What PRCS Data Users Need to Know i

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

Issued

March 2009

U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

USCENSUSBUREAU

Helping You Make Informed Decisions

A Compass for Understanding and Using 

American Community Survey Data

What Puerto Rico Community Survey Data Users 

Need to Know
Acknowledgments Linda A. Jacobsen, Vice President, Domestic Programs, Population 

Reference Bureau and Mark Mather, Associate Vice President, Domestic 

Programs, Population Reference Bureau, drafted the What General Data Users 

Need to Know handbook for the U.S. Census Bureau’s American Community 

Survey Offi ce. Kennon R. Copeland and John H. Thompson of National 

Opinion Research Center at the University of Chicago drafted the technical 

appendixes. Edward J. Spar, Executive Director, Council of Professional 

Associations on Federal Statistics, Frederick J. Cavanaugh, Executive 

Business Director, Sabre Systems, Inc., and Susan P. Love, Consultant, 

provided initial review of this handbook. 

Enid Santana coordinated the adaptation of this handbook for Puerto Rico 

with assistance from Francisco Acevedo, Statistical Project Manager, Puerto 

Rico Statistical Institute, Luis Avilés, President Board of Directors, Puerto 

Rico Institute of Statistics, Mario Marazzi, Executive Director Puerto Rico 

Institute of Statistics, Alfredo Navarro, Assistant Division Chief, Decennial 

Statistical Studies Division, Census Bureau, and Idania Rodríguez, Statistical 

Project Manager, Puerto Rico Institute of Statistics.

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

Community Survey Offi ce, provided the concept and directed the 

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

Understanding and Using American Community Survey Data. Cheryl V. 

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

Hostetter, Pamela M. Klein, Anna M. Owens, Clive R. Richmond, and 

Nancy K. Torrieri contributed to the planning and review of this handbook 

series.

The American Community Survey program is under the direction of 

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

Weinberg, Assistant Director for the American Community Survey and 

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

Offi ce.

Other individuals who contributed to the review and release of these 

handbooks include Dee Alexander, Herman Alvarado, Mark Asiala,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Zimolzak.

Linda Chen and Amanda Perry of the Administrative and Customer Services 

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

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

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

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

and production management.
A Compass for Understanding and Using 

American Community Survey Data Issued March 2009

U.S. Department of Commerce

Vacant, 

Secretary

Vacant, 

Deputy Secretary

Economics and Statistics Administration

Vacant,

Acting Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg, 

Acting Director

What Puerto Rico Community Survey Data Users Need to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What Puerto Rico Community 

Survey Data Users Need to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2009.

Economics and Statistics 

Administration

Vacant,

Acting Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg,

Acting Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Offi cer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Offi ce

ECONOMICS

AND STATISTICS

ADMINISTRATION
What PRCS Data Users Need to Know iii

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

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

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

About the Puerto Rico Community Survey ....................................................1

Who Uses the PRCS and Why? .......................................................................2 

How the PRCS Works .....................................................................................3

Diff erences Between the PRCS and the Decennial Census ............................3

Geographic Areas Covered in the PRCS ........................................................4

Benefi ts and Challenges of PRCS Data ..........................................................7

Understanding Single-Year and Multiyear Estimates ....................................7

Making Comparisons With Multiyear Estimates ..................................................8

Understanding Margin of Error ....................................................................9

Accessing PRCS Data .................................................................................. 10

Case Studies Using PRCS Data .................................................................... 14

Case Study #1: The Local Program Administrator ............................................14

Case Study #2: The Consultant........................................................................16

Case Study #3: The Local Planner ....................................................................19

Avoiding Pitfalls When Working With PRCS Data ........................................ 22

Other Resources for Working With ACS and PRCS Data .............................. 22

Conclusion .................................................................................................. 23

Glossary...................................................................................................... 24

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

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

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

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

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

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

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

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

 Appendix 8. Other ACS Resources ...................................................................A-27
iv What PRCS Data Users Need to Know

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

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

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

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

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

concepts that are fundamentally diff erent from those associated with sample 

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

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

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

The ACS Compass Products. 

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

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

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

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

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

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

they cover major stakeholder groups familiar to the Census Bureau.

General data users Congress 

High school teachers Puerto Rico Community Survey data

 users (in English and Spanish)

Business community Public Use Microdata Sample (PUMS) data

 users

Researchers Users of data for rural areas

Federal agencies State and local governments

Media Users of data for American Indians and 

 Alaska Natives

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

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

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

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

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

target audience. The Census Bureau developed additional ACS Compass Products

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

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

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

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

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

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

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

well as concerns and questions that will arise. 

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

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

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

you have learned. 
What PRCS Data Users Need to Know 1

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

This handbook provides an overview of the Census 

Bureau’s Puerto Rico Community Survey (PRCS), which 

is the equivalent of the American Community Survey 

for Puerto Rico. The purpose is to help general audiences understand the basics of the PRCS, its opportunities and challenges, and how to access and use 

the PRCS data on the Census Bureau’s Web site. It 

concludes with examples of how PRCS data can be 

used to answer real questions about demographic and 

social-economic aspects of the Puerto Rican society. 

A glossary and a series of technical appendixes are 

also included at the back of this handbook for those 

interested in more advanced ACS/PRCS issues and 

applications.

Introduction

About the Puerto Rico Community Survey

The War Department of the United States carried out 

an enumeration in Puerto Rico in 1899 following the 

island’s acquisition from Spain in 1898 and there have 

been decennial censuses from 1910 to the present. 

Recent censuses have 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 that included not only the basic “shortform” questions but also detailed questions about 

socioeconomic and housing characteristics. 

The PRCS is an islandwide survey that started in 2005. 

It is conducted by the Census Bureau and is designed 

to provide communities with reliable and timely demographic, housing, social, and economic data every year. 

It will replace 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. The PRCS data will provide a continuous stream 

of updated information for Puerto Rico and municipio 

governments and will revolutionize the way we use 

data to understand our communities.

The PRCS covers a broad range of topics about demographic, housing, social, and economic characteristics 

of the population as shown in Table 1. As with the 

decennial census, strict confi dentiality laws protect all 

information that could be used to identify individuals 

or households.

 Table 1. Subjects Included in the PRCS

Demographic Characteristics

Age

Sex

Hispanic Origin

Race

Relationship to Householder 

(e.g., spouse)

Economic Characteristics

Income

Food Stamps Benefi t

Labor Force Status

Industry, Occupation, and Class 

of Worker

Place of Work and Journey to 

Work

Work Status Last Year

Vehicles Available

Health Insurance Coverage*

Social Characteristics 

Marital Status and Marital History*

Fertility

Grandparents as Caregivers

Ancestry

Place of Birth, Citizenship, and 

Year of Entry

Language Spoken at Home

Educational Attainment and 

School Enrollment

Residence One Year Ago

Veteran Status, Period of Military 

Service, and VA ServiceConnected Disability Rating*

Disability

Housing Characteristics

Year Structure Built

Units in Structure

Year Moved Into Unit

Rooms

Bedrooms

Kitchen Facilities

Plumbing Facilities

House Heating Fuel

Telephone Service Available

Farm Residence

Financial Characteristics

Tenure (Owner/Renter)

Housing Value

Rent

Selected Monthly Owner Costs

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

 Source: U.S. Census Bureau.
2 What PRCS Data Users Need to Know

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

The PRCS off ers up-to-date information every year 

about important social and economic issues. The 

information obtained by the PRCS is used to eff ectively 

manage and evaluate the island and local government programs and to monitor year-to-year changes. 

Puerto Rico and municipio governments are involved in 

administering and evaluating programs involving federal funding through block grants. Community offi cials 

can use the data to monitor the well being of children, 

families, and the elderly; and to determine where to 

locate highways and new day care centers. Local businesses also use this vital information to determine if a 

town has the workforce needed to support business. 

Nongovernmental organizations also use the PRCS in 

a variety of ways to monitor changes among important population groups. Journalists use PRCS data to 

report on changes in the characteristics of our society 

or to put a piece of anecdotal evidence into a broader 

context. The text box provides more examples of how 

PRCS data are being used.

Much of the PRCS data provided on the Census Bureau’s 

Web site are available separately by age group, race, 

Hispanic origin, and sex. For example, data users can 

compare the poverty status of children and the elderly, 

college enrollment rates for men and women, or housing costs among municipios. No other resource provides such a wealth of social, economic, housing, and 

demographic information about Puerto Rican society. 

See Appendix 8 for Web site links to additional background information about the ACS/PRCS.

Who Uses the PRCS and Why?

1 For more information about federal uses of ACS/PRCS data, see U.S. Census Bureau, Subjects Planned for the 2010 

Census and American Community Survey: Federal Legislative and Program Uses, <http://www.census.gov/acs

/www/Downloads/Final_2010_Census_and_American_Community_Survey_Subjects_Notebook.pdf>.

2 El Nuevo Día, “Que nos dice el indicador?,” San Juan, Puerto Rico, November 9, 2008, <http://www.elnuevodia.com

/diario/noticia/negocios/negocios/%C2%BFque_nos_dice_el_indicador?/479634>.

State government. The U.S. Department of Health and Human Services and the 

Puerto Rico local social services departments use data on age and relationship to 

plan programs for older people living alone and to develop economic and social 

programs necessary to support grandparents as caregivers. The Special Supplemental Food Program for Women, Infants, and Children (WIC) uses income data 

from the PRCS to determine the potential demand for food assistance in Puerto 

Rico and the municipios.1

Nongovernmental organizations. The Sociedad de Educación y Rehabilitación (SER) de Puerto Rico uses PRCS data to monitor and provide programs and 

services to children with disabilities across the island. ASPIRA de Puerto Rico is 

an organization that uses PRCS data on poverty, educational attainment, school 

enrollment, and employment status to develop scholarships and grants for leadership programs for children in low-income families.

Journalists. An article on income by El Nuevo Día used PRCS data to report that 

there is a relationship between educational attainment, income level, and workforce participation. The article states that the risk of unemployment decreases 

when a person stays longer in school and it is reduced even more if they earn an 

academic degree.2

Community and local planners. Information from the PRCS is critical for community and local planners, who need up-to-date information about their communities to decide where to locate facilities and services for people with disabilities, 

evaluate the need for new roads, schools, senior centers, and other basic services. For example, the Puerto Rico Department of Transportation uses information about origins and destinations of journey to work, travel times, and means 

of transportation to implement the Transportation Equity Act for the 21st Century 

and other federal and state laws.
What PRCS Data Users Need to Know 3

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

The PRCS samples about 36,000 addresses each year, 

resulting in about 22,000 fi nal interviews in Puerto 

Rico. The annual PRCS sample is smaller than that of 

the Census 2000 long-form sample. As a result, the 

PRCS needs to combine population and housing data 

from multiple years to produce reliable estimates for 

small municipios and other local areas. To provide 

information for communities each year, the PRCS will 

provide 1-year, 3-year, and 5-year estimates.

PRCS data are very timely because they are released 

in the year immediately following the year in which 

they are collected. As shown in Table 2, starting with 

the data collected in 2005, PRCS 1-year estimates 

have been published for all areas with populations of 

How the PRCS Works

Diff erences Between the PRCS and the Decennial 

Census

65,000 or more. This includes the Commonwealth of 

Puerto Rico, 12 municipios and 7 cities, among other 

geographic areas. In December 2008, the fi rst 3-year 

estimates were released based on data from the 2005, 

2006, and 2007 surveys. By December 2009, the PRCS 

will have sampled approximately 180,000 addresses 

and, by 2010, the PRCS will provide the fi rst 5-year 

estimates of demographic, housing, social, and economic data for municipios, cities, and other small geographic areas in Puerto Rico. These 5-year estimates 

will be updated annually by removing the earliest year 

and replacing it with the latest one, and, for the fi rst 

time will provide the ability to monitor social and economic trends in local communities. Spanish versions 

for these data releases are also available.

