Web Survey Bibliography
Survey quality is a multi-faceted concept that originates from two different development paths. One path is the total survey error paradigm that rests on four pillars providing principles that guide survey design, survey implementation, survey evaluation, and survey data analysis. We should design surveys so that the mean squared error of an estimate is minimized given budget and other constraints. It is important to take all known error sources into account, to monitor major error sources during implementation, to periodically evaluate major error sources and combinations of these sources after the survey is completed, and to study the effects of errors on the survey analysis. In this context survey quality can be measured by the mean squared error and controlled by observations made during implementation and improved by evaluation studies. The paradigm has both strengths and weaknesses. One strength is that research can be defined by error sources and one weakness is that most total survey error assessments are incomplete in the sense that it is not possible to include the effects of all the error sources. The second path is influenced by ideas from the quality management sciences. These sciences concern business excellence in providing products and services with a focus on customers and competition from other providers. These ideas have had a great influence on many statistical organizations. One effect is the acceptance among data providers that product quality cannot be achieved without a sufficient underlying process quality and process quality cannot be achieved without a good organizational quality. These levels can be controlled and evaluated by service level agreements, customer surveys, paradata analysis using statistical process control, and organizational assessment using business excellence models or other sets of criteria. All levels can be improved by conducting improvement projects chosen by means of priority functions. The ultimate goal of improvement projects is that the processes involved should gradually approach a state where they are error-free. Of course, this might be an unattainable goal, albeit one to strive for. It is not realistic to hope for continuous measurements of the total survey error using the mean squared error. Instead one can hope that continuous quality improvement using management science ideas and statistical methods can minimize biases and other survey process problems so that the variance becomes an approximation of the mean squared error. If that can be achieved we have made the two development paths approximately coincide.
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Web survey bibliography (4086)
- Boosting Web pick-up Rates by referring to Compliance Principles ; 2012; Falnes-Dalheim, E., Haraldsen, G., Sundvoll, A.
- Choosing a Data Collection Approach: Mixed Mode Design Experiences in Statistics Finland; 2012; Taskinen, P., Kiianmaa, N.
- Ebook readings jumps, print book reading declines; 2012; Rainie, L., Duggan, M.
- Digital Divides: A connectivity continuum for the United States. Data from the 2011 Current Population...; 2012; File, T.
- How Should Debriefing Be Undertaken in Web-Based Studies? Findings From a Randomized Controlled Trial...; 2012; McCambridge, J., Kypri, K., Wilson, A.
- Better customer in sight in real time; 2012; Macdonald, E., Wilson, H. N., Konus, H.
- Best practices in data cleaning: A complete guide to everything you need to do before and after collecting...; 2012; Osborne, J. W.
- Benchmarking for better surveys; 2012; Nallan, S.
- Adult gadget ownership over time (2006-2012); 2012
- Subjective Well-being Of Spanish Workers: Continuous Voluntary Web Survey Examination; 2012; de Pedraza, P., Guzi, M.
- Specific mixed-mode methodology to reach sensory disabled people in quantitative surveys; 2012; Fontaine, S.
- Response Mode Choice and the Hard-to-Interview in the American Community Survey; 2012; Nichols, E. M., Horwitz, R., Guarino Tancreto, J.
- Recruiting in an Internet panel using respondent driven sampling; 2012; Schonlau, M.
- A Choice in Mode: A Solution for Increasing Response Rates of Hard-to-Survey Populations?; 2012; Haan, M., Ongena, Y. P.
- The Feasibility of Conducting a Web Survey Using Respondent Driven Sampling among Transgenders in the...; 2012; Kappelhof, J.
- Multi-Language Multi-Continent B2B Community Panel: How B2B research can effectively span the world; 2012; Morden, M., Accomando, E.
- Can Survey Gaming Techniques Cross Continents? Examining cross cultural reactions to creative questioning...; 2012; Puleston, J.
- Facing The Future Webcams as a survey tool in China; 2012; Gordon, A., Llewellyn, T., Gu, E.
