What Researchers Need to Know i

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

Issued

May 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 Researchers Need to Know
Acknowledgments Warren A. Brown, University of Georgia at Athens, drafted this handbook 

for the U.S. Census Bureau’s American Community Survey Offi ce. Kennon R. 

Copeland and John H. Thompson of National Opinion Research Center at 

the University of Chicago drafted the technical appendixes. Edward J. Spar, 

Executive Director, Council of Professional Associations on Federal Statistics, 

Frederick J. Cavanaugh, Executive Business Director, Sabre Systems, Inc., 

Susan P. Love, Consultant, Linda A. Jacobsen, Vice President, Domestic 

Programs, Population Reference Bureau, and Mark Mather, Associate Vice 

President, Domestic Programs, Population Reference Bureau, provided initial 

review of this handbook.

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

Community Survey Offi ce, provided the concept and directed the 

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

Understanding and Using American Community Survey Data. Cheryl V. 

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

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

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

this handbook series.

The American Community Survey program is under the direction of 

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

Weinberg, Assistant Director for the American Community Survey and 

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

Offi ce.

Other individuals who contributed to the review and release of these 

handbooks include Dee Alexander, Herman Alvarado, Mark Asiala,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Zimolzak.

Linda Chen and Amanda Perry of the Administrative and Customer Services 

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

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

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

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

and production management.
A Compass for Understanding and Using 

American Community Survey Data Issued May 2009

U.S. Department of Commerce

Gary Locke, 

Secretary

Vacant, 

Deputy Secretary

Economics and Statistics Administration

Vacant, 

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg, 

Acting Director

What Researchers Need to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What Researchers Need to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2009.

Economics and Statistics 

Administration

Vacant,

Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg,

Acting Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Offi cer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Offi ce

ECONOMICS

AND STATISTICS

ADMINISTRATION
What Researchers Need to Know iii

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

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

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

Information Available From the ACS .............................................................2

A Reengineered Census ................................................................................3

Comparison of ACS and Decennial Census Sample Data ..............................3 

Period and Point-in-Time Estimates .......................................................................3

 Residence Rules ...................................................................................................3

 Estimates of Income Characteristics .....................................................................4

 Population Below the Poverty Level ......................................................................5

Geographic Mobility and Migration .......................................................................5

Sampling Error ..............................................................................................6

Data Release and Geographic Areas .............................................................7

Access to Data Products ...............................................................................8

ACS Summary Files ...............................................................................................8

 ACS Public Use Microdata Sample Files .................................................................9

Case Studies ............................................................................................... 11 

Case Study 1: Interpreting Small Area 5-Year Period Estimates ............................11

 Case Study 2: Estimates of Income .....................................................................12

 Case Study 3: Trends in the Poverty Rate ............................................................14

Exercises .............................................................................................................15

Exercise 1: Access Summary Tabulations ............................................................15

Exercise 2: Access, Tabulate, and Download ACS Microdata Records ..................18

Exercise 3: Compare ACS Poverty Data With Census 2000 ..................................20

A Resource for Academic Researchers ....................................................... 22

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

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

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

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

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

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

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

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

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

 Appendix 8. Other ACS Resources ...................................................................A-27
iv What Researchers Need to Know

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

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

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

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

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

concepts that are fundamentally diff erent from those associated with sample 

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

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

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

The ACS Compass Products. 

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

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

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

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

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

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

they cover major stakeholder groups familiar to the Census Bureau.

General data users Congress 

High school teachers Puerto Rico Community Survey data

 users (in Spanish)

Business community Public Use Microdata Sample (PUMS) data

 users

Researchers Users of data for rural areas

Federal agencies State and local governments

Media Users of data for American Indians and 

 Alaska Natives

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

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

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

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

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

target audience. The Census Bureau developed additional ACS Compass Products

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

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

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

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

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

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

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

well as concerns and questions that will arise. 

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

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

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

you have learned. 
What Researchers Need to Know 1

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

Academic researchers have mined decennial census 

data on a variety of topics with geographic perspectives ranging from neighborhoods to the nation. The 

American Community Survey (ACS) is the new source 

for detailed demographic, social, economic, and housing characteristics. The Census Bureau is discontinuing 

the decennial long-form sample in the 2010 Census. 

For more information about the decennial census long 

form, review the text box on this page. The ACS is not 

simply a replacement, but rather a diff erent kind of 

survey, utilizing continuous measurement approaches 

and a rolling sample. This handbook is aimed at three 

groups of academic researchers:

• Those who are familiar with the data fi les from 

the decennial census long form and are looking 

to continue their research with the ACS.

• Those who are familiar with using data—summary tabulations and microdata records—from 

complex sample surveys, although not necessarily decennial census data.

• Those who are learning about research methods 

and analysis of data from sample surveys—such 

as graduate students in methods classes. The 

materials in this handbook are no substitute for 

a methods course where analysis of secondary 

data from sample surveys is dealt with, along 

with the concepts of survey methodology and 

statistics for sampling. The handbook should, 

however, serve as an excellent supplementary 

text in such courses.

This handbook is intended to acquaint researchers 

with issues that may aff ect their analysis of ACS data. 

We have included case studies to illustrate uses of the 

ACS and exercises for readers to practice accessing and 

analyzing ACS data. The handbook does not cover all 

the issues of survey design and methodology associated with the ACS. The Census Bureau has produced 

a detailed summary of the methods used in the ACS 

entitled, Design and Methodology: American 

Community Survey. It can be accessed at 

<http://www.census.gov/acs/www/Downloads

/tp67.pdf>. This document provides a comprehensive 

discussion and is an essential reference for academic 

users of the ACS. 

This handbook is supplemented with a set of technical appendixes. The appendixes vary in their level of 

methodological sophistication. Some readers may fi nd 

one or two of the appendixes too elementary while 

others may fi nd a few are too advanced. Throughout 

the handbook, we will refer to appendixes that provide 

expanded explanations of ACS issues. A glossary at 

the back of the handbook provides defi nitions of key 

terms.

The handbook focuses on the following topics relating 

to use of the ACS in academic research: 

• Estimates for geographic areas

• Access to data products

• Sampling error

• Residence rules 

• Income and poverty 

• Migration

• Comparisons with Census 2000

Much of the academic research utilizing data from the 

decennial census long-form sample and now the ACS 

can be divided into three categories: 

• An informational query for a table or graph to 

illustrate a point, a use that university researchers have in common with most users of census 

data. This includes looking for data tables and 

fi gures illustrating a point such as diff erences in 

the poverty rate or the percent in poverty that 

are recent migrants.

Introduction

THE DECENNIAL CENSUS LONG-FORM SAMPLE

Every 10 years since 1790, Congress has authorized funds to conduct a national census of the U.S. 

population. The decennial census is required by the U.S. Constitution. Recent censuses have consisted 

of a “short form,” which included basic questions about age, sex, race, Hispanic origin, household 

relationship, and owner/renter status, and a “long form.” The long form was used at only a sample of 

households and included not only the basic questions on the short form but also detailed questions 

about social, economic, and housing characteristics. The questions on the long form supplied the raw 

data needed for a range of programs aff ecting education, veterans, employment, housing and community development, public health care, commuting, services for the elderly and disabled, and assistance 

programs for low-income families and children. About $300 billion in federal program funds are distributed each year based, in whole or in part, on these data.
2 What Researchers Need to Know

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

• A query for records with specifi c attributes for 

individual housing units, households, and people 

to create a data fi le with numerous observations 

that will be statistically analyzed. These are data 

fi les where the unit of analysis is the housing 

unit, household, or person extracted from the 

microdata fi les—either for the entire nation or 

for selected geographic areas. 

• A query for records with specifi c attributes for 

geographic summary areas to create a data fi le 

with numerous observations that will be statistically analyzed. This involves data fi les for 

geographic areas, such as metropolitan areas, 

counties, places, minor civil divisions, tracts, or 

block groups, based on tabulations presented 

in summary fi les, such as Summary File 3 (SF3) 

from the Census 2000 long-form sample.

 Table 1. Subjects Included in the American Community Survey

Demographic Characteristics

Age

Sex

Hispanic Origin

Race

Relationship to Householder 

(e.g., spouse)

Economic Characteristics

Income

Food Stamps Benefi t

Labor Force Status

Industry, Occupation, and Class 

of Worker

Place of Work and Journey to 

Work

Work Status Last Year

Vehicles Available

Health Insurance Coverage*

Social Characteristics 

Marital Status and Marital History*

Fertility

Grandparents as Caregivers

Ancestry

Place of Birth, Citizenship, and 

Year of Entry

Language Spoken at Home

Educational Attainment and 

School Enrollment

Residence One Year Ago

Veteran Status, Period of Military 

Service, and VA ServiceConnected Disability Rating*

Disability

Housing Characteristics

Year Structure Built

Units in Structure

Year Moved Into Unit

Rooms

Bedrooms

Kitchen Facilities

Plumbing Facilities

House Heating Fuel

Telephone Service Available

Farm Residence

Financial Characteristics

Tenure (Owner/Renter)

Housing Value

Rent

Selected Monthly Owner Costs

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

 Source: U.S. Census Bureau.

Information Available From the ACS

The primary reason for the ACS is to help Congress 

determine funding and policies for a wide variety of 

federal programs. For this reason, the topics covered 

by the ACS are diverse. Nearly all topics in the ACS 

were also included on the Census 2000 long form. 

The ACS produces data on the demographic, social, 

and economic characteristics of the U.S. population 

and the physical and fi nancial characteristics of the 

nation’s housing. Table 1 lists the subjects included in 

the ACS. Many of the subjects contain numerous 

characteristics. For example, “Journey to Work” 

includes data on means of transportation (automobile, 

bus, bicycle, walking), travel time (both duration and 

time departed), and whether or not a carpool is used. 

For more information about these subjects and the 

federal legislative and program uses of the data, see 

<http://www.census.gov/acs/www/Downloads

/Final_2010_Census_and_American_Community

_Survey_Subjects_Notebook.pdf>.
What Researchers Need to Know 3

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

produces the nation’s offi cial estimates of poverty. 

The Census Bureau is preparing geographic area profi les, summary tabulations, and public use microdata 

fi les for each year’s release of ACS data. 

In order to deliver more timely information for all the 

geographic areas served by the decennial long form, 

the Census Bureau has designed the ACS as a sample 

survey using a continuous measurement approach 

to data collection. A sample of 3 million addresses is 

drawn from the Census Bureau’s Master Address File 

(MAF) each year. For geographic areas with populations larger than 65,000, the sample is suffi cient to 

produce reliable estimates based on a year’s worth 

of responses. However, in order to provide estimates 

for areas with smaller populations, the sample must 

be accumulated over a number of years. The Census 

Bureau is producing 3-year estimates for areas down 

to populations of 20,000 or more and 5-year estimates 

for all units of census geography. This requires data 

users to understand the diff erences between 1-, 3-, and 

5-year estimates—their availability and reliability. Refer 

to Appendix 1 for a more thorough explanation of 

multiyear estimates and Appendix 2 for greater detail 

on continuous measurement procedures and how they 

compare with “point-in-time” estimates. 

The American Community Survey is one component 

of a reengineered decennial census and is designed to 

address the nation’s need for more current information 

on the characteristics of its population and housing. 

Originally, the ACS was seen as a “replacement” for the 

long form. There was concern that the term “replacement” may have given users the impression that the 

parts were interchangeable, as in replacing a worn out 

set of spark plugs on your car. Rather, the change is 

more akin to switching from reliance on an internal 

combustion engine to a hybrid. On the outside, it looks 

the same and performs the same functions—only it is 

substantially diff erent once you look under the hood 

and understand what provides the propulsion.

The ACS off ers a number of benefi ts to researchers: 

timeliness, comparability, reliability, and numerous 

data products. The ACS data are timely and updated 

annually, compared with the decennial census longform data that were released once a decade. The 

ACS uses uniform methods across the United States, 

enabling comparisons across geographic areas and 

over time. While the annual ACS sample size (about 2.5 

percent) is smaller than the decennial long-form sample 

(about 16.7 percent), it is roughly 40 times larger than 

the Current Population Survey’s Annual Social and 

Economic Supplement (March Supplement), which 

A Reengineered Census

Comparison of ACS and Decennial Census 

Sample Data

Diff erences between the design and methodology of 

the ACS and the decennial census can aff ect analyses 

of change based on data from the two surveys. A few 

of these diff erences are discussed here. For more information on this topic see:

• Appendix 2, “Diff erences Between ACS and 

Decennial Census Sample Data”

• Census Bureau’s Web page “How to Use the Data: 

Guidance on Comparing ACS Data to Other 

Sources” <http://www.census.gov/acs/www

/UseData/compACS.htm> 

• Ten Things to Know About the American 

Community Survey (2005 Edition) <http://mcdc2

.missouri.edu/pub/data/acs2005/Ten_things_to

_know.shtml> by the Missouri State Data Center

Period and Point-in-Time Estimates

A major diff erence for users to understand is the 

change from estimates referenced to a specifi c date, 

as in April 1, 2000, for Census 2000, to ACS period 

estimates based on nearly daily collection of information over the entire calendar year. In practice, most of 

the decennial census socioeconomic sample data only 

approximate April 1 since the data are collected over 

a 6-month period and do not measure these characteristics as they existed on Census Day. For geographic 

areas of fewer than 65,000 people, it is necessary 

for the Census Bureau to accumulate 36 months of 

responses or even 60 months of responses prior to 

releasing ACS multiyear estimates. All ACS estimates 

are period estimates and are interpreted as the average 

values over the full time period. For example, 2007 

ACS 1-year estimates describe the average characteristics for calendar year 2007 while 2005–2007 ACS 

3-year estimates describe the average characteristics 

for the 3-year period of 2005, 2006, and 2007. 

Residence Rules

Residence rules defi ne who is included in a census or 

survey. The ACS does not use the same residence rule 
4 What Researchers Need to Know

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

and the census may provide diff erent pictures of the 

college town’s characteristics. 

Estimates of Income Characteristics

The Census Bureau cautions that income characteristics in the ACS and the decennial census may not be 

comparable because of some important diff erences in 

the universe and data collection. These diff erences are 

explained on the Census Bureau Web site at <http://

www.census.gov/acs/www/UseData/Comparison

_Guidance.htm#srcinc>. 