While the main function of the decennial census is to 

provide counts of people for the purpose of legislative 

redistricting, the primary purpose of the PRCS is to 

measure the changing social and economic characteristics of the Puerto Rico population by collecting information throughout the decade. As a result, the PRCS 

does not provide offi cial counts of the population. The 

Census Bureau’s Population Estimates Program will continue to be the offi cial source for Puerto Rico annual 

population totals, by age and sex. For more information about population estimates for Puerto Rico, visit 

the Census Bureau’s Web site at <www.census.gov

/popest/puerto_rico/> or see Appendix 7 of this handbook. PRCS estimates are controlled at the municipio 

level to match the Census Bureau’s annual population 

estimates by age and sex. 

Although the questions used in the PRCS are very 

similar to those included on the long form used in 

Census 2000, there are some important diff erences 

between the two surveys. While the decennial census 

has provided a snapshot of the Puerto Rico population 

 Table 2. Release Schedule for PRCS Data

Year of Data Release

Data Population 2006 2007 2008 2009 2010 2011 2012 2013

product threshold 

 Year(s) of Data Collection

 1-year 

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

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

 estimates 20,000+ 2007 2008 2009 2010 2011 2012

 5-year 2005– 2006– 2007– 2008–

 estimates All areas* 2009 2010 2011 2012

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

Source: U.S. Census Bureau.
4 What PRCS Data Users Need to Know

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

on the data, such as seasonal variation in population 

and minor diff erences in question wording and question order, are described in more detail in Appendix 

2 of this handbook. Additional guidance on making 

comparisons with PRCS and census data is provided in 

the section on “Making Comparisons” in Appendix 4. 

Readers should note that in 2006, the PRCS began 

including samples of the population living in group 

quarters (e.g., jails, college dormitories, and nursing 

homes) for the fi rst time. As a result, 2006 PRCS data 

may not be comparable with data from 2005 PRCS. 

This is especially true for estimates of young adults 

and the elderly, who are more likely than other population groups to be living in group quarters facilities. 

once every 10 years, the PRCS has been described as a 

“moving video image, continually updated to provide 

much needed data about” the people “in today’s fastmoving world.”3

Because PRCS data are collected continuously, they are 

not always comparable with data collected from the 

decennial census. For example, in the case of employment statistics, both surveys ask about employment 

status during the week prior to the survey. However, 

data from the decennial census are typically collected 

between March and June, whereas data from the PRCS 

are collected nearly every day and refl ect employment 

throughout the year. Diff erences in these responses 

may in turn aff ect data on commuting, occupation, and 

industry.4 Other factors that may also have an impact 

3 Kathleen B. Cooper. “Halfway to the 2010 Census: The Countdown 

and Components to a Successful Decennial Census,” paper presented 

to the House Subcommittee on Federalism and the Census, U.S. House 

of Representatives, Washington, DC, April 19, 2005, <http://www.ogc

.doc.gov/ogc/legreg/testimon/109f/Cooper0419.htm>.

4 U.S. Census Bureau, Meeting 21st Century Demographic Data 

Needs— Implementing the American Community Survey: Report 5: 

Comparing Economic Characteristics With Census 2000, Washington 

DC, 2004, <http://www.census.gov/acs/www/Downloads/Report05

.pdf>.

5 Public Use Microdata Areas, or PUMAs, are census-constructed geographic areas, each with approximately a population of 100,000.

Geographic Areas Covered in the PRCS

The PRCS data are tabulated for a variety of geographic 

areas. Table 3 shows the type and number of geographic areas included in the PRCS, as well as the type 

of PRCS estimates each will receive. The table is not 

exhaustive; it only covers major types of geographic 

areas. The information is based on geographic boundaries and population counts in 2007 and is therefore 

subject to change in future years. 

Since 2006, data were available for geographic areas 

with at least 65,000 people, including municipios, 

urban areas, places, metropolitan areas, cities, school 

districts, and Public Use Microdata Areas.5 Starting in 

December 2008, 3-year estimates are available for all 

areas with at least 20,000 people, and by 2010, 5-year 

estimates for geographic areas down to the block 

group level will be available. This means that by 2010, 

for example, there will be three sets of numbers available for San Juan Municipio—1-year, 3-year, and 5-year 

estimates. Less populous areas, such as Barrio 

Miradero in Mayagüez Municipio, will receive only 

5-year estimates. As shown in Table 3 the majority of 

areas will receive only 5-year estimates.

In Figures 1 through 3, sample maps for Puerto Rico 

show users some of the key geographic areas available 

through the PRCS: Public Use Microdata Areas, municipios (county equivalent), and tracts.

The Census Bureau divides Puerto Rico into 30 Public 

Use Microdata Areas, or PUMAs, each of which has a 

minimum population of 100,000. They are constructed 

based on municipio, census tract, and place boundaries. Typically, municipios with populations of 200,000 

or more are subdivided into multiple PUMAs, while 

PUMAs in less populous areas encompass groups of 

contiguous municipios. Because all PUMAs meet the 

65,000-population threshold that is needed to produce 

1-year PRCS estimates, they provide a useful geographic area for presenting data for less populous 

areas that otherwise would not receive 1-year estimates. The Census Bureau released PUMA-level data 

starting in 2006, based on the 2005 PRCS.

Municipios are also important because they are the 

primary legal subdivision in Puerto Rico. Of Puerto 

Rico’s 78 municipios (see Figure 2), there are 12 that 

are large enough to receive 1-year estimates. These 

areas also receive 3-year and 5-year estimates. Of 

the remaining 66 municipios, 53 are large enough to 

receive 3-year estimates. Only 13 out of 78 municipios 

will only receive 5-year estimates each year starting in 

2010.
What PRCS Data Users Need to Know 5

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

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

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

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

may diff er from the numbers in this table.

 

 Type of geographic area 

Total 

number of 

areas

Percent of total areas receiving . . .

1-year, 

3-year, 

& 5-year 

estimates

3-year & 

5-year 

estimates 

only

5-year 

estimates 

only

Puerto Rico 1 100.0 0.0 0.0

Public Use Microdata Areas 30 100.0 0.0 0.0

Metropolitan statistical areas 8 100.0 0.0 0.0

Micropolitan statistical areas 5 0.0 60.0 40.0

Municipios 78 15.4 67.9 16.7

Urban areas 18 55.6 16.7 27.8

School districts (elementary, secondary, and unifi ed) 1 100.0 0.0 0.0

Places (cities, towns, and census designated places) 225 3.1 4.4 92.4

Census tracts 861 0.0 0.0 100.0

Census block groups 2477 0.0 0.0 100.0

Zip code tabulation areas 134 0.0 0.0 100.0

 Table 3. Major Geographic Areas and Type of PRCS Estimates Received

 Figure 1. Census Public Use Microdata Areas in Puerto Rico: 2007

Source: U.S. Census Bureau.
6 What PRCS Data Users Need to Know

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

 Figure 2. Municipios in Puerto Rico by Population Size: 2007

 Figure 3. Census Tracts in Puerto Rico: 2007

Source: U.S. Census Bureau, 2007 Population Estimates Program.

Source: U.S. Census Bureau.

Census tracts are small geographic areas—with an 

average of about 4,000 people—that are commonly 

used to present information for small towns, rural 

areas, and neighborhoods. PRCS data are not currently 

available at the tract level because of their small population sizes. Figure 3 shows the type of geographic 

detail that will be available once enough data are collected to produce reliable data for census tracts.
What PRCS Data Users Need to Know 7

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

The PRCS provides several advantages over the information that has been collected in the past through the 

decennial census long-form samples. The main benefi ts of the PRCS are timeliness and access to annual 

data for municipios, local areas, and small population 

groups. The PRCS will deliver useful, relevant data, 

similar to data from previous census long forms, but 

updated every year instead of every 10 years.

Puerto Rico and municipio governments and planners 

rely on demographic, housing, social, and economic 

data for their budget formulations and to allocate 

funds. For example, PRCS data can be used to determine funding levels for nutritional assistance programs 

or to help decide where to build a day care center. Corporations, small businesses, and individuals can use 

these data to develop business plans, to set strategies 

for expansion or starting a business, and to determine 

if their service areas meet current and future needs. 

Small towns and rural communities have much to 

gain from the PRCS. Lacking the staff and resources to 

conduct their own research, many local communities 

rely on decennial census information that becomes 

increasingly outdated throughout the decade, or use 

local administrative records that are not comparable 

with information collected in neighboring areas.6 The 

PRCS is the fi rst islandwide survey that can be used to 

monitor changes in local areas and to make valid, middecade comparisons between diff erent communities.

The PRCS also provides tools for those who want to 

conduct their own research. The PRCS includes a PUMS 

fi le each year that enables researchers to create custom universes and tabulations from individual PRCS 

records that have been stripped of personally identifi -

able information. The 2007 PRCS PUMS fi le includes 

housing units that were interviewed and included in 

the published PRCS tabulations for 2007.7

The main challenges for PRCS data users are understanding and using multiyear estimates and the relatively large confi dence intervals associated with PRCS 

data for smaller geographic areas and groups of the 

population. Both of these issues are addressed in more 

detail below. PRCS data will be produced every year, 

but in exchange for this benefi t, the sample size of the 

PRCS needs to be smaller than that of the Census 2000 

long-form sample.

In late 2010, data users will have access to 5-year estimates of PRCS data. The sample size based on 5-year 

period estimates of PRCS data is still smaller than the 

long-form sample in the decennial census, resulting 

in larger standard errors in the PRCS 5-year estimates. 

Census Bureau researchers expect that the higher 

sampling error in the PRCS is off set, to some extent, 

by a reduction in nonsampling error, due to the use of 

experienced PRCS interviewers and the refusal to allow 

survey data to be obtained from neighbors.8

Refer to Appendix 8, particularly the section “ACS 

Design, Methodology, Operations” for more information. Further information on the methodology of the 

ACS/PRCS is available on the Census Bureau Web site. 

Benefi ts and Challenges of PRCS Data

Understanding Single-Year and Multiyear Estimates

6 David Swanson and George Hough, “An Evaluation of the American 

Community Survey: Preliminary Results from a County Level Analysis 

of the Oregon Test Site,” paper presented at the Annual Meeting of the 

Mississippi Academy of Sciences, Biloxi, Mississippi, February 19–20, 

2004, <http://www.olemiss.edu/depts/population_studies

/WorkingPapers/MAS_ACS_FINAL_REV1a.pdf>.

7 The PRCS includes nearly 36,000 addresses in its initial sample each 

year. However, a subsample of the addresses that do not respond via 

mail or telephone is selected to be visited in person, resulting in 

approximately 23,000 fi nal interviews each year.

8 Charles H. Alexander, “American Community Survey Data for Economic Analysis,” paper presented to the Census Advisory Committee 

of the American Economic Association, Washington, DC, October 18–

19, 2001, <http://www.census.gov/acs/www/Downloads/ACS

/Paper32.doc>. 

Multiyear estimates are likely to confuse some data 

users, in part because of their statistical properties, 

and in part because this is a new product from the 

Census Bureau. The PRCS provides municipios and 

communities that have at least 65,000 residents with 

1-year estimates of demographic, housing, social, 

and economic characteristics—a benefi t to government agencies that need to budget and plan for public 

services like transportation, medical care, and schools. 

For geographic areas with smaller populations, the 

PRCS samples too few households to provide reliable 

1-year estimates. For these communities, several years 

of data are pooled together to create reliable 3-year or 

5-year estimates. 

Note that 1-year, 3-year, and 5-year estimates from 

the PRCS are all “period” estimates that represent data 

collected over a period of time (as opposed to “pointin-time” estimates, such as the decennial census, that 
8 What PRCS Data Users Need to Know

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

The primary advantage of using multiyear estimates 

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

for smaller geographic areas and small population 

groups. A good illustration comes from the Multiyear 

Estimates Study—a research project undertaken by the 

Census Bureau to study the characteristics of multiyear 

estimates. Although this study did not include any 

geographic areas in Puerto Rico, we can draw conclusions about PRCS data from test areas in the United 

States. An example of 1-, 3-, and 5-year estimates for 

Rockland County, New York, is shown in the chart in 

Figure 4. The lines above and below the point estimates represent the confi dence intervals, or ranges of 

uncertainty, around each estimate. As you can see, the 

level of precision improves dramatically with the 3- and 

5-year estimates as indicated by the shorter lines. 

approximate the characteristics of an area on a specifi c 

date). While a 1-year estimate includes information 

collected over a 12-month period, a 3-year estimate 

represents data collected over a 36-month period, 

and a 5-year estimate includes data collected over a 

60-month period. Therefore, PRCS estimates based on 

data collected from 2005–2007 should not be called 

“2006” or “2007” estimates. Nor should 2005–2009 

period estimates be labeled “2007” estimates, 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 2005–2007 was X percent”). For more information 

about understanding single-year and multiyear estimates, see Appendix 1 of this handbook.

