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 - 2012 (371)
- 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.
- Detecting Satisficing In Online Surveys: What we found ; 2012; Salifu, S.
- Challenges of assessing the quality of a prerecruited probability-based panel of internet users in...; 2012; Struminskaya, B., Kaczmirek, L.
- Assessing Cross-National Equivalence of Measures of Xenophobia: Evidence from Probing in Web Surveys; 2012; Behr, D., Braun, M., Kaczmirek, L.
- Impact and the Research Excellence Framework: New challenges for universities; 2012; Grant, J.
- Impact of Fixed Choice Design on Blockmodeling Outcomes; 2012; Znidarsic, A.
- Panel Conditioning in Online Survey Panels: Problems of Increased Sophistication and Decreased Engagemeent...; 2012; Adams, A. N., Atkeson, L. R., Karp, J. A.
- Efficiency of Different Recruitment Strategies for Web Panels; 2012; Hansen, K. M., Pedersen, R. T.
- Understanding Mode Effects between Mobile Web and Mobile SMS Surveys; 2012; Poduska, B., Johnson, E. P.
- Paper-and-Pencil versus Web Administration of a Student Satisfaction Survey; 2012; Bowen, C.-C.
- Nonresponse and Online Student Evaluations of Teaching: Understanding the Influence of Salience...; 2012; Adams, M. J. D., Umbach, P. D.
- Disfluencies and Gaze Aversion in Unreliable Responses to Survey Questions; 2012; Schober, M. F., Conrad, F. G., Dijkstra, W., Ongena, Y. P.
- Use of Paradata in a Responsive Design Framework to Manage a Field Data Collection; 2012; Wagner, J., West, B. T., Kirgis, N., Lepkowski, J. M., Axinn, W., Kruger-Ndiaye, S.
- Recruiting A Probability Sample For An Online Panel: Effects Of Contact Mode, Incentives, And Information...; 2012; Scherpenzeel, A., Toepoel, V.
- Do Questions about Watching Internet Pornography Make People Watch Internet Pornography? A Comparison...; 2012; Peter, J., Valkenburg, P. M.