What PUMS Data Users Need to Know i

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

U.S. Department of Commerce

Economics and Statistics Administration

U.S. CENSUS BUREAU

USCENSUSBUREAU

Helping You Make Informed Decisions

A Compass for Understanding and Using 

American Community Survey Data

What PUMS Data Users Need to Know

Issued

February 2009
Acknowledgments Leonard M. Gaines, Consultant, 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 February 2009

U.S. Department of Commerce

Vacant, 

Secretary

Vacant, 

Deputy Secretary

Economics and Statistics Administration

Kim White, 

Acting Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg, 

Acting Director

What PUMS Data Users Need to Know
Suggested Citation

 U.S. Census Bureau, 

A Compass for Understanding 

and Using American 

Community Survey Data:

What PUMS Data Users 

Need to Know

U.S. Government Printing Offi ce, 

Washington, DC, 

2009.

Economics and Statistics 

Administration

Kim White,

Acting Under Secretary for Economic Aff airs

U.S. CENSUS BUREAU

Thomas L. Mesenbourg,

Acting Director

Thomas L. Mesenbourg,

Deputy Director and 

Chief Operating Offi cer

Arnold A. Jackson

Associate Director for Decennial Census

Daniel H. Weinberg

Assistant Director for ACS and Decennial Census 

Susan Schechter

Chief, American Community Survey Offi ce

ECONOMICS

AND STATISTICS

ADMINISTRATION
What PUMS Data Users Need to Know iii

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

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

Background ...................................................................................................1 

What Is the ACS and Why Is It Important? ...........................................................1

 What Are the Public Use Microdata Sample (PUMS) Files? ....................................1

 Confi dentiality of the ACS PUMS Data ................................................................2

 Who Should Use the PUMS and Why? ..................................................................3

PUMS Geography ...........................................................................................3

Identifying PUMAs .............................................................................................4

Creating PUMS Tabulations ...........................................................................6 

Accessing PUMS Files ........................................................................................6

 Creating PUMS Tables Using General Statistical Software .................................10

 Getting Started ............................................................................................10

 Using General Statistical Software ...............................................................12

 Creating PUMS Tables Using DataFerrett .........................................................12

 Getting Stated .............................................................................................13

 Using DataFerrett ........................................................................................15

Data Quality in PUMS .................................................................................. 24

Measuring Statistical Accuracy ........................................................................25

 Generalized Standard Error Formula Method ...............................................25

 Replicate Weights Method ............................................................................26

Margin of Error and Confi dence Intervals ........................................................26

Summary ..................................................................................................... 26

Glossary...................................................................................................... 27

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 PUMS Data Users Need to Know

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

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

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

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

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

concepts that are fundamentally diff erent from those associated with sample 

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

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

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

The ACS Compass Products. 

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

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

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

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

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

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

they cover major stakeholder groups familiar to the Census Bureau.

General data users Congress 

High school teachers Puerto Rico Community Survey data

 users (in 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 PUMS Data Users Need to Know 1

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

The American Community Survey (ACS) is the new 

source for the information previously collected through 

the decennial census long form. This information 

includes topics such as income, employment status, 

housing costs, and housing conditions. Unlike the 

decennial census, ACS data are collected on a continuous basis. This presents a number of challenges and 

benefi ts for data users.

This handbook is primarily intended for users of the 

ACS who are looking for more information than is available in the profi les and tables produced by the 

Census Bureau. In this handbook, you will learn how 

the Public Use Microdata Sample (PUMS) fi les diff er 

from the pretabulated products, how to access the 

data, and some ways to produce your own tables. Data 

users already familiar with the PUMS fi les available 

from the decennial censuses can learn how those fi les 

diff er from the ACS PUMS. 

While a common use of PUMS data is to develop statistical models describing the relationship between 

variables, that use of the data is beyond the scope of 

this handbook. Researchers developing these kinds of 

models should nonetheless fi nd the information in this 

handbook helpful if they are not familiar with the ACS 

PUMS fi les. 

A glossary and a series of technical appendixes are 

included at the back of this handbook for those interested in more advanced ACS issues.

What Is the ACS and Why Is It Important?

As in the past, the 2010 Census of Population and 

Housing will collect data about the number of people 

residing in the United States and their relationship 

within a household, age, race, Hispanic origin (ethnicity), and sex. It will also collect information about the 

number, occupancy status, and tenure (ownership 

status) of the nation’s housing units. However, unlike 

previous censuses, information about topics such 

as income, education, employment status, disability 

status, housing value, housing costs, and number of 

bedrooms will not be asked as part of the 

2010 Census. Instead, these data on these topics will 

come from the ACS. In this way, the ACS can be considered the replacement for the decennial census long 

form.

While the ACS takes the place of the long form as 

the source for similar information, it is not the same 

thing. Instead of collecting data from about 1 in every 

6 households once every 10 years, like the decennial 

census long form, the ACS samples about 1 in every 

40 addresses every year, or 250,000 addresses every 

month. This allows the Census Bureau to produce data 

every year rather than every decade. For areas with 

large populations (65,000 or more), survey estimates 

are based on 12 months (1 year) of ACS data. For all 

areas with populations of 20,000 or more, the survey 

estimates are based on 36 months (3 years) of ACS 

data. The Census Bureau will produce estimates for 

all areas, down to the census tract and block group 

levels, based on 60 months (5 years) of ACS data. How 

these estimates are produced is detailed in the ACS 

Design and Methodology report (Technical Paper 67) on 

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

.gov/acs/www/Downloads/tp67.pdf>. Information on 

the basic set of ACS data products and guidance on 

how to interpret ACS data are provided in other handbooks in the ACS Compass Products. The appendixes 

at the back of this report also provide important information about the use and interpretation of multiyear 

estimates. 

What Are the Public Use Microdata Sample 

(PUMS) Files?

The Census Bureau produces a large number of data 

profi les, tables, and maps showing a massive amount 

of pretabulated data from the ACS. However, these 

products cannot meet the needs of every data user. 

The Census Bureau produces the Public Use Microdata 

Sample (PUMS) fi les so that data users can create custom tables that are not available through pretabulated 

ACS products. 

The PUMS fi les are a set of untabulated records about 

individual people or housing units. They diff er from 

the ACS summary products, which show data that have 

already been tabulated for specifi c geographic areas. 

The diff erence between these kinds of products can be 

seen in Table 1. Summary products display summary 

statistics such as estimates of the number of males 

and females; the median age of the population; and 

estimates of the number of occupied housing units by 

tenure. These estimates are specifi c to a geographic 

area. (In Table 1, for example, State 1 and County A.)

PUMS fi les, in contrast, include population and housing unit records with individual response information 

such as relationship, sex, educational attainment, and 

employment status. 

The Census Bureau plans to produce 1-year, 3-year, 

and 5-year ACS PUMS fi les. The 3-year and 5-year PUMS 

fi les are multiyear combinations of the 1-year PUMS 

fi les with appropriate adjustments to the weights and 

infl ation adjustment factors described later in this 

handbook.

Background
2 What PUMS Data Users Need to Know

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

Confi dentiality of the ACS PUMS Data

As required by federal law, the confi dentiality of the 

ACS respondents is protected through a variety of 

means, ensuring that it is impossible to identify individuals who provide any response. The fi rst means of 

protecting confi dentiality is the removal of all personal 

identifi cation, such as name and address, from the 

record. Next, a small number of records are switched 

with similar records from a neighboring area, reducing 

the ability to identify individuals from their responses. 

Then, the answers to open-ended questions, such as 

age, income, or housing unit value—where an extreme 

value might identify an individual—are top-coded. Top 

coding is the process of taking any response exceeding 

a particular value and replacing it with a predetermined 

value. These predetermined values vary by state. For 

example, if someone in New York reports their age as 

103, it will be recorded in the ACS PUMS fi le as 94 (the 

maximum value shown for New York). 

In addition to modifying the individual records, respondent confi dentiality is protected in the PUMS because 

only a sample of ACS responses is included in the 

PUMS. The 1-year tabulated ACS products found on the 

American FactFinder are based on all of the ACS data 

collected for that year, about 2.5 percent of the population. The 1-year ACS PUMS fi les contain a sample of 

the ACS housing unit and group quarters population 

records representing about 1 percent of the population. So, in New York State’s 2006 ACS PUMS fi le, there 

are 187,143 population records or 0.969 percent of 

the estimated 19,306,183 people residing in the state. 

There are also a total of 85,108 records on the PUMS 

housing unit fi le for the state of New York (79,075 

housing unit records and 6,033 group quarters person 

placeholders). 

The Census Bureau also protects confi dentiality by limiting the geographic area codes available on the PUMS 

fi les. The only geographic codes available in the PUMS 

records are those for regions, divisions, states, and 

Public Use Microdata Areas or “PUMAs.”1 PUMAs, which 

are described in more detail in a later section, were 

defi ned for Census 2000 to represent geographic areas 

with populations of at least 100,000. 

 Table 1. Conceptual Comparison of ACS Summary Products and Public Use Microdata Samples

Note: In the actual PUMS fi les, many variables, such as tenure and relationship, are represented by numeric codes rather than descriptive text.

Source: U.S. Census Bureau, artifi cial data.

Example Summary Product

Geography Males Females Median age Occupied 

housing 

units

Owneroccupied 

units

Renteroccupied 

units

State 1 7,345,968 7,952,709 35.9 5,689,354 3,005,973 2,683,381

County A 45,678 49,852 33.5 40,678 15,961 24,717

Example Public Use Microdata Sample Housing Unit Household Records

Household 

ID

PUMA Tenure Rooms Bedrooms Value Contract 

rent

105 00100 Owned 8 3 236,500 N/A

106 00100 Rented 3 1 N/A 1,250

Example Public Use Microdata Sample Population Records

Household 

ID

Person ID PUMA Relationship Sex Educational 

attainment

Employment 

status

105 1 00100 Householder Female Bachelors Working

105 2 00100 Spouse Male Masters Working

105 3 00100 Child Male Some high school N/A

1 Regions and divisions are collections of states.
What PUMS Data Users Need to Know 3

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

Who Should Use the PUMS and Why?

The PUMS fi les should be used by people who are 

looking for data tables that are not presented by the 

Census Bureau in the pretabulated products available 

through American FactFinder.2 These fi les can be used 

to extract custom data for particular population groups 

(e.g., veterans, college students) or when it is not possible to get particular data categories from the standard tables (e.g., families with income between 90 and 

99 percent of the offi cial poverty threshold). While it 

is possible to request that the Census Bureau produce 

custom tabulations for a fee, the PUMS fi les provide a 

much less expensive—and often faster—way to get the 

data.

One common group of users of the PUMS fi les are 

academic researchers interested in modeling relationships between the variables collected as part of the 

ACS. Another common group of users are researchers 

working in government and business looking at either 

characteristics that are not usually cross-tabulated 

2 You can determine if the data you are interested in are found in a 

standard ACS table by going to American FactFinder and searching the 

tables by subject or keyword. A detailed list of American FactFinder 

table shells for the 2007 ACS is also available at <http://www.census

.gov/acs/www/Products/users_guide/2007/index.htm>.

against each other or are categorized in diff erent ways 

than is done in the standard tables. 

While the standard ACS products answer the majority of questions data users are interested in, some 

questions cannot be answered by these products. For 

example, the standard products do not provide a table 

showing the poverty status of foreign-born residents 

by education. This can be produced using the PUMS 

fi les. 

The PUMS fi les also can be used when the standard 

tables do not provide the categories that a data user is 

interested in seeing. For example, many of the standard tables use age breaks like 55 to 64 years and 65 

years and older. But in New York State, many of the 

programs administered by the Offi ce for the Aging are 

designed for the population aged 60 and older. So if 

the Offi ce for the Aging wants to study the impact of 

changing any of their programs, they would need to 

look to the PUMS fi les as a primary source of information.

As noted earlier, to ensure the confi dentiality of ACS 

respondents, PUMS fi les present data for a much more 

limited set of geographic areas than the pretabulated 

ACS products. PUMS fi les cannot be used to summarize data for individual counties, cities, or other small 

areas. It is possible to summarize data for the nation, 

each of the states, the District of Columbia, Puerto 

Rico, and areas known as Public Use Microdata Areas 

(PUMAs).

As part of Census 2000, PUMAs were defi ned as areas 

with 100,000 residents or more based on the populations reported in Census 2000. The ACS uses these 

same PUMAs. In most states, PUMA boundaries were 

defi ned by the State Data Center. If the State Data 

Center chose not to defi ne these areas, the Census 

Bureau’s regional offi ce geographic staff defi ned them.

In addition to having a minimum population of 

100,000 residents, the PUMAs had to be combinations 

of contiguous counties or census tracts. While attempts 

were made to create PUMAs that represented entire 

communities on their own, this was not always possible. For example, the city of Albany, New York, had 

PUMS Geography

a population of 95,658—not quite large enough to be 

its own PUMA. So, in order to get the population over 

100,000 and create a PUMA that essentially represents 

the city, Albany was combined with one census tract 

from the adjacent town with similar characteristics. 

As much as possible, PUMAs were designed to contain 

areas with similar characteristics. However, this was 

not always possible, so users need to consider the 

potential impacts of these tract combinations on the 

overall PUMA populations. Figure 1 shows one PUMA 

(01700), that comprises two counties in New York 

State: Seneca County and Tompkins County. In one 

regard these are very diff erent counties. As shown 

in Table 2, Tompkins County’s college and graduate 

school population accounts for about 31 percent of 

the population, while in Seneca County, this group 

represents about 5 percent of the total population. Yet 

outside the urban center of Tompkins County, these 

two counties are very similar. In a situation like this, 

the data user needs to consider the impact of such a 

large diff erence in the composition of the PUMA.
4 What PUMS Data Users Need to Know

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

Identifying PUMAs

PUMAs are identifi ed by a 5-digit number that is unique 

within state. Generally, the 5-digit PUMA codes are not 

useful in identifying where in a state a PUMA is located. 

To show where a particular PUMA is located, the 

Census Bureau has provided both maps and geographic 

equivalency fi les. The geographic equivalency fi les 

show which counties, places, and census tracts are 

included in each PUMA. 

Finding the PUMA maps on the Census Bureau’s Web 

site is fairly easy. As shown in Figure 2, from the 

Census Bureau’s home page <www.census.gov>, click 

on “Maps” under the “Geography” section.

 Figure 1. Sample PUMA Map

Source: U.S. Census Bureau, accessed at <http://ftp2.census.gov/geo

/maps/puma/puma2k/ny_puma5.pdf>.

