Web Survey Bibliography
Analysts working with data generated by different modes of data collection often want to be sure that their measurements are comparable. If a set of questions is designed to measure the same latent trait, confirmatory factor analysis (CFA) is a useful analytic tool for this purpose. It can be applied to assess, whether properties, such as measurement error, the association between latent traits and questions (measurement invariance) and the means of latent traits, are equivalent across survey modes. We illustrate an application using empirical data from an experiment based on a national probability sample, in which 4048 respondents were randomly assigned to either face-to-face, telephone, mail or web interviewing. Two related traits were measured with three questions respectively, “moral support of the police” and the “obligation to obey”, which form the basis of our CFA model. The association between latent traits and questions was invariant across modes. However, measurement errors differed between modes. In particular, the self-administered modes yielded more reliable indicators than the interviewer modes. Moreover, we find systematic bias between modes on the mean of one of the traits. The effect signs suggest that respondents gave socially desirable answers in the intervieweradministered modes. A particularity of survey modes is that sample compositions are often heterogeneous. If the selective process is correlated with model elements, such as traits, it can bias invariance tests and decrease fit. We illustrate available options to adjust for this problem, for example propensity score methods or covariate adjustment, all based on the use of auxiliary variables, such as socio-demographics. In conclusion, self-administered modes seem to produce measurements of lower quality than interviewer-administered modes with respect to random error and systematic bias. Modes may thus affect both, the error variance and bias of an estimate. An effect can be suspected particularly between interviewer and non-interviewer modes.
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Web Survey Bibliography - Web surveys (3851)
- Effects of Lotteries on Response Behavior in Online Panels; 2013; Goeritz, A., Luthe, S. C.
- Lotteries and study results in market research online panels; 2013; Goeritz, A., Luthe, S. C.
- Using Paradata to Study Response to Within-Survey Requests; 2013; Sakshaug, J. W.
- Paradata for Coverage Research ; 2013; Eckman, S.
- Online Fundraising Essentials, Second Edition; 2013; Stevenson, S. C.
- Tips for Evaluating Online Effectiveness; 2013; Stevenson, S. C.
- Ten questions to ask your online survey provider; 2013; Williams, D.
- Survey quality prediction system 2.0; 2013
- Practical tools for designing and weighting survey samples; 2013; Valliant, R. L., Daver, J. A., Kreuter, F.
- Paradata in web surveys; 2013; Callegaro, M.
- A nationwide web-based freight data collection; 2013; Samimi, A., Mohammadian, A., Kawamura, K.
- Mode Matters: Evaluating Response Comparability in a Mixed-Mode Survey; 2013; Bowyer, B. T., Rogowski, J. C.
- Comparing Survey Results Obtained via Mobile Devices and Computers: An Experiment With a Mobile Web...; 2013; de Bruijne, M., Wijnant, A.
- Cognitive Probes in Web Surveys: On the Effect of Different Text Box Size and Probing Exposure on Response...; 2013; Behr, D., Bandilla, W., Kaczmirek, L., Braun, M.
- Best Practice in Online Survey Research with Sensitive Topics; 2013; Kays, K., Keith, T. L., Broughal, M. T.
- Research Intentions are Nothing without Technology: Mixed-Method Web Surveys and the Coberen Wall of...; 2013; Ganassali, S., Rodriguez-Santos, C.
- Reducing Response Burden for Enterprises Combining Methods for Data Collection on the Internet; 2013; Vik, T.
- Measuring Wages Worldwide: Exploring the Potentials and Constraints of Volunteer Web Surveys; 2013; Steinmetz, S., Raess, D., Tijdens, K., de Pedraza, P.
- The Distinctiveness of Online Research: Descriptive Assemblages, Unobtrusiveness, and Novel Kinds of...; 2013; Lanfrey, D.
- Sampling, Channels, and Contact Strategies in Internet Survey; 2013; Macrì, E., Tessitore, C.
- Data Quality in PC and Mobile Web Surveys; 2013; Mavletova, A. M.
