers. Students can also 

look at the educational attainment levels of people 

in particular occupations and the average incomes 

in those jobs. This information could be particularly 

useful for students thinking about college and their 

occupational futures.

The ability to compare data sets from the ACS is also 

important. For many students, the most meaningful results will come from comparing fi gures across 

diff erent geographic areas, comparing population 
14 What High School Teachers Need to Know

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

subgroups, or looking at trends over time. Sometimes 

more than one perspective can be included in an 

analysis, such as comparing change over time between 

two population subgroups in the same community. 

For example, one could look at changes in the poverty 

rates of various population groups over time.

Some examples might help make this perspective 

clearer. The 2006 ACS shows that the poverty rate 

(percent of people below the poverty level in the past 

12 months) for the state of Ohio is 13.3 percent. For 

many students, it would be helpful to know if the 

poverty rate in Ohio is higher or lower than the poverty 

rate for another state, for example, Michigan. Since the 

ACS provides consistent data for cities and states, it is 

easy to make this comparison. The 2006 ACS indicates 

that the poverty rate for Ohio is 13.3 percent and for 

Michigan it is 13.5 percent. Students may observe that 

the poverty rate in Ohio appears to be lower than the 

poverty rate for Michigan. But they cannot draw that 

conclusion without looking more closely at these data. 

To provide additional perspective, students can access 

an ACS Ranking Table to see how poverty in Ohio compares with poverty levels in other states (see Figure 8).

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

 Figure 8. Poverty Rates by State
What High School Teachers Need to Know 15

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

This ACS table shows that Ohio ranks twenty-fi rst 

among the states in terms of the poverty rate and 

students can see that Michigan ranks twentieth. (Note 

that a rank of 1 indicates the highest estimated poverty 

rate, not the best poverty rate.) Students can also see 

from this ranking function that the poverty rate among 

states varies from a low of 7.8 percent in Maryland (not 

visible in Figure 8) to a high of 21.1 percent in 

Mississippi. Clicking on the “with statistical signifi cance” link in the left sidebar provides important 

results of testing to determine if the estimates for 

these two states are diff erent beyond sampling error. 

Figure 9 shows that the poverty rate in Ohio is not 

statistically diff erent from the rate for Michigan. 

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

 Figure 9. Poverty Rates—Statistical Signifi cance

Students may also be interested in using the ACS Subject Tables to explore how diff erent groups in a city 

such as Columbus, Ohio, compare with each other in 

terms of poverty (see Figure 10). Subject Tables is also 

accessed from the link shown in Figure 3. After you 

click on Subject Tables, a geography selection screen 

will pop up as illustrated previously in Figure 4. 

This ACS Subject Table shows that the poverty rate for 

the White alone, not Hispanic or Latino population in 

Columbus is 14.6 percent compared with 33.1 percent 

for Blacks or African Americans and 25.9 percent for 

people of Hispanic or Latino origin. One lesson from 

this type of “decomposition” is that the overall poverty 

rate in Columbus, Ohio, masks very diff erent rates for 

subgroups such as Whites, Blacks, and Latinos. Such 

decomposition could focus on other characteristics 

such as age, sex, or home ownership. The breadth of 

data in the ACS enables teachers to customize their 

lesson plans based on local issues.
16 What High School Teachers Need to Know

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

This table also illustrates one of the challenges in using 

ACS data for smaller geographic units, as described 

earlier. Note that the poverty rate is not shown for 

American Indians and Alaska Natives, Asians, Native 

Hawaiians and Other Pacifi c Islanders, Some Other 

Race, or Two or More Races. This is because there 

were not suffi cient numbers of people in these racial 

groups in Columbus for the Census Bureau to provide 

reliable estimates. The prevalence of missing data in 

some tables will be reduced when multiyear estimates 

become available starting in December 2008. 

The ACS can also be used to examine changes over 

time. As noted above, the 2006 poverty rate in Ohio 

was 13.3 percent (with a margin of error of 0.3 percent). But what was it in earlier years? The ACS shows 

that in 2005 the poverty rate in Ohio was 13.0 percent 

(with a margin of error of 0.3 percent). This comparison suggests that poverty has increased in Ohio 

between 2005 and 2006, but students would need to 

conduct a statistical test to determine if the estimate 

for 2006 is actually statistically diff erent from the estimate for 2005. For more information about conducting 

such statistical tests, see Appendixes 3 and 4 at the 

end of this handbook.

These are just a few examples of some of the important issues teachers can have students address with 

data from the ACS. Others could include such topics as:

• What is the average (mean) income of families in 

your community compared with the average for 

the state?

• What percentage of the population in your state is 

not working or looking for work?

• What is the median value of houses in your community?

• How many households in your county have no 

vehicles?

• What is the high school dropout rate (percent of 

16-to-19-year-olds who are not in school and do 

not have a high school diploma) in your city?

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

 Figure 10. Poverty Rates Among Subgroups in Columbus, Ohio
What High School Teachers Need to Know 17

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

Standards for the national 

council for social studies

Questions and topics that can be 

examined with ACS data

Data elements in the ACS that 

pertain to this question or issue

Culture and cultural 

 diversity

Describe cultural similarities and 

 diff erences between the local 

 community and the state or nation.

Race, Hispanic origin, ancestry, place 

 of birth, language spoken at home

People, places, and 

 environments

Describe links of people in your 

 community with other places. 

 Compare the population density of 

 your community with that of other 

 communities in the state.

Place of birth, language spoken 

 at home, residence one year ago, 

 population density

Production, distribution,

 and consumption

Describe the workforce in the local 

 community.

Occupation, industry, class of worker

Science, technology, and 

 society

Describe the kinds of scientists who 

 work in your community.

Occupation, industry, class of worker

Global connections Describe the people in your community 

 or state who were born outside the 

 United States.

Place of birth, language spoken at 

 home, ability to speak English

Civic ideals and practices Describe the potential voters in your 

 community.

Number and characteristics of 

 people over age 18, number and 

 characteristics of citizens over age 18

Source: U.S. Census Bureau, based on the National Standards for Social Studies Teachers.

 Table 5. Examples of Using ACS Data in the Context of Standards From the National Council for the 

 Social Studies

The ACS can also be used to address several specifi c 

social science standards as specifi ed by the National 

Council for the Social Studies (NCSS).5 We briefl y 

describe several of these standards in Table 5 and 

suggest how ACS data can be used to address each 

standard. 

5 National Council for the Social Studies, 2002, National Standards for 

Social Studies Teachers, revised 2002.

6 Geography Education Standards Project. 1994. Geography for Life: 

The National Geography Standards. Washington, DC: National 

Geographic Society Committee on Research and Exploration.

Using the ACS in Geography Courses

The American FactFinder Web site also provides students with the capability to interactively create thematic maps using ACS data. Teachers can use ACS data 

and the American FactFinder to address several of the 

National Geography Standards, such as understanding the characteristics, distribution, and migration of 

human populations on Earth’s surface, and using maps 

and other geographic representations, tools, and technologies to acquire, process, and report information 

from a spatial perspective.6 

Students can explore the spatial distribution of immigrants across the United States using ACS data on the 

percent of foreign-born individuals by state. Students 

can use Thematic Maps in the American FactFinder 

to create a choropleth map by state (see Figure 11).7 

Thematic Maps is accessed through the link provided 

on the ACS Data Sets page as shown in Figure 3. After 

you click on Thematic Maps, the Select Geography

screen will appear. For maps, you can either select 

“nation” to get a map of the entire United States or 

7 A choropleth map is a thematic map in which areas are shaded or 

patterned in proportion to the measurement of the characteristic that 

is being displayed on the map.
18 What High School Teachers Need to Know

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

“state” to get a map of just one state. Once you select 

a geography, the Select Theme screen will appear as 

shown in Figure 11. To fi nd the data you want, you can 

search by subject or keyword or see a list of all possible themes or tables. In this example, we search by the 

keyword “foreign-born,” and select the theme Percent 

of People Who Are Foreign Born: 2006.

With nation selected as the geography, the thematic 

map is shown in Figure 12.

