Practicability of Including Cell Phone Numbers in Random Digit Dialed 

Surveys: Pilot Study Results from the 

Behavioral Risk Factor Surveillance System 

Michael W. Link1, Michael P. Battaglia2, Martin R. Frankel3, Larry Osborn2, Ali H. Mokdad4 

1

 Nielsen Media Research 

3784 Ardsley Ct, Marietta, GA, 30062. Michael.Link@Nielsen.com 

2 Abt Associates Inc. 

55 Wheeler St., Cambridge, MA 02138, Mike_Battaglia@abtassoc.com, Larry_Osborn@abtassoc.com 

3 Abt Associates Inc. and Baruch College, CUNY 

14 Patricia Lane, Cos Cob, CT 06807, Martin_Frankel@abtassoc.com 

4

 Centers for Disease Control and Prevention 

4770 Buford Highway NE,MS K-66, Atlanta, GA, 30341, AMokdad@cdc.gov 

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the 

Centers for Disease Control and Prevention, Abt Associates Inc, Baruch College, CUNY, or Nielsen Media 

Research. 

Abstract 

Noncoverage rates in U.S. landline-based telephone samples due to cell phone–only households (i.e., households 

with no landline but accessible by cell phone) and the corresponding potential for bias in estimates from surveys that 

sample only from landline frames are growing issues. Building on some of the few published studies that focus on 

this problem, a study was conducted in three states (Georgia, New Mexico, and Pennsylvania) as part of the 

Behavioral Risk Factor Surveillance System (BRFSS), the world’s largest ongoing public health telephone survey, 

to evaluate the effectiveness of conducting the BRFSS interview with a sample drawn from dedicated cell phone 

telephone exchanges and mixed-use (landline and cell phone) exchanges. Approximately 600 interviews were 

conducted in each of two groups: cell phone–only adults (n = 572) and adults with both a landline and a cell phone 

(n = 592). Making comparisons with data from the ongoing, landline-based BRFSS survey, we report on response 

rates, demographic characteristics of respondents, key survey estimates of health conditions and risk behaviors, and 

survey costs. The methodology employed in this study and the “lessons learned,” including the costs of conducting 

surveys over cell phones, have wide application for other telephone surveys. 

Key words: cell phones, cell phone sampling, telephone surveys, public health surveillance 

Introduction 

For the past several decades, random-digit-dial (RDD) telephone sampling has provided a cost-efficient strategy for 

conducting surveys of the U.S. household population. However, as the percentage of cell phone–only households 

(households with no landline but accessible by cell phone) continues to grow, the validity of the basic RDD landline 

sampling model has come open to question. The continually increasing percentage of households that are 

abandoning their landline telephones for cell phones has significantly eroded the population coverage provided by 

landline-based surveys to pre-1970s levels. For the second half of 2006, the percentage of cell phone–only 

households was 12.8 percent (Blumberg and Luke 2007). Moreover, more than half (54.0 percent) of all adults 

living with unrelated roommates and one in four (25.2 percent) adults aged 18 to 24 years live in cell phone–only 

households. These adults are not covered by current RDD landline sampling procedures, which exclude telephone 
 

 

 

 

 

exchanges and 1,000 banks used exclusively for cell phones, and the percentages are trending upward. These are 

some of the same groups that are increasingly under-represented in current RDD landline telephone surveys due to 

differential nonresponse. 

Because cell phone use has grown so quickly since 2000, the empirical literature in this area is sparse. Moreover, 

because of the rapid changes in the telephone landscape, findings from just a few years ago may already require 

updating. In 2003, Steeh (2004) conducted one of the first national surveys by cell phone. The study found that 

although conducting surveys with sampled cell phone numbers was feasible, the approach faced a number of 

challenges, including lower response rates than in landline surveys, higher refusal rates, and lower refusal 

conversion rates. Brick et al. (2007) reached similar conclusions, finding as well that although the use of incentives 

could boost response rates, sending text messages did not improve participation. 

In terms of the potential for bias in survey estimates, Keeter (2006) provided reason for optimism by demonstrating 

that bias in 2004 presidential voting preferences due to exclusion of cell phone–only households was essentially 

eliminated when age was incorporated into the weighting methodology. In their analysis of a February 2004 

supplement to the Current Population Survey, however, Tucker, Brick, and Meekins (2007) struck a more cautionary 

tone. They warned of a high potential for bias in telephone survey estimates that sample only from landline frames 

because of increasing undercoverage caused by cell phone-only households and the different demographic profile of 

this group (cell phone–only respondents were more likely to be younger, unmarried, and Hispanic). Brick et al. 

(2006) found that topic salience and household inaccessibility resulted in substantial nonresponse bias in estimates 

of households by type of telephone service (landline, cell phone, or both). Topic salience was a larger factor for 

households selected from a landline frame, and inaccessibility was a greater concern for those sampled from a cell 

phone frame. Additionally, for the cell phone sample, a higher than expected percentage of respondents were from 

cell phone–only households, resulting from differential nonresponse rates among those who used their cell phones 

often versus those who used their cell phones less frequently. 

The research presented here builds upon these earlier works. First, we examine how respondents sampled and 

interviewed by cell phone differ from those from a landline frame with respect to an ongoing survey in the U.S., the 

Behavioral Risk Factor Surveillance System (BRFSS), one of the world’s largest health telephone surveys. Health 

and risk behaviors have not, heretofore, been the focus of a survey conducted using a sample of cell phone numbers. 

Second, we provide a first look at state-based data from the cell phone population. In contrast, all of the previously 

cited studies were focused at the national level, thereby potentially masking differences that may be seen at the state 

level. Third, we provide comparisons of two different vendors of cell phone numbers and compare resulting 

residential working number rates from each. Finally, in addition to comparing response rates, respondents’ 

demographic characteristics, and survey estimates for key health conditions and related risk factors, we also 

analyzed the costs of conducting surveys with cell phone samples. 

Methods and Design 

The BRFSS collects uniform, state-specific data on preventive health practices and risk behaviors that are linked to 

morbidity and mortality among adults. The survey is conducted by state health departments with assistance from the 

Centers for Disease Control and Prevention (further details on the BRFSS survey design, methodology, and 

questionnaire are available at http://www.cdc.gov/brfss). 

Because significant bias may be introduced into BRFSS estimates as the percentage of cell phone–only households 

increases, a study was conducted in 2007 to develop and test two approaches for conducting BRFSS interviews by 

cell phone. The first involved screening persons reached by cell phone to identify and interview those persons living 

in cell phone–only households. The second involved interviewing sample members reached at cell phone numbers 

regardless of their household cell or landline telephone status. The goals of the study were to determine the 

feasibility and costs of conducting surveys with sampled cell phone numbers and to explore the similarities and 

differences between those interviewed by landline and those interviewed by cell phone. 

Sampling 

Three states were selected for participation in the study: Georgia, New Mexico, and Pennsylvania. The three states 

were chosen because they represent various geographic regions of the United States and combined they provide a 

good representation of both the urban/rural and racial/ethnic mix of the U.S. population. The universe for the study 
 

 

 

 

 

 

 

 

 

 

 

 

 

consisted of all noninstitutionalized adults aged 18 and older living in the three states. Cell phone numbers were 

sampled and screened for the presence of adults living in private residences within three states. Because this was a 

household-based survey, interviews were not conducted with those who lived in institutions or group quarters. 

Two methods of sampling and interviewing were used in this study: (1) screening persons reached at cell phone 

numbers to identify and attempt interviews with all such adults living in cell phone–only households; and (2) 

screening persons reached at cell phone numbers to identify and attempt interviews with a random subsample of 

such adults living in households with landline and cell phone service. 

