Understanding and Using 

American Community Survey Data

What All Data Users Need to Know

Issued July 2018
Acknowledgments Linda A. Jacobsen, Vice President, U.S. Programs, Population Reference 

Bureau (PRB), and Mark Mather, Associate Vice President, U.S. Programs, 

PRB, drafted this handbook in partnership with the U.S. Census Bureau’s 

American Community Survey Office. Other PRB staff who assisted in 

drafting and reviewing the handbook include: Beth Jarosz, Kelvin Pollard, 

Paola Scommegna, and Marissa Slowey. 

American Community Survey data users who provided feedback and case 

studies for this handbook include: Joel Alvarez, Mike Carnathan, Clifford 

Cook, Kenneth Hodges, Lisa Neidert, Joseph Salvo, Jim Skinner, Taylor 

Tyger, Keith Wiley, and Oliver Wise.

Nicole Scanniello, Janice Valdisera, and Gretchen Gooding, Census Bureau, 

contributed to the planning and review of the handbook series. 

The American Community Survey program is under the direction of Albert 

E. Fontenot Jr., Associate Director for Decennial Census Programs, James 

Treat, Assistant Director for Decennial Census Programs, and Victoria

Velkoff, Chief, American Community Survey Office.

Other individuals from the Census Bureau who contributed to the review and 

release of these handbooks include:

Mark Asiala, Karen Battle, Judy Belton, Jennifer Berkley, Patrick Cantwell, 

Gary Chappell, Barbara Downs, Sirius Fuller, Colleen Keating, Justin Keller, 

Amelia Kermis, Karen King, Maria Olmedo-Malagon, Leanna Mellott, 

Jennifer Ortman, Nathan Ramsey, KaNin Reese, Jennifer Reichert, Jonathan 

Rothbaum, Michael Starsinic, David Waddington, and Kai Wu. 

Linda Chen and Amanda Perry of the Public Information Office and Janet

Sweeney, Creative Services Manager, provided publication management, 

graphic design and composition, and editorial review for print and electronic 

media.
U.S. Department of Commerce

Wilbur Ross,

Secretary

Karen Dunn Kelley, 

Performing the Nonexclusive Duties

 of the Deputy Secretary 

Economics and Statistics Administration

Karen Dunn Kelley,

Under Secretary for Economic Affairs

U.S. CENSUS BUREAU

Ron Jarmin,

Performing the Nonexclusive Functions and

Duties of the Director

Understanding and Using 

American Community Survey Data

What All Data Users Need to Know

Issued July 2018
Suggested Citation

 U.S. Census Bureau, 

Understanding and Using 

American Community Survey 

Data: What All Data Users Need to 

Know, U.S. Government Printing 

Office, Washington, DC, 2018.

ECONOMICS

AND STATISTICS

ADMINISTRATION

Economics and Statistics 

Administration

Karen Dunn Kelley,

Under Secretary for Economic Affairs

U.S. CENSUS BUREAU

Ron Jarmin,

Performing the Nonexclusive Functions and

Duties of the Director

Enrique Lamas,

Performing the Nonexclusive Functions and Duties 

of the Deputy Director and Chief Operating Officer

Albert Fontenot Jr.,

Associate Director for Decennial Census Programs

James Treat,

Assistant Director for Decennial Census Programs

Victoria Velkoff,

Chief, American Community Survey Office
Understanding and Using American Community Survey Data iii 

U.S. Census Bureau What All Data Users Need to Know iii

Contents 1. Understanding the ACS: The Basics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Geographic Areas Covered in the ACS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

3. Understanding and Using ACS Single-Year and Multiyear Estimates. . . . . . . 13

4. Making Comparisons With ACS Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

5. Accessing ACS Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

6. Case Studies Using ACS Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

7. Understanding Error and Determining Statistical Significance. . . . . . . . . . . . 44

8. Calculating Measures of Error for Derived Estimates. . . . . . . . . . . . . . . . . . . . 51

9. Differences Between the ACS and the Decennial Census. . . . . . . . . . . . . . . . 60

10. Using Dollar-Denominated Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

11. Measures of Nonsampling Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

12. Glossary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 

Understanding and Using American Community Survey Data 1

U.S. Census Bureau What All Data Users Need to Know 1

UNDERSTANDING AND USING AMERICAN 

COMMUNITY SURVEY DATA: 

WHAT ALL DATA USERS NEED TO KNOW 

Have you ever thought about how, or why, new roads, 

schools, after-school programs, or hospitals are placed 

in communities? What makes communities attractive 

to new businesses or tourists? Why there is no ATM or 

bike-share kiosk on a street corner? The answers often 

lie in the numbers—numbers that reflect what our communities look like, how our communities have changed, 

and how those changes impact our daily lives. 

The U.S. Census Bureau’s American Community Survey 

(ACS) is designed to answer these types of questions 

and to meet the needs of policymakers, business leaders, planners, and others nationwide who need good 

data to make informed decisions. The ACS provides a 

detailed portrait of the social, economic, housing, and 

demographic characteristics of America’s communities. 

This handbook provides an overview of the ACS to 

help data users understand the basics of the survey, 

how the data can be used, how to judge the accuracy 

of ACS estimates, and how to access ACS data on the 

Census Bureau’s Web site. It also includes some recent 

case studies that show how ACS data are being used 

to help address important policy and program issues. 

Links to additional ACS resources, including technical 

documentation for more advanced users, are included 

throughout the handbook.

1. UNDERSTANDING THE ACS: THE BASICS

What Is the ACS?

The American Community Survey (ACS) is a nationwide survey designed to provide communities with 

reliable and timely social, economic, housing, and 

demographic data every year. A separate annual survey, called the Puerto Rico Community Survey (PRCS), 

collects similar data about the population and housing 

units in Puerto Rico. The Census Bureau uses data collected in the ACS and the PRCS to provide estimates 

on a broad range of population, housing unit, and 

household characteristics for states, counties, cities, 

school districts, congressional districts, census tracts, 

block groups, and many other geographic areas. 

The ACS has an annual sample size of about 3.5 million 

addresses, with survey information collected nearly 

every day of the year. Data are pooled across a calendar year to produce estimates for that year. As a result, 

ACS estimates reflect data that have been collected 

over a period of time rather than for a single point in 

time as in the decennial census, which is conducted 

every 10 years and provides population counts as of 

April 1. 

ACS 1-year estimates are data that have been collected over a 12-month period and are available for 

geographic areas with at least 65,000 people. The 

Census Bureau combines 5 consecutive years of ACS 

data to produce estimates for geographic areas with 

fewer than 65,000 residents. These 5-year estimates 

represent data collected over a period of 60 months.1

1 The Census Bureau previously released 3-year estimates based on 

36 months of data collection. In 2015, the 3-year products were discontinued. The 2011–2013 ACS 3-year estimates, released in 2014, are the 

last release of this product.

Starting with the 2014 ACS, the Census Bureau is also 

producing “1-year Supplemental Estimates”—simplified 

versions of popular ACS tables—for geographic areas 

with at least 20,000 people. 

Because the ACS is based on a sample, rather than all 

housing units and people, ACS estimates have a degree 

of uncertainty associated with them, called sampling 

error. In general, the larger the sample, the smaller the 

level of sampling error. To help users understand the 

impact of sampling error on data reliability, the Census 

Bureau provides a “margin of error” for each published 

ACS estimate. The margin of error, combined with the 

ACS estimate, give users a range of values within which 

the actual “real-world” value is likely to fall.

TIP: In general, data users should be careful in drawing conclusions about small differences between two 

ACS estimates because they may not be statistically 

different.

The ACS provides vital information on a yearly basis 

about our nation and its people, helping local officials, 

community leaders, businesses, and the public plan 

and make decisions based on the changes taking place 

in their communities. Through the ACS, we know more 

about demographic trends, jobs and occupations, 

educational attainment, veterans, homeownership, and 

many other topics. Because data collection is ongoing, 

the ACS also provides essential, up-to-date information about population and housing characteristics both 

before and after natural disasters like Super Storm 

Sandy or economic crises like the Great Recession of 

2007 to 2009. 
2 Understanding and Using American Community Survey Data 

2 What All Data Users Need to Know U.S. Census Bureau

 Table 1.1. Population and Housing Data Included in American Community Survey Data Products 

Social Characteristics

Ancestry

Citizenship Status

Disability Status1

Educational Attainment

Fertility

Grandparents as Caregivers

Language Spoken at Home

Marital History2

Marital Status

Migration/Residence 1 Year Ago

Period of Military Service

Place of Birth

School Enrollment

Undergraduate Field of 

Degree3

Veteran Status2

Year of Entry

Economic Characteristics

Class of Worker

Commuting (Journey to Work)

Employment Status

Food Stamps/Supplemental 

Nutrition Assistance Program 

(SNAP)4

Health Insurance Coverage2

Income and Earnings

Industry and Occupation

Place of Work

Poverty Status

Work Status Last Year

Housing Characteristics

Computer and Internet Use5

House Heating Fuel

Kitchen Facilities

Occupancy/Vacancy Status

Occupants Per Room

Plumbing Facilities6

Rent

Rooms/Bedrooms

Selected Monthly Owner Costs

Telephone Service Available

Tenure (Owner/Renter)

Units in Structure

Value of Home

Vehicles Available

Year Householder Moved Into 

Unit

Year Structure Built

Demographics Characteristics

Age and Sex

Group Quarters Population

Hispanic or Latino Origin

Race

Relationship to Householder

Total Population

TIP: The ACS was designed to provide estimates of 

the characteristics of the population, not to provide 

counts of the population in different geographic areas 

or population subgroups. For basic counts of the 

U.S. population by age, sex, race, and Hispanic origin, 

visit the Census Bureau’s Population and Housing Unit 

Estimates Web page.2

The content collected through the ACS can be 

grouped into four main types of characteristics: social, 

economic, housing, and demographic, as shown in 

Table 1.1. Various tables in the ACS have different 

“universes,” or base reference totals against which all 

other characteristics are compared. Some tables cover 

population characteristics, while others cover housing 

characteristics. Among the population tables, some 

cover the entire population (such as tables of the 

population by age), while some cover only a subset of 

the population (such as tables of employment status, 

which include data only for the population aged 16 and 

older). 

ACS content is designed to meet the needs of federal 

government agencies, and every question in the ACS is 

asked for a reason. For example, questions about how 

2 U.S. Census Bureau, Population and Housing Unit Estimates, 

<www.census.gov/popest/>. 

people get to work, when they leave, and the length 

of their commutes are used for planning improvements to roads, highways, rail lines, and bus routes, 

and for planning emergency response routes. Because 

participation in the ACS is mandatory, the Office of 

Management and Budget (OMB) will only approve necessary questions for inclusion in the ACS. The OMB’s 

responsibility under the Paperwork Reduction Act 

requires that new questions demonstrate the practical 

utility of the data and minimize “respondent burden.” 

Respondent burden can be defined in different ways, 

but is often related to the length of the interview or 

questionnaire, or the extent to which questions are 

viewed as being intrusive or too personal. 

Some people are reluctant to respond to the ACS 

because of concerns about the confidentiality of the 

data. However, strict confidentiality laws protect all 

ACS information that could be used to identify individuals or households under Title 13 of the U.S. Code.

3

This is true even for interagency communication: other 

government agencies do not have the legal right to 

access individuals’ confidential information. 

3 U.S. Census Bureau, Title 13 - Protection of Confidential 

Information, <www.census.gov/about/policies/privacy/data 

_protection/title_13_-_protection_of_confidential_information.html>. 

1

 Questions on Disability Status were significantly revised in the 2008 survey to cause a break in series. 

2 Marital History, Veterans’ Service-Connected Disability Status and Ratings, and Health Insurance Coverage were added in the 2008 survey. 

3 Undergraduate Field of Degree was added in the 2009 survey. 

4 Food Stamp Benefit amount was removed in 2008. 

5 Computer and Internet Use was added to the 2013 survey. 

6 One of the components of Plumbing Facilities, flush toilet, and Business or Medical Office on Property questions were removed in 2016.

Source: U.S. Census Bureau. 
Understanding and Using American Community Survey Data 3

U.S. Census Bureau What All Data Users Need to Know 3

Who Uses the ACS and Why?

The ACS puts up-to-date information about important social issues at the fingertips of people who need 

it, including policymakers, researchers, businesses, 

nongovernmental organizations, journalists, teachers, 

students, and the public (see Box 1.1). Businesses use 

ACS data to better understand their current or potential customers. The federal government uses ACS information to evaluate the need for federal programs and 

to run those programs effectively. Nongovernmental 

organizations use the ACS in a variety of ways to 

monitor trends among important subgroups of the 

population. Journalists use ACS data to report on new 

or emerging social trends or to put a piece of anecdotal evidence into a broader context. State and local 

governments are using ACS information to keep track 

of year-to-year changes in their jurisdictions so they 

can better address the needs of their constituents.

Box 1.1. How Different Data-User Communities Use ACS Data

Federal agencies: ACS data help determine how 

more than $675 billion in federal funds are distributed to state and local areas each year.1

 Federal 

agencies rely on the ACS to help them make operational decisions, including managing and evaluating 

programs, determining eligibility for programs, and 

benchmarking other statistics. For example, the 

U.S. Department of Veterans Affairs uses ACS data 

on the characteristics of veterans to evaluate the 

need for educational, employment, and health care 

programs to assist those who have served in the 

military. The Special Supplemental Food Program 

for Women, Infants, and Children (WIC) uses 

income data from the ACS to determine the potential demand for food assistance across states and 

counties. The Appalachian Regional Commission 

(ARC) uses ACS data to assess the status of communities in the Appalachian Region on a host 

of social and economic measures, which in turn 

enables ARC to develop strategies to improve 

conditions in Appalachia. For more information 

about how federal agencies use ACS data, visit 

the Census Bureau’s American Community Survey 

Handbook of Questions and Current Federal Uses.

2

Nongovernmental organizations: The Lucile 

Packard Foundation for Children’s Health uses ACS 

data to track annual changes in the well-being of 

children in California, including measures of child 

poverty, family structure, school enrollment, and 

employment status of parents. The Migration Policy 

Institute uses ACS data to present detailed, statelevel information about the 42.4 million current U.S. 

residents who were born outside the United States. 

The State Health Access Data Assistance Center 

uses ACS data to monitor trends in health insurance coverage.

1

 U.S. Census Bureau, Uses of Census Bureau Data in Federal 

Funds Distribution, 2017, <www2.census.gov/programs-surveys 

/decennial/2020/program-management/working-papers/Uses-of 

-Census-Bureau-Data-in-Federal-Funds-Distribution.pdf>.

2 U.S. Census Bureau, American Community Survey Handbook 

of Questions and Current Federal Uses, 2014, <www.census.gov 

/programs-surveysw/acs/operations-and-administration 

/2014-content-review/federal-uses.html>.

Journalists: Journalists regularly report ACS data 

to keep the public informed about emerging social, 

economic, housing, and demographic trends. 

For example, the New York Times used ACS data 

to map poverty rates and men’s employment in 

America.3 The Wall Street Journal reported on the 

cities with the fastest-growing median household 

incomes, based on ACS data.4 The Durango Herald

(Durango, Colorado) used ACS 5-year data to show 

that many of Colorado’s newest residents are arriving from California and Texas.5

State and local governments: Information from 

the ACS is critical for state and local policymakers and planners who need up-to-date information 

about their communities to evaluate the need for 

new roads, hospitals, schools, senior centers, and 

other basic services. For example, the Council on 

Virginia’s Future, which advises the Governor and 

the Virginia General Assembly, relies on ACS data 

to monitor annual trends in travel time to work. 

New York City’s Department of City Planning used 

ACS data to identify the need for bilingual voting 

materials in certain New York neighborhoods.6

Businesses: Businesses use information from the 

ACS to understand their customers, make location 

or relocation decisions, and provide background 

information in loan applications. For example, ACS 

data are a key component of Zillow’s database of 

more than 110 million U.S. homes, helping homeowners and potential buyers learn more about their 

3 Gregor Aisch, Josh Katz, and David Leonhardt, “Where Men 

Aren’t Working,” Dec. 11, 2014, <www.nytimes.com/interactive

/2014/12/12/upshot/where-men-arent-working-map.html?

_r=0#>. 4 Josh Zumbrun, “For Cities Getting Richer the Fastest, Look to 

the South,” Sept. 15, 2016, <http://blogs.wsj.com/economics

/2016/09/15/for-cities-getting-richer-the-fastest-look-to-the

-south/>. 5 Jessica Pace, “Texas, California Sending Many New Residents 

to Colorado,” Dec. 2, 2016, <https://durangoherald.com

/articles/118903-texas-california-sending-many-new-residents

-to-colorado>. 6 National Research Council, “Chapter 7: Legal and Social 

Equity Uses of ACS Data,” in Benefits, Burdens, and Prospects of 

the American Community Survey: Summary of a Workshop, Daniel 

L. Cork, rapporteur, Committee on National Statistics, Division of 

Behavioral and Social Sciences and Education, Washington, DC: 

The National Academies Press, 2013, <www.nap.edu/read/18259

/chapter/8>.
4 Understanding and Using American Community Survey Data

4 What All Data Users Need to Know U.S. Census Bureau

One of the main strengths of the ACS is the ability to 

disaggregate the data by age group, race, Hispanic 

origin, sex, and other characteristics. For example, 

data users can compare the poverty status of children 

and older adults, college enrollment rates for men 

and women, or housing costs for African Americans 

and non-Hispanic Whites. No other resource provides 

such a wealth of social, economic, housing, and 

demographic information for the nation, states, and 

substate geographic areas. 

History of the ACS 

Every 10 years since 1790, Congress has authorized 

funds to conduct a national census of the U.S. population, as required by the U.S. Constitution. Censuses 

conducted between 1940 and 2000 consisted of a 

“short form,” which included basic questions about 

age, sex, race, Hispanic origin, household relationship, 

and owner/renter status, and a “long form” used for 

only a sample of households. The “long form” included 

not only the basic “short-form” questions but also 

detailed questions about social, economic, and housing characteristics.

Data from the census long form provided a detailed 

snapshot, every 10 years, of America’s population 

and households. However, in today’s world, our communities can change very quickly. Between decennial censuses, local governments, organizations, and 

businesses need timely data to assess and plan for 

local needs. Costly mistakes can result when planners 

and policymakers do not have current data on which 

to base their decisions. That is one of the key reasons 

the Census Bureau moved to a new way of gathering 

data. Rather than taking a snapshot of communities 

once every 10 years, the ACS was designed to provide 

a dynamic and timely picture of the nation every year. 

The ACS underwent years of extensive testing, including demonstration surveys conducted in parallel with 

the 2000 Census to evaluate the reliability of survey 

results. The ACS achieved full, nationwide implementation in 2005 for the household population and was 

expanded to cover the full population (including group 

quarters—such as college dormitories) in 2006. In 

2010, the ACS replaced the census long form as the 

nation’s source of social and economic data for population and housing characteristics.

Box 1.1. How Different Data-User Communities Use ACS Data—Con.

communities.7 Many data vendors, such as Esri and 

Nielsen, also incorporate ACS data either directly 

or indirectly into their commercial data products, 

which businesses use for market segmentation, site 

selection, and marketing strategies.8

Researchers: Researchers both inside and outside of academia regularly use ACS data to test 

hypotheses, analyze data patterns across different 

geographic areas, and to investigate trends over 

time. For example, researchers at the Pew Research 

Center use ACS data—often in combination with 

decennial census data—to monitor long-term trends 

in U.S. education, income, employment, marriage, 

and family patterns. At Portland State University, 

researchers used ACS data to study migration 

patterns among young adults before and after the 

Great Recession.9

7 U.S. Census Bureau, Stats in Action: Zillow, 2015, 

<www.census.gov/library/video/sia-zillow.html>. 8 U.S. Department of Commerce, Economics and Statistics 

Administration, The Value of the American Community Survey: 

Smart Government, Competitive Businesses, and Informed Citizens, 

2015, <www.esa.gov/sites/default/files/the-value-of-the-acs.pdf>.

9 Jason R. Jurjevich and Greg Schrock, America on the Move, 

<www.americamoves.org/>. 

Disaster response and recovery: The 

American Red Cross uses ACS data to help identify 

vulnerable populations—before a disaster strikes—

and to monitor the impacts of a disaster during the 

response and recovery phases.10 In the aftermath 

of Hurricane Katrina in 2005, up-to-date, beforeand-after snapshots of community characteristics 

helped first and subsequent responders to better target preparedness, response, and recovery 

efforts.11

10 National Research Council, “Chapter 3: Planning Social 

Services and Responding to Disasters,” in Benefits, Burdens, and 

Prospects of the American Community Survey: Summary of a 

Workshop. Daniel L. Cork, rapporteur, Committee on National 

Statistics, Division of Behavioral and Social Sciences and 

Education, Washington, DC: The National Academies Press, 2013, 

<www.nap.edu/read/18259/chapter/4#51>. 11 Kin Koerber, Migration Patterns and Mover Characteristics 

from the 2005 ACS Gulf Coast Area Special Products, 2006, 

<www.census.gov/library/working-papers/2006/demo/koerber-01

.html>.
Understanding and Using American Community Survey Data 5

U.S. Census Bureau What All Data Users Need to Know 5

Over time, questions have been added, revised, or 

removed from the survey, as shown in Table 1.1. For 

example, in 2008 three new questions on marital history, health insurance coverage, and veteran’s serviceconnected disability were added, while the questions 

on disability were significantly revised to cause a break 

in series. The data from these new and revised questions collected in 2008 were first available in the ACS 

products released in 2009. A new question on bachelor’s field of degree was added in 2009—with data 

available in 2010—while in 2013, three new questions 

on computer ownership and Internet access were 

added, with data available in 2014. 

When a new question is added to the survey, 1-year 

estimates are available the following year, but it takes 

5 years to accumulate data for small geographic areas. 

While ACS 1-year estimates of health insurance coverage were first available in 2009, ACS 5-year estimates 

of coverage (for 2008–2012) were first available in 

2013. 

In 2014, the Census Bureau conducted a comprehensive assessment of the ACS program, including a 

review of each ACS question. This ACS Content Review

sought to understand which federal programs use the 

information collected by each question and assess how 

the Census Bureau might reduce respondent burden.4

Based on this assessment, the questions on the presence of a flush toilet and whether there is a business or 

medical office on the property were removed from the 

ACS, beginning with the 2016 survey.

The sample size of the ACS and the ways data are collected have also changed over time, as described in 

more detail in the next section. 

How Are ACS Data Collected?

From 2005 through 2012, the ACS collected data using 

three sequential methods, or “modes”: paper questionnaires through the mail, phone interviews, and personal visits with a Census Bureau interviewer. Starting 

in 2013, the Census Bureau added a fourth mode—an 

Internet response option—that simplified data collection and reduced survey costs. Starting in late 2017, 

based on declining response rates and increasing 

costs, the Census Bureau discontinued using phone 

interviews to follow up with nonrespondents. The 

annual sample size of the ACS has also increased over 

time, from 2.9 million addresses in 2005 to more than 

3.5 million addresses in 2015. This increased sample 

4 U.S. Census Bureau, American Community Survey, 2014 Content 

Review, <www.census.gov/programs-surveys/acs/operations-and

-administration/2014-content-review.html>. 

size has improved the precision of the ACS estimates. 

Over a 5-year period, the Census Bureau samples 

approximately 1-in-9 households nationwide, but the 

sampling rate is higher in areas with small populations 

and low predicted response rates. 

Of the 3.5 million addresses selected for ACS interviews in 2015, about 2.3 million resulted in final interviews. The number of final interviews is smaller than 

the number of initial addresses selected because the 

Census Bureau conducts in-person interviews with 

only a subset of those who do not respond by Internet, 

mail, or phone. Addresses are also excluded if they 

are determined to be invalid or commercial, or if a 

household refuses to participate. In 2015, the national 

response rate for the ACS was 95.8 percent.5 For more 

information about ACS sample size and response rates, 

visit the Census Bureau’s Web page on Sample Size 

and Data Quality.

6

The annual ACS sample is smaller than that of the 

2000 Census long-form sample, which included about 

18 million housing units. As a result, the ACS needs 

to combine population or housing data from multiple 

years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide 

information for communities each year, the ACS currently provides 1-year estimates for geographic areas 

with at least 65,000 people and 5-year estimates for 

smaller geographic areas down to the census tract 

and block-group level. Starting with the 2014 ACS, the 

Census Bureau is also producing 1-year Supplemental 

Estimates—simplified versions of popular ACS tables 

for geographic areas with populations of 20,000 or 

more.

