What Users of Data for Rural Areas Need to Know i

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

Issued 

August 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 Users of Data for Rural Areas Need to Know 
 

Acknowledgments Paul R. Voss, Senior Spatial Analyst, Odum Institute for Research in Social 

Science, University of North Carolina at Chapel Hill, Katherine J. Curtis, 

Department of Rural Sociology, University of Wisconsin-Madison/Extension, 

and Daniel Veroff, Director, Applied Population Laboratory, Department 

of Rural Sociology, University of Wisconsin-Madison/Extension, drafted this 

handbook for the U.S. Census Bureau’s American Community Survey Office. 

Kennon R. Copeland and John H. Thompson of National Opinion Research 

Center at the University of Chicago drafted the technical appendixes. 

Edward J. Spar, Executive Director, Council of Professional Associations on 

Federal Statistics, Frederick J. Cavanaugh, Executive Business Director, 

Sabre Systems, Inc., Susan P. Love, Consultant, Linda A. Jacobsen, Vice 

President, Domestic Programs, Population Reference Bureau, and 

Mark Mather, Associate Vice President, Domestic Programs, Population 

Reference Bureau, provided initial review of this handbook. 

Deborah H. Griffin, Special Assistant to the Chief of the American 

Community Survey Office, provided the concept and directed the 

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

Understanding and Using American Community Survey Data. Cheryl V. 

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

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

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

this handbook series. 

The American Community Survey program is under the direction of 

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

Weinberg, Assistant Director for the American Community Survey and 

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

Office. 

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 

What Users of Data for Rural Areas Need to Know 

U.S. Department of Commerce 

Gary Locke, 

Secretary 

Dennis F. Hightower, 

Deputy Secretary 

Economics and Statistics Administration 

Rebecca M. Blank, 

Under Secretary for Economic Aff airs 

U.S. CENSUS BUREAU 

Robert M. Groves, 

Director 

Issued August 2009 
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data: 

What Users of Data for Rural Areas 

Need to Know 

U.S. Government Printing Office, 

Washington, DC, 

2009. 

ECONOMICS 

AND STATISTICS
 

ADMINISTRATION
 

Economics and Statistics 

Administration 

Rebecca M. Blank, 

Under Secretary for Economic Aff airs 

U.S. CENSUS BUREAU 

Robert M. Groves, 

Director 

Thomas L. Mesenbourg, 

Deputy Director and 

Chief Operating Officer 

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 
 

 

 

 

 

 

 

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

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

The American Community Survey.................................................................2
 

Accessing American Community Survey Data ..............................................4
 

Using This Case Study..................................................................................7
 

Introducing Wolf Lake ...................................................................................7
 

The Need for a Comprehensive Plan.............................................................8
 

Commence the Dialogue ...............................................................................9
 

Epilogue ..................................................................................................... 22
 

Reference.................................................................................................... 22
 

Glossary...................................................................................................... 23
 

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


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


 Appendix 2. Differences 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
 

What Users of Data for Rural Areas Need to Know iii
 

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 different 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 different kinds of explanations and guidance to 

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

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

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

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

they cover major stakeholder groups familiar to the Census Bureau. 

General data users Congress 

High school teachers Puerto Rico Community Survey data 

users (in Spanish) 

Business community Public Use Microdata Sample (PUMS) data 

users 

Researchers Users of data for rural areas 

Federal agencies State and local governments 

Media Users of data for American Indians and 

Alaska Natives 

The handbooks differ 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 differences 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 differences 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 finding 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 pathfinder in leading others to share what 

you have learned. 

iv What Users of Data for Rural Areas Need to Know 

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

The purpose of this handbook is to introduce the 

American Community Survey (ACS) to people who care 

about, and use, social, economic, and housing data for 

rural communities. This handbook addresses ACS data 

issues for areas with populations as large as 20,000. 

That’s a somewhat high-population threshold for a 

handbook focusing on small, rural communities, as it 

sweeps under our microscope thousands of cities and 

other incorporated places along with virtually all rural 

territory. Our inclination is to remain focused primarily 

on small, rural areas, and our examples are designed to 

address rural, more than small area, issues. 

This handbook is written with language that should not 

appear overly technical and specialized for most potential ACS data users. We do presume, however, that 

someone using this handbook to navigate through the 

possibly unfamiliar waters of the ACS does have some 

minimal understanding of the mission and purpose 

of the U.S. Census Bureau and at least some limited 

experience accessing data from the Census Bureau’s 

Web site. The reader should have some appreciation 

for data gathered using a sample survey including the 

statistical imprecision of survey data (often referred to 

as the “margin of error”) that is occasionally published 

with sample-based estimates, most often with the outcome of public opinion polls. 

We use the device of a single case study to illustrate a 

variety of aspects of this new national survey. While the 

case study involves a small rural community faced with 

the requirement of updating its social and economic 

profile as part of a larger land use planning exercise, 

many of the data issues confronted by the local official 

also apply to small urban settlements and small cities. 

We show how results from the ACS are both similar to, 

and different from, data formerly gathered in conjunction with decennial censuses using the long-form 

sample survey. Data once available for small governmental units and other small geographic entities from 

familiar census files called Summary File 3 (SF3) and 

Summary File 4 (SF4) have been supplanted by data 

from the ACS. 

This change has been guided partly by demands from 

community leaders for data that are more current and 

relevant than traditional long-form sample results. By 

the early 1990s, it had become clear that, for many 

areas, census long-form data were outdated and 

irrelevant almost before they were released some 18 

months following the actual census. The introduction 

of the ACS has also been guided by ongoing and intensive discussions between Census Bureau staff and data 

users so that ACS data products would meet the needs 

of community leaders, regardless of the size of their 

community. The development of the ACS has benefi ted 

from a decade-long testing and evaluation period to 

ensure that the transition from census long-form data 

to ACS data is as smooth as possible and that training 

materials would be available to ease that transition. 

This handbook is one of several similar publications 

designed to meet this goal. 

Data users who are not yet familiar with the ACS will 

be comforted by the similarities between the long-form 

survey and the ACS (e.g., similar questionnaire format 

and content and similar data products). However, for 

all ACS data users, there will be a necessary reorientation that alerts the data analyst to some major diff erences between the census long-form survey and the 

ACS. For example, the sampling strategy and rules for 

determining residence in the ACS are diff erent from 

the long-form survey. These differences will require 

users of small area data to invest time and eff ort to 

gain familiarity with the new ACS and to discover how 

ACS data can be used in fresh and innovative ways to 

address questions frequently asked by rural community leaders, data analysts, and planners. The Census 

Bureau has provided several documents to assist in 

understanding these differences. Many can be found 

on the Internet at <http://www.census.gov/acs 

/www/>. 

Technical and statistical issues related to using ACS 

data are presented as appendix material. We point to 

these technical materials, as necessary, when describing how one small community, the hypothetical town 

of Wolf Lake, confronted the need to consult ACS data 

to develop a comprehensive town planning document. 

In addition, the Census Bureau’s Web site <www 

.census.gov> has a wealth of information regarding 

the ACS. A particularly useful link provides specifi c 

guidance regarding cautions when comparing ACS 

data with data from other data sources, including the 

Census 2000 long-form sample data at <http://www 

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

What Users of Data for Rural Areas Need to Know 1 

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

 

The American Community Survey
 

The ACS is an innovative national initiative designed 

to collect statistically reliable and timely information 

needed for critical government functions. See the 

“What Is the ACS?” text box for details. While the ACS 

was tested and evaluated in a handful of counties for 

more than a decade and nationally for 5 years, it only 

commenced full implementation in 2005. Counties 

and cities with populations of 20,000 and more have 

already received ACS data. Smaller geographic areas, 

specifically those with populations below 20,000, must 

wait until 2010 before ACS data are available for their 

communities. Thus, beginning in 2010, the ACS will 

provide data users across the country with annually 

updated information about America’s small communities, neighborhoods, and rural areas. 

The principal feature of the ACS that makes it diff erent 

from the census long-form sample survey is that the 

ACS is a continuous, ongoing data collection eff ort. 

Every month, a fresh sample of about 250,000 

addresses is identified for interviewing. The data products from the ACS resemble those produced from the 

decennial census. In contrast with traditional census 

products that approximated data for a point in time 

called Census Day, the tables of data from the ACS 

represent the accumulation of data gathered over the 

course of 1-year, 3-year, and 5-year time periods. Like 

the census long-form sample, a larger percentage of 

addresses are selected in sparsely settled rural areas. 

Despite this “rural oversample,” ACS interviews must 

be accumulated over several years to achieve reasonable statistical reliability in smaller areas. 

Table 1 shows the kinds and proportions of geographic 

areas for which cumulations of 12 months of data are 

sufficient to generate statistically reliable data. These 

cumulations generally are referred to as “1-year estimates” and are released by the Census Bureau every 

What Is the ACS? 

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

with an updated statistical portrait every year. The U.S. Census Bureau, under the authority of Title 

13, U.S. Code, Sections 141 and 193, conducts the survey. Title 13 also requires that the Census 

Bureau use this information only for statistical purposes. Thus, all statistical tables and public use 

files based on ACS results strictly maintain the confidentiality of individual responses. 

Survey questionnaires are sent to approximately 250,000 addresses across the country every 

month. Addresses from which a questionnaire is not returned by mail are followed-up, first in an 

attempt to obtain the information by telephone, and then, for a sample of nonresponding households, in person by a Census Bureau fi eld interviewer. 

Based on responses from the series of 12 independent monthly samples each calendar year, the 

ACS can provide estimates of demographic, housing, social, and economic characteristics for 

all states, as well as for cities, counties, metropolitan areas, and population groups of 65,000 

or more. These estimates, based on a full year’s worth of collected ACS data, are called “1-year 

estimates.” For less populated areas, such as rural villages and towns, 3 or 5 years of ACS data are 

accumulated to produce statistically reliable estimates of population and housing characteristics. 

Estimates for areas with populations of 20,000 or more are based on data collected over 3 years 

(“3-year estimates”). 

For rural areas, urban neighborhoods, census tracts, block groups, and population groups of fewer 

than 20,000 people, it will take 5 years to accumulate enough survey data to achieve data estimates with statistical reliability that is similar to that of the Census 2000 long-form sample survey. 

These latter survey estimates, called “5-year estimates,” will be published for areas with small 

populations each year beginning in 2010. The Census Bureau will also produce 3-year and 1-year 

data products for larger geographic areas. 

A detailed description of ACS data collection methodology and the survey’s sample design may be 

found at <http://www.census.gov/acs/www/SBasics/desgn_meth.htm>. 

2 What Users of Data for Rural Areas Need to Know 

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

 Table 1. Major Geographic Areas and Type of ACS Estimates Published 

 Type of geographic area 

Total 

number of 

areas 

Percent of total areas receiving . . . 

1-year, 

3-year, 

& 5-year 

estimates 

3-year & 

5-year 

estimates 

only 

5-year 

estimates 

only 

States and District of Columbia 51 100.0 0.0 0.0 

Congressional districts 435 100.0 0.0 0.0 

Public Use Microdata Areas* 2,071 99.9 0.1 0.0 

Metropolitan statistical areas 363 99.4 0.6 0.0 

Micropolitan statistical areas 576 24.3 71.2 4.5 

Counties and county equivalents 3,141 25.0 32.8 42.2 

Urban areas 3,607 10.4 12.9 76.7 

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

American Indian areas, Alaska Native areas, and 

Hawaiian homelands 607 2.5 3.5 94.1 

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

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

ZIP Code tabulation areas 32,154 0.0 0.0 100.0 

Census tracts 65,442 0.0 0.0 100.0 

Census block groups 208,801 0.0 0.0 100.0 

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

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

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

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

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

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

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

may differ from the numbers in this table. 

year for geographic areas with at least 65,000 people. 

