The ACS employs three modes of data collection:

• Internet
• Mailout/Mailback
• Computer Assisted Telephone Interview (CATI)²
• 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 an initial mailing package - this package contains information for completing the ACS questionnaire on the internet (on-line). If a sample address has not responded online within approximately two weeks of the initial mailing, then a second mailing package with a paper questionnaire is sent. Sampled addresses then have the option of which mode to use to complete the interview.

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 in sample are sent to CAPI and assigned to one of two data collection periods: January-April or September-December.³ Up to four months is allowed to complete the assigned interviews. As we do not mail to any remote Alaska addresses, this is the only data collection mode available.


Footnotes:

² Note that all CATI operations ended at the end of September, 2017
³ Prior to the 2011 sample year, all remote Alaska sample cases were subsampled for CAPI at a rate of 2-in-3.

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 housing unit (HU) addresses, 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.

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 a personal interview with a 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.

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 that are eligible for data collection. Beginning with the 2019 sample, we restricted the universe of eligible addresses further to exclude a small proportion of addresses that do not meet a set of minimum address criteria. The Master Address File (MAF) is a database maintained by the Census Bureau containing a listing of residential, group quarters, and commercial addresses in the U.S. and Puerto Rico. The MAF is normally updated twice each year with the Delivery Sequence Files (DSF) provided by the U.S. Postal Service. The DSF covers 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.

Due to operational difficulties associated with data collection, the ACS excludes certain types of GQs from the sampling universe and data collection operations. The weighting and estimation accounts for this segment of the population as they are included in the population controls. The following GQ types are those that are removed from the GQ universe:

1. Soup kitchens
2. Domestic violence shelters
3. Regularly scheduled mobile food vans
4. Targeted non-sheltered outdoor locations
5. Maritime/merchant vessels
6. Living quarters for victims of natural disasters
7. Dangerous encampments

The ACS GQ universe file contains both valid and invalid GQs, but only valid GQs are eligible for sampling. We maintain an inventory of all GQ records regardless of their valid status. In this way, any updates to the GQ universe can be applied to the combined valid and invalid file.

The ACS employs a two-phase, two-stage sample design. The first-phase sample consists of two separate address samples: Period 1 and Period 2. These samples are chosen at different points in time. Both samples are selected in two stages of sampling, a first-stage and a secondstage. Subsequent to second-stage sampling, the majority of sample addresses are randomly assigned to one of the twelve months of the sample year (the exception is for addresses in remote Alaska, which are assigned to either January or September). The second-phase of sampling occurs when the CAPI sample is selected.

The Period 1 sample is selected during September and October of the year prior to the sample year (the 2019 Period 1 sample was selected in September and October of 2017). Approximately half of a year's sample is selected at this time. Sample addresses that are not in remote Alaska are randomly assigned to one of the first six months of the sample year; sample addresses in remote Alaska are assigned to January.

Period 2 sampling occurs in January and February of the sample year (the 2019 Period 2 sample was selected during January and February of 2019). This sample accounts for the remaining half of the overall first-phase sample. Period 2 sample addresses that are not in remote Alaska are randomly assigned to one of the last six months of the sample year; Period 2 sample addresses in remote Alaska are assigned to September.⁴

A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.⁵

The following steps are used to select the first-phase and second-phase samples in both periods.



Footnotes:

⁴ Remote Alaska assignments are made so that the sample addresses are approximately evenly distributed between the two data collection periods.
⁵ Beginning with the August, 2011 CAPI sample all non-mailable and non-responding addresses in the following areas are now sent to CAPI: all Hawaiian Homelands, all Alaska Native Village Statistical areas, American Indian areas with an estimated proportion of American Indian population ≥ 10%

The 2019 group quarters (GQ) sampling frame was divided into two strata: a small GQ stratum and a large GQ stratum. Small GQs are defined to have expected populations of fifteen or fewer residents, while large GQs have expected populations of more than fifteen residents.

Samples were selected in two phases within each stratum. In general, GQs were selected in the first phase and then persons/residents were selected in the second phase. Both phases differ between the two strata. Each sampled GQ was randomly assigned to one or more months in 2019 - it was in these months that their person samples were selected.

Counts of sample addresses and GQ persons can be found in two locations on the US Census Bureau website. On data.census.gov, base tables B98001 and B98002 provide sample size counts for the nation, states, and counties. Sample size counts for the nation and states are also available in the Sample Size and Data Quality Section of the ACS website, at https://www.census.gov/acs/www/methodology/sample-size-and-data-quality

Starting with the weighting for the 2011 1-year ACS estimates, the group quarters (GQ) person weighting changed in important ways from previous years' weighting. The GQ population sample was supplemented by a large-scale whole person imputation into not-in-sample GQ facilities. For the 2019 ACS GQ data, roughly as many GQ persons were imputed as interviewed. The goal of the imputation methodology was two-fold.