One important fact to remember about the ACS is that 

the request to complete the survey is not mailed to 

specific people, but rather to specific addresses. The 

Census Bureau selects a random sample of addresses 

to be included in the ACS. Each address has about a 

1-in-480 chance of being selected in a given month, 

and no address should be selected more than once 

every 5 years. Each month, the Census Bureau sends 

an initial mail package to approximately 295,000 

addresses across the United States. This is a small 

number of housing units considering there are more 

than 180 million addresses in the United States.

5 The survey response rate is the ratio of the estimate of housing 

units interviewed after data collection is complete to the estimate of 

all units that should have been interviewed. Interviews include complete and partial interviews with enough information to be processed. 6 U.S. Census Bureau, American Community Survey, Sample Size 

and Data Quality, <www.census.gov/acs/www/methodology/sample

-size-and-data-quality/>.
6 Understanding and Using American Community Survey Data

6 What All Data Users Need to Know U.S. Census Bureau

Until 2015, the Census Bureau sent all selected 

addresses an advance notification letter informing people living at that address that they had been 

selected to participate in the ACS. Shortly thereafter 

(for most U.S. addresses), instructions for completing the survey by Internet were mailed. Beginning 

in August 2015, the Census Bureau eliminated the 

advance notification letter and instead included 

instructions in the initial mail package for completing the survey by Internet or over the phone through 

a toll-free Telephone Questionnaire Assistance (TQA) 

line. If households do not respond by Internet or TQA, 

then a paper questionnaire is mailed to the address. 

In Puerto Rico and some hard-to-reach areas, only a 

paper questionnaire is mailed. 

Until 2017, if no response was received by Internet, 

TQA, or mail within a month following the initial mailing, the Census Bureau followed up with a telephone 

interview when a telephone number was available. 

However, beginning in October 2017, the Census 

Bureau discontinued the telephone Nonresponse 

Followup operation because of declining response 

rates and increasing costs. Respondent data are still 

collected via telephone through the TQA operation.

If the Census Bureau is unable to get a response 

by Internet, mail, or TQA, then the address may be 

selected for an in-person interview. Because of the 

high cost per completed interview, the Census Bureau 

samples about one in three nonrespondent housing 

units for personal visit interviews. The proportion of 

nonresponding households selected for in-person 

interviews is higher in areas with lower predicted 

response rates. A sample of people living in group 

quarters facilities—such as college dorms, skilled nursing facilities, or correctional facilities—is also interviewed in person to ensure coverage of people who 

are not living in housing units. 

Population Controls

As with most household surveys, the ACS data are 

controlled so that the numbers of housing units and 

people in certain categories agree with the Census 

Bureau’s official estimates. The ACS uses a weighting 

method to ensure that ACS estimates are consistent 

with official Census Bureau population estimates by 

age, sex, race, and Hispanic origin—as well as estimates of total housing units. ACS estimates are controlled to official population and housing units at the 

county level. ACS single-year estimates are controlled 

to population and total housing unit estimates as of 

July 1 of the survey year, while ACS 5-year estimates 

are controlled to the average of the July 1 population 

and housing unit estimates over the 5-year period.

Starting with the 2009 survey, ACS estimates of the 

total population of incorporated places (self-governing 

cities, towns, or villages) and minor civil divisions (such 

as county precincts) are also adjusted so that they are 

consistent with official population estimates. 

TIP: ACS data for small statistical areas (such as 

census tracts) have no control totals, which may lead 

to errors in the population and housing unit estimates. In such cases, data users are encouraged to 

rely more upon noncount statistics, such as percent 

distributions or averages.

For more information about ACS methods, visit the 

Census Bureau’s Design and Methodology Report Web 

page.7

When Are ACS Data Released?

ACS data are very timely because they are released 

in the year immediately following the year in which 

they are collected (see Table 1.2). Beginning with data 

collected in 2005, 1-year estimates have been published for areas with populations of 65,000 or more, 

including all states, the District of Columbia, and many 

large counties and cities. In 2010, the Census Bureau 

released the first ACS 5-year estimates for the nation, 

states, cities, counties, and other small geographic 

areas. These 5-year estimates have been updated 

annually by removing the earliest year and replacing it 

with the latest one, thus providing an unprecedented 

ability to annually monitor social and economic trends 

in local communities.

The Census Bureau also produced ACS 3-year estimates, starting in 2008, but that series was discontinued in 2015. However, every community in the nation 

will continue to receive a detailed statistical portrait 

of its social, economic, housing, and demographic 

characteristics through ACS 1-year and 5-year data 

products. 

In July 2016, the Census Bureau released a series of 

Supplemental Estimates, consisting of new 1-year 

estimates for geographic areas with populations of 

20,000 or more. These tables provide 1-year estimates 

for many geographic areas that were previously only 

available through the 3-year or 5-year data products.

7 U.S. Census Bureau, American Community Survey, American 

Community Survey Design and Methodology, <www.census.gov

/programs-surveys/acs/methodology/design-and-methodology.html>.
Understanding and Using American Community Survey Data 7

U.S. Census Bureau What All Data Users Need to Know 7

 Table 1.2. Release Schedule for ACS Data

NA Not available.

1 The Census Bureau produced ACS 3-year estimates starting in 2008, but that series was discontinued in 2015. 

2 Five-year estimates are available for areas as small as census tracts and block groups. 

Source: U.S. Census Bureau.

ACS data collected for earlier years, from 2000 

through 2004, are also available for areas with 

250,000 people or more, including all states, the 

District of Columbia, and many large counties and 

cities.8

Additional Background Information

American Community Survey Site Map

<www.census.gov/acs/www/sitemap.php>

The ACS site map provides an overview of the ACSrelated links and materials that are available on the 

Census Bureau’s Web site.

What Is the ACS?

<www.census.gov/programs-surveys/acs/about.html>

This Web page includes basic information about the 

ACS and provides links to additional background 

materials. 

ACS Questionnaire Archive

<www.census.gov/programs-surveys/acs/methodology

/questionnaire-archive.html>

Browse archived sample ACS questionnaires for the 

household and group quarters populations in English 

and Spanish with instruction guides from 1996 through 

the present.

8 U.S. Census Bureau, American Community Survey, American 

Community Survey Design and Methodology, <www.census.gov

/programs-surveys/acs/methodology/design-and-methodology.html>.

Methodology

<www.census.gov/programs-surveys/acs/methodology

.html>

This Web page contains links to information on ACS 

data collection and processing, evaluation reports, and 

related topics.

Questions on the Form and Why We Ask

<www.census.gov/acs/www/about/why-we-ask-each

-question/>

This Web page provides more information about how 

federal agencies and other data users use the ACS in 

their work.

ACS Data Releases

<www.census.gov/programs-surveys/acs/news/data

-releases.html>

This Web page includes information about the ACS 

data release schedule, guidance on using the latest 

ACS data, and technical information about geography 

and product changes. Users can also browse the notes 

from previous years.

Table and Geography Changes

<www.census.gov/programs-surveys/acs/technical

-documentation/table-and-geography-changes.html>

This Web page provides information about changes to 

tables and geography for each ACS data release.

Year of data release

Data product, population threshold, and year(s) of data collection

1-year estimates 

(65,000+)

1-year Supplemental 

Estimates (20,000+)

3-year estimates 

(20,000+)

5-year estimates 

(All areas)

2006 2005 NA NA NA

2007 2006 NA NA NA

2008 2007 NA 2005–2007 NA

2009 2008 NA 2006–2008 NA

2010 2009 NA 2007–2009 2005–2009

2011 2010 NA 2008–2010 2006–2010

2012 2011 NA 2009–2011 2007–2011

2013 2012 NA 2010–2012 2008–2012

2014 2013 NA 2011–2013 2009–2013

2015 2014 NA NA 2010–2014

2016 2015 2014/2015 NA 2011–2015

2017 2016 2016 NA 2012–2016
8 Understanding and Using American Community Survey Data

8 What All Data Users Need to Know U.S. Census Bureau

2. GEOGRAPHIC AREAS COVERED IN THE ACS

Data from the American Community Survey (ACS) are 

tabulated for a variety of different geographic areas 

ranging in size from broad geographic regions 

(Northeast, Midwest, South, and West) to states, cities, 

towns, census tracts, and block groups (see Box 2.1). 

Table 2.1 shows the type and number of geographic 

areas included in the ACS 1-year and 5-year products 

for 2015. For example, in 2015, ACS 1-year data were 

available for 830 counties (26 percent of all counties), 

while the remaining 2,390 counties (74 percent of all 

counties) received 5-year estimates. Note that the 

information in this table is based on current geographic boundaries and is expected to change over 

time. The ACS uses boundaries as of January 1 of the 

last year of the estimate period. For example, the 

2011–2015 ACS 5-year estimates use boundaries as of 

January 1, 2015, as reported to the U.S. Census Bureau. 

Box 2.1. Explaining Census Bureau Geography

For reporting purposes, the nation is subdivided 

into two main types of geographic areas, legal and 

statistical. Legal areas are defined specifically by 

law, and include state, local, and tribal government 

units, as well as some specially defined administrative areas like congressional districts. Many, but 

not all, are represented by elected officials. An 

example of a legal area is New York State.

Statistical areas are defined directly by the Census 

Bureau and state, regional, or local authorities, and 

include census tracts and urban areas. The primary 

purpose of statistical areas is to tabulate and 

present census data. An example of a statistical 

area is the Boston-Cambridge-Newton, MA-NH 

Metropolitan Statistical Area. 

Geographic areas are organized in a geographic 

hierarchy (see Figure 2.1). Larger units, such as 

states, include smaller units, such as counties and 

census tracts. This structure is derived from the 

legal, administrative, or areal relationships of the 

entities.

REGIONS

DIVISIONS

Counties

Census Tracts*

Block Groups*

American Indian Areas/

Alaska Native Areas/

Hawaiian Home Lands

Urban Areas

Metropolitan and 

Micropolitan Areas

Public Use Microdata Areas

State Legislative Districts*

Alaska Native Regional Areas

Places

STATES

NATION

ZIP Code Tabulation Areas 

(ZCTA)**

School Districts

County Subdivisions

Congressional Districts

 *Five-year estimates only.

 **Five-year estimates only, first released in 2012 for the 2007–2011 ACS estimates.

 Figure 2.1. Hierarchy of Select Geographic Entities in the ACS
Understanding and Using American Community Survey Data 9

U.S. Census Bureau What All Data Users Need to Know 9

Geographic areas Total 

number of areas

Areas receiving 1-year and 

5-year estimates 

Areas receiving only 5-year 

estimates1

Number Percent Number Percent

United States 1 1 100.0 0 0.0

Region 4 4 100.0 0 0.0

Division 9 9 100.0 0 0.0

States, the District of Columbia, and 

Puerto Rico 52 52 100.0 0 0.0

County or equivalent2 3,220 830 25.8 2,390 74.2

County subdivision3 36,631 223 0.6 36,408 99.4

Subminor civil division (Puerto Rico only) 145 NA NA 145 100.0

Census tract 74,001 NA NA 74,001 100.0

Block group 220,333 NA NA 220,333 100.0

Place (incorporated places and census 

designated places) 29,574 596 2.0 28,978 98.0

Consolidated city 8 NA NA 8 100.0

Alaska Native Regional Corporation 12 3 25.0 9 75.0

American Indian Area/Alaska Native Area/

Hawaiian Home Land 693 12 1.7 681 98.3

Specified American Indian Area-Tribal 

Census Tract 481 NA NA 481 100.0

Specified American Indian Area-Tribal 

Census Tract-Tribal Block Group 915 NA NA 915 100.0

Metropolitan Statistical/Micropolitan 

Statistical Area 929 516 55.5 413 44.5

Principal City of Metropolitan or 

Micropolitan Statistical Areas 1,249 388 31.1 861 68.9

Metropolitan Division 31 31 100.0 0 0.0

Combined Statistical Area 169 167 98.8 2 1.2

Combined New England City and Town 

Area 6 6 100.0 0 0.0

New England City and Town Area 38 25 65.8 13 34.2

Principal Cities of New England City and 

Town Areas 58 19 32.8 39 67.2

New England City and Town Area 

Division 10 10 100.0 0 0.0

Urban Area 3,592 432 12.0 3,160 88.0

Congressional Districts, 114th Congress 435 435 100.0 0 0.0

Delegate District, 114th Congress (at Large, 

District of Columbia) 1 1 100.0 0 0.0

Resident Commissioner District, 114th 

Congress (at Large, Puerto Rico) 1 1 100.0 0 0.0

State Legislative District, Upper 

Chamber4 1,954 NA NA 1,954 100.0

State Legislative District, Lower 

Chamber4 4,825 NA NA 4,825 100.0

Public Use Microdata Area 2,378 2,378 100.0 0 0.0

5-digit ZIP Code Tabulation Area 33,120 NA NA 33,120 100.0

Elementary School District 2,181 76 3.5 2,105 96.5

Secondary School District 538 90 16.7 448 83.3

Unified School District 10,923 851 7.8 10,072 92.2

TOTAL 428,517 7,156 1.7 421,361 98.3

 Table 2.1. Selected Geographic Areas Published in the 2015 American Community Survey 1-Year 

 and 5-Year Estimates

NA Not available. 1

 Geographic areas with populations of 20,000 or more also receive 1-year Supplemental Estimates, which are simplified versions of popular 

ACS tables. 2 County equivalents include Alaska boroughs, municipalities, city and boroughs, and census areas; Louisiana parishes; Puerto Rico municipios; and independent cities in Maryland, Missouri, Nevada, and Virginia. 3 For 1-year estimates, qualifying Minor Civil Divisions in 20 states only. For 5-year estimates, all county subdivisions. 4 Legislative session year 2014. 

Note: Figures based on geographic area boundaries as of January 1, 2014, new and dissolved incorporations as of January 1, 2015, and population 

estimates from the July 1, 2015, Census Bureau Population Estimates. The Census Bureau does not publish ACS data for individual blocks.
10 Understanding and Using American Community Survey Data

10 What All Data Users Need to Know U.S. Census Bureau

Key Geographic Areas in the ACS

In Figures 2.2 through 2.5, sample maps for four different states—Utah, Kentucky, Iowa, and Vermont—show 

data users some of the key geographic areas available 

through the ACS: congressional districts, Public Use 

Microdata Areas, counties, and census tracts.

Congressional districts are redrawn after each census 

for the purpose of electing the members of the 

U.S. House of Representatives. Each of Utah’s four 

congressional districts (shown in Figure 2.2) includes 

approximately 750,000 people. ACS data on congressional districts can be used to compare the home 

districts of the 435 House members and how they have 

changed over time. 

The Census Bureau also divides each state into a series 

of Public Use Microdata Areas, or PUMAs, each of 

which has a minimum population of 100,000. PUMAs 

are constructed based on county and neighborhood 

boundaries and do not cross state lines. Typically, 

counties with large populations are subdivided into 

multiple PUMAs, while PUMAs in more rural areas 

are made up of groups of adjacent counties. PUMAs 

are especially useful for rural areas because, unlike 

counties, they all meet the 65,000-population threshold that is needed to produce ACS 1-year estimates. 

Kentucky’s PUMAs are shown below in Figure 2.3. 

Counties are also important because they are the 

primary legal subdivision within each state. ACS 1-year 

estimates are currently available for 10 of Iowa’s 99 

counties—those with populations of 65,000 or more 

in 2015 (see Figure 2.4). Iowa has 34 counties with 

populations of at least 20,000 people that receive 

1-year Supplemental Estimates. The 65 counties in Iowa 

with fewer than 20,000 people only receive 5-year 

estimates.

 Figure 2.2. Congressional Districts in Utah

Source: U.S. Census Bureau, Congressional District Cartographic Boundary Shapefiles, accessed at <www.census.gov

/geo/maps-data/data/cbf/cbf_cds.html>. 
Understanding and Using American Community Survey Data 11

U.S. Census Bureau What All Data Users Need to Know 11

Source: U.S. Census Bureau, Public Use Microdata Sample Cartographic Boundary Shapefiles, accessed at <www.census

.gov/geo/maps-data/data/cbf/cbf_puma.html>. 

Source: U.S. Census Bureau, Population Estimates; and County Cartographic Boundary Shapefiles, accessed at 

<www.census.gov/geo/maps-data/data/cbf/cbf_counties.html>. 

 Figure 2.3. Public Use Microdata Areas in Kentucky

 Figure 2.4. Counties in Iowa by Population Size in 2015
12 Understanding and Using American Community Survey Data

12 What All Data Users Need to Know U.S. Census Bureau

Census tracts are small geographic areas—with an 

average of about 4,000 people each—that are commonly used to present information for small towns, 

rural areas, and neighborhoods. For example, in 

Vermont, there are currently 184 census tracts with 

data available through the ACS 5-year data products 

(see Figure 2.5). 

There are also more than 300 ACS data tables available for block groups—subdivisions of census tracts—

that include between 600 and 3,000 people each. In 

the ACS, block groups are the lowest (smallest) level 

of geography published. Block group data are only 

available in the ACS 5-year data products. 

Additional Background Information

Geography & ACS

<www.census.gov/programs-surveys/acs/geography

-acs.html>

This Web page includes information about changes in 

geographic boundaries in the ACS, key concepts and 

definitions, and reference maps.

 Figure 2.5. Census Tracts in Vermont

Source: U.S. Census Bureau, Census Tract Cartographic Boundary Shapefiles, accessed at <www.census.gov/geo/maps

-data/data/cbf/cbf_tracts.html>. 
Understanding and Using American Community Survey Data 13

U.S. Census Bureau What All Data Users Need to Know 13

3. UNDERSTANDING AND USING ACS SINGLEYEAR AND MULTIYEAR ESTIMATES

Each year, the U.S. Census Bureau publishes American 

Community Survey (ACS) 1-year estimates for geographic areas with populations of 65,000 or more. 

The 65,000-population threshold ensures that 1-year 

data are available for all regions, divisions, states, 

the District of Columbia, Puerto Rico, congressional districts, Public Use Microdata Areas, and many 

large counties and county equivalents, metropolitan 

and micropolitan areas, cities, school districts, and 

American Indian areas.9 The 1-year Supplemental 

Estimates, simplified versions of popular ACS tables, 

are also available for geographic areas with at least 

20,000 people. These annual data provide policymakers, planners, business leaders, and others with a 

critical source of up-to-date information to plan for 

services such as transportation, medical care, housing, 

and schools.

For geographic areas with smaller populations, the 

ACS samples too few housing units to provide reliable 

single-year estimates. For these areas, several years of 

data are pooled together to create more precise 

multiyear estimates. Since 2010, the ACS has published 

5-year data (beginning with 2005–2009 estimates) 

for all geographic areas down to the census tract and 

block group levels.10

This means that there are two sets of numbers—both 

1-year estimates and 5-year estimates—available for 

geographic areas with at least 65,000 people, such 

as the state of Virginia. Less populous areas, such as 

Bath County in Virginia’s Shenandoah Valley, receive 

only 5-year estimates. As shown in Table 2.1 in the section on “Geographic Areas Covered in the ACS,” the 

vast majority of geographic areas receive only 5-year 

estimates. 

Understanding Period Estimates

Single-year and multiyear estimates from the ACS are 

all “period” estimates derived from a sample collected 

over a period of time, as opposed to “point-in-time” 

estimates such as those from past decennial censuses. 

For example, the 2000 Census “long form” sampled 

the resident U.S. population as of April 1, 2000. 

9 Public Use Microdata Areas, or PUMAs, are collections of counties—or tracts within counties—with approximately 100,000 people 

each. PUMAs do not cross state lines. 10 For several years, the Census Bureau produced both ACS 3-year 

estimates (for areas with populations of 20,000 or more) and ACS 

5-year estimates (for all geographic areas). Starting with the 2014 data 

release, the 3-year products were discontinued, but 5-year estimates 

are still published each year.

While an ACS 1-year estimate includes information collected over a 12-month period, an ACS 5-year estimate 

includes data collected over a 60-month period. 

In the case of ACS 1-year estimates, the period is the 

calendar year (e.g., the 2015 ACS covers the period 

from January 2015 through December 2015). In the 

case of ACS multiyear estimates, the period is 5 calendar years (e.g., the 2011–2015 ACS estimates cover 

the period from January 2011 through December 2015). 

Therefore, ACS estimates based on data collected from 

2011–2015 should not be labeled “2013,” even though 

that is the midpoint of the 5-year period.

Multiyear estimates should be labeled to indicate clearly 

the full period of time (e.g., “The child poverty rate in 

2011–2015 was X percent.”). They do not describe any 

specific day, month, or year within that time period.

Multiyear estimates require some considerations that 

single-year estimates do not. For example, multiyear 

estimates released in consecutive years consist mostly 

of overlapping years and shared data.

TIP: As shown in Figure 3.1, consecutive 5-year estimates contain 4 years of overlapping coverage (for 

example, the 2010–2014 ACS 5-year estimates share 

sample data from 2011 through 2014 with the 2011–

2015 ACS 5-year estimates). Because of this overlap, 

users should use extreme caution in making comparisons with consecutive years of multiyear estimates.

The primary advantage of using multiyear estimates 

is the increased statistical reliability of the data compared with that of single-year estimates, particularly 

for small geographic areas and small population subgroups. Figure 3.2 shows the improved precision of an 

ACS 5-year estimate, compared with a 1-year estimate, 

for child poverty statistics in Rice County, Minnesota—a 

county with about 65,000 residents in 2015. The lines 

above and below the point estimates represent the 

confidence intervals, or ranges of uncertainty, around 

each estimate. The confidence interval for the 1-year 

child poverty estimate ranges from 1.4 percent to 9.4 

percent (8 percentage points) while the interval for the 

5-year estimate is narrower, ranging from 12.8 percent to 19.2 percent (6 percentage points). (Refer to 

the section on “Understanding Error and Determining 

Statistical Significance” for a detailed explanation of 

uncertainty in ACS data.)
14 Understanding and Using American Community Survey Data

14 What All Data Users Need to Know U.S. Census Bureau

Percent of children in poverty

1-year estimate

(2015)

5-year estimate

(2011–2015)

0

5

10

15

20

25

Source: U.S. Census Bureau, American FactFinder, Table DP03: Selected Economic 

Characteristics in the United States.

 Figure 3.2. Child Poverty Rate in Rice County, Minnesota: 2015 and 

 2011–2015

 Figure 3.1. Sample Cases Used in Producing ACS 5-Year Estimates

Year of data release

Data collection period

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

2010

2011

2012

2013

2014

2015

2016

Source: U.S. Census Bureau, “When to Use 1-year, 3-year, or 5-year Estimates,” accessed at <www.census.gov/programs-surveys

/acs/guidance/estimates.html>.
Understanding and Using American Community Survey Data 15

U.S. Census Bureau What All Data Users Need to Know 15

 Figure 3.3. Percentage of People Aged 5 and Older Who Speak Spanish at Home in the Prince 

 George’s County, MD, Public School District 

0

2

4

6

8

10

12

14

2011 2012 2013 2014 2015

Data release year

1-year estimates

5-year estimates

Source: U.S. Census Bureau, American FactFinder, DP02: Selected Social Characteristics in the United States. 

Deciding Which ACS Estimate to Use

For data users interested in obtaining detailed ACS 

data for small geographic areas (areas with fewer than 

65,000 residents), ACS 5-year estimates are the only 

option.11 However, data users interested in estimates 

for areas with populations of 65,000 or more have 

a choice between the 1-year and 5-year data series. 

Which data should be used?

The 1-year estimates for an area reflect the most current data but they have larger margins of error than 

the 5-year estimates because they are based on a 

smaller sample. The 5-year estimates for an area have 

larger samples and smaller margins of error than 

the 1-year estimates. However, they are less current 

because the larger samples include data that were 

collected in earlier years. The main advantage of using 

multiyear estimates is the increased statistical reliability for smaller geographic areas and small population 

groups.

One-year estimates are particularly useful for geographic areas with rapidly changing characteristics 

because they are based on the most current data—

data from the past year. For example, ACS 1-year data 

were used to compare poverty rates before, during, 

and after the 2007–2009 recession. In contrast, 5-year 

11 In July 2016, the Census Bureau released a series of Supplemental 

Estimates, consisting of new 1-year estimates for geographic areas 

with populations of 20,000 or more. These tables provide 1-year estimates for many geographic areas that were previously only available 

through the 3-year or 5-year data products.

estimates provide less current information because 

they are based on both data from the previous year 

and data that are 2 to 5 years old. For many areas 

undergoing minimal change, using the “less current” 

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

major changes over a given time period, the multiyear 

estimates may be quite different from the single-year 

estimates for any of the individual years. The singleyear and multiyear estimates will not 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., 

2013 single year, 2011–2015 multiyear). 