The sample was obtained from two vendors using a split sample design across the three states to compare the 

efficiency of the sampling methods used by the two vendors (that is, half of the numbers in each state were drawn 

from one vendor and the other half from the other vendor). Each sample was derived from the Telecordia database 

of telephone numbers, but structured and sampled from in slightly different ways. The first vendor, Survey Sampling 

International (SSI), used both dedicated cell phone banks and “mixed use” banks (banks of numbers, containing no 

residential directory-listed residential numbers, which are used for cell phone and one or more other telephone uses 

such as landline service, paging, etc.) partitions the frame into 100-blocks of numbers (that is, blocks of numbers 

with an identical combination of area code, exchange, and first two digits of the last four digits of the telephone 

number), sorted by state FIPS (Federal Information Processing Standards) code, telephone carrier, and sequential 

100-block identification. The intent is to provide a stratified sample that is representative both geographically and by 

large and small cell phone service carriers. A systematic sampling interval was determined by dividing the universe 

of eligible 100-blocks by the desired sample size. Using a random start less than or equal to the sampling interval, a 

systematic k-th selection of 100-blocks was performed and a two-digit random number between 00 and 99 was 

appended to each selected 100-block stem. 

The second vendor, Marketing Systems Group (MSG), used dedicated cell phone 1,000 banks sorted on the basis of 

area code and exchange. An interval, K, was formed by dividing the population count of telephone numbers in the 

frame, N, by the desired sample size, n. The frame of telephone numbers was divided into n intervals of size K 

telephone numbers. From each interval, one 10-digit telephone number was drawn at random. 

Overall, a sample of 23,397 telephone numbers was drawn from cell phone exchanges across the three states. For 

sample release and management purposes, the sample from each sampling vendor was divided into random 

subsamples called replicates within each state. 

Recruitment 

When a potential respondent was contacted, he or she was first asked questions to determine study eligibility (see 

Appendix A for text of the screening questions). Specifically, the person answering the telephone was asked whether 

he or she had been reached on a cell phone, lived in a private residence, was aged 18 years or older, and resided in 

one of the specified states. For those who responded “no” to any of these questions, the interview was terminated. 

Those answering “yes” to all of the screening questions were then asked whether they also had a landline telephone 

in their home (that is, a “regular” telephone that is connected to outside telephone lines through a cable or cord and 

is used for making or receiving calls). All of those who had only a cell phone were interviewed, whereas persons 

who had both types of telephones were subsampled for administration of the full interview; the subsampling rate 

varied depending on state, sampling vendor and sample replicate release. The subsampling procedure was used 

within each state in order to achieve approximately 200 interviews with each of the two cell phone groups. 

For this study we made the assumption that cell phones are individual devices and not shared household devices; 

that is, there is a one-to-one correspondence between the person answering the cell phone (assumed to be the cell 

phone owner) and the cell phone itself. As a result, no additional within household selection was undertaken. 

Since little is known, however, about the extent of cell phone sharing within households, a series of questions was 

asked to determine the extent to which cell phones are shared among adults and if, in future studies, within 

household selection is warranted for households with multiple adults. The first question (Appendix B: #1) 

established whether or not the respondent had a cell phone for their personal use, including phones that were used 

for both personal and business purposes. Those with a negative response to the first question (or who answered 

don’t know or refused) were asked a follow-up question (#2) to determine whether or not the respondent shared a 

cell phone for personal use with other adults, to ensure that all cell phone users were identified, regardless of phone 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

“ownership.” Those with a positive response to the first question were asked whether they usually shared the cell 

phone with any other adults (#3). All respondents who were determined to share their cell phone were next asked 

the number of other adults who used the cell phone at least one-third of the time (#4). These questions were asked 

in all three states for the cell phone sample and in the landline survey in New Mexico and Pennsylvania (because of 

concerns about interview length, the sharing questions were not added to Georgia landline interview). 

Data Collection 

A shortened (approximately 12-minute) version of the 2007 BRFSS core questionnaire was programmed using a 

computer-assisted telephone interviewing (CATI) system. Interviewing was conducted in English and Spanish. A 

translated questionnaire and bilingual interviewers were used for respondents who preferred to complete the 

interview in Spanish. Respondents who did not speak either English or Spanish were excluded from the study. Data 

were collected from January 18 through April 5, 2007. 

While existing BRFSS protocols were used for the cell phone study to the extent possible, it was necessary to 

implement some study-specific protocols to account for differences between the cell phone and landline studies. 

Some of these contacting and interviewing protocols were developed on the basis of recommendations from a group 

of survey experts convened in 2005 to address issues related to cell phone interviews (Lavrakas and Shuttles 2005). 

The study-specific protocols were then updated throughout the field period as new situations were encountered. In 

particular, a series of interviewer debriefings was held in order to learn more about the process of collecting data via 

cell phone and to increase data collection productivity and quality, and feedback from the debriefings was used to 

refine the protocols. Some of the more important study-specific protocols included: 

Calling hours: Although considerable evidence shows that specific times of day, as well as days of the week, are 

particularly productive for achieving contact with landline households, little is known about the best times to make

calls to cell phone holders (Brick et al. 2007). It was initially assumed that weekday evening and weekend calling 

hours would be most productive, since those are the hours that are least restrictive in terms of cost to potential 

respondents in many cellular service plans. A productivity analysis conducted after the first week of interviewing 

showed that evening calls were more productive than daytime calls, but weekend calling was not particularly 

productive. Therefore, an increased emphasis was placed on weekday evening calling for the remainder of the data 

collection period. 

Call answering and ring tones: Some cell phone companies offer a service in which the customer can set 

personalized ring tones so that incoming callers hear music rather than a usual ring. Therefore, it was necessary for 

interviewers hearing music after dialing to remain on the line for a short period of time to see whether the 

respondent or their voicemail would pick up the call. In addition, many cell phone voicemail systems do not pick up 

until after six or more rings. To ensure that voicemail messages were left appropriately, interviewers were required 

to allow the phone to ring at least seven times before exiting a case. 

Respondent location: Because cell phone users may take calls in a variety of situations, such as during a routine 

errand or while driving, it was important for interviewers to determine whether or not the respondent’s location 

would hinder the conduct of an interview or place the respondent in a position of undue risk. The informed consent 

language read to all respondents asked them to confirm that they were in a place where they could continue with the 

interview at the time of contact. Even when respondents agreed that they would continue with the interview, 

interviewers were encouraged to listen for cues that the respondent might be in a distracting situation and, if so, to 

offer to set an appointment to complete the interview at another time. Respondents were also allowed to make 

arrangements to be called back on their landline telephone to complete the interview if they preferred. 

Identifying business-only cell phone numbers: A substantial number of cell phone customers use their phones for 

personal as well as business purposes, making them eligible for the cell phone study (analogous to the approach used 

for business numbers in landline surveys). Only those using their phone exclusively for business purposes were 

ineligible for the study. Therefore, if an interviewer reached voicemail suggesting that a cellular number was used 

for business purposes, a pending disposition code was assigned and the case was re-contacted until it could be 

definitively determined whether or not the number was solely for business use. 

Identifying child/teen cell phones: Persons under the age of 18 were ineligible for the study. However, if a cell 

phone was used by both children or teenagers and an adult aged 18 or older, attempts were made to contact the adult 
 

 

 

 

 

 

 

 

 

 

 

 

 

user in order to conduct the interview. Thus, when interviewers reached an answering party under age 18, they 

probed to determine whether or not the cell phone belonged to the child or teenager exclusively or if it was shared 

by their parent or another adult, and the case was coded appropriately. 