1. The primary objective was to establish representation of county by major GQ type group in the tabulations for each combination that exists on the ACS GQ sample frame. The seven major GQ type groups are defined by the Population Estimates Program and are given in Table 4.
   
2. A secondary objective was to establish representation of tract by major GQ type group for each combination that exists on the ACS GQ sample frame.

The GQ sampling frame was modified to create an imputation frame from which all imputed GQs were selected from. The frame was updated with the actual populations and GQ type changes from ACS interviews, as well as any subsequent information gathered in other processes since the sampling frame was initially created. The change in populations for ACS GQ interviews was used to calculate a not-in-sample adjustment factor that was used to update the population for all GQs on the frame not selected for sample. This adjustment factor was calculated at the following level:

GQ Major Type x GQ Size Stratum

There were three size strata used for this process: GQs in sample with certainty, GQs with 16 or more persons, and GQs with less than 16 persons.

For all not-in-sample GQ facilities with an expected population of 16 or more persons (large facilities), we imputed a number of GQ persons equal to 2.5% of the expected population. For those GQ facilities with an expected population of fewer than 16 persons (small facilities), we selected a random sample of GQ facilities as needed to accomplish the two objectives given above. For those selected small GQ facilities, we imputed a number of GQ persons equal to 20% of the facility's expected population.

Interviewed GQ person records were then sampled at random to be donors for the imputed persons of the selected not-in-sample GQ facilities. An expanding search algorithm searched for donors within the same specific type of GQ facility and the same county. If that failed, the search included all GQ facilities of the same major GQ type group. If that still failed, the search expanded to a specific type within state, then a major GQ type group within state. This expanding search continued through division, region, and the entire nation until suitable donors were found.

The weighting procedure made no distinction between sampled and imputed GQ person records. The initial weights of person records in the large GQ facilities equaled the observed or expected population of the GQ facility divided by the number of person records. The initial weights of person records in small GQ facilities equaled the observed or expected population of the GQ facility divided by the number of records, multiplied by the inverse of the fraction of small GQ facilities represented in the weighting to the number on the frame of that tract by major GQ type group combination.

The population totals on the imputation frame are used to ensure that the sub-state distribution of GQ weighting preserves the distribution from the frame. This accomplished through a series of three constraints:

1. Tract Constraint (TRCON) - This factor makes the total weight within each tract by major type group equal the total population from the imputation frame.
   
2. County Constraint (CYCON) - This factor makes the total weight within each county by major type group equal the total population from the imputation frame.
   
3. State Constraint (STCON) - This factor makes the total weight within each state by major type group equal the total population from the imputation frame.

As was done in previous years' weighting, we controlled the final weights to an independent set of GQ population estimates produced by the Population Estimates Program for each state by each of the seven major GQ type groups.

Lastly, the final GQ person weight was rounded to an integer. Rounding was performed so that the sum of the rounded weights were within one person of the sum of the unrounded weights for any of the groups listed below:

Major GQ Type Group

Major GQ Type Group x County

The housing unit and household person weighting uses two types of geographic areas for adjustments: weighting areas and subcounty areas. Weighting areas are county-based and have been used since the first year of the ACS. Subcounty areas are based on incorporated place and minor civil divisions (MCD). Their use was introduced into the ACS in 2010.

Weighting areas were built from collections of whole counties. 2010 Census data and 2007-2011 ACS 5-year data were used to group counties of similar demographic and social characteristics. The characteristics considered in the formation included:

1. Percent in poverty (the only characteristic using ACS 5-year data)
2. Percent renting
3. Density of housing units (a proxy for rural areas)
4. Race, ethnicity, age, and sex distribution
5. Distance between the centroids of the counties
6. Core-based Statistical Area status

Each weighting area was also required to meet a threshold of 400 expected person interviews in the 2011 ACS. The process also tried to preserve as many counties that meet the threshold to form their own weighting areas. In total, there are 2,130 weighting areas formed from the 3,143 counties and county equivalents.

Subcounty areas are built from incorporated places and MCDs, with MCDs only being used in the 20 states where MCDs serve as functioning governmental units. Each subcounty area formed has a total population of at least 24,000, as determined by the July 1, 2019 Population Estimates data, which are based on the 2010 Census estimates of the population on April 1, 2010, updated using births, deaths, and migration. The subcounty areas can be incorporated places, MCDs, place/MCD intersections (in counties where places and MCDs are not coexistent), 'balance of MCD,' and 'balance of county.' The latter two types group together unincorporated areas and places/MCDs that do not meet the population threshold. If two or more subcounty areas cannot be formed within a county, then the entire county is treated as a single area. Thus, all counties whose total population is less than 48,000 will be treated as a single area since it is not possible to form two areas that satisfy the minimum size threshold.

