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 (4086)
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- Web-Based Survey Methodology; 2017; Wright, K. B.
- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
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- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
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- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- Measuring Subjective Health and Life Satisfaction with U.S. Hispanics; 2017; Lee, S.; Davis, R.
- Humanizing Cues in Internet Surveys: Investigating Respondent Cognitive Processes; 2017; Jablonski, W.; Grzeszkiewicz-Radulska, K.; Krzewinska, A.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
- The Effect of Respondent Commitment on Response Quality in Two Online Surveys; 2017; Cibelli Hibben, K.
- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
- Push2web or less is more? Experimental evidence from a mixed-mode population survey at the community...; 2017; Neumann, R.; Haeder, M.; Brust, O.; Dittrich, E.; von Hermanni, H.
- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- Redirected Inbound Call Sampling (RICS); A New Methodology ; 2017; Krotki, K.; Bobashev, G.; Levine, B.; Richards, S.
- An Empirical Process for Using Non-probability Survey for Inference; 2017; Tortora, R.; Iachan, R.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Rates, Delays, and Completeness of General Practitioners’ Responses to a Postal Versus Web-Based...; 2017; Sebo, P.; Maisonneuve, H.; Cerutti, B.; Pascal Fournier, J.; Haller, D. M.
- Necessary but Insufficient: Why Measurement Invariance Tests Need Online Probing as a Complementary...; 2017; Meitinger, K.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Is There a Future for Surveys; 2017; Miller, P. V.
- Reducing speeding in web surveys by providing immediate feedback; 2017; Conrad, F.; Tourangeau, R.; Couper, M. P.; Zhang, C.
- Social Desirability and Undesirability Effects on Survey Response latencies; 2017; Andersen, H.; Mayerl, J.
- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.