Response Profile of the 2005 ACS 

Geoffrey I. Jackson 

United States Census Bureau 

Room 4H268C, Washington DC 20233 

Geoffrey.I.Jackson@census.gov 

“Any views expressed on statistical, methodological, or technical issues are those of the author and not necessarily those of 

the U.S. Census Bureau.”

ABSTRACT 

(ACS Response Profile Logistic Regression) 

The objective of this paper is to use 2005 American Community Survey (ACS) data to compare and analyze the 

demographic, social, and economic characteristics of people who respond by mail, Computer Automated Telephone 

Interview (CATI), and Computer Automated Personal Interview (CAPI). There is a high cost involved with ensuring a high 

response rate for the ACS. Potential respondents are first contacted through a mail questionnaire. Those that don’t respond 

to the mail questionnaire are followed up with by CATI. Finally, a portion of CATI nonrespondents is followed up by CAPI. 

We created a profile of who responds to what mode. Survey nonresponse is typically due to three reasons: noncontact, 

resistance, and inability to complete the survey. We tested mail questionnaire nonresponse due to resistance and those who 

were unable to respond. A logistic regression model for nonresponse due to resistance and inability to respond was created. 

1. INTRODUCTION 

The American Community Survey (ACS) is a national survey conducted throughout the year by the U.S. Census Bureau. 

The ACS was designed to replace the decennial census long-form survey and allows the Census Bureau to produce smallarea estimates annually. The ACS is a multi-mode survey that employs three sequential modes of data collection to achieve 

high levels of survey response. Initially in the ACS, a paper questionnaire is mailed to all sampled housing units with a 

mailable address. Mail nonrespondents are then followed up with a Computer Assisted Telephone Interview (CATI). 

Finally, a Computer Assisted Personal Interview (CAPI) is conducted on a subsample of the CATI nonrespondents and a 

subsample of unmailable addresses (U.S. Census Bureau 2006). Ultimately, the vast majority of mail nonrespondents 

complete the ACS by telephone or face-to-face interviews. This paper will look at the nonrespondents to the mail portion of 

the ACS. We hypothesize that certain sample units are more likely to be interviewed by the mail component than the 

CATI/CAPI component. We will try to determine what types of people choose to mail back the ACS mail questionnaire. 

Two studies by McGovern and Griffin (2003) and Salvo and Lobo (2002), suggest reasons why people don’t respond to the 

ACS mail questionnaires. In addition to those studies, Leslie, Raglin, and Braker (2002) studied overall ACS response. 

These papers stated that nonresponse was correlated with the respondent’s race, language spoken, Hispanic origin, if they 

rent, and if they live in poverty. McGovern and Griffin (2003) examined data from the Census 2000 Supplementary Survey 

(C2SS) and 2001 Supplementary Survey (01SS), and reported an association between the use of different languages other 

than English at home and the mode of completion of the survey. The C2SS and 01SS were tests of the ACS’s feasibility and 

used the same three modes of data collection as the ACS. They found that a higher percentage of those who speak English at 

home completed the mail questionnaire than households that spoke Spanish or other languages (McGovern and Griffin 2003). 

Salvo and Lobo (2002) looked at the quality of response to the 2000 ACS and also found differences in who completed 

which mode. They found that the Bronx in New York had a much lower mail return rate than the national average. 

Compared to national averages, the Bronx had higher levels of households in poverty, Hispanics, Black Nonhispanics, and 

renters (Salvo and Lobo 2002). Leslie, Raglin, and Braker (2002) studied differences between 2000 ACS respondents and 

nonrespondents, using Census 2000 data for the nonrespondents. The study showed that, overall, nonrespondents were more 

likely to be male, single, Black, renters, and living alone (Leslie, Raglin, and Braker 2002). 

These three studies suggest that mail and overall nonresponse is mainly due to inability to respond and social or linguistic 

isolation. My hypothesis is that the various demographic groups that are likely not to respond by mail are those that are more 

likely to be isolated socially or by virtue of their language. We also hypothesize that people who have vision difficulties or 

other physical difficulties will be less likely to respond by mail. This account of ACS mail nonresponse is supported by 

Groves (2004), who states that survey nonresponse (other than noncontact) is typically due to two reasons: refusals and the 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

inability to complete the survey (Groves 2004). Social isolation can lead to a high refusal rate, and linguistic isolation is a 

major source of inability to complete the mail questionnaire. 