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

This process produced the following factors:

Base Weight (BW)

This initial weight is assigned to every housing unit as the inverse of its block’s sampling rate.

CAPI Subsampling Factor (SSF)

The weights of the CAPI cases are adjusted to reflect the results of CAPI subsampling.

This factor is assigned to each record as follows:

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 are unmailable. A two-thirds sample of these is sent directly to CAPI and for these cases SSF = 1.5.

Sample addresses in Remote Alaska, Hawaiian Homelands, Alaska Native Village Statistical areas and a subset of American Indian areas are selected for CAPI at 100% sampling rate and for these cases SSF = 1.0.

Variation in Monthly Response by Mode (VMS)

This factor makes the total weight of the Mail, CATI, and CAPI records to be tabulated in a month equal to the total base weight of all cases originally mailed for that month. For all cases, VMS is computed and assigned based on the following groups:

Weighting Area x Month

Noninterview Factor (NIF)

This factor adjusts the weight of all responding occupied housing units to account for nonresponding housing units. The factor is a ratio adjustment that is computed and assigned to occupied housings units based on the following groups:

Weighting Area x Building Type (single or multi unit) x Tract

Vacant housing units are assigned a value of NIF = 1.0. Nonresponding housing units are assigned a weight of 0.0.

Housing Unit Post-Stratification Factor (HPF)

This factor makes the total weight of all housing units agree with the 2019 independent housing unit estimates at the subcounty level.

Initially the person weight of each person in an occupied housing unit is 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. The person weighting is done in a series of three steps, which are repeated until a stopping criterion is met. These three steps form a raking ratio or raking process. These person weights are individually adjusted for each person as described below.

The three steps are as follows:

Subcounty Area Controls Raking Factor (SUBEQRF)

This factor is applied to individuals based on their geography. It adjusts the person weights so that the weighted sample counts equal independent population estimates of total population for the subcounty area. Because of later adjustment to the person weights, total population is not assured of agreeing exactly with the official 2019 population estimates at the subcounty level.

Spouse Equalization/Householder Equalization Raking Factor (SPHHEQRF)

This factor is applied to individuals based on the combination of their status of being in a married-couple or unmarried-partner household and whether they are the householder.

All persons are assigned to one of four groups:

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

2. Spouse or unmarried partner in a married-couple or unmarried-partner household (non-householder)

3. Other householder

4. Other non-householder

The weights of persons in the first two groups are adjusted so that their sums are each equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). At the same time, the weights of persons in the first and third groups are adjusted so that their sum is equal to the total estimate of occupied housing units 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 while simultaneously producing more consistent estimates of householders, occupied housing units, and households.

Demographic Raking Factor (DEMORF)

This factor is applied to individuals based on their age, race, sex and Hispanic origin. It adjusts the person weights so that the weighted sample counts equal independent population estimates by age, race, sex, and Hispanic origin at the weighting area. Because of collapsing of groups in applying this factor, only total population is assured of agreeing with the official 2019 population estimates at the weighting area level.

This uses the following groups (note that there are 13 Age groupings):

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.

These three steps are repeated several times until the estimates at the national level achieve their optimal consistency with regard to the spouse and householder equalization.

The effect Person Post-Stratification Factor (PPSF) is then equal to the product (SUBEQRF x SPHHEQRF 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).

The final product of all person weights (BW x ... x HPF x PPSF) is rounded to an integer.

Rounding is performed so that the sum of the rounded weights is within one person of the sum of the unrounded weights for any of the groups listed below:

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 is within one of the number produced using the unrounded weights.

This process produces the following factors:

Householder Factor (HHF)

This factor adjusts for differential response depending on the race, Hispanic origin, sex, and age of the householder. The value of HHF for an occupied housing unit is the PPSF of the householder. Since there is no householder for vacant units, the value of HHF = 1.0 for all vacant units.

Rounding

The final product of all housing unit weights (BW x ... x HHF) is rounded to an integer. For occupied units, the rounded housing unit weight is the same as the rounded person weight of the householder. This ensures that both the rounded and unrounded householder weights are equal to the occupied housing unit weight. The rounding for vacant housing units is then performed so that total rounded weight is within one housing unit of the total unrounded weight for any of the groups listed below:

County

County x Tract

County x Tract x Block

Title 13 of the United States Code authorizes the Census Bureau to conduct censuses and surveys. Section 9 of the same Title requires that any information collected from the public under the authority of Title 13 be maintained as confidential. Section 214 of Title 13 and Sections 3559 and 3571 of Title 18 of the United States Code provide for the imposition of penalties of up to five years in prison and up to $250,000 in fines for wrongful disclosure of confidential census information.