Seneca 

County

Tompkins 

County

PUMA 

01700

Population 34,279 100,590 134,869

College or graduate school enrollees 1,822 31,326 33,148

Percent of population enrolled in college or graduate school 5.3 31.1 24.6

 Table 2. Selected Characteristics of New York State PUMA 01700 and Component Counties

Source: U.S. Census Bureau, 2005–2007 ACS 3-year Estimates, Social Data Profi le.
What PUMS Data Users Need to Know 5

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

 Figure 2. Census Bureau Home Page

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

This will bring you to the geography page, shown in 

Figure 3. Clicking on “MAP PRODUCTS” will take you 

to the Census Bureau’s Map Products page, Figure 4, 

which includes links to a variety of map products, 

including the Census 2000 PUMA maps that show the 

current set of PUMAs being used for the ACS PUMS 

fi les. The Census 2000 PUMAs will be used until the 

new PUMAs are delineated using 2010 Census counts. 

You will have to scroll down the page to see these fi les. 

Clicking on the 2000 link under the 5 percent sample 

will take you to a list of the states.3 Simply choose the 

state you are interested in seeing and you will get a 

PDF fi le with a map showing the Super-PUMAs.4 Individual maps for each Super-PUMA show the individual 

PUMAs.

If you would prefer to look at a list of the components of PUMAs, you need to look at the geographic 

equivalency fi les online. A separate geographic equivalency fi le exists for each state. They are found in the 

main PUMS link from the Census Bureau Web site: 

<http://www.census.gov/main/www/pums.html>. 

For instance, using the New York equivalency fi le and 

sorting the data by summary level code, one can see 

which census tracts are grouped together in a PUMA or 

which PUMAs compose New York County [Manhattan], 

(03801-03810). Geographic Equivalency Files can be 

accessed at the following FTP site: <http://www2

.census.gov/census_2000/datasets/PUMS/FivePercent/>. 

The Missouri State Data Center created a tool that 

allows PUMA users to enter the geography that they 

are interested in to identify PUMA codes and 

equivalent geographies. For more information, go to 

<http://mcdc2.missouri.edu/websas/geocorr2k.html>.

3 The Census Bureau produced both 1percent and 5 percent PUMS 

fi les for Census 2000. The ACS PUMS uses the same geography as the 

Census 2000, 5 percent PUMS fi les even though it contains a 1 percent 

sample.

4 A Super-PUMA is a collection of PUMAs with a minimum combined 

population of 400,000. These were used as the geographic areas for 

the Census 2000, 1 percent PUMS fi les. The ACS does not use SuperPUMAs.
6 What PUMS Data Users Need to Know

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

Creating tables from the PUMS fi les is easy if you have 

the right software and some basic knowledge to use 

the fi les correctly. As a rule, these fi les are too large to 

work with in a spreadsheet, and databases are not well 

suited to cross-tabulating data. Fortunately, a number 

of other applications do this very well. Many of these 

are general statistical programs, such as SPSS, SAS, 

S-Plus, and R Statistical Software. The Census Bureau 

also provides the ability to produce cross-tabulations 

using the DataFerrett system. Each of these programs 

has its own advantages and disadvantages, and users 

can choose the one that best meets their needs. 

This section describes how to access the ACS PUMS 

fi les for use in any of the general statistical programs. 

It also shows how to use the DataFerrett program.

For some PUMS applications it is necessary to apply 

infl ation adjustments. For the 1-year PUMS the infl ation adjustment variable (ADJUST) should be used to 

produce income characteristics. The 3-year and 5-year 

PUMS will carry two infl ation adjustment variables—

ADJINC for income applications and ADJHOUS for housing cost applications. For additional information about 

dollar-denominated data in the ACS, refer to Appendix 5.

Accessing PUMS Files

The Census Bureau has made it fairly easy to access 

the PUMS data fi les. These fi les are available in two 

basic formats, as ASCII text fi les with comma-separated 

values (CSV) and in two versions of SAS data sets (PCSAS fi les and UNIX fi les). Most statistical programs can 

read fi les in at least one of these formats.

The easiest way to access the PUMS fi les is through the 

Census Bureau’s American FactFinder system. One way 

to get to American FactFinder is to click on the 

“American FactFinder” button on the left side of the 

Census Bureau’s home page <www.census.gov> as 

shown in Figure 5. This will take you to the American 

FactFinder home page, shown in Figure 6. To get to the 

ACS PUMS data, click on “Data Sets” and then 

“American Community Survey.” This will bring you 

to the ACS data sets page shown in Figure 7. 

 Figure 3. Geography Page

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

 Figure 4. Census 2000 PUMA Map Products Page

Source: U.S. Census Bureau, accessed at <www.census.gov/geo/www/maps/CP_MapProducts.htm>.

Creating PUMS Tabulations
What PUMS Data Users Need to Know 7

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

 Figure 5. Census Bureau Home Page Link to American FactFinder

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

 Figure 6. American FactFinder Home Page

Source: U.S. Census Bureau, American FactFinder, accessed at <http://factfi nder.census.gov>.
8 What PUMS Data Users Need to Know

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

In Figure 7, please note that the ACS data products are 

listed with the most recent years at the top of the list. 

In order to select a year other than the most recent 

one, click on the radio button for the year desired. To 

get to the ACS PUMS fi les, click on the “Download PUMS 

data” menu item near the bottom of the list of options 

on the right. This will take you to the ACS PUMS download page shown in Figure 8.

You can also access the PUMS page through the Census 

Bureau’s ACS Web site. The advantage of going through 

the ACS Web site is that the PUMS user verifi cation fi les 

are listed. User verifi cation fi les provide estimates for 

selected housing and population characteristics to help 

data users determine that they are using the weights 

correctly. The ACS Web site also includes a brief 

description of the PUMS. To access PUMS through the 

 Figure 7. American FactFinder ACS Data Sets Page

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

.

ACS Web site, start at the ACS Web site <www.census

.gov/acs/www>. Click on the “Access Data” tab and 

under “GET DATA” select “Public Use Microdata 

Sample (PUMS) Files.” On this page you will fi nd links 

to documentation and to the PUMS fi les.

The ACS PUMS download page contains information 

about the ACS PUMS fi les in addition to providing 

access to the fi les themselves. This background information is on the right side of the page and includes the 

following:

• Lists of the subjects included in each of the housing and population record fi les.

• The state-specifi c values used in top coding the 

variables for confi dentiality protection.
What PUMS Data Users Need to Know 9

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

 Figure 8. ACS PUMS Download Page

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

.

• Detailed codes for the variables that contain a 

large number of coded responses, such as ancestry and occupation.

• Links to the geographic equivalency fi les mentioned earlier. These are actually links to the 

Census 2000 PUMS fi les and documentation.

• A PDF fi le containing information about the accuracy of the PUMS and methods of calculating the 

standard errors and related measures.

• A PDF fi le containing the PUMS data dictionary. This has the names of each of the variables 

included in the PUMS fi les, a description of the 

variables’ contents, the possible values for each 

variable, and the meanings of these values.

Some of these links are to specifi c fi les and others are 

to Web pages.

The middle column of the ACS PUMS download page 

(Figure 8) shows the options for downloading the ACS 

PUMS data. The fi rst choice to make is which records 

to download, by choosing either population records or 

housing records. Table 3 shows the variables included 

in each of these record types for the 2007 ACS.

The next choice is which of the three formats to download. The fi rst option is CSV (ASCII comma separated 

values) with the variable names in the fi rst line. The 

second option is a PC SAS data set. The third option is 

a UNIX SAS data set. Choose the format that is easiest 

to import into your software. For example, when you 

are working with SAS on a PC, the PC SAS version is 
10 What PUMS Data Users Need to Know

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

Items in the housing record include: Items in the person record include:

Bedrooms Meals included in rent Ability to speak English Mobility status

Condominium status

Mortgage status and 

selected monthly owner 

costs

Age Occupation

Contract rent (monthly 

rent) Plumbing facilities Ancestry Personal care limitations

Cost of utilities and fuels Presence and age of own 

children Citizenship Place of birth

Family income Presence of subfamilies 

in household Class of worker Place of work

Family, subfamily, and 

household relationships Property value Disability status Poverty status

Farm status and value Real estate taxes Educational attainment Race

Fire, hazard, and fl ood 

insurance Residence state Fertility Relationship

Food stamps Rooms Hispanic origin School enrollment and type 

of school

Fuel used Telephone in housing 

unit Income by type Sex

Gross rent Tenure Industry Time of departure for work

House heating fuel Units in structure Language spoken at 

home Travel time to work

Household income Vacancy status Last week work status Vehicle occupancy

Household type Vehicles available Marital status Weeks worked

Kitchen facilities Year householder moved 

into unit

Means of transportation 

to work Work status

Linguistic isolation* Year structure built Migration Work limitation status

Military status, periods 

of active duty military 

service, veteran period 

of service

Year of entry

 Table 3. Topics Included in the 2007 ACS PUMS Files by Record Type

* Households in which no person, age 14 or over, speaks only English or speaks English very well. 

Source: U.S. Census Bureau, accessed online at <www.census.gov/acs/www/Products/PUMS/PUMS3.htm>.

.

best. When using SPSS, either the CSV fi le or the PC SAS 

is easy to read, but using the PC SAS fi les will provide 

the meaningful variable labels. The CSV format is a 

good choice if you plan to load the data into a relational database system such as Oracle, Access, or 

MySQL.

Finally, choose the geographic area. You can download 

the entire nation or any individual state by selecting 

the area and pressing the “GO” button. The national fi le 

is a single large data set containing all of the ACS PUMS 

records for the nation. If you want to look at just a few 

states, you can save time by downloading just those 

rather than the entire nation. If you want items from 

the housing records and the population records, you 

need to download both fi le types and merge them with 

your software.

The data fi le is sent as a compressed ZIP fi le. Once you 

save it on your computer, you will have to uncompress 

the fi le and read it into your software.

Creating PUMS Tables Using General Statistical 

Software

Getting Started

To demonstrate how to use the PUMS fi les to produce 

a table, we will ask the question, “What is the employment status of college students living in rental units 

by gross rent in Tompkins County, New York?”5 We will 

5 While this example was created for this handbook, it is based on 

many requests the author has seen from policy analysts, local governments, and the private sector over the years.
What PUMS Data Users Need to Know 11

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

look at how this table can be produced using the 2006 

ACS PUMS fi le and general statistical software.

The fi rst steps are to identify the relevant PUMAs and 

the variables of interest. From the map shown in Figure 

1, we know that we are interested in PUMA 01700 

in New York State. Given that Tompkins is by far the 

larger county and—with Cornell University and Ithaca 

College in the county—has many more college students 

than Seneca County, it is reasonable to use this PUMA 

as a proxy for just Tompkins County. If there were 

more college students within Seneca County, we might 

assume that the characteristics of the students in the 

two counties were the same and could estimate the 

number of students in Tompkins County by assigning it 

the county’s share of college students in 2000. 

Table 3 shows the topics included in the housing unit 

records and the population records. To answer our 

question, we can use data on tenure (owned or rented) 

and gross rent from the housing unit records and data 

on school enrollment and employment status from the 

population records. This means we need to combine 

both record types in order to produce the table. 

Figure 9 shows the data dictionary information for the 

tenure variable. It includes the variable name, value 

length, description, value codes, and the descriptions. 

From this part of the data dictionary we determine that 

we want the people with tenure (TEN) values of “3” 

(rented for cash rent) or “4” (no cash rent). Reviewing 

the data dictionary for the other variables, we determine that:

• We need to recode gross rent of the housing unit 

(GRNTP) into categories since the ACS PUMS fi le 

lists individual values. Recoding creates a new 

set of values for a variable by consolidating one 

or more values found in the data set into a new 

recoded value. For example, the age variable can 

be recoded to consolidate ages 18, 19, 20, and 

21 into a new value of “18–21.” Frequently, recoding is done by creating new variables because of 

diff erences in format, numeric versus text, or just 

to help keep things clearer when doing the analysis.

• We want to include people with a response of 

“6” (college undergraduate) or “7” (graduate or 

professional school) for the grade level attending 

(SCHG) variable. 

• We need to recode the employment (ESR or 

employment status recode) variable so that the 

two civilian employed values are combined, as 

are the two Armed Forces values.

 Tenure

 b .N/A (GQ/vacant)

 1 .Owned with mortgage or loan

 2 .Owned free and clear

 3 .Rented for cash rent

 4 .No cash rent

VACS 1

 Vacancy status

 b .N/A (occupied/GQ)

 1 .For rent

 2 .Rented, not occupied

 3 .For sale only

 4 .Sold, not occupied

 5 .For seasonal/recreational/occasional use

 6 .For migratory workers

 7 .Other vacant

VAL 2

 Property value

 bb .N/A (GQ/rental unit/vacant, not for sale only)

 01 .Less than $ 10000

 02 .$ 10000 - $ 14999

 03 .$ 15000 - $ 19999

 04 .$ 20000 - $ 24999

 05 .$ 25000 - $ 29999

 06 .$ 30000 - $ 34999

07 $ 35000 - $ 39999

 Figure 9. Selected Variables From the Data Dictionary

Source: U.S. Census Bureau, 2006 ACS PUMS Data Dictionary, accessed online at <www.census.gov/acs/www/Downloads

/PUMSDataDict06.pdf>.

TEN 1
12 What PUMS Data Users Need to Know

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

Using General Statistical Software

Although the programming syntax varies across the 

diff erent general statistical programs, there are also 

many similarities in terms of approaches to working 

with the PUMS fi les. SPSS produced the work shown 

here.

As mentioned above, fi rst combine the housing unit 

and population records. To do this, merge the two data 

sets together to match the records on the SERIALNO 

variable. The SERIALNO variable is a 7-digit code that 

is unique across the nation. This appends the housing 

unit variables onto the population records.

After combining the records, limiting the number of 

records being processed (by selecting those of interest) 

will often increase processing speed. For this example, 

the universe of interest includes college students 

(grade level attending equals 6 or 7) renting apartments (tenure equals 3 or 4) in PUMA 01700. Selecting 

only these individuals reduces the number of records 

from 193,742 in the state to 60 college-student renters 

in Tompkins and Seneca Counties in New York. 

Now that the records are limited to those of interest, 

recode the variables that need additional manipulation. 

Exactly how to do this varies with the software. In this 

example, 

1. Recode gross rent (GRNTP) into a new variable for 

grouped gross rent (GGRNTP) with the categories 

of no cash rent, under $500, $500 to $999, and 

$1,000 or more.

