Web Survey Bibliography
AGS covers many topics, including student satisfaction for his/her university experience. Using the complete AGS data set, we calculate two satisfaction factors from a set of satisfaction questions. This is done using the factor analysis (via principal components) method. First factor may be interpreted as a “general satisfaction” index, the second is a contrast between software (teaching, exams, graduation organization) and hardware (classrooms, libraries, cafeterias) evaluation. Factors are not directly observable, but we treat them as they are, for simplicity’s sake. They are continous, approximately normal variables.
Is the non-response process MAR (“Missing at Random”)? To say this we regress the response indicators on all the administrative variables (including sex, age, number of years needed for graduating, graduation mark, high school final mark, faculty, type of course) and the two satisfaction factors. There is a weak evidence that the respondents and non-respondents have different distribution of factor scores.
If we have to take any inference on the non-respondents we must assume that they are related to respondents in some way. The way is often the assumption that they are related through the auxiliary information, that is through variables known for both respondent and non-respondents.
We bootstrap the population to evaluate the ability of the calibration correction to improve the estimators of non-response. We try first to use the quasi-randomization approach to estimate propensity, then use these weights as a basis for calibration. Several combination for calibration variables are used. Faculty is always included as they are main subdivisions of the University and estimates by faculty are routinely required.
The paper analyzes reweighting adjustments for non-response in surveys carrying out a bootstrap evaluation of non-response adjusted estimators. In our study we consider a population made of students from the Bergamo University graduating in a specific period of time.
This population has been surveyed twice (web mode in both cases), before and after graduation. The ante-graduation survey (from now on, AGS) is a compulsory survey, the post-graduation survey (PGS) is not compulsory, therefore there was 56% non-response rate. Administrative (archive) data available for all the students. We apply the non-response process of the PGS in the analysis of AGS data. In this way, we have a controlled situation in which all survey variables, for both respondents and non-respondents are known. We avoid artificial assumptions on the non-response process.
Web Survey Bibliography - Weighting & imputation (279)
- Measurement error calibration in mixed-mode sample surveys; 2013; Buelens, B., van der Brakel, J.
- Assessing Nonresponse Bias in the Green Technologies and Practices Survey; 2013; Meekins, B., Sverchkov, M., Stang, S.
- Web Panel Representativeness; 2013; Bianchi, A., Biffignandi, S.
- Methodological Issues in the Design of Online Surveys for Measuring Unethical Work Behavior: Recommendations...; 2013; Wouters, K., Maesschalck, J., Peeters, C. F. W., Roosen, M.
- On the Impact of Response Patterns on Survey Estimates from Access Panels; 2013; Enderle, T., Muennich, R., Bruch, C.
- Unit Nonresponse and Weighting Adjustments: A Critical Review; 2013; Brick, J. M.
- Adjusting for bias in a mixed-mode CAWI survey on University students ; 2013; Clerici, R., Giraldo, A.
- A probability-based web panel for the UK: What could it look like?; 2013; Nicolaas, G.
- Panel Attrition: Separating Stayers, Sleepers and Other Types of Drop-Out in an Internet Panel; 2013; Lugtig, P. J.
- Speeding and Non-Differentiation in Web Surveys: Evidence of Correlation and Strategies for Reduction...; 2013; Zhang, C.
- Web Versus Outbound: A Mode Face-Off Following the Presidential Debate; 2013; Marlar, J.
- The Effects of Errors in Paradata on Weighting Class Adjustments: A Simulation Study; 2013; West, B. T.
- Improving Surveys with Paradata: Analytic Uses of Process Information; 2013; Kreuter, F.
- Ten questions to ask your online survey provider; 2013; Williams, D.
- Practical tools for designing and weighting survey samples; 2013; Valliant, R. L., Daver, J. A., Kreuter, F.
- Measuring Wages Worldwide: Exploring the Potentials and Constraints of Volunteer Web Surveys; 2013; Steinmetz, S., Raess, D., Tijdens, K., de Pedraza, P.