Disclosure avoidance is the process for protecting the confidentiality of data. A disclosure of data occurs when someone can use published statistical information to identify an individual who has provided information under a pledge of confidentiality. For data tabulations, the Census Bureau uses disclosure avoidance procedures to modify or remove the characteristics that put confidential information at risk for disclosure. Although it may appear that a table shows information about a specific individual, the Census Bureau has taken steps to disguise or suppress the original data while making sure the results are still useful. The techniques used by the Census Bureau to protect confidentiality in tabulations vary, depending on the type of data. All disclosure avoidance procedures are done prior to the whole person imputation into not-insample GQ facilities.

Data swapping is a method of disclosure avoidance designed to protect confidentiality in tables of frequency data (the number or percent of the population with certain characteristics). Data swapping is done by editing the source data or exchanging records for a sample of cases when creating a table. A sample of households is selected and matched on a set of selected key variables with households in neighboring geographic areas that have similar characteristics (such as the same number of adults and same number of children). Because the swap often occurs within a neighboring area, there is no effect on the marginal totals for the area or for totals that include data from multiple areas. Because of data swapping, users should not assume that tables with cells having a value of one or two reveal information about specific individuals. Data swapping procedures were first used in the 1990 Census, and were used again in Census 2000 and the 2010 Census.

The goals of using synthetic data are the same as the goals of data swapping, namely to protect the confidentiality in tables of frequency data. Persons are identified as being at risk for disclosure based on certain characteristics. The synthetic data technique then models the values for another collection of characteristics to protect the confidentiality of that individual.

The data in ACS products are estimates of the actual figures that would be obtained by interviewing the entire population. The estimates are a result of the chosen sample, and are subject to sample-to-sample variation. Sampling error in data arises due to the use of probability sampling, which is necessary to ensure the integrity and representativeness of sample survey results. The implementation of statistical sampling procedures provides the basis for the statistical analysis of sample data. Measures used to estimate the sampling error are provided in the next section.

Other types of errors might be introduced during any of the various complex operations used to collect and process survey data. For example, data entry from questionnaires and editing may introduce error into the estimates. Another potential source of error is the use of controls in the weighting. These controls are based on Population Estimates and are designed to reduce variance and mitigate the effects of systematic undercoverage of groups who are difficult to enumerate. However, if the extrapolation methods used in generating the Population Estimates do not properly reflect the population, error can be introduced into the data. This potential risk is offset by the many benefits the controls provide to the ACS estimates, which include the reduction of issues with survey coverage and the reduction of standard errors of ACS estimates. These and other sources of error contribute to the nonsampling error component of the total error of survey estimates.

Nonsampling errors may affect the data in two ways. Errors that are introduced randomly increase the variability of the data. Systematic errors, or errors that consistently skew the data in one direction, introduce bias into the results of a sample survey. The Census Bureau protects against the effect of systematic errors on survey estimates by conducting extensive research and evaluation programs on sampling techniques, questionnaire design, and data collection and processing procedures.

An important goal of the ACS is to minimize the amount of nonsampling error introduced through nonresponse for sample housing units. One way of accomplishing this is by following up on mail nonrespondents during the CATI and CAPI phases. For more information, please see the section entitled "Control of Nonsampling Error".

Users should be careful when computing and interpreting confidence intervals.

Previously, this document included formulas for approximating the standard error (SE) for various types of estimates. For example, summing estimates or calculating a ratio of two or more estimates. These formulas are also found in the Instruction for Statistical Testing documents, which is located at https://www.census.gov/programs-surveys/acs/technicaldocumentation/code-lists.html. In addition, the worked examples have also been placed in the same location in the document called "Worked Examples for Approximating Margins of Error".

It is possible for some sample housing units or persons to be missed entirely by the survey (undercoverage). It is also possible for some sample housing units and persons to be counted more than once (overcoverage). Both undercoverage and overcoverage of persons and housing units can introduce bias into the data. Coverage error can also increase both respondent burden and survey costs.

To avoid coverage error in a survey, the frame must be as complete and accurate as possible. For the ACS, the frame is an address list in each state, the source of which is the Master Address File (MAF). An attempt is made to assign each MAF address to the appropriate geographic codes 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 that could not be automatically coded.

In 2019, 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. Sometimes the MAF contains duplicates of addresses. This could occur when there is a slight difference in the address such as 123 Main Street versus 123 Maine Street, and can introduce overcoverage.

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 mistakenly interviewed instead of units in sample. 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 became eligible for CAPI. During the 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. Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet ACS residency rules.

Coverage rates give a measure of undercoverage or overcoverage of persons or housing units in a given geographic area. Rates below 100 percent indicate undercoverage, while rates above 100 percent indicate overcoverage. Coverage rates are released concurrent with the release of estimates on data.census.gov¹¹ in the B98 series of detailed tables (Table IDs B98011, B98012, B98013, and B980014). Coverage rate definitions and coverage rates for total population for nation and states are also available in the Sample Size and Data Quality Section of the ACS website, at https://www.census.gov/acs/www/methodology/sample-size-and-data-quality/.