2. Recode the employment status (ESR) variable into a 

new variable (EMPSTAT) with the categories of Civilian Employed (ESR equals 1 or 2), Unemployed (ESR 

equals 3), Armed Forces (ESR equals 4 or 5), and 

Not in Labor Force (ESR equals 6). 

After creating these new variables, we are ready to 

create the data table. When producing the table, it 

is critical to remember that each person in the PUMS 

represents a diff erent number of people in the population because of the ACS’s sampling and weighting 

procedures. To account for this, the PUMS fi le contains a weighting factor for each population (PWGTP) 

and housing unit record (WGTP) that we will use to 

infl ate the sample to the full population. Because we 

are interested in students (population), we use the 

PWGTP to weight the input records to estimate the 

total population. There are an additional 80 population 

and housing unit weight variables on the fi le, but the 

main purpose of these replicate weights is to calculate standard errors described later in this handbook. 

Again, exactly how you apply the weights and create 

the tables depends on the software. Table 4 shows the 

data produced through SPSS for our example.

Creating PUMS Tables Using DataFerrett

If you do not have access to a general statistical 

program, it is still possible to create PUMS tabulations 

through the Census Bureau’s DataFerrett program. 

DataFerrett is a tool developed by Census Bureau staff 

for extracting data and producing tables from a wide 

range of data products generated by a number of 

federal government agencies. DataFerrett functionality 

goes far beyond the simple example in this handbook. 

ACS data users are encouraged to work with 

DataFerrett to produce maps and charts and to develop 

complex recoding applications. 

To demonstrate how to use DataFerrett to produce 

tables from the PUMS data, we will walk through an 

example of how to use the 2006 ACS PUMS to fi nd the 

number and percentage of people aged 75 and older 

in every state with graduate or professional degrees. 

While it is possible to get the number of people aged 

 Table 4. PUMS Tabulation Results—2006 ACS

Source: U.S. Census Bureau, 2006 American Community Survey, Public Use Microdata Sample.

Employment Status

Civilian Total

employed

Unemployed Not in 

labor force

Grouped 

gross 

rent

Under $500 36 0 279 315

$500 to $999 2338 181 1439 3958

$1,000 or more 1805 85 2421 4311

No cash rent 217 0 14 231

Total 4396 266 4153 8815

Grouped Gross Rent and Employment Status Crosstabulation
What PUMS Data Users Need to Know 13

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

65 and older with bachelor or higher degrees from a 

pretabulated ACS tables, the age breakout we are interested in (75 and older) and the restriction to graduate and professional degrees are not available in any 

pretabulated table.

Getting Started

Since DataFerrett is a software application, download it 

from the Census Bureau’s Web site and install it on your 

computer. Starting at the Census Bureau’s home page 

<www.census.gov>, click on “Data Tools” on the 

left side of the screen as shown in Figure 10. This will 

take you to the page shown in Figure 11; click on the 

“DataFerrett” link. This will take you to the 

DataFerrett home page shown in Figure 12. In the 

upper-right hand corner of this page are a number of 

diff erent versions of DataFerrett that you can download 

and install on your computer. The installation process 

is fairly standard and the prompts are clearly noted. 

This page also contains a number of useful tutorials 

and other support material about DataFerrett.

 Figure 10. Census Bureau’s Home Page for Accessing DataFerrett

Source: U.S. Census Bureau, accessed online at <www.census.gov>.

 Figure 11. Census Bureau’s Data Tools Page

Source: U.S. Census Bureau, accessed online at <www.census.gov/main/www/access.html>.
14 What PUMS Data Users Need to Know

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

 Figure 12. DataFerrett Home Page

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

After downloading and installing DataFerrett, you 

can run it as you would any other program; Figure 13 

shows the opening screen of the program. Please note 

that you will need to sign onto DataFerrett with an 

e-mail address. After signing onto the program, you 

will see the screen shown in Figure 14. In order to get 

the data, click on the “Get Data Now” button. 

Clicking on the “Get Data Now” button opens 

DataFerrett’s data selection screen shown in Figure 15. 

This screen allows you to fi nd the data sets you want 

by type (Microdata, Aggregate Data, Longitudinal Data, 

Time Series Data), name of the data set (along the left 

side), or by the variable or subject you are interested in 

(along the top).

 Figure 13. DataFerrett Opening Page

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census.gov/>.
What PUMS Data Users Need to Know 15

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

 Figure 14. DataFerrett Introduction Page

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

Using DataFerrett

In this example we are using DataFerrett to produce 

2006 ACS state-level tables of the number and percentage of people aged 75 and older with graduate or professional degrees. We are therefore interested in data 

from the 2006 ACS, specifi cally age and educational 

attainment data that are on the population record. In 

addition we want to have data by state. 

The fi rst step is to select our data set and topics. In the 

left sidebar, under “Search All Datasets” is an alphabetical list of data sets available to DataFerrett. Doubleclicking on the “American Community Survey” folder 

(see Figure 15) reveals two options—the Public Use 

Microdata Sample and the Puerto Rico Public Use Microdata Sample. Opening either PUMS folder identifi es the 

specifi c PUMS fi les that are available. For this example 

we will select “Public Use Microdata Sample” and 

 Figure 15. DataFerrett Data Selection Screen

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census.gov/>.
16 What PUMS Data Users Need to Know

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

 Figure 16. 2006 ACS PUMS Data Category Selection Screen

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

“2006.” As shown in Figure 16, once we have selected 

“2006” we have a choice of reading a description of 

this data set (description) or reviewing the specifi c 

variables in the data set (view variables). 

Select the “View Variables” button to get more information about the variables. This will open a screen 

similar to that shown in Figure 16. You are prompted 

to select the topics of interest. In this case, check the 

“Selectable Geographies” box to obtain state-level 

summaries and check the “Population” box to obtain 

age (AGEP) and educational attainment variables 

(SCHL). 

A list of variables will appear for you to review, as 

shown in Figure 17. It is possible to sort this list by 

clicking on the column headings. You can select multiple variables by using the standard Window’s CtrlClick method to select additional individual variables 

or Shift-Click to select a range of variables. Select the 

“PUMS Age,” “Geographic Items,” and the “Educational Attainment” lines using the Ctrl-Click method. 

Click the “Search Variables” button. 

After highlighting the variables you are interested in, 

click on the “Browse/Select Highlighted Variables” 

button circled in Figure 17. If you select a mix of 

geographic selection and nongeographic variables, you 

will receive a warning box. Clicking “OK” allows you 

to proceed to selecting the geographic area of interest, 

as shown in Figure 18. Available geographies associated with the data set you selected will be displayed. 

For the 2006 ACS this includes states and PUMAs. For 

this example we would select “FIPS State Code—ST 

—State of current residence.” Clicking the “Next>” 

button updates the selection box by displaying a list of 

states as shown in Figure 19. For this example we click 

on the “Select All” line and click on “Next>” to move 

this selection into the “Geographies Selected” box. 

After you have selected the geographic area or areas of 

interest, click “Finish.” This will take you to the variable selection menu shown in Figures 20 and 21.

As you highlight a specifi c variable, details about that 

variable are displayed. Hightlight the “PUMS Age” 

variable. Figure 20 shows the details of the age variable. Note that the documentation states that the 

PUMS fi le includes continuous values of the variable 

AGEP between 0 and 99. This means that it is possible 

to obtain data for single years of age or to use single 

years of age to defi ne specifi c age ranges within this 

interval. For this example, change the 0 to 75 in the 

“Continuous values of AGEP” limited to the universe 

to people aged 75 and older. 
What PUMS Data Users Need to Know 17

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

 Figure 17. DataFerrett Variable Selection Screen

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

 Figure 18. DataFerrett Geographic Selection Dialog Box

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census.gov/>.
18 What PUMS Data Users Need to Know

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

 Figure 19. DataFerrett State Selection Screen

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

 Figure 20. DataFerrett Variable and Value Selection Screen for 

 a Continuous Variable—Age

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

Highlight the “Educational attainment” variable. 

Figure 21 shows the selection screen for the variable 

SCHL that has categorical or discrete values. Here you 

can choose selected values from a list of options by 

checking the boxes next to the values you want to 

include. Since our interest is fi nding the percentage of 

people aged 75 and older with a graduate or professional degree, we need to include all of the values in 

order to get the total population. So, we leave all of the 

choices checked. After you have selected the variables 
What PUMS Data Users Need to Know 19

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

 Figure 21. DataFerrett Variable and Value Selection Screen for a 

 Discrete Variable—Educational Attainment

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

and values you want to include, check the “Select ALL 

Variables” box and click “OK.” You will then receive a 

confi rmation about how many variables you have put 

into your data basket. 

At this point, you are ready to download the data or 

create a data table. You can do this by clicking on the 

“Step 2: DataBasket/Download/Make A Table” 

tab at the top of the screen as shown in Figure 17. 

This will take you to a screen similar to that shown in 

Figure 22. First you need to determine if you want to 

combine existing categories into new categories. This 

is called “recoding.” Highlighting the “SCHL—educational attainment” variable and click on the “Recode 
20 What PUMS Data Users Need to Know

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

Variable(s)” button on the right will bring up a recode 

dialog box like the one shown in Figure 23. 

This dialog box allows you to recode the values of 

individual variables into new categories. The exact look 

of this box will vary depending on the variable you 

are recoding. Double-clicking on a label allows you to 

edit the label itself. Once you have recoded a variable, 

click “OK.” Then you can recode another variable. The 

“Modify Variable(s)” button in Figure 22 allows you to 

change selected values of existing variables and labels 

of recode variables.

For this example you should recode the SCHL variable 

to identify people with a master’s degree, professional 

degree, or a doctorate degree. Highlight the SCHL 

variable and click the “Recode Variable(s)” button. 

A default label will be provided for this recode. It is 

best to rename it so you can remember it later. For this 

example, double-click on the default “Recode1” label 

and type “Grad School.” Use the “Shift” key to select 

multiple values to highlight the values 14, 15, and 

16 and then click the “Recode” button creates a new 

value that is defi ned as the sum of these three values. 

By default the remainder is assigned a value of 2. You 

should also edit the labels of this new value.

Once you have your variables the way you want them, 

you can create the table by clicking on the “Make A 

Table” button shown in Figure 22. This will open a 

table window like the one shown in Figure 24. In order 

to set up the table, simply drag and drop the variables 

you want in the table. Specifi cally for this example 

you would drag the variable that you want in the rows 

 Figure 22. DataFerrett DataBasket Screen

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

 Figure 23. DataFerrett Recode Dialog Box

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census.gov/>.
What PUMS Data Users Need to Know 21

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

(GEOG-101) and drop it in cell R1/C1. The list of 

states will appear. Then drag the variable that you want 

in the columns (Record 1 Grad School) and drop it in 

cell R1/C2. The resulting table is shown in Figure 25. 

Once you have your data shell set up the way you want 

it, simply press the “GO Get Data” button. This will 

create the table shown in Figure 26. Recall that we had 

restricted our universe to the population aged 75 and 

older. This table therefore provides us with the values 

we need to determine the percentage of the total population aged 75 and older with professional or graduate 

degrees. From this table, we see, for example, that 

California has about 1,925,000 people aged 75 and 

older. Of these, 154,178 have graduate or professional 

degrees. To convert these values to percentages, select 

the “Percent of fi rst column” option from the tool bar. 

By selecting diff erent options from the tool bar, it is 

also possible to sort on one of these variables, display 

graphs and maps, and more. 

DataFerrett automatically chooses either the population weight (PWGTP) or the housing unit weight (WGTP) 

based on the variables you have chosen for your 

tabulations. It is often helpful to check the weight 

chosen by DataFerrett to ensure that the correct weight 

is being used for your analysis. For this example, click 

on the “Options” pull-down menu, select “Weighting” 

from the drop-down menu and then select “Unweighted” before running your tabulation. Click on “Go get 

data.” If you think you should use a diff erent one, you 

can simply click on that one. 

 Figure 24. DataFerrett Tabulation Shell

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census.gov/>.
22 What PUMS Data Users Need to Know

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

 Figure 25. DataFerrett Tabulation Shell Ready for Data Tabulation

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census.gov/>.
What PUMS Data Users Need to Know 23

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

 Figure 26. DataFerrett Tabulated Results

Source: U.S. Census Bureau, accessed online at <http://dataferrett.census

.gov/>.
24 What PUMS Data Users Need to Know

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

Because data produced from the PUMS are based on a 

sample, there is little chance that the numbers shown 

in these tables are the exact numbers that would be 

obtained if everyone in the population were counted. 

For example, the number of college students in 

Tompkins and Seneca Counties paying gross rent less 

than $500 and employed could be 36, as shown in 

Table 4, but it is also possible that there is only one 

person in this situation and that one person just happened to be in the sample with a weight of 36. 

One way to quickly check for results based on an 

extremely small sample is to reproduce the tables 

without using weights. Table 5 shows the unweighted 

sample counts for the data shown in Table 4. From this 

table, you see that in fact only one student was paying 

less than $500 in gross rent and was employed. You 

also see that several cells are based on samples of one 

to six people. This suggests that you should use these 

results with great care. If the cells of interest are those 

with 12, 13, or 21 students in the sample, then this 

tabulation may be more useful. The best way to determine that is to look at the sampling error for your estimates. One way to improve the quality of this analysis 

would be to use either the 3- or 5-year PUMS fi les for 

the area (when available). Another method would be to 

look at these data for a larger geographic area.

Looking at the unweighted counts for the graduate 

degree example produced using DataFerrett, shown 

in Figure 27; we see that this tabulation is based on 

a very robust sample of 214,072 people, of whom 

14,253 have graduate or professional degrees. The 

larger sample size indicates that the results are generally going to be fairly accurate. However, we also see 

that in Alaska only 10 people are aged 75 and older 

with graduate or professional degrees. This suggests 

that we need to be a bit more careful about conclusions drawn for this particular state.

Data Quality in PUMS

 Table 5. Unweighted Sample Counts for Tompkins-Seneca County Example

Employment Status

Civilian Total

employed

Unemployed Not in 

labor force

Grouped 

gross 

rent

Under $500 1 0 3 4

$500 to $999 21 1 13 35

$1,000 or more 6 1 12 19

No cash rent 1 0 1 2

Total 29 2 29 60

Source: U.S. Census Bureau, 2006 American Community Survey, Public Use Microdata Sample.