Percent of children in poverty

1-year estimate

(2005)

3-year estimate

(2003–2005)

5-year estimate

(2001–2005)

0

5

10

15

20

Figure 4. Child Poverty Rate in Rockland County, 

 New York, Selected Year

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

Making Comparisons With Multiyear Estimates

One of the challenges for data users is deciding how 

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

people are produced only in the form of 5-year estimates, while 1-year estimates are available for areas 

with populations of 65,000 or more. To facilitate such 

geographic comparisons, the Census Bureau plans to 

publish 5-year estimates for all geographic areas and 

3-year estimates for all areas with 20,000 or more 

residents. The Census Bureau recommends comparing 

estimates based only on the same type of estimates. 

For example, suppose you want to compare estimates 

for Barrio Playa Guayanés in Yabucoa Municipio, a 

small place on the east coast of the island, with estimates for Caguas Municipio. Even though the PRCS 

publishes 1-year estimates for Caguas, only 5-year 

estimates will be published for Barrio Playa Guayanés. 

Thus, in 2010, when 5-year estimates for smaller geographic areas become available, you should compare 

2005–2009 estimates for Barrio Playa Guayanés with 

2005–2009 estimates for Caguas Municipio, even 

though more recent 1-year estimates will be available 

for Caguas. 

While multiyear estimates provide valuable data for 

small geographic areas, using them to look at trends 

for small populations can be challenging because they 

rely on pooled data for 3 or 5 years. For example, 

comparisons of 3-year estimates from 2005–2007 

and 2006–2008 are unlikely to show much diff erence 

because two of the years overlap (both sets of estimates include the same data collected in 2006 and 

2007). The overlap is even greater for the 5-year estimates used for areas with fewer than 20,000 people. 
What PRCS Data Users Need to Know 9

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

Data users also need to use caution in looking at trends 

involving income or other measures that are adjusted 

for infl ation, such as income and monetary variables. 

Appendix 5 provides information on the adjustment 

of single-year and multiyear ACS/PRCS estimates for 

infl ation. Note that infl ation adjustment is based on a 

national-level consumer price index: it does not adjust 

for diff erences in costs of living across diff erent geographic areas.

The Census Bureau suggests comparing periods that 

do not overlap—comparing 2005–2007 estimates with 

2008–2010 estimates, for example. This means waiting 

longer to identify a trend. However, in areas undergoing fundamental shifts in the size or composition of the 

population, change may be so substantial that it will be 

obvious after only a few years. Additional guidance on 

the use and interpretation of single-year and multiyear 

estimates is provided in Appendix 1. 

Understanding Margin of Error

All data that are based on samples, such as the PRCS 

and the census long-form samples include a range of 

uncertainty. Two broad types of error can occur: sampling error and nonsampling error. Nonsampling errors 

can result from mistakes in how the data are reported 

or coded, problems in the sampling frame or survey 

questionnaires, or problems related to nonresponse or 

interviewer bias. The Census Bureau tries to minimize 

nonsampling errors by using trained interviewers and 

by carefully reviewing the survey’s sampling methods, 

data processing techniques, and questionnaire design. 

Appendix 6 includes a more detailed description of 

diff erent types of errors in the ACS/PRCS and other 

measures of ACS/PRCS quality.

Sampling error occurs when data are based on a 

sample rather than the full population. Sampling error 

is easier to measure than nonsampling error and can 

be used to assess the statistical reliability of survey 

data. For any given area, the larger the sample, the 

greater the confi dence in the estimate. The Census 

Bureau reported the 90-percent confi dence interval on 

all PRCS estimates produced for 2005. The 90-percent 

confi dence intervals defi ne a range expected to contain 

the true value of an estimate with a level of confi dence 

of 90 percent. Margins of error are easily converted 

into these confi dence ranges. With the release of the 

2006 PRCS data, margins of error are now provided 

for every PRCS estimate. For example, the 2007 PRCS 

data profi le for Puerto Rico, shown in Figure 5, shows 

that 548,833 married-couple families resided in Puerto 

Rico in 2007. By adding and subtracting the margin of 

error from the point estimate, we can calculate the 90- 

percent confi dence interval for that estimate: 

548,833 – 8,553 = 540,280 = Lower-bound interval

548,833 + 8,553 = 557,386 = Upper-bound interval

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

 Figure 5. Sample Estimate and Margin of Error in American FactFinder
10 What PRCS Data Users Need to Know

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

geographic areas or population groups or it may be 

necessary to derive a new percentage, proportion, or 

ratio from published PRCS data. In such cases, additional calculations are needed to produce confi dence 

intervals and margins of error for the derived estimates. Appendix 3 provides detailed instructions on 

how to make these calculations. Note that these error 

measures do not tell us about the magnitude of nonsampling errors. 

Some advanced data users will also want to construct 

custom PRCS estimates from the PRCS’ Public Use 

Microdata Sample (PUMS). There are separate instructions for conducting signifi cance tests for PUMS 

estimates, available on the Census Bureau’s American 

FactFinder Web site at <http://factfi nder.census.gov

/home/en/acs_pums_2007.html>. (Click on 2007 PUMS 

Accuracy if using the 2007 ACS PUMS.) Readers can 

also consult the Public Use Microdata Sample handbook 

in this handbook series.

Therefore, we can be 90-percent confi dent that the true 

number of married-couple families in Puerto Rico falls 

somewhere between 540,280 and 557,386. Detailed 

information about sampling error and instructions for 

calculating confi dence intervals and margins of error 

are included in Appendix 3 of this handbook.

The margin of error around an estimate is important 

because it helps you draw conclusions about the data. 

Small diff erences between two estimates may not be 

statistically signifi cant if the confi dence intervals of 

those estimates overlap. However, the Census Bureau 

cautions data users not to rely on overlapping confi dence intervals as a test for statistical signifi cance, 

because this method will not always produce accurate 

results. Instead, the Census Bureau recommends following the detailed instructions provided in Appendix 

4 of this handbook for conducting statistical signifi -

cance tests. 

In some cases, data users will need to construct custom PRCS estimates by combining data across multiple 

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

 Figure 6. American FactFinder Web Site

Accessing PRCS Data

All PRCS data are available through the Census Bureau’s 

American FactFinder Web site at <http://factfi nder

.census.gov>.9 The link to the Puerto Rico 

Community Survey data sets can be found under the 

Getting Detailed Data section on the American 

FactFinder home page. For the Spanish language 

version of the PRCS click on en español, as shown in 

Figure 6.10

9 The only exceptions are the ACS/PRCS Summary Tables available for 

download through the Census Bureau’s FTP server, at <http://www2

.census.gov/acs2006/Final_Summaryfi le/>. These fi les are designed 

for advanced users with data processing and or programming capabilities.

10 Data for Puerto Rico can be accessed from both the ACS and the 

Puerto Rico Community Survey data sets.
What PRCS Data Users Need to Know 11

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

all correspond to this specifi c data set. All tables are 

clearly labeled, identifying the data set. 

As shown in Figure 7, a link to explain 1-year vs. 3-year 

estimates is available. This link off ers basic guidance 

to users about which estimates to select with a basic 

comparison of 1-year and 3-year estimates. Also available is the 2007 Quick Guide, which provides detailed 

descriptions of the diff erent ACS/PRCS data products 

that are available. 

Figure 7 is a screenshot of the data sets page and the 

links to the various data products available from the 

2007 PRCS. For the 2007 PRCS, two data sets are 

shown—the 3-year estimates (based on the 2005–2007 

PRCS) and the 1-year estimates (based on the 2007 

PRCS). Tabs also index 2006 and 2005 PRCS data 

sets. The AFF will default to the most recent data set 

but users can choose from any of these data sets. For 

2007 data, AFF will default to the 3-year estimates data 

set. It is important for all users to understand that 

once a data set is selected, the accessed tables will 

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

 Figure 7. Data Sets Page
12 What PRCS Data Users Need to Know

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

• Selected population profi les. Detailed race/ethnic data are available through the selected population profi les. Beginning with the 2007 PRCS, the 

selected population profi les also included country 

of birth. Due to population thresholds, most of 

these PRCS data tables will be available with the 

release of the 5-year estimates. 

• Comparison profi les. The comparison profi les 

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

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

estimates. Comparison profi les are only available 

for 1-year estimates.

• Detailed tables and summary fi les. The 

detailed tables are the best source for advanced 

data users or those who want access to the most 

comprehensive PRCS tables. For more advanced 

users, detailed tables are also available for download through the ACS Summary File on the Census 

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

/www/Special/acsftp.html>.

• Public Use Microdata Samples. Those with 

expertise in using statistical analysis software 

packages may also be interested in the Public Use 

Microdata Sample (PUMS) fi les, which contain a 

sample of individual records of people and households that responded to the survey (stripped of all 

personal identifi able information). The PUMS fi les 

permit analysis of specifi c population groups and 

custom variables that are not available through 

the American FactFinder. For example, PUMS data 

users can look at the proportion of children ages 5 

to 11 living in low-income working families or the 

number of chemical engineers making $100,000 

in San Juan. More information about the PUMS is 

available in the PUMS Handbook. 

• Custom tables. The custom tables option allows 

you to customize specifi c detailed tables to meet 

your needs. You can extract selected rows of data 

from one or more detailed tables to create a table 

with just the estimates you want to include. 

Many of the PRCS data products are similar to those 

available for Census 2000, but several new data products are also available. All of the tables and graphs are 

easy to use and include measures of statistical reliability. The set of PRCS/ACS data products are described 

below. Table 4 provides a quick summary of key PRCS 

data products.

• Data profi les and narrative profi les. The data 

profi les provide a good starting place for novice 

data users. Data profi les provide separate fact 

sheets on the social, economic, demographic, and 

housing characteristics for diff erent geographic 

areas. Narrative profi les also provide clear, concise 

textual descriptions of the data included in the 

data profi les. They must be accessed by selecting 

data profi les from the menu. 

• Geographic comparison tables. Those interested in geographic comparisons for areas within 

Puerto Rico may be interested in the geographic 

comparison tables, which allow comparison of 

PRCS data across a variety of geographic areas, 

including metropolitan areas, places, and 

municipios. 

• Thematic maps. The thematic maps provide 

graphic displays of the data available through the 

geographic comparison tables. Diff erent shades 

of color are used to display variations in the data 

across geographic areas. Data users can also highlight areas with statistically diff erent values from a 

selected municipio or metropolitan area of 

interest.

• Subject tables. For information about a particular 

topic (e.g., employment, education, and income), 

start with the subject tables, which provide 

pretabulated numbers and percentages for a wide 

variety of topics, often available separately by age 

and sex. 
What PRCS Data Users Need to Know 13

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

Data product

Geographic areas 

covered Description

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

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

concise, nontechnical text.

Selected population 

 profi les All*

Provide social, economic, and housing profi les for a 

number of race, ethnic, ancestry, and country of birth 

groups.

Ranking tables States, DC, 

Puerto Rico

Provide state rankings of estimates across key 

variables.

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

data, classifi ed by subject.

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

Geographic comparison

 tables All* Compare geographic areas other than states (e.g., 

municipios) for key variables.

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

data.

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

PRCS detailed tables.

Summary fi les All* 

Provide access to the detailed tables through a series of 

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

site <www2.census.gov>.

Public Use Microdata

 Sample fi les Puerto Rico, PUMAs Provide access to PRCS microdata for data users with statistical analysis experience.