While the Census Bureau designed the ACS questions, 

edits, and imputation procedures to be the same as 

those used in Census 2000, a major diff erence is 

the reference period. While Census 2000 asked for a 

respondent’s income during 1999, the ACS collects 

data throughout the year on an on-going, monthly 

basis and asks for a respondent’s income over the past 

12 months. A paper presented by Census Bureau staff 

at the 2003 American Statistical Association Meetings 

reports on a comparison study of income data in 

Census 2000 and the 2000 ACS. The paper is available 

on the Census Bureau Web site at <http://www.census

.gov/acs/www/Downloads/ACS/ASA_nelson.pdf>. 

Using a split panel test of the diff erences in reported 

income associated with the diff erent reference periods, the investigators found income from wages and 

salary to be statistically higher for the panel asked to 

report on income for the previous calendar year when 

compared with income reported throughout the year. 

Income from self-employment was also signifi cantly 

lower.

Data on income are collected from respondents as 

“Income in the past 12 months.” Instructions to the 

respondent explain that the past 12 months refer to 

“the period from today’s date one year ago up through 

today.” See examples of ACS questionnaires at <http://

www.census.gov/acs/www/SBasics/SQuest/SQuest1

.htm> and Subject Defi nitions at <http://www.census

.gov/acs/www/SBasics/SDef.htm>. This means that 

responses collected in January report income almost 

entirely from the calendar year prior to the reporting year and responses collected in December are for 

income received almost entirely in the reporting calendar year. The other months are a mix of income from 

the prior calendar year and the reporting calendar year. 

In order to reduce the eff ect of infl ation on the estimates of income, all income data are adjusted using 

the Consumer Price Index and stated in terms of annual 

dollars for the most recent year covered in the release. 

Only the income dollar amounts are adjusted for 

infl ation in ACS single-year estimates. The household 

fi nancial data (e.g., rent and mortgage payments, utility costs) are not adjusted when 1-year estimates are 

produced but are adjusted when 3- and 5- year estias the decennial census. Rather than the decennial census rule of “usual place of residence,” the ACS uses a 

2-month residence rule. Residents who are living at the 

sample address at the time of the interview and who 

have lived or plan to live at that housing unit for more 

than 2 months are in scope for the survey. For areas 

with substantial seasonal populations, the diff erence 

in residence rules has the potential to aff ect characteristics such as occupancy rates, age composition, and 

household types. This is one of the clearest diff erences 

between the decennial census long-form data of the 

past, the upcoming short-form only 2010 Census, and 

the ACS. A detailed discussion of the impact of residence rules in general as well as diff erences between 

the ACS and the decennial census is presented in 

“Once, Only Once, and in the Right Place: Residence 

Rules in the Decennial Census (2006),” written by the 

Committee on National Statistics and published by the 

National Academies Press. Appendix 2 also addresses 

some of the key implications of these diff erences in 

residence rules for data users.

Vacant Units

The vacancy rate for an area where a high proportion of the housing units are owned by retirees could 

look diff erent depending on the data source. Imagine 

a retired couple that occupies their second home for 

4 months of the year—January through April. From 

the perspective of the local real estate market, the 

unit is not vacant because it is not for sale or rent. 

In the decennial census, it is not the “usual place of 

residence” for the couple and so even though they are 

living there on Census Day, April 1, the unit should 

be classifi ed as “Vacant: For seasonal, recreational, or 

occasional use.” If the same couple receives an ACS 

questionnaire during the time they are living there, and 

since they are “living or staying here for more than two 

months,” the unit is classifi ed as occupied. But if the 

unit falls into the sample during the other 8 months of 

the year, it is classifi ed as vacant. 

Age and Household Composition

The decennial census and the ACS may show diff erent 

measures of the age of the population and the types 

of households in an area with a college or university. 

College students, whether living on or off campus, are 

considered to be residents of the area where they live 

while attending college—although they may not be 

year-round residents. The decennial census will include 

characteristics of the students in an area’s age and 

household composition. 

Because the ACS conducts ongoing interviews throughout the year, for 3 or more months of the year, it will 

interview many students where they are spending their 

summer, not in their college town. The ACS interviews 

are averaged over the collection period, thus the ACS 
What Researchers Need to Know 5

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

Our focus for poverty status in this handbook is to 

illustrate comparisons between the ACS and Census 

2000. The Census Bureau’s ACS Web page on “How 

to Use the Data: Guidance on Comparing ACS Data to 

Other Sources” <http://www.census.gov/acs/www

/UseData/compACS.htm> provides general guidelines 

regarding global diff erences between the 2006 and 

2007 ACS and Census 2000 as well as guidance for 

specifi c items and subjects. The guidelines are available for all subjects in the ACS and are organized by 

subject area such as Age, Poverty, Housing, and Group 

Quarters. For example, two categories are included 

under the heading for Poverty: “Poverty Status of 

Families and People in Families” and “Poverty Status of 

All People in the Poverty Universe.” The tables provide 

shorthand advice on comparisons that include 

“Compare,” “Compare With Caution,” and “Do Not 

Compare.” In addition, there are links to “more info” 

and to crosswalks for comparable summary tables in 

the 2006 ACS and the Census 2000 Summary File 3 

(SF3).

Geographic Mobility and Migration

In order to distinguish between movers and 

nonmovers, the ACS questionnaire asks, “Did this 

person live in this house or apartment one year ago?” 

Follow-up questions and response categories further 

classify individuals as local movers, domestic migrants, 

or immigrants from abroad. These data can be used to 

construct migration streams of people coming into and 

moving out of an area. For the out-migration stream, 

only those with a destination in the United States 

and Purto Rico can be identifi ed. The Census Bureau 

produces summary tabulations and users have the 

option of constructing their own estimates—especially 

with detailed characteristics of subpopulations—using 

the ACS PUMS fi les. The ACS summary tabulations of 

mobility data that are available on the American 

FactFinder provide estimates of nonmovers, local 

movers, domestic in-migrants, and immigrants from 

abroad. Additional detailed tables providing estimates 

of the number of out-migrants are available beginning 

with the 2007 ACS data release.

The ACS data on mobility and migration are not 

comparable with the Census 2000 and earlier decennial data. The ACS asks about residence 1 year ago 

whereas Census 2000 asked about residence 5 years 

ago. The shorter reference period used in the ACS will 

do a better job of capturing short-term and subsequent 

return migration. Census 2000 captured longer-term 

migration. The proportion of the resident population 

that will be classifi ed as migrants will generally be 

higher in Census 2000 than the ACS since it represents 

5 years of accumulated migration instead of 1 year—

minus return migrants that are undetected in a 5-year 

reference period. An excellent resource on the mobility 

and migration data from the 2006 ACS is the Census 

mates are produced. Dollar values of income variables 

on the PUMS fi les, which consist of responses only for 

a single year are reported in nominal dollars and will 

need to be infl ation-adjusted using the variable ADJUST. 

If the analyst combines PUMS fi les for diff erent years, 

the dollar values for all fi nancial variables should be 

adjusted to the latest year. Refer to Appendix 5 for 

more information on dollar-denominated data.

ACS estimates of income can be aff ected by the ACS 

methodology when a substantial portion of the population is not composed of year-round residents but rather 

includes a substantial proportion of those who live 

elsewhere for a portion of the year. The impact of the 

diff erent residence rules used in the ACS on estimates 

of income for small areas will be greatest for areas with 

seasonal populations such as college towns, resort 

areas, and retirement communities.

Population Below the Poverty Level

The number and proportion of people living below the 

poverty threshold serve as indicators of the social and 

economic well being of an area. The Census Bureau 

produces a number of data sources that provide estimates of an area’s population living below the poverty 

threshold. The Census Bureau’s Housing and Household Economic Statistics Division maintains a Web site 

that provides users with details on the major national 

household surveys and programs that produce poverty 

statistics <http://www.census.gov/hhes/www

/poverty/newguidance.html>. These major sources of 

estimates of poverty are: 

• The American Community Survey (ACS)

• The Annual Social and Economic Supplement to 

the Current Population Survey (CPS ASEC)

• The Census 2000 long-form sample survey

• The Small Area Income and Poverty Estimates 

(SAIPE) program 

• The Survey of Income and Program Participation 

(SIPP)

Diff erences in methodology for estimating the poverty 

rate among these sources can produce estimates that 

are not comparable. For example, there have been 

considerable changes in the SAIPE model-based estimates of poverty for counties when the Census Bureau 

switched from using poverty data measured by the 

CPS ASEC to data from the ACS as input to their model. 

A detailed explanation of the SAIPE estimation procedures is presented by the Census Bureau at 

<http://www.census.gov/hhes/www/saipe/methods

/index.html>. As a second example, the Census Bureau 

provides a summary of key diff erences between the 

CPS ASEC and ACS poverty estimates at <http://www

.census.gov/hhes/www/poverty/factsheet.html>. 
6 What Researchers Need to Know

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

Kin Koerber, of the Census Bureau’s Housing and 

Household Economic Statistics Division, produced a 

working paper examining domestic migration fl ows 

for states from the 2005 ACS (available on the Census 

Bureau Web site at <http://www.census.gov/acs/www

/Downloads/State_Migration_Flows_Paper.doc>). 

Koerber states that while direct comparisons of migration data cannot be made between the ACS and Census 

2000, it is possible to compare the relative size of 

fl ows. Koerber’s paper could be useful to anyone who 

will be analyzing migration data from the ACS and in 

particular those who will be looking at trends involving 

decennial census data.

Bureau’s “2006 Subject Defi nitions” for the ACS and 

the Puerto Rico Community Survey (PRCS), available at 

<http://www.census.gov/acs/www/Downloads/2006

/usedata/Subject_Defi nitions.pdf>. The section on 

“Residence One Year Ago” provides excellent information on the collection and processing of the data and 

the preparation of summary tabulations of the data. In 

addition, the publication covers limitations of the data 

and the history of the concept and question in prior 

versions of the ACS.

Beginning with the 1940 Census, some characteristics 

reported from the decennial census have been estimates based on a sample of housing units and population. Sample estimates have variability associated with 

them, which is termed sampling error. Information 

about ACS sampling error is provided on the ACS Web 

site and measures of sampling error are included in the 

tables of tabulated characteristics. For Census 2000 

and most other surveys, the documentation on sampling error is included in the detailed documentation 

for a fi le and users calculate margins of error on their 

own. Even academic researchers sometimes ignored 

the issues of sampling variability—which can be 

problematic when analyzing diff erences in small area 

estimates. Rather than taking into account that the 

estimates were derived from a complex sample survey, 

the estimates were sometimes treated as values for the 

population. 

In the online presentation of ACS tables in the 

American FactFinder, the margins of error are presented alongside the estimates. The column heading 

“Margin of Error” is a hyperlink to the glossary that 

provides additional information about the margin of 

error, the confi dence interval, and its lower and upper 

bounds. The margin of error presented in the American 

FactFinder is based on a 90-percent confi dence level. 

Note that in the presentation of the margin of error 

prior to 2006, the Census Bureau used a factor of 1.65 

in combination with the standard error of the estimate 

to derive the margin of error. In 2006 and subsequent 

years, the Census Bureau increased the precision by 

using a factor of 1.645.

By presenting the margin of error alongside the point 

estimates, users can more easily determine whether 

diff erences they observe over time and space are 

statistically signifi cant or within the bounds of random 

variation. The Census Bureau uses a 90-percent 

confi dence level to determine the margin of error in 

the published tabulations. Depending on the application, a user may wish to increase the confi dence level 

to 95 percent or 99 percent to conduct a more rigorous 

test of signifi cant diff erences. Some users may combine geographic areas, thereby increasing the sample 

size and reducing the margin of error. Information 

about calculating the margin of error, addressing variations such as these, as well as examples, is included in 

Appendix 3.

Researchers using the microdata fi les need to calculate 

their own estimates of standard error due to sampling, 

using either the formula for generalized standard 

errors or by using the replicate weights. The Census 

Bureau uses the replicate weights method to estimate 

the standard error in the summary tabulations and 

also the table for “Tabulated 2006 PUMS Estimates for 

User Verifi cation.” The Census Bureau presents both 

approaches in Section 6, “Calculation of Standard 

Errors” of the document PUMS Accuracy of the Data 

(2006) <http://www.census.gov/acs/www

/Downloads/2006/AccuracyPUMS.pdf>. According to 

the Census Bureau, “It is important to keep in mind 

that there will be diff erences between the standard 

error approximations computed by the two methods. 

Generally, using the replicate weights will produce a 

more accurate estimate of a standard error.” Assuming 

that more users will opt for the formula to calculate 

generalized standard errors than will calculate standard errors using the replicate weights, the question 

becomes, “What diff erence does it make?” In Table 2, 

“Comparison of Two Approaches to Calculating Standard Errors in the ACS PUMS 2006,” the two 

approaches in terms of Standard Error (SE), Margin of 

Error (MOE), and Coeffi cient of Variation (CV) for statelevel estimates of the number of people by 5-year age 

categories are compared.

Sampling Error
What Researchers Need to Know 7

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

error measures displayed in the tables, there are very 

slight diff erences resulting from the method used to 

calculate the SEs. For example, the SE for the estimate 

of the population age 85 and over is 6,970 based on 

replicate weights and 7,131 based on generalized standard errors. The CV for this estimate is therefore higher 

for the generalized standard error approach (1.962 

percent versus 1.917 percent). There is a tendency for 

the formula for generalized standard errors to estimate 

a higher standard error than the replicate weights 

method. This means that by using the generalized formula instead of the replicate weights, one is less likely 

to fi nd statistically signifi cant diff erences where they 

actually do exist. The lesson to take away from this is 

the necessity to learn how to use the more complex 

method with replicate weights to produce estimates of 

standard error.