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

 Figure 11. Selecting Themes for Maps

 Figure 12. Sample Thematic Map of the United States

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
What High School Teachers Need to Know 19

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

This map quickly reveals the states with the highest 

proportion of immigrants including California, Nevada, 

Florida, New Jersey, and New York, where 18.9 percent or 

more of the population was born outside of the United 

States. Students could also use ACS data to drill down 

within a particular state like California to examine how 

immigrants are spatially distributed across counties 

(see Figure 13). They would do this by selecting state 

instead of nation in the Select Geography screen. Of 

course, as explained above, single-year estimates are 

available only for counties with a population of at least 

65,000. That is why some California counties are not 

shown. Once 3-year estimates for 2005–2007 are 

available in December of 2008, data will be shown for 

all counties with populations of at least 20,000.

Based on the county map, students discover that 

immigrants are distributed throughout California, but 

are most concentrated in counties like Los Angeles and 

Imperial, where the share who are foreign born can 

reach as high as 36 percent. Although the thematic 

maps in the American FactFinder pop up with the 

ranges or data classes and color scheme automatically 

selected, the navigation tools in the left sidebar (circled 

in red in Figure 13) provide options for students to 

change the Data Classes and color scheme.

 Figure 13. Foreign-Born People by County for California

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
20 What High School Teachers Need to Know

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

If students click on the View as a table navigation 

tool in the left sidebar (circled in red in Figure 13), a 

screen will pop up with a table showing the data for 

every state (see Figure 14). Students can download 

data tables like this one in Microsoft Excel format to 

import for use with other mapping software such as 

ArcView or MapViewer. To do this, they simply click on 

the Download option as highlighted in Figure 14, and a 

window will pop up with download options, including 

Microsoft Excel.

 Figure 14. Downloading Data From the American FactFinder

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

If students click on the navigation tool View with 

statistical signifi cance (also circled in Figure 13), the 

thematic map will change to indicate with crosshatching which counties have an estimate of percent 

foreign born that is not statistically diff erent from the 

percentage in a reference county (see Figure 15). 

With Los Angeles County selected as the reference 

county, this map indicates that the estimates for every 

other county are statistically diff erent from those for 

Los Angeles County. This tool provides a quick way for 

students to assess statistical signifi cance without having to calculate it themselves. 

The wealth of data in the ACS combined with this easyto-use thematic mapping capability off er a number of 

opportunities for geographic activities on the 

American FactFinder Web site. Students can also download a wide variety of ACS data tables for use with 

other mapping software applications. However, students who download such data tables will need to be 

aware of missing geographic areas when they import 

these tables for use with other mapping packages. 

For example, if they download a 2006 ACS table with 

county-level data for California, the resulting Excel fi le 

will be missing data for all counties with fewer than 

65,000 people. 
What High School Teachers Need to Know 21

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

 Figure 15. Thematic Map for Counties Showing Statistical Signifi cance

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

Using the ACS in Mathematics and Statistics 

Courses

Mathematics and statistics teachers have a multitude 

of data sources they can use in developing homework 

problems, project assignments, and examples for 

course lectures. The ACS off ers three unique advantages as a potential data source:

1. The ACS provides a rich source of current data about 

students’ local communities, their state, and the 

nation. Students may fi nd it easier to understand 

and remember mathematical and statistical concepts 

and skills they learn with data and examples they 

fi nd both interesting and personally relevant.

2. ACS data are easily accessible online and are 

updated annually. This saves teachers the work of 

creating and maintaining data sets each year for 

students to use for assignments and projects.

3. The ACS estimates provided online include their 

margins of error, making it easy for teachers and 

students to use these data to calculate additional 

measures of error (e.g., standard errors or coeffi -

cients of variation) and to conduct tests of statistical 

signifi cance. 
22 What High School Teachers Need to Know

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

In selecting examples to illustrate potential applications of ACS data in math and statistics courses, we 

consulted the Principles and Standards for School 

Mathematics, published by the National Council of 

Teachers of Mathematics (NCTM), as well as the 

2007–2008 detailed course description for AP 

Statistics from the College Board.8, 9 Our review suggests that ACS data and information can be used to 

address components of several of the NCTM standards, 

including Measurement and Data Analysis and Probability, as well as the section on Statistical Inference of the 

AP statistics course. Teachers and students in AP 

statistics may also be interested in more detailed information about the sample design and methodology of the 

ACS. A technical paper is available online at <http://

www.census.gov/acs/www/Downloads/tp67.pdf>.

Proportions and Ratios

ACS data can be used to teach students how to calculate proportions and ratios for a variety of topics for 

their school district, city, county, or state. For example, 

to analyze the Hispanic population of their school district, students in Fairfax County, Virginia, could access 

the table “Hispanic or Latino Origin by Specifi c Origin” 

from the ACS product Detailed Tables (see Figure 16).

The data in this table could be used to calculate the 

proportion of the population in this school district that 

is Hispanic (e.g., 130,753/1,010,443) or the proportion 

of the Hispanic population that is Mexican (e.g., 19,624 

/130,753). It could also be used to calculate the ratio 

of Central Americans to Mexicans within the school 

district (e.g., 42,762/19,624).

Statistical Inference 

As described previously in this handbook, all estimates 

provided in ACS online data products either include 

the margin of error or the upper and lower bounds of 

a 90-percent confi dence interval. Another measure of 

the precision of an estimate is the standard error. Standard errors measure the variability of an estimate due 

to sampling, and they are needed to conduct tests of 

statistical signifi cance. Statistical signifi cance indicates 

whether the diff erence between two estimates is likely 

to represent a real diff erence that exists within the 

full population or whether instead it has occurred by 

chance due to sampling. In this example, we illustrate 

8 National Council of Teachers of Mathematics, Principles and Standards for School Mathematics, Reston, Virginia: 2000.

9 College Board, AP Statistics Course Description: May 2007–2008.

 Figure 16. Hispanic Population in Fairfax County, Virginia, Public Schools

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
What High School Teachers Need to Know 23

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

how teachers and students can use the MOEs for ACS 

estimates to calculate additional MOEs and to test for 

statistical signifi cance on a topic related to current 

events. For more information on how to calculate and 

interpret measures of sampling error, see Appendix 3 

at the end of this handbook.

In the last year, foreclosures and falling housing values 

have been widely reported. However, prior to 2007, 

real estate markets in many metropolitan areas were 

still booming, and students can use the ACS to track 

the changes in home values in their communities. In 

this example, we’ll assume that students in Prince 

William County, Virginia, want to determine if their 

county experienced a statistically signifi cant increase 

in housing values between 2005 and 2006.10 Because 

they want to establish a baseline for homes that could 

potentially be aff ected by rising interest rates and foreclosure, they only want to examine homes that have a 

mortgage, not those that are owned free and clear. 

To fi nd ACS data on housing values, students can check 

the Subject Tables found on the Data Sets page on the 

American FactFinder (AFF) Web site (shown in Figure 3). 

When they click on Subject Tables, the Select Geography screen shown in Figure 17 appears. After making 

the selections shown below to get data for Prince 

William County, they click Next. 

10 The 2005 ACS survey did not include group quarters population, 

while the 2006 ACS did. Therefore, as noted earlier in this handbook, 

students need to be careful when comparing data estimates from 

2005 and 2006. In this example, the data item of interest is collected 

only for the household population, not the group quarters population. 

Therefore, it is reasonable to make comparisons between 2005 and 

2006 ACS estimates for this data item.

 Figure 17. Selecting Geography: Prince William County, Virginia

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
24 What High School Teachers Need to Know

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

 Figure 18. Searching Subject Tables by Major Topic

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

 Figure 19. Finding Tables With Data on Housing Values

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

On the main Subject Tables screen students can use the 

drop-down menu to search tables by major topic areas 

(see Figure 18). For this example, they would select the 

subject Housing Financial Characteristics to get the 

three table choices shown in Figure 19. 

By selecting Table S2506, Financial Characteristics for 

Housing Units With a Mortgage, they obtain the distribution of owner-occupied housing units by value for 

2006.
What High School Teachers Need to Know 25

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

By clicking on the View this table from 2005 navigation 

tool in the left sidebar (circled in red in Figure 20), students can quickly and easily get the comparable data 

for Prince William County for 2005 (see Figure 21).