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

 Table 4. Summary of PRCS Data Products
14 What PRCS Data Users Need to Know

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

By now you should have a fairly good understanding of 

the PRCS, the opportunities it provides, and its limitations. In this last section, we provide some concrete 

examples of how PRCS data can be used to answer 

specifi c questions about social and economic patterns 

in Puerto Rico. 

Case Studies Using PRCS Data

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

 Figure 8. PRCS Data Profi les Available Through American FactFinder

Case Study #1: The Local Program 

Administrator

Jaime is the administrator of a local program in the 

Ponce Municipio. In 2006, the city put into action a 

plan to reduce unemployment rates through a series 

of seminars and job training initiatives that help the 

unemployed develop skills that correspond with available job opportunities. He wants to know if these 

initiatives have reduced the level of unemployment in 

the city. 

Jaime starts his analysis by going to the American 

FactFinder and selecting the 2006 PRCS Data Profi le for 

Ponce Municipio (see Figure 8). 

The 2006 PRCS Data Profi le has fi ve summaries (see 

circled list at top of Figure 9), including one that 

focuses on economic characteristics. The economic 

profi le indicates that Ponce’s unemployed rate was 

18.8 percent with a margin of error of ±3.0 percentage 

points. Next, he uses the navigation tools in the left 

sidebar to view the same PRCS table for the 2007 PRCS 

(see Figure 9). Jaime fi nds that the unemployment rate 

was 16.8 percent with a margin of error of 2.5 percentage points.
What PRCS Data Users Need to Know 15

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

He then uses a second formula to test whether the difference between the two estimates is signifi cant at the 

90-percent confi dence level.

Where X1 is the 2007 estimate and X2 is the 2006 estimate, SE1 is the standard error for the 2007 estimate 

and SE2 is the standard error for the 2006 estimate.

 is the critical value for the desired confi dence 

level (=1.645 for 90 percent).

Since 0.84 is smaller than 1.645, the diff erence 

between the two estimates is not statistically signifi -

cant and Jaime cannot conclude that the unemployment rate in Ponce Municipio has dropped since 2006. 

He tells his constituents that the data show no change 

in the unemployment rate but he will continue to monitor these data to see if the 2008 PRCS suggests that 

unemployment has dropped. 

He pastes the poverty data from these tables into a 

spreadsheet, including the confi dence intervals around 

each estimate.11 Based on the point estimates, it 

appears that the unemployment rate has declined in 

Ponce since the initiative started. 

Jaime is interested in the overall change from 2006, 

but he is not sure from the data whether the decrease 

over this time period represents a statistically signifi cant change. He uses the instructions included in 

Appendix 4 of this handbook to fi nd the formulas he 

needs to test for a signifi cant diff erence between two 

PRCS estimates. First, he needs to calculate the standard error associated with each of the two estimates:

Where is the PRCS published margin of error 

for the estimate.

For the 2007 estimate:

For the 2006 estimate:

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

 Figure 9. Example of PRCS Data Profi les and Options for Viewing Tables

11 One way to present confi dence intervals in Excel is to use “Stock” 

charts to display the data. 

1.645

MOEPRCS SE 

MOEPRCS

1.645

2.5 SE = 1.52 

ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ

1.645

(1.52) (1.82)

16.8 18.8

2 2

!





ZCL

1.645

3.0 SE = 1.82 
16 What PRCS Data Users Need to Know

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

Antonio starts by going to the American FactFinder and 

pulling up the detailed tables from the 2005–2007 ACS 

3-year estimates data set, as shown in Figure 10.

The default menu allows him to select geographic 

areas within a specifi c state, but the geo within geo 

menu provides additional options for selecting geographic areas. For example, Antonio can select all 

municipios within Puerto Rico or output data for places 

within metropolitan statistical areas, urban areas, or 

municipios. 

Case Study #2: The Consultant

Antonio is an independent statistical consultant for 

real estate investors. He received a call from Mario, 

one of his best clients, asking for up-to-date information about urban areas with the highest median home 

value in Puerto Rico. Antonio explains to his client the 

current limitations of the PRCS data. Right now, he can 

only get information for geographic areas with at least 

20,000 people, and there are margin of errors associated with all of the PRCS estimates, particularly for 

smaller geographic areas. But he tells Mario he will see 

what he can fi nd. 

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

 Figure 10. Data Products Available Through American FactFinder: Detailed Tables

Figure 11 shows the geo within geo tab, which allows 

him to extract data for all large places (this includes 

cities and towns with a population of 20,000 or more) 

in Puerto Rico. He uses the drop-down menu to select 

Places, Puerto Rico, and All Places. On the next page, 

he browses the by subject menu and selects the 

Value of Home (Owned and Vacant) topic, which is 

listed under Housing Characteristics. He selects Table 

B25077 (Median Value (Dollars) for Owner-Occupied 

Housing Units) and clicks on the button to show the 

results. However, since data are displayed online only 

for the fi rst few places, he decides to use the Print/

Download tab to export the full table into a spreadsheet (see Figures 12 and 13).

Given Mario’s criteria, Guaynabo urban area topped the 

list. Rather than just considering data for that single 

place, Antonio puts together the following table (Table 

5) showing Median Home Values for the available 

urban areas in Puerto Rico. He also warns Mario about 

the margin of error around the estimates. Finally, 

Antonio directs Mario to the data profi les on the 

American FactFinder to learn more about the characteristics of people and housing in these selected areas. 
What PRCS Data Users Need to Know 17

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

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

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

 Figure 11. Using the “geo within geo” Tab in American FactFinder

 Figure 12. Print/Download Data From American FactFinder
18 What PRCS Data Users Need to Know

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

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

 Figure 13. Downloading Data From American FactFinder Into MS Excel

Median home value, 2005–2007 Margin of error (+/–)

Guaynabo zona urbana, Puerto Rico $210,900 $6,238

Trujillo Alto zona urbana, Puerto Rico $157,700 $5,030

San Juan zona urbana, Puerto Rico $153,800 $2,286

Carolina zona urbana, Puerto Rico $142,600 $2,065

Levittown comunidad, Puerto Rico $136,800 $3,675

Bayamón zona urbana, Puerto Rico $131,000 $1,897

Caguas zona urbana, Puerto Rico $119,100 $1,611

Cataño zona urbana, Puerto Rico $107,000 $6,362

Humacao zona urbana, Puerto Rico $101,900 $6,140

Fajardo zona urbana, Puerto Rico $95,500 $1,958

Vega Baja zona urbana, Puerto Rico $94,200 $2,828

Ponce zona urbana, Puerto Rico $93,400 $1,445

Arecibo zona urbana, Puerto Rico $92,000 $2,326

Guayama zona urbana, Puerto Rico $91,400 $3,627

Yauco zona urbana, Puerto Rico $88,400 $3,502

Mayagüez zona urbana, Puerto Rico $86,000 $2,356

Note: Because of sampling variability, some of the estimates in this table may not be statistically diff erent from one another or from 

estimates for other geographic areas not listed in the table.

Source: U.S. Census Bureau, 2005–2007 American Community Survey.

 Table 5. Median Home Value in Dollars for Places in Puerto Rico With Population 20,000 

 or More People: 2005—2007
What PRCS Data Users Need to Know 19

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

Virginia starts by going to the 2007 Detailed Tables in 

the American FactFinder. First she selects Metropolitan 

Statistical Areas/Micropolitan Statistical Areas and then 

San Juan-Caguas-Guaynabo PR Metro Area from the list 

of geographic areas available (see Figure 14). 

On the next page Virginia uses the by keyword tab to 

pull up a list of all of the transportation tables (see 

Figure 15). From that list, she chooses Table B08122 

(Means of Transportation to Work by Poverty Status in 

the Past 12 Months). The next page shows her a table 

with the results.

Case Study #3: The Local Planner 

As a member of the planning commission of the 

Department of Transportation of the Commonwealth 

of Puerto Rico, Virginia is interested in the changing 

characteristics of workers and the means of transportation of workers who work in the San Juan Metropolitan 

Area. She wants to know how workers who drive to 

work alone compare with those who take public transportation and how this might aff ect the demand for 

transportation services in the coming years. In particular, she wants to compare the proportion of workers 16 

years and over for whom poverty status is determined 

and who drive to work alone with those who take public transportation under the same circumstances.

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

 Figure 14. Selecting Geographies in American FactFinder
20 What PRCS Data Users Need to Know

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

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

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

 Figure 15. Selecting Variables in American FactFinder

 Figure 16. Example of Transportation Data Available in American FactFinder
What PRCS Data Users Need to Know 21

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

For the poverty rate of workers that drive alone:

Therefore, the proportion of workers that drive alone 

( ) is 0.161 with a margin of error ( ) of ± 

0.0070. Applying the same formulas to the PRCS data 

for workers that take public transportation and multiplying all of the results by 100, Virginia comes up with 

the data shown in Table 6.

Since the margin of error for workers who take public 

transportation is relatively large, she may want to use 

the instructions in Appendix 4 of this handbook to test 

whether the diff erence between these two PRCS estimates is statistically signifi cant. Based on her preliminary results, she reports to the other members of the 

planning commission that the poverty rate for workers 

that take public transportation to work in San JuanCaguas-Guaynabo Metro Area, who numbered more 

than 32,000 just in the past year, may be more than 

two times higher than the poverty rate for workers that 

drive alone. 

Next, she needs to derive the poverty rates for those 

workers who drive to work and those who take public 

transportation. In Appendix 3 of this handbook, she 

fi nds the formulas needed to calculate the margin of 

error for a derived proportion. She calculates the proportion of workers who drive alone to work for whom 

poverty status is determined. 

Where is the estimate used in the numerator, 

the number of workers who drive alone (car, truck, or 

van—drove alone) and who are below 100 percent of 

the poverty level (95,419). is the estimate used 

in the denominator, the total number of workers who 

drive alone (592,808).

Next she needs to calculate the margin of error associated with her derived proportion. Again, she uses the 

appropriate formula from Appendix 3:

den

num

X

X

p ˆ

ˆ ˆ 

(592,808)

(95,419)

pˆ = 0.1610 

pˆ

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

den

num den

p

X

MOE p MOE

MOE ˆ

( ˆ * ) 2 2 2 r  

    

592,808

4,516 ( 0.161 * 11,362 ) 2 2 2 r  MOEp 

592,808

r 20,394,256  (0.025921*129,095,044) MOEp 

592,808

r 4,129 MOEp = r .007 

MOEp

X num

ˆ

X num

ˆ

Poverty rate Margin of error (+/–)

Drive Alone 16.09 0.70

Take Public Transportation 36.29 4.56

Source: U.S. Census Bureau, 2007 American Community Survey.

 Table 6. Poverty Rates for Workers That Drive Alone and Workers That Take Public Transportation 

 in San Juan-Caguas-Guaynabo PR Metro Area: 2007

To calculate poverty rates and their associated margins of error, Virginia fi rst needs to put the data into 

a spreadsheet. Since this is a small table, she uses 

her mouse to copy and paste the data directly into a 

spreadsheet program. Others may prefer to download 

the data using the Print/Download option, as shown in 

Figure 16.
22 What PRCS Data Users Need to Know

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

The PRCS data are complex and cover a broad range of 

topics and geographic areas. Because this is a relatively 

new survey, many people do not fully understand how 

to interpret and use the PRCS data. Some key points 

are summarized below to guide you as you explore the 

PRCS data. 

• Use caution in comparing PRCS data with data 

from the decennial census or other sources. Every 

survey uses diff erent methods, which could aff ect 

the comparability of the numbers.

• The PRCS was designed to provide estimates 

of the characteristics of the population, not to 

provide counts of the population in diff erent geographic areas or population groups.

• Be careful in drawing conclusions about small 

diff erences between two estimates because they 

may not be statistically diff erent.

• Data users need to be careful not to interpret 

annual fl uctuations in the data as long-term 

trends. 

• Use caution in comparing data from 2006 and

later surveys with data from the 2005 PRCS. 