Group Gross Rent and Employment Status Crosstabulation
What PUMS Data Users Need to Know 25

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

 Figure 27. Unweighted Counts for the Graduate-Professional 

 Degree Example

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

Measuring Statistical Accuracy

Most researchers want more formal measures of the 

impact of sampling on the results. The most commonly 

used measures are the standard error, the margin of 

error, and the confi dence interval. Both the margin of 

error and the confi dence interval are based on the standard error. The relationships between these measures 

are described in more detail in Appendix 3.

There are two ways to calculate the standard error. The 

fi rst is a generalized standard error formula provided 

by the Census Bureau. The second is through the use 

of the replicate weights provided by the Census Bureau 

as part of the PUMS fi le.

Generalized Standard Error Formula Method

The Census Bureau provides a number of formulas to 

approximate the standard error for most of the situations PUMS users are likely to encounter. These are 

included in the “Accuracy of the PUMS” provided for 

each year’s PUMS fi les. The most commonly used formulas are those for totals and percentages.

To fi nd the standard error for the estimated 2,338 

employed college students paying between $500 and 

$999 a month on rental housing, use the standard 

error formula for the totals. This formula is:

)

ˆ 1(ˆ *99*)ˆ ( N

Y YSE DF Y 
26 What PUMS Data Users Need to Know

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

where:

 = design factor

 = size of the geographic area (total population or 

 housing units)

 = estimated value. 

The design factor is found in a state-specifi c table in 

the “Accuracy Statement” and varies for the diff erent characteristics being considered, such as tenure, 

employment status, and gross rent. When more than 

one characteristic is involved in the analysis, it is best 

to use the largest design factor for the factors being 

considered. In this example, that would be the design 

factor for gross rent, which in New York State is 1.8.

The estimate ( ) from Table 4 is 2,338. The area size 

is the combined population of Seneca and Tompkins 

Counties, because we are looking at people (college 

students). This value ( ) from Table 2 is 131,869.

Using these numbers in the above equation shows the 

standard error of this estimated value to be 858. 

Replicate Weights Method

Another method of producing the standard error takes 

advantage of the 80 replicate weights provided for 

each population and each housing unit record in the 

PUMS. While this method is a bit more accurate than 

the generalized formula above, it is also more computationally intense. 

The fi rst step is to produce the estimate. In the example above, this is 2,338. Then you would produce this 

same estimate 80 times, using each of the 80 diff erent 

replicate weights. Once you have these 81 estimated 

values, you can calculate the standard error using the 

following formula:

where:

 = the estimate based on the original weight, e.g., 

 2,338

 = the 80 individual estimates based on each of 

 the replicate weights.

The ease with which the 80 replicated estimates can 

be produced depends largely on the software used to 

produce data from the PUMS fi les. 

Margin of Error and Confi dence Intervals

Two measures that are particularly useful in looking 

at the quality of the PUMS estimates are the margin of 

error (MOE) and the confi dence interval. The 

Census Bureau reports these for a 90-percent confi -

dence interval. Once you have the standard error, the 

margin of error is very easy to calculate. It is the standard error times 1.645, for the 90-percent confi dence 

level. In our example, where the standard error was 

858, the margin of error is 1,411. 

The confi dence interval is the estimate plus or minus 

the margin of error. In this case, the 90-percent confi dence level would run from 927 to 3,749. In other 

words, there is a 90 percent chance that this interval 

would contain the average estimate of employed college students paying between $500 and $999 a month 

for rent, taken over all possible samples. How useful 

this estimated 2,338 answer is depends on the sensitivity of the decision being made to variations in the 

estimate.

Care is required when reporting values when the confi dence interval drops below zero or above the area’s 

total population. In these cases, consider those values 

as logical limits when reporting the confi dence intervals.

DF

N

Yˆ

Yˆ

N

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80

1

2

80

4 )( r

XSE X r X

X r

X 

The ACS PUMS fi les are designed to allow data users 

to produce their own tabulations of ACS data without 

the expense or time required when the Census Bureau 

produces custom tabulations. Producing PUMS tabulations or doing more sophisticated modeling of the 

data requires specialized software, such as SPSS, SAS, 

another statistical program, a relational database management program, or the Census Bureau’s DataFerrett 

program. 

While the ACS PUMS fi les provide great fl exibility, the 

need to protect the confi dentiality of the respondents 

has imposed some limitations on the data. These 

include the use of a limited portion of ACS responses 

and a limited set of geographic areas with populations 

of 100,000 or more.

The smaller sample size of the PUMS increases the 

need to calculate measures of uncertainty, such as 

margins of error, around the estimates. As described 

above, there are two ways to do this. The general 

method is less accurate than the replicate weights 

method, but it is less cumbersome for the data user. 

The choice of method is one that has to be guided by 

balancing these factors.

Summary
What PUMS Data Users Need to Know 27

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 
28 What PUMS Data Users 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 PUMS Data Users Need to Know 29

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

Period estimates. An estimate based on information 

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

either 1 year, 3 years, or 5 years.

Point-in-time estimates. An estimate based on 

one point in time. The decennial census long-form 

estimates for Census 2000 were based on information 

collected as of April 1, 2000.

Population Estimates Program. Offi cial Census 

Bureau estimates of the population of the United 

States, states, metropolitan areas, cities and towns, 

and counties; also offi cial Census Bureau estimates of 

housing units (HUs). 

Public Use Microdata Area (PUMA). An area that 

defi nes the extent of territory for which the Census 

Bureau releases Public Use Microdata Sample (PUMS) 

records. 

Public Use Microdata Sample (PUMS) fi les. 

Computerized fi les that contain a sample of individual 

records, with identifying information removed, 

showing the population and housing characteristics of 

the units, and people included on those forms. 

Puerto Rico Community Survey (PRCS). The 

counterpart to the ACS that is conducted in Puerto 

Rico. 

Quality measures. Statistics that provide information 

about the quality of the ACS data. The ACS releases 

four diff erent quality measures with the annual data 

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

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

allocation rates for all collected variables. The ACS 

Quality Measures Web site provides these statistics 

each year. In addition, the coverage rates are also 

available for males and females separately. 

Reference period. Time interval to which survey 

responses refer. For example, many ACS questions 

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

past 12 months” or “last week.” 

Residence rules. The series of rules that defi ne who 

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

address for purposes of the survey or census. 

Sampling error. Errors that occur because only 

part of the population is directly contacted. With any 

sample, diff erences are likely to exist between the 

characteristics of the sampled population and the 

larger group from which the sample was chosen.

Sampling variability. Variation that occurs by chance 

because a sample is surveyed rather than the entire 

population. 

Selected population profi les. An ACS data product 

that provides certain characteristics for a specifi c race 

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

population subgroup (for example, people aged 60 

years and over). This data product is produced directly 

from the sample microdata (that is, not a derived 

product). 

Single-year estimates. Estimates based on the set 

of ACS interviews conducted from January through 

December of a given calendar year. These estimates 

are published each year for geographic areas with a 

population of 65,000 or more. 

Standard error. The standard error is a measure of 

the deviation of a sample estimate from the average of 

all possible samples. 

Statistical signifi cance. The determination of 

whether the diff erence between two estimates is not 

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

This determination is based on both the estimates 

themselves and their standard errors. For ACS data, 

two estimates are “signifi cantly diff erent at the 90 

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

that there was a less than 10 percent chance that the 

diff erence came entirely from random variation. 

Subject tables. Data products organized by subject 

area that present an overview of the information that 

analysts most often receive requests for from data 

users. 

Summary fi les. Consist of detailed tables of Census 

2000 social, economic, and housing characteristics 

compiled from a sample of approximately 19 million 

housing units (about 1 in 6 households) that received 

the Census 2000 long-form questionnaire.

Thematic maps. Display geographic variation in map 

format from the geographic ranking tables.

Three-year estimates. Estimates based on 3 years 

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

characteristics of a geographic area over the entire 

3-year period. These estimates will be published for 

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

Appendix A-1

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

What Are Single-Year and Multiyear 

Estimates?

Understanding Period Estimates

The ACS produces period estimates of socioeconomic 

and housing characteristics. It is designed to provide 

estimates that describe the average characteristics of 

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

single-year estimates, the period is the calendar year 

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

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

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

2007 ACS estimates cover January 2005 through 

December 2007, and the 2006–2010 ACS estimates 

cover January 2006 through December 2010). The ACS 

multiyear estimates are similar in many ways to the 

ACS single-year estimates, however they encompass a 

longer time period. As discussed later in this appendix, 

the diff erences in time periods between single-year 

and multiyear ACS estimates aff ect decisions about 

which set of estimates should be used for a particular 

analysis.

While one may think of these estimates as representing 

average characteristics over a single calendar year or 

multiple calendar years, it must be remembered that 

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

12 monthly values and the multiyear estimates are not 

calculated as the average of either 36 or 60 monthly 

values. Nor are the multiyear estimates calculated as 

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

ACS collects survey information continuously nearly 

every day of the year and then aggregates the results 

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

The data collection is spread evenly across the entire 

period represented so as not to over-represent any 

particular month or year within the period. 

Because ACS estimates provide information about 

the characteristics of the population and housing 

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

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

estimates, such as those from the decennial census 

long-form samples or monthly employment estimates 

from the Current Population Survey (CPS), which are 

designed to measure characteristics as of a certain 

date or narrow time period. For example, Census 2000 

was designed to measure the characteristics of the 

population and housing in the United States based 

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

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

monthly CPS collects data for an even narrower time 

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

Implications of Period Estimates

Most areas have consistent population characteristics 

throughout the calendar year, and their period 

estimates may not look much diff erent from estimates 

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

design. However, some areas may experience changes 

in the estimated characteristics of the population, 

depending on when in the calendar year measurement 

occurred. For these areas, the ACS period estimates 

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

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

noticeable in smaller areas where changes such as a 

factory closing can have a large impact on population 

characteristics, and in areas with a large physical event 

such as Hurricane Katrina’s impact on the New Orleans 

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

year and 5-year estimates where the periods involved 

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

example, 36 months) there have been major or 

consistent changes in certain population or housing 

characteristics for an area, a period estimate for that 

area could diff er markedly from estimates based on a 

“point-in-time” survey.

An extreme illustration of how the single-year estimate 

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

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

of Mexico whose population is dominated by retirees 

in the winter months and by locals in the summer 

months. While the percentage of the population in the 

labor force across the entire year is about 45 percent 

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

estimates ranging from 20 percent to 60 percent.

Understanding and Using ACS Single-Year and Multiyear Estimates

Appendix 1.

 Table 1. Percent in Labor Force—Winter Village

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

Month

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

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

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

The important thing to keep in mind is that ACS 

single-year estimates describe the population and 

characteristics of an area for the full year, not for 

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

multiyear estimates describe the population and 

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

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

the multiyear time period. 

Release of Single-Year and Multiyear Estimates

The Census Bureau has released single-year estimates 

from the full ACS sample beginning with data from 

the 2005 ACS. ACS 1-year estimates are published 

annually for geographic areas with populations of 

65,000 or more. Beginning in 2008 and encompassing 

2005–2007, the Census Bureau will publish annual 

ACS 3-year estimates for geographic areas with 

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

the Census Bureau will release ACS 5-year estimates 

(encompassing 2005–2009) for all geographic areas 

—down to the tract and block group levels. While 

eventually all three data series will be available each 

year, the ACS must collect 5 years of sample before 

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

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

available for use, which means that data are only 

available for areas with populations of 20,000 and 

greater.

New issues will arise when multiple sets of multiyear 

estimates are released. The multiyear estimates 

released in consecutive years consist mostly of 

overlapping years and shared data. As shown in Table 

2, consecutive 3-year estimates contain 2 years of 

overlapping coverage (for example, the 2005–2007 

ACS estimates share 2006 and 2007 sample data with 

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

estimates contain 4 years of overlapping coverage. 

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

Type of estimate

Year of Data Release

2008 2009 2010 2011 2012

Years of Data Collection

3-year 

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

5-year

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

Diff erences Between Single-Year and Multiyear ACS Estimates

Currency

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

housing characteristics because they are based on the 

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

multiyear estimates provide less current information 

because they are based on both data from the previous 

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

using the “less current” sample used to produce the 

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

major changes over a given time period, the multiyear 

estimates may be quite diff erent from the single-year 

estimates for any of the individual years. Single-year 

and multiyear estimates are not expected to be the 

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

single year is the midyear of the ACS multiyear period 

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

For example, suppose an area has a growing Hispanic 

population and is interested in measuring the percent 

of the population who speak Spanish at home. Table 3 

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

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

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

Reliability

Multiyear estimates are based on larger sample sizes 

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

as the 1-year estimates. For some characteristics this 

increased sample is needed for the estimates to be 

reliable enough for use in certain applications. For 

other characteristics the increased sample may not be 

necessary. 

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

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

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

 Who Speak Spanish at Home

Year of data

 release 1-year estimates 3-year estimates

Time period Estimate Time period Estimate

2003 2002 13.7 2000–2002 13.4

2004 2003 15.1 2001–2003 14.4

2005 2004 15.9 2002–2004 14.9

2006 2005 16.8 2003–2005 15.9

the estimates. All of these factors, along with an 

understanding of the diff erences between single-year 

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

Understanding Characteristics

For users interested in obtaining estimates for small 

geographic areas, multiyear ACS estimates will be the 

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

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

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

two sets of multiyear estimates when analyzing data 

for small geographic areas with populations of at least 

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

available. Only the largest areas with populations of 

65,000 and more receive all three data series. 

The key trade-off to be made in deciding whether 

to use single-year or multiyear estimates is between 

currency and precision. In general, the single-year 

estimates are preferred, as they will be more relevant 

to the current conditions. However, the user must take 

into account the level of uncertainty present in the 

single-year estimates, which may be large for small 

subpopulation groups and rare characteristics. While 

single-year estimates off er more current estimates, 

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

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

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

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

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

a small CV is usually preferable to a multiyear estimate 

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

are an alternative option when a single-year estimate 

has an unacceptably high CV. 

Multiyear estimates are the only type of estimates 

available for geographic areas with populations of less 

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

use multiyear estimates when they are working with 

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

geographic areas benefi t from the increased sample 

resulting in more precise estimates of population and 

housing characteristics, especially for subpopulations 

within those areas. 

In addition, users may determine that they want to use 

single-year estimates, despite their reduced reliability, 

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

similarly benefi t from the increased sample sizes and 

gain reliability. 