 Figure 20. Housing Values in Prince William County, Virginia, in 2006

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

 Figure 21. Housing Values in Prince William County, Virginia, in 2005

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
26 What High School Teachers Need to Know

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

These data indicate that the median value of owneroccupied homes with a mortgage increased from 

$397,800 in 2005 to $445,300 in 2006 in this county. 

These data also show an increase from 29.6 percent 

to 38.9 percent in the share of homes with values of 

$500,000 or more. It appears that this shift of homes 

into the highest value category may be resulting in the 

jump in median home value from 2005 to 2006. Using 

the margin of error provided in the tables, students 

can conduct a statistical test to determine whether the 

change from 2005 to 2006 in the share of homes in the 

top value category is statistically signifi cant.

To determine whether the observed diff erence between 

29.6 percent in 2005 and 38.9 percent in 2006 is 

statistically signifi cant, students must conduct a test 

using the following formula:11

then the diff erence between estimates and is 

statistically signifi cant at the specifi ed confi dence level, 

CL

where is estimate i( ) = 2,1

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

 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).12

First, students need to derive the standard error for 

each of these estimates using the following formula: 

 

Substituting the values for 2006 yields: 

The standard error for the 2005 estimate is: 

Filling in the values for the statistical test for this 

example results in the following:

Since the test value (4.41) is greater than the critical 

value for a confi dence level of 90 percent (1.645), then 

students could conclude that the percent of owneroccupied homes with values of $500,000 or more 

in their county had indeed increased signifi cantly 

between 2005 and 2006.

Data users can also examine the relative amount of 

sampling error associated with a particular estimate 

by calculating a coeffi cient of variation (CV). CVs relate 

the size of the standard error to the size of the estimate itself. Larger CVs indicate less reliable estimates. 

A general guideline is that CVs should be less than 30 

percent for estimates to be considered reliable. For the 

example above, the CV for the 2005 estimate of the 

percent of homes with values of $500,000 or more is:

The CV for the 2006 estimate is:

Since both of these CVs are below 10 percent, these 

estimates can be considered to be reliable. 

The availability of ACS data for multiple years, time 

periods (2005–2007, 2005–2009), and geographic 

areas off ers students extensive choices in making 

comparisons. However, it is important to follow the 

general guidelines for interpreting single- and multiyear estimates when making comparisons and conducting tests of statistical signifi cance. In addition, due to 

changes in geographic boundaries, sample frame, and 

collection methods in the ACS over time, students may 

fi nd the guidance in Appendix 4 of this handbook helpful in determining whether a particular comparison is 

appropriate and valid. 

11 Step-by-step instructions for this calculation as well as general guidance on making comparisons with ACS data are included in Appendix 

4 at the end of this handbook. Instructions for calculating the MOE, 

confi dence interval, standard error, and coeffi cient of variation, are 

detailed in Appendix 3.

12 The MOEs published with the ACS estimates correspond to the 90-

percent confi dence level. If students want to conduct a test at the 95- 

percent or 99-percent confi dence level, then the published MOEs must 

be converted to the desired confi dence level by using an adjustment 

factor. Detailed instructions for making these adjustments are provided 

in Appendix 3 of this handbook. 

13 Note that for ACS data from 2005 and earlier years, the value 1.65 

was used to derive the MOE, and therefore is used in the denominator 

in this calculation rather than 1.645, which was used beginning with 

the 2006 ACS data.

0532.0

6.29

576.1 CV * 100 = 5.3%

ZCL

SESE

XX

!





2

2

2

1

21

ˆˆ

Xi

ˆ

SEi

X1

ˆ X 2

ˆ

ZCL

645.1

MOE SE 

398.1

645.1

2.3 SE 

576.1

65.1

6.2 SE 13

   576.1398.1

6.299.38 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

414.4

107.2

3.9

438.4

3.9

484.2954.1

3.9 

0351.0

9.38

398.1 CV * 100 = 3.5% 
What High School Teachers Need to Know 27

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

Teachers and students can fi nd a wealth of additional 

information about the ACS on the Census Bureau’s Web 

site <www.census.gov/acs>. However, sorting through 

all of the information available on the ACS would take a 

long time without a good map of the available resources. 

In Appendix 8, we provide links to online resources 

that data users are likely to fi nd the most useful. The 

resources listed below cover many of the topics discussed in this handbook, but in greater detail: 

Other Resources for Working With ACS Data

 ACS Data Resources

• FactFinder Help (online help, census data information, glossary, and tutorial)

<http://factfi nder.census.gov/home/en/epss/main.html>

• 2007 Guide to the Data Products (Web page)

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

• American Community Survey Design and Methodology (technical paper) 

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

• Accuracy of the ACS Data (online document)

<http://www.census.gov/acs/www/Downloads/ACS/accuracy2007.pdf>

• ACS Sample Size (Web page)

<http://www.census.gov/acs/www/SBasics/SSizes/SSizes07.htm>

• ACS Quality Measures (Web page)

<http://www.census.gov/acs/www/UseData/sse/>

• 2006 Data Users Handbook (online document) 

<http://www.census.gov/acs/www/Downloads/Handbook2006.pdf>

• How to Use the ACS Data (Web page)

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

• Guidance on Comparing 2007 ACS Data (online tables)

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

• Detailed information on ACS Topics

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

Conclusion

Now that you’ve been introduced to the ACS and 

viewed a small sample of the many ways it can be 

used in courses from social studies to geography to 

mathematics and statistics, we hope you are motivated 

to explore the ACS data on American FactFinder on 

your own. The ACS provides an unparalleled source of 

rich information that students and teachers can exploit 

to learn more about their community and their country while they master concepts and skills in the core 

subjects of social studies, geography, and mathematics. It is important to note that this handbook is being 

written in the summer of 2008, and data from many 

smaller places are not yet available. However, the fi rst 

set of ACS products with 3-year estimates will be available by December 2008. Beginning in 2010 when ACS 

data become available for every geographic level on 

a regular basis, teachers and students from even the 

smallest places in the United States will have access 

to rich information about their communities that is 

updated each year. 
28 What High School Teachers Need to Know

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

Glossary

Accuracy. One of four key dimensions of survey 

quality. Accuracy refers to the 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 
What High School Teachers Need to Know 29

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

sets of data for standard census geographic areas, 

including both political and statistical geography. 

These products are available on the American 

FactFinder and the ACS Web site.

For users with data needs not met through the general 

purpose products, the Census Bureau off ers “custom” 

tabulations on a cost-reimbursable basis, with the 

American Community Survey Custom Tabulation 

program. Custom tabulations are created by tabulating 

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

complexity, and cost depending on the needs of the 

sponsoring client.

Data profi les. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profi le that should be used if offi cial 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 diff erent 

tables that contain basic distributions of 

characteristics. These tables provide the most detailed 

data and are the basis for other ACS products.

Disclosure avoidance (DA). Statistical methods 

used in the tabulation of data prior to releasing data 

products to ensure the confi dentiality of responses. 

See Confi dentiality. 

Estimates. Numerical values obtained from a 

statistical sample and assigned to a population 

parameter. Data produced from the ACS interviews are 

collected from samples of housing units. These data 

are used to produce estimates of the actual fi gures that 

would have been obtained by interviewing the entire 

population using the same methodology. 

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

allows data fi les to be downloaded from the Census 

Bureau Web site.

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates refl ect the characteristics 

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

will be published for all geographic areas down to the 

census block group level.

Geographic comparison tables. More than 80 

single-variable tables comparing key indicators for 

geographies other than states.

Geographic summary level. A geographic summary 

level specifi es the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifi es the state-county 

hierarchy. Thus, both the state code and the county 

code are required to uniquely identify a county in the 

United States or Puerto Rico. 

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

place where people live or stay that is normally owned 

or managed by an entity or organization providing 

housing and/or services for the residents. These 

services may include custodial or medical care, as well 

as other types of assistance. Residency is commonly 

restricted to those receiving these services. People 

living in GQ facilities are usually not related to each 

other. The ACS collects data from people living in both 

housing units and GQ facilities.