- Moving an established survey online – or not?; 2013; Barber, T., Chilvers, D., Kaul, S.
- The comparison of road safety survey answers between web-panel and face-to-face; Dutch results of SARTRE...; 2013; Goldenbeld, C., de Craen, S.
- Measuring working conditions in a volunteer web survey; 2013; de Pedraza, P., Villacampa, A.
- Propensity Score Weighting – Can Personality Adjust for Selectivity?; 2013; Glantz, A., Greszki, R.
- Sampling Frame Coverage and Domain Adjustment Procedures for Internet Surveys; 2013; Asan, Z., Ayhan, H. O.
- The rise of the "connected viewer"; 2012; Smith, A., Boyles, J. L.
- Eurobarometer Special surveys: Special Eurobarometer 381; 2012
- Computation of Survey Weights: Bridging Theory and Practice; 2012; Debell, M.
- Modes of Data Collection; 2012; Tourangeau, R.
- An experimental investigation of the effects of noncontingent and contingent incentives in recruiting...; 2012; Lavrakas, P. J., Dennis, J. M., Peugh, J., Shand-Lubbers, J., Lee, E., Peugh, J., Charlebois, O., Murakami...
- Rules of engagement: The war against poorly engaged respondents - guidelines for elimination; 2012; Gittelman, S. H., Trimarchi, E.
- Web Panels; 2012; Bethlehem, J., Biffignandi, S.
- Use of Response Propensities; 2012; Bethlehem, J., Biffignandi, S.
- Weighting Adjustment Techniques; 2012; Bethlehem, J., Biffignandi, S.
- The Problem of Self-Selection; 2012; Bethlehem, J.,Biffignandi, S.
- The Problem of Undercoverage; 2012; Bethlehem, J., Biffignandi, S.
- Respondent-driven sampling; 2012; Schonlau, M., Liebau, E.
- A Structural Analysis Based on Similarity between Fuzzy Clusters and Its Application to Evaluation Data...; 2012; Chiba, R., Furutani, T., Sato-Ilic, M.
- Why one should incorporate the design weights when adjusting for unit nonresponse using response homogeneity...; 2012; Kott, P. S.
- Cell Sample Demographics under Alternative Dual-Frame Sample Designs; 2012; Montgomery, R., Morrison, H., Zeng, W., Wolter, K., Blumberg, S. J., O'Connor, K.
- Data Quality from Low Cost Data Collection Methodologies; 2012; Traugott, M. W.
- To Weight, or Not to Weight, That is the Question: Survey Weights and Multivariate Analysis; 2012; Young, R., Johnson, D. R.
- Multiple Imputation for Unit Nonresponse and Measurement Error; 2012; Peytchev, A.
- Assessing the Quality of Survey Data ; 2012; Blasius, J.
- Collecting, Managing, and Assessing Data Using Sample Surveys; 2012; Stopher, P.
- Can Weighting Compensate for Sampling Issues in Internet Surveys?; 2011; Vaske, J. J., Jacobs, M. H., Sijtsma, M. T. J., Beaman, J.
- Online Appendix for “Surveying the General Public Over the Internet Using Address-Based Sampling...; 2011; Dillman, D. A., Messer, B. L.
- Online survey research: Findings, best practices, and future research. Report prepared for the Advertising...; 2011; Vannette, D.
- Online survey research: Findings, Best practices, and future research; 2011
- Just published: Forrester Wave™ of enterprise feedback management satisfaction and loyalty solutions...; 2011; McInnes, A.
- In search of a new approach to measure newspaper audiences in Canada: The journey continues; 2011; Crassweller, A., Rogers, J., Graves, F., Gauthier, E., Charlebois, O.
- Households with Computers, Telephone Subscriptions, and Internet Access, Selected Years, 1997 - 2010; 2011
- Eurobarometer Special surveys: EB75.1 E-Communications Household Survey. Special Eurobarometer 362; 2011
- A meta-analysis of experiments manipulating progress indicators in Web surveys; 2011; Callegaro, M., Villar, A., Yang, Y.