Web Survey Bibliography
Title Internet and Smartphone Coverage in a National Health Survey: Implications for Alternative Modes
Author Couper, M. P.; Kelley, J.; Axinn, W.; Guyer, H.; Wagner, J.; West, B. T.
Year 2015
Access date 22.08.2016
Abstract
The rise of Internet-enabled smartphones presents an opportunity to re-examine the issue of Internet coverage and its implications for coverage bias. While a number of papers have examined cell phone coverage and Internet coverage separately, few have looked at the possible compensatory effects of joint coverage. We added two interviewer observations (one on Internet access and the other on smartphone ownership among respondents) to the National Survey of Family Growth (NSFG) with a view to exploring the feasibility of Internet-based follow-up surveys. NSFG is a national probability survey of women and men age 15-44, using a continuous design. We examine 8 quarters (2 years) of data, from September 2012 to August 2014.
Overall, we find that 82.2% of respondent report Internet access and 76.1% report having a smartphone (estimates weighted for differential selection and nonresponse). Combined, this means that 89.4% have access to the Internet, either via traditional devices or via a smartphone. We also find some evidence of compensatory coverage when looking at key gender/race/age subgroups. For instance, while Black male teens (15-17) have the lowest rate of Internet access (74.9%) and the lowest rate of smartphone usage (58.9%), when combined 82.6% have some form of Internet access.
We propose to examine the socio-demographic correlates of Internet and smartphone (and combined) coverage (access) in this population. In addition, we propose to look at the effect of differential coverage on key estimates produced by the NSFG, related to fertility, family formation, and sexual activity. While this does not address nonresponse bias issues related to alternative modes, our paper has implications for possible coverage biases that may arise in switching to a Web-based mode of data collection, either for follow-up surveys or to replace the main face-to-face data collection.
Overall, we find that 82.2% of respondent report Internet access and 76.1% report having a smartphone (estimates weighted for differential selection and nonresponse). Combined, this means that 89.4% have access to the Internet, either via traditional devices or via a smartphone. We also find some evidence of compensatory coverage when looking at key gender/race/age subgroups. For instance, while Black male teens (15-17) have the lowest rate of Internet access (74.9%) and the lowest rate of smartphone usage (58.9%), when combined 82.6% have some form of Internet access.
We propose to examine the socio-demographic correlates of Internet and smartphone (and combined) coverage (access) in this population. In addition, we propose to look at the effect of differential coverage on key estimates produced by the NSFG, related to fertility, family formation, and sexual activity. While this does not address nonresponse bias issues related to alternative modes, our paper has implications for possible coverage biases that may arise in switching to a Web-based mode of data collection, either for follow-up surveys or to replace the main face-to-face data collection.
Access/Direct link FCSM Research Conference Homepage (Abstract) / (Full text)
Year of publication2015
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - 2015 (291)
- Taking MARS Digital; 2015; Melton, E.; Krahn, J.
- A Comparison of the Effects of Face-to-Face and Online Deliberation on Young Students’ Attitudes...; 2015; Triantafillidou, A.; Yannas, P.; Lappas, G.; Kleftodimos, A.
- A Privacy-Friendly Method to Reward Participants of Online-Surveys; 2015; Herfert, M.; Lange, B.; Selzer, A.; Waldmann, U.
- Doing Online Surveys: Zum Einsatz in der sozialwissenschaftlichen Raumforschung; 2015; Nadler, R.; Petzold, K.; Schoenduwe, R.
- Are Fast Responses More Random? Testing the Effect of Response Time on Scale in an Online Choice Experiment...; 2015; Boerger, T.
- The impact of frequency rating scale formats on the measurement of latent variables in web surveys -...; 2015; Menold, N.; Kemper, C. J.
- Investigating response order effects in web surveys using eye tracking; 2015; Karem Hoehne, J.; Lenzner, T.
- Implementation of the forced answering option within online surveys: Do higher item response rates come...; 2015; Decieux, J. P.; Mergener, A.; Neufang, K.; Sischka, P.
- Internet Panels, Professional Respondents, and Data Quality; 2015; Matthijsse, S.; De Leeuw, E. D.; Hox, J.
- Self-administered Questions and Interviewer–Respondent Familiarity; 2015; Rodriguez, L. A., Sana, M., Sisk, B.
- Comparing Food Label Experiments Using Samples from Web Panels versus Mall Intercepts; 2015; Chang, L. C., Lin, C. T. J.
- Translating Answers to Open-ended Survey Questions in Cross-cultural Research: A Case Study on the Interplay...; 2015; Behr, D.
- The impact of gamifying to increase spontaneous awareness; 2015; Cape, P.
- Using eye-tracking to understand how fourth grade students answer matrix items; 2015; Maitland, A.; Sun, H.; Caporaso, A.; Tourangeau, R.; Bertling, J.; Almonte, D.
- Incentive Types and Amounts in a Web-based Survey of College Students; 2015; Krebs, C.; Planty, M.; Stroop, J.; Berzofsky, M.; Lindquist, C.
- Response Rates and Response Bias in Web Panel Surveys; 2015; Boyle, J.; Berman, L.; Dayton, Ja.; Fakhouri, T.; Iachan, R.; Courtright, M.; Pashupati, K.