Group quarters (GQ) population. The number of 

persons residing in GQ facilities. 

Item allocation rates. Allocation is a method 

of imputation used when values for missing or 

inconsistent items cannot be derived from the existing 

response record. In these cases, the imputation 

must be based on other techniques such as using 

answers from other people in the household, other 

responding housing units, or people believed to have 

similar characteristics. Such donors are refl ected in a 

table referred to as an allocation matrix. The rate is 

percentage of times this method is used.

Margin of error (MOE). Some ACS products provide 

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

diff erence between an estimate and its upper or lower 

confi dence bounds. Confi dence bounds can be created 

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

upper bound) and subtracting the margin of error from 

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

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

level. 

Multiyear estimates. Three- and fi ve-year estimates 

based on multiple years of ACS data. Three-year 

estimates will be published for geographic areas with 

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

will be published for all geographic areas down to the 

census block group level. 

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

Nonsampling error. Total survey error can be 

classifi ed into two categories—sampling error and 

nonsampling error. Nonsampling error includes 

measurement errors due to interviewers, respondents, 

instruments, and mode; nonresponse error; coverage 

error; and processing error.
30 What High School Teachers Need to Know

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

Period estimates. An estimate based on information 

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

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population Estimates Program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

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

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

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

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

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

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

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

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

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

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

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

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

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

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

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

geographic areas with a population of 20,000 or more.
Appendix A-1

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

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

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

single-year estimates, the period is the calendar year 

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

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

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

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

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

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

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

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

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

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

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

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

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

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

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

monthly CPS collects data for an even narrower time 

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

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

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

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

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

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

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

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

year and 5-year estimates where the periods involved 

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

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

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

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

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

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

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

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

Month

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

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

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

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

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

multiyear estimates describe the population and 

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

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

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

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

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

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

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

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

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

estimates contain 4 years of overlapping coverage. 

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

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

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

5-year

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

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

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

housing characteristics because they are based on the 

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

multiyear estimates provide less current information 

because they are based on both data from the previous 

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

using the “less current” sample used to produce the 

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

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

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

single year is the midyear of the ACS multiyear period 

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

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

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

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

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

Reliability

Multiyear estimates are based on larger sample sizes 

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

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

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

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

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

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

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

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

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

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

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

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

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

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

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

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

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

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

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

a small CV is usually preferable to a multiyear estimate 

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

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

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

use multiyear estimates when they are working with 

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

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

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

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

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

for local planning needs, make comparisons across 

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

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

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

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

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

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

errors and CVs associated with those estimates are also 

shown.

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

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

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

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

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

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

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

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

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

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

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

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

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

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

SE of 4.9 percentage points that yields a CV of 12.2 

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

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

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

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

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

more acceptable level.

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

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

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

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

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

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

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

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

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

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

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

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

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

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

the 2006 ACS. 

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

in a particular characteristic. This is because roughly 

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

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

as shown in Figure 1, when comparing 2006–2008 

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

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

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

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

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

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

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

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

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

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

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

or the 5-year estimates because they are available for 

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

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

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

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

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

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

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

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

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

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

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

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

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

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

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

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

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

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

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

larger geographies and populations when currency is 

more important than the precision of the estimates. 

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

availability of a single-year estimate, calculating the CV 

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

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

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

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

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

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

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

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

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

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

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

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

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

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

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

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

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

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

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

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

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

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

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

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

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

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

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

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

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

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

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

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

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

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

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

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

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

60-month period. 

Most decennial census sample estimates are anchored 

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

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

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

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

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

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

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

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

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

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

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

housing unit or group quarters, some people may stay 

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

course of the year. For example, migrant workers move 

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

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

estimates of the characteristics of some areas. 

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

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

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

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

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

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

to enumerate people at the place where they live or stay 

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

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

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

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

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

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

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

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

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

there for more than 2 months.

More on Reference Periods

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

being that the remaining basic demographic questions 

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

the date the interview is provided. Questions with their 

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

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

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

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

they refl ect the circumstances on the day the data are 

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

represent the average characteristics over a full year (or 

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

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

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

comparing ACS estimates with Census 2000. 

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

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

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

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

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

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

while Census 2000 collected income information for 

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

responses are a mixture of 12 reference periods ranging 

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

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

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

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

period is used for all interviews. In contrast, 

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

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

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

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

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

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

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

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

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

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

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

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

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

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

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

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

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

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

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

(such as estimates from the ACS or the decennial 

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

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

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

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

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

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

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

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

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

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

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

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

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

larger the sample size, the smaller the standard error 

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

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

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

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

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

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

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

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

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

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

MOEs published with the ACS estimates correspond 

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

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

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

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

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

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

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

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

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

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

Measures of Sampling Error

Appendix 3.
A-12 Appendix

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

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

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

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

would be derived as 

Confi dence Intervals

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

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

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

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

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

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

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

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

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

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

(20,000 + 1,645).

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

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

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

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

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

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

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

1.960/1.645.1

 If the desired confi dence level is 99 

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

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

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

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

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

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

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

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

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

should be set to zero instead.

1

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

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

error from the standard error in those years.

2

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

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 645.1

960.1

95 

Factors Associated With Margins of

Error for Commonly Used Confi dence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

  723,14357,12 645.1

960.1 MOE95 r r 

645.1

MOEACS SE 

512,7 645.1

357,12 SE 

CL XL  MOECL ˆ

CL XU  MOECL ˆ

MOECL

  CL ULCI CLCL , .

MOEACS

MOEACS

Xˆ

MOE MOEACS 645.1

576.2

99 
Appendix A-13

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

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

confi dence level for the number of civilian veterans in 

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

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

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

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

757,059 to 786,505.

The CI is also useful when graphing estimates, to show 

the extent of sampling error present in the estimates, 

and for visually comparing estimates. For example, 

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

in constructing the CI above, the user could be 90 

percent certain that the value for the population was 

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

Coeffi cients of Variation

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

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

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

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

barometer of the stability, and thus the usability of a 

sample estimate. It can also help a user decide whether 

a single-year or multiyear estimate should be used for 

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

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

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

estimation period increases, the sample size increases 

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

indicates that the sampling error is small relative to the 

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

the estimate is close to the population value. In some 

applications a small CV for an estimate is desirable and 

use of a multiyear estimate will therefore be preferable 

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

desired level of precision.

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

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

the estimate is very reliable. If the CV was noticeably 

larger, the usability of the estimate could be greatly 

diminished. 

While it is true that estimates with high CVs have 

important limitations, they can still be valuable as 

L95  059,757723,14782,771

U95  505,786723,14782,771

( )

18,355 20,000 21,645

building blocks to develop estimates for higher levels 

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

improve the reliability of those estimates as evidenced 

by reductions in the CVs.

Calculating Coeffi cients of Variation From Standard 

Errors

The CV can be expressed as

where is the ACS estimate and is the derived SE 

for the ACS estimate.

For example, to determine the CV for the estimated 

number of civilian veterans in the state of Virginia in 

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

and the SE derived previously (7,512).

This means that the amount of sampling error present 

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

of the estimate.

The text box below summarizes the formulas used 

when deriving alternative sampling error measures 

from the margin or error published with ACS estimates.

100 ˆ u 

X

SE CV

Xˆ

%1.0100

782,771

512,7 CV u 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confi dence Levels

Standard Error (SE)

Confi dence Interval (CI)

Coeffi cient of Variation (CV)

MOE MOE ACS

.1 645

.1 960 95 

MOE MOE ACS

.1 645

.2 576 99 

.1 645

MOE ACS SE 

100 ˆ u 

X

SE CV

SE

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

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

Calculating Margins of Error for Derived 

Estimates

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

the ability to develop unique estimates called derived 

estimates. These derived estimates are usually based 

on aggregating estimates across geographic areas or 

population subgroups for which combined estimates 

are not published in American FactFinder (AFF) tables 

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

four age groups not collapsed).

ACS tabulations provided through AFF contain the 

associated confi dence intervals (pre-2005) or margins 

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

confi dence level. However, when derived estimates are 

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

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

the MOE for these derived estimates. The MOE helps 

protect against misinterpreting small or nonexistent 

diff erences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

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

confi dence level other than 90 percent is desired, the 

user should fi rst calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confi dence level as described earlier in this appendix.