Social isolation describes the condition of people who do not consider themselves to be a part of a larger society or to be 

bound by its norms. Groups who have suffered historical inequities at the hands of major societal institutions and those 

identifying strongly with a specific subculture may consider themselves socially isolated (Groves and Couper 1998). The 

ACS is a government survey and the packet that includes the mail questionnaire features a cover letter that uses the phrase, 

“The U.S. Census Bureau is conducting a survey ...” The follow-up letter for nonrespondents includes the following 

wording: “You are required by U.S. law to respond to this survey” (U.S. Census Bureau 2006). Socially isolated groups may 

see themselves as alienated from central institutions such as the Federal Government and therefore they might be less likely 

to complete a government survey (Groves and Couper 1998). 

Linguistic isolation describes people who speak a different language and have difficulty communicating with the majority of 

the population. If a mail nonrespondent does not speak English well, they could be described as linguistically isolated. The 

ACS does offer people who cannot read English the opportunity to call a toll-free number to receive a copy of the 

questionnaire in Spanish. However, a person who is linguistically isolated might not take the time to find the telephone 

number within the packet. The ACS collects information on English proficiency and language spoken at home. 

The ACS also collects information on physical/mental difficulties and limitations in vision or hearing. (Hearing and vision 

difficulties are covered by a single question and so these two impairments cannot be differentiated.) Vision problems as well 

as other mental or physical difficulties could make a person unable to complete the mail questionnaire. 

Noncontacts due to unmailable questionnaires and Undeliverable-As-Addressed (UAA) questionnaires could have a large 

effect on ACS mail nonresponse.1 Therefore noncontacts will be excluded from the analysis of nonresponse. This is to 

distinguish mail refusals from noncontacts. Sosdian and Sharp (1980) state the inability to access mail respondents is a larger 

factor in mail nonresponse than resistance. Certain demographic groups may be more likely to not receive their ACS mail 

questionnaire due to living in areas with non-city-style addresses. A non-city-style address is a mailing address that does not 

use house number and street or road name (U.S. Census Bureau 2006). 

2. ACS BACKGROUND

The ACS is a monthly sampled national survey conducted by the U.S. Census Bureau. In 2005, the housing unit (HU) 

component of the ACS was fully implemented throughout the United States of America and Puerto Rico. The 2005 ACS 

sample consisted of housing units in all 3,141 counties and county equivalents, including the District of Columbia and all of 

Puerto Rico’s 78 municipios (U.S. Census Bureau 2006). The ACS sample is taken from the Master Address File (MAF), 

which is the Census Bureau’s official inventory of HUs and group quarters. The MAF is constructed from addresses that 

existed during Census 2000, post census adds and deletes from the United States Postal Service’s (USPS) delivery sequence 

file, adds from the demographic area address listing, and count question resolution adds (U.S. Census Bureau 2006). There 

are 80 replicate weights for each sampled housing unit to reflect the population. 

Data collection for HUs in the ACS is done in one of three modes. Sampled housing units with mailable addresses are first 

mailed a paper questionnaire. An unmailable address is a sample address that is inadequate for delivery by the USPS (U.S. 

Census Bureau 2006). The USPS does not deliver to some mailable addresses. They are identified as UAAs and occur for 

many reasons including bad addresses. Included with the paper questionnaire is a cover letter, a guide to the ACS, a 

frequently asked questions brochure, and a return envelope (U.S. Census Bureau 2006). If the Census Bureau does not 

receive a completed paper questionnaire within approximately three and a half weeks, a replacement questionnaire is sent out. 

If there is still no response after a month, a CATI interview is attempted if there is a telephone number for the address. The 

final phase of ACS data collection is CAPI. The CAPI sample is a subsample of the CATI nonrespondents plus a subsample 

of the unmailable addresses. The CAPI sampling rates depend on whether the HU has a mailable address. Addresses with an 

unmailable address are sampled at the rate of two-in-three (U.S. Census Bureau 2006). The sample rates for units with 

mailable addresses are based on their predicted levels of completed interviews prior to the CAPI stage. The sample rates are 

one-in-two, two-in-five, or one-in-three (U.S. Census Bureau 2006). Census Bureau Field Representatives conduct the CAPI 

component. 

1 Unmailable questionnaires correspond to sample addresses that were too incomplete to meet the requirements for mailing. 

