Web Survey Bibliography
The goal in this paper is to explain the academic performance for PhD students as measured by the the number and type of publications and conference papers co-authored during the last three years. In the literature, performance in creative jobs such as those involving knowledge creation is explained from different types of variables. Some authors stress the role of social background variables; some other authors use mainly attitudinal variables such as job satisfaction or job motivation, and some others focus on network variables such as centrality, density or closeness. Very rarely have all types of variables been used together. The data were collected with a web-administered questionnaire from the whole population of PhD students at the University of Girona (Spain). The egocentered networks of both the PhD student and his/her supervisor were merged into what is known as a nosduocentered network (Coromina et al. 2005). The population size did not make it possible to use formal measurement error models for the attitudinal variables, which were measured using Summated Rating Scales (SRS). Appropriate reliability measures were computed from exploratory factor analysis models and correlations were corrected for attenuation. Since the complete population was available, formal statistical tests were not interpretable and the explanator power of the variables was assessed by means of standardized regression coefficients, partial correlations and adjusted R2. We started by specifying three regression models, one for each group of variables. The best background variable model included supervisor performance, seniority at the department, field of study, having children and age. The best attitudinal variable model included different motivations to start a PhD and job satisfaction. The best network variable model is the maximum density for nosduocentered networks. Then, we fitted three regression models combining the predictors of all possible pairs of groups and a regression model with the predictors of all three groups. The comparison of the adjusted R2 statistics made it possible to see which groups of predictors added explanatory power with respect to the other groups. The final model to explain PhD student performance is composed by background and attitudinal SRS variables. The lack of predictive power of network variables cannot be understood as the network being completely irrelevant. In fact, the most relevant predictor in the model is the supervisors’ academic performance, and the supervisor should be a very important source of social capital for the PhD student. The fact that the field of study is significant still reveals very different traditions of publishing. Age, having children and the amount of years working in the department also have a positive effect on performance. The last variables with predictive power for performance are motivations to start a PhD such as greater work autonomy and career advantages.
Web survey bibliography - 2005 (76)
- The ethics of research using electronic mail discussion groups; 2005; Kralik, D., Warren, J., Koch, T., Pignone, G., Price, K.
- The Analyses of Domestic Study about Internet Survey; 2005; Rui, L., Tie-ying, S.
- Controlling the Baseline Speed of Respondents: An Empirical Evaluation of Data Treatment Methods of...; 2005; Mayerl, J.
- Determinanten der Rücklaufquote in Online-Panels; 2005; Batanic, B., Moser, K.
- On the cost-efficiency of probability sampling based mail surveys with a Web response option; 2005; Werner, P.
- Expert workshop on mixed mode data collection in comparative social surveys; 2005; Roberts, C.
- The Effect Of A Simultaneous Mixed-Mode (Mail And Web) Survey On Respondent Characteristics And Survey...; 2005; Brennan, M.
- The total survey error approach. A guide to the new science of survey research; 2005; Weisberg, H. F.
- The professional respondent problem in online panel surveys today; 2005; Fulgoni, G.
- Satisficing behavior in online panelists; 2005; Downes-Le Guin, T.
- Reading behavior in the digital environment: Changes in reading behavior over the past ten years; 2005; Liu, Z.
- Rating versus comparative trade-off measures. Trending changes in political issues across time and predictive...; 2005; Thomas, R. K., Behnke, S., Johnson, Al., Sanders, M.
- Publication bias: Recognizing the problem, understanding its origins and scope, and preventing harm; 2005; Dickersin, K.
- Panel proliferation and quality concerns; 2005; Faasse, J.
- Gricean effects in self-administered survey. Ph.D. Dissertation; 2005; Yan, T.
- Drop-down boxes, radio buttons, or fill-in-the-blank? Web survey scale-type effects; 2005
- Does weighting for nonresponse increase the variance of survey means?; 2005; Little, R. J., Vartivarian, S.
- Big scale observations gathered with the help of client side paradata; 2005; Haraldsen, G., Kleven, O., Sundvoll, A.
- User Interface Design and Evaluation ; 2005; Stone, D., Jarrett, C., Woodroffe, M., Minocha, S.
- Adding Value to Data Through Improved Access. The Case for Web Portals; 2005; Baker, R. P.