- Device Diversity: Understanding the complexity of varied devices for taking surveys – Case study...; 2012; Pearson, C., Backlund, K., Veling, L., Tsvelik, M., Jehoel, S.
- Research Goes Mobile: Findings from initial smartphone application research; 2012; Dubreuil, C., Joubert, S.
- Better Answers to Basic Questions: Enhancing the accuracy of online reach and audience metrics; 2012; van Dam, P. H., van Ossenbruggen, R., Voorend, R.
- Rules of engagement: The war against poorly engaged respondents - guidelines for elimination; 2012; Gittelman, S. H., Trimarchi, E.
- Reality check in the digital age: The relationship between what we ask and what people actually do; 2012; Hofmeyr, J., Louw, A.
- Dimensions of Online Survey Data Quality What really matters?; 2012; Puleston, J., Eggers, M.
- WEBDATANET: web-based data-collection methodological challenges, solutions and implementations. Action...; 2012; de Pedraza, P.
- WebSM Study: Survey software features overview ; 2012; Vehovar, V., Cehovin, G., Kavcic, L., Lenar, J.
- Examining Contexts-of-Use for Web-Based and Paper-Based Questionnaires; 2012; Hardré, P. L., Crowson, H. M., Xie, K.
- Probabilistic survey questions and incorrect answers: Retirement income replacement rates; 2012; van Santen, P., Alessie, R., Kalwij, A.
- Survey Quality; 2012; Lyberg, L. E.
- Prenotification, Incentives, and Survey Modality: An Experimental Test of Methods to Increase Survey...; 2012; Tepper, J. R., Jacob, B.
- Using Free Online Survey Software in Your Teaching; 2012; Chippindall, J.
- Comparability of Survey Measurements; 2012; Oberski, D.
- Why People Agree to Participate in Surveys; 2012; Albaum, G., Smith, S. M.
- Unit Non-Response Due to Refusal; 2012; Stoop, I.
- Classification of Surveys; 2012; Stoop, I., Harrison, E.
- What Survey Modes are Most Effective in Eliciting Self-Reports of Criminal or Delinquent Behavior?; 2012; Kleck, G., Roberts, K.
- Non-Response and Measurement Error; 2012; Billiet, J., Matsuo, H.
- An Overlooked Approach in Survey Research: Total Survey Error; 2012; Bautista, R.
- An assessment of equivalence between Internet and paper-based surveys: evidence from collectivistic...; 2012; Fang, J., Wen, C., Prybutok, V.
- Effects of Incentives in Surveys; 2012; Toepoel, V.
- Respondents Cooperation: Demographic Profile of Survey Respondents and Its Implication; 2012; Glaser, P.
- Costs and Errors in Fixed and Mobile Phone Surveys; 2012; Vehovar, V., Slavec, A., Berzelak, N.
- E-Mail Surveys; 2012; Mesch, G.
- Does survey experience affect respondents’ reported level of satisfaction?; 2012; Schultz Christensen, A., Ladenburg, J.
- Building Your Own Online Panel Via E-Mail and Other Digital Media; 2012; Toepoel, V.
- Data Quality in HIV/AIDS Web-Based Surveys: Handling Invalid and Suspicious Data; 2012; Bauermeister, J. A., Pingel, E., Zimmerman, M., Couper, M. P., Carballo-Diéguez, A., Strecher, V. J.
- Use of Web 2.0 to Recruit Australian Gay Men to an Online HIV/AIDS Survey; 2012; Theriault, N., Bi, P., Hiller, J. E., Nor, M.
- Web and Mail Surveys: An Experimental Comparison of Methods for Nonprofit Research; 2012; Lin, W., Van Ryzin, G. G.
- Evaluation of an online (opt-in) panel for public participation geographic information systems surveys...; 2012; Brown, G., Weber, D., Zanon, D., de Bie, K.
- Survey Data Collection and Integration; 2012; Davino, C., Fabbris, L.