Refusal Conversion: If respondents asked not to be called on their cell phone, interviewers attempted to avert a 

refusal by asking for another telephone number at which the respondent could be contacted or if there was a better 

time for them to take a call via cell phone (e.g., when incoming calls would not incur a cost). No further attempt 

was made to contact respondents who would not provide this information. However, more general, “non-hostile” 

refusals were re-contacted once for a conversion attempt. 

Coding of Recorded Messages: Early in the data collection period, it became clear that a wide variety of recorded 

messages were used by cell phone companies to indicate a similarly wide variety of circumstances, including 

respondent unavailability, that the number dialed was for company business purposes, that the number was 

temporarily or permanently out of services, etc. To facilitate the coding of these messages, additional disposition 

codes for messages indicating that the respondent was temporarily unavailable or not accepting calls and for 

operator messages that were unclassifiable were added to the study. 

Voicemail messages: Given the increased use of voicemail in the United States, special procedures were developed 

for leaving messages on respondents’ voicemail. This procedure is similar to leaving messages on answering 

machines in landline surveys (Link and Mokdad 2005). Interviewers were restricted to leaving one voicemail 

message per week for a given telephone number. When voicemail was reached before eligibility could be 

established, the following message was left: 

“Hello. The [STATE HEALTH DEPARTMENT] is conducting a study about the health of adults in 

[STATE]. Your telephone number has been selected at random. Would you please call us, toll-free, at 1-

800-555-5555 to determine whether or not you are eligible for the study? The toll-free number again is 1-

800-555-5555. Thank you.” 

A slightly different wording was used when callbacks were required to continue an interview that had been 

suspended. 

Text messages: We also tested the effectiveness of sending text messages to sampled telephone numbers with high 

call attempts. This was conducted with only one large carrier, which operated in all three states, for cases that had no 

actual contact after 10 attempts (i.e., a mixture of no answer, busy, voicemail, or other types of automated 

messages). The message was worded as follows: 

"Please participate in a survey for the Centers for Disease Control. If eligible, you will get $10 to pay for 

your cell time. Please call 1-800-555-5555." 

Remuneration for minutes used: To increase response and offset any charges incurred by respondents, a modest 

payment was provided to respondents. Those who completed the full screening portion of the interview but were not 

eligible for the interview because of cell or landline subsampling were offered $1. Eligible respondents who 

completed the detailed interview were offered $10. 

BRFSS Landline Telephone Survey 

The cell phone survey was conducted in parallel with the ongoing, monthly BRFSS RDD data collection, thereby 

facilitating the comparison of results between the two approaches. Telephone survey data from the three 

participating states for January through March 2007 were used in this analysis. As discussed above, additional 

questions were added to the landline telephone survey to determine the type of telephone access in the household 

(landline and cell phone or landline only) facilitating comparison with the cell phone study data. More details on the

BRFSS design and methodology are available elsewhere (Mokdad, Stroup, and Giles 2003). 

Weighting Adjustments 

Design weights were calculated for the cell phone sample in the three states. The design consists of six strata-- three 

states by two sample vendors. The base sampling weight of each sampled cell telephone number in a stratum equals 

the population count of telephone numbers divided by the sample size of telephone numbers in the released 
 

 

 

 

 

 

 

 

 

 

 

 

replicates. For adults living in households with landline and cell telephone service, the base sampling weight was 

adjusted to reflect the subsampling that took place in each stratum. Finally, within each stratum, an interview unit 

nonresponse adjustment was performed separately for cell phone–only adults and for cell phone plus landline adults. 

These design weights were then divided by two so that the samples from the two sampling vendors could be 

combined within a state. Finally, the design weights in a state were ratio-adjusted, separately for cell phone–only 

and cell phone plus landline adults, so that the sum of the design weights in each state equaled the same total. This 

“equalized” design weight was used in the comparison of demographic characteristics across four key analysis 

groups determined by sample frame (landline versus cell phone) and household telephone access: (A) landline 

sample with landline-only telephone access, (B) landline sample with landline and cell phone access, (C) cell phone 

sample with landline and cell phone access, and (D) cell phone sample with cell phone–only access (see figure 1 – 

note that groups B and C differ in terms of the frame from which they are sampled). The “equalized” design weight 

was used to produce the estimates presented in the analyses below because it gave each state an “equal” contribution 

to the combined state estimates (i.e., the estimates were not dominated by Pennsylvania or Georgia, the two larger 

states). 

For the analyses conducted here, the landline data in each state were weighted to account for the probability of 

selection of the telephone number, the number of voice-use landline telephone numbers in the household, and for the 

random selection of one adult from the household. Next, mirroring the process followed with the cell phone data, the 

design weight was ratio-adjusted, separately for landline-only adults and landline plus cell phone adults, so that the 

sum of the design weights in each state equaled the same total. 

Response Rate Calculations 

To maximize comparability between the landline and cell phone surveys, we used response rate calculations 

recommended by the American Association for Public Opinion Research (AAPOR 2006). For the BRFSS landline 

telephone survey, the original BRFSS disposition codes were mapped to the AAPOR specified codes, and response 

rates were calculated using AAPOR response rate formula #4. Interviewing by cell phone requires, however, the 

addition of case disposition codes beyond those recommended by AAPOR to deal with some of the unique situations 

encountered, such as when a respondent is in an area with limited or no cell phone service, the call fails for some 

unknown reason, or the interviewer receives one of a variety of operator messages. Using the disposition code 

categories recommended by Callegaro et al. (2007) as a starting point, we developed a set of interim and final 

dispositions that could be mapped back to the broader AAPOR disposition categories (eligible, ineligible, unknown 

eligibility, and out-of-scope) 

Because we subsampled cases in the cell phone survey, to improve comparability we calculated two interim 

participation rates: a screening completion rate and a full interview completion rate. The former rate indicated 

whether the respondent successfully completed the initial screening portion of the questionnaire and was calculated 

with AAPOR formula #4 (AAPOR 2006). The latter rate reflected the proportion of those who were successfully 

screened as being eligible for the study, were subsampled for the full interview, and actually completed the full 

interview. Multiplying these rates provided an overall response rate. 

Prevalence Estimates 

Differences in demographic characteristics of respondents by the four analysis groups were examined, including sex, 

age, race/ethnicity, education, employment status, marital status, presence of children in the household, and veteran 

status. We also used self-reports of survey participants to assess the prevalence of 10 important health practice and 

risk behavior measures: access to health care coverage, cost as a barrier to health care, physical activity, asthma, 

diabetes, high blood pressure, obesity, current smoking, binge drinking, and human immunodeficiency virus (HIV) 

testing. 

Cost Calculations 

Cost is an important component in the evaluation of any survey design. The data collection costs per interview were 

calculated for both the landline and cell phone surveys, using the following: (1) actual unit costs for materials and 

supplies derived from the study experience, (2) production statistics from the effort, and (3) estimates of industry 

averages for direct hourly rates and indirect cost rates (i.e., fringe benefits, general and administrative expenses, 

indirect technical costs, and materials support expenses). Other costs assumed to be nearly equivalent regardless of 

the survey design were not included, such as overall project management, survey design development, and post–data 

collection weighting and analysis. Cell phone costs were calculated separately for two scenarios: (1) inclusion of all 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

completed interviews regardless of type of household telephone access, and (2) screening for cell phone–only 

households. 

Analytic Considerations 

Analyses were initially conducted at the state level. Respondent demographic characteristics and responses to health 

questions varied little, however, at the state level. Additionally, it was determined that presentation of results by 

state by the four analysis groups would be cumbersome and lengthy, resulting in confusion over clarity. As a result, 

we chose for some analyses (comparison of demographic characteristics and health and risk factor estimates) to take 

a more parsimonious analytic approach, combining the data from the three states with weighting adjustments to 

ensure that the results were not dominated by the larger of the three states. The few significant differences identified 

in the state-level analyses have been noted in the text. 