These areas will also receive 3-year and 5-year estimates. Also shown is the proportion of major types of 

geographic areas for which cumulations of 3 years or 

36 months of data (“3-year estimates”) and cumulations 

of 5 years or 60 months of data (“5-year estimates”) 

will be released. Table 1 also shows that of the 3,141 

counties and county equivalents, 25 percent are large 

enough to receive 1-year, 3-year, and 5-year estimates 

every year. Most counties (and equivalents) will only 

receive 5-year estimates each year since they have 

populations of less than 20,000. All census tracts, 

ZIP Code Tabulation Areas, and census block groups, 

regardless of size, receive only 5-year estimates. Most 

cities, towns, census designated places, townships, 

and villages (minor civil divisions) will only receive 

5-year estimates. 

What Users of Data for Rural Areas Need to Know 3 

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

 

 

 

 

 Table 2. ACS Data Collection and Release Dates 

Year of Data Release 

Data 

product 

Population 

threshold 

2006 2007 2008 2009 2010 2011 2012 2013 

1-year 

estimates 65,000+ 2005 

Year(s) of Data Collection

2006 2007 2008 2009 2010 2011 2012

 3-year 

estimates 20,000+ 

2005– 

2007 

2006– 

2008 

2007– 

2009 

2008– 

2010 

2009– 

2011 

2010–

2012

 5-year 

estimates All areas* 

2005– 

2009 

2006– 

2010 

2007– 

2011 

2008–

2012 

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

Source: U.S. Census Bureau. 

Table 2 summarizes the data release schedule for each also be released. These releases will include the 1-year 

of these types of estimates. For example, in 2011, estimates, based on data collected in 2010 for areas 

when basic demographic data from the 2010 Census with at least a 65,000 population; 3-year estimates, 

begin to be released, detailed social, economic, and based on data collected in 2008 through 2010, for 

housing data from the 5 years of ACS interviews span- areas with at least a 20,000 population; and 5-year 

ning the period January 2006 to December 2010 will estimates for all areas. 

Accessing American Community Survey Data 

ACS data are presented in a number of diff erent products, some of which are mentioned in the text box 

“ACS Data Products.” They range from profiles of broad 

social, economic, housing, and demographic characteristics to very detailed tables that focus on a specifi c 

variable. ACS data products include thematic maps and 

tables that compare data across geographies. 

These products cover a wide array of subjects, listed 

in Table 3 and are generally equivalent to the subjects 

released based on the long-form data from Census 

2000. A highly useful link on the Census Bureau’s ACS 

Web site provides a technical definition of each published data item in the ACS at <http://www.census 

.gov/acs/www/UseData/Def.htm>. 

The ACS data products can be accessed through the 

Census Bureau’s American FactFinder Web site: 

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

From this main page, two links under the “Getting 

Detailed Data” section lead to the ACS. The “learn 

more” link provides users with a basic overview of the 

ACS and its uses, and a link to the Census Bureau’s ACS 

home page. The “get data” link leads to the ACS data 

sets Web page where users can select the products and 

the subjects that fit their data needs. A world of alternative data options awaits the user here. For example, 

a mouse click on “detailed tables” opens a dialogue 

section that will appear familiar to anyone who has 

used the American FactFinder to obtain decennial census tables, except that here the tables are generated 

from ACS data. 

4 What Users of Data for Rural Areas Need to Know 

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

While ACS data are currently being released each year for geographic areas with populations of at least 

20,000, the survey is preparing for cumulations of data for the smallest geographic areas. The fi rst 

3-year estimates were released in 2008, based on data collected in 2005, 2006, and 2007. Beginning 

in 2010, based on data gathered from 2005 through 2009, annual 5-year estimates from ACS surveys 

will be released for all census geographic units, regardless of size, down to the census block group 

level of geography. These annual publications will include the following product types in addition to 

others: 

• Detailed tables 

• Subject tables 

• Geographic comparison tables 

• Thematic maps 

• Data profi les 

The Census Bureau also releases each year single- and multiyear public use microdata sample (PUMS) 

files based on a sample of housing units and people living in group quarters. These PUMS fi les permit 

the creation of customized tables and the detailed analysis of statistically “rare” population groups 

(e.g., households that include members spanning three generations). The microdata records from the 

PUMS files preserve the confidentiality of individual responses in a variety of ways. Tabulations of 

microdata can only be produced for states and predesignated Public Use Microdata Areas (PUMAs), 

each having a population of at least 100,000 people. This limitation of geographic detail helps ensure 

that PUMS data remain confi dential. 

The Census Bureau also plans to release two specialized data files twice each decade, beginning in 

2012. These will be based on 5-year estimates: 

• American Indian and Alaska Native Summary File 

• Summary File 4 (detailed tables by race, ethnicity, and selected ancestry groups) 

Table 3: Subjects Included in the American Community Survey 

Demographic Characteristics 

Age 

Sex 

Hispanic Origin 

Race 

Relationship to Householder 

(e.g., spouse) 

Economic Characteristics 

Income 

Food Stamps Benefi t 

Labor Force Status 

Industry, Occupation, and Class 

of Worker 

Place of Work and Journey to 

Work 

Work Status Last Year 

Vehicles Available 

Health Insurance Coverage* 

Social Characteristics 

Marital Status and Marital History* 

Fertility 

Grandparents as Caregivers 

Ancestry 

Place of Birth, Citizenship, and 

Year of Entry 

Language Spoken at Home 

Educational Attainment and 

School Enrollment 

Residence One Year Ago 

Veteran Status, Period of Military 

Service, and VA ServiceConnected Disability Rating* 

Disability 

Housing Characteristics 

Year Structure Built 

Units in Structure 

Year Moved Into Unit 

Rooms 

Bedrooms 

Kitchen Facilities 

Plumbing Facilities 

House Heating Fuel 

Telephone Service Available 

Farm Residence 

Financial Characteristics 

Tenure (Owner/Renter) 

Housing Value 

Rent 

Selected Monthly Owner Costs

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

 Source: U.S. Census Bureau. 

What Users of Data for Rural Areas Need to Know 5 

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

Getting ACS data from the American FactFinder generally involves a fi ve-step process: 

1. 	 Select the ACS data set (for example, the 2007 

ACS 1-year estimates or the 2005–2007 ACS 

3-year estimates). 

2. 	 Select the data product from the options provided in the right-hand margin. 

3. 	 Select the geography (or geographies) of interest 

from the drop-down lists of available geographies. 

4. 	 Select the specific table or tables desired. 

5. 	 View or download the data. The results are 

provided on screen and can be downloaded in 

a variety of file formats (e.g., comma delimited, 

tab delimited, Excel). 

More experienced data users may choose to download 

ACS data using the File Transfer Protocol (FTP). This 

option is provided on the ACS data release page at 

<http://www.census.gov/acs/www/Products/index 

.html> or directly at <http://www.census.gov/acs 

/www/Special/acsftp.html>. 

For someone approaching the Census Bureau’s ACS site 

and the American FactFinder interface for the fi rst time, 

the process may appear intimidating. But a bit of practice retrieving data tables for different kinds of geographic units and for different years will fairly quickly 

make even a novice user comfortable. The best source 

for assistance in many cases will be the specialists at 

their State Data Centers: <http://www.census.gov 

/sdc/www/>. In the text box, “Examples Where ACS 

Data Might Be Used,” we briefly list several examples. 

Examples Where ACS Data Might Be Used 

• 	 A consortium of human services agencies and a rural county health facility use ACS data on 

age, poverty, disability, and access to transportation to examine issues of health, aging, and 

rural poverty. They use ACS data to compete for a variety of human services grants and loans 

and to tell the local story to pursue funding for a satellite clinic to serve low-income families 

and the elderly. 

• 	 A county public health department uses an array of ACS data (age, race/ethnicity, poverty, 

education level of parents, and single parents) as key indicators of populations at risk for 

diabetes and asthma in children. They supplement the ACS data with data from local clinics 

to help target education and intervention programs through community organizations and 

school systems. The goal is to teach children and their parents about healthy lifestyles, living 

conditions, and diets. 

• 	 A state low-income housing coalition uses PUMS data from the ACS on income by household 

size and contract rents to evaluate the supply of affordable rental housing. 

• 	 A school district compares ACS data with Census 2000 to track trends in the Spanishspeaking population. Combined with administrative data collected by the district, the ACS 

data help the district to reach a decision about the need to hire more bilingual teachers. 

• 	 In preparing an application for Community Development Block Grant funding for a new 

library/senior community center, a village uses ACS data to document that at least 51 percent 

of those benefiting from the project will be low-income or moderate-income individuals. The 

village also uses ACS data on age and income to make the case for a seniors’ facility. 

6	 What Users of Data for Rural Areas Need to Know 

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

Using This Case Study
 

The following case study uses a fictitious e-mail dialog 

between a data user and a State Data Center demographer to touch on key issues and instructions about the 

use of ACS data. You will note that in addition to this 

dialog, summaries of important points are provided in 

the left margin. These summaries can serve as indices 

for finding guidance on particular topics. The data from 

the 1990 census, Census 2000, and the Population 

Estimates Program that are cited in this case study are 

real, although we have renamed the area Wolf Lake. 

Introducing Wolf Lake
 

Data from the 2001–2005 ACS Multiyear Estimates 

Study were used in this case study as the 2005–2009 

ACS estimates.1 The 2005–2009 ACS margins of error 

were not based on the Multiyear Estimates Study; 

rather, they were approximated by using a constant 

relationship between the Census 2000 margins of 

error and the ACS margins of error. To illustrate certain 

concepts more clearly, reasonable (but fi ctitious) data 

are used for the 2010 Census and for several projections. 

Located in the heart of Lake County, Wolf Lake is a 

town steeped in the folklore of mythical lumberjack 

Paul Bunyan. Local history and geography are a rich 

mixture of colorful legends of lumber camp high jinks, 

true stories of devastating forest fires and selfl ess 

heroism, and ever-embellished tales of invasive bears, 

record deep snowdrifts, and bigger-than-life game fi sh. 

The local chamber of commerce advertises Wolf Lake 

as a “vacation paradise.” Wolf Lake is a rural town of 

friendly people and 36 square miles of a welcoming 

blend of forests, lakes, rivers, and natural amenities 

that draw both tourists and new residents to the area. 

The town is a functioning governmental unit with a 

town chairman, a three-member board of supervisors, 

and a single, paid, town employee—part-time clerk/ 

treasurer, Pat Smith. A few relevant demographic statistics for the town are shown in Table 4. 

