Documentation: | ACS 2006 (1-Year Estimates) |

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Publisher: U.S. Census Bureau

Survey: ACS 2006 (1-Year Estimates)

Document: | ACS 2006-1yr Summary File: Technical Documentation |

citation: | Social Explorer; U.S. Census Bureau; American Community Survey 2006 Summary File: Technical Documentation. |

Chapter Contents

The data contained in these data products are based on the American Community Survey (ACS) and Puerto Rico Community Survey (PRCS) sample interviewed from January 1, 2006 through December 31, 2006. [Unless otherwise specified, the term ACS in this document will refer to both the ACS and PRCS.] The ACS sample is selected from all counties and county-equivalents in the United States, and all municipios in Puerto Rico (PR). In 2006, the ACS began collection of data from sampled persons in group quarters (GQs) - for example, military barracks, college dormitories, nursing homes, and correctional facilities. Persons in group quarters are included with persons in housing units (HUs) in all 2006 ACS estimates based on the total population. All ACS population estimates from previous years include only persons in housing units. The ACS, like any other statistical activity, is subject to error. The purpose of this documentation is to provide data users with a basic understanding of the ACS sample design, estimation methodology, and accuracy of the ACS data. The ACS is sponsored by the U.S. Census Bureau, and is part of the 2010 Decennial Census Program.

Additional information on the operational aspects of the ACS, including data collection and processing, can be found in the ACS Operations Plan (http://www.census.gov/acs/www/Downloads/OpsPlanfinal.pdf).

Additional information on the operational aspects of the ACS, including data collection and processing, can be found in the ACS Operations Plan (http://www.census.gov/acs/www/Downloads/OpsPlanfinal.pdf).

The ACS and PRCS employ three modes of data collection:

With the exception of addresses in Remote Alaska, the general timing of data collection is:

Month 1: Addresses in sample that are determined to be mailable are sent a questionnaire via the U.S. Postal Service.

Month 2: All mail non-responding addresses with an available phone number are sent to CATI.

Month 3: A sample of mail non-responses without a phone number, CATI non-responses, and unmailable addresses are selected and sent to CAPI.

Note that mail responses are accepted during all three months of data collection.

All Remote Alaska addresses are assigned to one of two data collection periods, January-April, or September-December and are sampled for CAPI at a rate of 2-in-3. Data for these addresses are collected using CAPI only and up to four months are given to complete the interviews in Remote Alaska for each data collection period.

- Mailout/Mailback
- Computer Assisted Telephone Interview (CATI)
- Computer Assisted Personal Interview (CAPI)

With the exception of addresses in Remote Alaska, the general timing of data collection is:

Month 1: Addresses in sample that are determined to be mailable are sent a questionnaire via the U.S. Postal Service.

Month 2: All mail non-responding addresses with an available phone number are sent to CATI.

Month 3: A sample of mail non-responses without a phone number, CATI non-responses, and unmailable addresses are selected and sent to CAPI.

Note that mail responses are accepted during all three months of data collection.

All Remote Alaska addresses are assigned to one of two data collection periods, January-April, or September-December and are sampled for CAPI at a rate of 2-in-3. Data for these addresses are collected using CAPI only and up to four months are given to complete the interviews in Remote Alaska for each data collection period.

Field representatives have several options available to them for data collection. These include completing the questionnaire while speaking to the resident in person or over the telephone, conducting an personal interview with proxy, such as a relative or guardian, or leaving paper questionnaires for residents to complete for themselves and then pick them up later. This last option is used for data collection in Federal prisons.

Group Quarters data collection spans six weeks, except in Remote Alaska and for Federal prisons, where the data collection time period is four months. As is done for HUs, Group Quarters in Remote Alaska are assigned to one of two data collection periods, January-April, or September-December and up to four months is allowed to complete the interviews. Similarly, all Federal prisons are assigned to September with a four month data collection window.

Group Quarters data collection spans six weeks, except in Remote Alaska and for Federal prisons, where the data collection time period is four months. As is done for HUs, Group Quarters in Remote Alaska are assigned to one of two data collection periods, January-April, or September-December and up to four months is allowed to complete the interviews. Similarly, all Federal prisons are assigned to September with a four month data collection window.

The universe for the ACS consists of all valid, residential housing unit addresses in all county and county equivalents in the 50 states, including the District of Columbia, and Puerto Rico. The Master Address File (MAF) is a database maintained by the Census Bureau containing a listing of residential and commercial addresses in the U.S. and Puerto Rico. The MAF is updated twice each year with the Delivery Sequence Files provided by the U.S. Postal Service which cover only the U.S. These files identify mail drop points and provide the best available source of changes and updates to the housing unit inventory. The MAF is also updated with the results from various Census Bureau field operations, including the ACS.

The group quarters (GQ) sampling frame is created from the Special Place (SP)/GQ facility files, obtained from decennial census operations, merged with the MAF. This frame includes GQs added from operations such as the GQ Incomplete Information Operation (IIO) at the Census Bureau's National Processing Center in Jeffersonville, Indiana, the Census Questionnaire Resolution (CQR) Program and GQs closed on Census day. The GQ frame underwent an unduplication process. GQs that were closed on Census day were not included in the SP/GQ inventory file received from Decennial Systems and Contract Management Office (DSCMO). These were added from a preliminary inventory file obtained from DSCMO since it was possible that while these GQs were closed on Census day, they could be open when the ACS contacts them. Headquarters Staff researched state prisons on the Internet to obtain the current operating status and the current population counts for state prisons. After the frame was put together from these different sources, it was then sorted geographically.

The ACS employs a two-stage, two-phase sample design. The ACS first-stage sample consists of two separate samples, Main and Supplemental, each chosen at different points in time. Together, these constitute the first-stage sample. Both the Main and the Supplemental samples are chosen in two phases referred to as first- and second-phase sampling. Subsequent to second-phase sampling, sample addresses are randomly assigned to one of the twelve months of the sample year. The second-stage of sampling occurs when the CAPI sample is selected (see Section 2 below).

The Main sample is selected during the summer preceding the sample year. Approximately 99 percent of the sample is selected at this time. Each address in sample is randomly assigned to one of the 12 months of the sample year. Supplemental sampling occurs in January/February of the sample year and accounts for approximately 1 percent of the overall first-stage sample. The Supplemental sample is allocated to the last nine months of the sample year. The sample of addresses from both Main and Supplemental is referred to as the initially selected sample. A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.

Several of the steps used to select the first-stage sample are common to both Main and Supplemental sampling. The descriptions of the steps included in the first-stage sample selection below indicate which are common to both and which are unique to either Main or Supplemental sampling.

1. First-Stage Sample Selection

First-phase sampling (*performed during both Main and Supplemental sampling*) - First stage sampling defines the universe for the second stage of sampling through two steps. First, all addresses that were in a first-stage sample within the past four years are excluded from eligibility. This ensures that no address is in sample more than once in any five-year period. The second step is to select a 20 percent systematic sample of new units, i.e. those units that have never appeared on a previous MAF extract. Each new address is systematically assigned to either the current year or to one of four back-samples. This procedure maintains five equal partitions of the universe.

