Web Survey Bibliography
The strengths and weaknesses of web surveys have been widely described in the literature. Of particular interest is the question of the quality and reliability of web surveys for scienti c use - more concretely, to which degree can the obtained results be generalised for the whole population? As respondents are not selected at random and the target population rather forms a convenience than a probability sample, particularly volunteer web surveys are subject to selection bias.
To deal with this problem, weighting adjustment, like post-strati cation and propensity score weighing, have been seen as a possible solution to reduce the biases in web surveys. However, particularly the post-strati cation weighting, aiming to adjust for demographic dierences between the sample and the population under consideration, seems to be limited. As some variables of interest often do not show a sufficiently strong relationship with the demographic weighting variables, this method can correct for proportionality but not necessarily for representativeness. As a consequence, another weighting technique called Propensity Score Adjustment (PSA) has been suggested as an alternative for statistically surmounting inherent problems in web survey data. It aims to correct for dierences caused by the varying inclinations of individuals to participate in web surveys. It adjusts for selection bias due to observed covariates which are demographic as well as webographic(lifestyle) variables. These variables measure general attitudes or behaviour that are hypothesised to dier between the online and the general population. In the scienti c community, however, this method has traditionally not been applied in the eld of surveys, and there has been a minimal amount of evidence for its applicability and performance, and the implications are not conclusive. Moreover, the statistical theory behind this approach is not well developed and the eectiveness and implications, particularly for survey methodology, still need to be better studied.
Against this background, the present paper attempts to explore various statistical weighting procedures for volunteer web surveys and evaluate their eectiveness in adjusting biases arising from non-randomised sample selection. In order to achieve the goal, three methods are compared in more detail: post-strati cation, correlations and nally PSA. A rst essential step for exploring the existing selection bias within the existing data-sets will be a detailed bias description. It is particularly needed for the application of PSA searching for the variables that
are going to be included in the calculation of the Propensity Score weights. The efficiency of dierent weights will then be tested by comparing unweighted and weighted results from the German, Dutch and Spanish sample of the Wage-indicator Survey 2006 with those that could be found using data from the German Socio-economic panel, the OSA Labour Supply Panel and the Spanish Structure Earnings Survey for the same year. In the framework of these examinations, analytical graphics and formal tests of signi cance will be used. Furthermore,
the sensitivity of the results, particularly to changes in the speci cation of the propensity score, will also be addressed.
Conference homepage (abstract)
Web Survey Bibliography - Weighting & imputation (262)
- 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.
- 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.
- 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. et al.
- 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.
- The Evolution of Edits in the Canadian Census of Population Online Questionnaires; 2011; Laroche, D.
- Current Projects at University of Ljubljana; 2011; Lozar Manfreda, K.
- Maintaining Cross-Sectional Representativeness in a Longitudinal General Population Survey ; 2011; Lynn, P.
- The German Access Panel and the Impact of Response Propensities; 2011; Amarov, B., Enderle, T., Muennich, R., Rendtel, U., Zins, S.
- A Bayesian analysis of small area probabilities under a constraint; 2011; Nandram, B., Sayit, H.
- The Impact of Non-Response Treatments on the Stability of Blockmodels; 2011; Znidarsic, A., Ferligoj, A., Doreian, P.
- Research on Internet survey errors and control methods; 2011; Mingyue, F., Xicang, Z.
- Separation of selection bias and mode effect in mixed-mode survey – Application to the face-to...; 2011; Bayart, C., Bonnel, P.
- Social Climate Survey of Tobacco Control: A mixed-mode approach; 2011; Klein, J. D., McMillen, R.
- Exploring use of information in paradata through calibration method to detect and adjust non-response...; 2011; Billiet, J. Matsuo, H.
- Assessment of propensity score methods on nonresponse bias adjustment; 2011; Alanya, A., Billiet, J., Matsuo, H.
- Nonsampling errors in dual frame telephone surveys ; 2011; Brick, J. M., Flores Cervantes, I., Lee, S., Norman, G.
- Handbook of Nonresponse in Household Surveys ; 2011; Bethlehem, J., Cobben, F., Schouten, B.
- Dropout in Web-based studies: Methodology; 2011; Reips, U. -D.