Wolf Lake’s Census 2000 population of 958 showed an 

increase of almost 10 percent over the 1990 census 

population. The state demographic agency’s annual 

population estimates for 2007 imply an increase of 

Table 4. Demographic Overview: Town of Wolf Lake 

1990 2000 2007 

Population 884 958 993

 In Households 884 958 NA

 In Group Quarters 0 0 NA 

Housing Units 1,240 1,407 1,461

 Occupied 376 445 NA

 Vacant 864 962 NA

 Percent vacant 69.7 68.4 NA 

NA Not available. 

about 35 people, representing a growth rate of almost 

4 percent since 2000. An annexation of land from a 

neighboring town in 2006 accounts for some of that 

growth. A total of 44 housing units were added to Wolf 

Lake. Most of the town population resides in housing 

units. A single nursing home was added to the community since 2000. The town has nearly 1,500 housing units, but more than 70 percent of these generally 

are found to be vacant at census time (April). Many of 

these vacant units are large lakeshore homes owned by 

families from the metropolitan city roughly 160 miles 

to the south. These homes are capable of supporting 

year-round occupancy, and some are used for winter 

vacations when owners come up for brief holidays of 

cross-country skiing and snowmobiling. Mostly, however, these places are occupied for various intervals of 

time during the summer months when families come 

to Wolf Lake to swim and fish, and enjoy some of the 

other tourist-oriented recreations that spring to life in 

early May. Some of these families are “snowbirds” that 

spend the winter months in Sun Belt communities but 

return to Wolf Lake for the summer each year. In these 

summer months, the area also experiences a large 

increase in the population who work in these tourist 

areas—high school and college students in particular. A 

map of the town is shown in Figure 1. 

Note to the reader: Fasten your seatbelt. For this case 

study to be realistic, we must fast-forward to the fall of 

2011. The ACS has been in full swing for over 5 years 

and the 2010 Census (with no long-form supplement) 

was conducted without major diffi culty or controversy. 

1 The ACS Multiyear Estimates Study includes a series of single- and 

multiyear estimates for a sample of areas across the country. These 

estimates were produced to dress rehearse production systems and to 

provide a preview of multiyear estimates. For more information, see 

<http://www.census.gov/acs/www/AdvMeth/Multi_Year_Estimates 

/overview.html>. 

What Users of Data for Rural Areas Need to Know 7 

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

 

 

 Figure 1. Map of the Town of Wolf Lake 

Source: U.S. Census Bureau. 

Table 5. Demographic Change 1990 to 2010:

 Town of Wolf Lake 

The 2010 Census population count for Wolf Lake was 

1,006—an increase of 48 persons (5 percent) over the 

Census 2000 count. The 2010 Census included 32 residents of the nursing home and a housing unit count of 

1,474, with 1,022 housing units (69.3 percent) enumerated as vacant (see Table 5). 

1990 2000 2010 

Population 884 958 1,006

 In Households 884 958 974

 In Group Quarters 0 0 32 

Housing Units 1,240 1,407 1,474

 Occupied 376 445 452

 Vacant 864 962 1,022

 Percent vacant 69.7 68.4 69.3 

NA Not available. 

The Need for a Comprehensive Plan 

In October 2011, the Lake County board of supervisors, upon advice from the county attorney, voted to 

bring the county into conformity with the State’s Smart 

Growth Law. The decision meant that Wolf Lake would 

join other governmental units in the county and commence a comprehensive planning process. The county 

board offered the assistance of the county geographic 

information system officer to each town government, but the supervisors in Wolf Lake decided also to 

contact State Data Center demographers at the state 

university. Terry Adams, a university demographer, 

agreed to work with town clerk, Pat Smith, to assist in 

understanding, accessing, and using ACS data that are 

available on the Census Bureau’s Web site at <www 

.census.gov>. They both agreed this would help speed 

the completion of the demographic, social, economic, 

and housing sections of the plan, and provide two 

sets of eyes to ensure that the data were extracted 

and interpreted properly. These four sections of the 

comprehensive plan cover a broad range of data and 

are intended to provide both a snapshot of current 

conditions and relevant trends as a backdrop for the 

town’s new land use plan. Over the last 10 years or 

so, the town has been growing modestly but has been 

dealing with an aging population, loss of jobs from a 

2008 manufacturing plant closing, and continuation 

of a long-familiar pattern: the exodus of young people 

as soon as they graduate from high school and, with 

troubling frequency, sometimes before graduating. So, 

along with the “snapshot” demographic data from the 

2010 Census, the clerk was instructed by the town 

supervisors to locate other data that might address 

some of these issues. 

8 What Users of Data for Rural Areas Need to Know 

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

Clerk Smith learns from the university demographer that she can access the data she needs on the Census Bureau’s 

Web site. She is asked to find the link to the ACS where she’ll be able to access rich, detailed socioeconomic data 

about Wolf Lake. She is encouraged by the demographer to read the documentation for the ACS before using the 

data. After briefly reviewing some of the documentation, she begins a series of e-mail exchanges with the 

university demographer, Terry Adams. Follow along. 

Understanding ACS data 

requires preparation. Use the 

Census Bureau’s Web site. 

Most geographic areas in 

the United States will have 

only the 5-year estimates 

from the ACS to update their 

social, economic, and housing 

characteristics. But they will 

receive a fresh update every 

year. 

When you are comparing ACS 

data for two diff erent areas, 

be sure to compare the same 

types of estimates (e.g., 5– 

year to 5–year). 

ACS period estimates may be 

interpreted as if they were 

averages of survey results 

gathered over the data collection period. 

[November 4] Clerk Smith: Hi, Terry, I’ve spent much of the week reading 

some of the ACS documentation on the Census Bureau’s Web site. I especially 

looked carefully at the ACS main page <http://www.census.gov/acs/www/>. 

It’s all pretty clear, but there’s really a lot there! I won’t get to it all, and I’m not 

even sure I entirely understand some of what I’ve looked at, but I think I’m on 

board. It seems that I will only have 5-year estimates for Wolf Lake, right? The 

ACS data released a couple weeks ago cover the period 2006 to 2010. Can you 

tell me what that means, exactly? Is it as simple as averaging all 5 years’ worth 

of data? Please remember, I’m not a statistician. 

[November 4] Demographer Adams: Great, Pat. I’m glad you found the 

documentation helpful. It sounds like we’re ready to get to work. Let me take 

your questions one at a time. You are correct that you will use the 5-year estimates from the ACS. Of more than 25,000 incorporated places in the United 

States, about 92 percent will only have ACS data from the 5-year estimates. The 

overwhelming majority of governmental units across the country have populations below 20,000, which means that you’re in good company. All of the other 

towns in Lake County will be using the 5-year estimates for their comprehensive plans, too. Lake County, as an entity, is large enough to have 3-year ACS 

estimates released for it, so the county has 3-year estimates for 2005–2007, 

2006–2008, 2007–2009, and 2008–2010. The county will also have 5-year estimates for 2005–2009 and 2006–2010, and it is these latter two ACS data sets 

that would provide the appropriate county data for the towns in Lake County 

to use when comparing their trends with those of the county. These are the 

only two sets of ACS estimates currently available from which you’ll be able to 

generate data for Wolf Lake showing detailed population and housing characteristics. But remember, these data are updated with fresh 5-year estimates every 

year. 

[November 7] Clerk Smith: And what about my second question? What do 

these 5-year estimates really mean? Are they some kind of average? 

[November 8] Demographer Adams: I think you’re asking how you should 

interpret a 5-year estimate, and you’re on the right track. In many respects, 

the period estimates of social and economic characteristics based on 5 successive years of ACS interviews are pretty straightforward. They’re more or less 

averages based on observations over the past 5 years, but the estimates are 

not simple averages determined by adding up all the data collected over the 5 

years and dividing by 5. The way the Census Bureau calculates a period estimate is a little more complicated, but, in reality, it’s not inappropriate to think 

of such numbers as being like an average. The estimates will reduce the high 

and low sample observations over the 5-year period. There are some issues 

that complicate matters just a bit, however, and I’ll raise these when we discuss 

the differences between the census long-form sample and the ACS. 

[November 8] Clerk Smith: Okay, I think I understand what these 5-year ACS 

estimates represent. I have used the census long-form data for these planning 

exercises in the past. Since you raised the issue, how do the 5-year period 

estimates from the ACS compare with what might have been discovered had 

the 2010 Census actually sampled and interviewed people using a long-form 

questionnaire? 

What Users of Data for Rural Areas Need To Know 9 

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

 

ACS period estimates take 

the place of the traditional 

census long-form sample 

estimates. 

The ACS and the old longform survey are diff erent in 

several respects. 

The ACS is based on a 

continuous data collection 

strategy, and the sample 

results must be accumulated 

over time until the data can 

be reported with statistical 

reliability. 

The way the census and 

the ACS determine whose 

characteristics are to be 

reflected in the estimates for 

a place diff ers. 

In the census, most people 

are assigned to a “usual” 

place of residence. 

In the ACS, people are 

assigned to a “current” place 

of residence, which may 

or may not be their “usual” 

residence. 

[November 9] Demographer Adams: I suspect you’re not the only town 

clerk asking this question about now, and I confess that it’s taken me some time 

to understand the differences. The switch to gathering and publishing detailed 

social and economic characteristics based on the ACS represents a remarkable 

shift for data analysts in the kinds and amounts of data available. For small 

communities like Wolf Lake and for rural areas in general, the chief benefi t of 

the ACS is that we’re going to get updated socioeconomic and housing data 

every year. No more waiting for the next census plus a couple more years to 

get the data checked and published. But you need to be prepared to understand 

this new alternative to the long-form estimates. In some ways, this new survey 

is very different from past census samples, and there are important tradeoff s 

involved in this change that we all need to understand. Indeed, some of the 

challenges of the ACS are considerably more troublesome for small communities and rural areas than they are for large cities. Therefore, an understanding 

of these differences is going to be the key to the successful use of this new 

survey. 

[November 9] Clerk Smith: Can you be specific about those diff erences? 

[November 10] Demographer Adams: You bet. The transition from traditional census long-form results to ACS results for most data analysts will be 

pretty straightforward, but for some it may not be quite so easy. You’re already 

aware of one of the important differences. The 2010 Census collected data over 

a relatively short time period—March through June of 2010. In comparison, the 

ACS collects data continuously throughout the entire year. This continuous measurement nature of the ACS is certainly one difference that will require a new 

way of interpreting some of the ACS data. The ACS data are estimates based 

on interviews spanning a period of time. For Wolf Lake, 5 years of data are 

required before estimates with good statistical reliability can be released. 

Another difference is that the rule for determining who should be included in 

the ACS is different from the rule in the census. In the 2010 Census, households are enumerated, and their characteristics are associated with their “usual 

place of residence.” This may or may not be where they happened to be living 

or staying on Census Day (April 1 in recent censuses) which makes collecting 

these data often difficult. However, households receiving the ACS questionnaire 

where they are living or staying will be interviewed, and their characteristics 

will be associated with that place, if their length of stay there is intended to be 

more than 2 consecutive months. The ACS refers to this concept as “current 

residence,” and for people who meet this “2-month rule,” the place where they 

are interviewed for ACS may or may not be their usual residence. It’s possible 

that the ACS is including, as it should, some of the summer residents of Wolf 

Lake who may not have been included there under the census rules. Given that 

you know the Wolf Lake community better than I do, I encourage you to think 

about how the presence of part-time summer residents in Wolf Lake might 

make the ACS estimates different from the Census 2000 long-form results and 

get back to me with your thoughts. 

[November 14] Clerk Smith: Let me be sure I understand this. Are you saying that people who vacation at Wolf Lake on weekends throughout the summer will be included in the ACS? I know they weren’t included in those vacation 

homes in the census—they were vacant units. 

[November 14] Demographer Adams: No. The ACS would treat those 

weekenders just like the census. It’s the households who live there for more 

than 2 months that are interviewed as Wolf Lake residents by ACS. For example, 

people who spend the winter in warmer places but move back to Wolf Lake for 

the summer should be included in the ACS if their address was in sample in 

the summer. If that address was in sample in the winter, when they were living 

10 What Users of Data for Rural Areas Need To Know 

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

If a large portion of the 

households in an area lives 

there for several months, 

but not for most of the 

year, the community may 

find noticeable diff erences 

between census data and 

ACS data. 

in that “warmer place,” the unit should be classified like the census, as vacant. 