Calculating MOEs for Aggregated Count Data

To calculate the MOE for aggregated count data:

1) Obtain the MOE of each component estimate.

2) Square the MOE of each component estimate.

3) Sum the squared MOEs.

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

 MOEs.

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

where is the MOE of the component estimate.

The example below shows how to calculate the MOE 

for the estimated total number of females living alone 

in the three Virginia counties/independent cities that 

border Washington, DC (Fairfax and Arlington counties, 

Alexandria city) from the 2006 ACS.

The aggregate estimate is:

Obtain MOEs of the component estimates: 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs.

Thus, the derived estimate of the number of females 

living alone in the three Virginia counties/independent 

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

MOE for the estimate is +4,289.

Calculating MOEs for Derived Proportions

The numerator of a proportion is a subset of the 

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

households that are female). To calculate the MOE for 

derived proportions, do the following:

1) Obtain the MOE for the numerator and the MOE 

for the denominator of the proportion.

2) Square the derived proportion.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator by 

the squared proportion.

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

the numerator.

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

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

proportion.

 Table 1. Data for Example 1 

Characteristic Estimate MOE

Females living alone in 

 Fairfax County 

 (Component 1)

52,354 +3,303

Females living alone in 

 Arlington County

 (Component 2)

19,464 +2,011

Females living alone in 

 Alexandria city

 (Component 3)

17,190 +1,854

Fairfax Arlington  XXXX ˆˆˆˆ Alexandria 

MOEFairfax r 303,3 , 

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

r ¦cMOEagg MOEc

2

MOEc

th

c

  008,89190,17464,19354,52

 011 MOEArlington r ,2 , 

r r 289,4246,391,18

MOEAlexandria r ,1854
Appendix A-15

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

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived proportion.

 is the estimate used as the numerator of the 

derived proportion.

 is the estimate used as the denominator of the 

derived proportion.

There are rare instances where this formula will fail—

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

happens, use the formula for derived ratios in the next 

section which will provide a conservative estimate of 

the MOE.

The example below shows how to derive the MOE for 

the estimated proportion of Black females 25 years of 

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

 The estimated proportion is:

where is the ACS estimate of Black females 25 

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

females 25 years of age and older in Fairfax County.

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older in Fairfax County 

with a graduate degree) and denominator (number 

of Black females 25 years of age and older in Fairfax 

County).

 Table 2. Data for Example 2

Characteristic Estimate MOE

Black females 25 years 

 and older with a graduate 

 degree (numerator)

4,634 +989

Black females 25 years 

 and older

 (denominator)

31,713 +601

1461.0

713,31

634,4

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

r 989 MOEnum , 601 MOEden r 

MOEnum

MOEden den

den

num

X

X

p ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ

X gradBF ˆ

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

Thus, the derived estimate of the proportion of Black 

females 25 years of age and older with a graduate 

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

MOE for the estimate is +0.0311. 

Calculating MOEs for Derived Ratios

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

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

1) Obtain the MOE for the numerator and the MOE 

 for the denominator of the ratio.

2) Square the derived ratio.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator 

 by the squared ratio.

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

 numerator.

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

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

 the ratio.

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived ratio.

 is the estimate used as the numerator of the 

derived ratio.

 is the estimate used as the denominator of the 

derived ratio.

0311.0

373,31

1.985

373,31

7.408,970 r 

r r MOEp 

MOEnum

MOEden

den

num

X

X

R ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ i

den

num den

p

X

MOE p MOE

MOE ˆ

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

     ]601*1461.0[989 2 2 2  

  7.408,9703.712,7121,978

den

num den

R

X

MOE R MOE

MOE ˆ

)* ˆ ( 2 2 2 r  
A-16 Appendix

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

The example below shows how to derive the MOE for 

the estimated ratio of Black females 25 years of age 

and older in Fairfax County, Virginia, with a graduate 

degree to Black males 25 years and older in Fairfax 

County with a graduate degree, based on the 2006 

ACS.

The estimated ratio is:

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County) and denominator (number 

of Black males 25 years of age and older in Fairfax 

County with a graduate degree).

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator.

Thus, the derived estimate of the ratio of the number 

of Black females 25 years of age and older in Fairfax 

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

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

the MOE for the estimate is +0.2135.

Calculating MOEs for the Product of Two Estimates

To calculate the MOE for the product of two estimates, 

do the following:

1) Obtain the MOEs for the two estimates being 

multiplied together.

2) Square the estimates and their MOEs.

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

4) Multiply the second squared estimate by the 

fi rst estimate’s squared MOE.

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

6) Take the square root of (5).

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

MOE for the product is calculated as:

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

respectively.

 is the MOE of the fi rst estimate.

 is the MOE of the second estimate.

The example below shows how to derive the MOE for 

the estimated number of Black workers 16 years and 

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

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

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

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

Thus, the derived estimate of Black workers 16 years 

and over who commute by public transportation is 

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

MOEnum r 989, 328 MOEden r ,1

2135.0

440,6

2.375,1

440,6

1.259,891,1 r 

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

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

Percent of Black workers 16 

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

13.4% +2.7%

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

 Table 3. Data for Example 3 

Characteristic Estimate MOE

Black females 25 years and 

 older with a graduate 

 degree (numerator)

4,634 +989

Black males 25 years and

 older with a graduate degree 

 (denominator)

6,440 +1,328

7200.0

440,6

634,4

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

2 22 2 MOE uBA A MOEB B uur MOEA

MOEA

MOEB

r 405,1

)* ˆ ( 2 2 2 MOEnum  R MOEden 

     ]328,1*7200.0[989 2 2 2  

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

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

Calculate the MOE by dividing the square root of the 

prior result by the denominator ( ). 

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

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

The text box below summarizes the formulas used to 

calculate the margin of error for several derived estimates.

Calculating MOEs for Estimates of “Percent Change” or 

“Percent Diff erence”

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

two estimates (for example, the same estimates in two 

diff erent years) is commonly calculated as

Because is not a subset of , the procedure 

to calculate the MOE of a ratio discussed previously 

should be used here to obtain the MOE of the percent 

change.

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

2005 estimates of the number of persons in Maryland 

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

The percent change is:

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

The MOEs for the numerator ( ) and denominator 

( ) are:

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

product of the squared ratio and the squared MOE of 

the denominator (MOE1):

 Table 5. Data for Example 5

Characteristic Estimate MOE

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2005

762,475 +22,666

0521.1

475,762

210,802

ˆ

ˆ ˆ

1

2 X

X

R

2 Xˆ 1 Xˆ

2 Xˆ

1 Xˆ

1 Xˆ

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

0433.0

475,762

3.038,33

475,762

529,528,091,1 r 

r r MOER 

r ¦cMOE agg MOE c

2

den

num den

p

X

MOE p MOE ) MOE ˆ

(ˆ * 2 2 2 r  

1

12

ˆ

ˆˆ *%100

X

XX

Percent Change  

ˆ

ˆˆ *%100

1

12  X

XX

Percent Change

%21.5

475,762

475,762210,802 *%100  ̧ 

1

·  ̈

©

§ 

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

)* ˆ ( 2

1

22 MOE2  R MOE 

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

 

529,528,091,1

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  
A-18 Appendix

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

One of the most important uses of the ACS estimates is 

to make comparisons between estimates. Several key 

types of comparisons are of general interest to users: 

1) comparisons of estimates from diff erent geographic 

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

proportion of people below the poverty level in two 

counties); 2) comparisons of estimates for the same 

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

the proportion of people below the poverty level in a 

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

estimates with the corresponding estimates from past 

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

2006 and 2000).

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

the comparison takes into account the sampling error 

associated with each estimate, thus determining 

whether the observed diff erences between estimates 

are statistically signifi cant. Statistical signifi cance 

means that there is statistical evidence that a true 

diff erence exists within the full population, and that 

the observed diff erence is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical signifi cance when making comparisons is 

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

be made are also discussed.

Determining Statistical Signifi cance

When comparing two estimates, one should use the 

test for signifi cance described below. This approach 

will allow the user to ascertain whether the observed 

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

is statistically signifi cant). 