- Multi-Mode Research and Data Linkage. Theoretical and Practical Advice; 2005; Terhanian, G.
- Architectural Design of a Survey Questionnaire and Respondent Data Repository. Practical Considerations...; 2005; Cookson, P., Sobell, J.
- Developing and validating a nursing website evaluation questionnaire; 2005; Tsai, S. - L., Chai, S.-K.
- Workaround: Site’s surveys beat pop-up blockers, yield responses; 2005; Arnold, C.
- The Story of Subject Naught: A Cautionary but Optimistic Tale of Internet Survey Research; 2005; Konstan, J. A., Ross, M. W., Rosser, B. R. S., Stanton, J. M., Edwards, W. M.
- Standards in Online Surveys. Sources for Professional Codes of Conduct, Ethical Guidelines and Quality...; 2005; Kaczmirek, L., Schulze, N.
- Computer adaptive testing; 2005; Gershon, R. C.
- Ego control and ego-resiliency: Generalization of self-report scales based on personality descriptions...; 2005; Block, J., Funder, D. C., Letzring, T. D.
- The Web experiment list: A Web service for the recruitment of participants and archiving of Internet...; 2005; Reips, U. -D., Lengler, R.
- Survey of substance use among high school students in Taipei: Web-based questionnaire versus paper-and...; 2005; Wang, Y. C., Lee, C. M., Lew-Ting, C. Y., Hsiao, C. K., Chen, W. J.
- Web Surveys. A Brief Guide on Usability and Implementation Issues; 2005; Kaczmirek, L.
- An assessment of measurement invariance between online and mail surveys ; 2005; Deutskens, E., de Ruyter, K., Wetzels, M.
- E-mail versus Web survey response rates among health education professionals; 2005; Kittleson, M. J., Brown, S. L.
- Toward An Open-Source Methodology: What We Can Learn From The Blogosphere; 2005; M.
- Aux Abonnes Absents: Liste Rouge Et Telephone Portable Dans Les Enquetes En Population Generale Sur...; 2005; Beck, F., ., Peretti-Watel, P.
- Web Versus Paper Questionnares: A Design and Functionality - Comparison; 2005; Jones, Ja., Fraser, C., Dowling, Z.
- Web Surveys and the new Disability Discrimination Act; 2005; Macer, T.
- Mixed-mode Surveys Using Mail and Web Questionnaires; 2005; Meckel, M., Baugh, P., Walters, D.
- Sampling procedure, questionnaire design, online implementation; 2005; Jackob, N., Arens, J., Zerback, T., Jowell, R., de Rouvray, C.
- Simple Approaches to Estimating the Variance of the Propensity Score Weighted Estimator Applied on Volunteer...; 2005; Isaksson, A., Lee, S., de Rouvray, C.
- Simple Approaches to Estimating the Variance of the Propensity Score Weighted Estimator Applied on Volunteer...; 2005; Isaksson, A., Lee, S.
- Alternative Modes for Health Surveillance Surveys: An Experiment with Web, Mail, and Telephone; 2005; Link, M. W., Mokdad, A.
- An Experimental Comparison Of Web And Telephone Surveys; 2005; Fricker, S., Galesic, M., Tourangeau, R., Yan, T.
- Organizational Virtual Communities: Exploring Motivations Behind Online Panel Participation; 2005; Daugherty, T., Lee, W.-N., Gangadharbatla, H., Kim, K., Outhavong, S.
- Promoting Uniform Question Understanding in Today's and Tomorrow's Surveys; 2005; Conrad, F. G., Schober, M. F.
- Is a Web survey as effective as a mail survey? A field experiment among computer users; 2005; Kiernan, N. E., Kiernan, M., Oyler, M. A., Gilles, C.
- The effect of personalization on response rates and data quality in web surveys; 2005; Heerwegh, D., Vanhove, T., Matthijs, K., Loosveldt, G.
- When Methodology Interferes With Substance; 2005; Schoen, H., Faas, T.
- Web-based and Mailed Questionnaires: A Comparison of Response Rates and Compliance; 2005; Baelter, K., Balter, O., Fondell, E., Trolle-Lagerros, Y.
- Bleeding Edge or Proven Technology? The Fact and the Fiction of Mobile Survey Computing; 2005; Cameron, M. R.