Also, people who work for the entire summer in Wolf Lake, but live somewhere 

else for the rest of the year, could be included in the ACS, but not in the census. 

[November 14] Clerk Smith: Terry, I get it now and you’re absolutely right. 

The data do look different on several measures. I looked at the 2005–2009 ACS 

5-year estimates and compared them with the data from Census 2000 and from 

the 2010 Census. Here’s some of what I see: 

Table A. A Few Wolf Lake Characteristics 

Census 

2000 

1,407 

445 

962 

68.4 

358 

958 

335 

70 

89 

49.6 

25.7 

2005–2009 

ACS 

1,587 

624 

963 

60.7 

405 

1,545 

573 

127 

399 

39.5 

22.3 

2010 

Census 

1,474 

452 

1,022 

69.3 

361 

1,006 

NA 

NA 

NA 

49.2 

24.3 

Total housing units 

Occupied housing units 

Vacant housing units 

Percent vacant 

Owner-occupied housing units 

Total population 

Total civilian employed population 

Employed in retail trade 

Employed in arts, entertainment, recreation, accommodation, and food service 

Median age 

Percent age 65 and over 

NA Not Available. 

The ACS data I found on the Census Bureau’s Web site also contained a plusminus margin of error for each of the estimates, but I need your help in interpreting these. I just wasn’t sure what to do with them. Any advice? 

[November 14] Demographer Adams: If it’s all right with you, I’d prefer 

to not talk about the margins of error yet. They’re very important, and we will 

discuss them, I promise. I like your data comparisons, and it does seem that 

the ACS is reflecting the characteristics of some of your summer residents. The 

ACS estimate of vacant units is less than the census count, and I think that’s 

to be expected. Due to your increased population in summer months, some of 

the homes reported as “occupied” by the ACS apparently were considered to 

be vacant by the census. From what you’ve told me about the folks who spend 

their winters in states like Florida or Arizona and summers in Wolf Lake, I’m 

surprised to see an ACS lower median age and a lower ACS estimate of people 

who are 65 and over. 

[November 14] Clerk Smith: Let me summarize what I think you were telling 

me about the differences that I might see in the ACS data that could be due to 

including some of the seasonal residents. In Wolf Lake, we do have some retired 

couples that live at Wolf Lake for only about 4 months but we also have younger 

people who come to work at Wolf Lake. I assume that the ACS data include both 

of these groups and that’s why the median age in the ACS is a little lower. This 

seems to be supported by the increase in the percent of people employed in 

retail trade, arts, and entertainment. 

[November 14] Demographer Adams: That sounds reasonable. 

What Users of Data for Rural Areas Need To Know 11 

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

The margins of error (MOE) 

attached to every ACS estimate are very important and 

signal the extent of statistical uncertainty associated 

with the estimate. This 

uncertainty results from 

the fact that the estimate is 

based on a sample and not 

on a complete count. 

Census long-form sample 

estimates also have levels of 

statistical uncertainty resulting from sampling error. 

Computing the MOE for a 

Census 2000 sample estimate is not difficult. 

[November 15] Clerk Smith: Can we begin the conversation about the 

margins of error attached to each number? I know a little about MOEs. I know 

that they are related to a specifi c confidence level. I think I read that the Census 

Bureau uses a 90-percent level of confidence. Also, the MOEs are calculated 

from standard errors using this confidence level. MOEs give us an idea of how 

reliable, or precise, the estimates are. 

[November 18] Demographer Adams: Yes, that’s right. I’m glad you know 

the basics. There are a couple of things I should say about sampling error. First 

is something I wanted to mention this past Monday when you presented your 

Census 2000 data and compared those estimates with ACS 5-year estimates. 

Remember that the numbers you are familiar with from the Census 2000 SF3 

file are also based on a sample. Most numbers in that file also have margins of 

error, and many of us simply ignored the fact that sampling error was associated with them because the MOEs were not published along with the long-form 

sample estimates. So, in addition to the MOEs for the ACS estimates, you also 

need to consider the MOEs for the long-form estimates from Census 2000. The 

Census Bureau provides very useful technical documentation for SF3 that shows 

how to compute estimates of these MOEs (see U.S. Census Bureau, 2002: Chapter 8; also available at <http://www.census.gov/prod/cen2000/doc/sf3.pdf>). 

The Census Bureau is producing MOEs for every ACS estimate. The Census 

Bureau’s current strategy is to make it clear to data users that there is statistical 

sampling error associated with estimates produced from samples. It was a lot 

simpler back in the SF3 days to just take the census long-form numbers as the 

“truth” and not bother with the details of sampling error and levels of uncertainty. However, what the Census Bureau is doing now with the ACS estimates 

is actually better and more informative. If you plan to compare Census 2000 

long-form estimates with your recent ACS estimates to see if they are statistically different, there are many caveats to consider, and I’ll continue to bring 

these up rather often, I’m afraid. 

[November 18] Clerk Smith: Do indeed bring them up whenever you think 

it’s helpful. But can you give me an example of how I can compute an MOE on a 

Census 2000 SF3 estimate? 

[November 21] Demographer Adams: Okay, I worked on this a bit over the 

weekend, and to help get you started I have computed the 90 percent MOE for 

the Census 2000 SF3 long-form sample estimate of the number of Wolf Lake’s 

civilian workers engaged in private wage and salary (W&S) work. The data 

come from Table P51 in the Census 2000 SF3 for Wolf Lake. This is the “Class 

of Worker” table. Private W&S workers include all civilian employees (age 16 

and over) who don’t work for the government, aren’t self-employed, and aren’t 

unpaid family workers. If you look at Table P51 in the American FactFinder, 

you’ll see that it’s a very large table with a lot of detail. I combined male and 

female workers and summed across several cells in the table to get a single 

total for W&S workers. 

To calculate the Census 2000 MOEs, you need four things: 

• 	 The unadjusted standard error or SE for the characteristic that would result 

from a simple random sample. 

• 	 The design factor for this characteristic to account for the Census 2000 


long-form sample design (which wasn’t a simple random sample).
 

• 	 The Census 2000 sampling rate for the geographic area. 

• 	 The total population of the area. 

Refer to “Example 1” in Chapter 8 of the Technical Documentation for SF3 (U.S. 

Census Bureau, 2002) to calculate the unadjusted SE for a total. Calculating 

standard errors for other kinds of estimates (e.g., percentages, means, and 

12 What Users of Data for Rural Areas Need To Know 

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

 

 

 

 

Refer to Appendix 3 for 

more information about 

margins of error and confi ­

dence levels. 

When comparing the diff erence between two samplebased estimates, formal 

tests for statistical significance of the diff erence 

should be made. Appendix 

4 presents a step-by-step 

procedure for making such 

comparisons. 

medians) are also shown in Chapter 8. The formula for the adjusted SE for a 

total is 

§ Yˆ · 

SECensus 2000 5Yˆ ̈1  ̧  ̈ N  ̧ © 1 

Where Yˆ = the estimate of the characteristic total and

 = the size of the publication area. N 1 

The value of 5 in this formula assumes a sampling rate of 1-in-6. We’ll have 

to adjust for this later since Wolf Lake was sampled at a higher rate. I found 

the observed Census 2000 sampling rate for Wolf Lake in SF3 Table P4. It was 

44.17 percent. 

In our example, the estimate of the characteristic total is the estimate of the 

civilian population 16 and over who are private wage and salary workers. From 

Table P51 in SF3, I found that estimate to be 335. The size of the publication 

area is the total population of Wolf Lake, 958. So the unadjusted SE is 33.00. 

335 · SECensus2000(unadjusted ) 5(335) ̈

§

1  ̧ 33.00
 © 958 1
 

The design factor adjustment of 0.5 can be found in Table C in this technical 

documentation. Note that these design factor tables are state specific. I found 

“Class of Worker” in the stub of our state’s table and then chose the fi nal column “35 percent or more,” given that the Census 2000 long-form sampling rate 

for Wolf Lake was 44.17 percent. Multiplying the unadjusted SE by the standard 

error design factor from Table C gives us an adjusted SE of 16.50 persons. 

SECensus2000(adjusted) 0.5(33.00) 16.50

I calculated the 90 percent margin of error for the estimate by simply taking the 

adjusted SE and multiplying it by 1.645, the factor for a 90-percent confi dence 

level. 

MOE(90%) 1.645(16.50) 27.14

This 90 percent MOE is used to construct a 90-percent “confi dence interval” 

around the Census 2000 long-form estimate—308 to 362, and this now can be 

compared to the 90-percent confidence intervals associated with the ACS 5-year 

estimate. I found the 90 percent MOEs for the ACS estimates on the Census 

Bureau’s Web site and created the corresponding confidence intervals. I found 

that interval to be 526 to 620. 

[November 22] Clerk Smith: Since those intervals don’t overlap, can I 

conclude with certainty that the ACS estimate is higher than the Census 2000 

sample estimate? 

[November 22] Demographer Adams: That’s a tempting conclusion to 

draw, but you shouldn’t do so before dealing with matters of “statistical significance.” The number of W&S workers seems higher in the ACS estimate, and 

this makes sense given what surely must be the higher number of W&S workers 

in the summer months when more of the lodging establishments, restaurants, 

bars, bait & tackle shops, and so forth are open. I calculated a formal test of 

significance on the difference between the census estimate (335) and the ACS 

5-year estimate (573), and the difference is statistically significant at the 90­

percent confi dence level. 

What Users of Data for Rural Areas Need To Know 13 

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

 

The number of completed 

interviews used to create the ACS 5-year period 

estimates will generally be 

smaller than the number 

used to produce the 

Census 2000 long-form 

sample estimates. 

ACS estimates for rural 

areas and small communities are based on proportionately larger samples 

than in large places. 

Note also that the 90-percent confidence interval for the ACS estimate is wider 

than the one I calculated for the Census 2000 long-form estimate. This is to 

be expected given that the 5-year accumulated ACS samples for Wolf Lake are 

somewhat smaller than the Census 2000 long-form sample—something that’s 

true for virtually all census geography. 

[November 23] Clerk Smith: This whole discussion of sampling errors has 

been very helpful. Thanks, Terry. I’ve looked at Chapter 8 of the SF3 Technical 

Documentation and generated standard errors for everything in my report that 

came from SF3. I revised my original table to reflect those estimates, and it’s 

clearer to me now that some of the estimates that looked very diff erent were 

really only artifacts of sampling error. I can see now the value of looking at 

sampling errors before drawing conclusions. Can you look at this table and tell 

me what you think? Also, on November 18 you said in your message that you 

wanted to talk about a couple of issues related to sampling errors in Census 

2000 and sampling errors in the ACS. Did I miss the second point? 

Table A (revised). A Few Wolf Lake Characteristics 

Census 

2000 

1,407 

445 

962 

68.4 

358 

958 

335 (±27) 

70 (±15) 

89 (±17) 

49.6 

25.7 

2005–2009 

ACS 

1,587 (±60) 

624 (±33) 

963 (±53) 

60.7 (±0.3) 

405 (±28) 

1,545 

573 (±48) 

127 (±26) 

399 (±29) 

39.5 (±2.8) 

22.3 (±2.1) 

2010 

Census 

1,474 

452 

1,022 

69.3 

361 

1,006 

NA 

NA 

NA 

49.2 

24.3 

Total housing units 

Occupied housing units 

Vacant housing units 

Percent vacant 

Owner-occupied housing units 

Total population 

Total civilian private wage and salary 

workers 

Employed in retail trade 

Employed in arts, entertainment, recreation, accommodation, and food service 

Median age 

Percent age 65 and over 

NA Not Available. 

