The CHUM: A Frame Supplementation Procedure for 

Address-Based Sampling

Joseph McMichael, Bonnie Shook-Sa, Jamie Ridenhour, and Rachel Harter

RTI International

Proceedings of the 2013 Federal Committee on Statistical Methodology (FCSM) Research Conference

1. Introduction

Scientific surveys must be based on sampling frames that provide reasonable coverage of the target population. 

Address-based sampling (ABS) studies have grown in use thanks to the availability of the U.S. Postal Service’s 

Computerized Delivery Sequence (CDS) File, which is made available to the public through non-exclusive licensing 

agreements with qualified vendors. The CDS file, in combination with a supplemental address list called the NoStat file1

, contains all U.S. postal delivery points. While there is not always a one-to-one correspondence between 

delivery points and dwelling units, mailing addresses often serve as reasonable proxies for the physical locations of 

dwellings. The number of mailing addresses on the CDS and No-Stat files compares favorably with housing unit 

estimates from Census (Iannacchione 2011)2

. Although these two files provide near-complete coverage of dwelling

units, certain types of postal delivery points (e.g., P.O. Boxes, Rural Route Boxes) are not suitable for in-person 

studies because they cannot be physically located based on their mailing addresses. Addresses that are suitable for 

in-person fieldwork are commonly referred to as city-style addresses3

. The combined CDS and No-Stat files allow

for the central development of sampling frames for in-person surveys without the need for costly field visits prior to 

sample selection. ABS is increasingly viewed as a lower cost alternative to field enumeration (FE) for in-person 

surveys of the general population (Iannacchione 2011). 

Most sampling frames have some coverage error. Traditional FE list frames contain neither the addresses of 

dwelling units built after the list frame was compiled nor units missed during enumeration. ABS frames for inperson surveys have been shown to suffer from undercoverage in rural areas where a higher proportion of mailing 

addresses are not locatable for the purposes of an in-person survey (Dohrmann et al. 2007; Iannacchione et al. 2007; 

O’Muircheartaigh et al. 2007). The process of allocating mailing addresses to area segments, called geocoding, can 

also lead to undercoverage in areas where geocoding is less accurate (Morton et al. 2007; Eckman and English 

2012). 

Traditional FE frames are often supplemented by a missed dwelling unit procedure during the interviewing stage of 

the study to ensure that any new dwelling units or units erroneously left off the FE list frame have a chance of 

selection. The most common procedure is the Half-Open Interval (HOI) method (Kish 1965) in which the 

interviewer searched from a sampled dwelling unit up to but not including the next dwelling unit on the ordered 

frame list, and any new dwelling units found in that interval are also selected into the sample. By linking the 

previously missed dwelling unit to the one listed just before it on the ordered list, the probabilities of selection for 

missed dwelling units are set to the be same as the sampled unit. This method requires a well-specified listing 

sequence so that the interviewer can follow the same path as the field enumerator whose information was used to 

create the FE frame. The problem with the HOI method for ABS is that the address lists are in mail delivery 

sequence order, which does not lend itself to well-defined half-open intervals because of the tendency of postal lists 

to cross streets and jump from block to block (McMichael et al. 2008A). 

Survey researchers have developed procedures to supplement the CDS to obtain more complete address frames

(Dohrmann and Sigman 2013; English et al. 2013). While some surveys are based on ABS or FE alone, most 

national, in-person, ABS designs now typically use a mixture of methods, depending on the expected coverage 

 

1 The USPS No-Stat file contains approximately seven million predominately rural city-style addresses not found on 

the CDS file (Shook-Sa et al, 2013). 

2 Housing unit estimates exclude group quarters units such as dormitories and group homes.

3 City-style addresses consist of a street number, street name, city, state, ZIP code, and unit designation (as 

appropriate).
provided by the CDS for the selected geographies. Enhanced listing combines ABS with FE by using the address 

list for the segment and asking interviewers to update it by enumerating dwelling units missing from the CDS file

prior to the selection of dwelling units as part of frame-building. Another approach is to base the sample on a hybrid 

sampling frame, which uses FE exclusively in segments where address coverage is predicted to be low and the CDS 

for the remainder.