- Characteristics of the Population of Internet Panel Members; 2015; Boyle, J; Freedner, N.; Fakhouri, T.
- Internet and Smartphone Coverage in a National Health Survey: Implications for Alternative Modes; 2015; Couper, M. P.; Kelley, J.; Axinn, W.; Guyer, H.; Wagner, J.; West, B. T.
- An Overview of Mobile CATI Issues in Europe; 2015; Slavec, A.; Toninelli, D.
- Using Mobile Phones for High-Frequency Data Collection; 2015; Azevedo, J. P.; Ballivian, A.; Durbin, W.
- Willingness of Online Access Panel Members to Participate in Smartphone Application-Based Research; 2015; Pinter, R.
- Who Has Access to Mobile Devices in an Online Opt-in Panel? An Analysis of Potential Respondents for...; 2015; Revilla, M.; Toninelli, D.; Ochoa, C.; Loewe, G.
- Who Are the Internet Users, Mobile Internet Users, and Mobile-Mostly Internet Users?: Demographic Differences...; 2015; Antoun, C.
- A Meta-Analysis of Breakoff Rates in Mobile Web Surveys; 2015; Mavletova, A. M.; Couper, M. P.
- The Best of Both Worlds? Combining Passive Data with Survey Data, its Opportunities, Challenges and...; 2015; Duivenvoorde, S.; Dillon, A.
- Optimizing the Decennial Census for Mobile – A Case Study; 2015; Nichols, E. M.; Hawala, E. O.; Horwitz, R.; Bentley, M.
- App vs. Web for Surveys of Smartphone Users: Experimenting with mobile apps for signal-contingent experience...; 2015; McGeeney, K.; Keeter, S.; Igielnik, R.; Smith, A.; Rainie, L.
- Using Video to Reinvigorate the Open Question; 2015; Cape, P.
- On the Go: How Mobile Participants Affect Survey Results; 2015; Barlas, F. M.; Thomas, R. K.
- The Matrix Lives On: Improving Grids for Online Surveys; 2015; Thomas, R. K.; Barlas, F. M.; Graham, P.; Subias, T.
- Variance Estimation for Surveys from Internet Panels ; 2015; Rivers, D.
- Sensitivity Analysis of Bias of Estimates from Web Surveys with Nonrandomized Panel Selection; 2015; Beresovsky, V.
- Detecting Fraud in a Survey Sample Recruited Online; 2015; Brown, D.; Dever, J. A.; Augustson, E.; Squiers, L.
- Survey Treatments and Response Modes: Bayesian Survival Analysis with Competing Risks; 2015; Minato, H.
- Purposefully Mobile: Experimentally Assessing Device Effects in an Online Survey ; 2015; Barlas, F. M.; Thomas, R. K.; Graham, P.
- Using equivalence testing to disentangle selection and measurement in mixed modes surveys ; 2015; Cernat, A.
- What do web survey panel respondents answer when asked “Do you have any other comment?”; 2015; Schonlau, M.
- On Climbing Stairs Many Steps at a Time: The New Normal in Survey Methodology; 2015; Dillman, D. A.
- Mobile Research Methods: Opportunities and challenges of mobile research methodologies. ; 2015; Toninelli, D. (Ed.); Pinter, R.; de Pedraza, P.
- Effect of Web-Based Versus Paper-Based Questionnaires and Follow-Up Strategies on Participation Rates...; 2015; Kilsdonk, E.; van den Heuvel-Eibrink, M. M.; van Dulmen-den Broeder, E.; van der Pal, H. J. H.; van...
- Polling Error in the 2015 UK General Election: An Analysis of YouGov’s Pre and Post-Election Polls...; 2015; Wells, A.; Rivers, D.
- Cell Phone and Face-to-face Interview Responses in Population-based Sur- veys - How Do They Compare?; 2015; Ghandour, L.; Ghandour, B.; Mahfoud, Z.; Mokdad, A.; Sibai, A. M.
- Collecting Health Research Data - Comparing Mobile Phone-assisted Personal Interviewing to Paper-and...; 2015; van Heerden, A. C.; Norris, S. A.; Tollman, S. M.; Richter, L. M.
- The Effects of Questionnaire Completion Using Mobile Devices on Data Quality. Evidence from a Probability...; 2015; Bosnjak, M.; Struminskaya, B.; Weyandt, K.
- Are Sliders Too Slick for Surveys? An Experiment Comparing Slider and Radio Button Scales for Smartphone...; 2015; Aadland, D.; Aalberg, T.
- Evaluation of an Adapted Design in a Multi-device Online Panel: A DemoSCOPE Case Study; 2015; Arn, B.; Klug, S.; Kolodziejski, J.
- Maximizing Data Quality using Mode Switching in Mixed-Device Survey Design: Nonresponse Bias and Models...; 2015; Axinn, W.; Gatny, H. H.; Wagner, J.
- Web Surveys Optimized for Smartphones: Are there Differences Between Computer and Smartphone Users?; 2015; Andreadis, I.
- Measuring Political Knowledge in Web-Based Surveys: An Experimental Validation of Visual Versus Verbal...; 2015; Munzert, S.; Selb, P.
- Validation of the new scale for measuring behaviors of Facebook users: Psycho-Social Aspects of Facebook...; 2015; Bodroza, B.; Jovanovic, T.