Deciding Which ACS Estimate to Use

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

for local planning needs, make comparisons across 

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

planning could include making local decisions such as 

where to locate schools or hospitals, determining the 

need for services or new businesses, and carrying out 

transportation or other infrastructure analysis. In the 

past, decennial census sample data provided the most 

comprehensive information. However, the currency 

of those data suff ered through the intercensal period, 

and the ability to assess change over time was limited. 

ACS estimates greatly improve the currency of data 

for understanding the characteristics of housing and 

population and enhance the ability to assess change 

over time.

Several key factors can guide users trying to decide 

whether to use single-year or multiyear ACS estimates 

for areas where both are available: intended use of the 

estimates, precision of the estimates, and currency of 

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

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

Table 4 illustrates how to assess the reliability of 

1-year estimates in order to determine if they should 

be used. The table shows the percentage of households 

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

errors and CVs associated with those estimates are also 

shown.

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

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

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

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

families with a female householder, no husband, and 

related children less than 18 years) will typically have 

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

for small subpopulations.

For example, consider Sevier County, Tennessee, which 

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

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

geographic area will be much smaller than 65,000. 

Table 5 shows an estimated 21,881 families in Sevier 

County based on the 2000–2004 multiyear estimate; 

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

husband present, with related children under 18 years. 

Not surprisingly, the 2004 ACS estimate of the poverty 

rate (38.3 percent) for this subpopulation has a large 

standard error (SE) of 13.0 percentage points. Using 

this information we can determine that the CV is 33.9 

percent (13.0/38.3).

For such small subpopulations, users obtain more 

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

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

SE of 4.9 percentage points that yields a CV of 12.2 

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

CV of 16.8 percent (6.8/40.4).

Users should think of the CV associated with an 

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

threshold that an individual should use will vary based 

on the application. In practice there will be many 

estimates with CVs over desirable levels. A general 

guideline when working with ACS estimates is that, 

while data are available at low geographic levels, in 

situations where the CVs for these estimates are high, 

the reliability of the estimates will be improved by 

aggregating such estimates to a higher geographic 

level. Similarly, collapsing characteristic detail (for 

example, combining individual age categories into 

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

more acceptable level.

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

 Who Speak Spanish at Home

County Estimate Standard error Coeffi cient of 

variation

Broward County, FL 19.9 0.2 1.0

Lake County, IL 15.9 0.2 1.3

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

2000–2004 2000–2004 2002–2004 2004

Total family

type

Pct. in

poverty

SE Pct. in

poverty

SE Pct. in

poverty

SE

All families 21,881 9.5 0.8 9.7 1.3 10.0 2.3

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

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

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

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

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

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

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

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

Assessing Change

Users are encouraged to make comparisons between 

sequential single-year estimates. Specifi c guidance on 

making these comparisons and interpreting the results 

are provided in Appendix 4. Starting with the 2007 

ACS, a new data product called the comparison profi le 

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

the 2006 ACS. 

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

in a particular characteristic. This is because roughly 

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

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

as shown in Figure 1, when comparing 2006–2008 

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

the diff erences in overlapping multiyear estimates are 

driven by diff erences in the nonoverlapping years. A 

data user interested in comparing 2009 with 2008 will 

not be able to isolate those diff erences using these two 

successive 3-year estimates. Figure 1 shows that the 

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

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

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

refer to Appendix 4 for more information.

Making Comparisons

Often users want to compare the characteristics of one 

area to those of another area. These comparisons can 

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

between two diff erent geographic areas you need to 

take the type of estimate into account. It is important 

that comparisons be made within the same estimate 

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

should only be compared with other 3-year estimates, 

and 5-year estimates should only be compared with 

other 5-year estimates. 

You certainly can compare characteristics for areas with 

populations of 30,000 to areas with populations of 

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

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

or the 5-year estimates because they are available for 

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

 Figure 1. Data Collection Periods for 3–Year Estimates 

Period

Jan. Dec.

2006

Jan. Dec.

2007

Jan. Dec.

2008

Jan. Dec.

2009

2007–2009

2006–2008

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

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

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

15,000

15,500

16,000

16,500

17,000

17,500

18,000

18,500

19,000

19,500

20,000

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Estimated Civilian Veterans

1-year estimate

3-year estimate

5-year estimate

Variability in single-year estimates for smaller areas 

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

examine trends. For example, single-year estimates for 

a characteristic with a high CV vary from year to year 

because of sampling variation obscuring an underlying 

trend. In this case, multiyear estimates may be useful 

for assessing an underlying, long-term trend. Here 

again, however, it must be recognized that because the 

multiyear estimates have an inherent smoothing, they 

will tend to mask rapidly developing changes. Plotting 

the multiyear estimates as representing the middle 

year is a useful tool to illustrate the smoothing eff ect 

of the multiyear weighting methodology. It also can 

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

estimates. As a general rule, users should not consider 

a multiyear estimate as a proxy for the middle year of 

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

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

As Figure 2 shows, while the single-year estimates 

fl uctuate from year to year without showing a smooth 

trend, the multiyear estimates, which incorporate data 

from multiple years, evidence a much smoother trend 

across time.

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

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

Summary of Guidelines 

Multiyear estimates should, in general, be used when 

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

used when analyzing data for smaller geographies and 

smaller populations in larger geographies. Multiyear 

estimates are also of value when examining change 

over nonoverlapping time periods and for smoothing 

data trends over time. 

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

larger geographies and populations when currency is 

more important than the precision of the estimates. 

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

availability of a single-year estimate, calculating the CV 

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

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

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

Local area variations may occur due to rapidly 

occurring changes. As discussed previously, multiyear 

estimates will tend to be insensitive to such changes 

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

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

5-year estimates can take advantage of the strengths 

of each set of estimates while using other estimates to 

compensate for the limitations of each set.

Figure 3 provides an illustration of how the various ACS 

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

The multiyear estimates provide a smoothing of the 

upward trend and likely provide a better portrayal of the 

change in proportion over time. Correspondingly, as the 

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

multiyear estimates, an observed change in the upward 

direction for consecutive single-year estimates could 

provide an early indicator of changes in the underlying 

trend that will be seen when the multiyear estimates 

encompassing the single years become available.

We hope that you will follow these guidelines to 

determine when to use single-year versus multiyear 

estimates, taking into account the intended use and 

CV associated with the estimate. The Census Bureau 

encourages you to include the MOE along with the 

estimate when producing reports, in order to provide 

the reader with information concerning the uncertainty 

associated with the estimate.

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

 Multiyear Estimates

2007

2006–2008

2008

2007–2009

2006–2010

2009

2008–2010

2007–2011

2010

2009–2011

2008–2012

2011

2010–2012

2012

Period

Percent of Population

1-year estimate

3-year estimate

5-year estimate

45

46

47

48

49

50

51

52

53

54

55

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

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

There are many similarities between the methods used 

in the decennial census sample and the ACS. Both the 

ACS and the decennial census sample data are based 

on information from a sample of the population. The 

data from the Census 2000 sample of about one-sixth 

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

questionnaire, whose content was the model for the 

ACS. While some diff erences exist in the specifi c 

Census 2000 question wording and that of the ACS, 

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

surveys are noted below with references provided to 

a series of evaluation studies that assess the degree 

to which these diff erences are likely to impact the 

estimates. As noted in Appendix 1, the ACS produces 

period estimates and these estimates do not measure 

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

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

Residence Rules, Reference Periods, and 

Defi nitions

The fundamentally diff erent purposes of the ACS and 

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

example, the residence rules for a census or survey 

determine the sample unit’s occupancy status and 

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

The Census 2000 residence rules, which determined 

where people should be counted, were based on the 

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

of congressional apportionment and state redistricting. 

To accomplish this the decennial census attempts to 

restrict and determine a principal place of residence 

on one specifi c date for everyone enumerated. The 

ACS residence rules are based on a “current residence” 

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

to the continuously changing survey interview dates. 

This method is consistent with the goal that the ACS 

produce estimates that refl ect annual averages of the 

characteristics of all areas. 

Estimates produced by the ACS are not measuring 

exactly what decennial samples have been measuring. 

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

the sampled unit was interviewed, whether it is the day 

that a mail questionnaire is completed or the day that 

an interview is conducted by telephone or personal 

visit. Individual questions with time references such as 

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

months prior to the day the question is answered. ACS 

interviews are conducted just about every day of the 

year, and all of the estimates that the survey releases 

are considered to be averages for a specifi c time 

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

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

60-month period. 

Most decennial census sample estimates are anchored 

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

obvious diff erence between the ACS and the census 

is the overall time frame in which they are conducted. 

The census enumeration time period is less than half 

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

that the distribution of census enumeration dates are 

highly clustered in March and April (when most census 

mail returns were received) with additional, smaller 

clusters seen in May and June (when nonresponse 

follow-up activities took place). 

This means that the data from the decennial census 

tend to describe the characteristics of the population 

and housing in the March through June time period 

(with an overrepresentation of March/April) while the 

ACS characteristics describe the characteristics nearly 

every day over the full calendar year. 

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

based on data collected in 2000 and 3-year ACS 

estimates based on data collected in 1999–2001 in 

selected counties. A series of reports summarize their 

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

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

ACS estimates were found to be quite similar to those 

produced from decennial census data. 

More on Residence Rules

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

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

housing unit or group quarters, some people may stay 

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

course of the year. For example, migrant workers move 

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

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

estimates of the characteristics of some areas. 

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

Diff erences Between ACS and Decennial Census Sample Data

Appendix 2.
Appendix A-9

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

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

while the ACS residence rules were designed to collect 

representative information to produce annual average 

estimates of the characteristics of all kinds of areas. 

When interviewing the population living in housing 

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

to enumerate people at the place where they live or stay 

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

residence” rule to interview people who are currently 

living or staying in the sample housing unit as long as 

their stay at that address will exceed 2 months. The 

residence rules governing the census enumerations of 

people in group quarters depend on the type of group 

quarter and where permitted, whether people claim a 

“usual residence” elsewhere. The ACS applies a straight 

de facto residence rule to every type of group quarter. 

Everyone living or staying in a group quarter on the day 

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

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

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

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

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

and current residence concepts could result in diff erent 

residence decisions. Appreciable diff erences may occur 

in areas where large proportions of the total population 

spend several months of the year in what would not be 

considered their residence under decennial census rules. 

In particular, data for areas that include large beach, 

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

there for more than 2 months.

More on Reference Periods

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

being that the remaining basic demographic questions 

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

the date the interview is provided. Questions with their 

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

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

all census data refl ect the characteristics as of April 1 

is a myth. Decennial census samples actually provide 

estimates based on aggregated data refl ecting the entire 

period of decennial data collection, and are greatly 

infl uenced by delivery dates of mail questionnaires, 

success of mail response, and data collection schedules 

for nonresponse follow-up. The ACS reference periods 

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

they refl ect the circumstances on the day the data are 

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

represent the average characteristics over a full year (or 

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

the census. 

Some specifi c diff erences in reference periods between 

the ACS and the decennial census are described below. 

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

comparing ACS estimates with Census 2000. 

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

Bureau’s guidance on comparisons that contrasts for 

each question the specifi c reference periods used in 

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

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

Income Data

To estimate annual income, the Census 2000 long-form 

sample used the calendar year prior to Census Day as 

the reference period, and the ACS uses the 12 months 

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

while Census 2000 collected income information for 

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

responses are a mixture of 12 reference periods ranging 

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

the full calendar year 2005 through November 2006. 

The ACS income responses for each of these reference 

periods are individually infl ation-adjusted to represent 

dollar values for the ACS collection year.

School Enrollment

The school enrollment question on the ACS asks if a 

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

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

period is used for all interviews. In contrast, 

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

February 1 attended a school or college.” Since 

Census 2000 data were collected from mid-March to 

late-August, the reference period could have been as 

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

Utility Costs

The reference periods for two utility cost questions—gas 

and electricity—diff er between Census 2000 and the 

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

asks for the utility costs in the previous month. 

Defi nitions

Some data items were collected by both the ACS and the 

Census 2000 long form with slightly diff erent defi nitions 

that could aff ect the comparability of the estimates for 

these items. One example is annual costs for a mobile 

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

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

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

example, the ACS could be expected to yield smaller 

estimates than Census 2000. 

Implementation

While diff erences discussed above were a part of the 

census and survey design objectives, other diff erences 

observed between ACS and census results were not 

by design, but due to nonsampling error—diff erences 

related to how well the surveys were conducted. 

Appendix 6 explains nonsampling error in more detail. 

The ACS and the census experience diff erent levels and 

types of coverage error, diff erent levels and treatment 

of unit and item nonresponse, and diff erent instances 

of measurement and processing error. Both 

Census 2000 and the ACS had similar high levels of 

survey coverage and low levels of unit nonresponse. 

Higher levels of unit nonresponse were found in the 

nonresponse follow-up stage of Census 2000. Higher 

item nonresponse rates were also found in 

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

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

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

All survey and census estimates include some amount 

of error. Estimates generated from sample survey data 

have uncertainty associated with them due to their 

being based on a sample of the population rather than 

the full population. This uncertainty, referred to as 

sampling error, means that the estimates derived from 

a sample survey will likely diff er from the values that 

would have been obtained if the entire population had 

been included in the survey, as well as from values 

that would have been obtained had a diff erent set of 

sample units been selected. All other forms of error are 

called nonsampling error and are discussed in greater 

detail in Appendix 6.

Sampling error can be expressed quantitatively in 

various ways, four of which are presented in this 

appendix—standard error, margin of error, confi dence 

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

with the estimates must be taken into account when 

analyzing individual estimates or comparing pairs of 

estimates across areas, population subgroups, or time 

periods. The information in this appendix describes 

each of these sampling error measures, explaining how 

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

to assist the user with analysis and interpretation of 

ACS estimates. Also included are instructions on how 

to compute margins of error for user-derived estimates. 

Sampling Error Measures and 

Their Derivations

Standard Errors

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

(such as estimates from the ACS or the decennial 

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

measured in the survey. The SE provides a quantitative 

measure of the extent to which an estimate derived 

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

measure from which other sampling error measures are 

derived. The SE is also used when comparing estimates 

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

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

sample of size two were selected from this population, 

the estimates of the average value would be 1.5 (units 

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

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

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

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

across all possible samples do equal the population 

value). The standard error would provide an indication 

of the extent of this variation.

The SE for an estimate depends upon the underlying 

variability in the population for the characteristic and 

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

larger the sample size, the smaller the standard error 

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

estimates for less populous areas are only published 

using multiple years of data: to take advantage of the 

larger sample size that results from aggregating data 

from more than one year.