Assignment of blocks to a second-phase sampling stratum (*performed during Main sampling only*) - Second-phase sampling uses seven distinct sampling rates in the U.S. and five in PR. These rates are applied at a block level to addresses in the U.S. and PR by calculating a measure of size for each of the following entities:

For American Indian and Alaska Native Village Statistical Areas, the measure of size is the estimated number of occupied HUs multiplied by the proportion of people reporting American Indian or Alaska Native (alone or in combination) in Census 2000.

Each block is then assigned the smallest measure of size from the set of all entities of which it is a part. The second-phase sampling strata are shown in Table 1 below.

**Table 1**. **First-Stage Sampling Rate Categories for the United States and Puerto Rico **

2. Second-Stage Sample Selection - Subsampling the Unmailable and Non-Responding Addresses

All addresses determined to be unmailable are subsampled for the CAPI phase of data collection at a rate of 2-in-3. Unmailable addresses, which include Remote Alaska addresses, do not go to the CATI phase of data collection. Subsequent to CATI, all addresses for which no response has been obtained prior to CAPI are subsampled based on the expected rate of completed interviews at the tract level using the rates shown in Table 2.

**Table 2. Second-Stage (CAPI) Subsampling Rates for the United States and Puerto Rico **

The Main sample is selected during the summer preceding the sample year. Approximately 99 percent of the sample is selected at this time. Each address in sample is randomly assigned to one of the 12 months of the sample year. Supplemental sampling occurs in January/February of the sample year and accounts for approximately 1 percent of the overall first-stage sample. The Supplemental sample is allocated to the last nine months of the sample year. The sample of addresses from both Main and Supplemental is referred to as the initially selected sample. A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.

Several of the steps used to select the first-stage sample are common to both Main and Supplemental sampling. The descriptions of the steps included in the first-stage sample selection below indicate which are common to both and which are unique to either Main or Supplemental sampling.

1. First-Stage Sample Selection

First-phase sampling (

Assignment of blocks to a second-phase sampling stratum (

- Counties
- Places (active, functioning governmental units)
- School Districts (elementary, secondary, and unified)
- American Indian Areas
- Alaska Native Village Statistical Areas
- Hawaiian Homelands
- Minor Civil Divisions (MCDs) - in Connecticut, Maine, Massachusetts, Michigan, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, and Wisconsin (these are the states where MCDs are active, functioning governmental units)
- Census Designated Places - in Hawaii only

For American Indian and Alaska Native Village Statistical Areas, the measure of size is the estimated number of occupied HUs multiplied by the proportion of people reporting American Indian or Alaska Native (alone or in combination) in Census 2000.

Each block is then assigned the smallest measure of size from the set of all entities of which it is a part. The second-phase sampling strata are shown in Table 1 below.

- Calculation of the second-phase sampling rates (
*performed during Main sampling only*)

- Second-phase sample selection (
*performed in Main and Supplemental)*

- Sample Month Assignment (
*performed in Main and Supplemental*)

Sampling Rate Category | Sampling Rates | |
---|---|---|

United States | Puerto Rico | |

Blocks in smallest governmental units (MOS^{1} < 200) |
10.0% | 10.0% |

Blocks in smaller governmental units (200 ≤ MOS < 800) | 6.8% | 8.1% |

Blocks in small governmental units (800 ≤ MOS ≤ 1200) | 3.4% | 4.0% |

Blocks in large tracts (MOS >1200, TRACTMOS^{2} ≥ 2000) where Mailable addresses^{3} ≥ 75% and predicted levels of completed mail and CATI interviews prior to second-stage sampling > 60% |
1.6% | 2.0% |

Other Blocks in large tracts (MOS >1200, TRACTMOS ≥ 2000) | 1.7% | |

All other blocks (MOS >1200, TRACTMOS < 2000) where Mailable addresses ≥ 75% and predicted levels of completed mail and CATI interviews prior to second-stage sampling > 60% | 2.1% | 2.7% |

All other blocks (MOS >1200, TRACTMOS < 2000) | 2.3% |

2. Second-Stage Sample Selection - Subsampling the Unmailable and Non-Responding Addresses

All addresses determined to be unmailable are subsampled for the CAPI phase of data collection at a rate of 2-in-3. Unmailable addresses, which include Remote Alaska addresses, do not go to the CATI phase of data collection. Subsequent to CATI, all addresses for which no response has been obtained prior to CAPI are subsampled based on the expected rate of completed interviews at the tract level using the rates shown in Table 2.

Address and Tract Characteristics | CAPI Subsampling Rate |
---|---|

United States | |

Unmailable addresses and addresses in Remote Alaska | 2-in-3 |

Mailable addresses in tracts with predicted levels of completed mail and CATI interviews prior to CAPI subsampling between 0% and less than 36% | 1-in-2 |

Mailable addresses in tracts with predicted levels of completed mail and CATI interviews prior to CAPI subsampling greater than 35% and less than 51% | 2-in-5 |

Mailable addresses in other tracts | 1-in-3 |

Puerto Rico | |

Unmailable addresses | 2-in-3 |

Mailable addresses | 1-in-2 |

The GQ sampling frame is divided into three strata: one for small GQs (having 15 or fewer people according to Census 2000 or updated information), one for GQs that were closed on Census Day 2000, and one for large GQs (having more than 15 people according to Census 2000 or updated information). GQs in the first two strata are sampled using the same procedure, and GQs in the large stratum are sampled using different a method. The small GQ stratum and the stratum for GQs closed on Census Day are combined into one sampling stratum and sorted geographically^{1}.

1. First Stage Sample Selection for Small Stratum

2. First Stage Sample Selection for the Large Stratum

3. Sample Month Assignment

In order to assign a panel month to each hit, all of the GQ samples from a state are combined and sorted by small/large stratum and second-phase order of selection. Consecutive samples are assigned to the twelve panel months in a predetermined order, starting with a randomly determined month, except for Federal prisons and remote Alaska. Remote Alaska GQs are assigned to January and September based on where the GQ is located. Correctional facilities have their sample clustered. All Federal prisons hits are assigned to the September panel. In non-Federal correctional facilities, all hits for a given GQ are assigned to the same panel month. However, unlike Federal prisons, the hits in state and local correctional facilities are assigned to randomly selected panels spread throughout the year.

4. Second Stage Sample: Selection of Persons in Small and Large GQs

Small GQs in the second phase sampling are take all, i.e., every person in the selected GQ is eligible to receive a questionnaire. If the actual number of persons in the GQ exceeds 15, a field subsampling operation is performed to reduce the total number of sample persons interviewed at the GQ to 10. If the actual number of persons in the GQ is 10 or fewer, then the group size will be less than 10.