For example, suppose a school district official in Prince 

George’s County, Maryland, is interested in measuring 

recent trends in the population speaking Spanish at 

home. Comparing data by release year shows that the 

5-year estimates for the Prince George’s County Public 

School District are lagging behind the 1-year estimates 

(see Figure 3.3). While the 1-year estimates show an 

increase in the share of people speaking Spanish at 

home, followed by a decline, the 5-year estimates show 

a steady increase over time. 

TIP: In general, ACS 1-year data are more likely to 

show year-to-year fluctuations, while consecutive 

5-year estimates are more likely to show a smooth 

trend, because 4 of the 5 years in the series overlap 

from one year to the next.
16 Understanding and Using American Community Survey Data

16 What All Data Users Need to Know U.S. Census Bureau

 Table 3.1. Distinguishing Features of ACS 1-Year, 1-Year Supplemental, 3-Year, and 5-Year Estimates

1-Year Estimates

1-Year Supplemental 

Estimates 3-Year Estimates1 5-Year Estimates

12 months of collected data

Example: 

2015 ACS 1-year estimates

Dates collected:

January 1, 2015, to

December 31, 2015

12 months of collected data

Example:

2015 ACS 1-year Supplemental Estimates

Dates collected:

January 1, 2015, to

December 31, 2015

36 months of collected data

Example:

2011-2013 ACS 3-year estimates

Dates collected:

January 1, 2011, to

December 31, 2013

60 months of collected data

Example:

2011-2015 ACS 5-year estimates

Dates collected:

January 1, 2011, to

December 31, 2015

Data for areas with populations of 65,000 and moreData for areas with populations of 20,000 and moreData for areas with populations of 20,000 and moreData for all areas

Smallest sample size Smallest sample size Larger sample size than 

1-year

Largest sample size

Less reliable than 3-year or 

5-year

Less reliable than 5-year More reliable than 1-year; less 

reliable than 5-year

Most reliable

Most current Most current Less current than 1-year; 

more current than 5-year

Least current

Annually released:

2005 ACS 1-year data to 

present

Annually released:

2014 ACS 1-year data to 

present

Annually released:

2005–2007 ACS 3-year data 

to 2011–2013 ACS 3-year 

data

Annually released:

2005–2009 ACS 5-year data 

to present

Best used when Best used when Best used when Best used when

Currency is more important 

than precision

Analyzing large populations

Currency is more important 

than precision

Analyzing smaller populations

Examining smaller geographic areas because the 

standard 1-year estimates 

are not available

More precise than 1-year, 

spans fewer years than 

5-year

Analyzing smaller populations

Examining smaller geographic areas because the 

standard 1-year estimates 

are not available

Precision is more important 

than currency

Analyzing very small populations

Examining tracts and other 

small geographic areas because 1-year estimates are 

not available

1

 ACS 3-year estimates have been discontinued. The 2005–2007, 2006–2008, 2007–2009, 2008–2010, 2009–2011, 2010–2012, and 2011–2013 

ACS 3-year estimates will remain available to data users, but no new 3-year estimates will be produced. Every community in the nation will 

continue to receive a detailed statistical portrait of its social, economic, housing, and demographic characteristics through 1-year and 5-year 

ACS products.

TIP: Data users may think that multiyear estimates 

are only appropriate when working with data for 

small areas, but this is not the case. Large geographic 

areas also benefit from the larger sample used for 

5-year estimates, resulting in more precise estimates 

of population and housing characteristics, especially 

for subpopulations within those areas. 

In addition, ACS 5-year estimates are not subject to the 

same data suppression rules that the Census Bureau 

applies to 1-year estimates. The Census Bureau restricts 

some 1-year data tables from publication because the 

estimates are not reliable. For more information, visit 

the Census Bureau’s Web page on Data Suppression.

12

Some users may prefer to use 1-year estimates, despite 

their reduced reliability, as building blocks to produce 

12 U.S. Census Bureau, American Community Survey, Data 

Suppression, <www.census.gov/programs-surveys/acs/technical

-documentation/data-suppression.html>.

estimates for meaningful higher levels of geography. 

For example, data for neighboring counties could be 

combined to produce estimates for a metropolitan or 

regional planning area. These aggregations will similarly benefit from the increased sample sizes, through 

improved precision. 

There are no hard-and-fast rules for choosing between 

1-year and 5-year data, but the margins of error 

provided with ACS data can help data users decide 

on the tradeoff between currency and reliability. 

Table 3.1 shows the different features of ACS 1-year, 

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

that data users can consider in choosing which estimates to use. Data users can also refer to the section 

on “Understanding Error and Determining Statistical 

Significance” for guidance on assessing the reliability 

of ACS estimates.
Understanding and Using American Community Survey Data 17

U.S. Census Bureau What All Data Users Need to Know 17

4. MAKING COMPARISONS WITH ACS DATA

One of the main benefits of the American Community 

Survey (ACS) is the ability to make comparisons—over 

time, across different geographic areas, and across different population subgroups. For example, data users 

may be interested in:

• Comparing the proportion of people without 

health insurance in two counties.

• Comparing the child poverty rate with the poverty 

rate for the working-age population.

• Comparing the proportion of people who are 

foreign-born in a city in 2015 with the corresponding share in 2014.

• Comparing the proportion of people with college 

degrees from 2000 to 2015 in a state, based on 

data from the ACS and the 2000 Census.

When making comparisons with ACS data, note that 

differences in survey design, questionnaire content and 

design, sample size, or geography may affect comparability of estimates. 

TIP: ACS data users interested in making comparisons also need to pay attention to sampling error 

because differences between estimates may, or may 

not, be statistically significant. 

This section describes some of the key considerations 

for data users making comparisons with ACS data. 

The U.S. Census Bureau also provides extensive guidance on comparing ACS data on their Web site.13 The 

section on “Understanding Error and Determining 

Statistical Significance” includes guidance on how to 

calculate and interpret the statistical significance of 

differences between estimates.

The Census Bureau created a statistical testing tool to 

help data users test whether ACS estimates are statistically different from one another.14

Comparing ACS Data Across 

Geographic Areas

One of the challenges for data users is deciding how 

to compare geographic areas with different population sizes. Estimates for areas with fewer than 20,000 

people are produced only in the form of 5-year estimates. However, for larger areas with at least 65,000 

people (or 20,000 people in the case of the 1-year 

Supplemental Estimates) both 1-year and 5-year data 

13 U.S. Census Bureau, American Community Survey (ACS), 

Comparing ACS Data, <www.census.gov/programs-surveys/acs 

/guidance/comparing-acs-data.html>. 14 U.S. Census Bureau, American Community Survey (ACS), 

Statistical Testing Tool, <www.census.gov/programs-surveys/acs 

/guidance/statistical-testing-tool.html>.

are available, so data users need to choose which estimates to use.15

TIP: When comparing ACS estimates across different geographic areas or population subgroups, data 

users should avoid comparing ACS single-year estimates with ACS multiyear estimates. That is, 1-year 

estimates should only be compared with other 1-year 

estimates, and 5-year estimates should only be compared with other 5-year estimates. 

For example, suppose a policymaker wanted to compare veterans’ characteristics in Athens, Texas—a small 

city southeast of Dallas—with veterans in Houston. 

Although the ACS publishes annual estimates on veterans for Houston, only 5-year estimates are available for 

Athens. Thus, the policymaker should compare 2011–

2015 ACS 5-year estimates for Athens with 2011–2015 

ACS 5-year estimates for Houston, even though more 

recent, single-year estimates for 2015 are available for 

Houston. 

TIP: Another option for presenting ACS data for less 

populated areas is to show single-year estimates for 

large counties in Texas and then combine the remaining counties into a state “residual” by subtracting 

the available single-year data from the state total. 

Alternatively, data users could present ACS estimates 

for Public Use Microdata Areas, since they meet the 

65,000-population threshold required for singleyear estimates and are often used as a substitute for 

county-level data.16

Comparing ACS Data Over Time

TIP: Data users are encouraged to compare ACS data 

over time based on nonoverlapping estimates. 

When using ACS 1-year data, these comparisons are 

generally straightforward. Using multiyear estimates to 

look at trends for small populations can be challenging because they rely on pooled data for 5 years. For 

example, comparisons of 5-year estimates from 2010 

to 2014 and 2011 to 2015 are unlikely to show much difference because four of the years overlap; both sets of 

estimates include the same data collected from 2011 

through 2014.17 The Census Bureau suggests comparing 

15 In July 2016, the Census Bureau also released a series of 1-year 

Supplemental Estimates—simplified versions of popular ACS tables 

available for geographic areas with at least 20,000 people. 16 Although Public Use Microdata Areas typically follow county 

boundaries, this is not always the case, particularly in some New 

England states. 17 While the interpretation of this difference is difficult, these comparisons can be made with caution. Users who are interested in comparing overlapping multiyear period estimates should refer to the section on “Understanding Error and Determining Statistical Significance” 

for more information.
18 Understanding and Using American Community Survey Data

18 What All Data Users Need to Know U.S. Census Bureau

5-year estimates that do not overlap—for example, comparing 2006–2010 ACS 5-year estimates with 2011–2015 

ACS 5-year estimates. When new ACS 5-year data are 

released, it will be possible to update this trend, based 

on data from 2007–2011 and 2012–2016. 

TIP: Changes to ACS questions over time may make it 

difficult to measure trends. For example, the Census 

Bureau made substantial changes to the 2008 ACS 

questions on labor force participation and number of 

weeks worked. As a result, the Census Bureau recommends using caution when comparing 2008 and later 

labor force data with 2007 and earlier estimates.

TIP: Data users are also encouraged to pay attention 

to sampling error associated with ACS data when 

looking at trends over time. For example, 1-year estimates for a relatively small population subgroup may 

vary widely 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.

However, because multiyear estimates have an inherent 

smoothing effect on trends—because of overlapping estimates from year to year—they will tend to mask rapidly 

developing changes. 

Changes in Geography

ACS data generally reflect the geographic boundaries 

as of the year the data are collected. While geographic 

boundary changes are somewhat infrequent, they do 

occur, and those changes can affect a data user’s ability to make comparisons over time. For example, congressional districts are redrawn every 10 years immediately following the decennial census. Congressional 

district data from the 2012 ACS and later years reflect 

the new boundaries that were drawn after the 2010 

Census, while ACS data for earlier years reflect the 

2000 Census boundaries. Given the major changes to 

district boundaries after each census, a comparison of 

congressional district data between 2011 and 2012 is 

not feasible.

ACS data are also regularly updated to reflect local 

changes in geographic boundaries. For example, the 

city of Jurupa Valley, California, incorporated in July 

2011. Data for this city was first published in 2012, and 

has been updated each subsequent year, but data 

are not available for Jurupa Valley for 2011 and earlier 

years. The Census Bureau does not revise ACS data 

for previous years to reflect changes in geographic 

boundaries. For more information, visit the Census 

Bureau’s Web page on Geography & ACS.

18

Changes in Population Controls

The ACS uses a weighting methodology to ensure 

that ACS estimates are consistent with official Census 

Bureau population estimates by age, sex, race, and 

Hispanic origin. With each annual release of population 

estimates, the Population Estimates Program revises 

and updates the entire time series of estimates from 

the previous decennial census to the current year. 

However, ACS estimates for prior years are not revised 

or reweighted based on updated population estimates. 

The change in the population estimates from 2009 to 

2010 was particularly significant. The 2010 ACS 1-year 

data and 2006–2010 ACS 5-year data were controlled 

to population estimates that reflected the results of 

the 2010 Census. However, the 1-year and 5-year data 

for 2009 and earlier years used population estimates 

that were based on the 2000 Census. 

TIP: Because the 2009 ACS and 2010 ACS 1-year 

estimates use controls that are based on different 

decennial census base years, data users need to 

use caution when making comparisons across these 

years. Specifically, estimates of the number of people 

in a given geographic area or population subgroup 

are not strictly comparable between these two years. 

However, rates and percentages—as well as monetary 

data, such as median income values—are generally 

comparable between the two periods.

Comparisons With Data From the 2000 

Census and the 2010 Census

The ACS was modeled after the long form of the 

decennial census, and data users interested in 

long-term trends can, in many cases, make valid 

comparisons between ACS and the 2000 Census (and 

earlier decennial census) estimates. Census Bureau 

subject matter specialists have reviewed the factors 

that could affect differences between ACS and the 

2000 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. 

However, differences in residence rules, universes 

(base reference totals against which all other characteristics are compared), and reference periods 

between the two surveys should be considered when 

making these comparisons. For example, the ACS 

18 U.S. Census Bureau, American Community Survey (ACS), 

Geography & ACS, <www.census.gov/programs-surveys/acs

/geography-acs.html>.
Understanding and Using American Community Survey Data 19

U.S. Census Bureau What All Data Users Need to Know 19

data are collected throughout the calendar year while 

the 2000 Census long form sampled the population 

as of April 1, 2000. Given the differences in the reference period, the two surveys may yield very different 

estimates for communities with large seasonal populations or those undergoing rapid change. The section 

on “Differences Between the ACS and the Decennial 

Census” provides more information about these differences.

The 2010 Census was a short-form only census so it 

does not include all of the detailed social, economic, 

and housing data available from previous censuses. 

However, data users can make valid comparisons 

between ACS estimates and basic characteristics from 

the 2010 Census, including age, sex, race, Hispanic 

origin, household relationship, and homeowner status. 

For basic counts of the U.S. population by age, sex, 

race, and Hispanic origin between censuses, data users 

are encouraged to use the Census Bureau’s official 

population estimates, available on the Census Bureau’s 

Population and Housing Unit Estimates Web site.19

Using Monetary Data

TIP: Data users also need to use caution in looking 

at trends involving income or other measures that 

are adjusted for inflation, such as rental costs, home 

values, and utility costs. 

For example, to compare published monetary data for 

the most recent year with data from the 2010 ACS, data 

users need to adjust the 2010 data for inflation, based 

on a national-level consumer price index. 

19 U.S. Census Bureau, Population and Housing Unit Estimates, 

<www.census.gov/programs-surveys/popest.html>.

ACS multiyear estimates with dollar values are adjusted 

for inflation to the final year of the period. For example, 

the 2011–2015 ACS 5-year estimates are tabulated using 

dollars adjusted to 2015. 

Note that inflation adjustment does not adjust for differences in costs of living across different geographic 

areas. The section on “Using Dollar-Denominated Data” 

provides more information on the adjustment of ACS 

single-year and multiyear estimates for inflation. 

Additional Background Information

Geography & ACS 

<www.census.gov/programs-surveys/acs

/geography-acs.html>

This Web page describes changes to local geographic 

area boundaries, which may affect comparisons of ACS 

data over time.

Comparing ACS Data 

<www.census.gov/programs-surveys/acs/guidance

/comparing-acs-data.html>

This Web page provides guidance on making valid 

comparisons between the latest ACS data and ACS 

data from the previous year, the 2000 Census, and the 

2010 Census.

Statistical Testing Tool

<www.census.gov/programs-surveys/acs/guidance

/statistical-testing-tool.html>

The Statistical Testing Tool is a spreadsheet that tests 

whether ACS estimates are statistically different from 

one another. Simply copy or download ACS estimates 

and their margins of error into the tool to get instant 

results of statistical tests.
20 Understanding and Using American Community Survey Data

20 What All Data Users Need to Know U.S. Census Bureau

5. ACCESSING ACS DATA

American FactFinder (AFF) is the U.S. Census Bureau’s 

primary tool for accessing population, housing, and 

economic data from the American Community Survey 

(ACS), the Puerto Rico Community Survey, the decennial census, and many other Census Bureau data sets.20

AFF provides comprehensive access to pretabulated 

ACS data for a wide range of geographic areas, including states, cities, counties, census tracts, and block 

groups.

Other specialized tools, such as My Congressional 

District and Census Business Builder, provide users 

with quick and easy access to statistics for particular 

geographic areas and topics.21 More advanced users 

also have several options to access more detailed ACS 

data through downloadable Summary Files, the Public 

20 U.S. Census Bureau, American FactFinder, <https://factfinder 

.census.gov/>. 21 U.S. Census Bureau, My Congressional District <www.census.gov 

/mycd/>; Census Business Builder (CBB) <www.census.gov/data 

/data-tools/cbb.html>.

Use Microdata Sample Files, or the Census Bureau’s 

Application Programming Interface (API).22

Accessing ACS Data in American 

FactFinder

With several different options for accessing ACS data, 

how should you decide which tool to use? For most 

data users, AFF is the best place to start because it 

houses almost all of the ACS data tables produced by 

the Census Bureau. Below, you will find a brief description of the different tools that can be found on AFF, 

along with some guidance to help you decide which 

tool is right for you. Click on a tool to navigate to a 

search results page prefiltered for the ACS program, a 

product type, and a dataset. From the search results 

page you can further filter your results by keywords 

and geographic areas.

22 U.S. Census Bureau, American Community Survey (ACS), 

Summary File Data, <www.census.gov/programs-surveys/acs/data

/summary-file.html>; American Community Survey (ACS), PUMS Data, 

<www.census.gov/programs-surveys/acs/data/pums.html>; 

Developers, <www.census.gov/developers/>.

Data Profiles: The Data Profiles are good places to start for novice data users, as they contain the most frequently requested social, economic, housing, and demographic data.23 Each of these four subject areas is a 

separate profile. The Data Profiles summarize the data for a single geographic area, providing both estimates 

and percentages, to cover the most basic data on all ACS topics.

 Geographic areas covered: All areas down to the neighborhood (census tract) level.

23 U.S. Census Bureau, American FactFinder, Data Profiles, <https://factfinder.census.gov/bkmk/navigation/1.0/en/d_program%3AACS 

/d_product_type%3ADATA_PROFILE>.

Why this tool may be right for you Why you may want to consider another tool

The profiles help novice users get acquainted with ACS 

data tables.

The tables may not contain enough detail for your topic(s) 

of interest.

The profiles provide broad social, economic, housing, and 

demographic statistics for many levels of geography.

Data are not available for block groups.
Understanding and Using American Community Survey Data 21

U.S. Census Bureau What All Data Users Need to Know 21

Subject Tables: Data users interested in a particular topic (e.g., employment, education, and income) may 

wish to start with the Subject Tables, which provide pretabulated estimates and percentages for a wide 

variety of topics, often available separately by age, sex, or race/ethnicity.24

 Geographic areas covered: All areas down to the neighborhood (census tract) level.

Why this tool may be right for you Why you may want to consider another tool

Tables are available for most of the subjects in the 

ACS.

Not all topics have a corresponding Subject Table.

It provides more in-depth statistics on a particular 

subject than either Data Profiles or Comparison 

Profiles.

The tables may not contain enough detail for your topic(s) of 

interest.

It provides estimates and percentages for many 

levels of geography.

Data are not available for block groups.

Detailed Tables: The Detailed Tables are designed for advanced data users or those who want access to the 

most comprehensive ACS tables.25 Detailed Tables are also available for download through the ACS Summary 

File on the Census Bureau’s File Transfer Protocol (FTP) Web site.26

 Geographic areas covered: All areas down to the block group level.*

24 U.S. Census Bureau, American FactFinder, Subject Tables, <https://factfinder.census.gov/bkmk/navigation/1.0/en/d_program%3AACS

/d_product_type%3ASUBJECT_TABLE/>. 25 U.S. Census Bureau, American FactFinder, Detailed Tables, <https://factfinder.census.gov/bkmk/navigation/1.0/en/d_program%3AACS

/d_product_type%3ADETAILED_TABLE/>. 26 U.S. Census Bureau, American Community Survey (ACS), Data via File Transfer Protocol, <www.census.gov/programs-surveys/acs

/data/data-via-ftp.html>.

Why this tool may be right for you Why you may want to consider another tool

Along with the Summary Files, the Detailed Tables 

are the best sources for the most comprehensive 

ACS tables.

The tables may contain more detail than needed for your topic.

The tool covers all subjects in the ACS. The tool does not provide percentages.

Beginning with the 2009–2013 ACS 5-year release, 

statistics are available for areas down to the block 

group level in AFF.

Block group level data prior to the 2009–2013 ACS 5-year 

release are not available in AFF.

* Block groups were available for the first time in American FactFinder with the 2009–2013 ACS 5-year data release. Previously, this geography level was only available in the ACS Summary File.
22 Understanding and Using American Community Survey Data

22 What All Data Users Need to Know U.S. Census Bureau

Supplemental Estimates: The Supplemental Estimates are simplified Detailed Tables that provide access to the 

most recent ACS data at a lower population threshold than the standard 1-year tables.27

 Geographic areas covered: All areas with populations of 20,000 or more.

Why this tool may be right for you Why you may want to consider another tool

The tables contain the most recent statistics for 

more geographic areas than are available from the 

standard 1-year data tables. 

Data are not available for geographic areas with fewer than 

20,000 people.

The tables include statistics on popular topics such 

as education, housing, and jobs.

The tables contain only a subset of all ACS topics.

Data are not available prior to the 2014 ACS release.

Geographic Comparison Tables: Those interested in geographic comparisons for areas other than states may 

be interested in the Geographic Comparison Tables, which allow comparison of ACS data across a variety of 

geographic areas.28 

 Geographic areas covered: States, metropolitan/micropolitan statistical areas, cities, counties, congressional 

 districts, and several other geographic areas.

27 U.S. Census Bureau, American FactFinder, Supplemental Estimates, <https://factfinder.census.gov/bkmk/navigation/1.0/en/d 

_program%3AACS/d_dataset%3AACS_16_SPL/>. 28 U.S. Census Bureau, American FactFinder, Geographic Comparison Tables, <https://factfinder.census.gov/bkmk/navigation /1.0/en/d 

_program%3AACS/d_product_type%3AGEO_COMPARISON_TABLE/>.

Why this tool may be right for you Why you may want to consider another tool

The tables readily compare geographic areas on a 

selected topic. 

The tables may not contain enough detail for your topic(s) of 

interest.

Tables are available for most of the subjects in the ACS. The tables do not contain data for some levels of geography.

Comparisons can be made for many levels of geography.
Understanding and Using American Community Survey Data 23

U.S. Census Bureau What All Data Users Need to Know 23

Selected Population Profiles: Detailed race/ethnicity data are available through the Selected Population 

Profiles, which provide summary tables separately for more than 100 detailed racial, ethnic, and tribal groups.29

Beginning with the 2007 ACS, the Selected Population Profiles also include country of birth. Selected 

Population Profiles are only available for 1-year estimates.

 Geographic areas covered: All areas with populations of 65,000 or more.

Why this tool may be right for you Why you may want to consider another tool

The profiles provide broad social, economic, housing, 

and demographic statistics for a variety of detailed 

race, ethnic, and tribal groups.

The profiles may not contain enough detail for your topic(s) of 

interest.

The profiles provide broad social, economic, housing, and demographic statistics for many levels of 

geography.

Data are not available for some levels of geography.

Comparison Profiles: The 5-year Comparison Profiles show data side-by-side from two different years, indicating where there is a statistically significant difference between the two sets of estimates. The 5-year 

Comparison Profiles first became available with the release of the 2011–2015 ACS 5-year data. The 1-year 

Comparison Profiles show data side-by-side for 5 years, indicating where there is a statistically significant difference between the most current year compared to 4 prior years of data.30

Geographic areas covered: Areas with populations of 5,000 or more. The 1-year Comparison Profiles provide 

estimates for populations of 65,000 or more for most available geographic areas. The 5-year Comparison 

Profiles are available for the nation, states, counties, places, congressional districts, and metropolitan/

micropolitan statistical areas with populations of 5,000 or more.

29 U.S. Census Bureau, American FactFinder, Selected Population Profiles, <https://factfinder.census.gov/bkmk/navigation/1.0/en 

/d_program%3AACS/d_product_type%3ASELECTED_POP_PROFILE/>. 30 U.S. Census Bureau, American FactFinder, Comparison Profiles, <https://factfinder.census.gov/bkmk/navigation/1.0/en/d 

_program%3AACS/d_product_type%3ACOMPARISON_PROFILE>.

Why this tool may be right for you Why you may want to consider another tool

The profiles help novice users get acquainted with 

ACS data tables.

The profiles may not contain enough detail for your topic(s) of 

interest.

The profiles provide broad social, economic, housing, and demographic statistics for many levels of 

geography.

Data are not available for some levels of geography.

The profiles show yearly data side-by-side, indicating 

statistical significance between sets of estimates.
24 Understanding and Using American Community Survey Data

24 What All Data Users Need to Know U.S. Census Bureau

Ranking Tables: As their name implies, Ranking Tables provide state-level rankings on more than 80 key ACS 

variables.31

 Geographic areas covered: Nation, 50 states, District of Columbia, and Puerto Rico.

Why this tool may be right for you Why you may want to consider another tool

The tables readily display state rankings on the 

topic(s) of your choice.

Data are only available at the state level.