Margins of Error

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

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

level associated with the MOE indicates the likelihood 

that the sample estimate is within a certain distance 

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

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

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

in a table and is easily used to construct confi dence 

intervals and test for statistical signifi cance.

The Census Bureau statistical standard for published 

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

MOEs published with the ACS estimates correspond 

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

want to use other confi dence levels, such as 

95 percent or 99 percent. The choice of confi dence 

level is usually a matter of preference, balancing risk 

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

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

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

incorrect conclusion is substantial, the user should 

consider increasing the confi dence level.

One commonly experienced situation where use of a 

95 percent or 99 percent MOE would be preferred is 

when conducting a number of tests to fi nd diff erences 

between sample estimates. For example, if one were 

conducting comparisons between male and female 

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

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

comparisons would be expected to be found signifi -

cant even if no diff erences actually existed. Using a 

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

Measures of Sampling Error

Appendix 3.
A-12 Appendix

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

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

For example, the ACS published MOE for estimated 

number of civilian veterans in the state of Virginia 

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

would be derived as 

Confi dence Intervals

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

to contain the average value of the characteristic that 

would result over all possible samples with a known 

probability. This probability is called the “level of 

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

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

construct the CI. 

Constructing a Confi dence Interval From a Margin of 

Error

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

the published MOE is used. The CI boundaries are 

determined by adding to and subtracting from a 

sample estimate, the estimate’s MOE.

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

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

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

(20,000 + 1,645).

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

level, the appropriate MOE must fi rst be derived as 

explained previously.

Construction of the lower and upper bounds for the CI 

can be expressed as

where is the ACS estimate and 

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

The CI can thus be expressed as the range 

3

Calculating Margins of Error for Alternative Confi dence 

Levels

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

dence level other than 90 percent, the published MOE 

can easily be converted by multiplying the published 

MOE by an adjustment factor. If the desired confi -

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

1.960/1.645.1

 If the desired confi dence level is 99 

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

Conversion of the published ACS MOE to the MOE for a 

diff erent confi dence level can be expressed as

where is the ACS published 90 percent MOE 

for the estimate.

For example, the ACS published MOE for the 2006 ACS 

estimated number of civilian veterans in the state of 

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

percent confi dence level would be derived as follows: 

Deriving the Standard Error From the MOE

When conducting exact tests of signifi cance (as 

discussed in Appendix 4) or calculating the CV for 

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

derive the SE, simply divide the positive value of the 

published MOE by 1.645.2

Derivation of SEs can thus be expressed as

3

 Users are cautioned to consider logical boundaries when creating 

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

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

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

should be set to zero instead.

1

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

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

error from the standard error in those years.

2

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

value 1.65 rather than 1.645 in the adjustment factor.

MOE MOEACS 645.1

960.1

95 

Factors Associated With Margins of

Error for Commonly Used Confi dence Levels

90 Percent: 1.645

95 Percent: 1.960

99 Percent: 2.576

Census Bureau standard for published MOE is 

90 percent.

  723,14357,12 645.1

960.1 MOE95 r r 

645.1

MOEACS SE 

512,7 645.1

357,12 SE 

CL XL  MOECL ˆ

CL XU  MOECL ˆ

MOECL

  CL ULCI CLCL , .

MOEACS 

MOEACS 

Xˆ

MOE MOEACS 645.1

576.2

99 
Appendix A-13

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

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

confi dence level for the number of civilian veterans in 

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

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

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

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

757,059 to 786,505.

The CI is also useful when graphing estimates, to show 

the extent of sampling error present in the estimates, 

and for visually comparing estimates. For example, 

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

in constructing the CI above, the user could be 90 

percent certain that the value for the population was 

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

Coeffi cients of Variation

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

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

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

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

barometer of the stability, and thus the usability of a 

sample estimate. It can also help a user decide whether 

a single-year or multiyear estimate should be used for 

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

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

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

estimation period increases, the sample size increases 

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

indicates that the sampling error is small relative to the 

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

the estimate is close to the population value. In some 

applications a small CV for an estimate is desirable and 

use of a multiyear estimate will therefore be preferable 

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

desired level of precision.

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

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

the estimate is very reliable. If the CV was noticeably 

larger, the usability of the estimate could be greatly 

diminished. 

While it is true that estimates with high CVs have 

important limitations, they can still be valuable as 

L95  059,757723,14782,771

U95  505,786723,14782,771

( )

18,355 20,000 21,645

building blocks to develop estimates for higher levels 

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

improve the reliability of those estimates as evidenced 

by reductions in the CVs.

Calculating Coeffi cients of Variation From Standard 

Errors

The CV can be expressed as

where is the ACS estimate and is the derived SE 

for the ACS estimate.

For example, to determine the CV for the estimated 

number of civilian veterans in the state of Virginia in 

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

and the SE derived previously (7,512).

This means that the amount of sampling error present 

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

of the estimate.

The text box below summarizes the formulas used 

when deriving alternative sampling error measures 

from the margin or error published with ACS estimates.

100 ˆ u 

X

SE CV

Xˆ

%1.0100

782,771

512,7 CV u 

Deriving Sampling Error Measures From 

Published MOE

Margin Error (MOE) for Alternate Confi dence Levels

Standard Error (SE)

Confi dence Interval (CI)

Coeffi cient of Variation (CV)

MOE MOE ACS

.1 645

.1 960 95 

MOE MOE ACS

.1 645

.2 576 99 

.1 645

MOE ACS SE 

100 ˆ u 

X

SE CV

SE

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

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

Calculating Margins of Error for Derived 

Estimates

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

the ability to develop unique estimates called derived 

estimates. These derived estimates are usually based 

on aggregating estimates across geographic areas or 

population subgroups for which combined estimates 

are not published in American FactFinder (AFF) tables 

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

four age groups not collapsed).

ACS tabulations provided through AFF contain the 

associated confi dence intervals (pre-2005) or margins 

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

confi dence level. However, when derived estimates are 

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

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

the MOE for these derived estimates. The MOE helps 

protect against misinterpreting small or nonexistent 

diff erences as meaningful. 

MOEs calculated based on information provided in AFF 

for the components of the derived estimates will be 

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

confi dence level other than 90 percent is desired, the 

user should fi rst calculate the MOE as instructed below 

and then convert the results to an MOE for the desired 

confi dence level as described earlier in this appendix.

Calculating MOEs for Aggregated Count Data

To calculate the MOE for aggregated count data:

1) Obtain the MOE of each component estimate.

2) Square the MOE of each component estimate.

3) Sum the squared MOEs.

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

 MOEs.

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

where is the MOE of the component estimate.

The example below shows how to calculate the MOE 

for the estimated total number of females living alone 

in the three Virginia counties/independent cities that 

border Washington, DC (Fairfax and Arlington counties, 

Alexandria city) from the 2006 ACS.

The aggregate estimate is:

Obtain MOEs of the component estimates: 

Calculate the MOE for the aggregate estimated as the 

square root of the sum of the squared MOEs.

Thus, the derived estimate of the number of females 

living alone in the three Virginia counties/independent 

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

MOE for the estimate is +4,289.

Calculating MOEs for Derived Proportions

The numerator of a proportion is a subset of the 

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

households that are female). To calculate the MOE for 

derived proportions, do the following:

1) Obtain the MOE for the numerator and the MOE 

for the denominator of the proportion.

2) Square the derived proportion.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator by 

the squared proportion.

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

the numerator.

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

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

proportion.

 Table 1. Data for Example 1 

Characteristic Estimate MOE

Females living alone in 

 Fairfax County 

 (Component 1)

52,354 +3,303

Females living alone in 

 Arlington County

 (Component 2)

19,464 +2,011

Females living alone in 

 Alexandria city

 (Component 3)

17,190 +1,854

Fairfax Arlington  XXXX ˆˆˆˆ Alexandria 

MOEFairfax r 303,3 , 

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

r ¦cMOEagg MOEc

2

MOEc

th

c

  008,89190,17464,19354,52 

 011 MOEArlington r ,2 , 

r r 289,4246,391,18

MOEAlexandria r ,1 854
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

1461.0

713,31

634,4

ˆ

ˆ

ˆ 

BF

gradBF

X

X

p

X BF ˆ

r 989 MOEnum , 601 MOEden r 

MOEnum

MOEden den

den

num

X

X

p ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ

X gradBF ˆ

Multiply the squared MOE of the denominator by the 

squared proportion and subtract the result from the 

squared MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator. 

Thus, the derived estimate of the proportion of Black 

females 25 years of age and older with a graduate 

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

MOE for the estimate is +0.0311. 

Calculating MOEs for Derived Ratios

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

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

1) Obtain the MOE for the numerator and the MOE 

 for the denominator of the ratio.

2) Square the derived ratio.

3) Square the MOE of the numerator.

4) Square the MOE of the denominator.

5) Multiply the squared MOE of the denominator 

 by the squared ratio.

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

 numerator.

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

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

 the ratio.

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

where is the MOE of the numerator.

 is the MOE of the denominator.

 is the derived ratio.

 is the estimate used as the numerator of the 

derived ratio.

 is the estimate used as the denominator of the 

derived ratio.

0311.0

373,31

1.985

373,31

7.408,970 r 

r r MOEp 

MOEnum

MOEden

den

num

X

X

R ˆ

ˆ ˆ 

X num

ˆ

X den

ˆ i

den

num den

p

X

MOE p MOE

MOE ˆ

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

     ]601*1461.0[989 2 2 2  

  7.408,9703.712,7121,978

den

num den

R

X

MOE R MOE

MOE ˆ

)* ˆ ( 2 2 2 r  
A-16 Appendix

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

The example below shows how to derive the MOE for 

the estimated ratio of Black females 25 years of age 

and older in Fairfax County, Virginia, with a graduate 

degree to Black males 25 years and older in Fairfax 

County with a graduate degree, based on the 2006 

ACS.

The estimated ratio is:

Obtain MOEs of the numerator (number of Black 

females 25 years of age and older with a graduate 

degree in Fairfax County) and denominator (number 

of Black males 25 years of age and older in Fairfax 

County with a graduate degree).

Multiply the squared MOE of the denominator by the 

squared proportion and add the result to the squared 

MOE of the numerator.

Calculate the MOE by dividing the square root of the 

prior result by the denominator.

Thus, the derived estimate of the ratio of the number 

of Black females 25 years of age and older in Fairfax 

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

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

the MOE for the estimate is +0.2135.

Calculating MOEs for the Product of Two Estimates

To calculate the MOE for the product of two estimates, 

do the following:

1) Obtain the MOEs for the two estimates being 

multiplied together.

2) Square the estimates and their MOEs.

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

4) Multiply the second squared estimate by the 

fi rst estimate’s squared MOE.

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

6) Take the square root of (5).

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

MOE for the product is calculated as:

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

respectively.

 is the MOE of the fi rst estimate.

 is the MOE of the second estimate.

The example below shows how to derive the MOE for 

the estimated number of Black workers 16 years and 

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

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

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

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

Thus, the derived estimate of Black workers 16 years 

and over who commute by public transportation is 

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

MOEnum r 989, 328 MOEden r ,1

2135.0

440,6

2.375,1

440,6

1.259,891,1 r 

r r MOER 

 Table 4. Data for Example 4

Characteristic Estimate MOE

Black workers 16 years and 

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

Percent of Black workers 16 

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

13.4% +2.7%

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

 Table 3. Data for Example 3 

Characteristic Estimate MOE

Black females 25 years and 

 older with a graduate 

 degree (numerator)

4,634 +989

Black males 25 years and

 older with a graduate degree 

 (denominator)

6,440 +1,328

7200.0

440,6

634,4

ˆ

ˆ ˆ 

gradBM

gradBF

X

X

R

2 22 2 MOE uBA A MOEB B uur MOEA

MOEA

MOEB

r 405,1

)* ˆ ( 2 2 2 MOEnum  R MOEden 

     ]328,1*7200.0[989 2 2 2  

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

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

Calculate the MOE by dividing the square root of the 

prior result by the denominator ( ). 

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

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

The text box below summarizes the formulas used to 

calculate the margin of error for several derived estimates.

Calculating MOEs for Estimates of “Percent Change” or 

“Percent Diff erence”

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

two estimates (for example, the same estimates in two 

diff erent years) is commonly calculated as

Because is not a subset of , the procedure 

to calculate the MOE of a ratio discussed previously 

should be used here to obtain the MOE of the percent 

change.

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

2005 estimates of the number of persons in Maryland 

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

The percent change is:

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

The MOEs for the numerator ( ) and denominator 

( ) are:

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

product of the squared ratio and the squared MOE of 

the denominator (MOE1):

 Table 5. Data for Example 5

Characteristic Estimate MOE

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2006

802,210 +22,866

Persons who lived in a 

 diff erent house in the U.S. 

 1 year ago, 2005

762,475 +22,666

0521.1

475,762

210,802

ˆ

ˆ ˆ

1

2 X

X

R

2 Xˆ 1 Xˆ

2 Xˆ

1 Xˆ

1 Xˆ

Calculating Margins of Error for Derived Estimates

Aggregated Count Data

Derived Proportions

Derived Ratios

0433.0

475,762

3.038,33

475,762

529,528,091,1 r 

r r MOER 

r ¦cMOE agg MOE c

2

den

num den

p

X

MOE p MOE ) MOE ˆ

 (ˆ * 2 2 2 r  

1

12

ˆ

ˆˆ *%100

X

XX

Percent Change  

ˆ

ˆˆ *%100

1

12  X

XX

Percent Change

%21.5

475,762

475,762210,802 *%100  ̧ 

1

·  ̈

©

§ 

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

)* ˆ ( 2

1

22 MOE2  R MOE 

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

 

529,528,091,1

den

num den

R

X

MOE (R MOE ) MOE ˆ

* ˆ 2 2 2 r  
A-18 Appendix

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

One of the most important uses of the ACS estimates is 

to make comparisons between estimates. Several key 

types of comparisons are of general interest to users: 

1) comparisons of estimates from diff erent geographic 

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

proportion of people below the poverty level in two 

counties); 2) comparisons of estimates for the same 

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

the proportion of people below the poverty level in a 

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

estimates with the corresponding estimates from past 

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

2006 and 2000).

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

the comparison takes into account the sampling error 

associated with each estimate, thus determining 

whether the observed diff erences between estimates 

are statistically signifi cant. Statistical signifi cance 

means that there is statistical evidence that a true 

diff erence exists within the full population, and that 

the observed diff erence is unlikely to have occurred 

by chance due to sampling. A method for determining 

statistical signifi cance when making comparisons is 

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

be made are also discussed.