[November 24] Demographer Adams: Your table looks great. I’m glad you 

are feeling more comfortable using MOEs and no, you didn’t miss the “other 

issues.” In my long-winded response, I did. I apologize; there’s so much to say 

on this topic! The second point I wanted to make about sampling errors actually 

takes us back to my comments on November 10 regarding diff erences between 

the census long-form sampling approach and that of the ACS. While there are 

differences between the ACS sample and the Census 2000 long-form sample, 

there’s actually one commonality that you’ll appreciate. For small governmental 

units like Wolf Lake, the census long-form questionnaire was sent to a higher 

proportion of housing units than in larger cities. The Census Bureau did this so 

that the data obtained from the long-form sample for small areas would achieve 

a level of statistical reliability comparable to that of larger cities. The ACS survey follows a similar pattern. It, too, oversamples addresses in small communities, but, as we’ve discussed, not at a rate high enough to produce statistically 

reliable 1-year or 3-year estimates. 

14 What Users of Data for Rural Areas Need To Know 

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data 
Assigning an ACS multiyear period estimate to the 

midpoint of the collection 

period is appealing but not 

a good idea. 

ACS multiyear estimates 

will not be able to identify 

abrupt changes in characteristics. These estimates will 

smooth the data gathered 

prior to the event with data 

gathered after the event. 

Consult Appendix 1 for 

additional detail. 

All ACS estimates use geographic boundaries existing 

on January 1 of the fi nal 

year of the survey collection 

period. 

Despite the increased sample size for small governmental units, the ACS currently is producing single-year results only for governmental areas and statistical areas with populations at least 65,000. For counties and other geographic 

entities with populations ranging from 20,000 to 65,000, ACS estimates require 

the pooled results of 3 years’ worth of ACS data. For governmental units like 

Wolf Lake, and for county subdivisions with fewer than 20,000 persons, ACS 

estimates require the pooled results of 5 years’ worth of ACS samples. These 

ACS 5-year estimates are what you’re looking at, and it takes some time to 

understand what these are, what they mean, and how they can be interpreted and used. 

For example, on November 7 you asked me what the 5-year estimates mean. I 

interpreted your question to be whether these period estimates can somehow 

be assigned to a specific year. The time frame described by multiyear estimates 

is the set of years included in the full range or period. The 2006–2010 ACS estimates are based on data collected in 2006, 2007, 2008, 2009, and 2010 and 

they therefore describe the characteristics of population and housing across 

those years. The ACS multiyear period estimates are not a simple average of 

several annual estimates. Some people think of these estimates as approximating conditions in 2008, recognizing full well that this midperiod estimate may 

not reflect at all the actual conditions in 2008. But this isn’t everyone’s preference—the Census Bureau encourages data users to think of the estimates as 

covering the full period of time. 

[November 25] Clerk Smith: Thanks. That really does help me understand 

these numbers. As you know, we had an employer shut down his small manufacturing plant in May of 2008. Forty-two people lost their jobs overnight. We’re 

still recovering from that event. How will the ACS results measure this change 

in the employment data? 

[November 28] Demographer Adams: One of the good things about the 

ACS is that it is designed to respond to such changes almost immediately, so 

that these kinds of abrupt events in large cities will show the effects of the 

event from one year’s estimates to the next. The ACS samples prior to May 

2008 probably picked up employees of that plant. Samples after May, of course, 

did not. Regrettably, for small places like Wolf Lake, the 5-year averages will 

smooth out what was, in fact, an abrupt moment for your community. Again, 

your ACS estimates are based on data collected over a 5-year period. I know 

that you wish that this abrupt change in manufacturing employment could be 

clearly reflected in your data, but it cannot. You certainly can, however, report 

the ACS data in your comprehensive plan and boldly footnote all employment 

data to indicate that the manufacturing plant closing aff ects them. 

[November 30] Clerk Smith: And what about the annexation I told you 

about? We acquired several acres of land from Mason Town in November 2006. 

The county surveyor said we picked up 44 additional housing units in the process. 

[December 1] Demographer Adams: I’m glad we came back to that, since 

this is an important aspect of ACS data. The Census Bureau regularly updates 

geographic boundaries of governmental units using its annual Boundary and 

Annexation Survey. No doubt you’ve had personal experience in responding 

to that survey over the years. As you probably also know, the Census Bureau 

sends out a request for school district boundaries to be updated every 2 years. 

Boundaries of statistical areas such as census tracts are updated each decade as 

part of the decennial census process. 

If the boundaries of a governmental unit change over the ACS estimation period 

(e.g., the result of an annexation like yours), the Census Bureau prepares the 

multiyear estimates using the geographic boundaries as of January 1 of the 

What Users of Data for Rural Areas Need To Know 15 

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

ACS period estimates are 

affected by both sampling 

and nonsampling error. The 

appendixes attached to this 

handbook contain additional 

information about these 

errors. 

Sampling error derives from 

the fact that the data are 

based on a sample of the 

population rather than the 

entire population. 

Nonsampling error includes 

a broad set of errors resulting from data collection or 

data processing problems. 

final year in the estimation period. The 2005–2009 estimates reported for 

Wolf Lake include the annexation because they’re reported for the town using 

boundaries as of January 1, 2009. The data from the earlier survey years have 

been adjusted for the annexation that occurred even after those surveys were 

out of the field and closed. In other words, if some of the 44 housing units 

had fallen into the ACS sample in February of 2006, the characteristics of 

those housing units and the people staying in them would initially have been 

assigned to census geography associated with Mason Town. By the time the 

2005–2009 ACS estimates were released, however, those housing and person 

characteristics would have been reassigned to Wolf Lake’s data set. 

[December 2] Clerk Smith: Okay, that seems clear enough. Now, I hate 

to ask this next question on a Friday afternoon, but my sense was the other 

day (the 24th) that you still wanted to say more about those margins of error. 

There’s more? 

[December 5] Demographer Adams: There’s always more! Remember I 

teach this material and what I said on November 24 really wasn’t enough. I 

think what you’re asking is, “How good are these estimates?” So let me pretend 

I’m talking to the students in my demography class for a moment. For them, I 

would address the issue in two parts: sampling error and nonsampling error. 

First, sampling error. The nature of inaccuracies that are associated with estimates based only on a sample of all possible observations is well understood 

and lies at the core of classical statistical theory. These errors are stated in 

terms of probabilities. When a sample of a population is taken, and an estimate 

of some attribute of that population is made, such as an estimate of the percent of all households having exactly 2 persons, the estimate will diff er from 

the true unknown percent. The difference that can be attributed to selecting a 

sample is called “sampling error” or sometimes “sampling variability.” Knowing exactly how the sample was drawn, or the “sample design,” and how many 

households were sampled, allow us to establish a range above and below the 

estimate, known as a “confidence interval,” that is likely at a given level of confidence to contain the true percent. Said another way, statistical theory can tell 

us how often the confidence interval will include the true unknown percentage. 

For all estimates derived from the ACS, the Census Bureau reports a margin of 

error at a confidence level of 90 percent. So, when you see an estimate in an 

ACS table of 40 percent ± 10 percent, you know that statistical theory has been 

used to guide your understanding of the estimate, i.e., your level of “trust” in 

the estimate. It tells you that the interval 30 percent to 50 percent has a 90 

percent probability of containing the true percentage. 

The strategy of the Census Bureau to cumulate many months of data before 

providing estimates based on the ACS for small communities like yours is 

driven by a goal to provide estimates with reasonable confidence intervals. In 

formal statistical terms that also have intuitive meaning, estimates with 

narrower confidence intervals are called “more reliable” or “more precise.” 

Now, let’s discuss nonsampling error. In addition to sampling error, for which 

rote formulas are available to compute it, there are other reasons why an 

estimate may differ from the true, but unknown, value. Nonsampling error can 

occur for a number of reasons and the consequences of this type of error are 

often poorly understood. Such error can result from missing data, response 

errors, or inadvertent errors in data collection or data processing. Some of 

these errors arise from Census Bureau procedures; others arise in the household as respondents deal with the ACS questionnaire. The ACS provides measures on its Quality Measures Web site <http://www.census.gov/acs/www 

/UseData/sse/index.htm> and detailed quality measures tables as data products in American FactFinder that indicate the potential for some types of nonsampling error. 

16 What Users of Data for Rural Areas Need To Know 

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

Nonsampling error in the 

ACS is smaller than nonsampling error associated with 

the Census 2000 long-form 

survey. 

The Census Bureau provides 

information about the sample sizes used to produce 

ACS estimates. 

An address can only be 

selected for the ACS once 

every 5 years. 

Persons living in group 

quarters are also included 

in the ACS sample (since 

2006). 

Okay, one more important point to make about nonsampling errors. Permanent, 

highly trained Census Bureau employees carry out the fieldwork for the ACS. 

Temporary field workers, often with minimal training given the speed with 

which the census fieldwork must be accomplished, did the decennial census 

fieldwork, including gathering much of the long-form data. As a consequence, 

ACS nonsampling error is lower than nonsampling error affecting the long-form 

sample data. That’s very good news! 

Enough. But do check out the Quality Measures Web site when you get a 

chance. This is part of the process when using ACS data. Class adjourned. 

[December 6] Clerk Smith: Actually, that helped a lot. But here’s another 

issue. A town resident called me yesterday to say that she had received a letter 

telling her that her household was going to receive a questionnaire in the mail 

for the American Community Survey. Boy was I ready for this call! We talked 

about the ACS for a while, and she promised me she’d fill it out for herself and 

her husband. Here’s my question. How many people in Wolf Lake are likely to be 

interviewed for the ACS over the course of a year? 

[December 6] Demographer Adams: One of the detailed tables produced 

from the ACS and available through American FactFinder (B00001) summarizes 

the unweighted sample count of the population for every geographic area for 

which data are published. This is the count of the total number of people in the 

sample that were used to produce the ACS estimates. For Wolf Lake the 5-year 

total of 624 tells you the total sample over 5 years. If we divide that by 5, we 

get an approximation of 125 people in sample each year in Wolf Lake. 

Oh, you might also tell the woman asking about her address being selected this 

month for ACS interviewing that the Census Bureau will ensure that her address 

isn’t sampled more than once every 5 years. 

[December 7] Clerk Smith: You didn’t mention the nursing home. What about 

the people who live there? 

[December 7] Demographer Adams: That estimate we just came up with is 

for the total population and would include any people who were interviewed in 

group quarters facilities (e.g., college dormitories, nursing homes, and prisons). 

Residents of your nursing home may not be interviewed every year but over a 

5-year period, the ACS estimates will probably include data for people living in 

this facility. 

[December 9] Clerk Smith: Terry, I’m beginning to put together some tables 

for our planning exercise, and I meant to mention this much earlier. I really 

don’t understand some of what I am seeing in the ACS data. Our Census 2000 

population was 958 and the 2010 Census count released with the redistricting 

file this past March pegged our population at 1,006. The demographic office 

in the state government estimated our population at 1,002 in 2007—entirely 

consistent with the census counts. The 2005–2009 ACS estimate of 1,545 just 

doesn’t fit with the others. What’s going on here? 

Table B. Population Counts and Population Estimates for Wolf Lake 

April 1, 

2000 

July 1, 

2007 

2005–2009 

ACS 

April 1, 

2010 

Census Count 958 1,006 

State Demographic Estimate 1,002 

Census Bureau Estimate 993 1,545 

What Users of Data for Rural Areas Need To Know 17 

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

Because independent 

population estimates are 

used to produce ACS estimates of total population, 

ACS estimates will usually 

be consistent with other 

census-based estimates at 

the county level. 