Findings 

For the cell phone study, 1,164 interviews were completed: 572 interviews with cell phone–only households and 592 

interviews with households that had both a cell phone and a landline (table 1). For the landline survey, 5,788 

interviews were completed: 1,972 interviews with persons in landline-only households and 3,816 interviews with 

those in households with both a landline and a cell phone. As noted earlier for the cell phone survey, all of those 

having only a cellular telephone were interviewed, while persons having both types of telephones were subsampled 

for administration of the full interview. The proportion of screened respondents having only cell phones was 35.5 

percent in Georgia, 38.5 percent in New Mexico, and 23.6 percent in Pennsylvania. A total of 1,049 potential 

respondents having both cell phones and landlines were categorized as not selected for interview based on the 

subsampling. 

For the cell phone sample, residential working number rates varied significantly by state, from a low of 63.2 percent 

in New Mexico to a high of 72.5 percent in Pennsylvania; the rate was 64.7 percent in Georgia (Table 1). Residential 

working number rates also varied significantly between sample vendors. Samples from SSI provided slightly higher 

working residential rates than did samples from MSG: Georgia, 66.1 percent versus 63.0 percent; New Mexico, 65.3 

percent versus 61.2 percent; and Pennsylvania, 74.5 percent versus 70.5 percent. If “mixed use” 100 banks with no 

residential directory-listed landline telephone numbers are more densely populated with working residential cell 

phone numbers, then this could account for some of the observed difference. 

Participation Rates 

Looking at the components of the overall response rate, we found that the screening completion rate to determine 

study eligibility was significantly higher in New Mexico (47.5 percent) than in Georgia (40.2 percent) or 

Pennsylvania (34.3 percent) (see Table 1). The interview completion rates, however, were closer in New Mexico 

(65.8 percent) and Pennsylvania (67.6 percent), but substantially lower in Georgia (60.8 percent). Interview 

completion rates also varied considerably by whether the household also had a landline telephone. The interview 

completion rate in cell phone–only households versus cell phone and landline households was higher in New 

Mexico (66.7 percent versus 64.1 percent) and Pennsylvania (70.2 percent versus 64.8 percent), but lower in 

Georgia (57.7 percent versus 66.1 percent). 

Overall, the cell phone response rate was considerably higher in New Mexico (31.3 percent) than in either Georgia 

(24.4 percent) or Pennsylvania (23.2 percent). These rates are similar to those reported for the JPSM Practicum 

Survey, which reported an AAPOR #3 response rate of 26.5 percent (Brick et al. 2007). 

The cell phone response rates were significantly lower than the landline response rates in New Mexico (51.9 percent 

versus 31.3 percent) and Pennsylvania (45.3 percent versus 23.2 percent). In Georgia, however, the landline and cell 

phone response rates were nearly identical (23.6 percent versus 24.4 percent). 

Because we did not run a split sample design, it is difficult to determine what role the incentive played in raising or 

lowering the participation rates. We do know that only 22.8 percent of those eligible for the $1 screener completion 

remuneration accepted the payment, and 87.1 percent of those eligible for the $10 payment for the full interview 

provided mailing information so they could receive the payment. Additionally, the text message did not appear to be 

very effective in encouraging participation among the hard-to-reach group. Of the 217 cell phone numbers to which 

the text message was sent, only two respondents ultimately completed the interview. Other researchers have reported 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

similar findings with regard to the apparent ineffectiveness of text messages in prompting participation in cell phone 

surveys (Brick et al. 2007; Steeh, Buskirk, and Callegaro 2007). 

Cell Phone Sharing 

Questions were added to both the cell phone and landline surveys to assess the percentage of respondents in 

households with at least one working cell phone who share a cell phone with one or more adults one-third of the 

time or more. Among the cell phone survey respondents, sharing was highest in Georgia (15.2 percent), followed by 

New Mexico (15.2 percent), and Pennsylvania (11.1 percent); none of these differences was statistically significant 

(table 2). The rates of sharing were somewhat higher in cell phone-only households than in households with both a 

landline and a cell phone in Georgia and New Mexico; again these differences were not statistically significant. 

For the landline sample, among those in households with at least one working cell phone, two-thirds of those 

interviewed in both New Mexico (64.5 percent) and Pennsylvania (64.3 percent) said they had a cell phone for 

personal use; in Georgia the rate was significantly higher (71.7 percent; p < .05). The prevalence of cell phone 

sharing overall was roughly equivalent in New Mexico (11.2 percent) and Pennsylvania (10.6 percent) [these data 

were not collected as part of the landline survey in Georgia]. Sharing was more prevalent in both states among those 

who indicated they had a personal cell phone compared to those who said they did not (p < >05). 

Demographic Comparisons 

Next we compared demographic characteristics across the four critical analysis groups, categorized by the frame 

from which their telephone number was sampled (landline or cell phone frame) and type of telephone access in the 

household (landline, cell phone, or both). The data were weighted using the “equalized” design weights (table 3). 

Focusing first on the two groups sampled from the cell phone sampling frame (groups C and D from Figure 1), we 

found that those from cell phone–only households, compared with those from households with both landline and cell 

phone access, were much more likely to be aged 18 to 34 years (51.4 percent versus 24.0 percent; p < .001), single 

or never married (43.9 percent versus 20.4 percent; p < .001), Hispanic (21.4 percent versus 15.2 percent; p < .01), a 

student (11.1 percent versus 4.8 percent; p < .001), and out of work (13.1 percent versus 7.6 percent; p < .01) (table 

2). Those in cell phone–only households were less likely to be married (32.0 percent versus 62.0 percent; p < .001), 

between the ages of 35 and 64 years (45.4 percent versus 65.6 percent; p < .001), a college degree recipient (20.7 

percent versus 34.0 percent; p < .001), non-Hispanic white (53.6 percent versus 64.6 percent; p < .001), or retired 

(3.6 percent versus 10.2 percent; p < .001).1 

Comparing the two extremes in telephone access (groups A and D from Figure 1), the cell phone–only group was 

significantly different from the landline-only group for all but two subgroups examined (having a high school 

diploma and having a college degree). Among those with access to a landline only, just over one-third of the 

respondents were younger than 50 years compared to more than three-quarters of those in cell phone-only 

households. Additionally, among the largest differences noted, those in landline-only households were less likely to 

be men (37.9 percent versus 51.1 percent; p < .001), currently employed (37.2 percent versus 66.9 percent; p < 

.001), single (16.4 percent versus 43.9 percent; p < .001), or to be a racial/ethnic minority (30.2 percent versus 51.1 

percent; p < .001). 

Looking next at differences between the two groups with access to both a cell phone and a landline (groups B and C 

from Figure 1), we found that these groups differed significantly across a number of characteristics. Compared with 

the landline survey, the cell phone survey resulted in a higher percentage of men (46.0 percent versus 38.2 percent; p 

< .001), non-Hispanic blacks (15.0 percent versus 7.5 percent; p < .001), employed persons (71.9 percent versus 

63.0 percent; p < .001), those who were single or never married (20.4 percent versus 13.7 percent; p < .001), and 

those with one or more children in the household (45.6 percent versus 38.9 percent; p < .001). In contrast, the cell 

phone survey resulted in a lower percentage of non-Hispanic whites (64.6 percent versus 77.3 percent; p < .001), 

those aged 65 years or older (10.4 percent versus 17.7 percent; p < .001), retired persons (10.2 percent versus 18.5 

percent; p < .001), and those who were married (62.0 percent versus 69.8 percent; p < .001). No significant 

differences were found between these groups with respect to education level or veteran status. 