Footnotes:

¹¹Beginning in 2019, data.census.gov replaces American Factfinder as the dissemination tool for publishing ACS tables and other data products.

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 the characteristics of nonrespondents differ from those of respondents. 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.

Measurement error can arise if the person completing the questionnaire or responding an interviewer's questions responds incorrectly. However, to mitigate this risk, the phrasing survey questions underwent cognitive testing and households were provided detailed instructions on how to complete the questionnaire.

Processing error can be introduced in numerous areas during data collection and capture, including during interviews, during data processing and during content editing

The PRCS employs three modes of data collection:

• Internet
• Mailout/Mailback
• Computer Assisted Telephone Interview (CATI)²
• 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 an initial mailing package - this package contains information for completing the ACS questionnaire on the internet (on-line). If a sample address has not responded online within approximately two weeks of the initial mailing, then a second mailing package with a paper questionnaire is sent. Sampled addresses then have the option of which mode to use to complete the interview.

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.



Footnotes:

² Note that all CATI operations ended at the end of September, 2017

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 housing unit (HU) addresses, 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.

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 a personal interview with a 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.

The universe for the PRCS consists of all valid, residential housing unit addresses in all municipios in Puerto Rico that are eligible for data collection. Beginning with the 2019 sample, we restricted the universe of eligible addresses further to exclude a small proportion of addresses that do not meet a set of minimum address criteria. The Master Address File (MAF) is a database maintained by the Census Bureau containing a listing of residential, group quarters, and commercial addresses in the U.S. and Puerto Rico. The MAF is normally updated twice each year with the Delivery Sequence Files provided by the U.S. Postal Service (in 2014, however, it was updated only once), however this update covers only the U.S. The DSF does not provide changes and updates to the MAF for Puerto Rico. The MAF is also updated with the results from various Census Bureau field operations, including the PRCS.

Due to operational difficulties associated with data collection, the PRCS excludes certain types of GQs from the sampling universe and data collection operations. The weighting and estimation accounts for this segment of the population as they are included in the population controls. The following GQ types are those that are removed from the GQ universe:

1. Soup kitchens
2. Domestic violence shelters
3. Regularly scheduled mobile food vans
4. Targeted non-sheltered outdoor locations
5. Maritime/merchant vessels
6. Living quarters for victims of natural disasters
7. Dangerous encampments

The PRCS GQ universe file contains both valid and invalid GQs, but only valid GQs are eligible for sampling. We maintain an inventory of all GQ records regardless of their valid status. In this way, any updates to the GQ universe can be applied to the combined valid and invalid file.

The PRCS employs a two-phase, two-stage sample design. The first-phase sample consists of two separate address samples: Period 1 and Period 2. These samples are chosen at different points in time. Both samples are selected in two stages of sampling, a first-stage and a secondstage. Subsequent to second-stage sampling, sample addresses are randomly assigned to one of the twelve months of the sample year. The second-phase of sampling occurs when the CAPI sample is selected.

The Period 1 sample is selected during September and October of the year prior to the sample year (the 2019 Period 1 sample was selected in September and October of 2017). Approximately half of a year's sample is selected at this time. Sample addresses that are not in remote Alaska are randomly assigned to one of the first six months of the sample year; sample addresses in remote Alaska are assigned to January.

Period 2 sampling occurs in January and February of the sample year (the 2019 Period 2 sample was selected during January and February of 2019). This sample accounts for the remaining half of the overall first-phase sample. Period 2 sample addresses that are not in remote Alaska are randomly assigned to one of the last six months of the sample year; Period 2 sample addresses in remote Alaska are assigned to September.

A sub-sample of non-responding addresses and of any addresses deemed unmailable is selected for the CAPI data collection mode.

The following steps are used to select the first-phase and second-phase samples in both periods.

The 2019 group quarters (GQ) sampling frame was divided into two strata: a small GQ stratum and a large GQ stratum. Small GQs are defined to have expected populations of fifteen or fewer residents, while large GQs have expected populations of more than fifteen residents.

Samples were selected in two phases within each stratum. In general, GQs were selected in the first phase and then persons/residents were selected in the second phase. Both phases differ between the two strata. Each sampled GQ was randomly assigned to one or more months in 2019 - it was in these months that their person samples were selected.

Counts of sample addresses and GQ persons can be found in two locations on the US Census Bureau website. On data.census.gov, base tables B98001 and B98002 provide sample size counts for the Puerto Rico and municipios. Sample size definitions and sample size counts for Puerto Rico are also available in the Sample Size and Data Quality Section of the ACS website, at https://www.census.gov/acs/www/methodology/sample-size-and-data-quality

Starting with the weighting for the 2011 1-year ACS estimates, the group quarters (GQ) person weighting changed in important ways from previous years' weighting. The GQ population sample was supplemented by a large-scale whole person imputation into not-in-sample GQ facilities. For the 2019 ACS GQ data, roughly as many GQ persons were imputed as interviewed. The goal of the imputation methodology was two-fold.