For each hit in the large GQs, the automated instrument uses the population count at the time of the visit and selects a subsample of 10 people from the roster. The people in this subsample receive the questionnaire.

1. First Stage Sample Selection for Small Stratum

- First phase sampling

- Second phase sampling

2. First Stage Sample Selection for the Large Stratum

- First Phase Sampling

- Second Phase Sampling

3. Sample Month Assignment

In order to assign a panel month to each hit, all of the GQ samples from a state are combined and sorted by small/large stratum and second-phase order of selection. Consecutive samples are assigned to the twelve panel months in a predetermined order, starting with a randomly determined month, except for Federal prisons and remote Alaska. Remote Alaska GQs are assigned to January and September based on where the GQ is located. Correctional facilities have their sample clustered. All Federal prisons hits are assigned to the September panel. In non-Federal correctional facilities, all hits for a given GQ are assigned to the same panel month. However, unlike Federal prisons, the hits in state and local correctional facilities are assigned to randomly selected panels spread throughout the year.

4. Second Stage Sample: Selection of Persons in Small and Large GQs

Small GQs in the second phase sampling are take all, i.e., every person in the selected GQ is eligible to receive a questionnaire. If the actual number of persons in the GQ exceeds 15, a field subsampling operation is performed to reduce the total number of sample persons interviewed at the GQ to 10. If the actual number of persons in the GQ is 10 or fewer, then the group size will be less than 10.

For each hit in the large GQs, the automated instrument uses the population count at the time of the visit and selects a subsample of 10 people from the roster. The people in this subsample receive the questionnaire.

The estimates that appear in this product were obtained from a raking ratio estimation procedure that resulted in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics were based on the person weight. Estimates of family, household, and housing unit characteristics were based on the housing unit weight. For any given tabulation area, a characteristic total was estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the tabulation area.

Each sample person or housing unit record was assigned exactly one weight to be used to produce estimates of all characteristics. For example, if the weight given to a sample person or housing unit had a value 40, all characteristics of that person or housing unit is tabulated with the weight of 40.

The weighting is conducted in two main operations: group quarters person weighting and a combined housing unit and household person weighting. The group quarters person weighting is conducted first with the household person weighting second. The household person weighting is dependent on the group quarters person weighting so that estimates for total population which include both group quarters and household population will be controlled to the Census Bureau's official 2006 total resident population estimates.

Each sample person or housing unit record was assigned exactly one weight to be used to produce estimates of all characteristics. For example, if the weight given to a sample person or housing unit had a value 40, all characteristics of that person or housing unit is tabulated with the weight of 40.

The weighting is conducted in two main operations: group quarters person weighting and a combined housing unit and household person weighting. The group quarters person weighting is conducted first with the household person weighting second. The household person weighting is dependent on the group quarters person weighting so that estimates for total population which include both group quarters and household population will be controlled to the Census Bureau's official 2006 total resident population estimates.

Each GQ person is first assigned to an Estimates Area Major GQ Type Group (the type groups used by the Population Estimates Program). The major type groups used are:

**Table 3: Estimates Area Major GQ Type Groups**

The procedure used to assign the weights to the GQ persons is performed independently within state. The steps are as follows:

State x Major GQ Type Group x County

State x Major GQ Type Group

Major GQ Type Group

Major GQ Type Group x County

Major GQ Type Group x County x Race

Major GQ Type Group x County x Race x Hispanic Origin

Major GQ Type Group x County x Race x Hispanic Origin x Sex

Major GQ Type Group x County x Race x Hispanic Origin x Sex x Age

Major GQ Type Group | Definition | Institutional / Non-Institutional |
---|---|---|

1 | Correctional Institutions | Institutional |

2 | Juvenile Detention Facilities | Institutional |

3 | Nursing Homes | Institutional |

4 | Other Long-Term Care Facilities | Institutional |

5 | College Dormitories | Non-Institutional |

6 | Military Facilities | Non-Institutional |

7 | Other Non-Institutional Facilities | Non-Institutional |

The procedure used to assign the weights to the GQ persons is performed independently within state. The steps are as follows:

- Base Weight

- Non-Interview Factor

State x Major GQ Type Group x County

- GQ Person Post-stratification Factor

State x Major GQ Type Group

- Rounding

Major GQ Type Group

Major GQ Type Group x County

Major GQ Type Group x County x Race

Major GQ Type Group x County x Race x Hispanic Origin

Major GQ Type Group x County x Race x Hispanic Origin x Sex

Major GQ Type Group x County x Race x Hispanic Origin x Sex x Age

The housing unit and household person weighting used weighting areas built from collections of whole counties. Census 2000 data were used to group counties of similar demographic and social characteristics. The characteristics considered in the formation included:

The estimation procedure used to assign the weights was then performed independently within each of the ACS weighting areas.

1. Initial Housing Unit Weighting Factors-This process produced the following factors:

Selected in CAPI subsampling: SSF = 2.0, 2.5, or 3.0 according to Table 2

Not selected in CAPI subsampling: SSF = 0.0

Not a CAPI case: SSF = 1.0

Some sample addresses were unmailable. A two-thirds sample of these were sent directly to CAPI and for these cases SSF = 1.5.

Weighting Area x Month

Weighting Area x Building Type x Tract

A second factor, NIF2, is a ratio adjustment that was computed and assigned to occupied housing units based on the following groups:

Weighting Area x Building Type x Month

NIF was then computed by applying NIF1 and NIF2 for each occupied housing unit. Vacant housing units were assigned a value of NIF = 1.0. Nonresponding housing units were now assigned a weight of 0.0.

NIFM was computed and assigned to occupied CAPI housing units based on the following groups:

Weighting Area x Building Type x Month

Vacant housing units or non-CAPI (mail and CATI) housing units received a value of NIFM = 1.0.

Weighting Area x Tenure (Owner or renter) x Month x Marital Status of the Householder (married/widowed or single)

Vacant housing units received a value of MBF = 1.0. MBF is applied to the weights computed through NIF.

2. Person Weighting Factors-Initially the person weight of each person in an occupied housing unit was the product of the weighting factors of their associated housing unit (BW x ... x HPF). At this point everyone in the household has the same weight. Beginning in 2006, the person weighting is done in a series of three steps which are repeated until a stopping criterion is met. These person weights were individually adjusted based for each person as described below.

The three steps are as follows:

- Householder in a married-couple or unmarried-partner household

- Spouse or unmarried partner in a married-couple or unmarried-partner household

- All others

The first two groups are adjusted so that the sum of their person weights is equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). The goal of this step is to produce more consistent estimates of spouses or unmarried partners and married-couple and unmarried-partner households.

Weighting Area x Race / Ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic American Indian or Alaskan Native, non-Hispanic Asian, non-Hispanic Native Hawaiian or Pacific Islander, and Hispanic (any race)) x Sex x Age Groups.

In Puerto Rico, this used only the Sex x Age Groups.