UAA questionnaires have mailable addresses identified by the USPS as addresses that they could not deliver to. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3. METHODS 

3.1. Datasets 

This analysis is based on information about the person who completed the ACS questionnaire (the respondents) and 

information about the respondent’s household. There were seven 2005 ACS datasets used to create this dataset that included 

edited and unedited person and household data. The edited and unedited person and household files were used because some 

variables of interest were only available within one of those files. Since this analysis is interested in respondent 

characteristics the final dataset contains only those households with identifiable respondents. All vacant housing units, 

housing units where a respondent name was not given, and housing units where a respondent could not be identified were 

considered out of scope. This resulted in a loss of 627,725 households (33% of the 2005 sample). Respondents’ 

demographic information and their household characteristics were analyzed in creating the response profile. To distinguish 

between the effects of mail nonresponse and mail noncontacts, any CATI or CAPI case that had either one of the two mailouts flagged as an UAA by the USPS or an address considered unmailable were not included in the analysis. A CATI/CAPI 

case with a UAA mail questionnaire or unmailable address was considered a mail noncontact instead of a mail refusal. 

Of the 1,280,989 households in the initial in-scope universe, there were 81,730 CATI/CAPI households classified as UAAs 

and 44,104 CAPI respondents classified as unmailable excluded from the final dataset. For more information on the UAA 

and unmailable cases excluded, see table 4.1. The final dataset contains 1,228,712 respondents. 

3.2. Weights 

The replicate weights used in the variance estimation reflect the ACS sample design and the CAPI subsample of mail and 

CATI nonrespondents. There are 80 replicate weights for each respondent. Weights to correct for coverage errors and 

noninterviews are not available for this analysis because of the use of respondent identifying information. Noninterview 

correction weights are only available for datasets where any respondent identifiers have been removed (U.S. Census Bureau 

2006). The successive differences variance estimation method was used with the replicate weights for variance estimation. 

The successive difference method is the Census Bureau’s required variance estimation method for ACS (U.S. Census Bureau 

2006). The formula for the successive differences method is below. 

Var(X ) =

4 80 

(X − X )

2

0 ∑ r 0 80 r=1 

Where X0 = the final sample estimate and Xr = the replicate weights. 

3.3. Analysis

Demographic information collected in either CATI or CAPI was used to examine the characteristics of ACS respondents and 

their households that were either mail questionnaire nonresponse or mail noncontacts. 

To calculate the weighted percentage of a given respondent or household characteristic that were UAAs and Unmailable, the 

following formulas were used with the final sample weights: 

UAAs Percentage of Characteristic that were UAAs = ×100

Initial In − Scope Universe

Unmailable Percentage of Characteristic that were Unmailable = ×100

Initial In − Scope Universe

These rates were calculated for each characteristic and are displayed in Table 4.1. 

To calculate the weighted distribution of responses by mode across respondent and respondent household characteristics, the 

following formula was used with the final sample weight. All UAA, unmailable, deleted, unidentified vacant, and 

nonresponding households were excluded from this completion rate. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Mail Total Percentage Mail = ×100

Mail Total + CATI Total +CAPI Total

CATI Total Percentage CATI = ×100

Mail Total + CATI Total +CAPI Total

CAPI Total Percentage CAPI = ×100

Mail Total + CATI Total +CAPI Total

In analyzing race, we used a six-category race variable created by the Census Bureau (White, Black, American Indian, Asian, 

Native Hawaiian and Other Pacific Islander, and some other race). In the six-category race variable, the races are mutually 

exclusive and respondents are only coded as having one race. However, in the ACS questionnaire, respondents can select 

more than one race. Respondents who selected more than one race category are randomly assigned a race category from one 

of their selections. 

I used logistic regression to model mail nonresponse. The dependent variable is whether a respondent responded by mail or 

by CATI or CAPI. The independent variables for the prediction model will consist of variables identified in the previous 

literature as having an effect on mail response such as race, Hispanic origin, tenure (i.e., owners vs. renters), poverty, living 

alone, receives public assistance, and households where a language other than English is spoken at home. Other variables 

that may be related to mail nonresponse were also included, such as the presence of someone under 18 years old in the house, 

the respondent being between 18 and 24 years old, the respondent having difficulty with seeing or hearing, and other physical 

and mental difficulties (includes difficulty remembering, dressing themselves, going outside, and physical difficulty). 