1. The primary objective was to establish representation of municipio by major GQ type group in the tabulations for each combination that exists on the PRCS GQ sample frame. The seven major GQ type groups are defined by the Population Estimates Program and are given in Table 3.
   
2. A secondary objective was to establish representation of tract by major GQ type group for each combination that exists on the PRCS GQ sample frame.

The GQ sampling frame was modified to create an imputation frame from which all imputed GQs were selected from. The frame was updated with the actual populations and GQ type changes from ACS interviews, as well as any subsequent information gathered in other processes since the sampling frame was initially created. The change in populations for ACS GQ interviews was used to calculate a not-in-sample adjustment factor that was used to update the population for all GQs on the frame not selected for sample. This adjustment factor was calculated at the following level:

GQ Major Type x GQ Size Stratum

There were three size strata used for this process: GQs in sample with certainty, GQs with 16 or more persons, and GQs with less than 16 persons.

For all not-in-sample GQ facilities with an expected population of 16 or more persons (large facilities), we imputed a number of GQ persons equal to 2.5% of the expected population. For those GQ facilities with an expected population of fewer than 16 persons (small facilities), we selected a random sample of GQ facilities as needed to accomplish the two objectives given above. For those selected small GQ facilities, we imputed a number of GQ persons equal to 20% of the facility's expected population.

Interviewed GQ person records were then sampled at random to be donors for the imputed persons of the selected not-in-sample GQ facilities. An expanding search algorithm searched for donors within the same specific type of GQ facility and the same county. If that failed, the search included all GQ facilities of the same major GQ type group. If that still failed, the search expanded to a specific type within state, then a major GQ type group within state. This expanding search continued through division, region, and the entire nation until suitable donors were found.

The weighting procedure made no distinction between sampled and imputed GQ person records. The initial weights of person records in the large GQ facilities equaled the observed or expected population of the GQ facility divided by the number of person records. The initial weights of person records in small GQ facilities equaled the observed or expected population of the GQ facility divided by the number of records, multiplied by the inverse of the fraction of small GQ facilities represented in the weighting to the number on the frame of that tract by major GQ type group combination.

The population totals on the imputation frame are used to ensure that the sub-state distribution of GQ weighting preserves the distribution from the frame. This accomplished through a series of three constraints:

1. Tract Constraint (TRCON) - This factor makes the total weight within each tract by major type group equal the total population from the imputation frame.
   
2. County Constraint (CYCON) - This factor makes the total weight within each county by major type group equal the total population from the imputation frame.
   
3. State Constraint (STCON) - This factor makes the total weight within each state by major type group equal the total population from the imputation frame.

As was done in previous years' weighting, we controlled the final weights to an independent set of GQ population estimates produced by the Population Estimates Program for each state by each of the seven major GQ type groups.

Lastly, the final GQ person weight was rounded to an integer. Rounding was performed so that the sum of the rounded weights were within one person of the sum of the unrounded weights for any of the groups listed below:

Major GQ Type Group

Major GQ Type Group x Municipio

The housing unit and household person weighting uses two types of geographic areas for adjustments: weighting areas and subcounty areas. Weighting areas are county-based and have been used since the first year of the ACS. Subcounty areas are based on incorporated place and minor civil divisions (MCD). Their use was introduced into the ACS in 2010.

Weighting areas were built from collections of whole counties. The 2010 Census data and 2007-2011 ACS 5-year data were used to group counties of similar demographic and social characteristics. The characteristics considered in the formation included:

1. Percent in poverty (the only characteristic using ACS 5-year data)
2. Percent renting
3. Density of housing units (a proxy for rural areas)
4. Race, ethnicity, age, and sex distribution
5. Distance between the centroids of the counties
6. Core-based Statistical Area status

Each weighting area was also required to meet a threshold of 400 expected person interviews in the 2011 PRCS. The process also tried to preserve as many municipios that met the threshold to form their own weighting areas. In total, there are 57 weighting areas formed from the 78 municipios in Puerto Rico.

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

This process produced the following factors:

Base Weight (BW)

This initial weight is assigned to every housing unit as the inverse of its block’s sampling rate.

CAPI Subsampling Factor (SSF)

The weights of the CAPI cases are adjusted to reflect the results of CAPI subsampling.

This factor is assigned to each record as follows:

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 are unmailable. A two-thirds sample of these is sent directly to CAPI and for these cases SSF = 1.5.