RTI has developed a method that can use the CDS exclusively with a frame supplementation procedure known as 

Check for Housing Units Missed (CHUM) for improving CDS coverage (McMichael et al. 2008). Unlike enhanced 

listing, CHUM can be performed after sample selection and usually during data collection. It is similar to the HOI 

procedure in that the interviewer searches the selected dwelling unit for any missed dwelling units and then searches 

from the selected dwelling unit to the next dwelling unit on the frame, following a prescribed order. The interviewer 

also searches a subset of selected blocks to ensure that dwelling units in blocks without city-style addresses on the 

CDS have a chance of selection. The CHUM procedure corrects for geocoding error and gives every dwelling unit a 

chance of selection with a defined probability. It has the same advantage as enhanced listing in that it can be applied 

to geographies where the CDS provides varying levels of coverage. It has the additional advantage of being less 

expensive, since interviewers are searching small areas corresponding to selected dwelling units rather than entire 

geographic clusters. The CHUM can be implemented within an ABS-only design or in a hybrid sampling frame 

context.

2. The CHUM Methodology

The CHUM is designed to give each dwelling unit missing from the ABS frame a known probability of selection, 

theoretically providing 100 percent coverage of dwelling units. In practice, the actual frame coverage is dependent 

on correct implementation of the procedure by field staff. For an ABS sample, the geographic layout of dwelling 

units within selected area segments is typically not known at the sample selection stage. As previously discussed, 

ABS frames are not amenable to half-open interval supplementation procedures that require pre-specified start and 

end points due to the lack of geographic ordering of addresses on the sampling frame (McMichael et al 2008A). 

Instead, the CHUM specifies a start point and instructs field staff to follow a pre-established path-of-travel to 

identify dwelling units missing from the sampling frame, determining the end point when they encounter a dwelling 

unit that has an address on the sampling frame. Unlike the HOI, the CHUM does not rely on a priori knowledge of 

the end point of the geographic interval.

To ensure that every DU has a known probability of selection, there are two components of the CHUM: the Check 

for Missed Units (CHUM1) and the Check for Missed Blocks (CHUM2). The Check for Missed Units provides 

coverage for dwelling units missing from the ABS frame that are located on city blocks that have ABS coverage, 

while the Check for Missed Blocks provides coverage for dwelling units on city blocks that have no ABS coverage. 

If the two components of CHUM, described below, are implemented correctly, then the ABS list frame, in 

combination with the CHUM, will provide coverage for all dwelling units within the selected area. 

2.1. Check for Missed Units (CHUM1)

The Check for Missed Units (CHUM1) is designed to identify missed dwelling units that are located on city blocks 

with ABS coverage (i.e., city blocks with at least one dwelling unit whose address is included on the ABS frame). 

The start point for the CHUM1 procedure is the dwelling unit associated with a sampled address from the ABS 

frame. The CHUM has a pre-established path-of-travel which allows field staff to identify the “next dwelling unit” 

from the starting point, where the path-of-travel is typically a clockwise direction around the block. Field personnel, 

often field interviewers, are instructed not to cross streets to ensure that any missed DU can be identified from a 

single address on the frame. After the address of the next dwelling unit is determined, field staff checks whether or 

not the address is included on the ABS frame. If the address is on the ABS frame, then the CHUM1 procedure is 

complete—there are no missed dwelling units in the interval following the start point. However, if the address of 

the next dwelling unit is not contained on the ABS frame, then the address is recorded and the field staff continues

to circumnavigate the block until a dwelling unit with its address on the ABS frame is located. After field staff 
encounter this already listed dwelling unit (the end point), the CHUM1 procedure is complete, and missed dwelling 

units between the start and end points are sampled for inclusion in the study (see Section 2.2.3 for further details on 

sampling missed dwelling units). Figure 1 demonstrates the CHUM 1 procedure for a typical CHUM interval. In 

the example figure, if dwelling unit 1 were selected the field interviewer would travel clockwise around the block 

until reaching dwelling unit 2, picking up the starred dwelling unit missing from the frame. Alternatively, if 

dwelling unit 2 was selected for the sample, the field interviewer would pick up two missed dwelling units before 

reaching dwelling unit 3. If dwelling unit 3 were selected, no missed units would be detected before reaching the 

endpoint (dwelling unit 1).