The test for signifi cance can be carried out by making 

several computations using the estimates and their 

corresponding standard errors (SEs). When working 

with ACS data, these computations are simple given 

the data provided in tables in the American FactFinder.

1) Determine the SE for each estimate (for ACS 

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

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

2) Square the resulting SE for each estimate.

3) Sum the squared SEs.

4) Calculate the square root of the sum of the 

squared SEs.

5) Calculate the diff erence between the two estimates.

6) Divide (5) by (4).

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

with the critical value for the desired level of 

confi dence (1.645 for 90 percent, 1.960 for 95 

percent, 2.576 for 99 percent).

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

between the two estimates can be considered 

statistically signifi cant at the level of confi dence 

corresponding to the critical value used in (7).

Algebraically, the signifi cance test can be expressed as 

follows:

If , then the diff erence 

between estimates and is statistically signifi cant 

at the specifi ed confi dence level, CL

where is estimate i (=1,2)

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

 is the critical value for the desired confi dence 

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

2.576 for 99 percent).

The example below shows how to determine if the 

diff erence in the estimated percentage of households 

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

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

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

statistically signifi cant. Using the formula above:

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

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

diff erence in the percentages is statistically signifi cant 

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

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

A rough interpretation of the result is that the user can 

be 99 percent certain that a diff erence exists between 

the percentages of households with one or more 

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

Making Comparisons

Appendix 4.

4

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

use the value 1.65 rather than 1.645. 

ZCL

SESE

XX

!





2

2

2

1

21

ˆˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   12.012.0

5.210.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

90.2

173.0

5.0

03.0

5.0

015.0015.0

5.0 

Ei
Appendix A-19

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

By contrast, if the corresponding estimates for State C 

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

would yield

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

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

A rough interpretation of the result is that the user 

cannot be certain to any suffi cient degree that the 

observed diff erence in the estimates was not due to 

chance.

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same 

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

3-year period) are straightforward and can be carried 

out as described in the previous section. There is, 

however, one statistical aspect related to the test for 

statistical signifi cance that users should be aware 

of. When comparing estimates within the same time 

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

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

In some cases, the comparison may involve a large 

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

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

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

estimates are not independent. The estimate for the 

large area is partially dependent on the estimate for the 

subset and, strictly speaking, the formula for testing 

diff erences should account for this partial dependence. 

However, unless the user has reason to believe that the 

two estimates are strongly correlated, it is acceptable 

to ignore the partial dependence and use the formula 

for testing diff erences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a fi nding of statistical signifi cance will still 

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

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

important to obtain a more exact test of signifi cance, 

the user should consult with a statistician about 

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

   25.020.0

1.225.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

25.1

320.0

4.0

1025.0

4.0

0625.004.0

4.0 

Comparisons Across Time Periods

Comparisons of estimates from diff erent time periods 

may involve diff erent single-year periods or diff erent 

multiyear periods of the same length within the same 

area. Comparisons across time periods should be made 

only with comparable time period estimates. Users are 

advised against comparing single-year estimates with 

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

2009) and against comparing multiyear estimates of 

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

2009–2014), as they are measuring the characteristics 

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

between such estimates are diffi cult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

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

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

the user should recognize that from 2006 on the ACS 

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

units. Many types of GQ populations have demographic, 

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

comparisons between 2005 and 2006 and later ACS 

estimates could be aff ected. This is particularly true 

for areas with a substantial GQ population. For most 

population characteristics, the Census Bureau suggests 

users make comparisons across these time periods 

only if the geographic area of interest does not include 

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

population, this is obviously not an issue. 

Comparisons Based on Overlapping Periods

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

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

periods is challenging since the diff erence is driven by 

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

for 2006 and 2007 are included in both estimates. 

Their contribution is subtracted out when the estimate 

of diff erences is calculated. While the interpretation 

of this diff erence is diffi cult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of diff erence should not be interpreted as a 

refl ection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

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

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

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

estimates will not want to wait until 2015 to evaluate 

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

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

estimates of the same geography that overlap in 

sample years one must account for this sample overlap. 

Thus to calculate the standard error of this diff erence 

use the following approximation to the standard error:

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

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

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

With this SE one can test for the statistical signifi cance 

of the diff erence between the two estimates using the 

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

 in the denominator of the formula for 

the signifi cance test.

Comparisons With Census 2000 Data

In Appendix 2, major diff erences between ACS data and 

decennial census sample data are discussed. Factors 

such as diff erences in residence rules, universes, and 

reference periods, while not discussed in detail in this 

appendix, should be considered when comparing ACS 

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

may aff ect comparisons between decennial census and 

ACS estimates when looking at data for areas such as 

college towns and resort areas. 

The Census Bureau subject matter specialists have 

reviewed the factors that could aff ect diff erences 

between ACS and decennial census estimates and they 

have determined that ACS estimates are similar to 

those obtained from past decennial census sample data 

for most areas and characteristics. The user should 

consider whether a particular analysis involves an area 

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

When comparing ACS and decennial census sample 

estimates, the user must remember that the decennial 

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

ACS and census estimates must be incorporated when 

performing tests of statistical signifi cance. Appendix 

3 provides the calculations necessary for determining 

statistical signifi cance of a diff erence between two 

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

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

.pdf> or the Census 2000 Summary File 4 Technical 

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

/cen2000/doc/sf4.pdf>.

A conservative approach to testing for statistical signifi -

cance when comparing ACS and Census 2000 

estimates that avoids deriving the SE for the 

Census 2000 estimate would be to assume the SE for 

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

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

assumed to be accurate (as the SE for the Census 2000 

estimate would be expected to be less than that for the 

ACS estimate), but a fi nding of no statistical signifi -

cance could be incorrect. In this case the user should 

calculate the census long-form standard error and follow the steps to conduct the statistical test.

Comparisons With 2010 Census Data

Looking ahead to the 2010 decennial census, data 

users need to remember that the socioeconomic data 

previously collected on the long form during the 

census will not be available for comparison with ACS 

estimates. The only common variables for the ACS and 

2010 Census are sex, age, race, ethnicity, household 

relationship, housing tenure, and vacancy status.

The critical factor that must be considered when comparing ACS estimates encompassing 2010 with the 

2010 Census is the potential impact of housing and 

population controls used for the ACS. As the housing 

and population controls used for 2010 ACS data will 

be based on the Population Estimates Program where 

the estimates are benchmarked on the Census 2000 

counts, they will not agree with the 2010 Census 

population counts for that year. The 2010 population 

estimates may diff er from the 2010 Census counts 

for two major reasons—the true change from 2000 to 

2010 is not accurately captured by the estimates and 

the completeness of coverage in the 2010 Census is 

diff erent than coverage of Census 2000. The impact of 

this diff erence will likely aff ect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large diff erences between the 

population controls and the 2010 Census population 

counts is greater.

Comparisons With Other Surveys

Comparisons of ACS estimates with estimates from 

other national surveys, such as the Current Population 

Survey, may be of interest to some users. A major consideration in making such comparisons will be that ACS 

5

 Further information concerning areas and characteristics that do not 

fi t the general pattern of comparability can be found on the ACS Web 

site at <http://www.census.gov/acs/www/UseData/compACS.htm>.

  2

2

2

21 1 1) ˆˆ ( XXSE #  SESEC

  2

2

2

1 1  SESEC

2

2

2

1  SESE
Appendix A-21

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

estimates include data for populations in both institutional and noninstitutional group quarters, and estimates from most national surveys do not include institutional populations. Another potential for large eff ects 

when comparing data from the ACS with data from 

other national surveys is the use of diff erent questions 

for measuring the same or similar information.

Sampling error and its impact on the estimates from 

the other survey should be considered if comparisons 

and statements of statistical diff erence are to be made, 

as described in Appendix 3. The standard errors on 

estimates from other surveys should be derived 

according to technical documentation provided for 

those individual surveys.