The tables include statistics on popular topics, such 

as finances, jobs, housing, age, and education.

The tables may not contain enough detail for your topic(s) of 

interest.

Thematic Maps: The Census Bureau’s Thematic Maps provide graphical displays of selected ACS data available 

through AFF tables (e.g., Geographic Comparison Tables, Ranking Tables).32 Different shades of color are used 

to display variations in the data across geographic areas. Data users can also highlight areas with statistically 

different values from a selected state, county, or metropolitan area of interest.

Creating a Thematic Map: From your Table Results page in AFF, click the Create a Map button. Select a data 

cell from the table that you want to map. The row and column headers corresponding to that data cell will 

determine the data item that will be mapped.

Why this tool may be right for you Why you may want to consider another tool

The maps provide a visual comparison of geographic 

areas on a selected topic. 

You prefer information in tabular format.

Maps are available for most of the subjects in the 

ACS.

Maps can only be displayed through selected AFF tables, which 

may not contain enough detail for your topic(s) of interest.

Maps are interactive and allow users to select topics 

of interest.

Maps cannot be displayed for some levels of geography.

Most of the maps can be shared. Maps contain only a subset of ACS topics.

31 U.S. Census Bureau, American Community Survey, Ranking Tables, <www.census.gov/acs/www/data/data-tables-and-tools/ranking-tables

/index.php>. 32 U.S. Census Bureau, American FactFinder, <https://factfinder.census.gov/>.
Understanding and Using American Community Survey Data 25

U.S. Census Bureau What All Data Users Need to Know 25

Other Specialized Tools for Accessing ACS Data

My Congressional District: My Congressional District gives users quick and easy access to selected statistics 

for U.S. congressional districts, based on data from the ACS and County Business Patterns database.33

 Geographic areas covered: Current U.S. congressional districts.

Why this tool may be right for you Why you may want to consider another tool

It contains statistics for every district. The geography selection is limited to congressional districts.

It includes statistics on popular topics such as education, housing, and jobs.It contains only a subset of all ACS topics.

The results page can be embedded on your Web 

site.

Maps cannot be displayed for some levels of geography.

My Tribal Area: My Tribal Area is a specialized tool that allows data users to easily access and view population 

and housing estimates for American Indian and Alaska Native areas from the ACS.34 The tool displays tables 

of the latest ACS 5-year estimates for selected demographic characteristics and measures of employment, 

income, health insurance, housing, and education.

Geographic areas covered: American Indian and Alaska Native areas.

Why this tool may be right for you Why you may want to consider another tool

Data are displayed for a large number of American 

Indian and Alaska Native areas.

It contains only a subset of all ACS topics.

The tables contain data on popular social, economic, 

housing, and demographic topics.

Data are only displayed for the most recent 5-year period.

The data are timely and easy to access. Data are not available for detailed tribal groups—only tribal 

areas.

33 U.S. Census Bureau, My Congressional District, <www.census.gov/mycd/>. 34 U.S. Census Bureau, My Tribal Area, <www.census.gov/tribal/index.html>.
26 Understanding and Using American Community Survey Data

26 What All Data Users Need to Know U.S. Census Bureau

Census Business Builder: As its name implies, Census Business Builder is designed for users needing data 

(e.g., potential customers, similar businesses, and consumer spending) to help start or grow a business, or to 

better understand an area’s business landscape.35 The data can be used in determining potential locations for 

new businesses or in creating a business plan for investors.

 Geographic areas covered: Custom regions built by county or groups of counties, cities/towns, ZIP codes, and 

 neighborhoods (census tracts).

Why this tool may be right for you Why you may want to consider another tool

You want to open a new business or expand an existing business.The geographic selection is limited to counties, places, ZIP 

codes, and census tracts.

It combines ACS statistics with economic data to 

guide your research.

It contains only a subset of all ACS topics.

The interactive map allows you to select areas and 

compare their results to those of neighboring areas.

Reports are downloadable and printable.

QuickFacts: As its name indicates, QuickFacts provides fast, easy access to the most requested social, economic, and housing characteristics of a given state, county, city, or town.36 QuickFacts uses data from the ACS, 

Population Estimates, and other databases.

 Geographic areas covered: All states and counties, plus cities and towns with 5,000 or more people.

Why this tool may be right for you Why you may want to consider another tool

The tool is easy to use—just choose your state, 

county, city, or town.

The geography selection is limited to state, county, city, and 

town.

It combines statistics from the ACS with other surveys to give a broader view of a particular geography in table, map, and chart formats.

It contains only a subset of all ACS topics.

Results can be downloaded or shared. The ACS data shown in this tool are only for the most recent 

5-year estimates.

35 U.S. Census Bureau, Census Business Builder, <www.census.gov/data/data-tools/cbb.html>. 36 U.S. Census Bureau, QuickFacts, <www.census.gov/quickfacts>.
Understanding and Using American Community Survey Data 27

U.S. Census Bureau What All Data Users Need to Know 27

Narrative Profiles: Narrative Profiles are short, analytic reports derived from the ACS 5-year estimates.37

Covering 15 different topic areas, each Narrative Profile provides text and bar charts to display highlights of 

selected social, economic, housing, and demographic estimates for a selected geographic area. 

 Geographic areas covered: Nation, states, metropolitan/micropolitan statistical areas, counties, cities, ZIP 

 Code Tabulation Areas, American Indian Areas/Alaska Native Areas/Hawaiian Home Lands, and neighborhoods 

 (census tracts).

OnTheMap for Emergency Management: OnTheMap for Emergency Management is a public data tool that 

provides an intuitive Web-based interface for accessing U.S. population and workforce statistics, in real time, 

for areas being affected by natural disasters.38 The tool allows users to retrieve reports containing detailed 

workforce, population, and housing characteristics for areas with federally declared disasters such as the ones 

affected by hurricanes, floods, and wildfires. 

 Geographic areas covered: Block groups aggregated to approximate event boundaries.

Why this tool may be right for you Why you may want to consider another tool

You need to access statistics for event areas in real 

time.

The geographic selection is limited to event areas.

It combines ACS statistics with data from other 

sources to give a broader view of an event area.

It contains only a subset of all ACS topics.

It has linkable maps and reports to share.

37 U.S. Census Bureau, American Community Survey, Narrative Profiles, <www.census.gov/acs/www/data/data-tables-and-tools 

/narrative-profiles/>. 38 U.S. Census Bureau, OnTheMap for Emergency Management, <https://onthemap.ces.census.gov/em/>.

Why this tool may be right for you Why you may want to consider another tool

The profiles provide a broad social, economic, 

housing, and demographic overview of a particular 

geographic area.

You prefer the information in tabular format.

The profiles provide not only summary text, but also 

colorful tables and charts to illuminate key highlights.

The profiles are only available for 5-year estimates.* 

The profiles include popular topics, such as finances, 

jobs, housing, age, and education.

The narrative may not contain enough detail for your topic of 

interest.

The profiles are only available for certain levels of geography.

* The Census Bureau discontinued the Narrative Profiles for both the 1-year and 3-year data releases. The 2012 ACS 1-year and 2010–

2012 ACS 3-year Narrative Profiles are the last release in American FactFinder. Beginning with the 2008–2012 ACS 5-year data release, only 

5-year Narrative Profiles are available.
28 Understanding and Using American Community Survey Data

28 What All Data Users Need to Know U.S. Census Bureau

Advanced Tools for Accessing ACS Data

In addition to the products described above, the Census Bureau makes ACS data available in a variety of formats 

for data users interested in using more advanced statistical, geographic, database, or programming applications.

Summary Files: The ACS Summary Files are comma-delimited text files that contain all of the Detailed Tables 

for the ACS data releases.39 The files are stored with only the data from the tables and without such 

information as the table title, description of the rows, or geographic identifiers. That information is located in 

other files that the user must merge with the data files to reproduce full tables. (Users can do so through such 

statistical packages as SAS, SPSS, or STATA.) 

 Geographic areas covered: All areas down to the block group level. The Summary Files are currently the only 

 source for accessing block group data from the 2005–2009 through 2008–2012 ACS 5-year data, since those 

 estimates are not available through AFF.

Why this tool may be right for you Why you may want to consider another tool

Along with the Detailed Tables, the Summary Files 

are the best source for the most comprehensive ACS 

tables.

You are able to use AFF to access your table(s) of interest (particularly the Detailed Tables).

The Summary Files cover all subjects in the ACS. The Summary Files may contain more detail than needed for 

your topic.

Statistics are available for areas down to the block 

group level.

The tool does not provide percentages.

You are a skilled programmer with access to statistical 

software to conduct analysis (e.g., SAS, SPSS, STATA).

Public Use Microdata (PUMS): Data users with expertise in using statistical packages may also be interested 

in the Public Use Microdata Sample (PUMS) files, which contain a sample of individual records of people and 

households that responded to the survey (stripped of all identifying information).40 The PUMS files permit analysis of specific population groups and custom variables that are not available through AFF. For example, PUMS 

data users can look at the proportion of children aged 5 to 11 living in low-income working families or the number of scientists and engineers earning more than $75,000. Data users can also combine multiple years of PUMS 

data to produce estimates for relatively small population subgroups (e.g., American Indian physicians).

 Geographic areas covered: Nation, states, and Public Use Microdata Areas (PUMAs). PUMAs are special 

 nonoverlapping areas that partition each state into contiguous geographic units containing no fewer than 

 100,000 people each. 

39 U.S. Census Bureau, American Community Survey (ACS), Summary File Data, <www.census.gov/programs-surveys/acs/data 

/summary-file.html>. 40 U.S. Census Bureau, American Community Survey (ACS), PUMS Data, <www.census.gov/programs-surveys/acs/data/pums.html>.

Why this tool may be right for you Why you may want to consider another tool

You are a skilled programmer with access to statistical 

software to conduct analysis (e.g., SAS, SPSS, STATA).

Your table of interest is already pretabulated in AFF.

You need to create a custom table that is not available on AFF.The PUMS geography selection is limited to the nation, states, 

and PUMAs.

You want to access a sample of actual ACS 

responses for the nation, a state, or a PUMA.
Understanding and Using American Community Survey Data 29

U.S. Census Bureau What All Data Users Need to Know 29

PUMS through DataFerrett: Users without access to statistical software can access the ACS PUMS through 

DataFerrett, a data analysis and extraction tool that allow users to develop customized tables, graphs, and 

maps without any additional software.41

Data via FTP: The Census Bureau produces downloadable ACS data files on the Census Bureau’s File Transfer 

Protocol (FTP) server.42 Files on the FTP server are intended for advanced users. The ACS directories contain a 

variety of data files and tables.

Why this tool may be right for you Why you may want to consider another tool

You are a skilled programmer with access to statistical software to conduct analysis (e.g., SAS, SPSS, 

STATA).

Your table of interest is already pretabulated in AFF.

You need to create a custom table that is not 

available in AFF.

Depending on the original data source accessible through FTP, 

the types of available geographic areas may be limited.

You want to access a sample of actual ACS 

responses for the nation, a state, or a PUMA.

Application Programming Interface (API): The Census Bureau’s API allows developers to design Web and 

mobile apps to explore or learn more about America’s changing population and economy.43 The API lets 

developers customize ACS statistics into Web or mobile apps that provide users quick and easy access to an 

ever-increasing pool of publicly available datasets. More data sets will be added over time. Developers could 

use the ACS statistics available through this API to create apps that:

• Show commuting patterns for every city in America.

• Display the latest numbers on owners and renters in a particular neighborhood. 

• Provide a local government a range of socioeconomic statistics on its population.

41 U.S. Census Bureau, DataFerrett, <https://dataferrett.census.gov/>.

42 U.S. Census Bureau, FTP Server, American Community Survey, <www2.census.gov/programs-surveys/acs/>. 43 U.S. Census Bureau, Developers, <www.census.gov/developers/>. 

Why this tool may be right for you Why you may want to consider another tool

You are a skilled programmer. You just want to download data, not create an app.

You wish to create a data retrieval app for use on 

your own Web site or as a mobile app.

There are limited datasets available.

Why this tool may be right for you Why you may want to consider another tool

You do not have access to statistical software. Your table of interest is already pretabulated in AFF.

You need to create a custom table that is not

available in AFF.

You are interested in conducting more complex statistical

analysis.

You want to download a subset of the variables or 

geographic areas available in the PUMS files.

The PUMS geography selection is limited to nation, region,

division, state, and PUMAs.
30 Understanding and Using American Community Survey Data

30 What All Data Users Need to Know U.S. Census Bureau

Restricted-Use Microdata: The Census Bureau partners with various universities, research institutions, and 

agencies to form a nationwide network of secure Federal Statistical Research Data Centers (FSRDC).44

At these secure centers, qualified researchers with approved projects (i.e., projects that benefit Census 

Bureau programs and demonstrate scientific merit) can perform statistical analysis on selected internal ACS 

microdata from the Census Bureau and other statistical agencies.

Why this tool may be right for you Why you may want to consider another tool

You have data needs that cannot be met with publicly available data.You need to submit a formal project proposal to the 

Census Bureau. 

You are affiliated with a FSRDC. All work must be conducted inside the secure FSRDC location. 

Research output is reviewed before release to ensure individual 

persons or businesses cannot be identified.

You must be specially sworn to protect the confidentiality of the 

data you access.

Custom Tables: The Census Bureau allows users to purchase custom tabulations of ACS data.45 However, the 

Census Bureau encourages all users to look at the other ACS data products available before considering such 

a request. 

Why this tool may be right for you Why you may want to consider another tool

You have data needs that cannot be met through 

publicly available data.

After initial discussions with Census Bureau staff, users must 

allow at least 8 weeks (and often longer) for the request to be 

completed. 

You are not an experienced data user with programming experience.The minimum cost for a custom tabulation is $3,000. 

You are not affiliated with a Census Bureau Research 

Data Center.

The Census Bureau’s Disclosure Review Board must review and 

approve all requests before work can begin.

44 U.S. Census Bureau, Research @ Census, Restricted-Use Microdata, <www.census.gov/research/data/restricted_use_microdata.html>. 45 U.S. Census Bureau, American Community Survey (ACS), Custom Tables, <www.census.gov/programs-surveys/acs/data/custom-tables

.html>.
Understanding and Using American Community Survey Data 31

U.S. Census Bureau What All Data Users Need to Know 31

GIS Shapefiles and Geodatabase Files: For GIS (Geographic Information Systems) software users, the Census 

Bureau produces TIGER/Line shapefiles and geodatabases.46 These products are spatial extracts from the 

Census Bureau’s Master Address File (MAF)/TIGER database, and they contain such features as legal and statistical geographic boundary areas, roads, railroads, and rivers. The geodatabases bring together geography 

from the TIGER/Line shapefiles and the ACS 5-year estimates. Users can incorporate shapefiles and geodatabases into a variety of GIS software applications, where they can map ACS data.

 Why this tool may be right for you Why you may want to consider another tool

You wish to display ACS data visually. You prefer the information in tabular format.

You are a skilled user of GIS software. You do not have access to GIS software.

You need to create a custom map that is not 

available through the Census Mapping Tools (as 

described above in “Thematic Maps”).

Your desired map can already be created through the Census 

Mapping Tools (as described above in “Thematic Maps”).

Maps can be created for a variety of geographic 

areas.

Census Flows Mapper: The Census Flows Mapper is a Web mapping application intended to provide users 

with a simple interface to view, save, and print county-to-county migration flows maps of the United States.47

Data users can choose inbound, outbound, or net migration flows within the United States for counties. Data 

are currently available from the ACS 5-year releases.

 Why this tool may be right for you Why you may want to consider another tool

You wish to display ACS data visually. You prefer information in tabular format.

You need to create a custom map that is not 

available through the Census mapping tools (as 

described above in “Thematic Maps”).

Geography selection is limited to counties.

It contains only a subset of all ACS topics.

46 U.S. Census Bureau, Geography, TIGER Products, <www.census.gov/geo/maps-data/data/tiger.html>. 47 U.S. Census Bureau, Census Flows Mapper, <flowsmapper.geo.census.gov/>.

Additional Background Information and 

Tools

How to Use American FactFinder

<https://factfinder.census.gov/help/en/index.htm#> 

This Web page describes how to access data you are 

looking for in AFF in three easy steps. You can also 

receive a virtual tour of the AFF site from this page.

ACS Data and Tools 

<www.census.gov/acs/www/data/data-tables-and

-tools/>

This Web page provides links to all of the major tools 

that the Census Bureau offers for accessing ACS data. 

Table and Geography Changes

<www.census.gov/programs-surveys/acs/technical

-documentation/table-and-geography-changes.html>

This Web page provides information about changes to 

tables and geography for each ACS data release.
32 Understanding and Using American Community Survey Data

32 What All Data Users Need to Know U.S. Census Bureau

6. CASE STUDIES USING ACS DATA

Case Study #1: New Orleans Smoke Alarm Outreach Program

Skill Level: Intermediate

Subject: Age, Income, Poverty, Year Structure Built, Year Householder Moved Into Unit

Type of Analysis: Analyses of trends/patterns within a community

Tool(s) Used: American FactFinder, statistical software, mapping software

Author(s): Oliver Wise, Director of the Office of Performance and Accountability, City of New Orleans

As a component of its fire prevention effort, the New Orleans Fire Department (NOFD) offers free smoke alarm 

installation to all city residents. Initially the program was fairly passive—requiring individuals to contact the fire 

department to request a smoke alarm, but local leaders wondered if the program was as effective as it could be.

To answer that question, and to help guide the city’s efforts in reducing fire fatalities, the Office of Performance 

and Accountability teamed up with the NOFD to pilot a more targeted approach to smoke alarm installation. The 

team developed a model to identify neighborhoods most at risk for fire fatalities. That model then helped NOFD 

prioritize a door-to-door effort to install free smoke alarms in homes across New Orleans. 

This analysis combined data from multiple sources, but would not have been possible without the American 

Community Survey (ACS). 

The first step was to estimate the likelihood that homes in a neighborhood were missing smoke alarms. From the 

American Housing Survey (AHS), the research team identified three key factors associated with lack of a smoke 

alarm: the age of the structure, the length of time the householder has lived in the structure, and the household’s 

ratio of income to the poverty level. However, AHS data are only available for relatively large areas. (Parishes, 

the equivalent of counties in other states, are the smallest reported geographies in Louisiana.) NOFD needed 

a smaller spatial scale for targeted outreach. The ACS filled that local data requirement because ACS data are 

available at a small neighborhood scale. For this project, we used block group data from the 2009–2013 ACS 

5-year estimates to identify neighborhoods most at risk for lack of smoke alarms.

To produce the block-group-level analysis of smoke alarm risk, we downloaded three ACS tables from American 

FactFinder (AFF): B25034 (Year Structure Built), C17002 (Ratio of Income to Poverty Level in the Past 12 

Months), and B25038 (Tenure by Year Householder Moved Into Unit).

• First, we went to the AFF Web site: <https://factfinder.census.gov>. 

• To access the data from AFF, we chose the Advanced Search option (see Figure 6.1).

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

 Figure 6.1. Advanced Search in American FactFinder
Understanding and Using American Community Survey Data 33

U.S. Census Bureau What All Data Users Need to Know 33

• We then selected Block Group from the “Geographies” drop-down menu and narrowed the query by selecting Louisiana as the state and Orleans as the county, or parish (see Figure 6.2). 

 Figure 6.2. Selecting Geographies in American FactFinder

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

• Highlighting the phrase “All Block Groups within Orleans Parish, Louisiana” and then clicking “Add to Your 

Selections” ensures that AFF will extract data for all block groups in Orleans Parish.

• Because we knew which tables we wanted to extract for the analysis, we typed a table number into the 

“topic or table name” search bar in AFF and clicked “Go” (see Figure 6.3).

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

 Figure 6.3. Selecting Tables in American FactFinder
34 Understanding and Using American Community Survey Data

34 What All Data Users Need to Know U.S. Census Bureau

• We then selected the year of interest (in this case the 2013 ACS 5-year data) to view the results.

• To format the data for downloading, we used the “Modify Table” option in AFF to Transpose Rows/Columns 

(see Figure 6.4).

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

 Figure 6.4. Transposing Table Rows and Columns in American FactFinder

• We then downloaded the data as a .csv file and repeated the process for the other two tables 

(see Figure 6.5).

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

 Figure 6.5. Downloading the Data
Understanding and Using American Community Survey Data 35

U.S. Census Bureau What All Data Users Need to Know 35

The research team then used a statistical program to aggregate the data from each table into relevant risk 

categories. We used data from Table C17002 to calculate the percentage of households in each block group 

with income below 200 percent of the federal poverty level, data from Table B25034 to identify the percentage 

of housing structures built before 1949, and data from Table B25038 to identify the percentage of householders who moved into their home before 2000. We then used these three indicators to assign a risk score to each 

block group.

It is important to note that some block groups are sparsely populated, and some have no population at all. For 

example, Lake Pontchartrain is probably not a relevant block group for smoke alarm outreach. To account for 

this, we removed large, sparsely populated (or completely unpopulated) block groups from the analysis.

The second step of the analysis was to estimate fire fatality risk. Since young children and older adults are most 

at risk of death in a fire, we used 2010 Census data to identify block groups with high proportions of people 

under the age of 5 or age 65 or older. In addition, we added fire frequency data from NOFD records for March 

2009 to March 2015. Using these three pieces of information, we compiled a fire fatality risk map.

Overlaying the smoke alarm risk map—based on ACS data—with the fire fatality risk map, we were able to highlight neighborhoods where fire mortality risk was high and where homes were unlikely to have smoke alarms. 

NOFD used that map to begin a door-to-door campaign in high-risk neighborhoods to install smoke alarms. We 

estimated that the program was twice as effective as random assignment would have been for contacting households in need of smoke alarms.

Shortly after the program began, a fire occurred in one of the homes in which a smoke alarm had been installed 

based on the targeted installation outreach. Eleven people survived that fire because they had an early warning 

from the alarm.

Because the model is based on ACS data available nationwide, the analysis could be replicated—and fire safety 

improved—for other communities around the nation. The code for this analysis can be found online.

48 You can 

also view the Census Bureau’s Stats in Action video to learn more about this project.49

48 See <https://github.com/cno-opa/smoke-alarm-outreach>.

49 U.S. Census Bureau, Stats in Action: New Orleans, LA: Smoke Alarm Outreach Program, 2016, <www.census.gov/library/video/2016 

/sia-nola-saop.html>. 
36 Understanding and Using American Community Survey Data

36 What All Data Users Need to Know U.S. Census Bureau

Case Study #2: Atlanta Region 20-County Data Dashboard

Skill Level: Intro/intermediate

Subject: County-level demographic and socioeconomic data

Type of Analysis: Analysis and visualization of ACS data across the 20-county metro Atlanta region 

Tool(s) Used: American FactFinder, Excel, data visualization tools

Author(s): Taylor Tyger, Senior Planner, Atlanta Regional Commission

Jim Skinner, Senior Principal Planner, Atlanta Regional Commission

Mike Carnathan, Division Manager, Atlanta Regional Commission

The Atlanta Regional Commission’s (ARC) Research and Analytics Division uses various databases, analysis tools, 

and visualization programs to improve data outreach in metro Atlanta. One of those tools is the Atlanta Region 

20-County Data Dashboard.

50 This data dashboard was created to provide an interactive platform for users 

seeking demographic and socioeconomic information at the county level. The dashboard consolidates data from 

various data sources into eight categorical “bins”: population, employment, housing, education, health, crime, 

income, and forecasts. The designated “bins” were identified based on common data requests ARC receives. 

We used the Census Bureau’s County Population Estimates and ACS 5-year estimates to “feed” parts of the dashboard (see Table 6.1). Some of the counties for which populations are included in the dashboard are fairly small. 

As a result, ACS 5-year estimates must be used instead of ACS 1-year estimates. Other data sources used in the 

dashboard include U.S. Bureau of Labor Statistics, U.S. Department of Housing and Urban Development, 

Georgia Department of Public Health, Georgia Department of Education, Federal Bureau of Investigation, and 

Atlanta Regional Commission forecasts. 

 Table 6.1. List of Variables Downloaded Through American FactFinder

Variable Dataset

Population County Population Estimates

Race and ethnicity County Population Estimates

Age County Population Estimates

Housing tenure ACS 5-Year Estimates

Vacancy rate ACS 5-Year Estimates

Household composition ACS 5-Year Estimates

School enrollment ACS 5-Year Estimates

Educational attainment ACS 5-Year Estimates

Median household income ACS 5-Year Estimates

Population below poverty level ACS 5-Year Estimates

While we recognize that there is a level of uncertainty associated with ACS estimates, the margins of error are 

not included in the dashboard. We considered several factors in the decision not to show margins of error. First, 

we wanted to present the information in a concise format for data users. Second, we wanted to present information in a consistent way across measures, and several measures in the dashboard from other sources do not have 

margins of sampling error. 