Figure 1: CHUM Components 1 and 2

Path of travel

DU not on ABS frame

DU on ABS frame

17th St

Ga

et wood Ave

nI d

ai na

Ave

15th St

N

START

1

3 2

Indiana Ave

16th St

CHUM1

Component

CHUM2

Component

Blackwater Ave

This path-of-travel works well when dwelling units are lined up along the street in a clear, clockwise geographic 

order (e.g., with single-family units and townhomes). However, multi-unit structures (e.g., apartment buildings) 

require a different path-of-travel to identify the next dwelling unit. With apartments, field staff is instructed to list 

the unit numbers within the building of the starting dwelling unit, to sort them alphanumerically, and to select the 

dwelling unit following the starting dwelling unit as the next dwelling unit. Like with the typical CHUM path-oftravel, field personnel continue listing units until they encounter one that is contained on the ABS frame (the end 

point). 

2.2. Check for Missed Blocks (CHUM2)

If only the CHUM1 procedure was implemented, then dwelling units in polygons (usually census blocks) within the 

selected area with no ABS coverage would not have a chance of selection. The Check for Missed Blocks (CHUM2) 

procedure is designed to ameliorate this problem. During the sample selection stage, one or more census blocks 

within each area segment is randomly selected for the CHUM2. CHUM2 blocks can be selected based on any 

random sampling method. To minimize design effects, CHUM2 blocks can be selected such that the weights of 

missed dwelling units picked up by the CHUM2 are similar to the weights of sampled addresses from the ABS 

frame. An alternative approach is to select CHUM2 blocks based on probability proportional to size (PPS) 

sampling, giving higher probabilities of selection to blocks expected to contain large numbers of dwelling units

missing from the frame. Special CHUM2 maps are created, identifying the CHUM2 start points. Start points are

selected to allow coverage of all areas of the census block based on the CHUM2 path-of-travel. Multiple CHUM2 

start points are sometimes required to allow complete coverage of the selected CHUM2 block. The start points are 
typically selected in a clearly defined location (e.g., the intersection of two streets) so that field staff can easily 

determine where to begin implementing the CHUM2 procedure.

The protocol for implementing the CHUM2 procedure is the same as the protocol for implementing the CHUM1 

procedure, except that the start point is a location specified on a map rather than a dwelling unit associated with an 

address on the ABS frame. From the start point, the field staff locates the “next dwelling unit” and checks whether 

or not the address of the next dwelling unit is on the ABS frame. If it is, the procedure is complete. Otherwise, the 

field staff continues listing and checking missed dwelling units until he/she either locate a dwelling unit with an 

address on the ABS frame or returns to the start point. If the CHUM2 interval contained any dwelling units

associated with ABS addresses, it is not a missed area and no dwelling units are added to the frame. In these cases, 

any missed dwelling units in the interval are covered by the CHUM1 procedure. However, if there were no dwelling 

units in the CHUM2 interval that were included on the ABS frame, this was a missed area and all dwelling units in 

the CHUM2 interval are sampled for inclusion. Figure 1 demonstrates the CHUM2 procedure. If the block at the 

top of the figure (bordered to the north by 17th Street and to the west by Indiana Avenue) was sampled for the 

CHUM2, the field interviewer would locate dwelling unit 1, which is contained on the ABS list. The CHUM2 

procedure would then terminate and no dwellings would be added to the frame. However, if the block at the bottom 

of the figure (bordered to the north by 16th Street and to the west by Indiana Avenue) was sampled for the CHUM2, 

the field interviewer would circumnavigate the entire block without finding the addresses of any dwelling units on 

the ABS frame. This is a missed block, and all three missed dwelling units on this block would be sampled for 

inclusion in the study.

2.3. Sampling Issues

The probability of selection for a missed dwelling unit identified with the CHUM1 procedure is the probability of 

selection for the sampled dwelling unit to which it is linked. For example, if the starting dwelling unit had a 

probability of selection of 0.05, then all missed dwelling units identified in the CHUM1 procedure would also have 

a probability of selection of 0.05. However, it is not always practical to sample all missed dwelling units identified 

in the CHUM1 procedure. For example, if 20 missed dwelling units were identified following the sampled 

dwelling, subsampling could be implemented to control the overall sample size in the segment. If subsampling is 

implemented, the probabilities of selection must be adjusted accordingly. In the example above, if the starting 

dwelling unit had a probability of selection of 0.05 and 7 of the 20 missed DUs were included in the sample, the 

subsampled missed dwelling units would have a probability of selection of 0.05*7/20 = 0.0175. 