Determining Statistical Signifi cance

When comparing two estimates, one should use the 

test for signifi cance described below. This approach 

will allow the user to ascertain whether the observed 

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

is statistically signifi cant). 

The test for signifi cance can be carried out by making 

several computations using the estimates and their 

corresponding standard errors (SEs). When working 

with ACS data, these computations are simple given 

the data provided in tables in the American FactFinder.

1) Determine the SE for each estimate (for ACS 

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

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

2) Square the resulting SE for each estimate.

3) Sum the squared SEs.

4) Calculate the square root of the sum of the 

squared SEs.

5) Calculate the diff erence between the two estimates.

6) Divide (5) by (4).

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

with the critical value for the desired level of 

confi dence (1.645 for 90 percent, 1.960 for 95 

percent, 2.576 for 99 percent).

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

between the two estimates can be considered 

statistically signifi cant at the level of confi dence 

corresponding to the critical value used in (7).

Algebraically, the signifi cance test can be expressed as 

follows:

If , then the diff erence 

between estimates and is statistically signifi cant 

at the specifi ed confi dence level, CL

where is estimate i (=1,2)

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

 is the critical value for the desired confi dence 

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

2.576 for 99 percent).

The example below shows how to determine if the 

diff erence in the estimated percentage of households 

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

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

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

statistically signifi cant. Using the formula above:

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

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

diff erence in the percentages is statistically signifi cant 

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

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

A rough interpretation of the result is that the user can 

be 99 percent certain that a diff erence exists between 

the percentages of households with one or more 

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

Making Comparisons

Appendix 4.

4

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

use the value 1.65 rather than 1.645. 

ZCL

SESE

XX

!





2

2

2

1

21

ˆˆ ,

SEi

ZCL

1 Xˆ 2 Xˆ

1 Xˆ

   12.012.0

5.210.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

90.2

173.0

5.0

03.0

5.0

015.0015.0

5.0 

Ei
Appendix A-19

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

By contrast, if the corresponding estimates for State C 

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

would yield

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

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

A rough interpretation of the result is that the user 

cannot be certain to any suffi cient degree that the 

observed diff erence in the estimates was not due to 

chance.

Comparisons Within the Same Time Period

Comparisons involving two estimates from the same 

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

3-year period) are straightforward and can be carried 

out as described in the previous section. There is, 

however, one statistical aspect related to the test for 

statistical signifi cance that users should be aware 

of. When comparing estimates within the same time 

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

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

In some cases, the comparison may involve a large 

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

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

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

estimates are not independent. The estimate for the 

large area is partially dependent on the estimate for the 

subset and, strictly speaking, the formula for testing 

diff erences should account for this partial dependence. 

However, unless the user has reason to believe that the 

two estimates are strongly correlated, it is acceptable 

to ignore the partial dependence and use the formula 

for testing diff erences as provided in the previous 

section. However, if the two estimates are positively 

correlated, a fi nding of statistical signifi cance will still 

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

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

important to obtain a more exact test of signifi cance, 

the user should consult with a statistician about 

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

   25.020.0

1.225.22 ˆˆ

2 2 2

2

2

1

21 



 





SESE

XX

25.1

320.0

4.0

1025.0

4.0

0625.004.0

4.0 

Comparisons Across Time Periods

Comparisons of estimates from diff erent time periods 

may involve diff erent single-year periods or diff erent 

multiyear periods of the same length within the same 

area. Comparisons across time periods should be made 

only with comparable time period estimates. Users are 

advised against comparing single-year estimates with 

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

2009) and against comparing multiyear estimates of 

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

2009–2014), as they are measuring the characteristics 

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

between such estimates are diffi cult to interpret. When 

carrying out any of these types of comparisons, users 

should take several other issues into consideration.

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

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

the user should recognize that from 2006 on the ACS 

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

units. Many types of GQ populations have demographic, 

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

comparisons between 2005 and 2006 and later ACS 

estimates could be aff ected. This is particularly true 

for areas with a substantial GQ population. For most 

population characteristics, the Census Bureau suggests 

users make comparisons across these time periods 

only if the geographic area of interest does not include 

a substantial GQ population. For housing characteristics or characteristics published only for the household 

population, this is obviously not an issue. 

Comparisons Based on Overlapping Periods

When comparing estimates from two multiyear periods, ideally comparisons should be based on nonoverlapping periods (e.g., comparing estimates from 

2006–2008 with estimates from 2009–2011). The comparison of two estimates for diff erent, but overlapping 

periods is challenging since the diff erence is driven by 

the nonoverlapping years. For example, when comparing the 2005–2007 ACS with the 2006–2008 ACS, data 

for 2006 and 2007 are included in both estimates. 

Their contribution is subtracted out when the estimate 

of diff erences is calculated. While the interpretation 

of this diff erence is diffi cult, these comparisons can 

be made with caution. Under most circumstances, the 

estimate of diff erence should not be interpreted as a 

refl ection of change between the last 2 years. 

The use of MOEs for assessing the reliability of change 

over time is complicated when change is being evaluated using multiyear estimates. From a technical standpoint, change over time is best evaluated with multiyear estimates that do not overlap. At the same time, 
A-20 Appendix

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

many areas whose only source of data will be 5-year 

estimates will not want to wait until 2015 to evaluate 

change (i.e., comparing 2005–2009 with 2010–2014). 

When comparing two 3-year estimates or two 5-year 

estimates of the same geography that overlap in 

sample years one must account for this sample overlap. 

Thus to calculate the standard error of this diff erence 

use the following approximation to the standard error:

where C is the fraction of overlapping years. For example, the periods 2005–2009 and 2007–2011 overlap for 

3 out of 5 years, so C=3/5=0.6. If the periods do not 

overlap, such as 2005–2007 and 2008–2010, then C=0.

With this SE one can test for the statistical signifi cance 

of the diff erence between the two estimates using the 

method outlined in the previous section with one modifi cation; substitute for 

 in the denominator of the formula for 

the signifi cance test.

Comparisons With Census 2000 Data

In Appendix 2, major diff erences between ACS data and 

decennial census sample data are discussed. Factors 

such as diff erences in residence rules, universes, and 

reference periods, while not discussed in detail in this 

appendix, should be considered when comparing ACS 

estimates with decennial census estimates. For example, given the reference period diff erences, seasonality 

may aff ect comparisons between decennial census and 

ACS estimates when looking at data for areas such as 

college towns and resort areas. 

The Census Bureau subject matter specialists have 

reviewed the factors that could aff ect diff erences 

between ACS and decennial census estimates and they 

have determined that ACS estimates are similar to 

those obtained from past decennial census sample data 

for most areas and characteristics. The user should 

consider whether a particular analysis involves an area 

or characteristic that might be aff ected by these diff erences.5

When comparing ACS and decennial census sample 

estimates, the user must remember that the decennial 

census sample estimates have sampling error associated with them and that the standard errors for both 

ACS and census estimates must be incorporated when 

performing tests of statistical signifi cance. Appendix 

3 provides the calculations necessary for determining 

statistical signifi cance of a diff erence between two 

estimates. To derive the SEs of census sample estimates, use the method described in Chapter 8 of either 

the Census 2000 Summary File 3 Technical Documentation <http://www.census.gov/prod/cen2000/doc/sf3

.pdf> or the Census 2000 Summary File 4 Technical 

Documentation <http://www.census.gov/prod

/cen2000/doc/sf4.pdf>.

A conservative approach to testing for statistical signifi -

cance when comparing ACS and Census 2000 

estimates that avoids deriving the SE for the 

Census 2000 estimate would be to assume the SE for 

the Census 2000 estimate is the same as that determined for the ACS estimate. The result of this approach 

would be that a fi nding of statistical signifi cance can be 

assumed to be accurate (as the SE for the Census 2000 

estimate would be expected to be less than that for the 

ACS estimate), but a fi nding of no statistical signifi -

cance could be incorrect. In this case the user should 

calculate the census long-form standard error and follow the steps to conduct the statistical test.

Comparisons With 2010 Census Data

Looking ahead to the 2010 decennial census, data 

users need to remember that the socioeconomic data 

previously collected on the long form during the 

census will not be available for comparison with ACS 

estimates. The only common variables for the ACS and 

2010 Census are sex, age, race, ethnicity, household 

relationship, housing tenure, and vacancy status.

The critical factor that must be considered when comparing ACS estimates encompassing 2010 with the 

2010 Census is the potential impact of housing and 

population controls used for the ACS. As the housing 

and population controls used for 2010 ACS data will 

be based on the Population Estimates Program where 

the estimates are benchmarked on the Census 2000 

counts, they will not agree with the 2010 Census 

population counts for that year. The 2010 population 

estimates may diff er from the 2010 Census counts 

for two major reasons—the true change from 2000 to 

2010 is not accurately captured by the estimates and 

the completeness of coverage in the 2010 Census is 

diff erent than coverage of Census 2000. The impact of 

this diff erence will likely aff ect most areas and states, 

and be most notable for smaller geographic areas 

where the potential for large diff erences between the 

population controls and the 2010 Census population 

counts is greater.

Comparisons With Other Surveys

Comparisons of ACS estimates with estimates from 

other national surveys, such as the Current Population 

Survey, may be of interest to some users. A major consideration in making such comparisons will be that ACS 

5

 Further information concerning areas and characteristics that do not 

fi t the general pattern of comparability can be found on the ACS Web 

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

  2

2

2

21 1 1) ˆˆ ( XXSE #  SESEC

  2

2

2

1 1  SESEC

2

2

2

1  SESE
Appendix A-21

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

estimates include data for populations in both institutional and noninstitutional group quarters, and estimates from most national surveys do not include institutional populations. Another potential for large eff ects 

when comparing data from the ACS with data from 

other national surveys is the use of diff erent questions 

for measuring the same or similar information.

Sampling error and its impact on the estimates from 

the other survey should be considered if comparisons 

and statements of statistical diff erence are to be made, 

as described in Appendix 3. The standard errors on 

estimates from other surveys should be derived 

according to technical documentation provided for 

those individual surveys.

Finally, the user wishing to compare ACS estimates 

with estimates from other national surveys should 

consider the potential impact of other factors, such 

as target population, sample design and size, survey 

period, reference period, residence rules, and interview 

modes on estimates from the two sources.
A-22 Appendix

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

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

for the earlier year (Y1).

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

more recent year (Y2).

 is the published ACS estimate for the earlier year 

(Y1).

The example below compares the national median 

value for owner-occupied mobile homes in 2005 

($37,700) and 2006 ($41,000). First adjust the 2005 

median value using the 2005 All Items CPI-U-RS Annual 

Average (286.7) and the 2006 All Items CPI-U-RS Annual 

Average (296.1) as follows:

Thus, the comparison of the national median value for 

owner-occupied mobile homes in 2005 and 2006, in 

2006 dollars, would be $38,936 (2005 infl ationadjusted to 2006 dollars) versus $41,000 

(2006 dollars).

Creating Values Used in Multiyear Estimates

Multiyear income, rent, home value, and energy cost 

values are created with infl ation adjustments. The 

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

factors for a time period are calculated as ratios of the 

CPI-U-RS Annual Average from its most recent year to 

the CPI-U-RS Annual Averages from each of its earlier 

years. The ACS values for each of the earlier years in 

the multiyear period are multiplied by the appropriate 

infl ation adjustment factors to produce the infl ationadjusted values. These values are then used to create 

the multiyear estimates. 

As an illustration, consider the time period 2004–2006, 

which consisted of individual reference-year income 

values of $30,000 for 2006, $20,000 for 2005, and 

$10,000 for 2004. The multiyear income components 

are created from infl ation-adjusted reference period 

income values using factors based on the All Items 

CPI-U-RS Annual Averages of 277.4 (for 2004), 286.7 

(for 2005), and 296.1 (for 2006). The adjusted 2005 

value is the ratio of 296.1 to 286.7 applied to $20,000, 

which equals $20,656. Similarly, the 2004 value is 

the ratio of 296.1 to 277.4 applied to $10,000, which 

equals $10,674.

Using Dollar-Denominated Data

Appendix 5.

Dollar-denominated data refer to any characteristics 

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

value, and energy costs are all dollar-denominated 

data.

Infl ation will aff ect the comparability of dollardenominated data across time periods. When ACS 

multiyear estimates for dollar-denominated data are 

generated, amounts are adjusted using infl ation factors based on the Consumer Price Index (CPI).

Given the potential impact of infl ation on observed 

diff erences of dollar-denominated data across time 

periods, users should adjust for the eff ects of infl ation. 

Such an adjustment will provide comparable estimates 

accounting for infl ation. In making adjustments, the 

Census Bureau recommends using factors based on 

the All Items CPI-U-RS (CPI research series). The Bureau 

of Labor Statistics CPI indexes through 2006 are found 

at <http://www.bls.gov/cpi/cpiurs1978_2006.pdf>. 

Explanations follow.

Creating Single-Year Income Values

ACS income values are reported based on the amount 

of income received during the 12 months preceding 

the interview month. This is the income reference 

period. Since there are 12 diff erent income reference 

periods throughout an interview year, 12 diff erent 

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

period incomes to a single reference period of January 

through December of the interview year. Note that 

there are no infl ation adjustments for single-year estimates of rent, home value, or energy cost values.

Adjusting Single-Year Estimates Over Time

When comparing single-year income, rent, home value, 

and energy cost value estimates from two diff erent 

years, adjustment should be made as follows: 

1) Obtain the All Items CPI-U-RS Annual Averages for 

the 2 years being compared.

2) Calculate the infl ation adjustment factor as the ratio 

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

CPI-U-RS from the earlier year.

3) Multiply the dollar-denominated data estimated for 

the earlier year by the infl ation adjustment factor.

The infl ation-adjusted estimate for the earlier year can 

be expressed as:

936,38$700,37$ 7.286

1.296 ˆ

X ,2005 Adj u 

1

1

2

,1 ˆ ˆY

Y

Y

Y Adj X

CPI

CPI X 

CPIY1

CPIY 2

1 ˆ

XY
Appendix A-23

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

As an illustration, consider ACS multiyear estimates for 

the two time periods of 2001–2003 and 2004–2006. 

