Nonresponse Bias in a Dual Frame Sample of Cell and Landline
                    Numbers

Brick, J. Michael; Dipko, Sarah; Presser, Stanley; Tucker, Clyde; Yuan, Yangyang

doi:10.1093/poq/nfl031

Cited by 88 publications

(61 citation statements)

References 6 publications

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“…Many of the large research studies on smoking and quitting beliefs have used telephone surveys to gather data (Balmford and Borland, 2008;Bansal et al, 2004;Brownson et al, 1992;Cummings et al, 2004;Hammond et al, 2004;McMenamin et al, 2006;Murphy et al, 2005), and impoverished individuals tend to be underrepresented in such surveys (Brick et al, 2006;Shebl et al, 2009). For instance, 21.6% of adults in low-income households had no landline service in 2007 (Blumberg and Luke, 2009), and 17% used wireless phones only (Blumberg and Luke, 2007).…”

mentioning

confidence: 99%

Barriers to Effective Tobacco-Dependence Treatment for the Very Poor

Christiansen

Reeder²,

Hill

et al. 2012

J. Stud. Alcohol Drugs

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ABSTRACT. Objective: People who live in poverty have a high prevalence of smoking, are less likely to engage in evidence-based treatment, and fi nd it harder to quit. Their beliefs about smoking and quitting can serve as barriers to quitting. Little is known about the smoking and quitting beliefs of the very poor (about U.S. $15,000 or less annual family income) because they tend not to be included in research. This study sought to assess beliefs about smoking and quitting by the very poor in relation to past quitting behavior and intention to quit in the future. Method: A survey was administered in person to residents in randomly selected addresses in two very impoverished Milwaukee, WI, ZIP codes during the day to ensure the inclusion of the very poor. Results: Six hundred fi fty-four people completed the survey, a response rate of 78.3%. Sixty-eight percent reported annual household incomes of less than $15,000 compared with 30.8% in the community as a whole and 13.0% of households nationally. Self-reported smoking prevalence was 42.1%. Specifi c beliefs about smoking and quitting were related to past quit attempts and intentions to quit in the future. Both race and income predicted beliefs and quitting-related variables independently and jointly. Conclusions: Continued tobacco-control progress requires addressing specifi c populations with known high tobacco use. One of these populations is those with low income. Efforts to engage them in treatment will have to address specifi c beliefs about smoking and quitting. (J. Stud. Alcohol Drugs, 73, 874-884, 2012) , 1964). Since then, tobacco use has become a class phenomenon with people living in poverty far more likely to smoke than others (Datta et al., 2006). In 2010, only 18.3% of adults with annual incomes at or above the poverty level were current smokers compared with 28.9% of adults below the poverty level (Centers for Disease Control and Prevention, 2011). Not surprisingly, this tobacco-use disparity contributes to the well-documented health disparity gap between income groups via tobacco-related diseases (Fagan et al., 2007;Fiscella and Williams, 2004;Jha et al., 2006;Vidrine et al., 2009). These class-based smoking disparities and their health consequences highlight the importance of bringing evidence-based tobacco-cessation treatments to those living in poverty.Poverty is not only related to smoking prevalence and related health risks but also to a reduced likelihood of smoking cessation and receipt of cessation treatment (Browning et al., 2008;Chase et al., 2007). Smokers facing educational and economic challenges are less likely to try to quit than are other smokers and are less successful when they do try (Gilman et al., 2003(Gilman et al., , 2008Giskes et al, 2006;Hiscock et al., 2011Hiscock et al., , 2012Levy et al., 2005;Sheffer et al., 2012). A lack of cessation success might refl ect, in part, a failure to use evidence-based treatment during a quit attempt. Cummings and Hyland (2005) found that when smokers living in poverty tried to qui...