With the CHUM2 procedure, the probability of selection for missed dwelling units is the probability of selection for 

the CHUM2 block. For example, if CHUM2 blocks were selected based on a simple random sample, and two of the 

30 census blocks in the segment were selected, the conditional probability of selection for dwellings on missed area 

blocks within the selected CHUM2 block would be 2/30=0.067. (The unconditional probability is the product of 

this conditional probability and the probability of selecting the segment.) As with the CHUM1, dwelling units on 

missed blocks can be subsampled if more missed units are identified than is practical to include in the sample, and 

the design weights must be adjusted accordingly. 

After identifying the base probabilities of selection for each sampled dwelling unit i (pi), the design weight is 

calculated as the inverse of the probability of selection, 1 pi

. Standard weighting procedures are then implemented 

on the entire sample, regardless of how the dwelling unit was identified (ABS frame vs. CHUM). 

Because subsampling of missed dwelling units identified in the CHUM procedures decreases the base probabilities 

of selection for missed units (and therefore increases their design weights), too much differential subsampling across 
the clusters can increase the variation in weights which can increase the design effect due to weighting4

and thereby 

lower the precision of certain estimates. This must be considered when implementing the CHUM in areas with very 

low ABS coverage because subsampling will likely be required to control the overall sample size in these areas. For 

example, if a sampled segment has only 5 percent ABS coverage, then we would expect 95 percent of the dwelling 

units to come into the sample via the CHUM. This can lead to challenges in controlling the sample size and 

sampling inefficiencies due to unequal weighting, so care should be taken when designing the study to account for 

the effects of missed dwelling units on the precision of survey estimates. 

3. CHUM Operational Issues

While the CHUM methodology theoretically provides complete coverage for dwelling units missing from ABS 

sampling frames, it is dependent on correct implementation of the procedure by field staff. The specific 

implementation of the CHUM can be tailored to the needs and limitations of each study. All of the following 

aspects of the CHUM can be optimized based on the goals and budget of the study: the selection of CHUM1 

addresses and CHUM2 starting points, when the CHUM is implemented, CHUM materials provided to field staff, 

field staff training and support, processing missed dwelling units identified with the CHUM, and quality control

procedures. The following subsections discuss operational issues associated with implementing the CHUM. These

subsections include case studies of how the CHUM has been implemented previously, as well as alternative 

implementations for future studies. 

3.1. Selecting CHUM1 Addresses and CHUM2 Points

The CHUM does not need to be completed for all sampled addresses and/or segments in the study. If a study does 

not have the resources available to implement the CHUM everywhere, or if the target population resides in areas 

where address undercoverage is less of a concern (e.g., urban areas), then the CHUM1 procedure can be completed 

for a subset of sampled addresses. However, implementing the CHUM1 for only a subset of addresses can impact 

the unequal weighting effect, as any added addresses will have a different weight than the original sample due to the 

subsampling of CHUM1 addresses. The more subsampling that occurs, the higher the variation in design weights 

between the selected addresses and addresses identified with the CHUM unless subsampling can be managed across 

clusters in a way that reduces or eliminates the impact on weight variation. If resources are limited, the CHUM can 

be focused on areas where CDS undercoverage is expected. CHUM1 can be completed for a higher proportion of 

addresses in segments that are expected to have undercoverage. CHUM2 blocks can be selected in a similar fashion. 

Depending on the resources available, the expected CDS coverage, and the sample design, any number of CHUM2 

blocks can be selected for inclusion.

In addition to tailoring the number of CHUM1 and CHUM2 intervals, researchers can tailor when the CHUM

procedure is implemented. The CHUM can be implemented anytime following sample selection of addresses. Field 

staff can be instructed to make a trip to sampled segments prior to screening and interviewing to complete the 

CHUM or the CHUM can be implemented during data collection. Implementing the CHUM prior to data collection 

allows time to develop the final sample of addresses before the start of screening and interviewing. This can allow 

researchers to determine appropriate subsampling rates and simplifies subsequent visits to the segment by field staff. 

