Geographic Inaccuracy of Cell Phone Samples and the Effect on Telephone Survey Bias, Variance, and Cost

Skalland, Benjamin; Khare, Meena

doi:10.1093/jssam/smt001

Cited by 10 publications

(15 citation statements)

References 2 publications

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“…Skalland and Khare (2013) Our study is the first population-based study to explore the association between this mobile population and their health behaviors (e.g., smoking, physical activity); chronic disease (diabetes, arthritis); and chronic conditions (overweight and obesity). Results indicate that this mobile population are more physically active, not a current smoker, not overweight or obese, and have lower prevalence of depression, diabetes, and arthritis compared to those who did not move.…”

Section: Results Resultsmentioning

confidence: 99%

“…According to Skalland and Khare (2013), in a single-state telephone survey, excluding the out-of-state respondents will increase the cost of the survey, as more cell phone samples are needed to complete the target number of interviews.…”

Section: Results Resultsmentioning

confidence: 99%

“…They estimated the geographic inaccuracy rates for cell phone samples (cell only, cell mostly, and landline mostly or dual users) for all adult users to be 6% at the census region level, 10% at the state level, and 41% at the county level. Similarly, Skalland and Khare (2013) estimated the national geographic inaccuracy (as sampling state vs. true state) of cell phone samples for adults in cell-only households to be 11.5% at the national level, with inaccuracy rates varying widely among states. Marken, Chattopadhyay and Chan (2016) reported an increase in cell phone mobility (described as "overcoverage" and "undercoverage") across states from 2013.…”

Section: Introduction Introductionmentioning

confidence: 99%

“…Marken, Chattopadhyay and Chan (2016) reported an increase in cell phone mobility (described as "overcoverage" and "undercoverage") across states from 2013. The out-of-state respondents are were more likely to be male, young, non-Hispanic white, college graduate, have high income, live in a household with no children, and living in Northeast census region (Christian, Dimock and Keeter 2009;Marken, Chattopadhyay, and Chan 2016;Skalland and Khare 2013). Inaccuracy in cell telephone sampling or overand undercoverage possesses a significant challenge in sampling, increases the costs of surveys, and can increase the variance (Skalland and Khare 2013) …”

Section: Introduction Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Health Behaviors and Chronic Conditions of Movers: Out-of-state Interviews Among Cell Phone Respondents, BRFSS 2014

Chowdhury¹,

Pierannunzi²,

Garvin³

et al. 2018

Surv Pract

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Since 2011, the Behavioral Risk Factor Surveillance System (BRFSS) has been conducting telephone surveys using landline and cell phones from all U.S. states.Due to the portability of cell phones, residents in one state can retain cell phone numbers with area codes from other states. Protocol dictates that BRFSS must interview such out-of-state respondents to complete the core BRFSS interview and collected data must then be transferred to the state of current residence.We used cell phone data from 2014 BRFSS to compare the demographic factors, health care access, health behaviors, history of chronic disease, and chronic conditions among out-of-state interview (movers) with those respondents whose cell phone numbers matched their current state of residence (did not move).The estimated weighted population percentage of movers was 10% nationwide and ranged from 1.5% in Hawaii to 21.0% in Nevada (median: 5.8%). Compared with respondents who did not move, movers were significantly more likely to be younger, white non-Hispanic, college graduate, never married, and more likely to have health care coverage. After adjusting for demographics, movers were 16% less likely to report no leisure time physical activity, 17% less likely to smoke, 7% less likely to be overweight or obese, 33% less likely to report diabetes, and 12% less likely to report having arthritis than respondents who did not move. Persons who might be left out of cell phone samples due to moving in or out of state may therefore represent a potential for bias in estimation of health behaviors and chronic conditions where transfer of data across state lines is not possible.

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Section: Results Resultsmentioning

confidence: 99%

Section: Results Resultsmentioning

confidence: 99%

Section: Introduction Introductionmentioning

confidence: 99%

Section: Introduction Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Health Behaviors and Chronic Conditions of Movers: Out-of-state Interviews Among Cell Phone Respondents, BRFSS 2014

Chowdhury¹,

Pierannunzi²,

Garvin³

et al. 2018

Surv Pract

View full text Add to dashboard Cite

show abstract

“…There is limited research on the consequences of including wireless phones in the construction of geographically specific sampling frames for random digit dialing (RDD) surveys (Christian et al 2009; Dutwin et al 2011; Skalland et al 2012). The challenge of sampling small geographic areas for dual-frame RDD surveys (that is, surveys that randomly select sample from two sampling frames, in this case landline telephone numbers and wireless telephone numbers) using switch center assignment has not been addressed.…”

Section: Introductionmentioning

confidence: 99%

The Accuracy of Small Area Sampling of Wireless Telephone Numbers

et al. 2015

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The value of telephone surveys for assessing effects at geographic areas is impacted by increasing wireless telephone use. Highly accurate landline samples may be drawn for national, state, county, or even smaller areas; however, wireless samples have less geographic precision. This requires additional data collection effort and screening costs in order to ensure the appropriate geographic area is surveyed. In this paper, we examine the accuracy of wireless sample frame from the 2010–2011 National Flu Surveys. We illustrate differences and variations in wireless sampling accuracy for different geographic areas, focusing on variability by area in placement of wire centers related to residences. Our results suggest that the accuracy of wireless sampling may be dependent on differences in geographic areas with the accuracy of wireless sampling decreasing as the level of geographic aggregation gets more specific; landline accuracy remains relatively stable regardless of geographic specificity. To explain this phenomenon, we examine patterns of geographic dispersion of wireless telephone numbers related to telephone switch centers and geographic area. Based on the evidence from these surveys we present several options to estimate the geographic specificity of an area prior to sampling.

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References

2014

Internet, Phone, Mail, and Mixed‐Mode Surveys

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Geographic Inaccuracy of Cell Phone Samples and the Effect on Telephone Survey Bias, Variance, and Cost

Cited by 10 publications

References 2 publications

Health Behaviors and Chronic Conditions of Movers: Out-of-state Interviews Among Cell Phone Respondents, BRFSS 2014

Health Behaviors and Chronic Conditions of Movers: Out-of-state Interviews Among Cell Phone Respondents, BRFSS 2014

The Accuracy of Small Area Sampling of Wireless Telephone Numbers

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