Comparison of Survey Estimates 

Prevalence estimates for 10 key health practices and risk behaviors were also examined across the four analysis 

groups. These data were weighted using the “equalized” design weight (table 4). 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The two groups sampled from the cell phone frame (groups C and D in Figure 1) differed significantly on 3 of the 

10 measures, with a higher percentage of those in cell phone-only households reporting they had engaged in binge 

drinking in the past 30 days (28.7 percent versus 15.5 percent; p < .001) and they had not received health care at 

some point during the past year due to health care costs (24.9 percent versus 16.3 percent; p < .001). Those in cell 

phone-only households were less likely to have any kind of health care coverage (70.1 percent versus 86.0 percent; p 

< .001).2 

Comparing the two groups with access to both a cell phone and a landline (groups B and C in Figure 1), a higher 

percentage of those from the cell phone frame indicated they had not received health care due to cost considerations 

(16.3 percent versus 10.2 percent; p < .01) and that they had been tested for HIV at some point in their lives (43.6 

percent versus 36.6 percent; p < .001). There were no significant differences across the other 8 variables examined. 

The differences in health estimates were starker when we compared the landline-only groups with the cell phoneonly group (groups A and D in Figure 1). Those in landline households were much more likely to say they had high 

blood pressure (38.8 percent versus 20.8 percent; p < .001), had some form of health coverage (78.7 percent versus 

70.1 percent; p < .001), and to have diabetes (15.6 percent versus 7.7 percent). Conversely, a lower percentage of 

those with only a landline said they engaged in binge drinking (11.0 percent versus 28.7 percent), have ever been 

tested for HIV (37.5 percent versus 53.4 percent), partake in some form of regular exercise (60.7 percent versus 70.8 

percent), and currently smoke cigarettes (31.1 percent versus 24.8 percent). Many of these differences are not 

surprising given the demographic composition, particularly in terms of age, of these two groups. 

Cost Comparisons 

The costs of conducting surveys with a cell phone sample are considerably higher than those of conducting a 

landline telephone survey. We calculated a generic cost model for obtaining 1,000 completed interviews with a 

sample drawn from a landline frame, a cell phone frame (with no screening), and a cell phone frame with screening 

for cell phone–only households (Table 5). The data represent call-center costs only and do not include other project 

costs (such as project management, statistical support, CATI and other programming support), which are assumed to 

be relatively equivalent across the two types of surveys. On a per-interview completed basis, these costs averaged 

approximately $64 for a landline survey, $74 for a cell phone survey (no screening), and $196 for a cell phone–-only 

household survey. It is important to place these numbers in context, however. Because of restrictions we placed on 

the calling protocols for cell phones (a single refusal conversion call was made and fewer call attempts were made

than for the landline sample), the overall level of effort per case was lower (averaging 3.2 call attempts for the cell 

phone sample and 7.4 call attempts for the landline sample). Additionally, the cell phone survey included an 

incentive, whereas the landline survey did not. 

Discussion 

The state-based cell phone study conducted here shows, like some previous studies (Brick et al. 2006; Fleeman 

2006; Steeh 2004), that conducting surveys by sampling cell phone numbers is feasible, but is costly and produces 

relatively low rates of participation. Study response rates varied by state, but were in line with those reported by 

other cell phone studies (Brick et al. 2007). Given some of the unique situations encountered when calling people on 

cell phones (e.g., respondents are driving, shopping, working, or otherwise engaged in a manner not typically 

encountered in landline telephone surveys), it has been recommended that special contacting protocols be put into 

place for cell phone surveys (Lavrakas and Shuttles 2005). Following these recommendations in this study, we used 

a protocol with fewer calls per case than would typically be used for the landline RDD survey. Further, only a single 

attempt was made to complete an interview once a respondent refused. Participation rates may have been improved 

in this study, therefore, had more rigorous methods been used, such as increasing the total call attempts and making 

an additional attempt (as warranted) to convert those who initially refused to complete the survey. 

The survey industry is, however, at a crossroads with regard to such actions. Although such attempts may indeed 

improve response rates modestly (a best guess on the basis of landline surveys would be 5-10 percentage points 

depending on the protocol used), they are just as likely to increase respondents’ frustration, which could cause 

already low cell phone survey response rates to plummet further. The consistent decline in response rates to landline 

telephone surveys (Battaglia et al. 2007) shows that a substantial percentage of the population does not want to 

participate in surveys, regardless of the study’s goals. There is no reason to think that respondents on cell phones 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

should be any different. Researchers who decide to conduct interviews by cell phone should tread cautiously in this 

area, balancing the desire to obtain valid and reliable data with the need to minimize the burden on respondents. 

The study also verifies at the state level what others have found in national surveys with regard to the significant 

differences between cell phone–only households and those with other types of telephone access. Differences in 

demographic characteristics of the respondents and in some of the health and risk behavior measures are similar to 

those reported elsewhere (Blumberg, Luke, and Cynamon 2006; Brick et al. 2006). The fact that this group differs 

significantly even after controlling for other potential confounders (e.g., age, sex, race, education, marital status, 

children in the household, and state of residence) means, however, that current weighting practices in most landline 

surveys are insufficient to account for these differences. Although most landline surveys post-stratify by factors such 

as sex, age, race, and education, it appears that many of those in cell phone–only households are significantly 

different in at least some of their health problems and behaviors despite these characteristics, such that weighting to 

these factors does not account fully for the differences. 

Problems associated with weighting extend as well to that portion of the population that has both a cell phone and a 

landline. As shown here, this group appears to have different demographic characteristics when sampled by landline 

versus being sampled by cell phone. Brick et al. (2006) come to a similar conclusion, noting differential 

nonresponse rates in this group depending on the type of telephone on which they make and receive most of their 

telephone calls. Those sampled from a landline telephone frame were more likely to report relying more on their 

landline than their cell phone, whereas this finding was reversed for those sampled according to their cell phone 

number. As a result, it appears that to fully account for the population, researchers will have to think of the 

telephone frame as a single frame with landlines and cell phones being separate strata and sample from each, without 

screening for cell phone–only households. 

Another explanation for some of the differences in health conditions and risk factors may be mode effects: that is, 

adults may answer questions differently on a landline phone in the household versus on a cell phone in a location 

outside the household. This is an area of scant research with regard to cell phones. At issue is the degree to which 

persons interviewed by cell phone are “cognitively engaged” in the activity. Cell phones are one of many modern 

tools that facilitate the “multitasking” so prominent in society today. Not only do researchers need to be concerned 

about questions of safety (e.g., potentially interviewing someone who is driving), but they also need to be concerned 

about how respondents answer questions while engaged in other activities, such as shopping, being outdoors, or 

dining in a restaurant. Ideally, researchers would like respondents to be “fully engaged” mentally in the interview 

process and not distracted by other activities or stimuli. We know very little, however, about the conditions in which 

respondents find themselves when they are taking part in an interview by cell phone. 