Population estimates for 

subcounty geography 

reported in the ACS may 

differ from other population 

estimates prepared by the 

Census Bureau or by state 

demographic offices. 

[December 12] Demographer Adams: We touched on this issue last month. 

What you are seeing here is a difference that some small towns may see 

between the ACS period estimates, the census counts, and other population 

estimates. 

In most cases, the Census Bureau forces ACS 1-year estimates of total population at the county level to match the estimates made by the Census Bureau’s 

Population Estimates Program. If you were to look at the 2007 ACS 1-year estimates of total population for Lake County, they probably would be reasonably 

close to the 2007 Population Estimates Program estimates. Most of the time, 

and for most counties, these procedures bring everything into alignment. While 

those independently derived county estimates aren’t perfect by any means, the 

numbers probably look like you would expect them to look. 

There are situations, however, when these procedures produce ACS period estimates for small towns and villages that may look very different from estimates 

from both the state and the federal estimates programs. I think you’ve spotted 

one such instance, as this can happen with ACS estimates of population for 

subcounty units of government. For subcounty units like Wolf Lake, even the 

estimate of total population is derived as a sample-based estimate. There are 

lots of reasons why these subcounty estimates from the ACS will diff er from 

other available subcounty estimates. If, for example, a geographic entity is 

affected by seasonal population swings, as Wolf Lake certainly is, the ACS estimate of total population can differ considerably from the most recent census 

count or the independent population estimate made by the Census Bureau or 

by a state statistical agency. This could happen in small college towns or resort 

and tourist communities like Wolf Lake. Small rural communities that bring in 

migrant farm workers for seasonal harvesting may also see diff erences from 

census estimates. The Census Bureau is aware of this and the problems that 

some users, like you, could have. They are currently researching options that 

would involve the use of subcounty population estimates in the ACS weighting 

in hopes that this would minimize these types of differences. But, in the short 

term, you should be prepared for these differences to exist. Stay tuned! 

It’s also important to recognize that the ACS 5-year estimates are not forced to 

match any single set of independent estimates. Instead, the Census Bureau uses 

a simple average of the independent estimates from each of the 5 years of the 

period. For this reason, you shouldn’t expect the estimate of total population 

in the 5-year estimates to match estimates from any given year’s Population 

Estimates Program exactly, even at the county level. 

I’ll make one additional comment about this. The ACS was designed to provide 

characteristics of the population, not estimates of total population or housing 

units. While they are released with the ACS data, I recommend that, when available, you use the state or Census Bureau independent population estimates 

whenever you need this kind of information. You will have to use the ACS data 

for an update of the other characteristics for your report, and to do this you 

should express both the census and ACS estimates as percentages. You will 

probably see differences between the Census 2000 and the ACS percentages, 

and you should do the requisite statistical testing of these differences as we 

have described to determine if any of the differences are just the result of sampling error. You will have to bring together everything we have talked about to 

do this, but give it a try. Good luck. Be in touch when you have your tables and 

we can review them together. 

[December 13] Clerk Smith: I agree that I should probably use our state 

demographic agency’s intercensal population estimates, but I anticipate the 

ACS estimates of population could cause us some trouble. We receive a sizable 

payment each year through the state’s revenue sharing program. This payment, 

in excess of $50,000 for Wolf Lake in recent years, is largely based on our 

18 What Users of Data for Rural Areas Need To Know 

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

 

ACS estimates of total population cannot be used to 

challenge other population 

estimates—for example, 

those estimates produced 

by the Census Bureau’s 

Population Estimation 

Branch or other state agency 

population estimates.

population size. Population size also determines our eligibility for other state 

and federal programs. And as fate would have it, a population size of 2,000 is 

the threshold level for some of these programs. The ACS estimates are inching toward the 2,000 mark. Our town board has already asked me if we should 

challenge the state demographic office’s estimates (and the Census Bureau’s 

own estimates!) based on the higher ACS estimates. When the ACS places us 

over the 2,000 level, which it appears could happen in a few years, there’s 

going to be a lot of questions regarding which estimate is the correct one! 

[December 13] Demographer Adams: There’s not much more I can say 

about this, Pat. I have been told that ACS estimates of total population cannot 

be used in population estimates challenges. But I agree that there may be some 

important questions later on. You at least have the advantage of now understanding why this is happening. 

[December 14] Clerk Smith: I’m not going to worry about that now. At 

the moment I’m getting some serious heat from the county to finish my work 

before the end of the year. They’re asking me for my draft tables ASAP, and my 

own town board is also pressuring me to get this off my desk and move on to 

other things. 

Some of my draft tables are shown here. I’m working on a lot of other tables, 

but I thought I would share these with you to get some feedback. I’ve broken 

the topics into several small tables because, for our final report, that’s the way 

the data will be shown: small tables with associated commentary. As you’ll see, 

the ACS data feature rather prominently in my tables. As usual, your comments 

will be gratefully appreciated. 

 Table 1. Total Population—Town of Wolf Lake 

Location 

Census 

2000 

2007 

Intercensal 

Estimate 

2008 

Intercensal 

Estimate 

2010 

Census 

Town of Wolf Lake 958 993 993 1,006 

Share of Lake County 4.6% 4.5% 4.5% 4.5% 

Lake County 21,033 22,047 21,919 22,361

Table 2. Population by Age—Town of Wolf Lake 

Census 2005–2009 2010 

2000 ACS Census 

Total population
 958 1,545 1,006
 

Percent 100.0 100.0 100.0 

Less than 5 3.8 2.7 (±0.7) 3.1 

5–9 4.9 3.4 (±0.8) 4.4 

10–14 5.1 3.9 (±0.8) 5.3 

15–19 5.1 12.2 (±1.3) 6.2 

20–24 2.3 10.7 (±1.8) 3.9 

25–34 7.3 10.4 (±1.6) 7.1 

35–44 12.9 13.7 (±1.4) 12.9 

45–54 16.5 19.2 (±1.7) 17.1 

55–59 7.7 2.9 (±0.4) 7.3 

60–64 8.7 1.7 (±0.4) 8.2 

65–74 14.8 7.8 (±1.1) 14.0 

75–84 8.2 8.9 (±1.8) 8.1 

85 years and over 2.6 2.6 (±0.6) 2.4 

What Users of Data for Rural Areas Need To Know 19 

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

ACS estimates won’t always 

be the best source.

 Table 3. Population by Race—Town of Wolf Lake

Census 2005–2009 2010 

2000 ACS Census 

Total population 958 1,545 1,006 

Percent 100.0 100.0 100.0 

One race 99.3 98.1 (±0.7) 98.6 

White 98.2 98.1 (±0.7) 98.1 

Black/African American 0.0 0.0 (±1.6) 0.0 

American Indian or Alaska Native 0.5 0.0 (±1.6) 0.3 

Asian 0.1 0.0 (±1.6) 0.0 

Native Hawaiian or other Pacifi c 


Islander
 0.0 0.0 (±1.6) 0.0 

Some other race 0.4 0.1 (±0.1) 0.2 

Two or more races 0.7 1.8 (±0.7) 1.4 

White and Black NA 0.0 (±1.6) 0.0 

White and American Indian or 

Alaska Native NA 1.8 (±0.7) 1.4 

 Table 4. Household Income—Town of Wolf Lake

Census 

2000 

36.6 (±3.5) 

59.2 (±3.8) 

4.2 (±2.0) 

68.6 (±3.7) 

44.3 (±3.7) 

18.9 (±2.9) 

2005–2009 

ACS 

19.9 (±6.1) 

73.8 (±6.7) 

6.3 (±3.5) 

75.5 (±6.5) 

35.1 (±6.5) 

11.9 (±5.1) 

Income less than $25,000 

Income less than $25,000 to $99,999 

Income greater than $100,000 

Households with earnings 

Households with social security income 

Households with retirement income 

 Table 5. Year Structure Built—Town of Wolf Lake 

Census 2005–2009 

2000 

1,407 

ACS 

Total Housing Units 1,587

Percent 100.0 100.0 

Built 1980 or later 35.2 (±1.4) 27.9 (±2.5) 

Built 1960 to 1979 26.6 (±0.9) 28.7 (±1.6) 

Built 1959 or earlier 38.2 (±1.5) 43.4 (±2.6) 

[December 17] Demographer Adams: Pat, you’ve really done a wonderful 

job. This is a great beginning. In general, I like the way you’ve presented the 

ACS data showing the margin of error. I checked a few of the 90 percent MOEs 

you’ve calculated for numbers based on the Census 2000 long-form sample, 

and those look correct. 

Table 1. This is good. The best source of estimates of the total population should 

come from the decennial census, the Population Estimates Program, or other 

state-based projections. The ACS is not the best source for this information. 

20 What Users of Data for Rural Areas Need To Know 

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

have large MOEs, it’s probably time to sacrifi ce detail 

and collapse the cells of 

the table in order to obtain 

estimates based on a larger 

sample size. 

Use ACS estimates to show 

the percent distribution of 

characteristics of your community’s population. 

The ACS will reveal information about your community 

that will no longer be available from the census. 

The ACS will permit a community to compare itself 

with its neighbors every 

year. 

Table 2. Since you have the census counts by age both for 2000 and 2010, I 

think I would just use those data. You have the basic demographics from the 

2010 Census with which to update the town’s demographic data, so leaving 

the ACS data out of these tables would be fine. There’s no right or wrong way. 

I’ll leave it up to you. You know your community and the Lake County planners 

who will be analyzing your report better than I. If you leave in the ACS data, 

you may want to consider collapsing some of the smallest age categories to 

improve the reliability of those estimates. You will also need to explain why 

some of the age distributions are so diff erent. 

Table 3. My comment here is similar to that for Table 2. The mingling of census and ACS data sometimes isn’t necessary. The ACS is designed to give small 

communities detailed estimates of characteristics that are not picked up in the 

census. Since you have 2000 and 2010 Census results for race, there’s no need 

to also show the ACS 5-year estimates. 

Table 4. This is great. It shows exactly what the ACS is for—to give you updated 

characteristics (in percentage terms) of items not covered in the census. You 

know how to compute statistical significance on differences based on our 

discussion on November 22. The higher incomes in the ACS may be telling you 

that the summer residents have higher incomes than the residents that are 

included in the census. The ACS data could also be indicating income increases 

in Wolf Lake since 2000. 

Table 5. Again, I like what you did. I see you collapsed some of the detailed 

categories—that’s a good idea. In general, people have trouble recalling the specific year that older homes were built so combining homes built 1939 or earlier 

with those built 1940 to 1959 is probably giving you better estimates anyway. 

The ACS data provide you with refreshed information on the housing stock of 

Wolf Lake. 

[December 17] Clerk Smith: I’m glad to hear you say that they look reasonable. I couldn’t have ever done this without you, Terry. You’ve helped me 

develop confidence in using these new data and I hope I can help others do the 

same. 

[December 17] Demographer Adams: I have just a few general fi nal comments. First, there’s no question that the ACS is adding richness to the community profile of Wolf Lake. It’s documenting the characteristics of your community 

on a regular basis. Without a long form in the 2010 Census, you need to get 

comfortable with the ACS. 

Second, as I looked at these estimates, I would love to have compared them 

with some of the other small towns in Lake County or with the corresponding 

tables for Lake County itself. Such comparisons really add context to the data 

tables you’ve created for Wolf Lake. I do understand that the other towns are 

also preparing reports like yours and that the county planners are preparing a 

report for the county. Since these all follow a basic tabular template, the comparisons will be easy to make. But, again, the beauty of the ACS is that you can 

track changes over time with respect to how Wolf Lake is changing relative to 

your neighbors and relative to the county as a whole. 

