Web Survey Bibliography
Probability sampling designs, those with samples selected with a reproducible random mechanism, are considered by many to be the gold standard for surveys. Theory has existed since the early 1930’s to produce population estimates from these samples under labels such as design-based, randomization-based, and model-assisted estimation. This theory ultimately requires that the sample units, excluded from the analysis files either because of non-sampling or nonresponse, are missing at random. This condition, however, is not always attainable. Studies involving samples without a necessarily reproducible design, referred to as non-probability surveys, have gained more attention in recent years but they are not new. Touted as cheaper, faster (even better) than probability designs, these surveys capture participants through various methods such as respondent-driven sampling or opt-in web surveys. For surveys required to produce population estimates to meet their stated fit for purpose, the link between the sample and the target population as well as the probability of participation must be addressed to justify the desired level of quality. Survey weights or analytic models have been proposed to provide the needed evidence of the data’s utility, but research findings on the effectiveness of these approaches are inconsistent. This suggests that probability and non-probability surveys sometimes “work” and sometimes they “fall apart.” Through this lens, we argue in this paper that probability and non-probability surveys are not two sides of a research coin but actually lie on a quality continuum. We first review the published material on the definition of quality, keeping in mind that surveys have a specific fit for purpose within this context. Next, we summarize research to date to measure the quality of non-probability survey estimates and compare these criteria with similar probability surveys. We conclude with components for a quality framework that encompasses all surveys to enable their objective comparison.
Web survey bibliography - The American Association for Public Opinion Research (AAPOR) 70th Annual Conference, 2015 (35)
- Effects of Mobile versus PC Web on Survey Response Quality: a Crossover Experiment in a Probability...; 2017; Antoun, C.; Couper, M. P.; G. G.Conrad, F. G.
- The Role of Device Type and Respondent Characteristics in Internet Panel Survey Breakoff; 2015; McCutcheon, A. L.
- Web Survey Invitations: Design Features to Improve Response Rates; 2015; Hughes, J.; Marlar, J.
- Advance Postcard Mailing Improves Web Panel Survey Participation; 2015; Bertoni, N.; Burkey, A.; Caldaro, M.; Keeter, S.; DiSogra, C.; McGeeney, K.
- Mobile Devices for the Collection of Sensitive Information; 2015; Maitland, A.; Mercer, A. W.; Tourangeau, K.; Williams, Do.
- What Is The Impact of Smartphone Optimization on Long Surveys?; 2015; Cole, J.; Brooks, K.; Sarraf, S.
- Examining the Impact of Mobile First and Responsive Web Design on Desktop and Mobile Respondents; 2015; Tharp, D.
- Can An Importance Prompt Reduce Item Nonresponse For Demographic Items Across Web and Mail Modes?; 2015; Israel, G. D.
- Leveraging Area Probability Sampling in Recruiting Households for Web Surveys; 2015; Copeland, K.; Pedlow, K.; Tupek, A.
- Reducing Coverage Error in a Web Survey of College Students; 2015; Daley, K.; Pacer, J.
- Influences on Response Latency in a Web Survey; 2015; Ackermann, A.; Cheng, H. W.; Howard Ecklund, E.; Kolenikov, S.; Phillips, B. T.
- App vs. Web for Surveys of Smartphone Users; 2015; Igielnik, R.; McGeeney, K.
- Where Does the Platform Matter: The Impact of Geographic Clustering in Device Ownership and Internet...; 2015; Bilgen, I.; English, N.; Stern, M. J.; Ventura, I.
- Methodological Considerations in the Use of Name Generators and Interpreters; 2015; Proeschold Bell, R. J.; Eagle, D. E.
- Survey Estimation: How Different Are Probability and Non-Probability Survey Designs?; 2015; Shook-Sa, B. E.; Dever, J. A.
- Experience of Multiple Approaches to Increase Response Rate in a Mixed-Mode Implementation of a Population...; 2015; Ding, M.;Leite-Bennett, A. K.; Landreman, U. E.; Johnson, D. R.; Mehrotra, K.; Rosenkranz, M.; Thompson...
- The Effect of Respondent Commitment on Response Quality in an Online Survey; 2015; Cibelli Hibben, K.; Conrad, F.
- Predictors of Completion Rates in Online Surveys; 2015; Cho, S.; Cohen, Jo.; Kuriakose, N.; Liu, M.
- Boosting Probability-Based Web Survey Response Rates via Nonresponse Follow-Up; 2015; Chew, K.; Fontes, A.; Lavrakas, P. J.
- Adding a Web Mode to Phone Surveys: Effectiveness and Cost Implications; 2015; Beebe, T. J.; Lien, R.; Luxenberg, H.; Rainey, J.
- Web Survey Response Examined from the Perspective of Leverage-Saliency Theory Within a Longitudinal...; 2015; Nares, Y. G.
- Challenging Survey Screen Designs on Smartphones; 2015; Nichols, E. M.; Olmsted, E. L.
- The Effect Usability Testing has on Data Quality: A Design of an Online Diary; 2015; Gentry, R. J.; Pens, Y.
- Making Usability-Testing a Standard Survey Pretesting Methodology; 2015; McFarlane, E.
- Measuring the Effects of Operational Designs on Response Rates and Nonresponse Bias; 2015; Anderson, Me.; Henrikson, N.; King, D.; Ulrich, K.
- A Systematic Generation of an Email Pool for Web Surveys; 2015; Silber, H.; Leibold, J.; Lischewski, J.; Schlosser, S.
- Are Tailored Outreach Efforts Too Costly? An Assessment of a Responsive Design Approach to Control Costs...; 2015; Epps, S. R.; Getman, D. P.; Hall, L. M.; Hunter, J. A.
- Nonresponse Analysis and Adjustment in the Follow- Up Study of a National Cohort of Gulf War And Gulf...; 2015; Dursa, E.; Hammer, H.; Kolenikov, S.; Schneiderman, A. I.
- Return To Sender: An Evaluation of Undeliverable (e)Mail in the Modern Age; 2015; Marlar, J.; Yu, D.
- Evaluating Visual Design Elements for Data Collection and Panelist Engagement; 2015; Christian, L. M.; Harm, D.; Langer Tesfaye, C.; Wells, T.
- Comparing Field and Laboratory Usability Tests to Assess the Consistency and Mistakes in Web Survey...; 2015; Croen, A.; Gonzales, N.; Ghandour, R.; Stern, M. J.
- Cell RDD Respondents Unmasked: Progress Report on Geo and Demo Appends to the Wireless Frame; 2015; DiSogra, C.; Kennedy, C.Mosher, M.
- Cognitive Testing of Survey Translations: Does Respondent Language Proficiency Matter?; 2015; Schoua-Glusberg, A.; Park, H.; Meyer, M.; Goerman, P. L.; Sha, M.
- Culturally-Related Response Styles for Attitude Questions: A Comparative Analysis of Chinese and American...; 2015; Wang, Me.
- Innovative Uses of Paradata Across Diverse Contexts ; 2015; Cheung, G.; Pennell, B.-E.