- Virtual research assistants: Replacing human interviewers by automated avatars in virtual worlds; 2013; Hasler, B. S., Tuchman, P., Friedman, D.
- Compared to a small, supervised lab experiment, a large, unsupervised web-based experiment on a previously...; 2013; Ryan, R. S., Wilde, M., Crist, S.
- From mixed-mode to multiple devices. Web surveys, smartphone surveys and apps: has the respondent gone...; 2013; Callegaro, M.
- Moving an established survey online – or not?; 2013; Barber, T., Chilvers, D., Kaul, S.
- On the Use of Latent Variable Models to Detect Differences in the Interpretation of Vague Quantifiers...; 2013; Griffin, J.
- An approach to selecting online respondents; 2013; Terhanian, G.
- By the Numbers: Theory of adaptation or survival of the fittest?; 2013; Cavallaro, K.
- Designing and conducting business surveys; 2013; Snijkers, G.,Araldsen, G., , Willimack, D. K.Jones, J.
- Modular Survey Design: A Bite Size Proposal; 2013; Kelly, F., Stevens, S., Johnson, A.
- Cyborgs vs. Monsters: Assembling Modular Surveys to Create Complete Datasets; 2013; Johnson, E. P., Siluk, L., Tarraf, S.
- Do I Have Your Full Attention?; 2013; Cape, P. J.
- Does Sample Size Still Matter?; 2013; Bakken, D. G., Bond, M.
- Optimizing Surveys for Smartphones: Maximizing Response Rates While Minimizing Bias; 2013; Lattery, K., Park Bartolone, G., Saunders, T.
- Shorter Isn't Always Better; 2013; Burdein, I.
- Mobile Research Risk: What Happens to Data Quality When Respondents Use a Mobile Device for a Survey...; 2013; Baker-Prewitt, J.
- Internet-Based Recruitment to a Depression Prevention Intervention: Lessons From the Mood Memos Study...; 2013; Morgan, A. J., Jorm, A. F., Mackinnon, A. J.
- Challenges for Researchers Investigating Contraceptive Use and Pregnancy Intentions of Young Women Living...; 2013; Herbert, D. L., Loxton, D., Bateson, D., Weisberg, E., Lucke, J. C.
- Using a web-based survey tool to undertake a Delphi study: Application for nurse education research; 2013; Gill, F. J., Leslie, G. D., Grech, C., Latour, J. M.
- The comparison of road safety survey answers between web-panel and face-to-face; Dutch results of SARTRE...; 2013; Goldenbeld, C., de Craen, S.
- Pros and cons of virtual interviewers – vote in the discussion about surveytainment; 2013; Póltorak, M., Kowalski, J.
- Addressing Disclosure Concerns and Analysis Demands in a Real-Time Online Analytic System; 2013; Krenzke, T., Gentleman, J. F., Li, J., Moriarity, C.
- Examination of the equivalence of self-report survey-based paper-and-pencil and internet data collection...; 2013; Weigold, A., Weigold, I. K., Russell, E. J.
- An Assessment of Incentive Versus Survey Length Trade-offs in a Web Survey of Radiologists; 2013; Ziegenfuss, J. Y., Niederhauser, B. D., Kallmes, D., Beebe, T. J.
- Clarifying Categorical Concepts in a Web Survey.; 2013; Redline, C. D.
- Using Online and Paper Surveys - The Effectiveness of Mixed-Mode Methodology for Populations Over 50; 2013; De Bernardo, D. H., Curtis, A.
- The fish model: What factors affect participants while filling in an online questionnaire?; 2013; Mohamed, B., Lorenz, A., Pscheida, D.
- Interview Duration in Web Surveys: Integrating Different Levels of Explanation; 2013; Rossmann, J., Gummer, T.
- The monetary value of good questionnaire design; 2013; Tress, F.
- Technical and methodological meta-information on current practices in online research: A full population...; 2013; Burger, C., Stieger, S.