I’ll say it once again—you’ve done a terrific job! Good luck with the next stage 

of this project! 

What Users of Data for Rural Areas Need To Know 21 

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

 

Epilogue
 

This 5-week conversation between Clerk Pat Smith and 

Demographer Terry Adams is entirely fictional. As a 

hypothetical small community invented for purposes of 

this handbook, Wolf Lake doesn’t really exist, but the 

kinds of issues highlighted in this handbook are quite 

real. Not all small communities in the United States 

are like Wolf Lake with its ACS estimates simultaneously influenced by seasonal swings in population, the 

abrupt closing of a major local firm, and geographic 

boundary changes. The combination of those forces 

was included in order to talk about them. But most 

small rural communities and small cities likely share at 

least one of these attributes, and for some such places, 

the attribute could be more extreme than illustrated 

here for Wolf Lake. 

It’s not clear how many communities share with Wolf 

Lake the influx of seasonal populations for 3 to 4 

months of the year. These communities include places, 

like Wolf Lake, where the seasonal peak occurs in the 

summer as well as places that have their seasonal peak 

Reference
 

in the winter months. There are many small towns with 

colleges and other institutions of higher learning where 

a common pattern of seasonality is shared. The same 

can be said about agricultural communities who rely on 

migrant farm workers to harvest and process seasonal 

agricultural commodities. 

In most aspects of our hypothetical dialogue presented 

here, Clerk Smith makes effective and good use of the 

ACS data. Her approach and her experience level are 

likely typical of small area data users interested in the 

characteristics of their communities. She dug into the 

technical documentation to learn about the ACS, and, 

wisely, she made excellent use of resource people to 

assist her in working through the ACS data and their 

complexities. Virtually all small community data users 

have access to demographic experts in their own 

state’s State Data Center agency at <http://www.census 

.gov/sdc/www/>. These agencies have experts on staff 

like Demographer Adams and are happy to work with 

data users like Clerk Smith. 

Citro, Constance F., and Graham Kalton (eds.). 2007. U.S. Census Bureau. 2002. Census 2000 Summary 


Using the American Community Survey: Benefi ts and File 3 Technical Documentation. Washington DC: U.S. 


Challenges. Washington DC: The National Academies Census Bureau.
 

Press.
 

22 What Users of Data for Rural Areas 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 offers 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. 

Coefficient of variation (CV). The ratio of the 

standard error (square root of the variance) to the 

value being estimated, usually expressed in terms 

of a percentage (also known as the relative standard 

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

reliability of the estimate. 

Comparison 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 confidence interval can be 

interpreted roughly as providing 90 percent certainty 

that the interval defined 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 Users of Data for Rural Areas Need To Know 23 

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 files. 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 profile that should be used if official 

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 confidentiality 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 files to be downloaded from the Census 

Bureau Web site. 

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates reflect 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 specifies the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifies 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 reflected in a 

table referred to as an allocation matrix. The rate is 

percentage of times this method is used. 

Margin of error (MOE). Some ACS products provide 

an MOE instead of confidence intervals. An MOE is the 

difference between an estimate and its upper or lower 

confidence bounds. Confidence bounds can be created 

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

upper bound) and subtracting the margin of error from 

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

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

level. 

Multiyear estimates. Three- and fi ve-year estimates 

based on multiple years of ACS data. Three-year 

estimates will be published for geographic areas with 

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

will be published for all geographic areas down to the 

census block group level. 

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

Nonsampling error. Total survey error can be 

classified 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. 

24 What Users of Data for Rural Areas 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 official Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defines the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized files 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 different 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, differences 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 difference 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 different at the 90 

percent level” if their difference is large enough to infer 

that there was a less than 10 percent chance that the 

difference 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. 

What Users of Data for Rural Areas Need to Know 25 

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

 

Appendix 1.
 

Understanding and Using ACS Single-Year and Multiyear Estimates
 

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 specific 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 differences in time periods between single-year 

and multiyear ACS estimates affect 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 specific 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 reflect 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 different 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 differ markedly from estimates based on a 

“point-in-time” survey. 

An extreme illustration of how the single-year estimate 

could differ 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. 

Table 1. Percent in Labor Force—Winter Village 

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 

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

Appendix A-1 

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 specific 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 specific 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 final 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 

Source: U.S. Census Bureau. 

Differences 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 different 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. 

A-2 Appendix 

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

 

Table 3. Example of Differences 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 

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

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 benefit 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 benefit 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 suffered 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 

the estimates. All of these factors, along with an 

understanding of the differences 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 offer more current estimates, 

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

determine the fitness 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. 

Appendix A-3 

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 sufficiently 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 “fitness 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
 

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

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

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

2000–2004 2000–2004 2002–2004 2004 

Total family Pct. in Pct. in Pct. in SE SE SE 

type poverty poverty poverty 

 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. 

A-4 Appendix 

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

 

Making Comparisons Assessing Change
 

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 specific pairs of comparisons. Whenever you want to make a comparison 

between two different 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.

Source: U.S. Census Bureau. 

Users are encouraged to make comparisons between 

sequential single-year estimates. Specific 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 differences 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-fifths for 5-year estimates). Thus, 

as shown in Figure 1, when comparing 2006–2008 

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

the differences in overlapping multiyear estimates are 

driven by differences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those differences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

of this difference is difficult, these comparisons can be 

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

refer to Appendix 4 for more information. 

Figure 1. Data Collection Periods for 3–Year Estimates 

Period 

2007–2009 

2006–2008 

Jan. Dec. 

2006 

Jan. Dec. 

2007 

Jan. Dec. 

2008 

Jan. Dec. 

2009 

Appendix A-5 

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

 

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 effect” 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 

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

As Figure 2 shows, while the single-year estimates 

fluctuate 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. 

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 

Estimated Civilian Veterans 

2007 

2006–2008 

2008 

2007–2009 

2006–2010 

2009 

2008–2010 

2007–2011 

2010 

2009–2011 

2008–2012 

Period 

2011 

2010–2012 

2012 

1-year estimate 

3-year estimate 

5-year estimate 

Source: U.S. Census Bureau. Based on data from the Multiyear Estimates Study. 

A-6 Appendix 

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 first occur. Single-year estimates, if associated with sufficiently 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 High

 Multiyear Estimates 

er, City X Single-Year, 

2007 

2006–2008 

2008 

2007–2009 

2006–2010 

2009 

2008–2010 

2007–2011 

2010 

2009–2011 

2008–2012 

Period 

Percent of Population 

45 

46 

47 

48 

49 

50 

51 

52 

53 

54 

55 

2011 

2010–2012 

2012 

1-year estimate 

3-year estimate 

5-year estimate 

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 
 

Appendix 2.
 

Differences Between ACS and Decennial Census Sample 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 differences 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 differences 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 different 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. Defining the rules in a dissimilar way can affect 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 specific 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 reflect 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 reflect the full calendar 

year; 3-year and 5-year estimates reflect 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 difference 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 

findings 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 definite ties to a single 

housing unit or group quarters, some people may stay 

in different places for significant 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. Differences 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 

A-8 Appendix 

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 differences in the ACS and census data as a consequence of the different residence rules are most likely 

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

and current residence concepts could result in diff erent 

residence decisions. Appreciable differences 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 reflect 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 reflect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data reflecting the entire 

period of decennial data collection, and are greatly 

influenced 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 reflect 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 different time, and reference period than 

the census. 

Some specifi c differences 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 specific 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 inflation-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—differ 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 affect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

home. Census 2000 included installment loan costs in 

Appendix A-9 

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 differences 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 different levels and 

types of coverage error, different 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. 

A-10 Appendix 

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

Appendix 3.
 

Measures of Sampling Error
 

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 differ from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a different set of 

sample units been selected. 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 coefficient 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 differ 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 differences 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 confidence. 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 confidence level. Thus, the 

MOEs published with the ACS estimates correspond 

to a 90-percent confidence level. However, users may 

want to use other confidence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

for the specific 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 confidence 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 confidence level would imply that 10 of the 

comparisons would be expected to be found signifi ­

cant even if no differences actually existed. Using a 

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

Appendix A-11 

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

 

 

 

 

 

 

 

 

 

 

 

 

 

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 confidence 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 confidence level can be expressed as 

1.960 MOE95 MOEACS 1.645 

2.576 MOE99 MOEACS 1.645 

where MOE is the ACS published 90 percent MOE ACS 

for the estimate. 

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. 

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 confidence level would be derived as follows: 

1.960 MOE95 r12,357 r14,723 1.645 

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 

MOEACS SE 

1.645 

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. 

where MOE is the positive value of the ACS pub- ACS 

lished 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 

12,357 SE 7,512 1.645 

Confi dence Intervals 

A confidence 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 

confidence” or “confidence 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 confidence 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 first be derived as 

explained previously. 

Construction of the lower and upper bounds for the CI 

can be expressed as 

L X CL 

ˆ  MOECL 

U X CL 

ˆ  MOECL 

where Xˆ is the ACS estimate and 

MOECL 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

CICL LCL ,UCL . 

3

 Users are cautioned to consider logical boundaries when creating 

confidence 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. 

A-12 Appendix 

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

 

 

 

 

 

 

 

 

 

 

 

 

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

confidence 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 confidence level derived above (+14,723). 

L95 771,782 14,723 757,059 

U95 771,782 14,723 786,505 

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 confidence 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 

( ) 

18,355 20,000 21,645 

Coeffi cients of Variation 

A coefficient 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 

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 Coefficients of Variation From Standard 

Errors 

The CV can be expressed as 

SE CV u100

X ˆ

where Xˆ is the ACS estimate and SE 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). 

7,512 CV u100 0.1% 

771,782 

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. 

Deriving Sampling Error Measures From 

Published MOE 

Margin Error (MOE) for Alternate Confi dence Levels 

MOE MOE ACS

.1 645

.1 960 95 

MOE MOE ACS

.1 645

.2 576 99 

Standard Error (SE) 

MOE ACS SE 

1.645 

Confidence Interval (CI) 

CI    X  MOE , X  MOE  CL CL CL

Coefficient of Variation (CV) 

SE CV u100

X ˆ

Appendix A-13 

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

 

 

 

 

 

 

 

 

 

 

 

Calculating Margins of Error for Derived 

Estimates 

One of the benefits 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 confidence intervals (pre-2005) or margins 

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

confidence 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 

differences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

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

confidence level other than 90 percent is desired, the 

user should first calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confidence 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: 

MOE r ¦ MOE 2 

agg c 

c 

where MOE is the MOE of the c

th component esti­ c mate. 

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. 

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 17,190 +1,854
 

 (Component 3)
 

The aggregate estimate is: 

X X ˆ ˆ ˆ ˆ

Fairfax Arlington Alexandria  X  X

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

Obtain MOEs of the component estimates: 

MOEFairfax r3,303 , 

MOEArlington r2,011, 

MOEAlexandria r1,854 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs. 

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

r 18,391,246 r4,289 

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. 

A-14 Appendix 

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

 

 

den

 

 

 

 

 

 

 

 

 

 

 

 

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

2 2 2 r MOE  ( pˆ * MOE ) num den MOEp

Xˆ 

den 

where MOE is the MOE of the numerator. num 

MOEden is the MOE of the denominator.

X ˆ

num pˆ is the derived proportion. X ˆ

den 

Xˆ 

num is the estimate used as the numerator of the 

derived proportion.