The formula for calculating generalized standard errors 

is below:

Because the ACS sample is not a simple random 

sample, the design factor is used. The design factor 

approximates the increase in variance from a simple 

random sample that is due to the “actual sample design 

and estimation procedures used for the ACS” (page 9 

of Accuracy of the Data). Reviewing the three sampling 

Total Formula:  ̧

 ̧

1

·

 ̈

 ̈

©

§  N

Y SE Y DF Y ˆ 1 ˆ ) * 99 * ˆ ( 

 Where: 

 DF = Design Factor 

N = Size of Geographic Area

Yˆ = Estimate of Characteristic Total 

 Table 2. Comparison of Two Approaches to Calculating Standard Errors in the 2006 ACS PUMS 

 Basic Demographic Characteristics, New York State

Standard

Error

(SE)

Coefficient of 

Variation

(CV)

Standard

Error

(SE)

Coefficient of 

Variation

(CV)

Margin of

Error

(MOE)

Total population* 19,306,183 NA NA NA NA NA NA

Age 0–4 1,212,345 10,233 0.844% 16,833 12,727 1.050% 20,936

Age 5–9 1,204,832 11,762 0.976% 19,348 12,690 1.053% 20,875

Age 10–14 1,289,058 11,972 0.929% 19,695 13,096 1.016% 21,542

Age 15–19 1,391,848 9,190 0.660% 15,118 13,569 0.975% 22,321

Age 20–24 1,362,487 10,710 0.786% 17,617 13,436 0.986% 22,102

Age 25–34 2,509,761 13,017 0.519% 21,413 17,643 0.703% 29,023

Age 35–44 2,909,825 12,556 0.432% 20,655 18,770 0.645% 30,876

Age 45–54 2,827,340 11,639 0.412% 19,147 18,548 0.656% 30,512

Age 55–59 1,187,611 12,154 1.023% 19,994 12,605 1.061% 20,736

Age 60–64 892,536 10,784 1.208% 17,740 11,016 1.234% 18,122

Age 65–74 1,262,855 8,821 0.698% 14,510 12,971 1.027% 21,338

Age 75–84 892,184 8,355 0.936% 13,743 11,014 1.235% 18,118

Age 85 and over 363,501 6,970 1.917% 11,466 7,131 1.962% 11,730

Basic Demographic 

Characteristics Estimate

Margin of

Error

(MOE)

Replicate Weights Generalized Standard Error

*Total population estimates are controlled and therefore not subject to sampling error. 

Adapted from “Tabulated 2006 PUMS Estimates for User Verifi cation.”

Data Release and Geographic Areas

Tabulations are prepared based on accumulated 

responses to the survey questionnaire. Depending 

on the population size of a geographic area, tabulations will be based on the responses accumulated for 

1-year, 3-year, or 5-year periods. Decennial long-form 

data were based on a “once a decade” 1-in-6 sample of 

housing units (with variations for rural and highdensity urban areas), and all data products fl owed from 

that collection period. ACS data are currently based on 

an annual sample that is approximately 1-in-40 housing units (with some variations), administered as 12 

independent monthly samples, and data products are 

produced for geographic areas once enough responses 

have been accumulated (see Table 3). Data for geographic areas with populations of 65,000 and more are 

summarized and produced annually based on the accumulated responses from the previous calendar year’s 

data collection. Areas with populations of 20,000 and 

more are summarized and produced annually based 

on the accumulated responses to the previous 3 years 

of data collection. All size areas are summarized with 

data produced annually based on the accumulated 

responses to the previous 5 years of data collection.
8 What Researchers Need to Know

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

When comparing smaller areas with larger areas, 

researchers need to keep in mind which tabulations 

they are using and should use consistent collection 

periods for comparison purposes. For example, when 

analyzing census tract characteristics for a major 

metropolitan area, data users should use the 5-year 

estimates for both the tract-level data (your only 

option) and the metropolitan area. 

Users of the ACS Public Use Microdata Samples (PUMS) 

will have access to the same geographic areas that 

were available from the Census 2000 PUMS. These 

Public Use Microdata Areas, or PUMAs, are the smallest geographic unit identifi ed in the public use fi les. 

PUMAs for both the ACS and Census 2000 contain a 

minimum of 100,000 people. Maps for PUMAs can be 

found on the Census Bureau’s Web site at <http://www

.census.gov/geo/www/maps/puma5pct.htm>. 

The Census Bureau is producing three sets of tabulations for geographic areas with a population size of 

65,000 and more: 1-year, 3-year, and 5-year estimates. 

The tabulations based on 5 years of data collection 

will have the smallest level of sampling error due to 

the larger sample size, that is, 5 years of accumulated 

responses. Two sets of tabulations are prepared for 

areas with populations of 20,000 and more. There will 

be only one set of small area estimates for areas with 

populations less than 20,000—which includes smaller 

counties and places along with census tracts and block 

groups. For these multiyear estimates, the records are 

pooled in a single data set and reweighted to a new set 

of controls based on the most current population estimates for each of the single years. For summary areas 

where boundaries change during the collection 

period—such as municipalities that may annex surrounding territory—the multiyear estimates are retabulated to fi t the boundaries of the area as it existed for 

the most recent year’s data. The individual housing and 

person records are also reweighted.

 Table 3. Release Schedule for ACS Data

Year of Data Release

Data Population 2006 2007 2008 2009 2010 2011 2012 2013

product threshold 

 Year(s) of Data Collection

 1-year 

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

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

 estimates 20,000+ 2007 2008 2009 2010 2011 2012

 5-year 2005– 2006– 2007– 2008–

 estimates All areas* 2009 2010 2011 2012

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

Source: U.S. Census Bureau.

Access to Data Products

A summary of the current ACS data products is available on the Census Bureau’s Web site at <http://www

.census.gov/acs/www/Products/>. Most ACS data 

products are accessed from the American FactFinder—

a data dissemination tool on the Census Bureau’s Web 

site. This section discusses two types of products of 

major interest to academic researchers, Summary Files 

and PUMS fi les. The Summary Files contain tabulations 

of data collected in the ACS presented as a series of 

tables with frequency distributions for a single characteristic, such as age, or cross-tabulations of two or 

more characteristics, such as age by sex by race. The 

records in the Summary Files refer to a specifi ed 

geographic area, such as a state, county, place, or census tract. Public use microdata fi les are like computerized versions of the completed and edited questionnaires—minus any personal identifying information. 

There are separate records for the housing units 

followed by individual records for each of the persons 

occupying the housing units.

ACS Summary Files

The ACS Summary Files are analogous to Summary File 

3 or SF3 from Census 2000. The data fi les and documentation are available from the Census Bureau’s ACS 
What Researchers Need to Know 9

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

interested in more information about this resource can 

also refer to the PUMS Handbook that is part of the ACS 

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

/UseData/Compass/compass_series.html>.

There are a number of reasons why academic researchers use the microdata fi les. Microdata provide the fl exibility to create custom tabulations or to investigate the 

relationship among characteristics captured by the survey questionnaire. For research aimed at understanding 

small population groups, analyzing individuals, households, or housing units, and not requiring small area 

characteristics, the PUMS fi les serve the need.

The ACS PUMS fi les are organized as hierarchical fi les, 

in the same manner as the Census 2000 PUMS fi les. 

There are separate records for housing units and population. The housing unit records have unique identifi ers 

that are repeated on each of the population records for 

people living in that housing unit. In this manner, housing unit characteristics can be appended to population 

records as needed in an analysis. For example, housing 

unit records contain variables providing the geographic 

location of the unit, and in order to identify a person’s 

geographic area of residence, it is necessary to link 

that information from the housing unit record to the 

population record. 

FTP site at <http://www.census.gov/acs/www

/Special/acsftp.html>. See Figure 1 for a display of the 

ACS FTP site. The 2006 ACS Summary File Technical 

Documentation provides users with all the information 

they need to access and process these data, including 

survey methods and links to sample SAS programs for 

processing the data fi les. Users should note that new 

ACS Summary Files are released each year, including 

recently released 2007 ACS 1-year Summary Files and 

2005–2007 ACS 3-year Summary Files. The documentation enables users to take advantage of the links to 

fi les on the Census Bureau Web site. The structure of 

the ACS Summary Files is slightly more complex than 

Census 2000 Summary Files because of the additional 

information on the reliability of sample estimates. Files 

that are merged with the ACS estimates provide information on the margin of error and the standard error.

ACS Public Use Microdata Sample Files

The ACS PUMS fi les are very similar to the PUMS fi les 

from Census 2000. Links to the data fi les and documentation are available from the Census Bureau’s ACS 

Web site by selecting the link to PUMS <http://www

.census.gov/acs/www/Products/PUMS/>. This Web 

page summarizes basic information about the ACS 

PUMS and provides necessary links to the data and documentation as illustrated in Figure 2. ACS data users 

Source: U.S. Census Bureau, accessed at <http://www.census.gov/acs>.

 Figure 1. Screenshot of ACS FTP Site
10 What Researchers Need to Know

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

On the ACS PUMS Web page at <http://www.census

.gov/acs/www/Products/PUMS>, there are fi les in various formats under the heading “Tabulated 2006 PUMS 

Estimates for User Verifi cation” for the user to verify 

they are accessing and processing the PUMS fi les correctly. Unlike the Current Population Survey (CPS) or the 

American Housing Survey (AHS), where the microdata 

fi les are the full fi les and produce the same results as 

the printed reports, the ACS PUMS fi les are a subsample 

drawn from the full sample. The ACS PUMS fi le is a systematic subsample of the full ACS responses, designed 

to yield an eff ective sampling rate of 1 percent of each 

state’s estimated population. Just like the Census 2000 

PUMS, tabulations from the ACS PUMS will not match 

the summary tables from the American FactFinder. In 

order to verify that you have correctly accessed and 

tabulated data from the ACS PUMS fi le, it is recommended that you fi rst replicate the values presented in 

“PUMS Estimates for User Verifi cation.” 

Because the records in the fi le represent a sample of 

the population, each record—housing and person—is 

assigned a weight. The weight is a numeric variable 

expressing the number of housing units or people that 

an individual microdata record represents. The sum of 

the housing unit and person weights for a geographic 

area is equal to the estimate of the total number of 

housing units and people in that area. Since the ACS is 

not a simple random sample survey but rather, a complex sample survey, the values of the weights vary. In 

order to generate estimates of the population based on 

the sample records, it is necessary to correctly use the 

weights assigned to each of the records. The Census 

Bureau provides basic tabulations of weighted characteristics from the ACS PUMS that researchers can use to 

verify the accuracy of their programming.

Source: U.S. Census Bureau, accessed at <http://www.census.gov/acs/www/Products/PUMS/>.

 Figure 2. ACS PUMS Web Page
What Researchers Need to Know 11

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

Case Study 1: Interpreting Small Area 5-Year 

Period Estimates

Purpose: Multilevel Analysis of Neighborhood Well 

Being

Data File: 2001–2005 ACS 5-Year Estimates From the 

Multiyear Estimates Study

Geographic Areas: Counties and Tracts

Background

This case study demonstrates how to use ACS data 

when you are working with diff erent size geographic 

areas. It highlights the comparisons that should (and 

should not) be made. If you want to know how unemployment and poverty rates for census tracts compare 

with those rates for their county or metropolitan area, 

you need to work with ACS multiyear estimates. The 

ACS estimates for census tracts are only produced as 

ACS 5-year (60-month) period estimates and will be 

released for the fi rst time for all census tracts in 2010 

based on responses collected during the 2005–2009 

calendar years. 

The ACS provides estimates of characteristics, such as 

poverty and unemployment that can be used as indicators of well-being. Census tracts are frequently used to 

represent neighborhoods while a county or metropolitan area may serve as a geographic unit to represent a 

regional labor market. The ACS estimates can be used 

for a multilevel analysis of the importance of regional 

labor market conditions upon neighborhood levels of 

well-being. If we believe that neighborhood conditions 

are in part a function of regional conditions, then in 

examining the diff erences in levels of well-being (such 

as rates of poverty and unemployment), we would 

want to control our analysis of tracts by characteristics 

of the county or metropolitan area.

Assuming that all the labor market regions we are 

analyzing—represented by individual counties, groups 

of counties, or metropolitan areas—are areas with 

populations greater than 65,000, then summary tabulations will be available in the form of 1-year, 3-year, and 

5-year estimates. For census tracts, only the 5-year 

estimates will be available. This case study will illustrate which estimates of county characteristics should 

be used in this analysis.

In December 2008, the Census Bureau released ACS 

3-year estimates on the American FactFinder. The 

Census Bureau will release 5-year estimates in 2010. 

Therefore, the 5-year estimates used in this case 

study are not available from the American FactFinder. 

However, the Census Bureau chose 36 counties as 

test sites for the development of the ACS and began 

collecting data in these counties prior to 2000. The 

Census Bureau released 1-, 3-, and 5-year estimates for 

these areas as part of the Multiyear Estimates Study. 

Researchers may access these data, as well as other 

useful information about the Census Bureau’s Multiyear 

Estimates Study at <http://www.census.gov/acs

/www/AdvMeth/Multi_Year_Estimates/overview.html>. 

Using this link, 1-, 3-, and 5-year estimates can be 

accessed for 34 of the test counties. 

Analysis

In this case study two counties are considered—Pima 

County, Arizona, and Rockland County, New York. 

Specifi cally we will analyze the 1-year and 5-year 

estimates of unemployment for selected tracts and 

compare them with the unemployment rates at the 

county level. Unemployment rates are displayed in 

Table 4 for Pima County, Arizona, Rockland County, 

New York, and selected census tracts in these counties 

to illustrate the options that are available. Both counties have populations of 65,000 or more and therefore 

they qualify for 1-year, 3-year, and 5-year estimates. 

The county-level data for 5-year and 1-year period 

estimates are presented. The only tract-level data available are 5-year estimates for the 2001–2005 period. 

The most current estimates in Table 4 are the 1-year 

estimates for 2005. Those estimates, however, are not 

available for census tracts. Although the impulse to 

use the most current estimates is strong, the proper 

county-level estimates to use in the multilevel analysis 

are the 5-year estimates for the period 2001–2005. 

This means that an unemployment rate of 2.3 percent 

for tract 112.00 in Rockland County, New York, should 

be compared with the 5-year estimate of the Rockland 

County rate of 3.1 percent. 

The Multiyear Estimates Study includes 5-year estimates for 1999–2003, 2000–2004, and 2001–2005. 

Trend analysis with the multiyear estimates should be 

done using estimates from nonoverlapping periods. 

For example, if you compare the 2000–2004 estimates 

with the 2001–2005 estimates you would be using the 

same observations for four of the component years. 