50 Atlanta Regional Commission, 20-County Data Dashboard, <http://33n.atlantaregional.com/20-county-data-dashboard>. 
Understanding and Using American Community Survey Data 37

U.S. Census Bureau What All Data Users Need to Know 37

To download the data, we used the AFF Advanced Search tool, as follows: 

• Go to the AFF Web site at <https://factfinder.census.gov>.

• Under “Geographies,” select “County” as a geographic type. Then select Georgia as the state. Holding down 

the CTRL key, select each of the 20 counties and then click “Add to Your Selections” (see Figure 6.6). We 

saved this search the first time—by clicking on the “save search” link in the “Your Selections” box—so that 

it could be reloaded later using the “load search” option. Reloading a saved query saves us time because it 

allows us to preselect the 20 counties of interest for each variable in the database.

 Figure 6.6. Geography Selection in American FactFinder

Source: U.S. Census Bureau, American FactFinder, accessed at 

<https://factfinder.census.gov>.
38 Understanding and Using American Community Survey Data

38 What All Data Users Need to Know U.S. Census Bureau

• Under “Topics,” expand the “Dataset” tab, and click “2014 ACS 5-year estimates.” 

• Many variables in the dashboard use the ACS as the data source. For example, to find housing tenure (owner 

vs. renter) under “Topics,” expand the “Housing” option, then expand “Occupancy Characteristic,” and then 

select “Owner/renter.” (See Figure 6.7 for an image of all the selections used.)

 Figure 6.7. Selections Used to Access the Housing Tenure Variable

• From the results window, the table “Tenure” (B25003) is downloaded and saved (see Figure 6.8). 

 Figure 6.8. American FactFinder Data Table B25003 Downloaded and Opened in Excel

• We repeat this process for all ACS topics in the dashboard.

• Once all the variables are downloaded from AFF as tables, we process the data in a spreadsheet. In the 

default format, each variable table is downloaded with each of the 20 counties’ identifiers in rows and then 

the data variables (including values and margins of error) in columns. In processing, two rows are added: a 

“10-county” and “20-county” summary geography. We use the 10- and 20-county definitions as references 

for comparing individual counties to the broader region.

• When needed, additional columns are added in order to aggregate or refine the data as downloaded. For 

example, we combined individual columns of age data to create an age range for the population aged 20 to 

34. We also calculated percentages by downloading the “universe” of data for selected variables. 

• Columns that are not needed are deleted, and the labels are edited for clarity. 

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

Source: U.S. Census Bureau, American FactFinder, accessed at <https://factfinder.census.gov>.
Understanding and Using American Community Survey Data 39

U.S. Census Bureau What All Data Users Need to Know 39

An initial analysis can be done in a spreadsheet, by sorting results from highest to lowest and comparing countylevel values to the 10- and 20-county reference points. However, most of the analysis and trend identification 

occurs in the visualization software, rather than in a spreadsheet. Figure 6.9 shows how the data are visualized in 

the final product. 

 Figure 6.9. Atlanta Region 20-County Data Dashboard

The data dashboard is updated throughout the year as updated data become available. The dashboard can be 

found on the Atlanta Regional Commission Web site.

51 The tool also has its own page on the Atlanta Regional 

Commission’s Research and Data Blog.

52

The dashboard has been viewed more than 3,000 times and is used as a resource by internal agency coworkers, external partners, nonprofits, elected officials, and the general public. For example, within ARC, the Aging 

Services division uses the 20-County Data Dashboard to quickly access and summarize demographic information 

for clients. It has even served as a model for Aging Services to develop their own data dashboards. Local media 

partners have used the 20-County Data Dashboard to quickly pull data that informs their stories. In addition, 

Tableau showcased the 20-County Data Dashboard as a best practice example for using data visualization to 

improve data outreach in their Webinar “Data Driven Government.” 

51 Atlanta Regional Commission, Atlanta Region 20-County Data Dashboard, <http://atlantaregional.org/atlanta-region-20-county-data

-dashboard/>. 52 Atlanta Regional Commission, 20-County Data Dashboard, <http://33n.atlantaregional.com/20-county-data-dashboard>. 

Source: Atlanta Regional Commission, Atlanta Region 20-County Data Dashboard, accessed at:

<https://atlantaregional.org/atlanta-region/regional-data-resources/atlanta-region-20-county>.
40 Understanding and Using American Community Survey Data

40 What All Data Users Need to Know U.S. Census Bureau

Case Study #3: USDA 515 Rental Housing Maturation

Skill Level: Intro/intermediate

Subject: County-level rental housing data

Type of Analysis: Analysis and visualization of counties’ affordable rental housing

Tool(s) Used: American FactFinder, spreadsheet, mapping software

Author(s): Keith Wiley, Research Associate, Housing Assistance Council

The 2010–2014 ACS 5-year data show that there are 5 million occupied rental housing units in rural areas.53 Since 

its inception in 1963, the U.S. Department of Agriculture (USDA) Section 515 Rural Rental Housing loan program 

has financed the construction of more than 533,000 affordable rental units, and it represents an important part 

of this housing stock. The program has received attention recently because a growing number of these loans will 

begin reaching maturity and will be paid off; an estimated 6,684 loans are expected to reach maturity over the 

next 20 years.54 After a loan is paid off, owners are under no obligation to maintain their properties as affordable 

housing and some fear many owners may no longer choose to do so.55

Given that the distribution of Section 515 units is not uniform across rural areas, the risk to affordable housing 

stocks associated with maturing loans will be greater for some areas than others. The following approach uses 

the 2010–2014 ACS 5-year data to identify those counties where the USDA Section 515 properties represent a 

relatively large portion of the overall rental housing stock. These are the areas where policymakers may want to 

concentrate efforts to ameliorate the potential loss of this important affordable housing option. 

The Census Bureau’s AFF Web site provides easy access to 2010–2014 ACS 5-year occupied-rental housing unit 

estimates. With these data, one can assess the role of the Section 515 program.

Steps:

• Go to the AFF Web site at: <https://factfinder.census.gov>.

• Select the “Guided Search” option, and click on “I’m looking for information about housing,” which limits the 

search to housing characteristics. Click “Next” (see Figure 6.10).

 Figure 6.10. Guided Search in American FactFinder

53 In this case study, rural refers to all counties that are not part of an Office of Management and Budget defined metropolitan area using the 

2013 classification. 54 Housing Assistance Council, Rural Policy Note: Maturing USDA Rural Rental Housing Loans: An Update, 2016, <www.ruralhome.org/storage 

/documents/policy-notes/rpn_maturing-mortgages-usda-2016.pdf>. 55 The New England Housing Network’s June 30, 2015, letter to the U.S. Senate Appropriations Subcommittee on Agriculture, Rural 

Development, Food and Drug Administration, and Related Agencies exemplifies the concern raised by local housing advocates about the potential loss of Section 515 housing units. The letter, as accessed on 8/28/16, can be found at <http://housingactionnh.org/wp-content 

/uploads/2015/07/RD515LETTER2015.pdf>.

Source: U.S. Census Bureau, American FactFinder, accessed at <https://factfinder.census.gov>.
Understanding and Using American Community Survey Data 41

U.S. Census Bureau What All Data Users Need to Know 41

• Housing topics appear. Click on the “Occupancy Characteristics” since a count of renter occupied units is an 

occupancy measure. Then select the “Owner/Renter (Tenure in Occupied Units)” option. Click “Next” (see 

Figure 6.11).

 Figure 6.11. Selecting Topics in American FactFinder Guided Search

• Geography options appear. Click on “Select a Geographic type:” and from the drop-down choices, select 

“County – 050.” Additional options for choosing counties then appear. Since this is a national analysis, click 

on “All Counties within United States” and then “Add to Your Selections.” Click “Next” (see Figure 6.12).

 Figure 6.12. Selecting Geographies in American FactFinder Guided Search

Source: U.S. Census Bureau, American FactFinder, Guided Search, <https://factfinder.census.gov/faces/nav/jsf/pages/guided_search

.xhtml>.

Source: U.S. Census Bureau, American FactFinder, Guided Search, <https://factfinder.census.gov/faces/nav/jsf/pages/guided_search.xhtml>.
42 Understanding and Using American Community Survey Data

42 What All Data Users Need to Know U.S. Census Bureau

• The final option is to select data by race/ethnicity, which is not relevant here, so click on “Skip this step.” 

• The available data tables appear with stars highlighting the best matches. Click on the first table labeled 

“Tenure” (Dataset: 2014 ACS 5-year estimates). The Tenure data in table format appear for review; however, 

for analysis purposes the data must be downloaded. Click on the “Download” option and in the screen that 

pops up, leave the settings as they are, comma-delimited file elements highlighted, and click “OK.”56 A popup window notes when “Your file is complete.” Click on the “Download” option in that window to download 

the zip file (see Figure 6.13).

 Figure 6.13. Results of Guided Search in American FactFinder

• Clicking on the downloaded zip file will open a folder containing four files. Clicking on the file with an ending 

name of “with_ann.csv” will open the Tenure table in a spreadsheet.57 The initial row can be deleted since the 

variables are already labeled. The data provide the user with estimates of the number of rental units in all 

counties.

The next step is to join the 2010–2014 ACS 5-year data with Section 515 property data, using a spreadsheet, and 

calculate the percentage of occupied rental units that are in the Section 515 program for each county.58 Counties 

where Section 515 occupied units make up 10 percent or more of the entire occupied rental housing stock are 

considered most at risk as these loans mature. The potential loss of 10 percent or more of all rental housing units 

would be problematic for most counties. These at-risk counties are home to approximately 13 percent of USDA 

Section 515 properties containing 48,378 occupied units (see Figure 6.14). 

56 There is a size limit on files that can be downloaded directly in Excel format. This request exceeds the column limits. There is no such restriction for comma delimited or CSV downloads, and they work just as well since the files also open in Excel. 57 The CSV file will open in Excel just fine. When saving the file, however, the user will want to specify that it will be saved in an Excel format. 

58 The USDA property data are publicly available at: <www.sc.egov.usda.gov/data/MFH.html>. This analysis involved aggregating these property data to the county level. The county-level USDA data were then linked to the ACS data using the state/county FIPS codes (labeled Id2 in 

ACS download data and State County FIPS Code in USDA data).

Data release year

53.2

34.5

12.4

10 percent or 

more of county 

rental market

5 percent to

 less than 10 percent 

of county rental market

Less than 5 percent

 of county

 rental market

Source: Analysis by the author of publicly available USDA Multi-Family Housing 515 (2014), and U.S. Census Bureau 2010–2014 American 

Community Survey 5-year data.

 Figure 6.14. Percentage of USDA Section 515 Properties by Share of County Rental Housing Market

Source: U.S. Census Bureau, American FactFinder, Guided Search, <https://factfinder.census.gov/faces/nav/jsf/pages/guided_search

.xhtml>.
Understanding and Using American Community Survey Data 43

U.S. Census Bureau What All Data Users Need to Know 43

The final step is to use the data to create a map of at-risk counties across the nation (see Figure 6.15). Certain 

states, such as Alabama, Maine, Mississippi, and South Dakota, contain many of these counties.

Policymakers may want to monitor what occurs with these maturing loans over the next 10 to 15 years, particularly in those counties where USDA Section 515 units represent at least one in every 10 rental units. If there is a 

considerable amount of attrition in these affordable housing units, it would most likely be in areas with higher 

concentrations of Section 515 housing units. Knowing where the loss would have the greatest impact can also 

help policymakers decide how best to use limited resources to address any problems. This issue is difficult to 

address, however, because it will unfold over many years. 

Sources: Analysis by the author of publicly available USDA Multi-Family Housing 515 (2014), and U.S. Census Bureau, 2010–2014 

American Community Survey 5-year data.

 Figure 6.15 USDA Section 515 Properties by Share of County Rental Housing Market
44 Understanding and Using American Community Survey Data

44 What All Data Users Need to Know U.S. Census Bureau

7. UNDERSTANDING ERROR AND DETERMINING 

STATISTICAL SIGNIFICANCE

Sources of Error

The data in American Community Survey (ACS) products are estimates of the actual figures that would have 

been obtained if the entire population—rather than the chosen ACS sample—had been interviewed using the 

same methodology. All estimates produced from sample surveys have uncertainty associated with them as a 

result of being based on a sample of the population rather than the full population. This uncertainty—called sampling error—means that estimates derived from the ACS will likely differ from the values that would have been 

obtained if the entire population had been included in the survey, as well as from values that would have been 

obtained had a different set of sample units been selected for the survey. 

Sampling error is the difference between an estimate based on a sample and the corresponding value that would 

be obtained if the estimate were based on the entire population. Measures of the magnitude of sampling error 

reflect the variation in the estimates over all possible samples that could have been selected from the population 

using the same sampling methodology. The margin of error is the measure of the magnitude of sampling error 

provided with all published ACS estimates.

In addition to sampling error, data users should recognize that other types of error—called nonsampling 

error—might also be introduced during any of the complex operations used to collect and process ACS data. 

Nonsampling error can result from problems in the sampling frame or survey questionnaires, mistakes in how the 

data are reported or coded, issues related to data processing or weighting, or problems related to interviewer 

bias or nonresponse bias. Nonresponse bias results when survey respondents differ in meaningful ways from nonrespondents. Nonsampling error may affect ACS data by increasing the variability of the estimates or introducing 

bias into ACS results. The U.S. Census Bureau tries to minimize nonsampling error through extensive research and 

evaluation of sampling techniques, questionnaire design, and data collection and processing procedures. 

Nonsampling error is very difficult to measure directly, but the Census Bureau provides a number of indirect measures to help inform users about the quality of ACS estimates. The section on “Measures of Nonsampling Error” 

includes a more detailed description of the different types of nonsampling error in the ACS and measures of ACS 

data quality. More information on ACS data quality measures for the nation and individual states is available on 

the Census Bureau’s Web page on Sample Size and Data Quality.

59 

Measures of Sampling Error

Margins of Error and Confidence Intervals

A margin of error (MOE) describes the precision of an ACS estimate at a given level of confidence. The confidence level associated with the MOE indicates the likelihood that the ACS sample estimate is within a certain 

range (the MOE) of the population value. The MOEs for published ACS estimates are provided at a 90 percent 

confidence level. From these MOEs, data users can easily calculate 90 percent confidence intervals that define a 

range expected to contain the true or population value of an estimate 90 percent of the time. For example, in the 

Data Profile for Selected Social Characteristics (Table DP02) for Colorado, a portion of which is shown in Table 

7.1, data from the 2015 ACS 1-year estimates indicate that there were 564,757 one-person households in the state 

in 2015 with an MOE of 10,127. By adding and subtracting the MOE from the point estimate, we can calculate the 

90 percent confidence interval for that estimate: 

564,757 – 10,127 = 554,630 = Lower bound of the interval

564,757 + 10,127 = 574,884 = Upper bound of the interval

59 U.S. Census Bureau, American Community Survey, Sample Size and Data Quality, <www.census.gov/acs/www/methodology 

/sample-size-and-data-quality/>.
Understanding and Using American Community Survey Data 45

U.S. Census Bureau What All Data Users Need to Know 45

564,757 – 10,127 = 554,630 = Lower bound of the interval 

564,757 + 10,127 = 574,884 = Upper bound of the interval

Source: U.S. Census Bureau, American FactFinder, Table DP02: Selected Social Characteristics in the United States.

 Table 7.1. Sample Estimates and Margins of Error in American FactFinder: 2015

Therefore, we can be 90 percent confident that the true number of one-person households in Colorado in 2015 

falls somewhere between 554,630 and 574,884. Put another way, if the ACS were independently conducted 100 

times in 2015, sampling theory suggests that 90 times the estimate of one-person households in Colorado would 

fall in the given confidence interval. Estimates with smaller MOEs—relative to the value of the estimate—will have 

narrower confidence intervals indicating that the estimate is more precise and has less sampling error associated 

with it. 

TIP: When constructing confidence intervals from MOEs, data users should be aware of any “natural” limits 

on the upper and lower bounds. For example, if a population estimate is near zero, the calculated value of the 

lower confidence bound may be less than zero. However, a negative number of people does not make sense, so 

the lower confidence bound should be reported as zero instead. 

For other estimates, such as income, negative values may be valid. Another natural limit would be 100 percent 

for the upper confidence bound of a percent estimate. Data users should always keep the context and meaning 

of an estimate in mind when creating and interpreting confidence intervals.

Standard Errors and Coefficients of Variation

A standard error (SE) measures the variability of an estimate due to sampling and provides the basis for calculating the MOE. The SE provides a quantitative measure of the extent to which an estimate derived from a 

sample can be expected to deviate from the value for the full population. SEs are needed to calculate coefficients of variation and to conduct tests of statistical significance. Data users can easily calculate the SE of an 

ACS estimate by dividing the positive value of its MOE by 1.645 as shown below:60

(1)

Using the data in Table 7.1, the SE for the number of one-person households in Colorado in 2015 would be:

60 Data users working with ACS 1-year estimates for 2005 or earlier should divide the MOE by the value 1.65 as that was the value used to derive the published MOE from the SE in those years.
46 Understanding and Using American Community Survey Data

46 What All Data Users Need to Know U.S. Census Bureau

The SE for an estimate depends on the underlying variability in the population for that characteristic and the 

sample size used for the survey. In general, the larger the sample size, the smaller the SE of the estimates produced from the sample data. This relationship between sample size and SE is the reason that ACS estimates for 

less populous areas are only published using multiple years of data. Combining data from multiple ACS 1-year 

files increases sample size and helps to reduce SEs.

Coefficients of variation are another useful measure of sampling error. A coefficient of variation (CV) measures 

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:

(2)

A small CV indicates that the SE is small relative to the estimate, and a data user can be more confident that the 

estimate is close to the population value. The CV is also an indicator of the reliability of an estimate. When the SE 

of an estimate is close to the value of the estimate, the CV will be larger, indicating that the estimate has a large 

amount of sampling error associated with it and is not very reliable. For the example of one-person households in 

Colorado, the CV would be calculated as:61 

 

 

A CV of 1.1 percent indicates that the ACS estimate of one-person households in Colorado has a relatively small 

amount of sampling error and is quite reliable. Data users often find it easier to interpret and compare CVs across 

a series of ACS estimates than to interpret and compare SEs.

There are no hard-and-fast rules for determining an acceptable range of error in ACS estimates. Instead, data users 

must evaluate each application to determine the level of precision that is needed for an ACS estimate to be useful.

For more information, visit the Census Bureau’s Web page on Data Suppression.

62

Determining Statistical Significance

One of the most important uses of ACS data is to make comparisons between estimates—across different geographic areas, different time periods, or different population subgroups. Data users may also want to compare 

ACS estimates with data from past decennial censuses. For any comparisons based on ACS data, it is important 

to take into account the sampling error associated with each estimate through the use of a statistical test for 

significance. This test shows whether the observed difference between estimates likely represents a true difference that exists within the full population (is statistically significant) or instead has occurred by chance because 

of sampling (is not statistically significant). Statistical significance means that there is strong statistical evidence 

that a true difference exists within the full population. Data users should not rely on overlapping confidence intervals as a test for statistical significance because this method will not always provide an accurate result. 

61 The examples provided in this section use unrounded values in their calculations, but the values displayed are rounded to three decimal 

places. For percentages that use a numerator or denominator from a prior example, the unrounded value is used, although the rounded value is 

displayed. 62 U.S. Census Bureau, American Community Survey, Data Suppression, <www.census.gov/programs-surveys/acs/technical-documentation

/data-suppression.html>.
Understanding and Using American Community Survey Data 47

U.S. Census Bureau What All Data Users Need to Know 47

When comparing two ACS estimates, a test for significance can be carried out by making several calculations 

using the estimates and their corresponding SEs. These calculations are straightforward given the published 

MOEs available for ACS estimates in American FactFinder (AFF) and many other Census Bureau data products. 

The steps to test for a statistically significant difference between two ACS estimates are as follows:

1. Calculate the SEs for the two ACS estimates using formula (1).

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. Divide the difference between the two ACS estimates by the square root of the sum of the squared SEs.

6. Compare the absolute value of the result from Step 5 with the critical value for the desired level of confidence (1.645 for 90 percent, 1.960 for 95 percent, or 2.576 for 99 percent).

7. If the absolute value of the result from Step 5 is greater than the critical value, then the difference between 

the two estimates can be considered statistically significant, at the level of confidence corresponding to the 

critical value selected in Step 6.

Algebraically, the significance test can be expressed as follows:

If (3)

then the difference between estimates and is statistically significant at the specified confidence 

level (CL)

where and are the estimates being compared 

SE1

 is the SE for estimate 

SE2 is the SE for estimate 

ZCL is the critical value for the desired confidence level (1.645 for 90 percent, 1.960 for 95 percent, and 2.576 for 

99 percent).

The example below shows how to determine if the difference in the estimated percentage of householders age 

65 or older who live alone between Florida (estimated percentage = 12.6, MOE = 0.2) and Arizona (estimated 

percentage = 10.5, MOE = 0.3) is statistically significant, based on 2015 ACS data. Using formula (1) above, first 

calculate the corresponding standard errors for Florida (0.122) and Arizona (0.182) by dividing the MOEs by 

1.645. Then, using formula (3) above, calculate the test value as follows:
48 Understanding and Using American Community Survey Data

48 What All Data Users Need to Know U.S. Census Bureau

Since the test value (9.581) is greater than the critical value for a confidence level of 90 percent (1.645), the difference in the percentages is statistically significant at a 90 percent confidence level. A rough interpretation of 

the result is that the user can be 90 percent certain that a difference exists between the percentage of householders aged 65 or older who live alone in Florida and in Arizona.

By contrast, if the corresponding estimate for Indiana (estimated percentage = 10.8, MOE = 0.2, SE = 0.122) were 

compared with the estimate for Arizona, formula (3) would yield:

Since the test value (1.369) is less than the critical value for a confidence level of 90 percent (1.645), the difference in percentages is not statistically significant. A rough interpretation of the result is that the user cannot be 

certain to any sufficient degree that the observed difference in the estimates between Indiana and Arizona was 

not due to chance.

The Census Bureau has produced a Statistical Testing Tool to make it easier for ACS data users to conduct tests 

of statistical significance when comparing ACS estimates.63 This tool consists of an Excel spreadsheet that will 

automatically calculate statistical significance when data users are comparing two ACS estimates or multiple 

estimates. Data users simply need to download ACS data from the Census Bureau’s AFF Web site and insert the 

estimate and MOE into the correct columns and cells in the spreadsheet. The results are calculated automatically. 

The result “Yes” indicates that estimates are statistically different and the result “No” indicates the estimates are 

not statistically different.64

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same time period (e.g., from the same year or the same 5-year 

period) are straightforward and can be carried out as described in the previous section as long as the areas 

or groups are nonoverlapping (e.g., comparing estimates for two different counties, or for two different age 

groups). On the other hand, if the comparison involves 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 African American females) then the two 

estimates may not be independent. In these cases, the data user may need to use a different approach that 

accounts for the correlation between the estimates in performing the statistical test of significance. 

63 U.S. Census Bureau, American Community Survey (ACS), Statistical Testing Tool, <www.census.gov/programs-surveys/acs/guidance

/statistical-testing-tool.html>. 64 This tool only conducts statistical testing on the estimates keyed in by the data user for comparison within the spreadsheet, and it does not 

adjust the MOE when making multiple comparisons, nor incorporate a Bonferroni correction or any other method in the results of the statistical 

testing.
Understanding and Using American Community Survey Data 49

U.S. Census Bureau What All Data Users Need to Know 49

Making Comparisons With Overlapping Multiyear Estimates

TIP: When comparing estimates from two multiyear periods, ideally comparisons should be based on nonoverlapping periods (e.g., comparing estimates from 2006–2010 with estimates from 2011–2015). 

The comparison of two estimates for different, but overlapping periods is challenging since the difference is driven 

by the nonoverlapping years. For example, when comparing the 2010–2014 ACS 5-year estimates with the 2011–2015 

ACS 5-year estimates, data for 2011 through 2014 are included in both estimates. Their contribution is subtracted 

out when the estimate of differences is calculated. While the interpretation of this difference is difficult, these comparisons can be made with caution. Under most circumstances, the estimate of difference should not be interpreted 

as a reflection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change over time is complicated when change is being evaluated 

using multiyear estimates. From a technical standpoint, change over time is best evaluated with multiyear estimates that do not overlap. At the same time, many data users will not want to wait until 2019 (when 2014–2018 

ACS 5-year data will be available) to evaluate change since 2009–2013. Users who need to compare two 5-year 

estimates of the same geography that overlap in sample years must use a different formula to calculate the SE of 

this difference. To account for the sample overlap, use the following approximation to the SE:

(4)

where C is the fraction of overlapping years. For example, the periods 2009–2013 and 2010–2014 overlap for 4 

out of 5 years, so C=4/5=0.8. If the periods do not overlap, such as 2006–2010 and 2011–2015, then C=0.