Unlike the 2005 survey, the 2006 PRCS survey 

includes samples of the population living in group 

quarters (e.g., college dorms and nursing homes), 

so the data may not be comparable, especially 

for young adults and the elderly who are more 

likely than other age groups to be living in group 

quarters facilities.

• Data users should not interpret or refer to 3-year 

or 5-year period estimates as estimates of the 

middle year or last year in the series. For example, a 2005–2007 estimate is not a “2006 average.”

• Data users should not rely on overlapping confi -

dence intervals as a test for statistical signifi cance 

because this method will not always provide an 

accurate result.

Avoiding Pitfalls When Working With PRCS Data 

Other Resources for Working With ACS and 

PRCS data 

Data users can fi nd a wealth of additional information 

about the ACS/PRCS on the Census Bureau’s Web site 

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

all of the information available on the ACS/PRCS can 

take a long time without a good map of the available 

resources. In Appendix 8, we provide links to online 

resources that data users are likely to fi nd the most 

useful. These resources cover many of the topics discussed in this handbook, but in greater detail. 
What PRCS Data Users Need to Know 23

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

being cautious in their approach to the PRCS, and 

rightly so. This is a relatively new survey with a new 

approach to measuring change in our communities. 

The PRCS has great potential, particularly as a source 

of annual data for local areas. By providing data for 

communities each year, the PRCS will provide critical 

information for communities when they need it most. 

Data from the PRCS can be extremely valuable for a 

variety of diff erent purposes that include: to monitor 

the well-being of Puerto Rico’s children and families, 

to investigate the characteristics of the workforce, to 

determine the economic well-being of working-poor 

families, or to track social, economic, and demographic 

changes in the general population. Many people are 

Conclusion 

 ACS/PRCS Data Resources

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

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

• 2007 Guide to the Data Products (Web page)

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

• Using Multiyear Estimates (Web page)

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

• American Community Survey Design and Methodology (technical paper) 

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

• Accuracy of the ACS Data (online document)

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

• ACS Sample Size (Web page)

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

• ACS Quality Measures (Web page)

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

• 2006 Data Users Handbook: PRCS (online document) 

<http://www.census.gov/acs/www/Downloads/2006%20PRCS%20Data

%20Users%20Handbook%20(English).pdf>

• How to Use the ACS Data (Web page)

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

• Guidance on Comparing 2007 ACS Data (online tables)

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

• Using Diff erent Sources of Data for Income and Poverty (online fact sheet)

<http://www.census.gov/hhes/www/income/factsheet.html>
24 What PRCS Data Users Need to Know

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

Glossary

Accuracy. One of four key dimensions of survey 

quality. Accuracy refers to the diff erence between 

the survey estimate and the true (unknown) value. 

Attributes are measured in terms of sources of error 

(for example, coverage, sampling, nonresponse, 

measurement, and processing). 

American Community Survey Alert. This periodic 

electronic newsletter informs data users and other 

interested parties about news, events, data releases, 

congressional actions, and other developments 

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

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

American FactFinder (AFF). An electronic system 

for access to and dissemination of Census Bureau 

data on the Internet. AFF off ers prepackaged data 

products and user-selected data tables and maps 

from Census 2000, the 1990 Census of Population 

and Housing, the 1997 and 2002 Economic 

Censuses, the Population Estimates Program, annual 

economic surveys, and the ACS. 

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

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

group is a cluster of blocks having the same fi rst 

digit of their four-digit identifying number within 

a census tract. 

Census geography. A collective term referring 

to the types of geographic areas used by the 

Census Bureau in its data collection and tabulation 

operations, including their structure, designations, 

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

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

Census tract. A small, relatively permanent 

statistical subdivision of a county delineated by a local 

committee of census data users for the purpose of 

presenting data. Census tract boundaries normally 

follow visible features, but may follow governmental 

unit boundaries and other nonvisible features; they 

always nest within counties. Designed to be relatively 

homogeneous units with respect to population 

characteristics, economic status, and living conditions 

at the time of establishment, census tracts average 

about 4,000 inhabitants. 

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

standard error (square root of the variance) to the 

value being estimated, usually expressed in terms 

of a percentage (also known as the relative standard 

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

reliability of the estimate. 

Comparison profi le. Comparison profi les are 

available from the American Community Survey for 

1-year estimates beginning in 2007. These tables 

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

Columbia, and geographic areas with a population of 

more than 65,000.

Confi dence interval. The sample estimate and its 

standard error permit the construction of a confi dence 

interval that represents the degree of uncertainty about 

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

interpreted roughly as providing 90 percent certainty 

that the interval defi ned by the upper and lower 

bounds contains the true value of the characteristic. 

Confi dentiality. The guarantee made by law (Title 

13, United States Code) to individuals who provide 

census information, regarding nondisclosure of that 

information to others. 

Consumer Price Index (CPI). The CPI program of 

the Bureau of Labor Statistics produces monthly data 

on changes in the prices paid by urban consumers for 

a representative basket of goods and services.

Controlled. During the ACS weighting process, the 

intercensal population and housing estimates are used 

as survey controls. Weights are adjusted so that ACS 

estimates conform to these controls. 

Current Population Survey (CPS). The CPS is 

a monthly survey of about 50,000 households 

conducted by the Census Bureau for the Bureau of 

Labor Statistics. The CPS is the primary source of 

information on the labor force characteristics of the 

U.S. population.

Current residence. The concept used in the ACS to 

determine who should be considered a resident of a 

sample address. Everyone who is currently living or 

staying at a sample address is considered a resident of 

that address, except people staying there for 2 months 

or less. People who have established residence at the 

sample unit and are away for only a short period of 

time are also considered to be current residents. 

Custom tabulations. The Census Bureau off ers a 

wide variety of general purpose data products from the 

ACS. These products are designed to meet the needs 

of the majority of data users and contain predefi ned 
What PRCS Data Users Need to Know 25

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

sets of data for standard census geographic areas, 

including both political and statistical geography. 

These products are available on the American 

FactFinder and the ACS Web site.

For users with data needs not met through the general 

purpose products, the Census Bureau off ers “custom” 

tabulations on a cost-reimbursable basis, with the 

American Community Survey Custom Tabulation 

program. Custom tabulations are created by tabulating 

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

complexity, and cost depending on the needs of the 

sponsoring client.

Data profi les. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profi le that should be used if offi cial 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 diff erent 

tables that contain basic distributions of 

characteristics. These tables provide the most detailed 

data and are the basis for other ACS products.

Disclosure avoidance (DA). Statistical methods 

used in the tabulation of data prior to releasing data 

products to ensure the confi dentiality of responses. 

See Confi dentiality. 

Estimates. Numerical values obtained from a 

statistical sample and assigned to a population 

parameter. Data produced from the ACS interviews are 

collected from samples of housing units. These data 

are used to produce estimates of the actual fi gures that 

would have been obtained by interviewing the entire 

population using the same methodology. 

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

allows data fi les to be downloaded from the Census 

Bureau Web site.

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates refl ect the characteristics 

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

will be published for all geographic areas down to the 

census block group level.

Geographic comparison tables. More than 80 

single-variable tables comparing key indicators for 

geographies other than states.

Geographic summary level. A geographic summary 

level specifi es the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifi es the state-county 

hierarchy. Thus, both the state code and the county 

code are required to uniquely identify a county in the 

United States or Puerto Rico. 

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

place where people live or stay that is normally owned 

or managed by an entity or organization providing 

housing and/or services for the residents. These 

services may include custodial or medical care, as well 

as other types of assistance. Residency is commonly 

restricted to those receiving these services. People 

living in GQ facilities are usually not related to each 

other. The ACS collects data from people living in both 

housing units and GQ facilities.

Group quarters (GQ) population. The number of 

persons residing in GQ facilities. 

Item allocation rates. Allocation is a method 

of imputation used when values for missing or 

inconsistent items cannot be derived from the existing 

response record. In these cases, the imputation must 

be based on other techniques such as using answers 

from other people in the household, other responding 

housing units, or people believed to have similar 

characteristics. Such donors are refl ected in a table 

referred to as an allocation matrix. The rate is the 

percentage of times this method is used.

Margin of error (MOE). Some ACS products provide 

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

diff erence between an estimate and its upper or lower 

confi dence bounds. Confi dence bounds can be created 

by adding the MOE to the estimate (for the upper 

bound) and subtracting the MOE from the estimate (for 

the lower bound). All published ACS MOE are based on 

a 90-percent confi dence level. 

Multiyear estimates. Three- and fi ve-year estimates 

based on multiple years of ACS data. Three-year 

estimates will be published for geographic areas with 

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

will be published for all geographic areas down to the 

census block group level. 

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

Nonsampling error. Total survey error can be 

classifi ed into two categories—sampling error and 

nonsampling error. Nonsampling error includes 

measurement errors due to interviewers, respondents, 

instruments, and mode; nonresponse error; coverage 

error; and processing error.
26 What PRCS Data Users Need to Know

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

Period estimates. An estimate based on information 

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

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population Estimates Program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

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

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

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

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

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

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

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

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

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

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

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

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

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

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

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

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

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

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

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

single-year estimates, the period is the calendar year 

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

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

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

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

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

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

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

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

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

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

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

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

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

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

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

monthly CPS collects data for an even narrower time 

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

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

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

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

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

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

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

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

year and 5-year estimates where the periods involved 

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

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

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

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

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

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

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

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

Month

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

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

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

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

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

multiyear estimates describe the population and 

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

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

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

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

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

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

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

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

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

estimates contain 4 years of overlapping coverage. 

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

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

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

5-year

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

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

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

housing characteristics because they are based on the 

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

multiyear estimates provide less current information 

because they are based on both data from the previous 

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

using the “less current” sample used to produce the 

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

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

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

single year is the midyear of the ACS multiyear period 

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

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

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

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

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

Reliability

Multiyear estimates are based on larger sample sizes 

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

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

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

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

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

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

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

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

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

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

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

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

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

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

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

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

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

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

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

a small CV is usually preferable to a multiyear estimate 

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

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

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

use multiyear estimates when they are working with 

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

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

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

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

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

for local planning needs, make comparisons across 

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

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

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

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

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

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

errors and CVs associated with those estimates are also 

shown.

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

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

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

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

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

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

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

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

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

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

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

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

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

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

SE of 4.9 percentage points that yields a CV of 12.2 

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

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

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

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

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

more acceptable level.

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

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

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

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

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

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

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

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

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

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

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

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

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

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

the 2006 ACS. 

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

in a particular characteristic. This is because roughly 

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

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

as shown in Figure 1, when comparing 2006–2008 

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

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

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

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

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

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

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

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

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

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

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

or the 5-year estimates because they are available for 

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

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

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

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

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

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

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

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

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

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

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

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

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

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

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

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

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

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

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

larger geographies and populations when currency is 

more important than the precision of the estimates. 

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

availability of a single-year estimate, calculating the CV 

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

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

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

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

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

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

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

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

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

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

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

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

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

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

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

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

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

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

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

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

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

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

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

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

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

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

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

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

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

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

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

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

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

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

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

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

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

60-month period. 

Most decennial census sample estimates are anchored 

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

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

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

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

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

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

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

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

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

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

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

housing unit or group quarters, some people may stay 

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

course of the year. For example, migrant workers move 

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

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

estimates of the characteristics of some areas. 

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

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

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

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

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

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

to enumerate people at the place where they live or stay 

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

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

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

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

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

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

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

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

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

there for more than 2 months.

More on Reference Periods

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

being that the remaining basic demographic questions 

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

the date the interview is provided. Questions with their 

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

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

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

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

they refl ect the circumstances on the day the data are 

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

represent the average characteristics over a full year (or 

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

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

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

comparing ACS estimates with Census 2000. 