That is, the responses collected in 2001, 2002, 2003, 

and 2004 are common to both sets of estimates. The 

optimal comparison would be between the 2000–2004 

estimates and the 2005–2009 estimates. Analysis of 

overlapping periods is possible but requires special 

attention to several factors. Appendix 4 discusses 

comparisons—including those involving overlapping 

periods—in greater detail. 

Case Studies
12 What Researchers Need to Know

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

Background

This case study illustrates how the ACS estimates of 

income from wages and salaries compare with other 

sources of income data. We use the Tucson, Arizona, 

metropolitan area for this example. It comprises one 

county, Pima County, which is a major retirement and 

seasonal destination as well as home to the University 

of Arizona. Pima County, Arizona, was a test site for 

the development of the ACS and therefore a series 

of 1-year estimates are available from the Multiyear 

Estimates Study for 2000 through 2006. Data on wages 

from the Bureau of Labor Statistics’ Quarterly Census 

of Employment and Wages (QCEW) are to be compared 

with the ACS estimates of aggregate wage or salary 

income for the metropolitan area. The ACS is a household survey and the data on earnings are based on 

place of residence, while the QCEW is based on administrative records fi led by employers that are based on 

place of work. The Census 2000 County-to-County 

Conclusion

This case study reminds researchers that they should 

make comparisons using the same data series. The 

1-year estimates should only be compared with other 

1-year estimates. It may be necessary to use less current data for a given area in order to make appropriate 

comparisons. Researchers should also avoid comparing 

ACS estimates for overlapping time periods. 

Case Study 2: Estimates of Income

Purpose: Compare ACS and Quarterly Census of 

 Employment and Wages (QCEW) Data on 

 Earnings

Data Files: ACS 1-Year Estimates for 2000–2006, 

 QCEW 

 Annual Earnings, 2000–2006

Geographic Areas: County

 Table 4. American Community Survey Tabulations of Unemployment Rate

Estimate MOE Estimate MOE Estimate MOE Estimate MOE Estimate MOE Estimate MOE

Pima County 4.1% +/-0.2 3.6% +/-0.3 4.5% +/-0.4 4.5% +/-0.4 3.6% +/-0.4 4.2% +/-0.4

Selected Tracts

40.59 1.7% +/-1.0 NA NA NA NA NA

44.16 2.2% +/-1.1

40.60 2.6% +/-1.2

47.12 3.9% +/-2.6

40.51 4.6% +/-2.6

40.61 6.0% +/-5.1

19.00 7.1% +/-4.8

7.00 8.1% +/-3.6

43.22 9.5% +/-4.1

39.02 13.0% +/-6.4

51.00 26.4% +/-11.5

Rockland County

3.1% +/-0.3 2.5% +/-0.4 2.7% +/-0.5 4.3% +/-0.7 3.5% +/-0.6 2.8% +/-0.6

Selected Tracts

112.00 2.3% +/-1.7 NA NA NA NA NA

105.02 2.9% +/-1.5

101.02 3.2% +/-2.3

105.01 3.7% +/-1.7

134.00 4.3% +/-2.5

115.01 5.0% +/-2.4

130.02 5.6% +/-2.8

115.04 6.4% +/-3.1

122.02 7.2% +/-3.7

113.01 8.2% +/-3.9

118.00 11.9% +/-5.5

2001-2005 2005

5-Year Estimate 1-Year Estimates

2001 2002 2003 2004

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

<http://www.census.gov/acs/www/AdvMeth/Multi_Year_Estimates/online_data_year.html>. 
What Researchers Need to Know 13

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

coverage in the ACS of workers who are not covered by 

unemployment insurance. Wage and salary income for 

1999 as reported in Census 2000 was also higher than 

income from the QCEW for 1999. The trend line for ACS 

data is not as smooth as the QCEW trend line, which 

is consistent with most comparisons of data from a 

sample survey and an enumeration. 

Conclusion

This case study highlights the need for researchers to 

understand that diff erent sources of data will provide 

diff erent measures for specifi c topics, such as income. 

It is important to spend the time understanding the 

data sources before you use them, to understand 

which source is the best source for your needs. In this 

example, the major diff erence in the two data sets is 

that the ACS is a sample survey of households while 

the QCEW is an enumeration of employers to collect 

data on employees covered in the unemployment 

insurance program.

Worker Flow Files reported that 97.5 percent of those 

working in the Tucson, Arizona, metropolitan area 

lived there as well, which minimizes the diff erential in 

earnings based on place of residence versus place of 

employment. The QCEW data on wages are for workers 

covered by unemployment insurance, while the ACS is 

for all workers.

Analysis

Table 5 and Figure 3 display the ACS 1-year estimates 

of wage and salary income for the Tucson, Arizona, 

Metropolitan Statistical Area (MSA) for the years 2000 

to 2006. As noted earlier, estimates of income characteristics in the ACS are split across the reporting 

year and prior year. To make the most reasonable 

comparison, we have averaged the QCEW data over 2 

years and “synched” them to the ACS reporting year. 

The ACS data show higher salary and wage levels than 

the QCEW data—even taking the margins of error into 

account. This is consistent with the more complete 

 Table 5. Comparison of Wage and Salary Income for Tucson, AZ MSA From the ACS and QCEW

 Figure 3. Comparison of Earnings Data for Tucson MSA From ACS and QCEW

0

5

10

15

20

2000 2001 2002 2003 2004 2005 2006

$ Billions

ACS

QCEW

ACS

Estimates

(in dollars)

MOE

(in dollars)

QCEW*

(in dollars)

2000 10,650,578 222,164 9,269,883

2001 10,961,591 235,276 9,810,476

2002 12,140,897 369,319 10,177,163

2003 12,430,720 372,496 10,540,859

2004 12,721,230 374,256 11,215,675

2005 14,244,673 408,409 12,133,677

2006 15,188,623 424,550 13,121,882

Source: U.S. Census Bureau, American Community Survey. U.S. Bureau of Labor Statistics, Quarterly Census of Employment and Wages.

*QCEW data have been averaged across 2 years—the reporting and prior year.

Source: U.S. Census Bureau and Bureau of Labor Statistics.
14 What Researchers Need to Know

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

sources than was available in August 2006. It demonstrates the extent to which estimates of the poverty 

rate are not comparable.

Analysis

Figure 4 shows the SAIPE series of estimates along 

with the estimates from Census 2000 and the ACS. 

SAIPE is the product of model-based estimates that 

used the Census Bureau’s Current Population Survey 

(CPS) as a major input through 2004. For 2005, SAIPE 

was based on the ACS, creating an abrupt break in the 

series between 2004 and 2005 for Tompkins County. 

The Census Bureau informed SAIPE users of this change 

in methodology and highlighted cautions relating to 

comparisons of 2004 to 2005 results. See <http://www

.census.gov/hhes/www/saipe/methods/cautions

.html>.

Conclusion

All of the estimates in Figure 4 are “right,” but they are 

based on diff erent data sources. There are important 

factors to consider if a newspaper wanting to compare 

annual fl uctuations in the poverty rate for a county 

contacted you. You would need to decide which data to 

recommend. You should avail yourself of the reference 

materials provided on the Census Bureau’s Web site. 

This case study also demonstrated the need to be clear 

about when it is and is not appropriate to compare 

data from two diff erent sources. In this example, comparisons of SAIPE with ACS data are inappropriate.

Case Study 3: Trends in the Poverty Rate

Purpose: Compare Poverty Rates Over Multiple Years

Data Files: ACS 1-Year Estimates, Small Area Income, 

 and Poverty Estimates (SAIPE) Annual 

 Estimates

Geographic Areas: County

Background

The following case is based on a newspaper article 

examining trends in the poverty rate for a county with 

a large college student population. The data points 

have been expanded to illustrate diff erences in the 

poverty rate from diff erence sources—especially when 

comparing trends over time. With the release of profi les 

from the 2005 ACS, journalists were mining the reports 

for newsworthy items. Tompkins County, New York, is 

home to Cornell University and approximately 10 percent of the county’s population is college students in 

dormitories. The Ithaca Journal reported, “In Tompkins 

County, 20.7 percent were living in poverty last year, 

a jump from 12.9 percent in 2004,” (Ithaca Journal, 

8/30/2006) citing the source simply as U.S. Census 

Bureau. There were two mistakes in the article. First, 

the estimate of a 12.9 percent poverty rate was not 

for 2004 but for 2003. Second, data from the 

Census Bureau’s Small Area Income and Poverty Estimates (SAIPE) for 2003 should not have been compared 

with the newly released 2005 ACS data. Figure 4 provides a more complete picture of the trend in poverty 

rates for Tompkins County, New York, from these data 

 Figure 4. Trends in Poverty Rate and Noncomparable Sources—Tompkins County, NY

0

5

10

15

20

25

1999 2000 2001 2002 2003 2004 2005 2006

Census 2000

SAIPE

ACS

Source: U.S. Census Bureau.
What Researchers Need to Know 15

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

Exercise 1: Access Summary Tabulations

Task: Access Detailed Poverty Data From ACS Detailed/

 Summary Tables

Data File: 2006 American Community Survey 

Geographic Area: States

Background

Accessing detailed tabulations from the ACS for a 

number of geographic areas can be accomplished in 

diff erent ways. This exercise provides an example 

using the American FactFinder to access 2006 ACS data 

on poverty status for all states. One could also use the 

American FactFinder to access poverty data for Core 

Based Statistical Areas (which include Metropolitan 

and Micropolitan Statistical Areas), counties, or a wide 

variety of census geographies meeting the population 

threshold of 65,000 for 1-year estimates. This exercise 

assumes a basic familiarity with using the American 

FactFinder interface to access census data.

Accessing and Downloading the Data

This section details the steps required to access the 

data of interest. It provides screen shots of some 

of the Web pages you will encounter. First go to the 

American FactFinder page on the Census Bureau’s Web 

site at <http://factfi nder.census.gov> and under the 

heading Getting Detailed Data select get data for 

the American Community Survey (see Figure 5).

This gets you to the ACS data sets page where you 

would fi rst select your data set. For this example, we 

want the 2006 American Community Survey. As 

shown in Figure 6, once you select a data set, a list 

of data products appears. For this exercise, we would 

select Detailed Tables.

Exercises

Source: U.S. Census Bureau, accessed at <http://www.census.gov/acs/www/Products/PUMS/>.

 Figure 5. Example of American FactFinder Web Page
16 What Researchers Need to Know

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

The Detailed Tables page displays the results of your 

query. A portion of this table is shown in Figure 8. 

• The table is arranged with rows for the population 

by ratio of income to poverty level. The fi rst row 

of data is for estimates of the Total Population for 

Whom Poverty Status Is Determined. 

• The columns are organized by the selected geographic area or areas and contain point estimates 

and the margins of error for those estimates. 

There are a total of 52 geographic areas in the 

table: the 50 states; Washington, DC; and Puerto 

Rico.

• By selecting to download the output (see circled 

tab in Figure 8) you have a number of formatting options that are shown in Figure 9. For large 

queries involving multiple tables and many geographic areas, an alternative is to use the ACS 

Summary File’s FTP server to download selected 

Summary Files at <http://www.census.gov/acs

/www/Special/acsftp.html>. The FTP site contains documentation, data fi les, SAS fi les, and an 

example illustrating how to access and merge the 

relevant fi les for one detailed table for all geographic areas covered in the 2006 ACS.

Now you must specify the geographic area or areas of 

interest. We are interested in state-level data so on the 

Select Geography page:

• For geographic type select State.

• For geographic areas select All States, Add, 

and then Next.

You are then requested to identify the specifi c table or 

tables. On the Select Tables page you will see a list 

of tables. See Figure 7 for an example. You can scroll 

down the full list or use the keyword or subject tabs 

to search for specifi c tables. For this exercise, entering 

“poverty” in the keyword box provides a shorter list of 

tables that include statistics on poverty. From this list:

• Select table B17002. Ratio of Income to 

Poverty Level in the Past 12 Months. 

• Select Add.

• You can select more than one table from this 

list and add them to your request. Sometimes 

if you are unsure of which estimate you need, 

it makes sense to list a few tables.

• Click Show Result.

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

 Figure 6. Example of ACS Data Sets Web Page
What Researchers Need to Know 17

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

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

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

 Figure 7. Example of Select Tables Page

 Figure 8. Example of ACS Detailed Tables Page
18 What Researchers Need to Know

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

Estimates for User Verifi cation. You can access this 

fi le from the ACS main page—access data tab by clicking on PUMS Files and PUMS Estimates for User Verifi cation. As described earlier, these fi les are designed to 

help data users verify that they are processing PUMS 

data correctly. The User Verifi cation fi le is presented in 

three formats: SAS Data Set, .lst text format, and .csv 

format. You need to download and display one of these 

formats. The fi le contains tabulations of people in 

housing units and group quarters, by sex and by age; 

and for housing units by occupancy status and tenure 

for the United States, 50 states, the District of 

Columbia, and Puerto Rico. 

For this example, we will focus on three population 

variables from the 2007 ACS PUMS—age, sex, and relationship. This exercise illustrates a basic methodology 

that can be applied to all ACS PUMS variables. We want 

to use DataFerrett to produce estimates of the total 

population living in housing units and in institutional 

and noninstitutional group quarters (GQs). Specifi cally, 

we want the following tallies:

• Total population

• Total males and total females

• People aged 0–4, 5–9, 10–14, 15–19, 20–24, 

25–34, 35–44, 45–54, 55–59, 60–64, 65–74, 

75–84, 85 and older

Exercise 2: Access, Tabulate, and Download ACS 

 Microdata Records

Task: Replicate 2007 ACS User Verifi cation Tabulations

Data File: 2007 American Community Survey PUMS 

Geographic Area: United States, States, and Puerto Rico

Background

DataFerrett is a software application for data extraction and mining. It is a joint eff ort developed and 

maintained by the Census Bureau and the Centers for 

Disease Control with major support from the Bureau 

of Labor Statistics and the National Center for Health 

Statistics. Information about DataFerrett, TheDataWeb, 

and downloads of the software are available from the 

Census Bureau at <http://dataferrett.census.gov/>. 

Some background on DataFerrett is assumed for the 

following exercise. For instruction on the basics of 

DataFerrett, refer to the tutorial on the DataFerrett 

Web page or the PUMS handbook in the ACS Compass 

Products. 