With this SE, data users can test for the statistical significance of the difference between the two estimates using 

formula (3) with the modification from formula (4):

Substitute in the denominator of formula (3) for the significance test.

The example below shows how to test for a statistically significant difference between the estimated percentages of householders aged 65 or older who lived alone in Holmes County, Florida, in 2010–2014 (estimated percentage = 13.1, MOE = 2.3, SE = 1.398) and in 2006–2010 (estimated percentage = 13.6, MOE = 2.3, SE = 1.398). In 

this example, only 1 of the 5 years overlaps, so C = 1/5=0.2. Using formula (3) with the modification from formula 

(4) yields:

Since the test value (0.283) is less than the critical value for a confidence level of 90 percent (1.645), the difference in the percentages is not statistically significant at a 90 percent confidence level. 
50 Understanding and Using American Community Survey Data

50 What All Data Users Need to Know U.S. Census Bureau

Custom (User-Derived) Estimates

In some cases, data users will need to construct custom ACS estimates by combining data across multiple geographic areas or population subgroups, or it may be necessary to derive a new percentage, proportion, or ratio 

from published ACS data. For example, one way to address the issue of unreliable estimates for individual census 

tracts or block groups is to aggregate geographic areas, yielding larger samples and more reliable estimates. 

In such cases, additional calculations are needed to produce MOEs and SEs, and to conduct tests of statistical 

significance for the derived estimates. The section on “Calculating Measures of Error for Derived Estimates” provides detailed instructions on how to make these calculations. 

Advanced users who are aggregating ACS estimates can use the Census Bureau’s Variance Replicate Tables

to produce MOEs for selected ACS 5-year Detailed Tables.65 Users can calculate MOEs for aggregated data by 

using the variance replicates. Unlike available approximation formulas, this method results in an exact MOE by 

incorporating the covariance. More information about the Variance Replicate Tables is available in the section on 

“Calculating Measures of Error for Derived Estimates.”

Some advanced data users will also want to construct custom ACS estimates from the Census Bureau’s Public 

Use Microdata Sample (PUMS) files. Separate instructions for calculating SEs and conducting significance tests 

for PUMS estimates are available on the Census Bureau’s PUMS Technical Documentation Web page.66

It is important for data users to remember that the error measures and statistical tests described in this section 

do not tell us about the magnitude of nonsampling errors. More information about those types of errors is available in the section on “Measures of Nonsampling Error.” 

Additional Background Information and Tools

Sample Size and Data Quality

<www.census.gov/acs/www/methodology/sample-size-and-data-quality/>

This Web page describes the steps the Census Bureau takes to ensure that ACS data are accurate and reliable. It 

also includes several measures of ACS data quality for the nation and states.

Statistical Testing Tool

<www.census.gov/programs-surveys/acs/guidance/statistical-testing-tool.html>

The Statistical Testing Tool is a spreadsheet that tests whether ACS estimates are statistically different from one 

another. Simply copy or download ACS estimates and their MOEs into the tool to get instant results of statistical 

tests.

Variance Replicate Tables

<www.census.gov/programs-surveys/acs/data/variance-tables.html> 

Variance replicate estimate tables include estimates, MOEs, and 80 variance replicates for selected ACS 5-year 

Detailed Tables. The tables are intended for advanced users who are aggregating ACS data within a table or 

across geographic areas. Unlike available approximation formulas, this method results in an exact MOE by 

incorporating the covariance.

65 U.S. Census Bureau, American Community Survey (ACS), Variance Replicate Tables, <www.census.gov/programs-surveys/acs/data/variance

-tables.html>. 66 U.S. Census Bureau, American Community Survey (ACS), PUMS Technical Documentation, <www.census.gov/programs-surveys/acs

/technical-documentation/pums/documentation.html>.
Understanding and Using American Community Survey Data 51

U.S. Census Bureau What All Data Users Need to Know 51

8. CALCULATING MEASURES OF ERROR FOR 

DERIVED ESTIMATES

The U.S. Census Bureau publishes a wide range of American Community Survey (ACS) estimates through the data 

products provided in American FactFinder (AFF) and ACS Summary Files. However, for some applications, data 

users may still need to construct custom ACS estimates and their associated margins of error (MOEs). For example, 

users may want to aggregate data across geographic areas or population subgroups, or may need to derive proportions, ratios, and percent change measures that are not provided in published ACS data products. 

TIP: Data users can also aggregate data across geographic areas—or across population subgroups—to produce 

more reliable estimates in cases where the underlying component areas have estimates with relatively large 

standard errors (SEs).

When such derived estimates are generated, the user must calculate the associated MOEs. As described in the 

section on “Understanding Error and Determining Statistical Significance,” MOEs are needed to calculate SEs and 

coefficients of variation (CVs) and to determine statistical significance. The steps outlined in the sections below 

illustrate how to calculate the MOEs for derived counts, proportions, percentages, ratios, and percent change, 

which can then be translated into SEs and CVs. 

Calculating Measures of Error for Aggregated Count Data

Aggregating Data Across Geographic Areas

For some applications, data users may want to know the number of people with certain characteristics within a 

particular geographic region that is not included as a standard geographic area in ACS products. For example, 

a user may be interested in the number of never-married females within a tricounty area. The example below 

shows how to calculate the MOE, SE, and CV for such a derived estimate. 

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:

(1)

The example below shows how to calculate the MOE and SE for the estimated number of never-married females 

living in the three Virginia counties/independent cities that border Washington, DC (Fairfax and Arlington counties, Alexandria City) from the 2015 ACS 1-year estimates.
52 Understanding and Using American Community Survey Data

52 What All Data Users Need to Know U.S. Census Bureau

Characteristic Estimate MOE

Never-married females living in Fairfax County (Component 1) 135,173 ±3,860

Never-married females living in Arlington County (Component 2) 43,104 ±2,642

Never-married females living in Alexandria City (Component 3) 24,842 ±1,957

 Table 8.1. Data for Example 1 From Three Virginia Counties/Independent Cities: 2015

 Source: U.S. Census Bureau, American FactFinder, Table B12001: Sex by Marital Status for the Population 15 Years and Over.

The aggregate estimate is:

Obtain MOEs of the component estimates:

 MOE (Fairfax) = ±3,860

 MOE (Arlington) = ±2,642

MOE (Alexandria) = ±1,957

Using formula (1), calculate the MOE for the aggregate estimate:

Thus, the derived estimate of the number of never-married females living in the three Virginia counties/independent cities that border Washington, DC, is 203,119, and the MOE for the estimate is ±5,070. 

The SE of this derived estimate can be calculated from the SEs of the component estimates as follows:

1. Calculate the SE of each component estimate from its MOE using: 

(2)

SE (Fairfax) = 3,860 / 1.645 = 2,347

SE (Arlington) = 2,642 / 1.645 = 1,606

 SE (Alexandria) = 1,957 / 1.645 = 1,190

2. Calculate the SE of the aggregate estimate:

(3)

With the three component estimates in this example, this becomes:
Understanding and Using American Community Survey Data 53

U.S. Census Bureau What All Data Users Need to Know 53

To assess the reliability of this derived estimate, users may find it helpful to calculate the CV as follows:

(4)

This CV indicates that the sampling error of this estimate is very small relative to the estimate itself, so the number of never-married females residing in the Virginia tri-county area bordering Washington, DC, can be considered a very reliable estimate. 

However, users should note that this method for calculating the MOE and SE for aggregated count data is an 

approximation, and caution is warranted because this method does not consider the correlation or covariance 

between the component estimates. This method may result in an overestimate or underestimate of the derived 

estimate’s SE depending on whether the component estimates are highly correlated in either a positive or negative direction. As a result, the approximated SE may not match the result from a direct calculation of the SE that 

does include a measure of covariance, such as the following:

(5)

Data users should also be aware that as the number of estimates involved in a sum or difference increases, the 

results of the approximation formula become increasingly different from the SE derived directly from the ACS 

microdata. Users are encouraged to work with the fewest number of estimates possible. If there are estimates 

involved in a sum that are controlled in the weighting, then the approximate SE can be increasingly different. 

Several examples are provided to demonstrate issues associated with approximating the SEs when summing 

large numbers of estimates together in the latest ACS Accuracy of the Data document.67

Aggregating Data Across Population Subgroups

For some applications, data users may wish to combine data across population subgroups, especially in ACS 

tables where some groups have low counts and large MOEs. Before aggregating categories in a Detailed Table 

to create a derived estimate, users should check to make sure there is not a collapsed version of the same table 

already available in AFF or the ACS Summary Files. The MOEs in the published, collapsed tables will be more 

accurate than those users can approximate using the methods described in this section. 

The example below illustrates the results from aggregating household income categories from a Detailed Table 

in AFF for Loudoun County, Virginia, from the 2015 ACS 1-year estimates. Income categories are organized into 

three subgroups for the purpose of this example.

67 U.S. Census Bureau, American Community Survey (ACS), Code Lists, Definitions, and Accuracy, <www.census.gov/programs-surveys/acs 

/technical-documentation/code-lists.html>. 
54 Understanding and Using American Community Survey Data 

54 What All Data Users Need to Know U.S. Census Bureau

Household 

Income Category Subgroup # Estimate MOE SE CV

 Less than $10,000 – 2,163 ±812 494 22.8

 $10,000 to $14,999 – 1,178 ±504 306 26.0

 $15,000 to $19,999 1 1,502 ±743 452 30.1

 $20,000 to $24,999 1 1,995 ±722 439 22.0

 $25,000 to $29,999 2 1,756 ±685 416 23.7

 $30,000 to $34,999 2 1,781 ±631 384 21.5

 $35,000 to $39,999 3 2,708 ±1,007 612 22.6

 $40,000 to $44,999 3 1,981 ±647 393 19.9

 $45,000 to $49,999 3 2,581 ±996 605 23.5

 $50,000 to $59,999 – 6,590 ±1,109 674 10.2

 $60,000 to $74,999 – 6,861 ±1,288 783 11.4

 $75,000 to $99,999 – 14,391 ±1,810 1,100 7.6

 $100,000 to $124,999 – 14,790 ±1,944 1,182 8.0

 $125,000 to $149,999 – 12,735 ±1,341 815 6.4

 $150,000 to $199,999 – 18,167 ±1,890 1,149 6.3

 $200,000 or more – 29,380 ±2,053 1,248 4.2

 Table 8.2. Data for Example 2 From Loudoun County, Virginia: 2015

Source: U.S. Census Bureau, American FactFinder, Table B19001: Household Income in the Past 12 Months (In 2015 InflationAdjusted Dollars).

After calculating the SEs and CVs shown in Table 8.2, a data user might want to combine some of the household 

income categories in the lower end of the income distribution to reduce the CVs and create more reliable estimates. For example, the income categories in Subgroup 1 ($15,000 to $19,999 and $20,000 to $24,999) could be

combined to form the first new subgroup, the two in Subgroup 2 ($25,000 to $29,999 and $30,000 to $34,999) 

to form a second new subgroup, and the three in Subgroup 3 ($35,000 to $39,999, $40,000 to $44,999, and 

$45,000 to $49,999) to form a third new subgroup. Following the same steps used to combine data across three

geographic areas in Example 1, derived estimates and MOEs for the three new subgroups could be calculated as 

follows:

1. Aggregate counts to form new subgroup estimates:

• Subgroup 1 ($15,000 to $24,999) = 1,502 + 1,995 = 3,497 

• Subgroup 2 ($25,000 to $34,999) = 1,756 + 1,781 = 3,537

• Subgroup 3 ($35,000 to $49,999) = 2,708 + 1,981 + 2,581 = 7,270

2. Using formula (1), calculate MOEs for each new subgroup estimate:

MOE (Subgroup 1) = ±√(743)2 + (722)2 = ±√1,073,333 = ±1,036

MOE (Subgroup 2) = ±√(685)2 + (631)2 = ±√867,386 = ±931 

MOE (Subgroup 3) = ±√(1,007)2 + (647)2 + (996)2 =

±√2,424,674 = ±1,557

 

MOE (Subgroup 2) = ±√(685)2 + (631)2 = ±√867,386 = ±931

 

MOE (Subgroup 3) = ±√(1,007)2 + (647)2 + (996)2 =

±√2,424,674 = ±1,557

1

MOE (Subgroup 1) = ±√(743)2 + (722)2 = ±√1,073,333 = ±1,036

MOE (Subgroup 3) = ±√(1,007)2 + (647)2 + (996)2 =

±√2,424,674 = ±1,557

MOE(Q̂) 100 [ 1

√(5

070)2 

[(0

322)2 (831)2]] 08%

MOE (Subgroup 1) = ±√(743)2 + (722)2 = ±√1,073,333 = ±1,036

MOE (Subgroup 2) = ±√(685)2 + (631)2 = ±√867,386 = ±931

±√2,424,674 = ±1,557

MOE(Q̂) = 100 ∗ [ 1

630498 √(5,070)2 − [(0.322)2 ∗ (831)2]] = 0.8%

MOE (Subgroup 1) = ±√(743)2 + (722)2 = ±√1,073,333 = ±1,036

MOE (Subgroup 2) = ±√(685)2 + (631)2 = ±√867,386 = ±931

 

MOE (Subgroup 3) = ±√(1,007)2 + (647)2 + (996)2 =

MOE(Q̂) = 100 ∗ [ 1

630498 √(5,070)2 − [(0.322)2 ∗ (831)2]] = 0.8%
Understanding and Using American Community Survey Data 55

U.S. Census Bureau What All Data Users Need to Know 55

3. Calculate the SE of each new subgroup estimate using formula (2): 

• SE (Subgroup 1) = 1,036 / 1.645 = 630

• SE (Subgroup 2) = 931 / 1.645 = 566

• SE (Subgroup 3) = 1,557 / 1.645 = 947

4. Using formula (4), calculate the CV for each new subgroup estimate:

• CV (Subgroup 1) = (630 / 3,497) * 100 = (0.180) * 100 = 18.0 percent

• CV (Subgroup 2) = (566 / 3,537) * 100 = (0.160) * 100 = 16.0 percent

• CV (Subgroup 3) = (947 / 7,270) * 100 = (0.130) * 100 = 13.0 percent 

Aggregating across income categories increases the reliability of the new subgroup estimates relative to the 

original component estimates. For example, the CV for the $15,000 to $19,999 category is 30 percent. When 

this category is combined with the $20,000 to $24,999 category, the CV for the derived estimate for the new 

Subgroup 1 drops to 18 percent—lower than the original CV for this category by itself. Similarly, the CVs for the 

component estimates of Subgroup 3 range from 20 percent to 24 percent, while the CV for the derived estimate 

for Subgroup 3 ($35,000 to $49,999) is only 13 percent.

AFF includes a collapsed income distribution within Demographic Profile DP03 that makes it possible to assess 

the difference in results from the approximation method illustrated above versus a direct estimation method that 

accounts for covariance between the component estimates of the subgroups. The relevant portion of this AFF 

table, from the 2015 ACS 1-year estimates, is shown in Table 8.3 below.

Household 

income category Subgroup # Estimate MOE SE CV

$15,000 to $24,999 1 3,497 ±1,037 630 18.0

$25,000 to $34,999 2 3,537 ±973 591 16.7

$35,000 to $49,999 3 7,270 ±1,554 945 13.0

 Table 8.3. Data From AFF Demographic Profile DP03 for Loudoun County, Virginia: 2015

 Source: U.S. Census Bureau, American FactFinder, Table DP03: Selected Economic Characteristics.

While the MOEs, SEs, and CVs for new Subgroup 1 and Subgroup 3 are almost identical to those derived from 

the approximation method, this is not the case for new Subgroup 2. The user-derived MOE is ±931 compared 

with the published MOE of ±973, and the derived CV is only 16.0 percent compared with a CV of 16.7 percent 

based on the published MOE for Subgroup 2. The approximation method slightly underestimates the MOE and 

SE for Subgroup 2 because it does not account for some covariance between the two component estimates. 

Although the differences between the user-derived and published MOE’s are small in this example, they can vary 

substantially in other cases, particularly for linear combinations of multiple estimates. Examples illustrating this 

problem are provided in the ACS Accuracy of the Data document (see section on “Issues with Approximating 

the Standard Error of Linear Combinations of Multiple Estimates”).68

Calculating Measures of Error for User-Derived Proportions and Percentages

When data users create derived estimates for aggregated count data, they are often interested in calculating 

additional measures such as proportions and percentages. For example, the user who calculated the number of 

never-married females in the tri-county area in northern Virginia might also want to know what proportion of all 

females in this region have never been married. With proportions, the numerator is a subset of the denominator. In this case, the number of never-married females is a subset of all females. This derived proportion would 

be calculated as the number of never-married females aged 15 and older divided by the total number of females 

aged 15 and older. This proportion could also be converted to a percentage by multiplying by 100. The 2015 

ACS 1-year estimates of the total number of females aged 15 and older in Fairfax County, Arlington County, and 

Alexandria City and their respective MOEs are shown in Table 8.4.

68 U.S. Census Bureau, American Community Survey, Code Lists, Definitions, and Accuracy, <www.census.gov/programs-surveys/acs/technical 

-documentation/code-lists.html>.
56 Understanding and Using American Community Survey Data

56 What All Data Users Need to Know U.S. Census Bureau

Characteristic Estimate MOE

Total females aged 15 and older living in Fairfax County (Component 1) 466,037 ±391

Total females aged 15 and older living in Arlington County (Component 2) 97,360 ±572

Total females aged 15 and older living in Alexandria City (Component 3) 67,101 ±459

 Table 8.4. Data for Example 3 From Three Virginia Counties/Independent Cities: 2015

 Source: U.S. Census Bureau, American FactFinder, Table B12001: Sex by Marital Status for the Population 15 Years and Over.

Using the data for never-married females from Example 1 in this section with the data in Table 8.4 yields the following:

Proportion = (Never-married females / Total females)

(203,119 / (466,037 + 97,360 + 67,101)) = (203,119 / 630,498) = 0.322

To calculate the MOE of this proportion, we need the MOEs of the numerator (203,119) and the denominator 

(630,498). We already calculated the MOE of the number of never-married females in Example 1 in this section 

as ±5,070. Using formula (1), the MOE of the denominator is calculated as:

MOE (Subgroup 1) = ±√(743)2 + (722)2 = ±√1,073,333 = ±1,036

MOE (Subgroup 2) = ±√(685)2 + (631)2 = ±√867,386 = ±931

 

MOE (Subgroup 3) = ±√(1,007)2 + (647)2 + (996)2 =

±√2,424,674 = ±1,557

MOE(Q̂) = 100 ∗ [ 1

630,498 √(5,070)2 − [(0.322)2 ∗ (831)2]] = 0.8%

MOE(X̂1 + X̂2 + X̂3) = ±√(391)2 + (572)2 + (459)2 = ±√690,746 = ±831

MOE(P̂) = 1

Ŷ √[MOE(X̂)]

2 − (P̂2 ∗ [MOE(Ŷ)]2 If we define the proportion as , then the MOE of this proportion is approximated as: )

MOE (Subgroup 1) = ±√(743)2 + (722)2 = ±√1,073,333 = ±1,036

MOE (Subgroup 2) = ±√(685)2 + (631)2 = ±√867,386 = ±931

 

MOE (Subgroup 3) = ±√(1,007)2 + (647)2 + (996)2 =

±√2,424,674 = ±1,557

MOE(Q̂) = 100 ∗ [ 1

630,498 √(5,070)2 − [(0.322)2 ∗ (831)2]] = 0.8%

MOE(X̂1 + X̂2 + X̂3) = ±√(391)2 + (572)2 + (459)2 = ±√690,746 = ±831

MOE(P̂) = Ŷ

2 √[MOE(X̂)] − (P̂2 ∗ [MOE(Ŷ 2 )] ) 1

(6)

However, the Census Bureau provides ACS estimates as percentages rather than proportions in American 

FactFinder. For this example, the proportion of never-married females (0.322) can be converted to t

 then the 

he percentage of never-married females (32.2%) by multiplying by 100. If we define this percentage as 

. 

Substituting the estimates for the numerator and denominator, and their respective MOEs in formula (6), and 

multiplying by 100 yields:

MOE (Subgroup 1) = ±√(743)2 + (722)2 = ±√1,073,333 = ±1,036

MOE (Subgroup 2) = ±√(685)2 + (631)2 = ±√867,386 = ±931

 

MOE (Subgroup 3) = ±√(1,007)2 + (647)2 + (996)2 =

±√2,424,674 = ±1,557

MOE(Q̂) = 100 ∗ [ 1

630,498 √(5,070)2 − [(0.322)2 ∗ (831)2]] = 0.8%

MOE(X̂1 + X̂2 + X̂3) = ±√(391)2 + (572)2 + (459)2 = ±√690,746 = ±831

MOE(P̂) = 1

Ŷ √[MOE(X̂)]

2 − (P̂2 ∗ [MOE(Ŷ)]2

)

Users should note that if the value under the square root is negative, then the formula for calculating the MOE 

of a ratio should be used instead, which substitutes a “plus” for the “minus” under the square root. Calculating 

MOEs for ratios is described in the next section below as formula (7).

Using formula (2), the SE of the percentage estimate of never-married females is 0.80 / 1.645 = 0.488. CVs can 

also be calculated for derived percentages using formula (4) and are interpreted in the same way as for derived 

count estimates. In this example, the CV for the percentage of never-married females is (0.488 / 32.2) * 100 = 1.5 

percent, indicating that this is a very reliable estimate with a standard error that is much smaller than the estimated percentage.
Understanding and Using American Community Survey Data 57

U.S. Census Bureau What All Data Users Need to Know 57

Calculating Measures of Error for Derived Ratios

Ratios are used to compare two estimates where the numerator is not a subset of the denominator. For example, 

the data user in Example 1 may want to compare the number of never-married males in the tri-county area to the 

number of never-married females. To do this, the user must first obtain the component estimates and MOEs for 

males, and calculate the derived estimate and MOE of the number of never-married males in the tri-county area. 

These data, from the 2015 ACS 1-year estimates, are shown in Table 8.5 below. 

Characteristic Estimate MOE

Never-married males living in Fairfax County (Component 1) 156,720 ±4,222

Never-married males living in Arlington County (Component 2) 44,613 ±2,819

Never-married males living in Alexandria City (Component 3) 25,507 ±2,259

 Table 8.5. Data for Example 4 From Three Virginia Counties/Independent Cities: 2015

 Source: U.S. Census Bureau, American FactFinder, Table B12001: Sex by Marital Status for the Population 15 Years and Over.

Using the derived estimate and MOE calculated in Example 1 in this section, and the data from Table 8.5, the ratio 

would be calculated as:

Ratio = (Never-married males / Never-married females)

Ratio = ((156,720 + 44,613 + 25,507) / 203,119) = (226,840 / 203,119) = 1.117

This means that there were approximately 112 never-married men for every 100 never-married women living in 

the three Virginia counties in 2015.

Using formula (1), the MOE of the derived estimate of never-married males is calculated as:

A ratio has a slightly different estimator for the MOE than a proportion. If , where = the number of 

never-married males and = the number of never-married females, then the MOE of this ratio is approximated 

as:

(7)

Substituting the estimates for the numerator and denominator, and their respective MOEs in this formula yields:

Using formula (2), the SE of the ratio is 0.039 / 1.645 = 0.024. Using formula (4), the CV for the ratio of nevermarried males to never-married females is (0.024 / 1.117) * 100 = 2.1 percent, indicating that this is a very reliable 

estimate with an SE that is much smaller than the value of the estimated ratio.
58 Understanding and Using American Community Survey Data

58 What All Data Users Need to Know U.S. Census Bureau

Calculating Measures of Error for Derived Estimates of Percent Change

One of the most important benefits of annual releases of ACS estimates is the ability it provides for data users 

to analyze change over time. A frequent application is to calculate the percent change from one time period to 

another. For example, users may want to calculate the percent change from a 2005–2009 ACS 5-year estimate 

to a 2010–2014 ACS 5-year estimate. Normally, the current estimate is compared with the older estimate. 

If the current estimate = and the earlier estimate = , then the MOE for percent change is calculated as follows:

(8)

Formula (8) reduces to a ratio, so the ratio formula (7) described in the section above should be used to calculate the MOE. As a caveat, users should be aware that this formula does not take into account the correlation 

when calculating a change between two overlapping time periods. To calculate standard errors for overlapping 

ACS multiyear estimates see the section on “Understanding Error and Determining Statistical Significance.” 