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

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

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

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

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

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

while Census 2000 collected income information for 

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

responses are a mixture of 12 reference periods ranging 

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

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

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

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

period is used for all interviews. In contrast, 

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

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

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

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

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

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

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

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

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

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

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

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

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

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

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

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

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

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

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

(such as estimates from the ACS or the decennial 

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

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

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

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

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

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

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

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

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

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

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

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

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

larger the sample size, the smaller the standard error 

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

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

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

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

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

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

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

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

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

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

MOEs published with the ACS estimates correspond 

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

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

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

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

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

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

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

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

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

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

Measures of Sampling Error

Appendix 3.
A-12 Appendix

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

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

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

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

would be derived as 

Confi dence Intervals

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

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

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

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

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

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

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

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

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

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

(20,000 + 1,645).

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

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

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

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

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

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

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

1.960/1.645.1

 If the desired confi dence level is 99 

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

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

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

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

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

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

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

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

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

should be set to zero instead.

1

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

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

error from the standard error in those years.

2

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

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 1.645

1.960

95 

Factors Associated With Margins of

Error for Commonly Used Confi dence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

MOECL

  CICL LCL UCL , .

MOEACS 

MOEACS 

Xˆ

MOE MOEACS 1.645

2.576

99 
Appendix A-13

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

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

confi dence level for the number of civilian veterans in 

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

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

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

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

757,059 to 786,505.

The CI is also useful when graphing estimates, to show 

the extent of sampling error present in the estimates, 

and for visually comparing estimates. For example, 

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

in constructing the CI above, the user could be 90 

percent certain that the value for the population was 

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

Coeffi cients of Variation

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

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

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

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

barometer of the stability, and thus the usability of a 

sample estimate. It can also help a user decide whether 

a single-year or multiyear estimate should be used for 

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

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

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

estimation period increases, the sample size increases 

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

indicates that the sampling error is small relative to the 

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

the estimate is close to the population value. In some 

applications a small CV for an estimate is desirable and 

use of a multiyear estimate will therefore be preferable 

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

desired level of precision.

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

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

the estimate is very reliable. If the CV was noticeably 

larger, the usability of the estimate could be greatly 

diminished. 

While it is true that estimates with high CVs have 

important limitations, they can still be valuable as 

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

( )

18,355 20,000 21,645

building blocks to develop estimates for higher levels 

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

improve the reliability of those estimates as evidenced 

by reductions in the CVs.

Calculating Coeffi cients of Variation From Standard 

Errors

The CV can be expressed as

where is the ACS estimate and is the derived SE 

for the ACS estimate.

For example, to determine the CV for the estimated 

number of civilian veterans in the state of Virginia in 

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

and the SE derived previously (7,512).

This means that the amount of sampling error present 

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

of the estimate.

The text box below summarizes the formulas used 

when deriving alternative sampling error measures 

from the margin or error published with ACS estimates.

100 ˆ u

X

SE CV

Xˆ

100 0.1%

771,782

7,512 CV u 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confi dence Levels

Standard Error (SE)

Confi dence Interval (CI)

Coeffi cient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

100 ˆ u

X

SE CV

SE

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

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

Calculating Margins of Error for Derived 

Estimates

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

the ability to develop unique estimates called derived 

estimates. These derived estimates are usually based 

on aggregating estimates across geographic areas or 

population subgroups for which combined estimates 

are not published in American FactFinder (AFF) tables 

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

four age groups not collapsed).

ACS tabulations provided through AFF contain the 

associated confi dence intervals (pre-2005) or margins 

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

confi dence level. However, when derived estimates are 

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

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

the MOE for these derived estimates. The MOE helps 

protect against misinterpreting small or nonexistent 

diff erences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

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

confi dence level other than 90 percent is desired, the 

user should fi rst calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confi dence level as described earlier in this appendix.

Calculating MOEs for Aggregated Count Data

To calculate the MOE for aggregated count data:

1) Obtain the MOE of each component estimate.

2) Square the MOE of each component estimate.

3) Sum the squared MOEs.

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

 MOEs.

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

where is the MOE of the component estimate.

The example below shows how to calculate the MOE 

for the estimated total number of females living alone 

in the three Virginia counties/independent cities that 

border Washington, DC (Fairfax and Arlington counties, 

Alexandria city) from the 2006 ACS.

The aggregate estimate is:

Obtain MOEs of the component estimates: 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs.

Thus, the derived estimate of the number of females 

living alone in the three Virginia counties/independent 

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

MOE for the estimate is +4,289.

Calculating MOEs for Derived Proportions

The numerator of a proportion is a subset of the 

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

households that are female). To calculate the MOE for 

derived proportions, do the following:

1) Obtain the MOE for the numerator and the MOE 

for the denominator of the proportion.

2) Square the derived proportion.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator by 

the squared proportion.

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

the numerator.

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

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

proportion.

 Table 1. Data for Example 1 

Characteristic Estimate MOE

Females living alone in 

 Fairfax County 

 (Component 1)

52,354 +3,303

Females living alone in 

 Arlington County

 (Component 2)

19,464 +2,011

Females living alone in 

 Alexandria city

 (Component 3)

17,190 +1,854

Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria 

MOEFairfax r3,303 , 

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

 r ¦cMOEagg MOEc

2

MOEc

th

c

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

 011 r2, MOEArlington , 

r 18,391,246 r4,289

MOEAlexandria r1,854
Appendix A-15

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

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived proportion.

 is the estimate used as the numerator of the 

derived proportion.

 is the estimate used as the denominator of the 

derived proportion.

There are rare instances where this formula will fail—

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

happens, use the formula for derived ratios in the next 

section which will provide a conservative estimate of 

the MOE.

The example below shows how to derive the MOE for 

the estimated proportion of Black females 25 years of 

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

 The estimated proportion is:

where is the ACS estimate of Black females 25 

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

females 25 years of age and older in Fairfax County.

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older in Fairfax County 

with a graduate degree) and denominator (number 

of Black females 25 years of age and older in Fairfax 

County).

 Table 2. Data for Example 2

Characteristic Estimate MOE

Black females 25 years 

 and older with a graduate 

 degree (numerator)

4,634 +989

Black females 25 years 

 and older

 (denominator)

31,713 +601

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

 r989 MOEnum , 601 MOEden r

MOEnum

MOEden den

den

num

X

X

p ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ

X gradBF ˆ

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

Thus, the derived estimate of the proportion of Black 

females 25 years of age and older with a graduate 

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

MOE for the estimate is +0.0311. 

Calculating MOEs for Derived Ratios

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

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

1) Obtain the MOE for the numerator and the MOE 

 for the denominator of the ratio.

2) Square the derived ratio.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator 

 by the squared ratio.

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

 numerator.

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

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

 the ratio.

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived ratio.

 is the estimate used as the numerator of the 

derived ratio.

 is the estimate used as the denominator of the 

derived ratio.

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

MOEnum

MOEden

den

num

X

X

R ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ i

den

num den

p

X

MOE p MOE

MOE ˆ

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

     989 [ 0.1461 * 601 ] 2 2 2  

978,121 7,712.3 970,408.7

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  
A-16 Appendix

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

The example below shows how to derive the MOE for 

the estimated ratio of Black females 25 years of age 

and older in Fairfax County, Virginia, with a graduate 

degree to Black males 25 years and older in Fairfax 

County with a graduate degree, based on the 2006 

ACS.

The estimated ratio is:

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County) and denominator (number 

of Black males 25 years of age and older in Fairfax 

County with a graduate degree).

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator.

Thus, the derived estimate of the ratio of the number 

of Black females 25 years of age and older in Fairfax 

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

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

the MOE for the estimate is +0.2135.

Calculating MOEs for the Product of Two Estimates

To calculate the MOE for the product of two estimates, 

do the following:

1) Obtain the MOEs for the two estimates being 

multiplied together.

2) Square the estimates and their MOEs.

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

4) Multiply the second squared estimate by the 

fi rst estimate’s squared MOE.

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

6) Take the square root of (5).

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

MOE for the product is calculated as:

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

respectively.

 is the MOE of the fi rst estimate.

 is the MOE of the second estimate.

The example below shows how to derive the MOE for 

the estimated number of Black workers 16 years and 

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

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

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

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

Thus, the derived estimate of Black workers 16 years 

and over who commute by public transportation is 

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

MOEnum r989, 328 r1, MOEden

0.2135

6,440

1,375.2

6,440

1,891,259.1 r

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

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

Percent of Black workers 16 

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

13.4% +2.7%

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

 Table 3. Data for Example 3 

Characteristic Estimate MOE

Black females 25 years and 

 older with a graduate 

 degree (numerator)

4,634 +989

Black males 25 years and

 older with a graduate degree 

 (denominator)

6,440 +1,328

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

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

MOEA

MOEB

 r1,405

* ) ˆ ( 2 2 2 MOEnum  R MOEden 

     989 [ 0.7200 * 1,328 ] 2 2 2  

978,121 913,318.1 1,891,259.1
Appendix A-17

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

Calculate the MOE by dividing the square root of the 

prior result by the denominator ( ). 

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

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

The text box below summarizes the formulas used to 

calculate the margin of error for several derived estimates.

Calculating MOEs for Estimates of “Percent Change” or 

“Percent Diff erence”

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

two estimates (for example, the same estimates in two 

diff erent years) is commonly calculated as

Because is not a subset of , the procedure 

to calculate the MOE of a ratio discussed previously 

should be used here to obtain the MOE of the percent 

change.

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

2005 estimates of the number of persons in Maryland 

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

The percent change is:

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

The MOEs for the numerator ( ) and denominator 

( ) are:

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

product of the squared ratio and the squared MOE of 

the denominator (MOE1):

 Table 5. Data for Example 5

Characteristic Estimate MOE

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2005

762,475 +22,666

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X

R

2 Xˆ 1 Xˆ

2 Xˆ

1 Xˆ

1 Xˆ

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

0.0433

762,475

33,038.3

762,475

1,091,528,529 r

r r MOER 

 r ¦cMOE agg MOE c

2

den

num den

p

X

MOE p MOE ) MOE ˆ

 (ˆ * 2 2 2 r  

1

2 1

ˆ

ˆ ˆ 100%*

X

X X

Percent Change  

ˆ

ˆ ˆ 100%*

1

2 1  X

X X

Percent Change

5.21%

762,475

802,210 762,475 100%*  ̧ 

1

·  ̈

©

§ 

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

* ) ˆ ( 2

1

2 2 MOE2  R MOE 

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

 

1,091,528,529

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  
A-18 Appendix

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

One of the most important uses of the ACS estimates is 

to make comparisons between estimates. Several key 

types of comparisons are of general interest to users: 

1) comparisons of estimates from diff erent geographic 

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

proportion of people below the poverty level in two 

counties); 2) comparisons of estimates for the same 

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

the proportion of people below the poverty level in a 

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

estimates with the corresponding estimates from past 

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

2006 and 2000).

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

the comparison takes into account the sampling error 

associated with each estimate, thus determining 

whether the observed diff erences between estimates 

are statistically signifi cant. Statistical signifi cance 

means that there is statistical evidence that a true 

diff erence exists within the full population, and that 

the observed diff erence is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical signifi cance when making comparisons is 

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

be made are also discussed.

Determining Statistical Signifi cance

When comparing two estimates, one should use the 

test for signifi cance described below. This approach 

will allow the user to ascertain whether the observed 

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

is statistically signifi cant). 

The test for signifi cance can be carried out by making 

several computations using the estimates and their 

corresponding standard errors (SEs). When working 

with ACS data, these computations are simple given 

the data provided in tables in the American FactFinder.

1) Determine the SE for each estimate (for ACS 

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

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

2) Square the resulting SE for each estimate.

3) Sum the squared SEs.

4) Calculate the square root of the sum of the 

squared SEs.

5) Calculate the diff erence between the two estimates.

6) Divide (5) by (4).

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

with the critical value for the desired level of 

confi dence (1.645 for 90 percent, 1.960 for 95 

percent, 2.576 for 99 percent).

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

between the two estimates can be considered 

statistically signifi cant at the level of confi dence 

corresponding to the critical value used in (7).

Algebraically, the signifi cance test can be expressed as 

follows:

If , then the diff erence 

between estimates and is statistically signifi cant 

at the specifi ed confi dence level, CL

where is estimate i (=1,2)

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

 is the critical value for the desired confi dence 

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

2.576 for 99 percent).