This exercise uses DataFerrett to extract data from the 

2007 ACS PUMS, tabulate selected variables from the 

population and housing records, and download the 

data to a desktop statistical software package. The task 

is to replicate the data in the 2007 ACS PUMS 

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

 Figure 9. Example of Download Web Page
What Researchers Need to Know 19

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

the bottom for Search Variables, a list of population variables is provided. You can scroll down the list 

to choose your variables or use the search tool at the 

top of the page to isolate a specifi c variable. If you are 

selecting more than one variable from the list, use Ctrl 

and Enter. For our example, select three variables: relationship (REL), sex (SEX), and age (AGEP). 

Click on the Browse/Select Highlighted Variables

box. A pop-up window will open to display details 

about these variables. Highlighting each variable, you 

can select all values by clicking select, OK, and OK. 

This moves the variables into your newly created Data 

Basket. There are applications when you might only 

need data for certain values—this is where you could 

identify the subset of interest. For this exercise we will 

need all values. After all selections are made, it’s wise 

to save your work. Using the File option in the tool bar, 

save this fi le. 

Recoding Variables

Clicking on the tab titled, Step 2—Data Basket

/Download/Make a Table, brings you to a new 

screen that summarizes the variables in your Data 

Basket. These variables include detailed categories that 

we need to combine. This is called recoding. We want 

Access DataFerrett

There are several ways to access DataFerrett. Links 

exist from the ACS PUMS page and from the American 

FactFinder page. From the American FactFinder page, 

select TOOLS AND REFERENCES from the left sidebar. 

Clicking on Tools brings you to a new page where, 

under Data Extraction Tools you can select Census 

Bureau’s DataFerrett. Download DataFerrett (if necessary) and then log in. Click the Get Data Now box to 

begin creating your tabulation. 

Select Data Set and Variables

You will start in DataFerrett under the tab Step 1: 

Select DataSet & Variable. The fi rst step is to select 

your data set from the list provided. For this exercise, 

double click on American Community Survey and 

Public Use Microdata Sample. Then select 2007 and 

View Variables. The View Variables screen identifi es 

the types of variables available for the selected data 

set. See Figure 10. If you need subnat-ional estimates 

you would need to check the box for Selectable 

Geographies. Similarly, if you need housing variables 

you would check that box. For our exercise, you only 

need population variables, so you only need to check 

that box. When you click on the magnifying glass at 

 Figure 10. Example of DataFerrett View Variables Screen

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census.gov/>.
20 What Researchers Need to Know

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

from among the six fi le formats off ered. Actually, the 

formats will all produce ascii text fi les for the data 

records, but will also produce program statements in 

SAS, SPSS, or STATA, enabling you to read in the data 

records and create a system fi le. Depending on the size 

of the fi le you created, you can choose to compress or 

not and to process batch mode or not.

Create the rest of the table values for the housing unit 

characteristics. If you match the Verify Tabulations 

values, then you did it correctly.

Exercise 3: Compare ACS Poverty Data With 

 Census 2000

Task: Compare Poverty Data in the ACS and Census

Data File: 2006 American Community Survey and 

 2000 Census Summary File 3 

Geographic Area: Jeff erson County, KY

Background

This exercise compares estimates of the number of 

school age children (5 to 17 years old) below the 

poverty level for Jeff erson County, Kentucky, from the 

2006 ACS with those from the Census 2000 Summary File 3 (SF3). It covers how to access comparable 

data on estimates of the number of children and how 

to determine whether the diff erences are statistically 

signifi cant. A basic familiarity with using the American 

FactFinder interface to census data is assumed.

Access the Data

Go to the Census Bureau’s ACS Web page. Select the 

How to Use the Data tab. From the left sidebar select 

Comparing ACS Data to Other Sources at 

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

/compACS.htm>. This page provides important information about comparing ACS data for a single year to 

ACS data for another year. It also provides guidance on 

comparing ACS data with data from Census 2000. Both 

general and item-specifi c guidance are provided. Scroll 

down to Subject Area/Item 17 – Poverty Status, 

and the row for “Poverty Status of All People in the 

Poverty Universe.” Note that comparisons between the 

2006 ACS and Census 2000 are identifi ed as “compare 

with caution.” The data are not directly comparable 

due to diff erences in reference period, data collection 

period, and residence rules—all of which have been 

discussed in this handbook. 

Click on the link to table crosswalk. The fi rst pair of 

tables is:

• Census 2000— P87: POVERTY STATUS IN 1999 BY 

AGE 

• 2006 ACS—B17001: POVERTY STATUS IN THE 

PAST 12 MONTHS BY SEX BY AGE

to recode the REL and AGEP variables into the specifi c 

categories in the verifi cation fi le.

Starting with the REL variable, highlight REL and click 

on Recode Variable(s) in the right tool bar. Change 

the default of Recode1 to RecodeREL. The variable 

name will remain Recode1 but this new label will help 

you remember which specifi c recoded variable it represents. REL is a categorical variable with 14 values. 

Highlight categories 1–12 using the Ctrl key to identify 

all values representing people living in housing units. 

Click on the Recode button to set them all to a value 

of 1. Double-clicking on the label box allows you to 

relabel this new category as “Living in housing units.” 

Similarly, recode Institutional and noninstitutional GQ 

population as 2 and 3 and relabel. Click OK to accept 

the recodes. 

Recoding the age variable involves a slightly diff erent approach since we want 5-year intervals from 1 

to 24, 10-year intervals from 25 to 84, and a separate 

category for people 85 and older. Highlight AGEP and 

click Recode Variable(s). Change the default label 

of Recode2 to RecodeAGEP. In the continuous variables box, the fi rst line states Within the range of 0 

through and the box is set to 99. Change 99 to 24. 

Then on the line that states subgroups repeat by

set the box to 5. This recodes people ages 0 to 24 into 

5-year age categories. Now the line states Within the 

range of 25 through 99. Use the same method to 

create 10-year intervals between 25 and 84 and then a 

fi nal category for people 85 and older. 

Creating a Table

Click on the Make a Table icon. The variables you 

have selected and the recode variables you have 

created are displayed. Click and drag the variables 

Recode1 (RecodeREL), SEX, and Recode2 (RecodeAGEP) 

to Column 1 in the order they are presented in the 

Verify Tabulations table. Be careful not to overlap 

the variables in column 1 or DataFerrett will think you 

want to cross tabulate them. Click on the green box—

GO Get Data. Note that DataFerrett used the variable 

PWGT (Population Weight) to estimate the population values since the variables are all for population 

records. Housing unit records are weighted by the 

variable for housing unit weight. Tables for population 

and housing unit records need to be created separately 

in DataFerrett. The data provided are national totals. 

To produce data for additional geographies you would 

have needed to identify an additional geography variable. 

Download the data set into a desktop statistical software package of your choice. Click the Download button to get an extract. (The Download button is located 

near the top of the page and has a picture of a ferret 

sitting on a hard drive.) A Download Data window pops 

up. Check the box next to Download Data. Choose 
What Researchers Need to Know 21

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

First determine the standard error (SE) of the estimate 

from the 2006 ACS. Appendix 3 details the steps 

needed to obtain Standard Errors for Derived Estimates. 

Algebraically, the MOE for the aggregated count is 

calculated as:

where is the MOE of the component estimate.

Table B17001 (2006 ACS) presents the period estimates 

and their margins of error (MOE). Use the formula 

above to calculate the MOE of the derived poverty 

estimate for children aged 5 to 17 years old. In order to 

derive the SE, divide the MOE by 1.645. (Answer: the SE 

of the derived estimate of 11,464 is 1,205.)

Next you need to determine the SE of the estimate from 

the Census 2000 Summary File 3 (SF3). The publication 

TECHNICAL DOCUMENTATION: Census 2000 Summary 

File 3 Technical Documentation prepared by the Census 

Bureau, 2002 is available on the Census Bureau’s Web 

site at <http://www.census.gov/prod/cen2000/doc

/sf3.pdf>. Beginning on page 8-6 in Chapter 8. Accuracy of the Data is the section Calculation of Standard Errors. Follow the steps outlined in the section 

to determine the:

• Unadjusted SE computed using the formula 

below Table A on page 8-22 (Answer: 316).

• Observed sampling rate from Table P4 of SF3 

(Answer: 17.1 percent).

• Design factor (Answer: 1.3—Poverty Status of 

Persons).

• SE (Answer: SE of the derived estimate of 20,583 

is 411).

Referring back to the signifi cance test, solve for Z. 

(Answer: 7.2) Since Z is greater than 1.645 we conclude that the diff erence in estimates of children aged 

5 to 17 years who are below the poverty level in 

Jeff erson County, Kentucky, between the Census 2000 

and the ACS 2006 is statistically signifi cant.

Go to the American FactFinder page on the Census 

Bureau’s Web site at <http://factfi nder.census.gov> 

and under the heading Getting Detailed Data select

get data for the Decennial Census. Select the button 

for Census 2000 Summary File 3 (SF 3)—Sample 

Data. To the right, a number of options appear under 

the heading Select from the following. Select Enter 

a Table Number and in the dialog box that appears 

enter P87 and click GO. For the geographic area, make 

the choice to select Jeff erson County, Kentucky, and 

then click GO. Note that Table P87. POVERTY STATUS 

IN 1999 BY AGE [17] – Universe: Population for who 

poverty status is determined is displayed. Download 

and save this table.

Use the American FactFinder to select 2006 American 

Community Survey and Detailed Tables. Depending on how you accessed the 2006 ACS, the geography 

selection you made could be saved or you will need 

to work through the selection options again. Using 

the table selection method Show all tables, scroll 

down to table B17001. Select and show the result. 

Note that table B17001. POVERTY STATUS IN THE PAST 

12 MONTHS BY SEX BY AGE – Universe: Population 

for whom poverty status is determined is displayed. 

Download and save this table.

Conduct Statistical Testing

You now have two estimates of the number of school 

age children (5 to 17 years old) below the poverty level 

for Jeff erson County, Kentucky. Before concluding if 

these two estimates are diff erent you must consider 

the sampling errors associated with each estimate 

and determine if the diff erence is statistically signifi -

cant. Appendix 4 describes the correct procedure for 

determining if the diff erence between two estimates is 

statistically signifi cant. Algebraically, the signifi cance 

test is expressed as:

then the diff erence between estimates and 

 is statistically signifi cant at the specifi ed confi -

dence level, CL

where is estimate 

 is the SE for the estimate 

 is the critical value for the desired confi dence 

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

2.576 for 99 percent).

In this exercise = 20,583 (Census 2000) and 

 = 11,464 (2006 ACS). If Z has an absolute value 

greater than 1.645 then the estimates are statistically 

diff erent at the 90-percent confi dence level.

If ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ , 

 r ¦cMOEagg MOEc

2

MOEc

th

c

1 Xˆ

1 Xˆ

2 Xˆ

2 Xˆ

Xi

ˆ

SEi

ZCL

i  1,2

i  1,2
22 What Researchers Need to Know

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

The ACS off ers a number of advantages to the research 

community. Data from the ACS are produced annually. 

The samples are far larger than those of the Current 

Population Survey (CPS) and the American Housing 

Survey (AHS), which were among the limited options 

for frequently updated annual data prior to the advent 

of the ACS. The data are comparable generally between 

years of the ACS and with caution for many items from 

Census 2000. The process of improving the ACS is 

ongoing, and the Census Bureau conducts methodological research of interest to academic researchers. 

A series of reports, papers, and presentations is publicly available on the Census Bureau’s Web site. The 

Evaluation Report Series for the ACS can be accessed at 

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

.htm>. A series of working papers and presentations is 

available at <http://www.census.gov/acs/www

/AdvMeth/Papers/Papers1.htm>. 

A Resource for Academic Researchers
What Researchers Need to Know 23

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

Glossary

Accuracy. One of four key dimensions of survey 

quality. Accuracy refers to the diff erence between 

the survey estimate and the true (unknown) value. 

Attributes are measured in terms of sources of error 

(for example, coverage, sampling, nonresponse, 

measurement, and processing). 

American Community Survey Alert. This periodic 

electronic newsletter informs data users and other 

interested parties about news, events, data releases, 

congressional actions, and other developments 

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

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

American FactFinder (AFF). An electronic system 

for access to and dissemination of Census Bureau 

data on the Internet. AFF off ers prepackaged data 

products and user-selected data tables and maps 

from Census 2000, the 1990 Census of Population 

and Housing, the 1997 and 2002 Economic 

Censuses, the Population Estimates Program, annual 

economic surveys, and the ACS. 

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

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

group is a cluster of blocks having the same fi rst 

digit of their four-digit identifying number within 

a census tract. 

Census geography. A collective term referring 

to the types of geographic areas used by the 

Census Bureau in its data collection and tabulation 

operations, including their structure, designations, 

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

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

Census tract. A small, relatively permanent 

statistical subdivision of a county delineated by a local 

committee of census data users for the purpose of 

presenting data. Census tract boundaries normally 

follow visible features, but may follow governmental 

unit boundaries and other nonvisible features; they 

always nest within counties. Designed to be relatively 

homogeneous units with respect to population 

characteristics, economic status, and living conditions 

at the time of establishment, census tracts average 

about 4,000 inhabitants. 

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

standard error (square root of the variance) to the 

value being estimated, usually expressed in terms 

of a percentage (also known as the relative standard 

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

reliability of the estimate. 

Comparison profi le. Comparison profi les are 

available from the American Community Survey for 

1-year estimates beginning in 2007. These tables 

are available for the United States, the 50 states, the 

District of Columbia, and geographic areas with a 

population of more than 65,000.

Confi dence interval. The sample estimate and its 

standard error permit the construction of a confi dence 

interval that represents the degree of uncertainty about 

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

interpreted roughly as providing 90 percent certainty 

that the interval defi ned by the upper and lower 

bounds contains the true value of the characteristic. 

Confi dentiality. The guarantee made by law 

(Title 13, U.S. 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 
24 What Researchers Need to Know

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

sets of data for standard census geographic areas, 

including both political and statistical geography. 

These products are available on the American 

FactFinder and the ACS Web site.

For users with data needs not met through the general 

purpose products, the Census Bureau off ers “custom” 

tabulations on a cost-reimbursable basis, with the 

American Community Survey Custom Tabulation 

program. Custom tabulations are created by tabulating 

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

complexity, and cost depending on the needs of the 

sponsoring client.