Calculating Measures of Error for the Product of Two Estimates 

In some instances, data users may need to derive an estimate by multiplying a published count by a published 

percentage. For example, a data user might be interested in the number of 1-unit detached owner-occupied 

housing units in a geographic area. In 2015, the number of owner-occupied housing units in the United States 

was 74,506,512 with an MOE of 228,238, and the percent of 1-unit detached owner-occupied housing units was 

82.4 (0.824) with an MOE of 0.1 percent (0.001).69 So, the number of 1-unit detached owner-occupied housing 

units was 74,506,512 * 0.824 = 61,393,366. The formula to calculate the MOE of a product is:

(9)

Substituting the estimates and their respective MOEs in formula (9) yields:

To obtain the lower and upper bounds of the 90 percent confidence interval around 61,393,366, add and subtract 

the MOE from 61,393,366. Thus, the 90 percent confidence interval for this estimate is [61,393,366 – 202,289] to 

[61,393,366 + 202,289] or 61,191,077 to 61,595,655. Using formula (2), the SE of the product is 202,289 / 1.645 = 

122,972. Using formula (4), the coefficient of variation for this derived estimate is (122,972 / 61,393,366) * 100 = 

0.2 percent, indicating that the derived estimate is very reliable.

69 U.S. Census Bureau, ACS 1-year estimates in American FactFinder, Table S2504: Physical Housing Characteristics for Occupied Housing 

Units.
Understanding and Using American Community Survey Data 59

U.S. Census Bureau What All Data Users Need to Know 59

Calculating Measures of Error Using Variance Replicate Tables

Advanced users may be interested in the Variance Replicate Tables, first released for the 2010–2014 ACS 5-year 

data in July 2016.70 These augmented ACS Detailed Tables include sets of 80 replicate estimates, which allow 

users to calculate measures of error for derived estimates using the same methods that are used to produce the 

published MOEs on AFF. These methods incorporate the covariance between estimates that the approximation 

formulas in this document leave out.

The Variance Replicate Tables are available for a subset of the 5-year Detailed Tables for 11 geographic summary 

levels, including the nation, states, counties, census tracts, and block groups. These tables will be released on an 

annual basis, shortly after the release of the standard 5-year data products. 

Variance Replicate Documentation, including lists of tables and summary levels, is available on the Census 

Bureau’s Web site.71

70 U.S. Census Bureau, American Community Survey (ACS), Variance Replicate Tables, <www.census.gov/programs-surveys/acs/data/variance

-tables.html>. 71 U.S. Census Bureau, American Community Survey (ACS), Variance Replicate Tables Documentation, <www.census.gov/programs-surveys

/acs/technical-documentation/variance-tables.html>.
60 Understanding and Using American Community Survey Data

60 What All Data Users Need to Know U.S. Census Bureau

9. DIFFERENCES BETWEEN THE ACS AND THE 

DECENNIAL CENSUS

While the main function of the U.S. decennial census is to provide counts of people for the purpose of 

congressional apportionment, the primary purpose of 

the American Community Survey (ACS) is to measure 

the changing social and economic characteristics of 

the U.S. population—our education, housing, jobs, and 

more. 

Every 10 years since 1790, Congress has authorized 

the government to conduct a national census of the 

U.S. population, as required by the U.S. Constitution. 

In every decennial census from 1940 through 2000, 

two questionnaires were used to collect information: 

a “short form” with only basic questions such as age, 

sex, race, and Hispanic origin; and a “long form” with 

the basic short-form questions plus additional questions on social, economic, and housing characteristics. 

Only a subset of households received the long-form 

questionnaire—about one in every six in 2000. 

After the 2000 Census, the long form was replaced 

by the ACS, which continued to collect long-formtype information throughout the decade. The ACS 

includes not only the basic short-form questions, but 

also detailed questions about population and housing 

characteristics. It is a nationwide, continuous survey 

designed to provide communities with reliable and 

timely social, economic, housing, and demographic 

data every year. Since its start, the ACS has been providing a continuous stream of updated information for 

states and local areas, and is revolutionizing the way 

we use statistics to understand our communities. 

There are many similarities between the ACS and 

the 2000 Census long form. Both data sources are 

based on information from samples of the population, 

and while there are some differences in the question 

wording between the ACS and the 2000 Census long 

form, many questions in the two forms are very similar. 

However, there are also important differences in residence rules, reference periods, definitions, and methods between the two data sources that can impact 

comparability between ACS and 2000 Census data. 

For detailed guidance on comparing ACS and 2000 

Census data, visit the U.S. Census Bureau’s Web page 

on Comparing ACS Data.

72

Residence Rules and Reference Periods

The fundamentally different purposes of the ACS and 

the decennial census and their timing led to important 

differences in the choice of data collection methods. 

72 U.S. Census Bureau, American Community Survey (ACS), 

Comparing ACS Data, <www.census.gov/programs-surveys/acs

/guidance/comparing-acs-data.html>. 

The decennial census residence rules, which determine where people should be counted, are based on 

the principle of “usual residence” on April 1, in keeping 

with the focus of the census on the requirements of 

congressional apportionment and state redistricting. 

To accomplish this, the decennial census attempts to 

restrict and determine a principal place of residence on 

one specific date for everyone enumerated. 

The ACS 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 (see Box 9.1 for more 

information). This method is consistent with the goal 

that the Census Bureau produce ACS estimates based 

on data collected over a period of time, rather than a 

single point in time. ACS 1-year estimates represent 

data that have been collected over a 12-month period, 

and 5-year estimates represent data collected over a 

period of 60 months.

Therefore, a key difference between the ACS and the 

decennial census is the overall time frame in which 

they are conducted. The distribution of census enumeration dates are highly clustered in March and 

April (when most census mail returns are received) 

with additional, smaller clusters seen in May and June 

(when nonresponse follow-up activities take 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 and April), while 

ACS data describe the characteristics nearly every day 

over the full calendar year. 

The differences in the ACS and census data as a 

consequence of the different residence rules are most 

likely minimal for most areas and most characteristics. 

However, for certain segments of the population, the 

usual and current residence concepts could result in 

different residence decisions. Appreciable differences 

may occur in areas where large proportions of the total 

population spend several months of the year in areas 

that would not be considered their residence under 

decennial census rules. In particular, data for areas that 

include large beach, lake, or mountain vacation areas, 

or large migrant worker communities may differ appreciably between the decennial census and the ACS if 

populations live there for more than 2 months.

Some of the specific differences in reference periods 

between the ACS and 2000 Census are described on 

the next page. Data users should consider the potential impact of these differences when comparing ACS 

with 2000 Census estimates. 
Understanding and Using American Community Survey Data 61

U.S. Census Bureau What All Data Users Need to Know 61

Employment Status (Compare With 

Caution)

The reference periods are different because of yearround ACS data collection. The ACS reference period 

is the week before the respondent completed the 

survey or the field representative conducted the 

interview. Because questionnaires are mailed and field 

interviews are conducted throughout the year, there 

is a revolving reference period. For the 2000 Census, 

the reference period was the week before Census Day 

(April 1, 2000).

Income and Earnings Data (Compare With 

Caution)

The ACS asks for a respondent’s income over the “past 

12 months.” For example, the 2015 ACS 1-year data 

reflect incomes over 2014–2015, and the 2011–2015 

ACS 5-year data reflect incomes over 2010–2015. The 

2000 Census, however, collected the income data for 

a fixed period of time—“during 1999” (the last calendar year). In a comparison study between the 2000 

Census income data and the 2000 ACS, income collected in the 2000 Census was found to be about 4 

percent higher than that in the 2000 ACS. For more 

information on the differences of income in the ACS 

and the 2000 Census, visit the Census Bureau’s Web 

page on Income in the American Community Survey: 

Comparison to Census 2000.

73

School Enrollment (Compare)

The ACS reference period was 3 months preceding 

the date of interview, while the 2000 Census reference 

period was any time since February 1, 2000.

73 U.S. Census Bureau, Income in the American Community Survey: 

Comparisons to Census 2000,<www.census.gov/library/working 

-papers/2003/acs/2003_Nelson_01.html>.

Box 9.1. Who Counts as a “Resident” in the ACS?

The ACS uses the concept of “current residence” to determine who should be considered residents of 

sample housing units. The basic idea behind this concept is that everyone who is currently living or staying at an address for more than 2 months is considered a current resident of that address.

This means that their expected length of stay is more than 2 months, not that they have been staying 

in the housing unit for more than 2 months at the time when the survey is conducted. A person away 

from their residence for 2 months or less, whether in the United States or overseas, on a vacation or on 

a business trip, is considered to be a “resident” at the address, and the unit is classified as occupied and 

eligible for inclusion in the survey. A person away from their residence for more than 2 months is considered not to be a resident. For the ACS, if no one is determined to be a current resident in the sampled 

housing unit, it is classified as “vacant.”

There are a few exceptions to the “2-month” rule:

People Without Another Place to Stay: Anyone staying at a residence who does not have 

another place to stay, even if they are at the residence for 2 months or less, are always considered current residents of the residence.

Children Away at School: Children (under college age) who are away at boarding school or 

summer camp for more than 2 months are always considered current residents of their parents’ 

homes. College students’ current residency is established by the 2-month rule.

Children in Joint Custody: Children who live under joint custody agreements and move between 

residences are always considered current residents of the sampled housing unit where they are 

staying at the time of the interview.

“Commuter Workers”: People who stay at a residence close to work and return regularly to 

another residence to be with their family are always considered current residents of the family 

residence, not the work-related residence.

Residency in group quarters facilities is determined differently. All people residing in the selected facility 

at the time of interview, regardless of the length of stay, are eligible to be selected to be interviewed in 

the ACS.
62 Understanding and Using American Community Survey Data

62 What All Data Users Need to Know U.S. Census Bureau

Definitions 

Many data items collected in both the ACS and the 

2000 Census long form have slightly different definitions that could affect the comparability of the estimates for these items. Some of the specific differences 

in subject or variable definitions between the ACS and 

the 2000 Census are described below. For a comprehensive list, visit the Census Bureau’s Web page on 

Subject Definitions.

74

Group Quarters

The total group quarters (GQ) population in the ACS 

may not be comparable with the 2000 Census or 2010 

Census counts because there are some GQ types that 

are out of scope in the ACS. These include domestic 

violence shelters, soup kitchens, regularly scheduled 

mobile food vans, targeted nonsheltered outdoor locations, crews on maritime vessels, and living quarters 

for victims of natural disasters. The exclusion of these 

GQ types from the ACS may result in a small bias in 

some ACS estimates to the extent that the excluded 

population is different from the included population. 

Furthermore, only a sample of GQ facilities throughout the United States and Puerto Rico are selected for 

the ACS. The ACS controls the GQ sample at the state 

level only. Therefore, for lower levels of geography, 

particularly when there are relatively few GQs in a geographic area, the ACS estimate of the GQ population 

may vary from the count from the decennial census.

Aggregate Gross Rent

Data on gross rent in the ACS should not be compared with 2000 Census gross rent data. For the 2000 

Census, tables were not released for total renteroccupied units. The universe in the 2000 Census 

was “specified renter-occupied housing units,” which 

excluded one-family houses on 10 acres or more, 

whereas the universe in the ACS is “renter-occupied 

housing units,” thus, comparisons cannot be made 

between these two data sets.

Occupants Per Room

Data on occupants per room in the ACS should be 

compared with the 2000 Census with caution. This is 

due to: 1) differences in residence rules, 2) the absence 

of population controls used to adjust for undercoverage in the reported number of current residents in the 

ACS used in this measure, and 3) differences in the 

reported number of rooms because of changes in the 

rooms question between the 2007 and 2008 ACS.

74 U.S. Census Bureau, American Community Survey (ACS), Code 

Lists, Definitions, and Accuracy, <www.census.gov/programs-surveys 

/acs/technical-documentation/code-lists.html>.

Data Collection Modes

Until 2015, the Census Bureau sent all selected 

addresses an advance notification letter informing people living at that address that they had been 

selected to participate in the ACS. Shortly thereafter 

(for most U.S. addresses), instructions for completing the survey by Internet were mailed. Beginning 

in August 2015, the Census Bureau eliminated the 

advance notification letter and instead included 

instructions in the initial mail package for completing the survey by Internet or over the phone through 

a toll-free Telephone Questionnaire Assistance (TQA) 

line. If households do not respond by Internet or TQA, 

then a paper questionnaire is mailed to the address. 

In Puerto Rico and some hard-to-reach areas, only a 

paper questionnaire is mailed. 

Until 2017, if no response was received by Internet, 

TQA, or mail within a month following the initial mailing, the Census Bureau followed up with a telephone 

interview when a telephone number was available. 

However, beginning in October 2017, the Census 

Bureau discontinued the telephone Nonresponse 

Followup operation because of declining response 

rates and increasing costs. Respondent data are still 

collected via telephone through the TQA operation.

If the Census Bureau is unable to get a response 

by Internet, mail, or TQA, then the address may be 

selected for an in-person interview. Because of the 

high cost per completed interview, the Census Bureau 

samples about one in three nonrespondent housing 

units for personal visit interviews. The proportion of 

nonresponding households selected for in-person 

interviews is higher in areas with lower predicted 

response rates. A sample of people living in group 

quarters facilities—such as college dorms, skilled nursing facilities, or correctional facilities—is also interviewed in person to ensure coverage of people who 

are not living in housing units.

Like the ACS, the primary modes of data collection in 

the 2000 Census and the 2010 Census were mail-out/

mail-back questionnaires, telephone, and in-person 

visits. Unlike the ACS, enumerators for the decennial 

census attempted to follow up with all nonresponding 

households through telephone or personal interviews. 

Data for those who could not be contacted or who 

refused to participate were collected through “proxy” 

interviews, which means interviewers attempted to find 

and get a response from a knowledgeable respondent 

who was not a member of the household. Neither the 

2000 Census nor the 2010 Census included an Internet 

response option, although the Census Bureau plans to 

provide this option in the 2020 Census. 
Understanding and Using American Community Survey Data 63

U.S. Census Bureau What All Data Users Need to Know 63

Sampling and Nonsampling Error

ACS data, like the data from the 2000 Census long 

form, are derived from a sample of the population 

and are therefore subject to sampling error. The 2000 

Census sample—consisting of about one in six households nationwide—is larger than the 5-year aggregated ACS sample designed to replace it. Therefore, 

there is more sampling error associated with the ACS 

5-year estimates, compared with estimates from the 

2000 Census long form. However, compared with 

the 2000 Census, the ACS has lower levels of nonresponse error—as measured through survey response 

rates and item nonresponse rates. Visit the sections 

on “Understanding Error and Determining Statistical 

Significance” and “Measures of Nonsampling Error” for 

more information. 

Additional Background Information

Design and Methodology Report

<www.census.gov/programs-surveys/acs

/methodology/design-and-methodology.html>

The 2014 Design and Methodology Report contains 

descriptions of the basic design of the ACS and details 

of the full set of methods and procedures.

American Community Survey and Puerto Rico 

Community Survey Subject Definitions 

<https://www.census.gov/programs-surveys/acs

/technical-documentation/code-lists.html> 

This document provides detailed definitions of population and housing variables in the ACS, as well as guidance on making comparisons with ACS data.

Comparing ACS Data

<www.census.gov/programs-surveys/acs/guidance

/comparing-acs-data.html>

This Web page provides guidance on making comparisons with ACS data. It includes a link to an ACS/

Census Table Comparison Tool to match ACS 5-year 

tables with corresponding tables from the 2000 

Census.75

75 U.S. Census Bureau, American Community Survey, ACS/Census 

Table Comparison, <www.census.gov/acs/www/guidance

/comparing-acs-data/acscensus-table-lookup/>.
64 Understanding and Using American Community Survey Data

64 What All Data Users Need to Know U.S. Census Bureau

10. USING DOLLAR-DENOMINATED DATA

Dollar-denominated data refer to any characteristics for which inflation adjustments are used when producing annual estimates or comparing estimates across time periods. For example, income, rent, home value, and 

energy costs across time periods are all dollar-denominated data.

Inflation will affect the comparability of dollar-denominated data across time periods. When American 

Community Survey (ACS) multiyear estimates for dollar-denominated data are generated, amounts are adjusted 

using national Consumer Price Index (CPI) annual averages, since a regional-based CPI is not available for the 

entire country.

Given the potential impact of inflation on observed differences of dollar-denominated data across time periods, 

ACS data users should adjust for the effects of inflation. Such an adjustment will provide comparable estimates 

accounting for inflation. In making adjustments, the U.S. Census Bureau recommends using factors based on the 

All-Items CPI-U-RS (CPI Research Series).

76 Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount of income received during the 12 months preceding the 

interview month. This is the income reference period. Since there are 12 different income reference periods 

throughout an interview year, 12 different income inflation adjustments are made. Monthly CPI-U-RSs are used to 

inflation-adjust the 12 reference period incomes to a single reference period of January through December of the 

interview year. Note that there are no inflation adjustments for 1-year estimates of rent, home value, or energy 

cost values.

Adjusting Single-Year Estimates Over Time

When comparing 1-year income, rent, home value, and energy cost value estimates from two different years, 

adjustments should be made as follows: 

1. Obtain the All Items CPI-U-RS Annual Averages for the 2 years being compared.

2. Calculate the inflation adjustment factor as the ratio of the CPI-U-RS from the more recent year to the 

 CPI-U-RS from the earlier year.

3. Multiply the dollar-denominated data estimated for the earlier year by the inflation adjustment factor. You 

 can also follow this same process to calculate the corresponding margin of error of the inflation-adjusted 

 estimate.

The inflation-adjusted estimate for the earlier year can be expressed as:

(1)

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); and is the published ACS estimate for the earlier year (Y1).

The example below compares the national median value for owner-occupied mobile homes in 2014 ($38,400) 

and 2015 ($44,000). First adjust the 2014 median value using the 2014 All Items CPI-U-RS Annual Average 

(347.8) and the 2015 All Items CPI-U-RS Annual Average (348.2) as follows:

Thus, the comparison of the national median value for owner-occupied mobile homes in 2014 and 2015, in 2015 

dollars, would be $38,444 (2014 inflation-adjusted to 2015 dollars) versus $44,000 (2015 dollars).

76 U.S. Bureau of Labor Statistics, Consumer Price Index, CPI Research Series Using Current Methods (CPI-U-RS), <www.bls.gov/cpi/cpiurs

.htm>.
Understanding and Using American Community Survey Data 65

U.S. Census Bureau What All Data Users Need to Know 65

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost values are created with inflation adjustments. The Census 

Bureau uses the All Items CPI-U-RS Annual Averages for each year in the multiyear time period to calculate a 

set of inflation adjustment factors. Adjustment factors for a time period are calculated as ratios of the CPI-U-RS 

Annual Average from the most recent year to the CPI-U-RS Annual Averages from each of the earlier years. The 

ACS values for each of the earlier years in the multiyear period are multiplied by the appropriate inflation adjustment factors to produce the inflation-adjusted values. These values are then used to create the multiyear estimates. 

As an illustration, consider the time period 2010-2014, with hypothetical individual reference-year income values 

ranging from $15,000 in 2010 to $30,000 in 2014 (see Table 10.1).

Reference Year Income CPI-U-RS Annual Averages Inflation-Adjusted Income

2014 $30,000 347.8 $30,000 

2013 $30,000 342.2 $30,491 

2012 $25,000 337.3 $25,778 

2011 $20,000 330.4 $21,053 

2010 $15,000 320.3 $16,288 

 Table 10.1. Hypothetical Income Values Adjusted for Inflation: 2010–2014

In this example, the multiyear income components are created from inflation-adjusted income values using the 

CPI-U-RS Annual Averages shown in the third column of the table. The inflation-adjusted 2013 value is the ratio 

of 347.8 to 342.2 applied to $30,000, which equals $30,491. The same calculation is applied to the income values 

from 2010 through 2012 to produce inflation-adjusted values for those years. These individual values are then 

used to create the multiyear estimate for the 2010-2014 time period.

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as follows:

1. Obtain the latest available All Items CPI-U-RS Annual Averages for the two periods being compared.

2. Calculate the inflation adjustment factor as the ratio of the CPI-U-RS Annual Average in formula (1) from the 

most recent year to the CPI-U-RS in formula (1) from the earlier year.

3. Multiply the dollar-denominated estimate for the earlier time period by the inflation adjustment factor. You 

can also follow this same process to calculate the corresponding margin of error of the inflation-adjusted 

estimate.

The inflation-adjusted estimate for the earlier year can be expressed as: 

(2)

where 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 current time period (P2). 

is the published ACS estimate for the earlier time period (P1).
66 Understanding and Using American Community Survey Data

66 What All Data Users Need to Know U.S. Census Bureau

As an illustration, consider ACS multiyear estimates for the two time periods of 2005-2009 and 2010-2014. To 

compare the median household income for owner-occupied mobile homes in 2005-2009 ($40,800) and 2010-

2014 ($39,000), first adjust the 2005-2009 median value using the 2009 All Items CPI-U-RS Annual Averages 

(315.2) and the 2014 All Items CPI-U-RS Annual Averages (347.8) as follows:

Thus, the comparison of the national median value for owner-occupied mobile homes in 2005-2009 and 2010-

2014, in 2014 dollars, would be $45,020 (2005-2009 inflation-adjusted to 2014 dollars) versus $39,000 (2010-

2014, already in 2014 dollars).

Issues Associated With Inflation Adjustment

The recommended inflation adjustment uses a national-level CPI and, thus, will not reflect inflation differences 

that may exist across different geographic areas. In addition, since the inflation adjustment uses the All Items 

CPI, it will not reflect differences that may exist across characteristics such as energy and housing costs.
Understanding and Using American Community Survey Data 67

U.S. Census Bureau What All Data Users Need to Know 67

11. MEASURES OF NONSAMPLING ERROR

All survey estimates are subject to both sampling and 

nonsampling error. In the section on “Understanding 

Error and Determining Statistical Significance,” the 

topic of sampling error and the various measures available for understanding the uncertainty in the estimates 

due to their being based on estimates derived from a 

sample, rather than from an entire population, are discussed. The margins of error published with American 

Community Survey (ACS) estimates measure only the 

effect of sampling error. Other errors that affect the 

overall accuracy of the survey estimates may occur in 

the course of collecting and processing the ACS and 

are referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any 

error affecting a survey estimate outside of sampling error. Nonsampling error can occur in complete 

censuses as well as in sample surveys, and is commonly recognized as including coverage error, unit 

nonresponse, item nonresponse, response error, and 

processing error. The U.S. Census Bureau has many 

procedures in place designed to reduce these sources 

of nonsampling error and thus improve the quality of 

the data. More information about these procedures is 

available in the section on “Improving Data Quality by 

Reducing Nonsampling Error” on the Census Bureau’s 

Design and Methodology Report Web page.77

Types of Nonsampling Error

Nonsampling error can result in both random errors 

and systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there 

is an error in the questionnaire design that negatively 

affects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the final results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

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 

77 U.S. Census Bureau, “Chapter 15: Improving Data Coverage by 

Reducing Non-Sampling Error,” American Community Survey Design 

and Methodology, 2014, <www.census.gov/programs-surveys/acs

/methodology/design-and-methodology.html>.

newly-constructed housing units; the estimates would 

suffer from errors because of housing undercoverage.

The final ACS estimates are adjusted for undercoverage 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, produced by the Census Bureau’s 

Population Estimates Program. However, it is important to measure the extent of coverage adjustment 

by comparing the precontrolled ACS estimates to the 

final controlled estimates. If the extent of coverage 

adjustments is large, there is a greater chance that differences in characteristics of undercovered or overcovered housing units or individuals differ from those 

eligible to be selected. When this occurs, the ACS 

may not provide an accurate picture of the population 

before the coverage adjustment, and the population 

controls may not eliminate or minimize that coverage 

error. For more information about coverage error visit 

the Census Bureau’s Coverage Rates Definitions Web 

page.78

However, the process of controlling ACS estimates to 

the official population estimates may lead to additional errors in the ACS data. The population controls 

used in the ACS are midyear, point-in-time population 

estimates based on the decennial census, which has 

different residence rules than the monthly samples 

on which ACS estimates are based. See the section 

on “Residence Rules and Reference Periods” for more 

information.

Unit nonresponse is the failure to obtain the minimum 

required information from a housing unit or a resident 

of group quarters in order for it to be considered a 

completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

or person. For example, if no one in a sampled housing 

unit is available during the time frame for data collection, unit nonresponse will result. 

