Review of Estimation Methods for Landline and Cell Phone Surveys

Caruz, Antonio; Rueda, María del Mar; Trujillo-Carmona, Manuel; Molina, David

doi:10.1177/0049124114546904

Cited by 8 publications

(7 citation statements)

References 19 publications

(18 reference statements)

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“…These results are similar to the findings by Skinner and Rao (1996) and Lohr and Rao (2000). These authors have suggested that when there is little empirical evidence to select a preferred estimator, the decision to choose one estimator over the others should be made on theoretical and practical considerations, such as internal consistency and the availability of good-quality poststratification raking for benchmarking (Brick 2013;Arcos et al 2014). …”

Section: Resultssupporting

confidence: 83%

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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Section: Resultssupporting

confidence: 83%

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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“…In those patients whom we had the opportunity to time, survey completion occurred at an average of 7 minutes, which may have decreased the accuracy of survey responses as a result of patient fatigue. 38 Similar to previous studies, this study has limited generalizability to nonacademic medical centers. 10,21 Incomplete RPDR administrative data also limited the results of this study.…”

Section: Figurementioning

confidence: 85%

Feasibility of the collection of patient-reported outcomes in an ambulatory neurology clinic

et al. 2016

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Objective: To determine whether patients could self-report physical and mental health assessments in the waiting room and whether these assessments would be associated with modified Rankin Scale (mRS) and Quality of Life in Epilepsy (QOLIE-10) scores.Methods: We offered iPad-based surveys to consecutive adult neurology patients at check-in to collect patient-reported outcome measures (PROMs). We collected demographic and clinical data on 6,075 patients through survey or administrative claims and PROMs from participating patients. We compared demographic characteristics of participants and nonparticipants and tested associations between physical and mental health scores and mRS and QOLIE-10.Results: Of 6,075 patients seen by neurologists during the study period, 2,992 (49.3%) participated in the survey. Compared to nonparticipating patients, participating patients more often were privately insured (53.5% vs 42.7%, p , 0.01), married (51.5% vs 47.9%, p , 0.01), and seen in general neurology (nonsubspecialty) clinics (53.1% vs 46.6%, p , 0.01) and more likely to report English as their preferred language (50.1% vs 38.4%, p , 0.01). Participating patients had a mean physical health T score of 28.7 (SD 15) and mental health T score of 33 (SD 15), which were 3 and 2 SD worse than the average for the US general population, respectively.

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“…While there is general agreement that non-coverage bias is reduced through inclusion of the mobile-only population ( Blumberg and Luke, 2009 , Mohorko et al, 2013 ), possible duplication of survey participants using both landline and mobile telephones (dual-users) adds complexity. This overlap is addressed through weighting strategies ( Hartley, 1974 ), however, there is no agreed consensus as to the optimal estimator ( Arcos et al, 2015 ). Furthermore, the choice of estimator is influenced by the type of information available about the mobile telephone population.…”

Section: Introductionmentioning

confidence: 99%

Including mobile-only telephone users in a statewide preventive health survey—Differences in the prevalence of health risk factors and impact on trends

Baffour

Roselli

Haynes

et al. 2017

Preventive Medicine Reports

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The Queensland preventive health survey is conducted annually to monitor the prevalence of behavioural risk factors in the north-east Australian state. Prompted by domestic and international trends in mobile telephone usage, the 2015 survey incorporated both mobile and landline telephone numbers from a list-based sampling frame. Estimates for landline-accessible and mobile-only respondents are compared to assess potential bias in landline-only surveys in the context of public health surveillance. Significant differences were found in subcategories of all health prevalence estimates considered (alcohol consumption, body mass index, smoking, and physical activity) from 2015 survey results. Results from Australian and international studies that have considered mobile telephone non-coverage bias are also summarised and discussed. We find that adjusting for sampling biases of telephone surveys by weighting does not fully compensate for the differences in prevalence estimates. However, predicted trends from previous years' surveys only differ significantly for the 2015 prevalence estimates of alcohol consumption. We conclude that the inclusion of mobile telephones into standard telephones surveys is important for obtaining valid, reliable and representative data to reduce bias in health prevalence estimates. Importantly, unlike some international experiences, the addition of mobiles telephones into the Queensland preventive health survey occurred before population trends were significantly affected.

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Review of Estimation Methods for Landline and Cell Phone Surveys

Cited by 8 publications

References 19 publications

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

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

Feasibility of the collection of patient-reported outcomes in an ambulatory neurology clinic

Including mobile-only telephone users in a statewide preventive health survey—Differences in the prevalence of health risk factors and impact on trends

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