However, additional resources are required to visit segments prior to data collection, and the results are less timely.

 

4 The design effect due to weighting is equal to one plus the relative variance of the sample weights, and is a 

measure of how unequal weighting impacts the efficiency of survey estimates (Kish, 1965; Valliant et al, 2013).
3.2. Creating CHUM Maps

Area probability surveys require maps to help field interviewers navigate to and between selected addresses. With 

ABS studies, the first task an interviewer completes in a segment may be to determine whether the selected 

addresses are actually within the segment boundaries. 

The materials field interviewers are provided for completing the CHUM are flexible. In general, they need maps 

that identify segment boundaries and the location of CHUM2 start points. Specialized maps can be created for the 

CHUM2 points showing only the blocks the interviewer needs to circumnavigate. Depending on the resources 

available for the study, these maps can be paper or electronic. 

To implement the CHUM, field interviewers also need a method for searching the ABS frame of the sample segment

for potential missed dwellings. This methodology can be as simple as a printout of addresses in that segment or as 

complex as an application that allows interviewers to search for addresses electronically. Electronic applications can 

be programmed to pre-populate candidate next addresses based on geographic proximity to the field interviewer’s 

location or the CHUM2 starting point. If this methodology is employed, care should be taken to avoid confirmation 

bias by FIs – that is, quality control measures should be employed to ensure that the FIs are not treating the list as 

correct and not updating it (Eckman and Kreuter 2011). 

3.3. CHUM Training

RTI studies that have used the CHUM to date have had trainings that lasted anywhere from thirty minutes to a full 

day, depending on the resources available and the expected reliance on the CHUM. A short training has lower upfront costs but will likely require more field support and more procedures for verifying quality of the frame 

augmentation process. Longer training is more expensive up front, but FIs should be better-prepared for what they 

encounter in the field. FIs can be trained on many of the rare situations they may encounter or they can be taught 

the basic CHUM approach while stressing to seek field support when they encounter something unusual. 

3.4. Support for Field Interviewers

While performing the CHUM, field interviewers will encounter situations that require them to seek help. If their

supervisor is not able to determine the correct course of action, the case may get routed back to the home office for

support from someone who understands the theoretical underpinnings of the CHUM. Field interviewers sometime

require help determining if the path-of-travel is taking them out of the segment; the FI may also need help to 

determine if the address is associated with a missed dwelling in a CHUM interval (e.g. if the address of the dwelling 

they encounter is similar but not identical to an address on the ABS frame). When field interviewers encounter a 

large number of missing units , a sampling statistician will need to establish the subsampling rate and associated 

procedures in that interval to mitigate the cost of all the additional cases. It is also important to have quality control 

checks in place; if an FI lists more units in a segment than are expected based on the predicted ABS coverage in that 

area, for example, perhaps he or she is off-course or not implementing the procedure correctly. How many units 

“more” is defined by collection resources and areas visited in a particular study – it may be acceptable for field staff 

to list up to three before seeking additional help or it may be acceptable for staff to list up to twenty.
3.5. Quality Checks for CHUM Implementation

Prior research has shown that interviewers do not always complete field work correctly, whether implementing the 

HOI, an enhanced listing procedure, or the CHUM (Eckman and Kreuter 2011; Eckman and O'Muircheartaigh 2011; 

Iannacchione et al. 2012). One method for monitoring the quality of CHUM fieldwork is to create situations where 

the field interviewer should find at least one missed dwelling unit by removing addresses from the field 

interviewer’s address list that are likely to be the next dwelling unit. While this seeding method is not perfect, it 

allows sampling staff to have a measure of how well the CHUM is being implemented. Seeding does have cost 

implications, however, as it puts an additional burden both on interviewers and sampling staff to CHUM and verify 

unnecessary intervals. The data quality benefits and validation that the CHUM is being implemented correctly 

typically outweigh these costs.