It is important to measure unit nonresponse because 

it has a direct effect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the final survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

unit nonresponse bias. This will happen if the characteristics of nonresponding units differ from the characteristics of responding units. For more information 

78 U.S. Census Bureau, American Community Survey (ACS), 

Coverage Rate Definitions, <www.census.gov/programs-surveys/acs 

/methodology/sample-size-and-data-quality/coverage-rates 

-definitions.html>.
68 Understanding and Using American Community Survey Data

68 What All Data Users Need to Know U.S. Census Bureau

about unit nonresponse, visit the Census Bureau’s 

Response Rates Definitions Web page.79

Item nonresponse occurs when a respondent fails 

to provide an answer to a required item or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question differ 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias. For more information about item 

nonresponse, visit the Census Bureau’s Item Allocation 

Rates Definitions Web page.80

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation of 

the final data files. For example, errors may occur if 

data entry of questionnaire information is incomplete 

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. 

79U.S. Census Bureau, American Community Survey (ACS), 

Response Rate Definitions, <www.census.gov/programs-surveys/acs 

/methodology/sample-size-and-data-quality/response-rates 

-definitions.html>. 80 U.S. Census Bureau, American Community Survey (ACS), Item 

Allocation Rates Definitions, <www.census.gov/programs-surveys 

/acs/methodology/sample-size-and-data-quality/item-allocation 

-rates-definitions.html>.

ACS Quality Measures

Nonsampling error is extremely difficult, if not impossible, to measure directly. However, the Census Bureau 

has developed a number of indirect measures of 

nonsampling error to help inform users of the quality of the ACS estimates: sample size, coverage rates, 

unit response rates and nonresponse rates by reason, 

and item allocation rates. These measures are available 

on the Census Bureau’s Sample Size and Data Quality

Web page.81

Sample size measures for the ACS summarize information for the housing unit and group quarters (GQ) 

samples. The measures available at the state level are:82

• Housing Units

• Number of initial addresses selected

• Number of final survey interviews

• Group Quarters People (beginning with the 

2006 ACS)

• Number of initial persons selected

• Number of final survey interviews

Sample size measures may be useful in special circumstances when determining whether to use 1-year 

or 5-year estimates in conjunction with estimates of 

the population of interest. While the coefficient of 

variation (CV) can be used to determine usability, as 

explained in the section on “Understanding Error and 

Determining Statistical Significance,” 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.

Coverage rates measure the ratio of ACS population 

or housing estimates of geographic areas or groups to 

the independent estimates for those areas or groups, 

multiplied by 100. National coverage rates are available 

for the total population by six race/ethnicity categories and the GQ population. Coverage rates are also 

available for housing units and total population by 

sex at both the state and national level. 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 

81 U.S. Census Bureau, American Community Survey, Sample Size 

and Data Quality, <www.census.gov/acs/www/methodology/sample

-size-and-data-quality/>. 82 The sample size measures for housing units (number of initial 

addresses selected and number of final survey interviews) and for 

persons in group quarters 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 Followup. 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. 
Understanding and Using American Community Survey Data 69

U.S. Census Bureau What All Data Users Need to Know 69

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, insufficient data, maximum contact attempts 

reached, and other.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. 

Missing data for a particular question or item is called 

item nonresponse. Item allocation involves the use of 

statistical procedures to impute the values for these 

missing data. Item allocation rates—the proportions of 

responses allocated for an item in a given geographic 

area—are determined by the content edits performed 

on the individual raw responses and closely correspond to item nonresponse rates. Overall housing unit 

and person characteristic allocation rates are available 

at the state and national levels, which combine many 

different characteristics. Allocation rates for individual 

items are published at the state and national levels. 

Allocation rates for other summary levels may be 

calculated from the B99 series of Detailed Tables in 

American FactFinder.

Item allocation rates differ by state, so data users are 

advised to examine the allocation rates for characteristics of interest before drawing conclusions from the 

published estimates.

Additional Background Information

Sample Size and Data Quality

<www.census.gov/acs/www/methodology

/sample-size-and-data-quality/>

The quality measures provided on this Web page illustrate the steps the Census Bureau takes to ensure that 

ACS data are accurate and reliable.

Design and Methodology Report

<www.census.gov/programs-surveys/acs

/methodology/design-and-methodology.html>

The 2014 Design and Methodology Report contains 

descriptions of the basic design of the ACS and details 

of the full set of methods and procedures.
70 Understanding and Using American Community Survey Data

70 What All Data Users Need to Know U.S. Census Bureau

12. GLOSSARY

Accuracy. One of four key dimensions of survey quality. Accuracy refers to the difference between the survey estimate and the true (unknown) value. Attributes 

are measured in terms of sources of error (for example, 

coverage, sampling, nonresponse, measurement, and 

processing).

Allocation. A commonly used approach to imputation 

(a statistical procedure to fill in missing responses) is 

known as hot-deck allocation, which uses a statistical 

method to supply responses for missing or inconsistent 

data from responding housing units or people in the 

sample who are similar. Certain values, such as a person’s educational attainment, are more accurate when 

provided from another housing unit or from a person 

with similar characteristics. Allocation rates measure 

the proportion of values that required hot-deck allocation and are an important measure of data quality.

American Community Survey (ACS). The ACS is a 

nationwide survey designed to provide communities a 

fresh look at how they are changing. The ACS replaced 

the decennial census long form in 2010 and thereafter 

by collecting long-form type information throughout 

the decade rather than only once every 10 years. Full 

national implementation of the ACS began in 2005. 

Questionnaires are mailed to a sample of addresses to 

obtain information about household residents and the 

housing unit itself.

The Census Bureau produces social, economic, housing, and demographic estimates from the ACS in the 

form of 1-year and 5-year estimates based on population thresholds. The strength of the ACS is in estimating population and housing characteristics. It produces 

estimates for small areas, including census tracts and 

block groups and population subgroups.

Although the ACS provides population and housing unit estimates, the Census Bureau’s Population 

Estimates Program produces and disseminates the official estimates of the population for the nation, states, 

counties, cities and towns, and estimates of housing units for states and counties. For 2010 and other 

decennial census years, the decennial census provides 

the official counts of population and housing units.

American FactFinder (AFF). A system for disseminating Census Bureau data on the Internet. The system 

offers prepackaged data and user-selected data tables 

and maps from the ACS and many other data sources. 

Application Programming Interface (API). API is a set 

of programs that allows an application to interact with 

other applications. The Census Bureau has developed 

the Census API, enabling developers to design Web 

and mobile apps to provide quick and easy access 

from sets of Census Bureau statistics.

Assignment. A type of imputation (a statistical procedure to fill in missing responses), assignment involves 

looking at other data, as reported by the respondent, 

to fill in missing responses. For example, when determining sex, if a person reports giving birth to children 

in the past 12 months, the Census Bureau verifies that 

the person is female. This approach also uses data as 

reported by other people in the household to fill in a 

blank or inconsistent field. For example, if the reference person is a U.S. citizen, a biological child with a 

blank response to citizenship is also assumed to be a 

citizen.

Block Group. A statistical subdivision of a census tract, 

generally defined to contain between 600 and 3,000 

people and between 240 and 1,200 housing units, and 

the smallest geographic unit for which the Census 

Bureau tabulates sample data. A subdivision of a census tract (or, before 2000, a block numbering area), 

a block group is a cluster of blocks having the same 

first digit of their four-digit identifying number within a 

census tract.

Census Geography. A collective term referring to 

the types of geographic areas used by the Census 

Bureau in its data collection and tabulation operations. This Web page shows the geographic entities for 

which data are available from the ACS, which provides 

information down to the block group level.83

With connecting lines, the diagram in the “Geographic 

Hierarchy” section shows the hierarchical relationships 

between geographic types. For example, a line extends 

from states to counties because a state is comprised 

of many counties, and a county can never cross a state 

boundary. 

If no line joins two geographic types, then an absolute 

and predictable relationship does not exist between 

them. For example, many places do not cross a county 

boundary (that is, only one county). However, some 

places extend over more than one county like New 

York City. Therefore, an absolute hierarchical relationship does not exist between counties and places, and 

any tabulation involving both of these geographic 

types may represent only a part of one county or one 

place.

83 U.S. Census Bureau, American Community Survey (ACS), Concept 

& Definitions <www.census.gov/programs-surveys/acs/geography-acs 

/concepts-definitions.html>.
Understanding and Using American Community Survey Data 71

U.S. Census Bureau What All Data Users Need to Know 71

Census Tract. A small, relatively permanent statistical 

subdivision of a county delineated by a local committee of census data users for presenting data. Census 

tracts nest within counties and their boundaries 

normally follow visible features, but may follow legal 

geography boundaries and other nonvisible features 

in some instances. Census tracts ideally contain about 

4,000 people and 1,600 housing units.

Coefficient of Variation (CV). The ratio of the standard 

error (square root of the variance) to the value being 

estimated, usually expressed in terms of a percentage 

(also known as the relative standard deviation). The 

lower the CV, the higher the relative reliability of the 

estimate.

Comparison Profiles (CP). The Comparison Profiles 

show ACS data side-by-side from different data 

releases, indicating where there is a statistically significant difference between estimates. Comparison 

Profiles are currently available for 1-year estimates 

and 5-year estimates starting with the release of the 

2011–2015 ACS 5-year data. The 5-year Comparison 

Profiles will compare data between two nonoverlapping 5-year periods. ACS Comparison Profiles begin 

with the letters “CP.”

Confidence Interval. The sample estimate and its margin of error permit the construction of a confidence 

interval that represents the degree of uncertainty 

about the estimate. A 90 percent confidence interval 

can be constructed by adding and subtracting the 

published margin of error from the ACS estimate. A 90 

percent confidence interval can be interpreted roughly 

as providing 90 percent certainty that the true number 

falls between the upper and lower bounds.

Confidentiality. The guarantee made by law (Title 13, 

U.S. Code) to individuals who provide census information regarding nondisclosure of that information 

to others. By law, the Census Bureau cannot disclose 

any private information that identifies a person or a 

business. Under federal law, the penalty for unlawful 

disclosure is a federal prison sentence of up to 5 years, 

a fine of up to $250,000, or both.

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.

Controls. During the ACS weighting process, the official county-level population and housing unit estimates 

are used as controls. Weights are adjusted so that ACS 

estimates conform to these controls (but do not necessarily exactly match). This is done to improve person 

and housing unit coverage and to reduce the variability of the ACS estimates.

Certain published estimates, such as total population 

estimates for states, do exactly match the controls. 

These estimates, which have five asterisks (*****) in the 

Margin of Error column on American FactFinder, are 

by definition fixed, and can be considered to have no 

sampling error.

Current Residence. The ACS uses a “current residence” concept to determine who should be considered a resident of a sample household. Everyone who 

is currently living or staying at a sample address is 

considered a resident of that address, except people 

staying there for 2 months or less. People who have 

established residence at the sample unit and are away 

for only a short period of time are also considered to 

be current residents.

Custom Tabulations. The Census Bureau offers a wide 

variety of general purpose data products from the 

ACS. These products are designed to meet the needs 

of the majority of data users and contain predefined 

sets of data for standard census geographic areas, 

including both political and statistical geographic 

areas. These products are available through American 

FactFinder and the ACS Web site. 

For users with data needs not met through the general 

purpose products, the Census Bureau offers Custom 

Tables on a cost-reimbursable basis, through the ACS 

Custom Tabulation program.84 Custom tables are created by tabulating data from ACS microdata files. They 

vary in size, complexity, and cost depending on the 

needs of the sponsoring client.

Data Profiles (DP). Data Profiles provide summaries 

of ACS data for various social, economic, housing, and 

demographic characteristics for the United States, 

regions, divisions, states, counties, county subdivisions, places, metropolitan areas, American Indian 

and Alaska Native areas, and other geographic areas. 

These profiles are similar in content to the demographic profiles from the decennial censuses. ACS 

Data Profiles begin with the letters “DP.” 

Decennial Census. The census of population and 

housing, taken by the Census Bureau in years ending 

in 0 (zero). Article I of the Constitution requires that 

a census be taken every 10 years for the purpose of 

reapportioning the U.S. House of Representatives. Title 

13 of the U.S. Code provides the authorization for conducting the census in Puerto Rico and the Island Areas.

84 U.S. Census Bureau, American Community Survey (ACS), Custom 

Tables, <www.census.gov/programs-surveys/acs/data/custom-tables

.html>.
72 Understanding and Using American Community Survey Data

72 What All Data Users Need to Know U.S. Census Bureau

Derived Estimates. One of the benefits of working 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 tables (e.g., aggregate estimates for a 

three-county area or for four age groups not collapsed).

Detailed Tables. Detailed Tables provide access to the 

most comprehensive ACS data tables on all topics and 

geographic areas. Tables include totals and subtotals. 

Users may choose more than one geographic area and 

more than one table that display in a scrolling list, but 

only what displays on the width of the screen will print.

ACS Detailed Tables begin with the letters “B” for base 

tables and “C” for collapsed tables. The “collapsed” 

tables cover the same topics as the base table, but 

with fewer categories.

Disclosure Avoidance. Statistical methods used before 

releasing data products to ensure the confidentiality of 

responses. 

Estimates. Data for the ACS are collected from a sample of housing units and used to produce estimates 

of the actual figures that would have been obtained 

by interviewing the entire population using the same 

methodology.

Five-Year Estimates. Estimates based on 5 years 

of ACS data. These estimates are meant to reflect the 

characteristics of a geographic area over the entire 

5-year period. These estimates are published for geographic areas down to the census block group level. 

File Transfer Protocol (FTP). A process that allows a 

user to download large census and survey files and 

datasets from the Census Bureau’s Web site.85

Geographic Comparison Tables (GCT). Allow users 

to compare ACS data across geographic areas in 

the same table (e.g., all counties in a state). ACS 

Geographic Comparison Tables begin with the letters 

“GCT.” 

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 one another. The 

ACS collects data from people living in both housing 

units and GQ facilities.

85 U.S. Census Bureau, American Community Survey (ACS), Data 

via FTP (File Transfer Protocol), <www.census.gov/programs-surveys

/acs/data/data-via-ftp.html>.

Group Quarters (GQ) Population. Includes all people 

living in group quarters instead of housing units. Group 

quarters are places where people live or stay, in a 

group living arrangement that is owned or managed 

by an entity or organization providing housing and/or 

services for the residents. The group quarters population lives in group quarters, of which there are two 

general categories:

Institutional group quarters are facilities that house 

those who are primarily ineligible, unable, or unlikely 

to participate in the labor force while resident. The 

institutionalized population is the population residing in institutional group quarters, such as adult correctional facilities, juvenile facilities, skilled-nursing 

facilities, and other institutional facilities such as 

mental (psychiatric) hospitals and in-patient hospice 

facilities.

Noninstitutional group quarters are facilities that 

house those who are primarily eligible, able, or likely 

to participate in the labor force while resident. The 

noninstitutionalized population lives in noninstitutional group quarters such as college/university 

student housing, military quarters, and other noninstitutional group quarters such as emergency and 

transitional shelters for people experiencing homelessness and group homes.

Housing Unit. A housing unit is a house, an apartment, 

a mobile home or trailer, a group of rooms, or a single 

room occupied as separate living quarters, or if vacant, 

intended for occupancy as separate living quarters. 

Separate living quarters are those in which the occupants live separately from any other individuals in the 

building and which have direct access from outside 

the building or through a common hall. For vacant 

units, the criteria of separateness and direct access are 

applied to the intended occupants whenever possible.

Imputation. When data are missing, it is standard practice to use a statistical procedure called imputation to 

fill in missing responses. Imputation is the placement 

of one or more estimated answers into a field of data 

records that previously had no data or had incorrect 

or implausible data. There are two principal imputation 

methods to deal with missing or inconsistent data—

assignment and allocation.

Margin of Error (MOE). The margin of error is the 

measure of sampling error published with each ACS 

estimate. A margin of error is the difference between 

an estimate and its upper or lower confidence bounds. 

Confidence bounds can be created by adding the margin of error to the estimate (for an upper bound) and 

subtracting the margin of error from the estimate (for 

a lower bound). All published margins of error for the 

ACS are based on a 90 percent confidence level.
Understanding and Using American Community Survey Data 73

U.S. Census Bureau What All Data Users Need to Know 73

Measurement Error. Also referred to as “response 

error,” measurement error occurs when the response 

received differs from the “true” value as a result of 

the respondent, the interviewer, the questionnaire, the 

mode of collection, the respondent’s record-keeping 

system(s), or other similar factors.

Multiyear Estimates. Three-year and five-year estimates based on multiple years of ACS data. ACS 

5-year estimates are published for geographic areas 

down to the census block group level. ACS 3-year 

estimates have been discontinued, but are available for 

2013 and earlier years for geographic areas with populations of 20,000 or more.

Narrative Profiles. Narrative Profiles provide text and 

bar charts to display highlights of selected social, 

economic, housing, and demographic estimates for a 

single geographic area. The topics include households 

and families, disability, travel to work, income, poverty, 

and a wide variety of other topics drawn from the ACS 

Data Profiles.

Nonsampling Error. Total survey error can be classified 

into two categories—sampling error and nonsampling 

error. Errors that occur during data collection (for 

example, nonresponse error, response error, and interviewer error) or data capture fall under the category of 

nonsampling error.

Overcoverage. Overcoverage exists when housing 

units or people have more than one chance of selection in the sample, or are included in the sample when 

they should not have been.

Period Estimates. An estimate based on information 

collected over a period of time. For ACS estimates, the 

period is either 1 year or 5 years. ACS 3-year estimates 

have been discontinued, but are available for 2013 and 

earlier years for geographic areas with populations of 

20,000 or more.

Point-in-Time Estimates. An estimate based on one 

point in time. The decennial census long-form estimates for the 2000 Census were based on information 

collected as of April 1, 2000.

Population Estimates Program. The Census Bureau’s 

Population Estimates Program (PEP) produces July 1 

estimates for years after the last published decennial 

census (2010), as well as for past decades. Existing 

data series—such as births, deaths, federal tax returns, 

Medicare enrollment, and immigration—are used to 

update the decennial census base counts. Population 

estimates are used in federal funding allocations, in 

setting the levels of national surveys, and in monitoring 

recent demographic changes.

Public Use Microdata Area (PUMA). A statistical area 

defined to contain a population of 100,000 or greater 

for which the Census Bureau tabulates Public Use 

Microdata Sample (PUMS) data. ACS and decennial 

census population and housing microdata are disseminated using these defined areas.

Public Use Microdata Sample (PUMS) Files. 

Computerized files containing a sample of individual 

records of people and households that responded 

to the ACS (stripped of all identifying information). 

The PUMS files permit analysis of specific population 

groups and custom variables that are not available 

through other ACS data products.

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 data from the ACS. Four 

different measures are provided with the annual data 

release: 1) initial sample size and final interviews, 2) 

coverage rates, 3) response rates, and 4) item allocation rates for all collected variables. Details are 

available in the technical documentation for the ACS 

products.

QuickFacts. A Census Bureau site that provides quick, 

easy access to facts about people, businesses, and 

geographic areas for all states, counties, and cities and 

towns with more than 5,000 people.

Ranking Tables. A table or product type that orders 

the states according to the numeric value of the data 

displayed. ACS Ranking Tables begin with the letter 

“R.”

For ACS Ranking Tables: The ordering provided by the 

“Rank” column is based only on the rounded value of 

the estimates and does not incorporate the margin of 

error. A difference in rankings between two states does 

not mean that the estimates are statistically different. 

Data users should use the “with statistical significance” 

version of the table to determine whether two estimates are statistically different.

Reference Week. The calendar week preceding the 

date on which the respondents completed their questionnaires or were enumerated. This calendar week 

may not be the same for all people since the enumeration may not be completed in 1 week.

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 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. See the entry 

on “Usual Residence” for information about residence 

rules in the decennial census.
74 Understanding and Using American Community Survey Data

74 What All Data Users Need to Know U.S. Census Bureau

Respondent. The person supplying survey or census 

information about his or her living quarters and its 

occupants.

Respondent Errors. The respondents’ failure to provide 

the correct answer to a survey question for any reason, 

such as poor comprehension of the question meaning, low motivation to answer the question, inability to 

retrieve the necessary information, or an unwillingness 

to answer the question truthfully.

Sample. Entities selected for a specific survey.

Sample Data. Population and housing information collected on a continuous basis for selected areas in the 

ACS and other surveys where data are gathered from 

a selected group of respondents. No sample data were 

collected in the 2010 Census.

Sample Survey. A data collection activity involving 

observations or questionnaires for a sample of a population. These data are used to produce estimates for 

the entire population.

Sampling Error. Errors that occur because only part of 

the population is directly contacted. With any sample, 

differences are likely to exist between the characteristics of the sampled population and the larger group 

from which the sample was chosen.

Sampling Rate. Proportion of the addresses in a 

geographic area, or residents of a GQ facility, who are 

selected for interview in a particular time period.

Sampling Variability. Variation that occurs by chance 

because a sample of the population is surveyed rather 

than the entire population. 

Selected Population Profiles. Selected Population 

Profiles can be used to show ACS data for a specific 

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

ancestry groups, or country of birth. 

Single-Year (1-Year) Estimates. Estimates based on 

1 year of ACS data. They are meant to reflect the 

characteristics of a geographic area over an entire 

12-month period. ACS 1-year estimates are published 

for geographic areas with populations of 65,000 or 

more. Starting with the 2014 ACS, the Census Bureau 

is also publishing 1-year Supplemental Estimates—simplified versions of popular ACS tables for areas with at 

least 20,000 people.

Standard Error. The standard error is a measure of the 

deviation of a sample estimate from the average of all 

possible samples. 

Statistical Significance, Test of. A test of statistical 

significance provides statistical evidence that indicates 

whether an observed difference between two estimates is likely due to chance (“not statistically significant”), or likely represents a true difference that exists 

in the population as a whole (“statistically significant”).

Statistical significance in census data products is usually reported at the 90 percent confidence level.

Note that some statistical significance results displayed in American FactFinder may be based on 

unrounded estimates and standard errors and users 

may not be able to duplicate the results using the 

rounded estimates and measures of error as displayed 

on American FactFinder.

Subject Tables. Subject Tables include ACS data organized by subject area, providing an overview of the 

information that analysts most often receive requests 

for from data users. ACS Subject Tables begin with the 

letter “S.” 

Summary Files. The ACS Summary Files are commadelimited text files that contain all of the Detailed 

Tables for the ACS data releases. 

Summary Level. Summary levels specify the content 

and hierarchical relationships of the geographic elements that are required to tabulate and summarize 

data. Each summary level has an assigned 3-digit summary level code to help programmers link each specific 

summary level to its appropriate use in a table, map, or 

other data summarization format. Some examples of 

summary levels are:

• 040: State

• 050: State-County

• 060: State-County-County Subdivision

• 150: State-County-Census Tract-Block Group

• 160: State-Place

• 314: Metropolitan Statistical Area-Metropolitan 

Division

• 430: Urban Area-State-County

It is important to distinguish between a summary level 

and a geographic area. A summary level represents the 

concept of a geographic level. For example, summary 

level 050, State-County, represents the concept of a 

county within a state. By comparison, a geographic 

area covers territory “on the ground,” such as Madison 

County, Indiana.

Supplemental Estimates. Simplified Detailed Tables 

that provide access to the most recent ACS data at a 

lower population threshold than the standard 1-year 

tables. Available for selected geographic areas with 

20,000 people or more. ACS Supplemental Estimates 

begin with the letter “K.”
Understanding and Using American Community Survey Data 75

U.S. Census Bureau What All Data Users Need to Know 75

Thematic Maps. Thematic Maps can be used to show 

the geographic patterns in statistical data. Thematic 

maps are a complement to the Ranking Tables and are 

a tool to visually display on a map the geographic variability of a key summary or derived measure. 

Undercoverage. The extent to which a sample does 

not include members of the target population thus 

preventing those members from having any chance of 

selection into the sample.

Unit Nonresponse. The failure to obtain the minimum 

required data from a unit in the sample.

Universe. The total number of units (e.g., individuals, 

households, or businesses) in the population of interest.

Usual Residence. Usual residence is a concept used 

in the decennial census to determine where a person 

should be counted in the census. Usual residence is 

defined as the place where a person lives and sleeps 

most of the time. This place is not necessarily the same 

as a person’s voting residence or legal residence.

Variance Replicate Tables. These augmented ACS 

Detailed Tables include sets of 80 replicate estimates, 

which allow advanced users to calculate measures 

of error for estimates using the same methods that 

are used to produce the published margins of error 

(MOEs) in American FactFinder. These methods incorporate the covariance between estimates that approximate MOE formulas do not. They are published for a 

subset of the 5-year Detailed Tables and at selected 

summary levels. The 2010–2014 ACS 5-year estimates 

were the first for which these were available.