These three steps were repeated several times until the estimates at the national level achieved their optimal consistency with regard to the spouse and householder equalization. The effect Person Post-Stratification Factor (PPSF) is then equal to the product (SPEQRF x HHEQRF x DEMORF) from all of iterations of these three adjustments. The unrounded person weight is then the equal to the product of PPSF times the housing unit weight (BW x ... x HPF x PPSF).

County

County x Race

County x Race x Hispanic Origin

County x Race x Hispanic Origin x Sex

County x Race x Hispanic Origin x Sex x Age

County x Race x Hispanic Origin x Sex x Age x Tract

County x Race x Hispanic Origin x Sex x Age x Tract x Block

For example, the number of White, Hispanic, Males, Age 30 estimated for a county using the rounded weights was within one of the number produced using the unrounded weights.

3. Final Housing Unit Weighting Factors-This process produced the following factors:

County County x Tract County x Tract x Block

- Percent in poverty
- Percent renting
- Percent in rural areas
- Race, ethnicity, age, and sex distribution
- Distance between the centroids of the counties
- Core-based Statistical Area status

The estimation procedure used to assign the weights was then performed independently within each of the ACS weighting areas.

1. Initial Housing Unit Weighting Factors-This process produced the following factors:

- Base Weight (BW)

- CAPI Subsampling Factor (SSF)

Selected in CAPI subsampling: SSF = 2.0, 2.5, or 3.0 according to Table 2

Not selected in CAPI subsampling: SSF = 0.0

Not a CAPI case: SSF = 1.0

Some sample addresses were unmailable. A two-thirds sample of these were sent directly to CAPI and for these cases SSF = 1.5.

- Variation in Monthly Response by Mode (VMS)

Weighting Area x Month

- Noninterview Factor (NIF)

Weighting Area x Building Type x Tract

A second factor, NIF2, is a ratio adjustment that was computed and assigned to occupied housing units based on the following groups:

Weighting Area x Building Type x Month

NIF was then computed by applying NIF1 and NIF2 for each occupied housing unit. Vacant housing units were assigned a value of NIF = 1.0. Nonresponding housing units were now assigned a weight of 0.0.

- Noninterview Factor-Mode (NIFM)

NIFM was computed and assigned to occupied CAPI housing units based on the following groups:

Weighting Area x Building Type x Month

Vacant housing units or non-CAPI (mail and CATI) housing units received a value of NIFM = 1.0.

- Mode Bias Factor (MBF)

Weighting Area x Tenure (Owner or renter) x Month x Marital Status of the Householder (married/widowed or single)

Vacant housing units received a value of MBF = 1.0. MBF is applied to the weights computed through NIF.

- Housing unit Post-stratification Factor (HPF)

2. Person Weighting Factors-Initially the person weight of each person in an occupied housing unit was the product of the weighting factors of their associated housing unit (BW x ... x HPF). At this point everyone in the household has the same weight. Beginning in 2006, the person weighting is done in a series of three steps which are repeated until a stopping criterion is met. These person weights were individually adjusted based for each person as described below.

The three steps are as follows:

- Spouse Equalization Raking Factor (SPEQRF)

- Householder in a married-couple or unmarried-partner household

- Spouse or unmarried partner in a married-couple or unmarried-partner household

- All others

The first two groups are adjusted so that the sum of their person weights is equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). The goal of this step is to produce more consistent estimates of spouses or unmarried partners and married-couple and unmarried-partner households.

- Householder Equalization Raking Factor (HHEQRF)

- Demographic Raking Factor (DEMORF)

Weighting Area x Race / Ethnicity (non-Hispanic White, non-Hispanic Black, non-Hispanic American Indian or Alaskan Native, non-Hispanic Asian, non-Hispanic Native Hawaiian or Pacific Islander, and Hispanic (any race)) x Sex x Age Groups.

In Puerto Rico, this used only the Sex x Age Groups.

These three steps were repeated several times until the estimates at the national level achieved their optimal consistency with regard to the spouse and householder equalization. The effect Person Post-Stratification Factor (PPSF) is then equal to the product (SPEQRF x HHEQRF x DEMORF) from all of iterations of these three adjustments. The unrounded person weight is then the equal to the product of PPSF times the housing unit weight (BW x ... x HPF x PPSF).

- Rounding

County

County x Race

County x Race x Hispanic Origin

County x Race x Hispanic Origin x Sex

County x Race x Hispanic Origin x Sex x Age

County x Race x Hispanic Origin x Sex x Age x Tract

County x Race x Hispanic Origin x Sex x Age x Tract x Block

For example, the number of White, Hispanic, Males, Age 30 estimated for a county using the rounded weights was within one of the number produced using the unrounded weights.

3. Final Housing Unit Weighting Factors-This process produced the following factors:

- Householder Factor (HHF)

- Rounding

County County x Tract County x Tract x Block

The Census Bureau has modified or suppressed some data on this site to protect confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified.

The Census Bureau's internal Disclosure Review Board sets the confidentiality rules for all data releases. A checklist approach is used to ensure that all potential risks to the confidentiality of the data are considered and addressed.

The Census Bureau's internal Disclosure Review Board sets the confidentiality rules for all data releases. A checklist approach is used to ensure that all potential risks to the confidentiality of the data are considered and addressed.

- Title 13, United States Code

- Disclosure Avoidance

- Data Swapping

- Synthetic Data

- Sampling Error

- Nonsampling Error

Sampling error is the difference between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population (as from a census). Note that sample-based estimates will vary depending on the particular sample selected from the population. Measures of the magnitude of sampling error reflect the variation in the estimates over all possible samples that could have been selected from the population using the same sampling methodology.

Estimates of the magnitude of sampling errors - in the form of margins of error - are provided with all published ACS data. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results.

Estimates of the magnitude of sampling errors - in the form of margins of error - are provided with all published ACS data. The Census Bureau recommends that data users incorporate this information into their analyses, as sampling error in survey estimates could impact the conclusions drawn from the results.

Confidence Intervals - A sample estimate and its estimated standard error may be used to construct confidence intervals about the estimate. These intervals are ranges that will contain the average value of the estimated characteristic that results over all possible samples, with a known probability.

For example, if all possible samples that could result under the ACS sample design were independently selected and surveyed under the same conditions, and if the estimate and its estimated standard error were calculated for each of these samples, then:

1. Approximately 68 percent of the intervals from one estimated standard error below the estimate to one estimated standard error above the estimate would contain the average result from all possible samples;

2. Approximately 90 percent of the intervals from 1.645 times the estimated standard error below the estimate to 1.645 times the estimated standard error above the estimate would contain the average result from all possible samples.

3. Approximately 95 percent of the intervals from two estimated standard errors below the estimate to two estimated standard errors above the estimate would contain the average result from all possible samples.

The intervals are referred to as 68 percent, 90 percent, and 95 percent confidence intervals, respectively.

Margin of Error - Instead of providing the upper and lower confidence bounds in published ACS tables, the margin of error is provided instead. The margin of error is the difference between an estimate and its upper or lower confidence bound. Both the confidence bounds and the standard error can easily be computed from the margin of error. All ACS published margins of error are based on a 90 percent confidence level.