The example below shows how to determine if the 

diff erence in the estimated percentage of households 

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

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

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

statistically signifi cant. Using the formula above:

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

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

diff erence in the percentages is statistically signifi cant 

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

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

A rough interpretation of the result is that the user can 

be 99 percent certain that a diff erence exists between 

the percentages of households with one or more 

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

Making Comparisons

Appendix 4.

4

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

use the value 1.65 rather than 1.645. 

ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2 



 





SE SE

X X

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5 

Ei
Appendix A-19

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

By contrast, if the corresponding estimates for State C 

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

would yield

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

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

A rough interpretation of the result is that the user 

cannot be certain to any suffi cient degree that the 

observed diff erence in the estimates was not due to 

chance.

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same 

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

3-year period) are straightforward and can be carried 

out as described in the previous section. There is, 

however, one statistical aspect related to the test for 

statistical signifi cance that users should be aware 

of. When comparing estimates within the same time 

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

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

In some cases, the comparison may involve a large 

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

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

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

estimates are not independent. The estimate for the 

large area is partially dependent on the estimate for the 

subset and, strictly speaking, the formula for testing 

diff erences should account for this partial dependence. 

However, unless the user has reason to believe that the 

two estimates are strongly correlated, it is acceptable 

to ignore the partial dependence and use the formula 

for testing diff erences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a fi nding of statistical signifi cance will still 

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

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

important to obtain a more exact test of signifi cance, 

the user should consult with a statistician about 

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

   0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2 



 





SE SE

X X

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4 

Comparisons Across Time Periods

Comparisons of estimates from diff erent time periods 

may involve diff erent single-year periods or diff erent 

multiyear periods of the same length within the same 

area. Comparisons across time periods should be made 

only with comparable time period estimates. Users are 

advised against comparing single-year estimates with 

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

2009) and against comparing multiyear estimates of 

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

2009–2014), as they are measuring the characteristics 

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

between such estimates are diffi cult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

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

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

the user should recognize that from 2006 on the ACS 

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

units. Many types of GQ populations have demographic, 

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

comparisons between 2005 and 2006 and later ACS 

estimates could be aff ected. This is particularly true 

for areas with a substantial GQ population. For most 

population characteristics, the Census Bureau suggests 

users make comparisons across these time periods 

only if the geographic area of interest does not include 

a substantial GQ population. For housing characteristics or characteristics published only for the household 

population, this is obviously not an issue. 

Comparisons Based on Overlapping Periods

When comparing estimates from two multiyear periods, ideally comparisons should be based on nonoverlapping periods (e.g., comparing estimates from 

2006–2008 with estimates from 2009–2011). The comparison of two estimates for diff erent, but overlapping 

periods is challenging since the diff erence is driven by 

the nonoverlapping years. For example, when comparing the 2005–2007 ACS with the 2006–2008 ACS, data 

for 2006 and 2007 are included in both estimates. 

Their contribution is subtracted out when the estimate 

of diff erences is calculated. While the interpretation 

of this diff erence is diffi cult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of diff erence should not be interpreted as a 

refl ection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

over time is complicated when change is being evaluated using multiyear estimates. From a technical standpoint, change over time is best evaluated with multiyear estimates that do not overlap. At the same time, 
A-20 Appendix

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

many areas whose only source of data will be 5-year 

estimates will not want to wait until 2015 to evaluate 

change (i.e., comparing 2005–2009 with 2010–2014). 

When comparing two 3-year estimates or two 5-year 

estimates of the same geography that overlap in 

sample years one must account for this sample overlap. 

Thus to calculate the standard error of this diff erence 

use the following approximation to the standard error:

where C is the fraction of overlapping years. For example, the periods 2005–2009 and 2007–2011 overlap for 

3 out of 5 years, so C=3/5=0.6. If the periods do not 

overlap, such as 2005–2007 and 2008–2010, then C=0.

With this SE one can test for the statistical signifi cance 

of the diff erence between the two estimates using the 

method outlined in the previous section with one modifi cation; substitute for 

 in the denominator of the formula for 

the signifi cance test.

Comparisons With Census 2000 Data

In Appendix 2, major diff erences between ACS data and 

decennial census sample data are discussed. Factors 

such as diff erences in residence rules, universes, and 

reference periods, while not discussed in detail in this 

appendix, should be considered when comparing ACS 

estimates with decennial census estimates. For example, given the reference period diff erences, seasonality 

may aff ect comparisons between decennial census and 

ACS estimates when looking at data for areas such as 

college towns and resort areas. 

The Census Bureau subject matter specialists have 

reviewed the factors that could aff ect diff erences 

between ACS and decennial census estimates and they 

have determined that ACS estimates are similar to 

those obtained from past decennial census sample data 

for most areas and characteristics. The user should 

consider whether a particular analysis involves an area 

or characteristic that might be aff ected by these diff erences.5

When comparing ACS and decennial census sample 

estimates, the user must remember that the decennial 

census sample estimates have sampling error associated with them and that the standard errors for both 

ACS and census estimates must be incorporated when 

performing tests of statistical signifi cance. Appendix 

3 provides the calculations necessary for determining 

statistical signifi cance of a diff erence between two 

estimates. To derive the SEs of census sample estimates, use the method described in Chapter 8 of either 

the Census 2000 Summary File 3 Technical Documentation <http://www.census.gov/prod/cen2000/doc/sf3

.pdf> or the Census 2000 Summary File 4 Technical 

Documentation <http://www.census.gov/prod

/cen2000/doc/sf4.pdf>.

A conservative approach to testing for statistical signifi -

cance when comparing ACS and Census 2000 

estimates that avoids deriving the SE for the 

Census 2000 estimate would be to assume the SE for 

the Census 2000 estimate is the same as that determined for the ACS estimate. The result of this approach 

would be that a fi nding of statistical signifi cance can be 

assumed to be accurate (as the SE for the Census 2000 

estimate would be expected to be less than that for the 

ACS estimate), but a fi nding of no statistical signifi -

cance could be incorrect. In this case the user should 

calculate the census long-form standard error and follow the steps to conduct the statistical test.

Comparisons With 2010 Census Data

Looking ahead to the 2010 decennial census, data 

users need to remember that the socioeconomic data 

previously collected on the long form during the 

census will not be available for comparison with ACS 

estimates. The only common variables for the ACS and 

2010 Census are sex, age, race, ethnicity, household 

relationship, housing tenure, and vacancy status.

The critical factor that must be considered when comparing ACS estimates encompassing 2010 with the 

2010 Census is the potential impact of housing and 

population controls used for the ACS. As the housing 

and population controls used for 2010 ACS data will 

be based on the Population Estimates Program where 

the estimates are benchmarked on the Census 2000 

counts, they will not agree with the 2010 Census 

population counts for that year. The 2010 population 

estimates may diff er from the 2010 Census counts 

for two major reasons—the true change from 2000 to 

2010 is not accurately captured by the estimates and 

the completeness of coverage in the 2010 Census is 

diff erent than coverage of Census 2000. The impact of 

this diff erence will likely aff ect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large diff erences between the 

population controls and the 2010 Census population 

counts is greater.

Comparisons With Other Surveys

Comparisons of ACS estimates with estimates from 

other national surveys, such as the Current Population 

Survey, may be of interest to some users. A major consideration in making such comparisons will be that ACS 

5

 Further information concerning areas and characteristics that do not 

fi t the general pattern of comparability can be found on the ACS Web 

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

  2

2

2

1 2 1 ) 1 ˆ ˆ SE(X  X #  C SE  SE

  2

2

2

1 1 C SE  SE

2

2

2

1 SE  SE
Appendix A-21

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

estimates include data for populations in both institutional and noninstitutional group quarters, and estimates from most national surveys do not include institutional populations. Another potential for large eff ects 

when comparing data from the ACS with data from 

other national surveys is the use of diff erent questions 

for measuring the same or similar information.

Sampling error and its impact on the estimates from 

the other survey should be considered if comparisons 

and statements of statistical diff erence are to be made, 

as described in Appendix 3. The standard errors on 

estimates from other surveys should be derived 

according to technical documentation provided for 

those individual surveys.

Finally, the user wishing to compare ACS estimates 

with estimates from other national surveys should 

consider the potential impact of other factors, such 

as target population, sample design and size, survey 

period, reference period, residence rules, and interview 

modes on estimates from the two sources.
A-22 Appendix

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

where is the All Items CPI-U-RS Annual Average 

for the earlier year (Y1).

 is the All Items CPI-U-RS Annual Average for the 

more recent year (Y2).

 is the published ACS estimate for the earlier year 

(Y1).

The example below compares the national median 

value for owner-occupied mobile homes in 2005 

($37,700) and 2006 ($41,000). First adjust the 2005 

median value using the 2005 All Items CPI-U-RS Annual 

Average (286.7) and the 2006 All Items CPI-U-RS Annual 

Average (296.1) as follows:

Thus, the comparison of the national median value for 

owner-occupied mobile homes in 2005 and 2006, in 

2006 dollars, would be $38,936 (2005 infl ationadjusted to 2006 dollars) versus $41,000 

(2006 dollars).

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost 

values are created with infl ation adjustments. The 

Census Bureau uses the All Items CPI-U-RS Annual Averages for each year in the multiyear time period to calculate a set of infl ation adjustment factors. Adjustment 

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from its most recent year to 

the CPI-U-RS Annual Averages from each of its earlier 

years. The ACS values for each of the earlier years in 

the multiyear period are multiplied by the appropriate 

infl ation adjustment factors to produce the infl ationadjusted values. These values are then used to create 

the multiyear estimates. 

As an illustration, consider the time period 2004–2006, 

which consisted of individual reference-year income 

values of $30,000 for 2006, $20,000 for 2005, and 

$10,000 for 2004. The multiyear income components 

are created from infl ation-adjusted reference period 

income values using factors based on the All Items 

CPI-U-RS Annual Averages of 277.4 (for 2004), 286.7 

(for 2005), and 296.1 (for 2006). The adjusted 2005 

value is the ratio of 296.1 to 286.7 applied to $20,000, 

which equals $20,656. Similarly, the 2004 value is 

the ratio of 296.1 to 277.4 applied to $10,000, which 

equals $10,674.

Using Dollar-Denominated Data

Appendix 5.

Dollar-denominated data refer to any characteristics 

for which infl ation adjustments are used when producing annual estimates. For example, income, rent, home 

value, and energy costs are all dollar-denominated 

data.

Infl ation will aff ect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using infl ation factors based on the Consumer Price Index (CPI).

Given the potential impact of infl ation on observed 

diff erences of dollar-denominated data across time 

periods, users should adjust for the eff ects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for infl ation. In making adjustments, the 

Census Bureau recommends using factors based on 

the All Items CPI-U-RS (CPI research series). The Bureau 

of Labor Statistics CPI indexes through 2006 are found 

at <http://www.bls.gov/cpi/cpiurs1978_2006.pdf>. 

Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount 

of income received during the 12 months preceding 

the interview month. This is the income reference 

period. Since there are 12 diff erent income reference 

periods throughout an interview year, 12 diff erent 

income infl ation adjustments are made. Monthly CPIU-RSs are used to infl ation-adjust the 12 reference 

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no infl ation adjustments for single-year estimates of rent, home value, or energy cost values.

Adjusting Single-Year Estimates Over Time

When comparing single-year income, rent, home value, 

and energy cost value estimates from two diff erent 

years, adjustment should be made as follows: 

1) Obtain the All Items CPI-U-RS Annual Averages for 

the 2 years being compared.

2) Calculate the infl ation adjustment factor as the ratio 

of the CPI-U-RS from the more recent year to the 

CPI-U-RS from the earlier year.

3) Multiply the dollar-denominated data estimated for 

the earlier year by the infl ation adjustment factor.

The infl ation-adjusted estimate for the earlier year can 

be expressed as:

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

CPIY1

CPIY 2

1 ˆ

XY
Appendix A-23

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

As an illustration, consider ACS multiyear estimates for 

the two time periods of 2001–2003 and 2004–2006. 