Finally, the user wishing to compare ACS estimates 

with estimates from other national surveys should 

consider the potential impact of other factors, such 

as target population, sample design and size, survey 

period, reference period, residence rules, and interview 

modes on estimates from the two sources.
A-22 Appendix

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

where is the All Items CPI-U-RS Annual Average 

for the earlier year (Y1).

 is the All Items CPI-U-RS Annual Average for the 

more recent year (Y2).

 is the published ACS estimate for the earlier year 

(Y1).

The example below compares the national median 

value for owner-occupied mobile homes in 2005 

($37,700) and 2006 ($41,000). First adjust the 2005 

median value using the 2005 All Items CPI-U-RS Annual 

Average (286.7) and the 2006 All Items CPI-U-RS Annual 

Average (296.1) as follows:

Thus, the comparison of the national median value for 

owner-occupied mobile homes in 2005 and 2006, in 

2006 dollars, would be $38,936 (2005 infl ationadjusted to 2006 dollars) versus $41,000 

(2006 dollars).

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost 

values are created with infl ation adjustments. The 

Census Bureau uses the All Items CPI-U-RS Annual Averages for each year in the multiyear time period to calculate a set of infl ation adjustment factors. Adjustment 

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from its most recent year to 

the CPI-U-RS Annual Averages from each of its earlier 

years. The ACS values for each of the earlier years in 

the multiyear period are multiplied by the appropriate 

infl ation adjustment factors to produce the infl ationadjusted values. These values are then used to create 

the multiyear estimates. 

As an illustration, consider the time period 2004–2006, 

which consisted of individual reference-year income 

values of $30,000 for 2006, $20,000 for 2005, and 

$10,000 for 2004. The multiyear income components 

are created from infl ation-adjusted reference period 

income values using factors based on the All Items 

CPI-U-RS Annual Averages of 277.4 (for 2004), 286.7 

(for 2005), and 296.1 (for 2006). The adjusted 2005 

value is the ratio of 296.1 to 286.7 applied to $20,000, 

which equals $20,656. Similarly, the 2004 value is 

the ratio of 296.1 to 277.4 applied to $10,000, which 

equals $10,674.

Using Dollar-Denominated Data

Appendix 5.

Dollar-denominated data refer to any characteristics 

for which infl ation adjustments are used when producing annual estimates. For example, income, rent, home 

value, and energy costs are all dollar-denominated 

data.

Infl ation will aff ect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using infl ation factors based on the Consumer Price Index (CPI).

Given the potential impact of infl ation on observed 

diff erences of dollar-denominated data across time 

periods, users should adjust for the eff ects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for infl ation. In making adjustments, the 

Census Bureau recommends using factors based on 

the All Items CPI-U-RS (CPI research series). The Bureau 

of Labor Statistics CPI indexes through 2006 are found 

at <http://www.bls.gov/cpi/cpiurs1978_2006.pdf>. 

Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount 

of income received during the 12 months preceding 

the interview month. This is the income reference 

period. Since there are 12 diff erent income reference 

periods throughout an interview year, 12 diff erent 

income infl ation adjustments are made. Monthly CPIU-RSs are used to infl ation-adjust the 12 reference 

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no infl ation adjustments for single-year estimates of rent, home value, or energy cost values.

Adjusting Single-Year Estimates Over Time

When comparing single-year income, rent, home value, 

and energy cost value estimates from two diff erent 

years, adjustment should be made as follows: 

1) Obtain the All Items CPI-U-RS Annual Averages for 

the 2 years being compared.

2) Calculate the infl ation adjustment factor as the ratio 

of the CPI-U-RS from the more recent year to the 

CPI-U-RS from the earlier year.

3) Multiply the dollar-denominated data estimated for 

the earlier year by the infl ation adjustment factor.

The infl ation-adjusted estimate for the earlier year can 

be expressed as:

936,38$700,37$ 7.286

1.296 ˆ

X ,2005 Adj u 

1

1

2

,1 ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

CPIY1

CPIY 2

1 ˆ

XY
Appendix A-23

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

As an illustration, consider ACS multiyear estimates for 

the two time periods of 2001–2003 and 2004–2006. 

To compare the national median value for owneroccupied mobile homes in 2001–2003 ($32,000) and 

2004–2006 ($39,000), fi rst adjust the 2001–2003 

median value using the 2003 All Items CPI-U-RS Annual 

Averages (270.1) and the 2006 All Items CPI-U-RS 

Annual Averages (296.1) as follows:

Thus, the comparison of the national median value 

for owner-occupied mobile homes in 2001–2003 

and 2004–2006, in 2006 dollars, would be $35,080 

(2001–2003 infl ation-adjusted to 2006 dollars) versus 

$39,000 (2004–2006, already in 2006 dollars).

Issues Associated With Infl ation Adjustment

The recommended infl ation adjustment uses a national 

level CPI and thus will not refl ect infl ation diff erences 

that may exist across geographies. In addition, since 

the infl ation adjustment uses the All Items CPI, it will 

not refl ect diff erences that may exist across characteristics such as energy and housing costs.

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as 

follows:

1) Obtain the All Items CPI-U-RS Annual Average for 

the most current year in each of the time periods 

being compared.

2) Calculate the infl ation adjustment factor as the 

ratio of the CPI-U-RS Annual Average in (1) from 

the most recent year to the CPI-U-RS in (1) from 

the earlier years.

3) Multiply the dollar-denominated estimate for the 

earlier time period by the infl ation adjustment 

factor.

The infl ation-adjusted estimate for the earlier years can 

be expressed as:

where CPI P1 is the All Items CPI-U-RS Annual Average 

for the last year in the earlier time period (P1). 

 is the All Items CPI-U-RS Annual Average for the 

last year in the most recent time period (P2).

 is the published ACS estimate for the earlier time 

period (P1).

080,35$000,32$ 1.270

1.296 ˆ

X  ,20032001 Adj u 

1

1

2

,1 ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

CPI P2

1 ˆ

X P
All survey estimates are subject to both sampling and 

nonsampling error. In Appendix 3, the topic of sampling error and the various measures available for 

understanding the uncertainty in the estimates due to 

their being derived from a sample, rather than from an 

entire population, are discussed. The margins of error 

published with ACS estimates measure only the eff ect 

of sampling error. Other errors that aff ect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any error 

aff ecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error.

Types of Nonsampling Errors

Coverage error occurs when a housing unit or person 

does not have a chance of selection in the sample 

(undercoverage), or when a housing unit or person has 

more than one chance of selection in the sample, or is 

included in the sample when they should not have been 

(overcoverage). For example, if the frame used for the 

ACS did not allow the selection of newly constructed 

housing units, the estimates would suff er from errors 

due to housing undercoverage.

The fi nal ACS estimates are adjusted for under- and 

overcoverage by controlling county-level estimates to 

independent total housing unit controls and to independent population controls by sex, age, race, and 

Hispanic origin (more information is provided on the 

coverage error defi nition page of the “ACS Quality Measures” Web site at <http://www.census.gov/acs/www

/UseData/sse/cov/cov_def.htm>). However, it is important to measure the extent of coverage adjustment by 

comparing the precontrolled ACS estimates to the fi nal 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that diff erences in characteristics of undercovered or overcovered 

housing units or individuals diff er from those eligible to 

be selected. When this occurs, the ACS may not provide 

an accurate picture of the population prior to the coverage adjustment, and the population controls may not 

eliminate or minimize that coverage error.

Unit nonresponse is the failure to obtain the minimum required information from a housing unit or a resident of a group quarter in order for it to be considered 

a completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

Appendix 6.

or person. For example, if no one in a sampled housing unit is available to be interviewed during the time 

frame for data collection, unit nonresponse will result.

It is important to measure unit nonresponse because 

it has a direct eff ect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the fi nal survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

unit nonresponse bias. This will happen if the characteristics of nonresponding units diff er from the characteristics of responding units.

Item nonresponse occurs when a respondent fails to 

provide an answer to a required question or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question diff er 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias.

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation 

of the fi nal data fi les. For example, errors may occur if 

data entry of questionnaire information is incomplete 

Measures of Nonsampling Error

A-24 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
Appendix A-25

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

or inaccurate. Coding of responses incorrectly also 

results in processing error. Critical reviews of edits and 

tabulations by subject matter experts are conducted to 

keep errors of this kind to a minimum.