Standard Error = Margin of Error / 1.645

Lower Confidence Bound = Estimate - Margin of Error

Upper Confidence Bound = Estimate + Margin of Error

Note that for 2006 and earlier estimates, ACS margins of error and confidence bounds were calculated using a 90 percent confidence level multiplier of 1.65. Beginning with the 2006 data release, we are now employing a more accurate multiplier of 1.645. Margins of error and confidence bounds from previously-published products will not be updated with the new multiplier. When calculating standard errors from margins of error or confidence bounds using published data for 2006 and earlier, use the 1.65 multiplier.

When constructing confidence bounds from the margin of error, the user should be aware of any natural limits on the bounds. For example, if a population estimate is near zero, the calculated value of the lower confidence bound may be negative. However, a negative number of people does not make sense, so the lower confidence bound should be reported as zero instead. However, for other estimates such as income, negative values do make sense. The context and meaning of the estimate must be kept in mind when creating these bounds. Another of these natural limits would be 100 percent for the upper bound of a percent estimate.

If the margin of error is displayed as '*****' (five asterisks), the estimate has been controlled to be equal to a fixed value and so it has no sampling error. When using any of the formulas in the following section, use a standard error of zero for these controlled estimates.

- Limitations -The user should be careful when computing and interpreting confidence intervals.

- The estimated standard errors (and thus margins of error) included in these data products do not include portions of the variability due to nonsampling error that may be present in the data. In particular, the standard errors do not reflect the effect of correlated errors introduced by interviewers, coders, or other field or processing personnel. Nor do they reflect the error from imputed values due to missing responses. Thus, the standard errors calculated represent a lower bound of the total error. As a result, confidence intervals formed using these estimated standard errors may not meet the stated levels of confidence (i.e., 68, 90, or 95 percent). Thus, some care must be exercised in the interpretation of the data in this data product based on the estimated standard errors.

- Zero or small estimates; very large estimates - The value of almost all ACS characteristics is greater than or equal to zero by definition. For zero or small estimates, use of the method given previously for calculating confidence intervals relies on large sample theory, and may result in negative values which for most characteristics are not admissible. In this case the lower limit of the confidence interval is set to zero by default. A similar caution holds for estimates of totals close to a control total or estimated proportions near one, where the upper limit of the confidence interval is set to its largest admissible value. In these situations the level of confidence of the adjusted range of values is less than the prescribed confidence level.

For example, if all possible samples that could result under the ACS sample design were independently selected and surveyed under the same conditions, and if the estimate and its estimated standard error were calculated for each of these samples, then:

1. Approximately 68 percent of the intervals from one estimated standard error below the estimate to one estimated standard error above the estimate would contain the average result from all possible samples;

2. Approximately 90 percent of the intervals from 1.645 times the estimated standard error below the estimate to 1.645 times the estimated standard error above the estimate would contain the average result from all possible samples.

3. Approximately 95 percent of the intervals from two estimated standard errors below the estimate to two estimated standard errors above the estimate would contain the average result from all possible samples.

The intervals are referred to as 68 percent, 90 percent, and 95 percent confidence intervals, respectively.

Margin of Error - Instead of providing the upper and lower confidence bounds in published ACS tables, the margin of error is provided instead. The margin of error is the difference between an estimate and its upper or lower confidence bound. Both the confidence bounds and the standard error can easily be computed from the margin of error. All ACS published margins of error are based on a 90 percent confidence level.

Standard Error = Margin of Error / 1.645

Lower Confidence Bound = Estimate - Margin of Error

Upper Confidence Bound = Estimate + Margin of Error

Note that for 2006 and earlier estimates, ACS margins of error and confidence bounds were calculated using a 90 percent confidence level multiplier of 1.65. Beginning with the 2006 data release, we are now employing a more accurate multiplier of 1.645. Margins of error and confidence bounds from previously-published products will not be updated with the new multiplier. When calculating standard errors from margins of error or confidence bounds using published data for 2006 and earlier, use the 1.65 multiplier.

When constructing confidence bounds from the margin of error, the user should be aware of any natural limits on the bounds. For example, if a population estimate is near zero, the calculated value of the lower confidence bound may be negative. However, a negative number of people does not make sense, so the lower confidence bound should be reported as zero instead. However, for other estimates such as income, negative values do make sense. The context and meaning of the estimate must be kept in mind when creating these bounds. Another of these natural limits would be 100 percent for the upper bound of a percent estimate.

If the margin of error is displayed as '*****' (five asterisks), the estimate has been controlled to be equal to a fixed value and so it has no sampling error. When using any of the formulas in the following section, use a standard error of zero for these controlled estimates.

- Limitations -The user should be careful when computing and interpreting confidence intervals.

- The estimated standard errors (and thus margins of error) included in these data products do not include portions of the variability due to nonsampling error that may be present in the data. In particular, the standard errors do not reflect the effect of correlated errors introduced by interviewers, coders, or other field or processing personnel. Nor do they reflect the error from imputed values due to missing responses. Thus, the standard errors calculated represent a lower bound of the total error. As a result, confidence intervals formed using these estimated standard errors may not meet the stated levels of confidence (i.e., 68, 90, or 95 percent). Thus, some care must be exercised in the interpretation of the data in this data product based on the estimated standard errors.

- Zero or small estimates; very large estimates - The value of almost all ACS characteristics is greater than or equal to zero by definition. For zero or small estimates, use of the method given previously for calculating confidence intervals relies on large sample theory, and may result in negative values which for most characteristics are not admissible. In this case the lower limit of the confidence interval is set to zero by default. A similar caution holds for estimates of totals close to a control total or estimated proportions near one, where the upper limit of the confidence interval is set to its largest admissible value. In these situations the level of confidence of the adjusted range of values is less than the prescribed confidence level.

Direct estimates of the standard errors were calculated for all estimates reported in this product. The standard errors, in most cases, are calculated using a replicate-based methodology that takes into account the sample design and estimation procedures. Exceptions include:

1. The estimate of the number or proportion of people, households, families, or housing units in a geographic area with a specific characteristic is zero. A special procedure is used to estimate the standard error.

2. There are either no sample observations available to compute an estimate or standard error of a median, an aggregate, a proportion, or some other ratio, or there are too few sample observations to compute a stable estimate of the standard error.. The estimate is represented in the tables by - and the margin of error by '**' (two asterisks).

3. The estimate of a median falls in the lower open-ended interval or upper open-ended interval of a distribution. If the median occurs in the lowest interval, then a "-" follows the estimate, and if the median occurs in the upper interval, then a "+" follows the estimate. In both cases the margin of error is represented in the tables by '***' (three asterisks).