To compare the national median value for owneroccupied mobile homes in 2001–2003 ($32,000) and 

2004–2006 ($39,000), fi rst adjust the 2001–2003 

median value using the 2003 All Items CPI-U-RS Annual 

Averages (270.1) and the 2006 All Items CPI-U-RS 

Annual Averages (296.1) as follows:

Thus, the comparison of the national median value 

for owner-occupied mobile homes in 2001–2003 

and 2004–2006, in 2006 dollars, would be $35,080 

(2001–2003 infl ation-adjusted to 2006 dollars) versus 

$39,000 (2004–2006, already in 2006 dollars).

Issues Associated With Infl ation Adjustment

The recommended infl ation adjustment uses a national 

level CPI and thus will not refl ect infl ation diff erences 

that may exist across geographies. In addition, since 

the infl ation adjustment uses the All Items CPI, it will 

not refl ect diff erences that may exist across characteristics such as energy and housing costs.

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as 

follows:

1) Obtain the All Items CPI-U-RS Annual Average for 

the most current year in each of the time periods 

being compared.

2) Calculate the infl ation adjustment factor as the 

ratio of the CPI-U-RS Annual Average in (1) from 

the most recent year to the CPI-U-RS in (1) from 

the earlier years.

3) Multiply the dollar-denominated estimate for the 

earlier time period by the infl ation adjustment 

factor.

The infl ation-adjusted estimate for the earlier years can 

be expressed as:

where CPI P1 is the All Items CPI-U-RS Annual Average 

for the last year in the earlier time period (P1). 

 is the All Items CPI-U-RS Annual Average for the 

last year in the most recent time period (P2).

 is the published ACS estimate for the earlier time 

period (P1).

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

CPI P2

1 ˆ

X P
All survey estimates are subject to both sampling and 

nonsampling error. In Appendix 3, the topic of sampling error and the various measures available for 

understanding the uncertainty in the estimates due to 

their being derived from a sample, rather than from an 

entire population, are discussed. The margins of error 

published with ACS estimates measure only the eff ect 

of sampling error. Other errors that aff ect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any error 

aff ecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error.

Types of Nonsampling Errors

Coverage error occurs when a housing unit or person 

does not have a chance of selection in the sample 

(undercoverage), or when a housing unit or person has 

more than one chance of selection in the sample, or is 

included in the sample when they should not have been 

(overcoverage). For example, if the frame used for the 

ACS did not allow the selection of newly constructed 

housing units, the estimates would suff er from errors 

due to housing undercoverage.

The fi nal ACS estimates are adjusted for under- and 

overcoverage by controlling county-level estimates to 

independent total housing unit controls and to independent population controls by sex, age, race, and 

Hispanic origin (more information is provided on the 

coverage error defi nition page of the “ACS Quality Measures” Web site at <http://www.census.gov/acs/www

/UseData/sse/cov/cov_def.htm>). However, it is important to measure the extent of coverage adjustment by 

comparing the precontrolled ACS estimates to the fi nal 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that diff erences in characteristics of undercovered or overcovered 

housing units or individuals diff er from those eligible to 

be selected. When this occurs, the ACS may not provide 

an accurate picture of the population prior to the coverage adjustment, and the population controls may not 

eliminate or minimize that coverage error.

Unit nonresponse is the failure to obtain the minimum required information from a housing unit or a resident of a group quarter in order for it to be considered 

a completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

Appendix 6.

or person. For example, if no one in a sampled housing unit is available to be interviewed during the time 

frame for data collection, unit nonresponse will result.

It is important to measure unit nonresponse because 

it has a direct eff ect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the fi nal survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

unit nonresponse bias. This will happen if the characteristics of nonresponding units diff er from the characteristics of responding units.

Item nonresponse occurs when a respondent fails to 

provide an answer to a required question or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question diff er 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias.

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation 

of the fi nal data fi les. For example, errors may occur if 

data entry of questionnaire information is incomplete 

Measures of Nonsampling Error

A-24 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
Appendix A-25

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

or inaccurate. Coding of responses incorrectly also 

results in processing error. Critical reviews of edits and 

tabulations by subject matter experts are conducted to 

keep errors of this kind to a minimum.

Nonsampling error can result in random errors and 

systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there is 

an error in the questionnaire design that negatively 

aff ects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the fi nal results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

ACS Quality Measures

Nonsampling error is extremely diffi cult, if not 

impossible, to measure directly. However, the Census 

Bureau has developed a number of indirect measures of 

nonsampling error to help inform users of the quality 

of the ACS estimates: sample size, coverage rates, unit 

response rates and nonresponse rates by reason, and 

item allocation rates. Starting with the 2007 ACS, these 

measures are available in the B98 series of detailed 

tables on AFF. Quality measures for previous years are 

available on the “ACS Quality Measures” Web site at 

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

Sample size measures for the ACS summarize information for the housing unit and GQ samples. The measures available at the state level are:6

Housing units

Number of initial addresses selected

Number of fi nal survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of fi nal survey interviews

Sample size measures may be useful in special circumstances when determining whether to use single-year 

or multiyear estimates in conjunction with estimates of 

the population of interest. While the coeffi cient of variation (CV) should typically be used to determine 

usability, as explained in Appendix 3, there may be 

some situations where the CV is small but the user 

has reason to believe the sample size for a subgroup 

is very small and the robustness of the estimate is in 

question. 

For example, the Asian-alone population makes up 

roughly 1 percent (8,418/656,700) of the population 

in Jeff erson County, Alabama. Given that the number of 

successful housing unit interviews in Jeff erson County 

for the 2006 ACS were 4,072 and assuming roughly 2.5 

persons per household (or roughly 12,500 completed 

person interviews), one could estimate that the 2006 

ACS data for Asians in Jeff erson County are based on 

roughly 150 completed person interviews.

Coverage rates are available for housing units, and 

total population by sex at both the state and national 

level. Coverage rates for total population by six race/

ethnicity categories and the GQ population are also 

available at the national level. These coverage rates are 

a measure of the extent of adjustment to the survey 

weights required during the component of the estimation methodology that adjusts to population controls. 

Low coverage rates are an indication of greater potential for coverage error in the estimates. 

Unit response and nonresponse rates for housing 

units are available at the county, state, and national 

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, other, and data insuffi cient to be considered 

an interview. Rates are also provided separately for persons in group quarters at the national and state levels.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. For example, 

the 2006 housing unit response rates are at least 94 

percent for all states. The response rate for the District 

of Columbia in 2006 was 91 percent.

Item allocation rates are determined by the content 

edits performed on the individual raw responses and 

closely correspond to item nonresponse rates. Overall 

housing unit and person characteristic allocation rates 

are available at the state and national levels, which 

combine many diff erent characteristics. Allocation rates 

for individual items may be calculated from the B99 

series of imputation detailed tables available in AFF. 

Item allocation rates do vary by state, so users are 

advised to examine the allocation rates for 

characteristics of interest before drawing conclusions 

from the published estimates.

6

 The sample size measures for housing units (number of initial addresses 

selected and number of fi nal survey interviews) and for group quarters 

people cannot be used to calculate response rates. For the housing unit 

sample, the number of initial addresses selected includes addresses 

that were determined not to identify housing units, as well as initial 

addresses that are subsequently subsampled out in preparation for personal visit nonresponse follow-up. Similarly, the initial sample of people 

in group quarters represents the expected sample size within selected 

group quarters prior to visiting and sampling of residents. 
A-26 Appendix

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

Implications of Population Controls on ACS Estimates

Appendix 7.

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defi ned by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s offi cial estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the offi cial count 

of the population comes from the previous census, 

updated by the Population Estimates Program.

In the case of the ACS, the total housing unit estimates 

and the total population estimates by age, sex, race 

and Hispanic origin are controlled at the county (or 

groups of counties) level. The group quarters total 

population is controlled at the state level by major type 

of group quarters. Such adjustments are important to 

correct the survey data for nonsampling and sampling 

errors. An important source of nonsampling error is 

the potential under-representation of hard-toenumerate demographic groups. The use of the 

population controls results in ACS estimates that more 

closely refl ect the level of coverage achieved for those 

groups in the preceding census. The use of the population estimates as controls partially corrects demographically implausible results from the ACS due to 

the ACS data being based on a sample of the population rather than a full count. For example, the use of 

the population controls “smooths out” demographic 

irregularities in the age structure of the population that 

result from random sampling variability in the ACS. 

When the controls are applied to a group of counties 

rather than a single county, the ACS estimates and the 

offi cial population estimates for the individual counties 

may not agree. There also may not be agreement 

between the ACS estimates and the population estimates for levels of geography such as subcounty areas 

where the population controls are not applied.

The use of population and housing unit controls also 

reduces random variability in the estimates from year 

to year. Without the controls, the sampling variability 

in the ACS could cause the population estimates to 

increase in one year and decrease in the next (especially for smaller areas or demographic groups), when 

the underlying trend is more stable. This reduction in 

variability on a time series basis is important since 

results from the ACS may be used to monitor trends 

over time. As more current data become available, the 

time series of estimates from the Population Estimates 

Program are revised back to the preceding census while 

the ACS estimates in previous years are not. Therefore, 

some diff erences in the ACS estimates across time may 

be due to changes in the population estimates. 

For single-year ACS estimates, the population and total 

housing unit estimates for July 1 of the survey year 

are used as controls. For multiyear ACS estimates, the 

controls are the average of the individual year population estimates.
Appendix A-27

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

Background and Overview Information

American Community Survey Web Page Site Map: 

<http://www.census.gov/acs/www/Site_Map.html>

This link is the site map for the ACS Web page. It provides an overview of the links and materials that are 

available online, including numerous reference documents.

What Is the ACS? <http://www.census.gov/acs/www

/SBasics/What/What1.htm> This Web page includes 

basic information about the ACS and has links to additional information including background materials.

ACS Design, Methodology, Operations

American Community Survey Design and Methodology 

Technical Paper: <http://www.census.gov/acs/www

/Downloads/tp67.pdf> This document describes the 

basic design of the 2005 ACS and details the full set 

of methods and procedures that were used in 2005. 

Please watch our Web site as a revised version will be 

released in the fall of 2008, detailing methods and 

procedures used in 2006 and 2007. 

About the Data (Methodology: <http://www.census

.gov/acs/www/AdvMeth/> This Web page contains 

links to information on ACS data collection and processing, evaluation reports, multiyear estimates study, 

and related topics.

ACS Quality

Accuracy of the Data (2007): <http://www.census.gov

/acs/www/Downloads/ACS/accuracy2007.pdf> This 

document provides data users with a basic understanding of the sample design, estimation methodology, and 

accuracy of the 2007 ACS data.

ACS Sample Size: <http://www.census.gov/acs/www

/SBasics/SSizes/SSizes06.htm> This link provides 

sample size information for the counties that were

published in the 2006 ACS. The initial sample size 

and the fi nal completed interviews are provided. The 

sample sizes for all published counties and county 

equivalents starting with the 2007 ACS will only be 

available in the B98 series of detailed tables on American FactFinder.

ACS Quality Measures: <http://www.census.gov/acs

/www/UseData/sse/> This Web page includes information about the steps taken by the Census Bureau to 

improve the accuracy of ACS data. Four indicators of 

survey quality are described and measures are provided at the national and state level.

Guidance on Data Products and Using the Data

How to Use the Data: <http://www.census.gov/acs

/www/UseData/> This Web page includes links to 

many documents and materials that explain the ACS 

data products. 

Comparing ACS Data to other sources: <http://www

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

are provided with guidance on comparing the 2007 

ACS data products to 2006 ACS data and Census 2000 

data. 

Fact Sheet on Using Diff erent Sources of Data for 

Income and Poverty: <http://www.census.gov/hhes

/www/income/factsheet.html> This fact sheet highlights the sources that should be used for data on 

income and poverty, focusing on comparing the ACS 

and the Current Population Survey (CPS).

Public Use Microdata Sample (PUMS): <http://www

.census.gov/acs/www/Products/PUMS/> This Web 

page provides guidance in accessing ACS microdata.

Other ACS Resources

Appendix 8.