Nonsampling error can result in random errors and 

systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there is 

an error in the questionnaire design that negatively 

aff ects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the fi nal results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

ACS Quality Measures

Nonsampling error is extremely diffi cult, if not 

impossible, to measure directly. However, the Census 

Bureau has developed a number of indirect measures of 

nonsampling error to help inform users of the quality 

of the ACS estimates: sample size, coverage rates, unit 

response rates and nonresponse rates by reason, and 

item allocation rates. Starting with the 2007 ACS, these 

measures are available in the B98 series of detailed 

tables on AFF. Quality measures for previous years are 

available on the “ACS Quality Measures” Web site at 

<http:/www.census.gov/acs/www/UseData/sse/>.

Sample size measures for the ACS summarize information for the housing unit and GQ samples. The measures available at the state level are:6

Housing units

Number of initial addresses selected

Number of fi nal survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of fi nal survey interviews

Sample size measures may be useful in special circumstances when determining whether to use single-year 

or multiyear estimates in conjunction with estimates of 

the population of interest. While the coeffi cient of variation (CV) should typically be used to determine 

usability, as explained in Appendix 3, there may be 

some situations where the CV is small but the user 

has reason to believe the sample size for a subgroup 

is very small and the robustness of the estimate is in 

question. 

For example, the Asian-alone population makes up 

roughly 1 percent (8,418/656,700) of the population 

in Jeff erson County, Alabama. Given that the number of 

successful housing unit interviews in Jeff erson County 

for the 2006 ACS were 4,072 and assuming roughly 2.5 

persons per household (or roughly 12,500 completed 

person interviews), one could estimate that the 2006 

ACS data for Asians in Jeff erson County are based on 

roughly 150 completed person interviews.

Coverage rates are available for housing units, and 

total population by sex at both the state and national 

level. Coverage rates for total population by six race/

ethnicity categories and the GQ population are also 

available at the national level. These coverage rates are 

a measure of the extent of adjustment to the survey 

weights required during the component of the estimation methodology that adjusts to population controls. 

Low coverage rates are an indication of greater potential for coverage error in the estimates. 

Unit response and nonresponse rates for housing 

units are available at the county, state, and national 

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, other, and data insuffi cient to be considered 

an interview. Rates are also provided separately for persons in group quarters at the national and state levels.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. For example, 

the 2006 housing unit response rates are at least 94 

percent for all states. The response rate for the District 

of Columbia in 2006 was 91 percent.

Item allocation rates are determined by the content 

edits performed on the individual raw responses and 

closely correspond to item nonresponse rates. Overall 

housing unit and person characteristic allocation rates 

are available at the state and national levels, which 

combine many diff erent characteristics. Allocation rates 

for individual items may be calculated from the B99 

series of imputation detailed tables available in AFF. 

Item allocation rates do vary by state, so users are 

advised to examine the allocation rates for 

characteristics of interest before drawing conclusions 

from the published estimates.

6

 The sample size measures for housing units (number of initial addresses 

selected and number of fi nal survey interviews) and for group quarters 

people cannot be used to calculate response rates. For the housing unit 

sample, the number of initial addresses selected includes addresses 

that were determined not to identify housing units, as well as initial 

addresses that are subsequently subsampled out in preparation for personal visit nonresponse follow-up. Similarly, the initial sample of people 

in group quarters represents the expected sample size within selected 

group quarters prior to visiting and sampling of residents. 
A-26 Appendix

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

Implications of Population Controls on ACS Estimates

Appendix 7.

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defi ned by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s offi cial estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the offi cial count 

of the population comes from the previous census, 

updated by the Population Estimates Program.

In the case of the ACS, the total housing unit estimates 

and the total population estimates by age, sex, race 

and Hispanic origin are controlled at the county (or 

groups of counties) level. The group quarters total 

population is controlled at the state level by major type 

of group quarters. Such adjustments are important to 

correct the survey data for nonsampling and sampling 

errors. An important source of nonsampling error is 

the potential under-representation of hard-toenumerate demographic groups. The use of the 

population controls results in ACS estimates that more 

closely refl ect the level of coverage achieved for those 

groups in the preceding census. The use of the population estimates as controls partially corrects demographically implausible results from the ACS due to 

the ACS data being based on a sample of the population rather than a full count. For example, the use of 

the population controls “smooths out” demographic 

irregularities in the age structure of the population that 

result from random sampling variability in the ACS. 

When the controls are applied to a group of counties 

rather than a single county, the ACS estimates and the 

offi cial population estimates for the individual counties 

may not agree. There also may not be agreement 

between the ACS estimates and the population estimates for levels of geography such as subcounty areas 

where the population controls are not applied.

The use of population and housing unit controls also 

reduces random variability in the estimates from year 

to year. Without the controls, the sampling variability 

in the ACS could cause the population estimates to 

increase in one year and decrease in the next (especially for smaller areas or demographic groups), when 

the underlying trend is more stable. This reduction in 

variability on a time series basis is important since 

results from the ACS may be used to monitor trends 

over time. As more current data become available, the 

time series of estimates from the Population Estimates 

Program are revised back to the preceding census while 

the ACS estimates in previous years are not. Therefore, 

some diff erences in the ACS estimates across time may 

be due to changes in the population estimates. 

For single-year ACS estimates, the population and total 

housing unit estimates for July 1 of the survey year 

are used as controls. For multiyear ACS estimates, the 

controls are the average of the individual year population estimates.
Appendix A-27

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

Background and Overview Information

American Community Survey Web Page Site Map: 

<http://www.census.gov/acs/www/Site_Map.html>

This link is the site map for the ACS Web page. It provides an overview of the links and materials that are 

available online, including numerous reference documents.

What Is the ACS? <http://www.census.gov/acs/www

/SBasics/What/What1.htm> This Web page includes 

basic information about the ACS and has links to additional information including background materials.

ACS Design, Methodology, Operations

American Community Survey Design and Methodology 

Technical Paper: <http://www.census.gov/acs/www

/Downloads/tp67.pdf> This document describes the 

basic design of the 2005 ACS and details the full set 

of methods and procedures that were used in 2005. 

Please watch our Web site as a revised version will be 

released in the fall of 2008, detailing methods and 

procedures used in 2006 and 2007. 

About the Data (Methodology: <http://www.census

.gov/acs/www/AdvMeth/> This Web page contains 

links to information on ACS data collection and processing, evaluation reports, multiyear estimates study, 

and related topics.

ACS Quality

Accuracy of the Data (2007): <http://www.census.gov

/acs/www/Downloads/ACS/accuracy2007.pdf> This 

document provides data users with a basic understanding of the sample design, estimation methodology, and 

accuracy of the 2007 ACS data.

ACS Sample Size: <http://www.census.gov/acs/www

/SBasics/SSizes/SSizes06.htm> This link provides 

sample size information for the counties that were

published in the 2006 ACS. The initial sample size 

and the fi nal completed interviews are provided. The 

sample sizes for all published counties and county 

equivalents starting with the 2007 ACS will only be 

available in the B98 series of detailed tables on American FactFinder.

ACS Quality Measures: <http://www.census.gov/acs

/www/UseData/sse/> This Web page includes information about the steps taken by the Census Bureau to 

improve the accuracy of ACS data. Four indicators of 

survey quality are described and measures are provided at the national and state level.

Guidance on Data Products and Using the Data

How to Use the Data: <http://www.census.gov/acs

/www/UseData/> This Web page includes links to 

many documents and materials that explain the ACS 

data products. 

Comparing ACS Data to other sources: <http://www

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

are provided with guidance on comparing the 2007 

ACS data products to 2006 ACS data and Census 2000 

data. 

Fact Sheet on Using Diff erent Sources of Data for 

Income and Poverty: <http://www.census.gov/hhes

/www/income/factsheet.html> This fact sheet highlights the sources that should be used for data on 

income and poverty, focusing on comparing the ACS 

and the Current Population Survey (CPS).

Public Use Microdata Sample (PUMS): <http://www

.census.gov/acs/www/Products/PUMS/> This Web 

page provides guidance in accessing ACS microdata.

Other ACS Resources

Appendix 8.