Sums and Differences of Direct Standard Errors - The standard errors estimated from these tables are for individual estimates. Additional calculations are required to estimate the standard errors for sums of and differences between two sample estimates. The estimate of the standard error of a sum or difference is approximately the square root of the sum of the two individual standard errors squared; that is, for standard errors SE(Xˆ) and SE(Yˆ) of estimates Xˆ and Yˆ:

This method, however, will underestimate (overestimate) the standard error if the two items in a sum are highly positively (negatively) correlated or if the two items in a difference are highly negatively (positively) correlated.

Ratios - The statistic of interest may be the ratio of two estimates. First is the case where the numerator*is not *a subset of the denominator. The standard error of this ratio between two sample estimates is approximated as:

Proportions/percents - For a proportion (or percent), a ratio where the numerator*is *a subset of the denominator, a slightly different estimator is used. Note the difference between the formulas for the standard error for proportions (below) and ratios (above) - the plus sign in the previous formula has been replaced with a minus sign. If the value under the square root sign is negative, use the ratio standard error formula above, instead. If *P *ˆ = Xˆ/Yˆ, then

Products - For a product of two estimates - for example if you want to estimate a proportions numerator by multiplying the proportion by its denominator - the standard error can be approximated as

Significant differences - Users may conduct a statistical test to see if the difference between an ACS estimate and any other chosen estimates is statistically significant at a given confidence level. Statistically significant means that the difference is not likely due to random chance alone. With the two estimates (Est1 and Est2) and their respective standard errors (SE1 and SE2), calculate

If Z > 1.645 or Z < -1.645, then the difference can be said to be statistically significant at the 90 percent confidence level. [Note that we are now recommending that +/-1.645 be used to determine significance. Previous ACS Accuracy of the Data documents suggested using +/-1.65.] Any estimate can be compared to an ACS estimate using this method, including other ACS estimates from the current year, the ACS estimate for the same characteristic and geographic area but from a previous year, Census 2000 100 percent counts and long form estimates, estimates from other Census Bureau surveys, and estimates from other sources. Not all estimates have sampling error - Census 2000 100 percent counts do not, for example, although Census 2000 long form estimates do - but they should be used if they exist to give the most accurate result of the test.

Users are also cautioned to*not *rely on looking at whether confidence intervals for two estimates overlap to determine statistical significance, because there are circumstances where that method will not give the correct test result. The Z calculation above is recommended in all cases.

All statistical testing in ACS data products is based on the 90 percent confidence level. Users should understand that all testing was done using*unrounded *estimates and standard errors, and it may not be possible to replicate test results using the rounded estimates and margins of error as published.

1. The estimate of the number or proportion of people, households, families, or housing units in a geographic area with a specific characteristic is zero. A special procedure is used to estimate the standard error.

2. There are either no sample observations available to compute an estimate or standard error of a median, an aggregate, a proportion, or some other ratio, or there are too few sample observations to compute a stable estimate of the standard error.. The estimate is represented in the tables by - and the margin of error by '**' (two asterisks).

3. The estimate of a median falls in the lower open-ended interval or upper open-ended interval of a distribution. If the median occurs in the lowest interval, then a "-" follows the estimate, and if the median occurs in the upper interval, then a "+" follows the estimate. In both cases the margin of error is represented in the tables by '***' (three asterisks).

Sums and Differences of Direct Standard Errors - The standard errors estimated from these tables are for individual estimates. Additional calculations are required to estimate the standard errors for sums of and differences between two sample estimates. The estimate of the standard error of a sum or difference is approximately the square root of the sum of the two individual standard errors squared; that is, for standard errors SE(Xˆ) and SE(Yˆ) of estimates Xˆ and Yˆ:

This method, however, will underestimate (overestimate) the standard error if the two items in a sum are highly positively (negatively) correlated or if the two items in a difference are highly negatively (positively) correlated.

Ratios - The statistic of interest may be the ratio of two estimates. First is the case where the numerator

Proportions/percents - For a proportion (or percent), a ratio where the numerator

Products - For a product of two estimates - for example if you want to estimate a proportions numerator by multiplying the proportion by its denominator - the standard error can be approximated as

Significant differences - Users may conduct a statistical test to see if the difference between an ACS estimate and any other chosen estimates is statistically significant at a given confidence level. Statistically significant means that the difference is not likely due to random chance alone. With the two estimates (Est1 and Est2) and their respective standard errors (SE1 and SE2), calculate

If Z > 1.645 or Z < -1.645, then the difference can be said to be statistically significant at the 90 percent confidence level. [Note that we are now recommending that +/-1.645 be used to determine significance. Previous ACS Accuracy of the Data documents suggested using +/-1.65.] Any estimate can be compared to an ACS estimate using this method, including other ACS estimates from the current year, the ACS estimate for the same characteristic and geographic area but from a previous year, Census 2000 100 percent counts and long form estimates, estimates from other Census Bureau surveys, and estimates from other sources. Not all estimates have sampling error - Census 2000 100 percent counts do not, for example, although Census 2000 long form estimates do - but they should be used if they exist to give the most accurate result of the test.

Users are also cautioned to

All statistical testing in ACS data products is based on the 90 percent confidence level. Users should understand that all testing was done using

We will present some examples based on the real data to demonstrate the use of the formulas.

The estimated number of males, never married is 39,401,560 from summary table B12001 for the United States for 2006. The margin of error is 99,234.

Standard Error = Margin of Error / 1.645

Calculating the standard error using the margin of error, we have:

SE(39,401,560) = 99,234 / 1.645 = 60,325.

We are interested in the number of people who have never been married. From Example 1, we know the number of males, never married is 39,401,560. From summary table B12001 we have the number of females, never married is 33,385,649 with a margin of error of 77,920. So, the estimated number of people who have never been married is 39,401,560 + 33,385,649 = 72,787,209. To calculate the standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of males never married from example 1 as 60,325. The standard error for the number of females never married is calculated using the margin of error:

SE(33,385,649) = 77,920 / 1.645 = 47,368.

So using the formula for the standard error of a sum or difference we have:

Caution: This method, however, will underestimate (overestimate) the standard error if the two items in a sum are highly positively (negatively) correlated or if the two items in a difference are highly negatively (positively) correlated.

To calculate the lower and upper bounds of the 90 percent confidence interval around 72,787,209 using the standard error, simply multiply 76,699 by 1.645, then add and subtract the product from 72,787,209. Thus the 90 percent confidence interval for this estimate is [72,787,209 - 1.645(76,699)] to [72,787,209 + 1.645(76,699)] or 72,661,039 to 72,913,379.

We are interested in the percentage of females who have never been married to the number of people who have never been married. The number of females, never married is 33,385,649 and the number of people who have never been married is 72,787,209. To calculate the standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of females never married from example 2 as 47,368 and the standard error for the number of people never married calculated from example 2 as 76,699.

The estimate is (33,385,649 / 72,787,209) * 100% = 45.9%

So, using the formula for the standard error of a proportion or percent, we have:

To calculate the lower and upper bounds of the 90 percent confidence interval around 45.9 using the standard error, simply multiply 0.05 by 1.645, then add and subtract the product from 45.9. Thus the 90 percent confidence interval for this estimate is

[45.9 - 1.645(0.04)] to [45.9 + 1.645(0.04)], or 45.8% to 46.0%.