More sophisticated monitoring techniques are possible when the field interviewers have Global Positioning System 

(GPS) capabilities in the field. Field interviewers’ locations can be monitored to ensure they are following the 

correct path-of-travel. Mistakes can be corrected quickly enough that the procedure can be repeated in areas where 

it was not implemented correctly. GPS can also ensure that missed dwellings identified through the CHUM are 

contained within the sampled segment.

4. Advantages and Disadvantages of ABS with CHUM

As discussed in the introduction, there are other options for reducing undercoverage in ABS studies. Traditional FE

and using the CDS alone with no supplementation procedures are approaches used in somewhat unique situations. 

Studies that use the CDS alone would either have to be restricted to areas where address coverage is quite high

(typically urban areas) or would have to be willing to accept undercoverage in some (typically rural) areas. 

Conducting FE for all segments in a large study is both expensive and time-consuming. A study using the hybrid FE 

and ABS design may not have a strong need for frame supplementation in segments where the predicted coverage 

rate5

is high.

Studies that do want to supplement the coverage of ABS frames in some manner have used either Enhanced Listing 

or the CHUM. Timeline, segment size, data collection budget, and impact on variances are all considerations in 

choosing the frame supplementation approach. Enhanced listing is completed prior to sample selection while 

CHUM is completed in the field at the start of screening and interviewing. Studies with an aggressive data 

collection timeline may not be able to add the extra time enhanced listing requires. Enhanced listing may be a better 

option in studies that are largely in areas where a large number of addresses are likely to be added to the frame. 

Including them ahead of time allows the statistician more control over the probabilities of selection associated with 

the added addresses. This can lead to more efficient sample designs.

While enhanced listing requires field staff to canvas the entire segment to supplement the CDS file, the CHUM is 

only implemented from randomly selected starting points. Because the CHUM is therefore less time consuming for 

field staff, it may be the better choice for studies with large segment sizes.

 

Some ABS studies use census block groups (CBGs) as the segments of dwelling units. CBGs average around 500 

households – checking all addresses associated with each CBG in the sample could be quite time-consuming relative 

to completing the CHUM1 interval for only the sampled addresses within that CBG.

 

5 The coverage rate is the predicted household coverage for a particular segment. The coverage threshold is the 

minimum rate at which the ABS frame is used in place of FE.
Both timeline and segment size are inherently related to budget constraints. Surveys typically require smaller 

segments when data collection costs are tight. Extra time spent listing the entire segment in a separate trip prior to

screening and interviewing is also an extra cost. 

Both supplementation procedures are dependent on field staff to implement the procedure correctly. Enhanced 

listing has been shown to suffer from confirmation bias (Eckman and Kreuter 2011). Without proper interviewer 

training, the CHUM can also lead to undercoverage (Iannacchione et al. 2012). Regardless of the method selected, 

resources must be available to appropriately train, monitor, and support field staff,

5. Conclusions

ABS is increasingly the preferred approach for in-person area probability studies. Although the combined CDS and 

No-Stat frames cover all Postal delivery points, they do not currently provide full housing unit coverage for inperson surveys. Several approaches have been developed to supplement the ABS frame where needed including 

hybrid sampling frames, enhanced listing, and the CHUM. Enhanced listing provides more statistical control for 

sampling added addresses, as researchers can select the sample of addresses from a complete frame that has already 

been supplemented, eliminating the need for subsampling that often occurs with the CHUM. However, the CHUM 

provides some operational advantages over enhanced listing. The CHUM occurs at the start of screening and 

interviewing rather than at the frame-building stage, so address updates are timelier. This also avoids multiple trips 

to the segment which saves resources. The CHUM is only implemented from a subsample of addresses in each 

segment, which makes it less time-consuming than enhanced listing, where field interviewers canvass the entire 

segment looking for missed addresses. In addition, the CHUM allows for geographic larger segment sizes compared 

to methods that canvas the entire segments. This has the likely benefit of lowering the intraclass correlation and thus 

decreasing design effects and variance (Valliant et al. 2013). Regardless of which supplementation technique is 

implemented, it is critical for field staff to be appropriately trained, monitored, and supported which performing 

field work. While the logistics of these procedures can be tailored to the needs and resources of the study, they are 

critical to maintaining high coverage and ensuring data quality.
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