4. RESULTS 

Table 4.1 is a summary of the characteristics of the population of mail questionnaires classified as noncontacts (unmailable 

and UAA) as a percentage of the in-scope universe. Poverty and Renters, two characteristics Salvo and Lobo (2002) 

associated with high mail nonresponse also have a higher level of noncontacts than the in-scope universe percentage of 

noncontacts. Other demographic characteristics found to have a high level of noncontacts are American Indians, people 

receiving public assistance, respondents between 18 and 24 years old, and people with difficulty seeing or hearing. The 

initial in-scope universe percentage of UAAs is much larger than the percentage of unmailables. American Indians are the 

only group with both an unmailable rate and UAA rate above 10 percent. If the UAAs and unmailable responses were 

included with CATI/CAPI responses due to refusals, the percentage of mail nonresponse would be inflated due to the 

noncontacts. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4.1. Distribution of Noncontact Mail Questionnaires by Respondent and Respondent Household 

Characteristics, with Standard Errors.

Percentage Percentage Percentage Percentage 

Unmailable Unmailable UAAs UAA 

Standard Standard 

Error Error 

In-Scope Universe 2.3 0.015 7.0 0.029 

Respondent’s Characteristics 

Race 

White 2.2 0.015 6.9 0.033 

Black 1.4 0.027 7.4 0.095 

American Indian 11.0 0.23 12.7 0.42 

Asian 0.37 0.027 4.0 0.14 

Native Hawaiian and Other Pacific 2.3 0.31 7.2 0.78 

Islander 

Some Other Race 1.0 0.046 6.7 0.16 

Hispanic Origin 

Yes 2.2 0.014 7.1 0.11 

No 1.3 0.031 7.0 0.031 

Between 18 and 24 years Old 

Yes 1.9 0.039 12.2 0.17 

No 2.4 0.016 6.7 0.027 

Speaks a Language Other than English at Home 

Yes 3.1 0.031 6.3 0.076 

No 2.2 0.015 7.2 0.033 

Difficulty Hearing or Seeing 

Yes 4.6 0.083 10.3 0.14 

No 2.2 0.014 6.8 0.030 

Other Difficulties 

Yes 3.8 0.044 9.1 0.070 

No 2.1 0.015 6.6 0.032 

Respondent’s Household Characteristics 

Living in Poverty 

Yes 3.0 0.045 10.3 0.10 

No 1.9 0.013 6.4 0.029 

Receiving Public Assistance 

Yes 2.3 0.081 9.7 0.25 

No 2.2 0.015 7.3 0.032 

Presence of Someone Under 18 years old in the 

Household 

Yes 2.1 0.022 6.5 0.054 

No 2.1 0.017 7.2 0.037 

Home Ownership 

Own 1.8 0.021 5.8 0.033 

Rent 2.7 0.018 9.5 0.065 

Living Alone 

Yes 1.9 0.021 7.6 0.068 

No 2.1 0.016 6.7 0.035 

Tables 4.2 and 4.3 show the distribution across modes of respondent and respondent household characteristics and their 

associated standard errors. This indicates how households or people with specific characteristics tend to be interviewed in 

the ACS. All UAAs, unmailables, vacants, deletes, unidentifiable respondents, and nonrespondents are excluded from tables 

4.2 and 4.3 
 

 

 

 

 

 

 

 

 

 

 

 

The 2005 ACS results support the past findings of McGovern and Griffin (2003) and Salvo and Lobo (2002). Non-whites 

(except Asians), Hispanics, those receiving public assistance, respondents with someone under the age of 18 present in their 

household, renters, and those living in poverty are less likely to respond via mail. 

The distribution for linguistically isolated ACS respondents also replicated earlier findings. Respondents who spoke a 

language other than English at home had a far lower percentage of interviews completed by mail than the respondents that 

spoke only English. 

Respondents who reported difficulty seeing or hearing showed that they were less likely to complete the mail questionnaire 

than those who do not have these difficulties. 

Table 4.2. Distribution Across Modes for Respondent’s Demographic Characteristics, with Standard Errors 

Percentage 

Mail 

Percentage 

CATI 

Percentage 

CAPI 

Total 

In-Scope Universe 56.1 

(0.059) 

12.4 

(0.029) 

31.5 

(0.061) 

100.0 

Race 

White 62.5 11.9 25.6 100.0 

Black 

(0.073) 

29.7 

(0.033) 

15.1 

(0.070) 

55.2 100.0 

American Indian 

(0.11) 

40.1 

(0.10) 

18.0 

(0.15) 

41.9 100.0 

Asian 

(0.56) 

52.7 

(0.36) 

9.7 

(0.69) 

37.6 100.0 

Native Hawaiian and Other Pacific 

(0.27) 

30.0 

(0.13) 

14.2 

(0.32) 

55.8 100.0 

Islander 

Some Other Race 

(1.3) 