Now, let us calculate the estimate of the ratio of the number of unmarried males to the number of unmarried females and its standard error. From the above examples, the estimate for the number of unmarried men is 39,401,560 with a standard error of 60,325, and the estimates for the number of unmarried women is 33,385,649 with a standard error of 47,368.

The estimate of the ratio is 39,401,560 / 33,385,649 = 1.180.

The standard error of this ratio is

The 90 percent margin of error for this estimate would be 0.00246 multiplied by 1.645, or about 0.004. The 90 percent lower and upper 90 percent confidence bounds would then be [1.180 - 0.004] to [1.180 + 0.004], or 1.176 and 1.184.

We are interested in the number of 1-unit detached owner-occupied housing units. The number of owner-occupied housing units is 75,086,485 with a margin of error of 218,471 from subject table S2504 for 2006, and the percent of 1-unit detached owner-occupied housing units is 81.4% (0.814) with a margin of error of 0.1 (0.001). So the number of 1-unit detached owner-occupied housing units is 75,086,485 * 0.814 = 61,120,399. Calculating the standard error for the estimates using the margin of error we have:

SE(75,086,485) = 218,471 / 1.645 = 132,809 and SE(0.814) = 0.001 / 1.645 = 0.0006079

The standard error for number of 1-unit detached owner-occupied housing units is calculated using the formula for products as:

To calculate the lower and upper bounds of the 90 percent confidence interval around 61,118,243 using the standard error, simply multiply 117,348 by 1.645, then add and subtract the product from 61,120,399. Thus the 90 percent confidence interval for this estimate is [61,120,399 - 1.645(117,348)] to [61,120,399 + 1.645(117,348)] or 60,927,362 to 61,313,436.

- Example 1.

The estimated number of males, never married is 39,401,560 from summary table B12001 for the United States for 2006. The margin of error is 99,234.

Standard Error = Margin of Error / 1.645

Calculating the standard error using the margin of error, we have:

SE(39,401,560) = 99,234 / 1.645 = 60,325.

- Example 2.

We are interested in the number of people who have never been married. From Example 1, we know the number of males, never married is 39,401,560. From summary table B12001 we have the number of females, never married is 33,385,649 with a margin of error of 77,920. So, the estimated number of people who have never been married is 39,401,560 + 33,385,649 = 72,787,209. To calculate the standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of males never married from example 1 as 60,325. The standard error for the number of females never married is calculated using the margin of error:

SE(33,385,649) = 77,920 / 1.645 = 47,368.

So using the formula for the standard error of a sum or difference we have:

Caution: This method, however, will underestimate (overestimate) the standard error if the two items in a sum are highly positively (negatively) correlated or if the two items in a difference are highly negatively (positively) correlated.

To calculate the lower and upper bounds of the 90 percent confidence interval around 72,787,209 using the standard error, simply multiply 76,699 by 1.645, then add and subtract the product from 72,787,209. Thus the 90 percent confidence interval for this estimate is [72,787,209 - 1.645(76,699)] to [72,787,209 + 1.645(76,699)] or 72,661,039 to 72,913,379.

- Example 3.

We are interested in the percentage of females who have never been married to the number of people who have never been married. The number of females, never married is 33,385,649 and the number of people who have never been married is 72,787,209. To calculate the standard error of this sum, we need the standard errors of the two estimates in the sum. We have the standard error for the number of females never married from example 2 as 47,368 and the standard error for the number of people never married calculated from example 2 as 76,699.

The estimate is (33,385,649 / 72,787,209) * 100% = 45.9%

So, using the formula for the standard error of a proportion or percent, we have:

To calculate the lower and upper bounds of the 90 percent confidence interval around 45.9 using the standard error, simply multiply 0.05 by 1.645, then add and subtract the product from 45.9. Thus the 90 percent confidence interval for this estimate is

[45.9 - 1.645(0.04)] to [45.9 + 1.645(0.04)], or 45.8% to 46.0%.

- Example 4.

Now, let us calculate the estimate of the ratio of the number of unmarried males to the number of unmarried females and its standard error. From the above examples, the estimate for the number of unmarried men is 39,401,560 with a standard error of 60,325, and the estimates for the number of unmarried women is 33,385,649 with a standard error of 47,368.

The estimate of the ratio is 39,401,560 / 33,385,649 = 1.180.

The standard error of this ratio is

The 90 percent margin of error for this estimate would be 0.00246 multiplied by 1.645, or about 0.004. The 90 percent lower and upper 90 percent confidence bounds would then be [1.180 - 0.004] to [1.180 + 0.004], or 1.176 and 1.184.

- Example 5.

We are interested in the number of 1-unit detached owner-occupied housing units. The number of owner-occupied housing units is 75,086,485 with a margin of error of 218,471 from subject table S2504 for 2006, and the percent of 1-unit detached owner-occupied housing units is 81.4% (0.814) with a margin of error of 0.1 (0.001). So the number of 1-unit detached owner-occupied housing units is 75,086,485 * 0.814 = 61,120,399. Calculating the standard error for the estimates using the margin of error we have:

SE(75,086,485) = 218,471 / 1.645 = 132,809 and SE(0.814) = 0.001 / 1.645 = 0.0006079

The standard error for number of 1-unit detached owner-occupied housing units is calculated using the formula for products as:

To calculate the lower and upper bounds of the 90 percent confidence interval around 61,118,243 using the standard error, simply multiply 117,348 by 1.645, then add and subtract the product from 61,120,399. Thus the 90 percent confidence interval for this estimate is [61,120,399 - 1.645(117,348)] to [61,120,399 + 1.645(117,348)] or 60,927,362 to 61,313,436.

As mentioned earlier, sample data are subject to nonsampling error. This component of error could introduce serious bias into the data, and the total error could increase dramatically over that which would result purely from sampling. While it is impossible to completely eliminate nonsampling error from a survey operation, the Census Bureau attempts to control the sources of such error during the collection and processing operations. Described below are the primary sources of nonsampling error and the programs instituted for control of this error. The success of these programs, however, is contingent upon how well the instructions were carried out during the survey.

It is possible for some sample housing units or persons to be missed entirely by the survey. The undercoverage of persons and housing units can introduce biases into the data. A major way to avoid undercoverage in a survey is to ensure that its sampling frame, for ACS an address list in each state, is as complete and accurate as possible.

The source of addresses was the MAF. The MAF is created by combining the Delivery Sequence File of the United States Postal Service and the address list for Census 2000. An attempt is made to assign all appropriate geographic codes to each MAF address via an automated procedure using the Census Bureau TIGER (Topologically Integrated Geographic Encoding and Referencing) files. A manual coding operation based in the appropriate regional offices is attempted for addresses which could not be automatically coded. The MAF was used as the source of addresses for selecting sample housing units and mailing questionnaires. TIGER produced the location maps for CAPI assignments.