Data profi les. Detailed tables that provide 

summaries by social, economic, and housing 

characteristics. There is a new ACS demographic and 

housing units profi le that should be used if offi cial 

estimates from the Population Estimates Program are 

not available.

Detailed tables. Approximately 1,200 diff erent 

tables that contain basic distributions of 

characteristics. These tables provide the most detailed 

data and are the basis for other ACS products.

Disclosure avoidance (DA). Statistical methods 

used in the tabulation of data prior to releasing data 

products to ensure the confi dentiality of responses. 

See Confi dentiality. 

Estimates. Numerical values obtained from a 

statistical sample and assigned to a population 

parameter. Data produced from the ACS interviews are 

collected from samples of housing units. These data 

are used to produce estimates of the actual fi gures that 

would have been obtained by interviewing the entire 

population using the same methodology. 

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

allows data fi les to be downloaded from the Census 

Bureau Web site.

Five-year estimates. Estimates based on 5 years of 

ACS data. These estimates refl ect the characteristics 

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

will be published for all geographic areas down to the 

census block group level.

Geographic comparison tables. More than 80 

single-variable tables comparing key indicators for 

geographies other than states.

Geographic summary level. A geographic summary 

level specifi es the content and the hierarchical 

relationships of the geographic elements that are 

required to tabulate and summarize data. For example, 

the county summary level specifi es the state-county 

hierarchy. Thus, both the state code and the county 

code are required to uniquely identify a county in the 

United States or Puerto Rico. 

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

place where people live or stay that is normally owned 

or managed by an entity or organization providing 

housing and/or services for the residents. These 

services may include custodial or medical care, as well 

as other types of assistance. Residency is commonly 

restricted to those receiving these services. People 

living in GQ facilities are usually not related to each 

other. The ACS collects data from people living in both 

housing units and GQ facilities.

Group quarters (GQ) population. The number of 

persons residing in GQ facilities. 

Item allocation rates. Allocation is a method 

of imputation used when values for missing or 

inconsistent items cannot be derived from the existing 

response record. In these cases, the imputation 

must be based on other techniques such as using 

answers from other people in the household, other 

responding housing units, or people believed to have 

similar characteristics. Such donors are refl ected in a 

table referred to as an allocation matrix. The rate is 

percentage of times this method is used.

Margin of error (MOE). Some ACS products provide 

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

diff erence between an estimate and its upper or lower 

confi dence bounds. Confi dence bounds can be created 

by adding the MOE to the estimate (for the upper 

bound) and subtracting the MOE from the estimate (for 

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

a 90-percent confi dence level. 

Multiyear estimates. Three- and fi ve-year estimates 

based on multiple years of ACS data. Three-year 

estimates will be published for geographic areas with 

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

will be published for all geographic areas down to the 

census block group level. 

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

Nonsampling error. Total survey error can be 

classifi ed into two categories—sampling error and 

nonsampling error. Nonsampling error includes 

measurement errors due to interviewers, respondents, 

instruments, and mode; nonresponse error; coverage 

error; and processing error.
What Researchers Need to Know 25

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

Period estimates. An estimate based on information 

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

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population Estimates Program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

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

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

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

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

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

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

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

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

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

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

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

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

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

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

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

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

Appendix A-1

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

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

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

single-year estimates, the period is the calendar year 

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

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

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

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

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

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

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

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

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

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

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

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

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

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

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

monthly CPS collects data for an even narrower time 

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

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

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

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

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

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

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

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

year and 5-year estimates where the periods involved 

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

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

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

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

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

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

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

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

Month

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

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

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

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

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

multiyear estimates describe the population and 

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

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

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

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

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

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

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

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

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

estimates contain 4 years of overlapping coverage. 

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

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

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

5-year

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

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

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

housing characteristics because they are based on the 

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

multiyear estimates provide less current information 

because they are based on both data from the previous 

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

using the “less current” sample used to produce the 

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

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

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

single year is the midyear of the ACS multiyear period 

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

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

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

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

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

Reliability

Multiyear estimates are based on larger sample sizes 

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

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

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

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

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

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

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

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

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

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

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

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

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

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

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

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

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

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

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

a small CV is usually preferable to a multiyear estimate 

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

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

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

use multiyear estimates when they are working with 

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

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

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

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

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

for local planning needs, make comparisons across 

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

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

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

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

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

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

errors and CVs associated with those estimates are also 

shown.

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

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

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

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

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

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

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

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

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

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

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

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

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

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

SE of 4.9 percentage points that yields a CV of 12.2 

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

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

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

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

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

more acceptable level.

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

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

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

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

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

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

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

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

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

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

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

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

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

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

the 2006 ACS. 

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

in a particular characteristic. This is because roughly 

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

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

as shown in Figure 1, when comparing 2006–2008 

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

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

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

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

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

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

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

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

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

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

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

or the 5-year estimates because they are available for 

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

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

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

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

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

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

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

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

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

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

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

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

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

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

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

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

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

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

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

larger geographies and populations when currency is 

more important than the precision of the estimates. 

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

availability of a single-year estimate, calculating the CV 

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

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

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

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

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

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

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

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

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

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

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

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

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

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

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

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

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

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

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

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

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

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

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

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

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

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

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

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

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

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

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

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

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

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

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

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

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

60-month period. 

Most decennial census sample estimates are anchored 

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

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

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

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

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

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

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

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

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

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

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

housing unit or group quarters, some people may stay 

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

course of the year. For example, migrant workers move 

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

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

estimates of the characteristics of some areas. 

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

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

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

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

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

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

to enumerate people at the place where they live or stay 

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

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

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

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

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

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

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

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

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

there for more than 2 months.

More on Reference Periods

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

being that the remaining basic demographic questions 

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

the date the interview is provided. Questions with their 

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

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

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

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

they refl ect the circumstances on the day the data are 

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

represent the average characteristics over a full year (or 

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

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

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

comparing ACS estimates with Census 2000. 

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

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

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

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

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

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

while Census 2000 collected income information for 

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

responses are a mixture of 12 reference periods ranging 

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

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

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

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

period is used for all interviews. In contrast, 

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

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

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

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

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

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

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

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

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

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

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

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

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

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

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

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

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

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

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

(such as estimates from the ACS or the decennial 

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

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

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

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

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

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

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

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

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

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

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

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

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

larger the sample size, the smaller the standard error 

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

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

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

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

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

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

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

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

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

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

MOEs published with the ACS estimates correspond 

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

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

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

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

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

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

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

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

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

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

Measures of Sampling Error

Appendix 3.
A-12 Appendix

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

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

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

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

would be derived as 

Confi dence Intervals

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

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

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

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

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

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

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

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

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

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

(20,000 + 1,645).

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

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

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

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

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

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

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

1.960/1.645.1

 If the desired confi dence level is 99 

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

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

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

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

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

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

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

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

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

should be set to zero instead.

1

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

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

error from the standard error in those years.

2

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

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 1.645

1.960

95 

Factors Associated With Margins of

Error for Commonly Used Confi dence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

  12,357 14,723

1.645

1.960 MOE95 r r

1.645

MOEACS SE 

7,512

1.645

12,357 SE 

LCL X  MOECL ˆ

UCL X  MOECL ˆ

MOECL

  CICL LCL UCL , .

MOEACS 

MOEACS 

Xˆ

MOE MOEACS 1.645

2.576

99 
Appendix A-13

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

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

confi dence level for the number of civilian veterans in 

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

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

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

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

757,059 to 786,505.

The CI is also useful when graphing estimates, to show 

the extent of sampling error present in the estimates, 

and for visually comparing estimates. For example, 

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

in constructing the CI above, the user could be 90 

percent certain that the value for the population was 

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

Coeffi cients of Variation

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

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

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

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

barometer of the stability, and thus the usability of a 

sample estimate. It can also help a user decide whether 

a single-year or multiyear estimate should be used for 

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

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

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

estimation period increases, the sample size increases 

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

indicates that the sampling error is small relative to the 

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

the estimate is close to the population value. In some 

applications a small CV for an estimate is desirable and 

use of a multiyear estimate will therefore be preferable 

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

desired level of precision.

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

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

the estimate is very reliable. If the CV was noticeably 

larger, the usability of the estimate could be greatly 

diminished. 

While it is true that estimates with high CVs have 

important limitations, they can still be valuable as 

L95 771,782 14,723 757,059

U95 771,782 14,723 786,505

( )

18,355 20,000 21,645

building blocks to develop estimates for higher levels 

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

improve the reliability of those estimates as evidenced 

by reductions in the CVs.

Calculating Coeffi cients of Variation From Standard 

Errors

The CV can be expressed as

where is the ACS estimate and is the derived SE 

for the ACS estimate.

For example, to determine the CV for the estimated 

number of civilian veterans in the state of Virginia in 

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

and the SE derived previously (7,512).

This means that the amount of sampling error present 

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

of the estimate.

The text box below summarizes the formulas used 

when deriving alternative sampling error measures 

from the margin or error published with ACS estimates.

100 ˆ u

X

SE CV

Xˆ

100 0.1%

771,782

7,512 CV u 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confi dence Levels

Standard Error (SE)

Confi dence Interval (CI)

Coeffi cient of Variation (CV)

MOE MOE ACS

1.645

1.960 95 

MOE MOE ACS

1.645

2.576 99 

1.645

MOE ACS SE 

100 ˆ u

X

SE CV

SE

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

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

Calculating Margins of Error for Derived 

Estimates

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

the ability to develop unique estimates called derived 

estimates. These derived estimates are usually based 

on aggregating estimates across geographic areas or 

population subgroups for which combined estimates 

are not published in American FactFinder (AFF) tables 

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

four age groups not collapsed).

ACS tabulations provided through AFF contain the 

associated confi dence intervals (pre-2005) or margins 

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

confi dence level. However, when derived estimates are 

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

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

the MOE for these derived estimates. The MOE helps 

protect against misinterpreting small or nonexistent 

diff erences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

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

confi dence level other than 90 percent is desired, the 

user should fi rst calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confi dence level as described earlier in this appendix.

Calculating MOEs for Aggregated Count Data

To calculate the MOE for aggregated count data:

1) Obtain the MOE of each component estimate.

2) Square the MOE of each component estimate.

3) Sum the squared MOEs.

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

 MOEs.

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

where is the MOE of the component estimate.

The example below shows how to calculate the MOE 

for the estimated total number of females living alone 

in the three Virginia counties/independent cities that 

border Washington, DC (Fairfax and Arlington counties, 

Alexandria city) from the 2006 ACS.

The aggregate estimate is:

Obtain MOEs of the component estimates: 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs.

Thus, the derived estimate of the number of females 

living alone in the three Virginia counties/independent 

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

MOE for the estimate is +4,289.

Calculating MOEs for Derived Proportions

The numerator of a proportion is a subset of the 

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

households that are female). To calculate the MOE for 

derived proportions, do the following:

1) Obtain the MOE for the numerator and the MOE 

for the denominator of the proportion.

2) Square the derived proportion.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator by 

the squared proportion.

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

the numerator.

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

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

proportion.

 Table 1. Data for Example 1 

Characteristic Estimate MOE

Females living alone in 

 Fairfax County 

 (Component 1)

52,354 +3,303

Females living alone in 

 Arlington County

 (Component 2)

19,464 +2,011

Females living alone in 

 Alexandria city

 (Component 3)

17,190 +1,854

Xˆ Xˆ Fairfax  Xˆ Arlington  Xˆ Alexandria 

MOEFairfax r3,303 , 

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

 r ¦cMOEagg MOEc

2

MOEc

th

c

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

 011 r2, MOEArlington , 

r 18,391,246 r4,289

MOEAlexandria r1,854
Appendix A-15

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

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived proportion.

 is the estimate used as the numerator of the 

derived proportion.

 is the estimate used as the denominator of the 

derived proportion.

There are rare instances where this formula will fail—

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

happens, use the formula for derived ratios in the next 

section which will provide a conservative estimate of 

the MOE.

The example below shows how to derive the MOE for 

the estimated proportion of Black females 25 years of 

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

 The estimated proportion is:

where is the ACS estimate of Black females 25 

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

females 25 years of age and older in Fairfax County.

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older in Fairfax County 

with a graduate degree) and denominator (number 

of Black females 25 years of age and older in Fairfax 

County).

 Table 2. Data for Example 2

Characteristic Estimate MOE

Black females 25 years 

 and older with a graduate 

 degree (numerator)

4,634 +989

Black females 25 years 

 and older

 (denominator)

31,713 +601

0.1461

31,713

4,634

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

 r989 MOEnum , 601 MOEden r

MOEnum

MOEden den

den

num

X

X

p ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ

X gradBF ˆ

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

Thus, the derived estimate of the proportion of Black 

females 25 years of age and older with a graduate 

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

MOE for the estimate is +0.0311. 

Calculating MOEs for Derived Ratios

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

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

1) Obtain the MOE for the numerator and the MOE 

 for the denominator of the ratio.

2) Square the derived ratio.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator 

 by the squared ratio.

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

 numerator.

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

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

 the ratio.

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived ratio.

 is the estimate used as the numerator of the 

derived ratio.

 is the estimate used as the denominator of the 

derived ratio.

0.0311

31,373

985.1

31,373

970,408.7 r

r r MOEp 

MOEnum

MOEden

den

num

X

X

R ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ i

den

num den

p

X

MOE p MOE

MOE ˆ

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

     989 [ 0.1461 * 601 ] 2 2 2  

978,121 7,712.3 970,408.7

den

num den

R

X

MOE R MOE

MOE ˆ

* ) ˆ ( 2 2 2 r  
A-16 Appendix

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

The example below shows how to derive the MOE for 

the estimated ratio of Black females 25 years of age 

and older in Fairfax County, Virginia, with a graduate 

degree to Black males 25 years and older in Fairfax 

County with a graduate degree, based on the 2006 

ACS.

The estimated ratio is:

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County) and denominator (number 

of Black males 25 years of age and older in Fairfax 

County with a graduate degree).

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator.

Thus, the derived estimate of the ratio of the number 

of Black females 25 years of age and older in Fairfax 

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

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

the MOE for the estimate is +0.2135.