Variation in Monthly Response by Mode (VMS)

This factor makes the total weight of the Mail, CATI, and CAPI records to be tabulated in a month equal to the total base weight of all cases originally mailed for that month. For all cases, VMS is computed and assigned based on the following groups:

Weighting Area x Month

Noninterview Factor (NIF)

This factor adjusts the weight of all responding occupied housing units to account for nonresponding housing units. The factor is a ratio adjustment that is computed and assigned to occupied housings units based on the following groups:

Weighting Area x Building Type (single or multi unit) x Tract

Vacant housing units are assigned a value of NIF = 1.0. Nonresponding housing units are assigned a weight of 0.0.

Initially the person weight of each person in an occupied housing unit is 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. The person weighting is done in a series of three steps, which are repeated until a stopping criterion is met. These three steps form a raking ratio or raking process. These person weights are individually adjusted for each person as described below.

The three steps are as follows:

Municipio Controls Raking Factor (SUBEQRF)

TThis factor is applied to individuals based on their geography. It adjusts the person weights so that the weighted sample counts equal independent population estimates of total population for the municipio. For those municipios that are their own weighting area, this adjustment factor will be 1.0. Because of later adjustments to the person weights, total population is not assured of agreeing exactly with the official 2019 population estimates for municipios which are not their own weighting area.

Spouse Equalization/Householder Equalization Raking Factor (SPHHEQRF)

This factor is applied to individuals based on the combination of their status of being in a married-couple or unmarried-partner household and whether they are the householder.

All persons are assigned to one of four groups:

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

2. Spouse or unmarried partner in a married-couple or unmarried-partner household (non-householder)

3. Other householder

4. Other non-householder

The weights of persons in the first two groups are adjusted so that their sums are each equal to the total estimate of married-couple or unmarried-partner households using the housing unit weight (BW x ... x HPF). At the same time, the weights of persons in the first and third groups are adjusted so that their sum is equal to the total estimate of occupied housing units 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 while simultaneously producing more consistent estimates of householders, occupied housing units, and households.

Demographic Raking Factor (DEMORF)

This factor is applied to individuals based on their age and sex in Puerto Rico (note that there are 13 Age groupings). It adjusts the person weights so that the weighted sample counts equal the independent population estimates by age and sex at the weighting area level. Because of collapsing of groups in applying this factor, only the total population is assured of agreeing with the official 2019 population estimates at the weighting area level.

These three steps are repeated several times until the estimates for Puerto Rico achieve their optimal consistency with regard to the spouse and householder equalization. The Person Post-Stratification Factor (PPSF) is then equal to the product (SUBEQRF x SPHHEQRF 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 MBF x PPSF).

The final product of all person weights (BW x ... x HPF x PPSF) is rounded to an integer.

Rounding is performed so that the sum of the rounded weights is within one person of the sum of the unrounded weights for any of the groups listed below:

Municipio

Municipio x Sex

Municipio x Sex x Age

Municipio x Sex x Age x Tract

Municipio x Sex x Age x Tract x Block

For example, the number of Males, Age 30 estimated for a municipio using the rounded weights is within one of the number produced using the unrounded weights.

This process produces the following factors:

Householder Factor (HHF)

This factor adjusts for differential response depending on the race, Hispanic origin, sex, and age of the householder. The value of HHF for an occupied housing unit is the PPSF of the householder. Since there is no householder for vacant units, the value of HHF = 1.0 for all vacant units.

Rounding

The final product of all housing unit weights (BW x ... x HHF) is rounded to an integer. For occupied units, the rounded housing unit weight is the same as the rounded person weight of the householder. This ensures that both the rounded and unrounded householder weights are equal to the occupied housing unit weight. The rounding for vacant housing units is then performed so that total rounded weight is within one housing unit of the total unrounded weight for any of the groups listed below:

Municipio

Municipio x Tract

Municipio x Tract x Block

Title 13 of the United States Code authorizes the Census Bureau to conduct censuses and surveys. Section 9 of the same Title requires that any information collected from the public under the authority of Title 13 be maintained as confidential. Section 214 of Title 13 and Sections 3559 and 3571 of Title 18 of the United States Code provide for the imposition of penalties of up to five years in prison and up to $250,000 in fines for wrongful disclosure of confidential census information.

Disclosure avoidance is the process for protecting the confidentiality of data. A disclosure of data occurs when someone can use published statistical information to identify an individual who has provided information under a pledge of confidentiality. For data tabulations, the Census Bureau uses disclosure avoidance procedures to modify or remove the characteristics that put confidential information at risk for disclosure. Although it may appear that a table shows information about a specific individual, the Census Bureau has taken steps to disguise or suppress the original data while making sure the results are still useful. The techniques used by the Census Bureau to protect confidentiality in tabulations vary, depending on the type of data. All disclosure avoidance procedures are done prior to the whole person imputation into not-insample GQ facilities.