In the CATI and CAPI nonresponse follow-up phases, efforts were made to minimize the chances that housing units that were not part of the sample were interviewed in place of units in sample by mistake. If a CATI interviewer called a mail nonresponse case and was not able to reach the exact address, no interview was conducted and the case was eligible for CAPI. During CAPI follow-up, the interviewer had to locate the exact address for each sample housing unit. If the interviewer could not locate the exact sample unit in a multi-unit structure, or found a different number of units than expected, the interviewers were instructed to list the units in the building and follow a specific procedure to select a replacement sample unit.

The source of addresses was the MAF. The MAF is created by combining the Delivery Sequence File of the United States Postal Service and the address list for Census 2000. An attempt is made to assign all appropriate geographic codes to each MAF address via an automated procedure using the Census Bureau TIGER (Topologically Integrated Geographic Encoding and Referencing) files. A manual coding operation based in the appropriate regional offices is attempted for addresses which could not be automatically coded. The MAF was used as the source of addresses for selecting sample housing units and mailing questionnaires. TIGER produced the location maps for CAPI assignments.

In the CATI and CAPI nonresponse follow-up phases, efforts were made to minimize the chances that housing units that were not part of the sample were interviewed in place of units in sample by mistake. If a CATI interviewer called a mail nonresponse case and was not able to reach the exact address, no interview was conducted and the case was eligible for CAPI. During CAPI follow-up, the interviewer had to locate the exact address for each sample housing unit. If the interviewer could not locate the exact sample unit in a multi-unit structure, or found a different number of units than expected, the interviewers were instructed to list the units in the building and follow a specific procedure to select a replacement sample unit.

It is possible for some sample housing units or persons to be sampled multiple times in the survey. The overcoverage of persons and housing units can introduce biases into the data, and increase respondent burden and survey costs. As with undercoverage, a major way to avoid housing unit overcoverage in a survey is to ensure that its sampling frame, for ACS an address list in each state, is as complete and accurate as possible.

The source of addresses was the MAF. The MAF is created by combining the Delivery Sequence File of the United States Postal Service and the address list for Census 2000. Sometimes the MAF has an address that is the duplicate of another address already on the MAF. This could occur when there is a slight difference in the address such as 123 Main Street versus 123 Maine Street.

Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet ACS residency rules.

The source of addresses was the MAF. The MAF is created by combining the Delivery Sequence File of the United States Postal Service and the address list for Census 2000. Sometimes the MAF has an address that is the duplicate of another address already on the MAF. This could occur when there is a slight difference in the address such as 123 Main Street versus 123 Maine Street.

Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet ACS residency rules.

Survey nonresponse is a well-known source of nonsampling error. There are two types of nonresponse error - unit nonresponse and item nonresponse. Nonresponse errors affect survey estimates to varying levels depending on amount of nonresponse and the extent to which nonrespondents differ from respondents on the characteristics measured by the survey. The exact amount of nonresponse error or bias on an estimate is almost never known. Therefore, survey researchers generally rely on proxy measures, such as the nonresponse rate, to indicate the potential for nonresponse error.

Unit nonresponse is the failure to obtain data from housing units in the sample. Unit nonresponse may occur because households are unwilling or unable to participate, or because an interviewer is unable to make contact with a housing unit. Unit nonresponse is problematic when there are systematic or variable differences between interviewed and noninterviewed housing units on the characteristics measured by the survey. Nonresponse bias is introduced into an estimate when differences are systematic, while nonresponse error for an estimate evolves from variable differences between interviewed and noninterviewed households. The ACS made every effort to minimize unit nonresponse, and thus, the potential for nonresponse error. First, the ACS used a combination of mail, CATI, and CAPI data collection modes to maximize response. The mail phase included a series of three to four mailings to encourage housing units to return the questionnaire. Subsequently, mail nonrespondents (for which phone numbers are available) were contacted by CATI for an interview. Finally, a subsample of the mail and telephone nonrespondents was contacted for by personal visit to attempt an interview. Combined, these three efforts resulted in a very high overall response rate for the ACS.

Nonresponse to particular questions on the survey questionnaire and instrument allows for the introduction of error or bias into the data, since the characteristics of the nonrespondents have not been observed and may differ from those reported by respondents. As a result, any imputation procedure using respondent data may not completely reflect this difference either at the elemental level (individual person or housing unit) or on average.

Some protection against the introduction of large errors or biases is afforded by minimizing nonresponse. In the ACS, item nonresponse for the CATI and CAPI operations was minimized by the requirement that the automated instrument receive a response to each question before the next one could be asked. Questionnaires returned by mail were edited for completeness and acceptability. They were reviewed by computer for content omissions and population coverage. If necessary, a telephone follow-up was made to obtain missing information. Potential coverage errors were included in this follow-up.

- Measurement and Processing Error - The person completing the questionnaire or responding to the questions posed by an interviewer could serve as a source of error, although the questions were cognitively tested for phrasing, and detailed instructions for completing the questionnaire were provided to each household.

Some protection against the introduction of large errors or biases is afforded by minimizing nonresponse. In the ACS, item nonresponse for the CATI and CAPI operations was minimized by the requirement that the automated instrument receive a response to each question before the next one could be asked. Questionnaires returned by mail were edited for completeness and acceptability. They were reviewed by computer for content omissions and population coverage. If necessary, a telephone follow-up was made to obtain missing information. Potential coverage errors were included in this follow-up.

- Measurement and Processing Error - The person completing the questionnaire or responding to the questions posed by an interviewer could serve as a source of error, although the questions were cognitively tested for phrasing, and detailed instructions for completing the questionnaire were provided to each household.

The interviewer may misinterpret or otherwise incorrectly enter information given by a respondent; may fail to collect some of the information for a person or household; or may collect data for households that were not designated as part of the sample. To control these problems, the work of interviewers was monitored carefully. Field staff were prepared for their tasks by using specially developed training packages that included hands-on experience in using survey materials. A sample of the households interviewed by CAPI interviewers was reinterviewed to control for the possibility that interviewers may have fabricated data.

The many phases involved in processing the survey data represent potential sources for the introduction of nonsampling error. The processing of the survey questionnaires includes the keying of data from completed questionnaires, automated clerical review, follow-up by telephone, manual coding of write-in responses, and automated data processing. The various field, coding and computer operations undergo a number of quality control checks to insure their accurate application.

After data collection was completed, any remaining incomplete or inconsistent information was imputed during the final content edit of the collected data. Imputations, or computer assignments of acceptable codes in place of unacceptable entries or blanks, were needed most often when an entry for a given item was missing or when the information reported for a person or housing unit on that item was inconsistent with other information for that same person or housing unit. As in other surveys and previous censuses, the general procedure for changing unacceptable entries was to allocate an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. Imputing acceptable values in place of blanks or unacceptable entries enhances the usefulness of the data.