Xˆ 

den 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.

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 31,713 +601 

(denominator) 

 The estimated proportion is: 

Xˆ 

gradBF 4,634 

pˆ 0.1461

X ˆ 31,713 BF 

where is the ACS estimate of Black females 25 Xˆ 

gradBF 

years of age and older in Fairfax County with a graduate degree and Xˆ 

BF 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). 

MOEnum r989, MOEden r601 

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator. 

2 2 2 MOE  ( pˆ * MOE ) num den 

989

2 [0.14612 * 6012

] 

978,121 7,712.3 970,408.7 

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

r 970,408.7 r 985.1 MOE p r0.0311

31,373 31,373 

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: 

2 2 2 r MOE  (Rˆ * MOE ) num den MOER X ˆ

den 

where MOE is the MOE of the numerator. num 

MOEden is the MOE of the denominator.

Xˆ ˆ num R is the derived ratio.

ˆ 

Xˆ 

X den 

num is the estimate used as the numerator of the 

derived ratio.

Xˆ 

den is the estimate used as the denominator of the i 

derived ratio. 

Appendix A-15 

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. 

Table 3. Data for Example 3 

+989 

+1,328 

Characteristic Estimate MOE 

Black females 25 years and 

older with a graduate 4,634 

 degree (numerator) 

Black males 25 years and

 older with a graduate degree 6,440 

(denominator) 

The estimated ratio is: 

ˆ Xˆ 

gradBF 4,634 R 0.7200 

Xˆ 6,440 gradBM 

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). 

MOEnum r989, MOEden r1,328 

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator. 

2 2 2 MOE  (Rˆ * MOE ) num den 

989

2 [0.72002 * 1,3282

]
 

978,121 913,318.1 1,891,259.1
 

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

r 1,891,259.1 r1,375.2 MOER r0.2135 

6,440 6,440 

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 first squared estimate by the second estimate’s squared MOE. 

4) Multiply the second squared estimate by the 

first 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: 

2 2 2 2 MOE AuB r A u MOE B  B u MOEA 

where A and B are the first and second estimates, 

respectively.

MOEA is the MOE of the fi rst estimate.

MOEB 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. 

Table 4. Data for Example 4 

+2,423 

+2.7% 

Characteristic Estimate MOE 

Black workers 16 years and 50,624 over (fi rst estimate) 

Percent of Black workers 16 

years and over who com­ 13.4% mute by public transportation (second estimate)
 

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: 

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

r1,405 

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. 

A-16 Appendix 

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Calculating MOEs for Estimates of “Percent Change” or Calculate the MOE by dividing the square root of the “Percent Diff erence” prior result by the denominator ( Xˆ ). 1 

The “percent change” or “percent diff erence” between r 1,091,528,529 r 33,038.3 two estimates (for example, the same estimates in two MOER r0.0433 

different years) is commonly calculated as 762,475 762,475 

X ˆ ˆ  X Finally, the MOE of the percent change is the MOE of Percent Change 100% * 2 1 

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

1 

The text box below summarizes the formulas used to Because Xˆ is not a subset of Xˆ , the procedure 2 1 calculate the margin of error for several derived esti­ to calculate the MOE of a ratio discussed previously mates. 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 different house in the U.S. 1 year ago. 

Derived Proportions 

Calculating Margins of Error for Derived Estimates 

Aggregated Count Data 

MOE r ¦c 

agg MOE c

2 

Table 5. Data for Example 5 

Characteristic Estimate MOE 

2 2 2 r MOE  (pˆ * MOE ) num den MOE p 

X ˆ Persons who lived in a den 

 different house in the U.S. 802,210 +22,866 

 1 year ago, 2006 

Persons who lived in a 


 different house in the U.S. 762,475 +22,666
 

 1 year ago, 2005
 

Derived Ratios 

den 

num den 

R 

X 

MOE (R * MOE ) MOE ˆ 

ˆ 2 2 2 r 

The percent change is: 

X ˆ ˆ

2  X1 Percent Change 100% * 

Xˆ

1 

§ 802,210  762,475 · 100% * ̈  ̧ 5.21% © 762,475 1 

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

X ˆ

2 802,210 R ˆ 1.0521 

Xˆ 

1 762,475 

The MOEs for the numerator ( Xˆ ) and denominator 2 

( Xˆ ) are: 1 

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

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

product of the squared ratio and the squared MOE of 

the denominator (MOE1): 

2 2 2 MOE2  (Rˆ * MOE1 ) 

22,866

2 

[1.0521

2 * 22,6662

] 

1,091,528,529 

Appendix A-17 

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

 

 

 

 

 

Appendix 4.
 

Making Comparisons
 

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 differences between estimates 

are statistically significant. Statistical signifi cance 

means that there is statistical evidence that a true 

difference exists within the full population, and that 

the observed difference is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical significance 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 significance described below. This approach 

will allow the user to ascertain whether the observed 

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

is statistically signifi cant). 

The test for significance 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 defined 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. 

4

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

use the value 1.65 rather than 1.645. 

5) Calculate the difference 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 

confidence (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 significant at the level of confi dence 

corresponding to the critical value used in (7). 

Algebraically, the significance test can be expressed as 

follows: 

Xˆ ˆ

1  X 2 If ! Z ,, then the diff erence CL

SE1

2  SE22 

between estimates Xˆ

1 and Xˆ2 is statistically signifi cant 

at the specifi ed confidence level, CL 

where XEˆ

1i is estimate i (=1,2)

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

 is the critical value for the desired confi ZCL 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 

difference 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 significant. Using the formula above: 

Xˆ ˆ

1  X 2 22.0  21.5 

SE1

2  SE22 0.12

2  0.122 

0.5 0.5 0.5 2.90 

0.015  0.015 0.03 0.173

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

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

difference in the percentages is statistically signifi cant 

at a 99-percent confidence level. This is also referred 

to as statistically significant at the alpha = 0.01 level. 

A rough interpretation of the result is that the user can 

be 99 percent certain that a difference exists between 

the percentages of households with one or more 

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

A-18 Appendix 

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 

Xˆ ˆ

1  X 2 22.5  22.1 

SE1

2  SE22 0.20

2  0.252 

0.4 0.4 0.4 1.25 

0.04  0.0625 0.1025 0.320

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

for a confidence 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 sufficient degree that the 

observed difference 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 significance 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 differences 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 

differences 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 differences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a finding of statistical significance will still 

be correct, but a finding 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. 

Comparisons Across Time Periods 

Comparisons of estimates from different time periods 

may involve different 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 

differing lengths (e.g., comparing 2006–2008 with 

2009–2014), as they are measuring the characteristics 

of the population in two different ways, so diff erences 

between such estimates are difficult 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 affected. 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 different, but overlapping 

periods is challenging since the difference 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 differences is calculated. While the interpretation 

of this difference is difficult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of difference should not be interpreted as a 

reflection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

over time is complicated when change is being evaluated using multiyear estimates. From a technical standpoint, change over time is best evaluated with multiyear estimates that do not overlap. At the same time, 

Appendix A-19 

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: 

SE(Xˆ

1  Xˆ 2 ) # 1 C SE1

2  SE22 

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 difference between the two estimates using the 

method outlined in the previous section with one modification; substitute 2 2 for 1 C SE1  SE2 

SE 2  SE 2 in the denominator of the formula for 1 2 

the signifi cance test. 

Comparisons With Census 2000 Data 

In Appendix 2, major differences between ACS data and 

decennial census sample data are discussed. Factors 

such as differences 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 affect 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 affected by these diff er­ 5 ences. 

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 

5

 Further information concerning areas and characteristics that do not 

fit the general pattern of comparability can be found on the ACS Web 

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

statistical significance of a difference 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 finding of statistical significance 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 finding 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 differ 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 

different than coverage of Census 2000. The impact of 

this difference will likely affect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large differences 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 

A-20 Appendix 

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 difference 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. 

Appendix A-21 

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

 

 

 

Appendix 5.
 

Using Dollar-Denominated Data
 

Dollar-denominated data refer to any characteristics 

for which inflation adjustments are used when producing annual estimates. For example, income, rent, home 

value, and energy costs are all dollar-denominated 

data. 

Inflation will affect 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 inflation on observed 

differences of dollar-denominated data across time 

periods, users should adjust for the effects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for inflation. 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 different income reference 

periods throughout an interview year, 12 diff erent 

income inflation adjustments are made. Monthly CPIU-RSs are used to inflation-adjust the 12 reference 

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no inflation adjustments for 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 inflation adjustment factor as the ratio 

of the CPI-U-RS from the more recent year to the 

CPI-U-RS from the earlier year. 

3) Multiply the dollar-denominated data estimated for 

the earlier year by the inflation adjustment factor. 

The inflation-adjusted estimate for the earlier year can 

be expressed as: 

ˆ CPI X Y 2 Xˆ Y1, Adj Y1 CPIY1 

where CPIY1 is the All Items CPI-U-RS Annual Average 

for the earlier year (Y1).

CPIY 2 is the All Items CPI-U-RS Annual Average for the 

more recent year (Y2).

Xˆ

Y1 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: 

296.1 Xˆ

2005, Adj u $37,700 $38,936 286.7 

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 inflation adjustments. The 

Census Bureau uses the All Items CPI-U-RS Annual Averages for each year in the multiyear time period to calculate a set of inflation adjustment factors. Adjustment 

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from 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 

inflation 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 inflation-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. 

A-22 Appendix 

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

 

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 inflation 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 inflation-adjusted estimate for the earlier years can 

be expressed as: 

CPI Xˆ P2 Xˆ P1, Adj P1 CPI P1 

where CPI P1 is the All Items CPI-U-RS Annual Average 

for the last year in the earlier time period (P1). 

CPI P2 is the All Items CPI-U-RS Annual Average for the 

last year in the most recent time period (P2).

Xˆ P1 is the published ACS estimate for the earlier time 

period (P1). 

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), first 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: 

296.1 Xˆ

20012003, Adj u$32,000 $35,080 270.1 

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 inflation-adjusted to 2006 dollars) versus 

$39,000 (2004–2006, already in 2006 dollars). 

Issues Associated With Infl ation Adjustment 

The recommended inflation 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 inflation adjustment uses the All Items CPI, it will 

not refl ect differences that may exist across characteristics such as energy and housing costs. 

Appendix A-23 

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

 

Appendix 6.
 

Measures of Nonsampling Error
 

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 affect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors. 

Broadly speaking, nonsampling error refers to any error 

affecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error. 

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 suffer from errors 

due to housing undercoverage. 

The final 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 definition 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 differ 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 

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 effect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the final survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

unit nonresponse bias. This will happen if the characteristics of nonresponding units differ from the characteristics of responding units. 

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 final data files. For example, errors may occur if 

data entry of questionnaire information is incomplete 

A-24 Appendix 

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 

affects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the final results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size. 

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 final survey interviews
 

Group quarters people (beginning with the 2006 ACS) 

Number of initial persons selected 

Number of final survey interviews 

Sample size measures may be useful in special circumstances when determining whether to use single-year 

or multiyear estimates in conjunction with estimates of 

6 The sample size measures for housing units (number of initial addresses 

selected and number of final 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. 

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 Jefferson 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 Jefferson 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 insufficient 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 different 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. 

Appendix A-25 

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

 

Appendix 7.
 

Implications of Population Controls on ACS Estimates
 

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defined 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 reflect 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 

official 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 differences 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. 

A-26 Appendix 

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data 
Appendix 8.
 

Other ACS Resources
 

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 final 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 Different 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. 

Appendix A-27 

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