Data swapping is a method of disclosure avoidance designed to protect confidentiality in tables of frequency data (the number or percent of the population with certain characteristics). Data swapping is done by editing the source data or exchanging records for a sample of cases when creating a table. A sample of households is selected and matched on a set of selected key variables with households in neighboring geographic areas that have similar characteristics (such as the same number of adults and same number of children). Because the swap often occurs within a neighboring area, there is no effect on the marginal totals for the area or for totals that include data from multiple areas. Because of data swapping, users should not assume that tables with cells having a value of one or two reveal information about specific individuals. Data swapping procedures were first used in the 1990 Census, and were used again in Census 2000 and the 2010 Census.

The goals of using synthetic data are the same as the goals of data swapping, namely to protect the confidentiality in tables of frequency data. Persons are identified as being at risk for disclosure based on certain characteristics. The synthetic data technique then models the values for another collection of characteristics to protect the confidentiality of that individual.

The data in PRCS products are estimates of the actual figures that would be obtained by interviewing the entire population. The estimates are a result pf the chosen sample, and are subject to sample-to-sample variation. Sampling error in data arises due to the use of probability sampling, which is necessary to ensure the integrity and representativeness of sample survey results. The implementation of statistical sampling procedures provides the basis for the statistical analysis of sample data. Measures used to estimate the sampling error are provided in the next section.

Other types of errors might be introduced during any of the various complex operations used to collect and process survey data. For example, data entry from questionnaires and editing may introduce error into the estimates. Another potential source of error is the use of controls in the weighting. These controls are based on Population Estimates and are designed to reduce variance and mitigate the effects of systematic undercoverage of groups who are difficult to enumerate. However, if the extrapolation methods used in generating the Population Estimates do not properly reflect the population, error can be introduced into the data. This potential risk is offset by the many benefits that the controls provide to the PRCS estimates, including the reduction of issues with survey coverage and the reduction of standard errors of PRCS estimates. These and other sources of error contribute to the nonsampling error component of the total error of survey estimates.

Nonsampling errors may affect the data in two ways. Errors that are introduced randomly increase the variability of the data. Systematic errors, or errors that consistently skew the data in one direction, introduce bias into the results of a sample survey. The Census Bureau protects against the effect of systematic errors on survey estimates by conducting extensive research and evaluation programs on sampling techniques, questionnaire design, and data collection and processing procedures.

An important goal of the PRCS is to minimize the amount of nonsampling error introduced through nonresponse for sample housing units. One way of accomplishing this is by following up on mail nonrespondents during the CATI and CAPI phases. For more information, see the section entitled "Control of Nonsampling Error".

Users should be careful when computing and interpreting confidence intervals.

Previously, this document included formulas for approximating the standard error (SE) for various types of estimates. For example, summing estimates or calculating a ratio of two or more estimates. These formulas are also found in the Instruction for Statistical Testing documents, which is located at https://www.census.gov/programs-surveys/acs/technicaldocumentation/code-lists.html. In addition, the worked examples have also been placed in the same location in the document called "Worked Examples for Approximating Margins of Error".

It is possible for some sample housing units or persons to be missed entirely by the survey (undercoverage). It is also possible for some sample housing units and persons to be counted more than once (overcoverage). Both undercoverage and overcoverage of persons and housing units can introduce bias into the data. Coverage error can also increase both respondent burden and survey costs.

To avoid coverage error in a survey, the frame must be as complete and accurate as possible. For the ACS, the frame is an address list in each state, the source of which is the Master Address File (MAF). An attempt is made to assign each MAF address to the appropriate geographic codes 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 that could not be automatically coded.

In 2019, 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. Sometimes the MAF contains duplicates of addresses. This could occur when there is a slight difference in the address such as 123 Main Street versus 123 Maine Street, and can introduce overcoverage.

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 mistakenly interviewed instead of units in sample. 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 became eligible for CAPI. During the 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. Person overcoverage can occur when an individual is included as a member of a housing unit but does not meet PRCS residency rules.

Coverage rates give a measure of undercoverage or overcoverage of persons or housing units in a given geographic area. Rates below 100 percent indicate undercoverage, while rates above 100 percent indicate overcoverage. Coverage rates are released concurrent with the release of estimates on data.census.gov⁶ in the B98 series of detailed tables (Table IDs B98011, B98012, B98013, and B980014). Coverage rate definitions and coverage rates for total population for Puerto Rico are also available in the Sample Size and Data Quality Section of the ACS website, at https://www.census.gov/acs/www/methodology/sample-size-and-data-quality/.



Footnotes:

⁶Beginning in 2019, data.census.gov replaces American Factfinder as the dissemination tool for publishing ACS tables and other data products.

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 the characteristics of nonrespondents differ from those of respondents. 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.

Measurement error can arise if the person completing the questionnaire or responding an interviewer's questions responds incorrectly. However, to mitigate this risk, the phrasing survey questions underwent cognitive testing and households were provided detailed instructions on how to complete the questionnaire.

Processing error can be introduced in numerous areas during data collection and capture, including during interviews, during data processing and during content editing