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confidence: 99%

Barriers to Effective Tobacco-Dependence Treatment for the Very Poor

Christiansen

Reeder²,

Hill

et al. 2012

J. Stud. Alcohol Drugs

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“…If we have independent population information about the landline and mobile frames, such as demographic and geographical distributions, then this can be used to poststratify the mobile and landline samples to the control population controls. This is what is suggested by Brick et al (2006) and Brick (2011). They suggest that by doing this, the fixed-weight average estimator is sufficient to reduce bias due to nonresponse, but they are dependent on knowing the response rates in the different domains.…”

Section: Comparison Of Composite Estimators With Census Figuresmentioning

confidence: 77%

“…Exclusion of mobile-only individuals from social surveys has adverse consequences for survey estimates, as there are sociodemographic differences between individuals who own a mobile telephone and those who own a landline telephone (Brick et al 2006 in the USA; Callegaro and Possio 2004 in Italy; Kuusela et al 2008 in Finland;Vicente and Reis 2009 in Portugal;and Arcos et al 2014 in Spain). Individuals living in mobile-only households are more likely to be younger, male, of a lower socioeconomic status, foreign-born, students, highly transient, in large cities, and in full-time employment .…”

Section: Introductionmentioning

confidence: 99%

Weighting Strategies for Combining Data from Dual-Frame Telephone Surveys: Emerging Evidence from Australia

Baffour

Haynes

Western

et al. 2016

Journal of Official Statistics

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Until quite recently, telephone surveys have typically relied on landline telephone numbers. However, with the increasing popularity and affordability of mobile phones, there has been a surge in households that do not have landline connections. Additionally, there has been a decline in the response rates and population coverage of landline telephone surveys, creating a challenge to collecting representative social data. Dual-frame telephone surveys that use both landline and mobile phone sampling frames can overcome the incompleteness of landline-only telephone sampling. However, surveying mobile phone users introduces new complexities in sampling, nonresponse measurement and statistical weighting. This article examines these issues and illustrates the consequences of failing to include mobile-phoneonly users in telephone surveys using data from Australia. Results show that there are significant differences in estimates of populations' characteristics when using information solely from the landline or mobile telephone sample. These biases in the population estimates are significantly reduced when data from the mobile and landline samples are combined and appropriate dual-frame survey estimators are used. The optimal choice of a dual-frame estimation strategy depends on the availability of good-quality information that can account for the differential patterns of nonresponse by frame.

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“…Since it is uncommon to have access to the domain membership information before collecting the survey data, this information should be obtained during the data collection. For example, information about landline telephone service should be obtained in the area-landline dual frame surveys (Lepkowski & Groves, 1986) or about the landline and cell phone services should be obtained in the landlinecell dual frame telephone surveys (Brick et al, 2006;Kennedy, 2007). Collecting this information could be burdensome for some respondents and could lead to more unit non-response.…”

Section: Domain Misclassification Bias In Dual Frame Estimationmentioning

confidence: 99%

Joint Calibration Estimator for Dual Frame Surveys

Elkasabi

Heeringa

Lepkowski

2015

Statistics in Transition New Series

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Many dual frame estimators have been proposed in the statistics literature. Some of these estimators are theoretically optimal but hard to apply in practice, whereas others are applicable but have larger variances than the first group. In this paper, a Joint Calibration Estimator (JCE) is proposed that is simple to apply in practice and meets many desirable properties for dual frame estimators. The JCE is asymptotically design unbiased conditional on the strong relationship between the estimation variable and the auxiliary variables employed in the calibration. The JCE achieves better performance when the auxiliary variables can fully explain the variability in the study variables or at least when the auxiliary variables are strong correlates of the estimation variables. As opposed to the standard dual frame estimators, the JCE does not require domain membership information. Even if included in the JCE auxiliary variables, the effect of the randomly misclassified domains does not exceed the random measurement error effect. Therefore, the JCE tends to be robust for the misclassified domains if included in the auxiliary variables. Meanwhile, the misclassified domains can significantly affect the unbiasedness of the standard dual frame estimators as proved theoretically and empirically in this paper.

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Nonresponse Bias in a Dual Frame Sample of Cell and Landline Numbers

Cited by 88 publications

References 6 publications

Barriers to Effective Tobacco-Dependence Treatment for the Very Poor

Barriers to Effective Tobacco-Dependence Treatment for the Very Poor

Weighting Strategies for Combining Data from Dual-Frame Telephone Surveys: Emerging Evidence from Australia

Joint Calibration Estimator for Dual Frame Surveys

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