Calculating MOEs for the Product of Two Estimates

To calculate the MOE for the product of two estimates, 

do the following:

1) Obtain the MOEs for the two estimates being 

multiplied together.

2) Square the estimates and their MOEs.

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

4) Multiply the second squared estimate by the 

fi rst estimate’s squared MOE.

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

6) Take the square root of (5).

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

MOE for the product is calculated as:

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

respectively.

 is the MOE of the fi rst estimate.

 is the MOE of the second estimate.

The example below shows how to derive the MOE for 

the estimated number of Black workers 16 years and 

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

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

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

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

Thus, the derived estimate of Black workers 16 years 

and over who commute by public transportation is 

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

MOEnum r989, 328 r1, MOEden

0.2135

6,440

1,375.2

6,440

1,891,259.1 r

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

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

Percent of Black workers 16 

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

13.4% +2.7%

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

 Table 3. Data for Example 3 

Characteristic Estimate MOE

Black females 25 years and 

 older with a graduate 

 degree (numerator)

4,634 +989

Black males 25 years and

 older with a graduate degree 

 (denominator)

6,440 +1,328

0.7200

6,440

4,634

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

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

MOEA

MOEB

 r1,405

* ) ˆ ( 2 2 2 MOEnum  R MOEden 

     989 [ 0.7200 * 1,328 ] 2 2 2  

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

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

Calculate the MOE by dividing the square root of the 

prior result by the denominator ( ). 

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

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

The text box below summarizes the formulas used to 

calculate the margin of error for several derived estimates.

Calculating MOEs for Estimates of “Percent Change” or 

“Percent Diff erence”

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

two estimates (for example, the same estimates in two 

diff erent years) is commonly calculated as

Because is not a subset of , the procedure 

to calculate the MOE of a ratio discussed previously 

should be used here to obtain the MOE of the percent 

change.

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

2005 estimates of the number of persons in Maryland 

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

The percent change is:

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

The MOEs for the numerator ( ) and denominator 

( ) are:

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

product of the squared ratio and the squared MOE of 

the denominator (MOE1):

 Table 5. Data for Example 5

Characteristic Estimate MOE

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2005

762,475 +22,666

1.0521

762,475

802,210

ˆ

ˆ ˆ

1

2 X

X

R

2 Xˆ 1 Xˆ

2 Xˆ

1 Xˆ

1 Xˆ

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

0.0433

762,475

33,038.3

762,475

1,091,528,529 r

r r MOER 

 r ¦cMOE agg MOE c

2

den

num den

p

X

MOE p MOE ) MOE ˆ

 (ˆ * 2 2 2 r  

1

2 1

ˆ

ˆ ˆ 100%*

X

X X

Percent Change  

ˆ

ˆ ˆ 100%*

1

2 1  X

X X

Percent Change

5.21%

762,475

802,210 762,475 100%*  ̧ 

1

·  ̈

©

§ 

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

* ) ˆ ( 2

1

2 2 MOE2  R MOE 

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

 

1,091,528,529

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  
A-18 Appendix

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

One of the most important uses of the ACS estimates is 

to make comparisons between estimates. Several key 

types of comparisons are of general interest to users: 

1) comparisons of estimates from diff erent geographic 

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

proportion of people below the poverty level in two 

counties); 2) comparisons of estimates for the same 

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

the proportion of people below the poverty level in a 

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

estimates with the corresponding estimates from past 

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

2006 and 2000).

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

the comparison takes into account the sampling error 

associated with each estimate, thus determining 

whether the observed diff erences between estimates 

are statistically signifi cant. Statistical signifi cance 

means that there is statistical evidence that a true 

diff erence exists within the full population, and that 

the observed diff erence is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical signifi cance when making comparisons is 

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

be made are also discussed.

Determining Statistical Signifi cance

When comparing two estimates, one should use the 

test for signifi cance described below. This approach 

will allow the user to ascertain whether the observed 

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

is statistically signifi cant). 

The test for signifi cance can be carried out by making 

several computations using the estimates and their 

corresponding standard errors (SEs). When working 

with ACS data, these computations are simple given 

the data provided in tables in the American FactFinder.

1) Determine the SE for each estimate (for ACS 

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

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

2) Square the resulting SE for each estimate.

3) Sum the squared SEs.

4) Calculate the square root of the sum of the 

squared SEs.

5) Calculate the diff erence between the two estimates.

6) Divide (5) by (4).

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

with the critical value for the desired level of 

confi dence (1.645 for 90 percent, 1.960 for 95 

percent, 2.576 for 99 percent).

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

between the two estimates can be considered 

statistically signifi cant at the level of confi dence 

corresponding to the critical value used in (7).

Algebraically, the signifi cance test can be expressed as 

follows:

If , then the diff erence 

between estimates and is statistically signifi cant 

at the specifi ed confi dence level, CL

where is estimate i (=1,2)

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

 is the critical value for the desired confi dence 

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

2.576 for 99 percent).

The example below shows how to determine if the 

diff erence in the estimated percentage of households 

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

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

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

statistically signifi cant. Using the formula above:

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

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

diff erence in the percentages is statistically signifi cant 

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

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

A rough interpretation of the result is that the user can 

be 99 percent certain that a diff erence exists between 

the percentages of households with one or more 

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

Making Comparisons

Appendix 4.

4

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

use the value 1.65 rather than 1.645. 

ZCL

SE SE

X X

!





2

2

2

1

1 2 ˆ ˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   0.12 0.12

22.0 21.5 ˆ ˆ

2 2 2

2

2

1

1 2 



 





SE SE

X X

2.90

0.173

0.5

0.03

0.5

0.015 0.015

0.5 

Ei
Appendix A-19

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

By contrast, if the corresponding estimates for State C 

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

would yield

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

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

A rough interpretation of the result is that the user 

cannot be certain to any suffi cient degree that the 

observed diff erence in the estimates was not due to 

chance.

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same 

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

3-year period) are straightforward and can be carried 

out as described in the previous section. There is, 

however, one statistical aspect related to the test for 

statistical signifi cance that users should be aware 

of. When comparing estimates within the same time 

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

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

In some cases, the comparison may involve a large 

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

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

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

estimates are not independent. The estimate for the 

large area is partially dependent on the estimate for the 

subset and, strictly speaking, the formula for testing 

diff erences should account for this partial dependence. 

However, unless the user has reason to believe that the 

two estimates are strongly correlated, it is acceptable 

to ignore the partial dependence and use the formula 

for testing diff erences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a fi nding of statistical signifi cance will still 

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

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

important to obtain a more exact test of signifi cance, 

the user should consult with a statistician about 

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

   0.20 0.25

22.5 22.1 ˆ ˆ

2 2 2

2

2

1

1 2 



 





SE SE

X X

1.25

0.320

0.4

0.1025

0.4

0.04 0.0625

0.4 

Comparisons Across Time Periods

Comparisons of estimates from diff erent time periods 

may involve diff erent single-year periods or diff erent 

multiyear periods of the same length within the same 

area. Comparisons across time periods should be made 

only with comparable time period estimates. Users are 

advised against comparing single-year estimates with 

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

2009) and against comparing multiyear estimates of 

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

2009–2014), as they are measuring the characteristics 

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

between such estimates are diffi cult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

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

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

the user should recognize that from 2006 on the ACS 

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

units. Many types of GQ populations have demographic, 

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

comparisons between 2005 and 2006 and later ACS 

estimates could be aff ected. This is particularly true 

for areas with a substantial GQ population. For most 

population characteristics, the Census Bureau suggests 

users make comparisons across these time periods 

only if the geographic area of interest does not include 

a substantial GQ population. For housing characteristics or characteristics published only for the household 

population, this is obviously not an issue. 

Comparisons Based on Overlapping Periods

When comparing estimates from two multiyear periods, ideally comparisons should be based on nonoverlapping periods (e.g., comparing estimates from 

2006–2008 with estimates from 2009–2011). The comparison of two estimates for diff erent, but overlapping 

periods is challenging since the diff erence is driven by 

the nonoverlapping years. For example, when comparing the 2005–2007 ACS with the 2006–2008 ACS, data 

for 2006 and 2007 are included in both estimates. 

Their contribution is subtracted out when the estimate 

of diff erences is calculated. While the interpretation 

of this diff erence is diffi cult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of diff erence should not be interpreted as a 

refl ection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

over time is complicated when change is being evaluated using multiyear estimates. From a technical standpoint, change over time is best evaluated with multiyear estimates that do not overlap. At the same time, 
A-20 Appendix

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

many areas whose only source of data will be 5-year 

estimates will not want to wait until 2015 to evaluate 

change (i.e., comparing 2005–2009 with 2010–2014). 

When comparing two 3-year estimates or two 5-year 

estimates of the same geography that overlap in 

sample years one must account for this sample overlap. 

Thus to calculate the standard error of this diff erence 

use the following approximation to the standard error:

where C is the fraction of overlapping years. For example, the periods 2005–2009 and 2007–2011 overlap for 

3 out of 5 years, so C=3/5=0.6. If the periods do not 

overlap, such as 2005–2007 and 2008–2010, then C=0.

With this SE one can test for the statistical signifi cance 

of the diff erence between the two estimates using the 

method outlined in the previous section with one modifi cation; substitute for 

 in the denominator of the formula for 

the signifi cance test.

Comparisons With Census 2000 Data

In Appendix 2, major diff erences between ACS data and 

decennial census sample data are discussed. Factors 

such as diff erences in residence rules, universes, and 

reference periods, while not discussed in detail in this 

appendix, should be considered when comparing ACS 

estimates with decennial census estimates. For example, given the reference period diff erences, seasonality 

may aff ect comparisons between decennial census and 

ACS estimates when looking at data for areas such as 

college towns and resort areas. 

The Census Bureau subject matter specialists have 

reviewed the factors that could aff ect diff erences 

between ACS and decennial census estimates and they 

have determined that ACS estimates are similar to 

those obtained from past decennial census sample data 

for most areas and characteristics. The user should 

consider whether a particular analysis involves an area 

or characteristic that might be aff ected by these diff erences.5

When comparing ACS and decennial census sample 

estimates, the user must remember that the decennial 

census sample estimates have sampling error associated with them and that the standard errors for both 

ACS and census estimates must be incorporated when 

performing tests of statistical signifi cance. Appendix 

3 provides the calculations necessary for determining 

statistical signifi cance of a diff erence between two 

estimates. To derive the SEs of census sample estimates, use the method described in Chapter 8 of either 

the Census 2000 Summary File 3 Technical Documentation <http://www.census.gov/prod/cen2000/doc/sf3

.pdf> or the Census 2000 Summary File 4 Technical 

Documentation <http://www.census.gov/prod

/cen2000/doc/sf4.pdf>.

A conservative approach to testing for statistical signifi -

cance when comparing ACS and Census 2000 

estimates that avoids deriving the SE for the 

Census 2000 estimate would be to assume the SE for 

the Census 2000 estimate is the same as that determined for the ACS estimate. The result of this approach 

would be that a fi nding of statistical signifi cance can be 

assumed to be accurate (as the SE for the Census 2000 

estimate would be expected to be less than that for the 

ACS estimate), but a fi nding of no statistical signifi -

cance could be incorrect. In this case the user should 

calculate the census long-form standard error and follow the steps to conduct the statistical test.

Comparisons With 2010 Census Data

Looking ahead to the 2010 decennial census, data 

users need to remember that the socioeconomic data 

previously collected on the long form during the 

census will not be available for comparison with ACS 

estimates. The only common variables for the ACS and 

2010 Census are sex, age, race, ethnicity, household 

relationship, housing tenure, and vacancy status.

The critical factor that must be considered when comparing ACS estimates encompassing 2010 with the 

2010 Census is the potential impact of housing and 

population controls used for the ACS. As the housing 

and population controls used for 2010 ACS data will 

be based on the Population Estimates Program where 

the estimates are benchmarked on the Census 2000 

counts, they will not agree with the 2010 Census 

population counts for that year. The 2010 population 

estimates may diff er from the 2010 Census counts 

for two major reasons—the true change from 2000 to 

2010 is not accurately captured by the estimates and 

the completeness of coverage in the 2010 Census is 

diff erent than coverage of Census 2000. The impact of 

this diff erence will likely aff ect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large diff erences between the 

population controls and the 2010 Census population 

counts is greater.

Comparisons With Other Surveys

Comparisons of ACS estimates with estimates from 

other national surveys, such as the Current Population 

Survey, may be of interest to some users. A major consideration in making such comparisons will be that ACS 

5

 Further information concerning areas and characteristics that do not 

fi t the general pattern of comparability can be found on the ACS Web 

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

  2

2

2

1 2 1 ) 1 ˆ ˆ SE(X  X #  C SE  SE

  2

2

2

1 1 C SE  SE

2

2

2

1 SE  SE
Appendix A-21

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

estimates include data for populations in both institutional and noninstitutional group quarters, and estimates from most national surveys do not include institutional populations. Another potential for large eff ects 

when comparing data from the ACS with data from 

other national surveys is the use of diff erent questions 

for measuring the same or similar information.

Sampling error and its impact on the estimates from 

the other survey should be considered if comparisons 

and statements of statistical diff erence are to be made, 

as described in Appendix 3. The standard errors on 

estimates from other surveys should be derived 

according to technical documentation provided for 

those individual surveys.

Finally, the user wishing to compare ACS estimates 

with estimates from other national surveys should 

consider the potential impact of other factors, such 

as target population, sample design and size, survey 

period, reference period, residence rules, and interview 

modes on estimates from the two sources.
A-22 Appendix

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

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

for the earlier year (Y1).

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

more recent year (Y2).

 is the published ACS estimate for the earlier year 

(Y1).

The example below compares the national median 

value for owner-occupied mobile homes in 2005 

($37,700) and 2006 ($41,000). First adjust the 2005 

median value using the 2005 All Items CPI-U-RS Annual 

Average (286.7) and the 2006 All Items CPI-U-RS Annual 

Average (296.1) as follows:

Thus, the comparison of the national median value for 

owner-occupied mobile homes in 2005 and 2006, in 

2006 dollars, would be $38,936 (2005 infl ationadjusted to 2006 dollars) versus $41,000 

(2006 dollars).