22.9 

(0.88) 

16.7 

(1.6) 

60.3 100.0 

(0.16) (0.16) (0.22) 

Hispanic Origin 

Yes 25.9 15.1 58.8 100.0 

(0.14) (0.097) (0.16) 

No 59.6 12.2 28.2 100.0 

(0.068) (0.033) (0.071) 

Speaks a Language Other than English at Home 

Yes 36.1 14.0 49.9 100.0 

(0.12) (0.085) (0.14) 

No 60.3 12.1 27.6 100.0 

(0.071) (0.031) (0.074) 

Difficulty Hearing or Seeing 

Yes 47.9 19.2 32.9 100.0 

(0.24) (0.15) (0.29) 

No 56.5 12.0 31.4 100.0 

(0.059) (0.029) (0.062) 

Other Difficulties 

Yes 51.8 17.2 31.0 100.0 

(0.14) (0.088) (0.17) 

No 56.9 11.5 31.6 100.0 

(0.065) (0.031) (0.065) 

Respondent Between 18 and 24 Years Old 

Yes 36.9 6.8 56.2 100.0 

(0.24) (0.082) (0.25) 

No 57.3 12.7 29.9 100.0 

(0.062) (0.030) (0.065) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4.3. Distribution Across Modes for Respondent’s Household Demographic Characteristics, with Standard

Errors 

Percentage Percentage Percentage Total 

Mail CATI CAPI 

In-Scope Universe 56.1 12.4 31.5 100.0 

(0.059) (0.029) (0.061) 

Living in Poverty 

Yes 38.7 11.4 49.8 100.0 

(0.14) (0.085) (0.15) 

No 58.8 12.6 28.6 100.0 

(0.065) (0.032) (0.066) 

Receiving Public Assistance 

Yes 35.7 11.1 53.1 100.0 

(0.36) (0.25) (0.042) 

No 54.4 13.2 32.4 100.0 

(0.063) (0.033) (0.066) 

Presence of Someone Under 18 Years Old in the 

Household 

Yes 44.5 15.9 39.6 100.0 

(0.097) (0.056) (0.12) 

No 62.4 10.6 26.9 100.0 

(0.083) (0.032) (0.080) 

Home Ownership 

Own 63.8 14.2 22.0 100.0 

(0.074) (0.037) (0.08) 

Rent 40.2 8.7 51.1 100.0 

(0.098) (0.041) (0.11) 

Living Alone 

Yes 61.7 9.1 29.2 100.0 

(0.011) (0.041) (0.11) 

No 54.1 13.7 32.1 100.0 

(0.065) (0.038) (0.071) 

In the prediction model, the dependent variable was mail response (versus response via another mode) and the independent 

variables were the demographic, social, and housing variables. The dataset used for the prediction model did not include 

UAAs, unmailables, vacants, unidentifiable respondents, deletes, and nonrespondents. Table 4.4 shows the estimated model 

parameters. 

In the logistic regression model, all of the parameter estimates were significant. The normalized r-square value of the model

is 0.1512. We expected negative parameter estimates for all of the variables except Living Alone. According to Table 4.3 

the proportion of people living alone had a higher proportion of responses returned by mail than was true overall, therefore as 

expected, has a positive coefficient. The coefficients with negative parameter estimates as expected due to having a lower 

proportion of responses returned by mail than the overall proportion of mail returns were Black, American Indian, Asian, 

Native Hawaiian, some other race, presence of someone under 18, renter, in poverty, ages 18 to 24, speaks a language other 

than English at home, difficulty seeing or hearing, and other physical or mental difficulties. However, the public assistance 

parameter did not have a negative value as expected. The proportion of persons receiving public assistance who were 

interviewed by mail was 35.7 percent. There is a significant difference in the number of persons that respond by mail 

between all of the parameter estimates and their control variable. For example, there is a significant difference in the 

percentage of respondents that complete the mail questionnaire for White respondents when compared to Black, American 

Indian, Asian, Native Hawaiian and Pacific Islander, and some other race. All demographic, social, and housing variables of 

interest are significantly different from their counterpart. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 4.4. Coefficients for logistic regression prediction model for mail response, with Standard Errors and P-values 