The finding that sharing of cell phones among adults appears to be taking place in 10-15 percent of households (a 

proportion many might consider “non-ignorable”) has both statistical and operational implications. In most 

probability sample designs the linkage between elements of the sampling frame and the population under study is 

viewed as static, although some may be time-based since linkages are defined as of a particular date or dates. In the 

case of sampling frames associated with telephones, the linkage between a telephone number and an individual is 

generally based on membership rather than behavior. Thus, if an individual is a member of a household, which is 

served by a landline telephone, that individual is assumed to be linked to the frame. Similarly, in the case of cellular 

telephone numbers, if an individual owns a cell phone (i.e., is the cell phone user or holder) that individual is 

assumed to be linked to the sampling frame through the cell number. What do we do, however, when two or more 

adults in a household share a cell phone? Because the linkage of individuals to phone numbers is a critical part of the 

sampling model, the weighting model, as well as models used for the computation of response rates, it may be 

necessary to obtain “usage” information from sample respondents and, in turn, use this behavior information to 

modify calculations of probabilities of selection, weighting as well as measures of response rate. It may also be 

necessary to develop (or adopt from existing approaches) methods for conducting within household selection, so that 

all adults who share the telephone have a positive probability of selection. As shown by Brick et al (2007), this can 

be operationally difficult and lead to increased nonresponse. More work in this area is required before a definitive 

set of recommendations can be developed, particularly in terms of the establishment of thresholds where sharing is 

determined to be non-ignorable (i.e., what percentage of the time does the cell phone need to be shared) and the 

most effective wording of usage questions to be asked. 
 

 

 

 

 

 

 

 

 

 

 

 

 

Finally, interviewing respondents by cell phone also appears much more costly than conducting landline telephone 

surveys, the costs of which are already high because of the need for increased effort to achieve acceptable response 

rates. As a result, researchers may want to conduct as few cell phone interviews as possible (but taking into account 

the inflation of sampling variances due to unequal weights) and simply weight these responses accordingly. A key 

difficulty with the weighting procedures at this point, however, is the lack of a good population standard denoting 

telephone access at the subnational level. Currently, the National Health Interview Survey (NHIS) provides the only 

external standard against which to adjust data by type of household telephone access; however, this survey provides 

data only down to the Census Region level, not the state, local, or other subnational level (Blumberg, Luke, and 

Cynamon 2006). Researchers must, therefore, either not account for this factor in their weighting schemes using the 

internal survey data (a potentially problematic approach) or assume that the national estimates fit equally at the 

subnational level and make adjustments accordingly (a dubious assumption, but a less problematic approach). 

Neither is likely to be a very good solution. Including questions on telephone access in other larger populationbased surveys, such as the Current Population Survey or American Community Survey, would help remedy this 

problem. 

Sampling and interviewing respondents by cell phone no longer appears to be a choice, but is now a necessity if 

surveys by telephone are to provide valid, reliable, and representative data. Such surveys, because of issues of 

differential nonresponse and cost per interview, should include all eligible sample members reached by telephone 

and not simply focus on cell phone–only households. Considerable work remains with regard to weighting and postsurvey adjustments, however, before landline and cell phone surveys can be combined to produce integrated 

estimates, particularly at the subnational level. 

Acknowledgements 

We would like to thank the following BRFSS Coordinators for their assistance in conducting this study: Leah Bryan 

(Georgia), Wayne A Honey (New Mexico), and Robert F. Dewar (Pennsylvania). Work by Michael Link was 

conducted while an employee of the Centers for Disease Control and Prevention. 

Notes 

1 With the exception of race, there were no significant differences in the demographic makeup of these two groups at 

the state level. Not surprising given the racial/ethnic makeup of the population in each state, New Mexico had a 

higher percentage of Hispanics, Georgia had a higher percentage of non-Hispanic blacks, and Pennsylvania had a 

higher percentage of non-Hispanic whites for both the landline and cell phone group and the cell phone–only group 

(data not shown). No other significant state-based differences in demographic characteristics were noted across these 

two groups from the cell phone frame. 

2

 Differences at the state level were found for only two variables. Among those in cell phone-only households, the 

percentage saying they had a health plan was significantly higher (p < .001) in Pennsylvania (85.5 percent) than in 

New Mexico (64.0 percent) or Georgia (60.7 percent). Conversely, a lower percentage of those in Pennsylvania 

(16.3 percent) reported that they had forgone health care in the past year due to costs than was the case with those in 

New Mexico (27.0 percent) or Georgia (31.4 percent). There were no statistically significant differences in survey 

estimates across the three states for those sampled from the cell phone frame who had both a cell phone and a 

landline. 

Appendix A 

Cell Phone Survey Screening Questions 

INITIAL INTRO 

HELLO, I am calling for the (HEALTH DEPARTMENT) . My name is (NAME) . We are gathering 

information about the health of (STATE) residents and you will be paid for any time you spend answering our 

questions on your cell phone. This project is conducted by the health department with assistance from the Centers 

for Disease Control and Prevention. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

I have just a few questions to find out if you are eligible for the study. 

1. Is this (PHONE NUMBER) ? 

IF NO: Thank you very much, but I seem to have dialed the wrong number. It’s possible that your number may be 

called at a later time. STOP 

2. Is this a cellular telephone? READ ONLY IF NECESSARY: “By cellular telephone, we mean a telephone that 

is mobile and usable outside of your neighborhood.” 

IF NO: Thank you very much, but we are only interviewing cell telephones at this time. STOP 

3. Are you 18 years of age or older? 

IF NO: Thank you very much, but we are only interviewing persons aged 18 or older at this time. STOP 

4. Do you live in a private residence, that is, not in a dormitory or other type of group living situation? 

IF NO: Thank you very much, but we are only interviewing private residences. STOP 

READ ONLY IF NECESSARY: “By private residence, we mean someplace like a house or apartment.” 

5. Are you a resident of (STATE)? 

IF NO: In what state do you live? COLLECT STATE 

Thank you very much, but we are not interviewing in your state at this time. STOP 

6. Do you also have a landline telephone that is used to make and receive calls? 

READ ONLY IF NECESSARY: “By landline telephone, we mean a “regular” telephone in your home that is 

connected to outside telephone lines through a cable or cord and is used for making or receiving calls.” 

INTERVIEWER: TELEPHONE SERVICE OVER THE INTERNET COUNTS AS LANDLINE SERVICE. 

IF "YES” AND NOT SUBSAMPLED: Thank you very much. Those are all the questions that I have for you 

today. In appreciation for the time you have spent answering our questions, we would like to provide you with one 

dollar in compensation. Would you please give me your name and address so that we can send you the one dollar 

payment? 

COLLECT NAME AND ADDRESS—STOP 

IF "NO” OR IF “YES” AND SUBSAMPLED, SKIP TO SURVEY INTRO 

Appendix B 

Household Cell Phone Sharing Questions 

1. Do you have a cell phone for personal use? Please include cell phones used for both business and personal use. 

IF YES: GO TO Q3 

IF NO: GO TO Q2 

2. Do you share a cell phone for personal use with other adults? 

IF YES: GO TO Q4 

IF NO: GO TO NEXT SECTION 
 

 

 

 

 

 

 

 

 

 

3. Do you usually share this cell phone with any other adults? 

IF YES: GO TO Q4 

IF NO: GO TO NEXT SECTION 

4. How many other adults use this cell phone at least one-third of the time? 

RECORD NUMBER: ______ 

References 

American Association for Public Opinion Research (AAPOR). 2006. Standard Definitions: Final 

Dispositions of Case Codes and Outcome Rates for Surveys. 4th ed. Ann Arbor, MI: AAPOR. 

Battaglia, Michael, Meena Khare, Martin Frankel, Mary Cay Murray, Paul Buckley, and Saralyn Peritz. 2007. 

Response rates: How have they changed and where are they headed? in Advances in Telephone Survey Methodology 

edited by James M. Lepkowski, Clyde Tucker, J. Michael Brick, Edith de Leeuw, Lilli Japec, Paul J. Lavrakas, 

Michael W. Link, and Roberta L. Sangster. John Wiley & Sons, New York. 