To compare the national median value for owneroccupied mobile homes in 2001–2003 ($32,000) and 

2004–2006 ($39,000), fi rst adjust the 2001–2003 

median value using the 2003 All Items CPI-U-RS Annual 

Averages (270.1) and the 2006 All Items CPI-U-RS 

Annual Averages (296.1) as follows:

Thus, the comparison of the national median value 

for owner-occupied mobile homes in 2001–2003 

and 2004–2006, in 2006 dollars, would be $35,080 

(2001–2003 infl ation-adjusted to 2006 dollars) versus 

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

Issues Associated With Infl ation Adjustment

The recommended infl ation adjustment uses a national 

level CPI and thus will not refl ect infl ation diff erences 

that may exist across geographies. In addition, since 

the infl ation adjustment uses the All Items CPI, it will 

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

Adjusting Multiyear Estimates Over Time

When comparing multiyear estimates from two different time periods, adjustments should be made as 

follows:

1) Obtain the All Items CPI-U-RS Annual Average for 

the most current year in each of the time periods 

being compared.

2) Calculate the infl ation adjustment factor as the 

ratio of the CPI-U-RS Annual Average in (1) from 

the most recent year to the CPI-U-RS in (1) from 

the earlier years.

3) Multiply the dollar-denominated estimate for the 

earlier time period by the infl ation adjustment 

factor.

The infl ation-adjusted estimate for the earlier years can 

be expressed as:

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

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

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

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

 is the published ACS estimate for the earlier time 

period (P1).

080,35$000,32$ 1.270

1.296 ˆ

X  ,20032001 Adj u 

1

1

2

,1 ˆ ˆ P

P

P

P Adj X

CPI

CPI X 

CPI P2

1 ˆ

X P
All survey estimates are subject to both sampling and 

nonsampling error. In Appendix 3, the topic of sampling error and the various measures available for 

understanding the uncertainty in the estimates due to 

their being derived from a sample, rather than from an 

entire population, are discussed. The margins of error 

published with ACS estimates measure only the eff ect 

of sampling error. Other errors that aff ect the overall 

accuracy of the survey estimates may occur in the 

course of collecting and processing the ACS, and are 

referred to collectively as nonsampling errors.

Broadly speaking, nonsampling error refers to any error 

aff ecting a survey estimate outside of sampling error. 

Nonsampling error can occur in complete censuses as 

well as in sample surveys, and is commonly recognized 

as including coverage error, unit nonresponse, item 

nonresponse, response error, and processing error.

Types of Nonsampling Errors

Coverage error occurs when a housing unit or person 

does not have a chance of selection in the sample 

(undercoverage), or when a housing unit or person has 

more than one chance of selection in the sample, or is 

included in the sample when they should not have been 

(overcoverage). For example, if the frame used for the 

ACS did not allow the selection of newly constructed 

housing units, the estimates would suff er from errors 

due to housing undercoverage.

The fi nal ACS estimates are adjusted for under- and 

overcoverage by controlling county-level estimates to 

independent total housing unit controls and to independent population controls by sex, age, race, and 

Hispanic origin (more information is provided on the 

coverage error defi nition page of the “ACS Quality Measures” Web site at <http://www.census.gov/acs/www

/UseData/sse/cov/cov_def.htm>). However, it is important to measure the extent of coverage adjustment by 

comparing the precontrolled ACS estimates to the fi nal 

controlled estimates. If the extent of coverage adjustments is large, there is a greater chance that diff erences in characteristics of undercovered or overcovered 

housing units or individuals diff er from those eligible to 

be selected. When this occurs, the ACS may not provide 

an accurate picture of the population prior to the coverage adjustment, and the population controls may not 

eliminate or minimize that coverage error.

Unit nonresponse is the failure to obtain the minimum required information from a housing unit or a resident of a group quarter in order for it to be considered 

a completed interview. Unit nonresponse means that no 

survey data are available for a particular sampled unit 

Appendix 6.

or person. For example, if no one in a sampled housing unit is available to be interviewed during the time 

frame for data collection, unit nonresponse will result.

It is important to measure unit nonresponse because 

it has a direct eff ect on the quality of the data. If the 

unit nonresponse rate is high, it increases the chance 

that the fi nal survey estimates may contain bias, even 

though the ACS estimation methodology includes a 

nonresponse adjustment intended to control potential 

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

Item nonresponse occurs when a respondent fails to 

provide an answer to a required question or when the 

answer given is inconsistent with other information. 

With item nonresponse, while some responses to 

the survey questionnaire for the unit are provided, 

responses to other questions are not obtained. For 

example, a respondent may be unwilling to respond 

to a question about income, resulting in item nonresponse for that question. Another reason for item nonresponse may be a lack of understanding of a particular question by a respondent.

Information on item nonresponse allows users to judge 

the completeness of the data on which the survey 

estimates are based. Final estimates can be adversely 

impacted when item nonresponse is high, because 

bias can be introduced if the actual characteristics of 

the people who do not respond to a question diff er 

from those of people who do respond to it. The ACS 

estimation methodology includes imputations for item 

nonresponse, intended to reduce the potential for item 

nonresponse bias.

Response error occurs when data are reported or 

recorded incorrectly. Response errors may be due to 

the respondent, the interviewer, the questionnaire, or 

the survey process itself. For example, if an interviewer 

conducting a telephone interview incorrectly records 

a respondent’s answer, response error results. In the 

same way, if the respondent fails to provide a correct 

response to a question, response error results. Another 

potential source of response error is a survey process 

that allows proxy responses to be obtained, wherein a 

knowledgeable person within the household provides 

responses for another person within the household 

who is unavailable for the interview. Even more error 

prone is allowing neighbors to respond.

Processing error can occur during the preparation 

of the fi nal data fi les. For example, errors may occur if 

data entry of questionnaire information is incomplete 

Measures of Nonsampling Error

A-24 Appendix

U.S. Census Bureau, A Compass for Understanding and Using American Community Survey Data
Appendix A-25

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

or inaccurate. Coding of responses incorrectly also 

results in processing error. Critical reviews of edits and 

tabulations by subject matter experts are conducted to 

keep errors of this kind to a minimum.

Nonsampling error can result in random errors and 

systematic errors. Of greatest concern are systematic errors. Random errors are less critical since they 

tend to cancel out at higher geographic levels in large 

samples such as the ACS.

On the other hand, systematic errors tend to accumulate over the entire sample. For example, if there is 

an error in the questionnaire design that negatively 

aff ects the accurate capture of respondents’ answers, 

processing errors are created. Systematic errors often 

lead to a bias in the fi nal results. Unlike sampling error 

and random error resulting from nonsampling error, 

bias caused by systematic errors cannot be reduced by 

increasing the sample size.

ACS Quality Measures

Nonsampling error is extremely diffi cult, if not 

impossible, to measure directly. However, the Census 

Bureau has developed a number of indirect measures of 

nonsampling error to help inform users of the quality 

of the ACS estimates: sample size, coverage rates, unit 

response rates and nonresponse rates by reason, and 

item allocation rates. Starting with the 2007 ACS, these 

measures are available in the B98 series of detailed 

tables on AFF. Quality measures for previous years are 

available on the “ACS Quality Measures” Web site at 

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

Sample size measures for the ACS summarize information for the housing unit and GQ samples. The measures available at the state level are:6

Housing units

Number of initial addresses selected

Number of fi nal survey interviews

Group quarters people (beginning with the 2006 ACS)

Number of initial persons selected

Number of fi nal survey interviews

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

or multiyear estimates in conjunction with estimates of 

the population of interest. While the coeffi cient of variation (CV) should typically be used to determine 

usability, as explained in Appendix 3, there may be 

some situations where the CV is small but the user 

has reason to believe the sample size for a subgroup 

is very small and the robustness of the estimate is in 

question. 

For example, the Asian-alone population makes up 

roughly 1 percent (8,418/656,700) of the population 

in Jeff erson County, Alabama. Given that the number of 

successful housing unit interviews in Jeff erson County 

for the 2006 ACS were 4,072 and assuming roughly 2.5 

persons per household (or roughly 12,500 completed 

person interviews), one could estimate that the 2006 

ACS data for Asians in Jeff erson County are based on 

roughly 150 completed person interviews.

Coverage rates are available for housing units, and 

total population by sex at both the state and national 

level. Coverage rates for total population by six race/

ethnicity categories and the GQ population are also 

available at the national level. These coverage rates are 

a measure of the extent of adjustment to the survey 

weights required during the component of the estimation methodology that adjusts to population controls. 

Low coverage rates are an indication of greater potential for coverage error in the estimates. 

Unit response and nonresponse rates for housing 

units are available at the county, state, and national 

level by reason for nonresponse: refusal, unable to 

locate, no one home, temporarily absent, language 

problem, other, and data insuffi cient to be considered 

an interview. Rates are also provided separately for persons in group quarters at the national and state levels.

A low unit response rate is an indication that there is 

potential for bias in the survey estimates. For example, 

the 2006 housing unit response rates are at least 94 

percent for all states. The response rate for the District 

of Columbia in 2006 was 91 percent.

Item allocation rates are determined by the content 

edits performed on the individual raw responses and 

closely correspond to item nonresponse rates. Overall 

housing unit and person characteristic allocation rates 

are available at the state and national levels, which 

combine many diff erent characteristics. Allocation rates 

for individual items may be calculated from the B99 

series of imputation detailed tables available in AFF. 

Item allocation rates do vary by state, so users are 

advised to examine the allocation rates for 

characteristics of interest before drawing conclusions 

from the published estimates.

6

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

selected and number of fi nal survey interviews) and for group quarters 

people cannot be used to calculate response rates. For the housing unit 

sample, the number of initial addresses selected includes addresses 

that were determined not to identify housing units, as well as initial 

addresses that are subsequently subsampled out in preparation for personal visit nonresponse follow-up. Similarly, the initial sample of people 

in group quarters represents the expected sample size within selected 

group quarters prior to visiting and sampling of residents. 
A-26 Appendix

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

Implications of Population Controls on ACS Estimates

Appendix 7.

As with most household surveys, the American 

Community Survey data are controlled so that the 

numbers of housing units and people in categories 

defi ned by age, sex, race, and Hispanic origin agree 

with the Census Bureau’s offi cial estimates. The 

American Community Survey (ACS) measures the 

characteristics of the population, but the offi cial count 

of the population comes from the previous census, 

updated by the Population Estimates Program.

In the case of the ACS, the total housing unit estimates 

and the total population estimates by age, sex, race 

and Hispanic origin are controlled at the county (or 

groups of counties) level. The group quarters total 

population is controlled at the state level by major type 

of group quarters. Such adjustments are important to 

correct the survey data for nonsampling and sampling 

errors. An important source of nonsampling error is 

the potential under-representation of hard-toenumerate demographic groups. The use of the 

population controls results in ACS estimates that more 

closely refl ect the level of coverage achieved for those 

groups in the preceding census. The use of the population estimates as controls partially corrects demographically implausible results from the ACS due to 

the ACS data being based on a sample of the population rather than a full count. For example, the use of 

the population controls “smooths out” demographic 

irregularities in the age structure of the population that 

result from random sampling variability in the ACS. 

When the controls are applied to a group of counties 

rather than a single county, the ACS estimates and the 

offi cial population estimates for the individual counties 

may not agree. There also may not be agreement 

between the ACS estimates and the population estimates for levels of geography such as subcounty areas 

where the population controls are not applied.

The use of population and housing unit controls also 

reduces random variability in the estimates from year 

to year. Without the controls, the sampling variability 

in the ACS could cause the population estimates to 

increase in one year and decrease in the next (especially for smaller areas or demographic groups), when 

the underlying trend is more stable. This reduction in 

variability on a time series basis is important since 

results from the ACS may be used to monitor trends 

over time. As more current data become available, the 

time series of estimates from the Population Estimates 

Program are revised back to the preceding census while 

the ACS estimates in previous years are not. Therefore, 

some diff erences in the ACS estimates across time may 

be due to changes in the population estimates. 

For single-year ACS estimates, the population and total 

housing unit estimates for July 1 of the survey year 

are used as controls. For multiyear ACS estimates, the 

controls are the average of the individual year population estimates.
Appendix A-27

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

Background and Overview Information

American Community Survey Web Page Site Map: 

<http://www.census.gov/acs/www/Site_Map.html>

This link is the site map for the ACS Web page. It provides an overview of the links and materials that are 

available online, including numerous reference documents.

What Is the ACS? <http://www.census.gov/acs/www

/SBasics/What/What1.htm> This Web page includes 

basic information about the ACS and has links to additional information including background materials.

ACS Design, Methodology, Operations

American Community Survey Design and Methodology 

Technical Paper: <http://www.census.gov/acs/www

/Downloads/tp67.pdf> This document describes the 

basic design of the 2005 ACS and details the full set 

of methods and procedures that were used in 2005. 

Please watch our Web site as a revised version will be 

released in the fall of 2008, detailing methods and 

procedures used in 2006 and 2007. 

About the Data (Methodology: <http://www.census

.gov/acs/www/AdvMeth/> This Web page contains 

links to information on ACS data collection and processing, evaluation reports, multiyear estimates study, 

and related topics.

ACS Quality

Accuracy of the Data (2007): <http://www.census.gov

/acs/www/Downloads/ACS/accuracy2007.pdf> This 

document provides data users with a basic understanding of the sample design, estimation methodology, and 

accuracy of the 2007 ACS data.

ACS Sample Size: <http://www.census.gov/acs/www

/SBasics/SSizes/SSizes06.htm> This link provides 

sample size information for the counties that were

published in the 2006 ACS. The initial sample size 

and the fi nal completed interviews are provided. The 

sample sizes for all published counties and county 

equivalents starting with the 2007 ACS will only be 

available in the B98 series of detailed tables on American FactFinder.

ACS Quality Measures: <http://www.census.gov/acs

/www/UseData/sse/> This Web page includes information about the steps taken by the Census Bureau to 

improve the accuracy of ACS data. Four indicators of 

survey quality are described and measures are provided at the national and state level.

Guidance on Data Products and Using the Data

How to Use the Data: <http://www.census.gov/acs

/www/UseData/> This Web page includes links to 

many documents and materials that explain the ACS 

data products. 

Comparing ACS Data to other sources: <http://www

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

are provided with guidance on comparing the 2007 

ACS data products to 2006 ACS data and Census 2000 

data. 

Fact Sheet on Using Diff erent Sources of Data for 

Income and Poverty: <http://www.census.gov/hhes

/www/income/factsheet.html> This fact sheet highlights the sources that should be used for data on 

income and poverty, focusing on comparing the ACS 

and the Current Population Survey (CPS).

Public Use Microdata Sample (PUMS): <http://www

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

page provides guidance in accessing ACS microdata.

Other ACS Resources

Appendix 8.