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost 

values are created with infl ation adjustments. The 

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

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from its most recent year to 

the CPI-U-RS Annual Averages from each of its earlier 

years. The ACS values for each of the earlier years in 

the multiyear period are multiplied by the appropriate 

infl ation adjustment factors to produce the infl ationadjusted values. These values are then used to create 

the multiyear estimates. 

As an illustration, consider the time period 2004–2006, 

which consisted of individual reference-year income 

values of $30,000 for 2006, $20,000 for 2005, and 

$10,000 for 2004. The multiyear income components 

are created from infl ation-adjusted reference period 

income values using factors based on the All Items 

CPI-U-RS Annual Averages of 277.4 (for 2004), 286.7 

(for 2005), and 296.1 (for 2006). The adjusted 2005 

value is the ratio of 296.1 to 286.7 applied to $20,000, 

which equals $20,656. Similarly, the 2004 value is 

the ratio of 296.1 to 277.4 applied to $10,000, which 

equals $10,674.

Using Dollar-Denominated Data

Appendix 5.

Dollar-denominated data refer to any characteristics 

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

value, and energy costs are all dollar-denominated 

data.

Infl ation will aff ect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using infl ation factors based on the Consumer Price Index (CPI).

Given the potential impact of infl ation on observed 

diff erences of dollar-denominated data across time 

periods, users should adjust for the eff ects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for infl ation. In making adjustments, the 

Census Bureau recommends using factors based on 

the All Items CPI-U-RS (CPI research series). The Bureau 

of Labor Statistics CPI indexes through 2006 are found 

at <http://www.bls.gov/cpi/cpiurs1978_2006.pdf>. 

Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount 

of income received during the 12 months preceding 

the interview month. This is the income reference 

period. Since there are 12 diff erent income reference 

periods throughout an interview year, 12 diff erent 

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

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no infl ation adjustments for single-year estimates of rent, home value, or energy cost values.

Adjusting Single-Year Estimates Over Time

When comparing single-year income, rent, home value, 

and energy cost value estimates from two diff erent 

years, adjustment should be made as follows: 

1) Obtain the All Items CPI-U-RS Annual Averages for 

the 2 years being compared.

2) Calculate the infl ation adjustment factor as the ratio 

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

CPI-U-RS from the earlier year.

3) Multiply the dollar-denominated data estimated for 

the earlier year by the infl ation adjustment factor.

The infl ation-adjusted estimate for the earlier year can 

be expressed as:

$37,700 $38,936

286.7

296.1 ˆ

X 2005,Adj u 

1

1

2

1, ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

CPIY1

CPIY 2

1 ˆ

XY
Appendix A-23

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

As an illustration, consider ACS multiyear estimates for 

the two time periods of 2001–2003 and 2004–2006. 

To compare the national median value for owneroccupied mobile homes in 2001–2003 ($32,000) and 

2004–2006 ($39,000), fi rst adjust the 2001–2003 

median value using the 2003 All Items CPI-U-RS Annual 

Averages (270.1) and the 2006 All Items CPI-U-RS 

Annual Averages (296.1) as follows:

Thus, the comparison of the national median value 

for owner-occupied mobile homes in 2001–2003 

and 2004–2006, in 2006 dollars, would be $35,080 

(2001–2003 infl ation-adjusted to 2006 dollars) versus 

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

Issues Associated With Infl ation Adjustment

The recommended infl ation adjustment uses a national 

level CPI and thus will not refl ect infl ation diff erences 

that may exist across geographies. In addition, since 

the infl ation adjustment uses the All Items CPI, it will 

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

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as 

follows:

1) Obtain the All Items CPI-U-RS Annual Average for 

the most current year in each of the time periods 

being compared.

2) Calculate the infl ation adjustment factor as the 

ratio of the CPI-U-RS Annual Average in (1) from 

the most recent year to the CPI-U-RS in (1) from 

the earlier years.

3) Multiply the dollar-denominated estimate for the 

earlier time period by the infl ation adjustment 

factor.

The infl ation-adjusted estimate for the earlier years can 

be expressed as:

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

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

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

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

 is the published ACS estimate for the earlier time 

period (P1).

$32,000 $35,080

270.1

296.1 ˆ

X 20012003,Adj u 

1

1

2

1, ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

CPI P2

1 ˆ

X P
All survey estimates are subject to both sampling and 

nonsampling error. In Appendix 3, the topic of sampling error and the various measures available for 

understanding the uncertainty in the estimates due to 

their being derived from a sample, rather than from an 

entire population, are discussed. The margins of error 

published with ACS estimates measure only the eff ect 

of sampling error. Other errors that aff ect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any error 

aff ecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error.

Types of Nonsampling Errors

Coverage error occurs when a housing unit or person 

does not have a chance of selection in the sample 

(undercoverage), or when a housing unit or person has 

more than one chance of selection in the sample, or is 

included in the sample when they should not have been 

(overcoverage). For example, if the frame used for the 

ACS did not allow the selection of newly constructed 

housing units, the estimates would suff er from errors 

due to housing undercoverage.

The fi nal ACS estimates are adjusted for under- and 

overcoverage by controlling county-level estimates to 

independent total housing unit controls and to independent population controls by sex, age, race, and 

Hispanic origin (more information is provided on the 

coverage error defi nition page of the “ACS Quality Measures” Web site at <http://www.census.gov/acs/www

/UseData/sse/cov/cov_def.htm>). However, it is important to measure the extent of coverage adjustment by 

comparing the precontrolled ACS estimates to the fi nal 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that diff erences in characteristics of undercovered or overcovered 

housing units or individuals diff er from those eligible to 

be selected. When this occurs, the ACS may not provide 

an accurate picture of the population prior to the coverage adjustment, and the population controls may not 

eliminate or minimize that coverage error.

Unit nonresponse is the failure to obtain the minimum required information from a housing unit or a resident of a group quarter in order for it to be considered 

a completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

Appendix 6.

or person. For example, if no one in a sampled housing unit is available to be interviewed during the time 

frame for data collection, unit nonresponse will result.

It is important to measure unit nonresponse because 

it has a direct eff ect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the fi nal survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

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

Item nonresponse occurs when a respondent fails to 

provide an answer to a required question or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question diff er 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias.

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation 

of the fi nal data fi les. For example, errors may occur if 

data entry of questionnaire information is incomplete 

Measures of Nonsampling Error

A-24 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
Appendix A-25

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

or inaccurate. Coding of responses incorrectly also 

results in processing error. Critical reviews of edits and 

tabulations by subject matter experts are conducted to 

keep errors of this kind to a minimum.

Nonsampling error can result in random errors and 

systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there is 

an error in the questionnaire design that negatively 

aff ects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the fi nal results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

ACS Quality Measures

Nonsampling error is extremely diffi cult, if not 

impossible, to measure directly. However, the Census 

Bureau has developed a number of indirect measures of 

nonsampling error to help inform users of the quality 

of the ACS estimates: sample size, coverage rates, unit 

response rates and nonresponse rates by reason, and 

item allocation rates. Starting with the 2007 ACS, these 

measures are available in the B98 series of detailed 

tables on AFF. Quality measures for previous years are 

available on the “ACS Quality Measures” Web site at 

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

Sample size measures for the ACS summarize information for the housing unit and GQ samples. The measures available at the state level are:6

Housing units

Number of initial addresses selected

Number of fi nal survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of fi nal survey interviews

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

or multiyear estimates in conjunction with estimates of 

the population of interest. While the coeffi cient of variation (CV) should typically be used to determine 

usability, as explained in Appendix 3, there may be 

some situations where the CV is small but the user 

has reason to believe the sample size for a subgroup 

is very small and the robustness of the estimate is in 

question. 

For example, the Asian-alone population makes up 

roughly 1 percent (8,418/656,700) of the population 

in Jeff erson County, Alabama. Given that the number of 

successful housing unit interviews in Jeff erson County 

for the 2006 ACS were 4,072 and assuming roughly 2.5 

persons per household (or roughly 12,500 completed 

person interviews), one could estimate that the 2006 

ACS data for Asians in Jeff erson County are based on 

roughly 150 completed person interviews.

Coverage rates are available for housing units, and 

total population by sex at both the state and national 

level. Coverage rates for total population by six race/

ethnicity categories and the GQ population are also 

available at the national level. These coverage rates are 

a measure of the extent of adjustment to the survey 

weights required during the component of the estimation methodology that adjusts to population controls. 

Low coverage rates are an indication of greater potential for coverage error in the estimates. 

Unit response and nonresponse rates for housing 

units are available at the county, state, and national 

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, other, and data insuffi cient to be considered 

an interview. Rates are also provided separately for persons in group quarters at the national and state levels.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. For example, 

the 2006 housing unit response rates are at least 94 

percent for all states. The response rate for the District 

of Columbia in 2006 was 91 percent.

Item allocation rates are determined by the content 

edits performed on the individual raw responses and 

closely correspond to item nonresponse rates. Overall 

housing unit and person characteristic allocation rates 

are available at the state and national levels, which 

combine many diff erent characteristics. Allocation rates 

for individual items may be calculated from the B99 

series of imputation detailed tables available in AFF. 

Item allocation rates do vary by state, so users are 

advised to examine the allocation rates for 

characteristics of interest before drawing conclusions 

from the published estimates.

6

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

selected and number of fi nal survey interviews) and for group quarters 

people cannot be used to calculate response rates. For the housing unit 

sample, the number of initial addresses selected includes addresses 

that were determined not to identify housing units, as well as initial 

addresses that are subsequently subsampled out in preparation for personal visit nonresponse follow-up. Similarly, the initial sample of people 

in group quarters represents the expected sample size within selected 

group quarters prior to visiting and sampling of residents. 
A-26 Appendix

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

Implications of Population Controls on ACS Estimates

Appendix 7.

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defi ned by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s offi cial estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the offi cial count 

of the population comes from the previous census, 

updated by the Population Estimates Program.

In the case of the ACS, the total housing unit estimates 

and the total population estimates by age, sex, race 

and Hispanic origin are controlled at the county (or 

groups of counties) level. The group quarters total 

population is controlled at the state level by major type 

of group quarters. Such adjustments are important to 

correct the survey data for nonsampling and sampling 

errors. An important source of nonsampling error is 

the potential under-representation of hard-toenumerate demographic groups. The use of the 

population controls results in ACS estimates that more 

closely refl ect the level of coverage achieved for those 

groups in the preceding census. The use of the population estimates as controls partially corrects demographically implausible results from the ACS due to 

the ACS data being based on a sample of the population rather than a full count. For example, the use of 

the population controls “smooths out” demographic 

irregularities in the age structure of the population that 

result from random sampling variability in the ACS. 

When the controls are applied to a group of counties 

rather than a single county, the ACS estimates and the 

offi cial population estimates for the individual counties 

may not agree. There also may not be agreement 

between the ACS estimates and the population estimates for levels of geography such as subcounty areas 

where the population controls are not applied.

The use of population and housing unit controls also 

reduces random variability in the estimates from year 

to year. Without the controls, the sampling variability 

in the ACS could cause the population estimates to 

increase in one year and decrease in the next (especially for smaller areas or demographic groups), when 

the underlying trend is more stable. This reduction in 

variability on a time series basis is important since 

results from the ACS may be used to monitor trends 

over time. As more current data become available, the 

time series of estimates from the Population Estimates 

Program are revised back to the preceding census while 

the ACS estimates in previous years are not. Therefore, 

some diff erences in the ACS estimates across time may 

be due to changes in the population estimates. 

For single-year ACS estimates, the population and total 

housing unit estimates for July 1 of the survey year 

are used as controls. For multiyear ACS estimates, the 

controls are the average of the individual year population estimates.
Appendix A-27

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

Background and Overview Information

American Community Survey Web Page Site Map: 

<http://www.census.gov/acs/www/Site_Map.html>

This link is the site map for the ACS Web page. It provides an overview of the links and materials that are 

available online, including numerous reference documents.

What Is the ACS? <http://www.census.gov/acs/www

/SBasics/What/What1.htm> This Web page includes 

basic information about the ACS and has links to additional information including background materials.

ACS Design, Methodology, Operations

American Community Survey Design and Methodology 

Technical Paper: <http://www.census.gov/acs/www

/Downloads/tp67.pdf> This document describes the 

basic design of the 2005 ACS and details the full set 

of methods and procedures that were used in 2005. 

Please watch our Web site as a revised version will be 

released in the fall of 2008, detailing methods and 

procedures used in 2006 and 2007. 

About the Data (Methodology: <http://www.census

.gov/acs/www/AdvMeth/> This Web page contains 

links to information on ACS data collection and processing, evaluation reports, multiyear estimates study, 

and related topics.

ACS Quality

Accuracy of the Data (2007): <http://www.census.gov

/acs/www/Downloads/ACS/accuracy2007.pdf> This 

document provides data users with a basic understanding of the sample design, estimation methodology, and 

accuracy of the 2007 ACS data.

ACS Sample Size: <http://www.census.gov/acs/www

/SBasics/SSizes/SSizes06.htm> This link provides 

sample size information for the counties that were

published in the 2006 ACS. The initial sample size 

and the fi nal completed interviews are provided. The 

sample sizes for all published counties and county 

equivalents starting with the 2007 ACS will only be 

available in the B98 series of detailed tables on American FactFinder.

ACS Quality Measures: <http://www.census.gov/acs

/www/UseData/sse/> This Web page includes information about the steps taken by the Census Bureau to 

improve the accuracy of ACS data. Four indicators of 

survey quality are described and measures are provided at the national and state level.

Guidance on Data Products and Using the Data

How to Use the Data: <http://www.census.gov/acs

/www/UseData/> This Web page includes links to 

many documents and materials that explain the ACS 

data products. 

Comparing ACS Data to other sources: <http://www

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

are provided with guidance on comparing the 2007 

ACS data products to 2006 ACS data and Census 2000 

data. 

Fact Sheet on Using Diff erent Sources of Data for 

Income and Poverty: <http://www.census.gov/hhes

/www/income/factsheet.html> This fact sheet highlights the sources that should be used for data on 

income and poverty, focusing on comparing the ACS 

and the Current Population Survey (CPS).

Public Use Microdata Sample (PUMS): <http://www

.census.gov/acs/www/Products/PUMS/> This Web 

page provides guidance in accessing ACS microdata.

Other ACS Resources

Appendix 8.