Blumberg, Stephen, and Julian Luke. 2007. “Wireless Substitution: Early Release of Estimates Based on Data from 

the National Health Interview Survey, July – December 2006.” Available online as of August 9, 2007 at 

http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless200705.pdf. 

Blumberg, Stephen J., Julian V. Luke, and Marcie L. Cynamon. 2006. “Telephone Coverage and Health Survey 

Estimates: Evaluating the Need for Concern About Wireless Substitution.” American Journal of Public Health 

96:926–31. 

Brick, J. Michael, Pat D. Brick, Sarah Dipko, Stanley Presser, Clyde Tucker, and Yangyang Yuan. 2007. “Cell 

Phone Survey Feasibility in the U.S.: Sampling and Calling Cell Numbers Versus Landline Numbers.” Public 

Opinion Quarterly 71:23–39. 

Brick, J. Michael, Sarah Dipko, Stanley Presser, Clyde Tucker, and Yangyang Yuan. 2006. “Nonresponse Bias in a 

Dual Frame Sample of Cell and Landline Numbers.” Public Opinion Quarterly 70:780–93. 

Callegaro, Mario, Charlotte Steeh, Trent D. Buskirk, Vasja Vehovar, Vesa Kuusela, and Linda Piekarski. 2007. 

“Fitting Disposition Codes to Mobile Phone Surveys: Experiences from Studies in Finland, Slovenia, and the USA.” 

Journal of the Royal Statistical Society 170:1–24. 

Fleeman, Anna. 2006. “Merging Cellular and Landline RDD Sample Frames: A Series of Three Cell Phone 

Studies.” Paper presented at the Second International Conference on Telephone Survey Methodology, Miami, FL. 

Keeter, Scott. 2006. “The Impact of Cell Phone Noncoverage on Polling in the 2004 Presidential Election.” Public 

Opinion Quarterly 70:88–98. 

Lavrakas, Paul, and Charles Shuttles. 2005. “Cell Phone Sampling Summit II Statements on Accounting for Cell 

Phones in Telephone Survey Research in the U.S.” Available online as of August 9, 2007 at 

http://www.nielsenmedia.com/cellphonesummit/statements.html. 

Link, Michael, and Ali Mokdad. 2005. “Leaving Answering Machine Messages: Do They Increase Response Rates 

for the Behavioral Risk Factor Surveillance System?” International Journal of Public Opinion Research 17:239–50. 

Mokdad, Ali, Donna Stroup, and Wayne Giles. 2003. “Public Health Surveillance for Behavioral Risk Factors in a 

Changing Environment: Recommendations from the Behavioral Risk Factor Surveillance Team.” MMWR 

Recommendations and Reports 52(RR09):1–12. 
 

Steeh, Charlotte. 2004. “A New Era for Telephone Surveys.” Paper presented at the Annual Conference of the 

American Association for Public Opinion Research, Phoenix, AZ. 

Steeh, Charlotte, Trent Buskirk, and Mario Callegaro. 2007. “Use of Text Messages in U.S. Mobile Phone Surveys.” 

Field Methods 19:59–75. 

Tucker, Clyde, J. Michael Brick, and Brian Meekins. 2007. “Household Telephone Service and Usage Patterns in 

the United States in 2004: Implications for Telephone Samples.” Public Opinion Quarterly 71:3–22. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Cell phone and landline survey response rates, by state 

GA NM PA 

Cell phone sample:

 Cell phone sample size (n) 9,000 4,400 9,997 

 Completed interviews – cell phone only (n) 206 215 151 

 Complete interviews – cell phone & landline (n) 199 198 195

 Cell phone-only adults (?) (%)1 35.5 38.5 23.6 

Working cell phone number rate (%) 64.7 63.2 72.5 

 Cell phone screener completion rate (%) 40.2 47.5 34.3 

 Cell phone interview completion rate (%) 60.8 65.8 67.6

 Cell phone-only response rate (%)2 23.2 31.7 24.1 

 Cell phone & (?) landline response rate (%)2,3 26.6 30.4 22.2 

 Cell phone overall response rate (%)2,3 24.4 31.3 23.2 

Landline sample:

 BRFSS landline overall response rate (%)2 23.6 51.9 45.3 

BRFSS = Behavioral Risk Factor Surveillance System. 

1

 Reflects the percentage of the total screened sample indicating they had only a cell phone and no landline in the 

household. 

2

 Response rate based on formula #4 recommended by the American Association for Public Opinion Research 

(APOR 2006). 

3

 Adjusted to account for subsampling among households with both a landline and a cell phone. 
 

 

 

 

 

 

 

 

 

 

 

 

Table 2. Cell phone sharing, by sample frame, by state

GA NM PA 

Cell phone sample: 

 Share cell phone - cell phone-only (%) 19.6 18.9 10.5 

 Share cell phone - cell phone & landline (%) 15.0 12.9 11.3 

 Share cell phone – total (%) 16.6 15.2 11.1 

Landline sample (cell phone & landline households only): 

 Have personal cell phone (%) 

 Share cell phone – have personal cell phone (%) 

 Share cell phone – do not have personal cell phone (%) 

 Share cell phone – total (%) 

71.7 

1

---

1

---

1

---

64.5 

 13.1 

 7.8 

 11.2 

64.3 

13.7 

5.0 

10.6 

Note: “Cell phone sharing” is defined as sharing a cell phone “one-third” of the time or more with 

another adult in the household, with the time allotment of “one-third” being self-defined by the respondent. 

1

 Data not collected for landline survey in Georgia. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3. Comparison of demographic characteristics by sample frame and type of household telephone access 

Landline survey Cell phone survey 

Landline Cell phone and Cell phone Cell phone 

Demographic characteristics 

Only

 (A) 

landline 

(B) 

and landline 

(C) 

Only

 (D) 

n 1,972 3,816 592 572 

Sex 

Male 37.9 38.2 46.0 51.1 

(34.8, 41.0) (36.1, 40.3) (41.9, 50.0) (46.9, 55.3) 

 Female 62.1 61.8 54.0 48.9 

(59.0, 65.2) (59.7, 63.9) (50.0, 58.1) (44.7, 53.1) 

Age (years) 

18-34 14.5 19.6 24.0 51.4 

(12.2, 17.1) (17.9, 21.6) (20.7, 27.7) (47.2, 55.5) 

35-49 20.9 32.0 35.2 26.5 

(18.3, 23.8) (30.1, 34.1) (31.5, 39.2) (22.9, 30.3) 

50-64 29.9 30.6 30.4 18.9 

(27.0, 32.9) (28.7, 32.6) (26.7, 34.2) (15.8, 22.4) 

65+ 34.7 17.7 10.4 3.3 

(31.9, 37.6) (16.2,19.3) (8.1, 13.1) (2.1, 5.1) 

Race/ethnicity 

Hispanic 16.8 12.2 15.2 21.4 

(14.5, 19.4) (10.8,13.7) (12.6, 18.3) (18.5, 24.8) 

Non-Hispanic white 69.8 77.3 64.6 53.6 

(66.7, 72.8) (75.3,79.1) (60.6, 68.3) (49.5,57.6) 

Non-Hispanic black 9.3 7.5 15.0 15.8 

(7.4, 11.6) (6.5, 8.8) (12.4, 18.1) (13.0, 19.0) 

Non-Hispanic other 4.1 3.0 5.2 9.2 

(3.0, 5.6) (2.3, 3.9) (3.7, 7.3) (7.1, 11.8) 

Education 

 Less than high school 20.4 7.1 10.1 12.7 

(18.0, 23.1) (6.0, 8.4) (8.0, 12.9) (10.2, 15.6) 

High school diploma 39.9 26.5 29.7 35.8 

(36.8, 43.0) (24.7, 28.4) (26.1, 33.5) (31.9, 39.9) 