Predictors Coefficient Standard Error* P-value 

Intercept 1.0233 0.005048 <0.0001 

Black -1.3424 0.007134 <0.0001 

American Indian -0.7212 0.02849 <0.0001 

Asian -0.0824 0.01375 <0.0001 

Native Hawaiian and Pacific Islander -1.0383 0.06481 <0.0001 

Some Other Race -0.3135 0.01497 <0.0001 

Hispanic Origin -0.9683 0.01160 <0.0001 

Receives Public Assistance 0.2400 0.01859 <0.0001 

Lives Alone 0.1312 0.006020 <0.0001 

Presence of Someone Under 18 -0.5735 0.006894 <0.0001 

Renter -0.7450 0.007170 <0.0001 

Poverty -0.3002 0.008259 <0.0001 

Age 18 to 24 -0.4090 0.01279 <0.0001 

Speaks a Language Other than English -0.4169 0.009103 <0.0001 

Difficulty Hearing or Seeing -0.2303 0.007987 <0.0001 

Other Difficulties -0.4553 0.01166 <0.0001 

*Using the successive differences variance estimation method, see section 3.2 for formula 

5. DISCUSSION 

The variables associated with inability to respond, and social (except living alone) and linguistic isolation seemed to have an 

effect on whether a person is likely to respond to the ACS by mail. As expected, respondents with these characteristics have 

lower proportions of mail interviews. These mail nonresponders will then respond to either CATI or CAPI. The high overall 

ACS response rate2

 dispels any notion of people who are socially or linguistic isolated of never responding. 

A continuum of resistance model could help explain the effects of mail response rates and overall nonresponse. The model 

assumes nonrespondents are similar to each other. In addition, it assumes overall nonrespondents are similar to respondents 

that required more than one contact to complete the survey (Lin and Schaeffer 1995). In a continuum of resistance model 

one could assume that CAPI respondents are similar to overall nonrespondents because of the difficulty involved in obtaining 

CAPI responses. An area of future research would be analyzing ACS data with a continuum of resistance model. 

When we did the analysis, we had no data on urbanicity or geographic information. We predict that the rate of mail response 

would be lower for people in urban environments than those in suburban and rural areas. People living in urban areas would 

be home less often and therefore less likely to complete a mail survey. Geographic information would also be helpful in the 

prediction model for mail response. 

Future research should look into CATI nonresponse. A similar analysis to this study could be done on CATI nonrespondents. 

Using CAPI response data one could test if different types of people are refusing the CATI portion of ACS. The effects of 

inability to respond and social and linguistic isolation on CATI could be different than their effects on mail nonresponse. 

Linguistic isolation should have less of an effect on CATI response, because CATI interviewers who speak different 

languages are used. 

6. REFERENCES 

Groves, Robert M. (2004). Survey Errors and Survey Costs. New York: John Wiley and Sons Inc. 

Groves, Robert M., and Couper, Mick P. (1998). Nonresponse in Household Interview Surveys. New York: John Wiley and 

Sons Inc. 

2 The 2005 ACS response rate was 97.3% (U.S. Census Bureau 2007) 
 

 

 

 

 

 

 

 

 

 

Leslie, Theresa F., Raglin, David A., and Braker, Emily M. (2002). “Can the American Community Survey trust using 

respondent data to impute data for survey nonrespondents? Are nonrespondents to the ACS different from respondents?” 

Bureau of the Census, Washington, DC. 

Lin, I-Fen, and Schaeffer, Nora Cate (1995). “Using Survey Participants to Estimate the Impact of Nonparticipation,” Public 

Opinion Quarterly, Vol. 59, No. 2. (Summer, 1995), pp. 236-258. 

McGovern, Pamela D., and Griffin, Deborah H. (2003). “Quality Assessment of Data Collected from Non-English Speaking 

Households in the American Community Survey,” Bureau of the Census, Washington, DC. 

Salvo, Joseph J., and Lobo, Arun Peter (2002). “The American Community Survey: Quality of Response by Mode of Data 

Collection in the Bronx Test Site,” 2002 Proceedings of the Joint Statistical Meeting of the American Statistical Association, 

New York City, August 2002. 

Sosdian, Carol P. and Sharp, Laure M. (1980). “Nonresponse in Mail Surveys: Access Failure or Respondent Resistance,” 

Public Opinion Quarterly, Vol. 44, No. 3. (Autumn 1980), pp. 396 – 402. 

U.S. Census Bureau (2006). “Design and Methodology American Community Survey,” U.S. Government Printing Office, 

Washington, DC, 2006. 

U.S. Census Bureau (2007). ACS Quality Measures: United States ACS Response Rates and Reasons for Noninterviews. 

http://www.census.gov/acs/www/acs-php/quality_measures_response_2005.php