Web Survey Bibliography
(a) Relevance & Research Question
High drop-out rates are considered a major shortcoming of web surveys and considerably threaten data quality. However, until recently survey breakoff has received limited scholarly attention and knowledge about the reasons causing respondents to terminate surveys early is still fractional. In political science, the topic has been particularly neglected. Enhanced understanding of the complex processes leading to breakoff is needed in order to develop standard guidelines for web surveys that help in minimising drop-out rates.
(b) Methods & Data
Connected to the German Longitudinal Election Study (GLES) a seven-wave campaign online tracking has been conducted with about 14,000 respondents, thereof 3,000 drop-outs. This data allows for a detailed analysis of drop-outs: Given the applied quota design, personal information is available even about those respondents who answered some questions but did not finish the survey. As many questions were included in each wave, yet being asked at different questionnaire positions and being surrounded by various items, contextual effects on drop-out can be analysed.
In terms of research methods, among other things, life tables were presented to observe how breakoffs are distributed throughout the survey. Discrete-time survival models with page-varying covariates are estimated for each wave separately, including both respondent characteristics as well as questionnaire and page characteristics.
(c) Results
Our main findings can be summarised as follows:
In accordance with recent research, we find drop-out to be a function of both respondent characteristics and page characteristics. For instance, higher educated people are less likely to break off, whereas open questions tend to produce significantly higher drop-outs.
In the course of the survey, the drop-out-risk tends to decrease.
Varying the context a question is embedded in, may affect the number of drop-outs, thus breakoff in web surveys is not unchallengeable.
(d) Added Value
Our findings confirm some results of previous research dealing with breakoff in web surveys. The reasons for breakoffs thus seem to be largely independent of the survey topic. Even more essential, based on the empirical results, some prospects for reducing the number of drop outs in web surveys by questionnaire and page design are provided.
Conference Homepage (abstract) / (presentation)
Web survey bibliography - General Online Research Conference (GOR) 2010 (17)
- Testing the Applicability of Respondent Driven Sampling as an Online Research Method to Sample Hidden...; 2010; Pajak, D.
- Seriousness Checks are Useful to Improve Data Validity in Online Research; 2010; Diedenhofen, D., Aust, F., Ullrich, S., Musch, J.
- Enrichment of Qualitative Research through Online Approaches: New Insights due to Online CoCreation...; 2010; Krischke-Ramaswamy, M., Knorr, H.
- Developing and Evaluating a Student Online Panel.; 2010; Stiglbauer, B., Gamsjäger, M., Gnambs, T., Batinic, B., Altrichter, H.
- Online Access Panels: A detailed look at different Ways of Entering, their Costs and Participation Behavior...; 2010; Führer, R., Keusch, F.
- Eye Tracking and Cognitive Interviewing: Steps to improve online questionnaires; 2010; Tries, S., Sattelberger, S.
- Trial by Ordeal, a medieval approach to a modern day problem; 2010; Cape, P., Cavallaro, K.
- How new engagement techniques and question approaches are revolutionizing online research data gathering...; 2010; Puleston, J.
- Social Networking Sites: New approaches for Online-Panels?; 2010; Drosdow, M., Geißler, H.
- The Impact of Visual and Functional Design Elements in Online Survey Research; 2010; Hammen, K.
- Theoretical model of context-sensitive mobile methods; 2010; Maxl, E.
- Can a professional questionnaire layout make up for a boring topic? The mediating role of topic interest...; 2010; Keusch, F., Mayerhofer, W., Jungreithmaier, S., Weilbuchner, N., Fuehrer, R., Kling, H.
- Using Propensity Score Weighting to Reduce Bias of a Swiss Market Research Web Panel; 2010; Wiegand, G., Jella, H., Beat, H., Stefan, L.
- Potentials and Constraints of Propensity Score Weighting to Improve Web Survey Quality; 2010; Steinmetz, S., Tijdens, K.
- Selection Bias in Web Surveys and the Use of Propensity Scores in Forecasting the Result of the 2009...; 2010; Musch, J., Ullrich, S., Diedenhofen, D.
- Breakoff in Web Surveys of the German Longitudinal Election Study (GLES); 2010; Blumenstiel, J. E., Roßmann, J., Steinbrecher, M.
- The longitudinal effect of incentives on participation and data quality in online panels; 2010; Neumann, B. P., Goeritz, A.