 Some college 20.9 26.7 26.2 30.8 

(18.4, 23.8) (24.8, 28.6) (22.8, 29.9) (27.1, 34.8) 

 College degree 18.8 39.7 34.0 20.7 

(16.4, 21.4) (37.7, 41.9) (30.2, 38.0) (17.5, 24.3) 

Employment status 

 Employed 37.2 63.0 71.9 66.9 

(34.1, 40.3) (60.9, 65.1) (68.1, 75.4) (62.8, 70.7) 

Homemaker 12.4 8.8 5.5 5.4 

(10.5,14.7) (7.6, 10.1) (3.9, 7.6) (3.8, 7.6) 

 Student 2.1 3 .9 4.8 11.1 

(1.3, 3.4) (3.0, 5.0) (3.3, 6.9) (8.7, 14.0) 

Retired 28.4 18.5 10.2 3.6 

(25.7, 31.1) (17.0, 20.2) (8.0, 13.0) (2.4, 5.5) 

Out of work/unable 19.9 5.8 7.6 13.1 

(17.3, 22.8) (4.9, 6.9) (5.7, 10.1) (10.5, 16.2) 

Marital status

 Married 49.5 69.8 62.0 32.0 

(46.3, 52.7) (67.9, 71.7) (57.9, 65.8) (28.2, 36.0) 

 Separated/divorced/widowed 34.1 16.5 17.7 24.2 

(31.2, 37.0) (15.1, 17.9) (14.8, 21.0) (20.8, 27.9) 

 Single/never married 16.4 13.7 20.4 43.9 

(14.1, 19.0) (12.2, 15.4) (17.3, 23.8) (39.8, 48.0) 
 

 

 

 

 

 

Children in household 

None 70.5 61.1 54.4 60.5 

(67.3, 73.4) (59.0, 63.2) (50.4, 58.4) (56.4, 64.5) 

One or more 29.5 38.9 45.6 39.5 

(26.6, 32.7) (36.8, 41.0) (41.6, 49.6) (35.5, 43.6) 

U.S. veteran status 

Veteran 87.1 87.1 87.3 90.6 

(85.7, 88.5) (84.8, 89.0) (84.3, 89.7) (87.9, 92.8) 

Not a veteran 12.9 12.9 12.7 9.4 

(11.5, 14.3) (11.0, 15.2) (10.3, 15.7) (7.2, 12.1) 

Note: Percentages are shown along with 95% confidence limits in parentheses. Data were weighted using the state 

“equalized” design weight. Standard errors were calculated using SPSS version 13 with the Complex Samples 

Module. 
 

 

 

 

 

 

 

Table 4. Comparison of estimates of key health practices and risk behaviors by sample frame and type of 

household telephone access 

Landline survey Cell phone survey 

Landline Cell phone and Cell phone and Cell phone 

Health condition or risk factor 

Only 

(A) 

landline 

(B) 

landline 

(C) 

only 

(D) 

n 1,972 3,816 592 572 

Any kind of health care coverage 78.7 89.0 86.0 70.1 

(75.9, 81.4) (87.6, 90.4) (82.9, 88.6) (66.3, 73.6) 

Had not received care because of cost 20.4 10.2 16.3 24.9 

(17.8, 23.3) (8.9, 11.6) (13.5, 19.5) (21.5, 28.6) 

Physical activities or exercise 60.7 75.1 75.1 70.8 

(57.5, 63.7) (73.2, 77.0) (71.4, 78.5) (66.9, 74.5) 

Ever had asthma 12.8 13.3 13.3 15.8 

(10.7, 15.1) (11.9, 14.9) (10.7, 16.3) (13.0, 19.1) 

Ever had diabetes 15.6 8.7 9.2 7.7 

(13.4, 18.1) (7.6, 10.0) (7.0, 11.8) (5.7, 10.2) 

Ever had high blood pressure 38.8 29.2 25.9 20.8 

Obesity (BMI ≥ 30 kg/m2

) 

(35.8, 41.9) 

27.6 

(27.3, 31.1) 

25.5 

(22.5, 29.6) 

30.9 

(17.6, 24.4) 

24.4 

(24.8, 30.6) (23.6, 27.4) (24.6, 37.9) (20.9, 28.2) 

Currently smoke cigarettes 24.8 17.3 23.4 31.1 

(22.1, 27.7) (15.7, 19.0) (18.0, 30.0) (27.3, 35.1) 

Binge drinking in past 30 days 11.0 21.1 15.5 28.7 

(9.0, 13.5) (18.9, 23.5) (10.9, 21.4) (22.0, 36.5) 

Ever tested for HIV* 37.5 36.6 43.6 53.4 

(33.5, 41.6) (34.3, 39.0) (39.4, 48.0) (45.0, 61.7) 

BMI = body mass index; HIV = human immunodeficiency virus. 

Note: Percentages are shown along with 95% confidence limits in parentheses. Data were weighted using the state 

“equalized” design weight. Standard errors were calculated using SPSS version 13 with the Complex Samples 

Module. 

*Asked only of adults aged 18 to 64 years old. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 5. Costs per 1,000 completed interviews comparing landline versus cell phone surveys 

Landline Cell Cell phone 

telephone phone only (via 

survey survey screening) 

Assumptions 

Number of sampled telephone numbers / addresses (per 1,000 

completed interviews)1 

5,920 12,800 38,400 

Average call attempts per sampled case1

 7.4 3.2 3.2 

 Total call attempts required (per 1,000 completed interviews) 43,808 40,960 122,880 

Cost calculations for materials/supplies 

 Telephone sample ($0.08 per case)2

 $474 $1,024 $3,072

 Telephone connect charges ($0.0625 per call)2

 $2,738 $2,560 $7,680 

$10 incentive plus mailing charges – materials and postage ($10.70 

per complete) 2 

NA $10,700 $10,700 

Subtotal for direct cost of materials/supplies $3,211 $14,284 $21,452 

Subtotal for indirect costs of materials/supplies (@ 25%)3

 $803

 Total cost for materials/supplies (direct and indirect) $4,014 $17,855 $26,815 

Cost calculations for labor 

Hours of interviewer time required (@ 20 calls/hour) 1

 2,190 2,048 6,144 

Hours of supervisor/monitor/quality control time required (@ 25% 

of interviewer time)3 

Interviewer time ($8.00 per hour) 3

548 

 $17,523 

512 

$16,384 

1,536 

$49,152 

 Supervisor or monitor time ($12.00 per hour) 3 $6,571 $6,144 $18,432 

Subtotal for direct labor costs $24,094 $22,528 $67,584 

Subtotal for indirect labor costs (fringe and other indirect costs (@ 

150%)3 

$36,142 $33,792 $101,376

 Total cost for labor (direct and indirect costs) $60,236 $56,320 $168,960 

Total cost of materials/supplies and labor (absent fee/profit) per $64,251 $74,175 $195,775 

1,000 completed interviews 

Cost per sampled telephone number $10.85 $5.79 $5.10 

Cost per completed interview $64.25 $74.18 $195.78 

NA = not applicable. 

Note: Comparison includes only costs of actual data collection and does not include other costs such as survey 

design, statistical support, analysis, or project management (other than direct supervision costs noted in the table). 

1

 Based on production statistics from cell phone study. 

2

 Based on cost data from cell phone study. 

3

 Based on estimates of average rates across survey research industry. 
Figure 1. Telephone access by sample frame 

(D) 

Cell phone 

(B) / (C) 

only 

Landline and 

cell phone 

Cellular Landline 

(A) 

Landline 

only 

telephone frame telephone 

frame