Web Survey Bibliography
Relevance & Research Question: Attrition is an important methodological challenge to panel surveys (Lynn 2009). Still, there is a remarkable shortage of variables which are associated with both, the propensity of respondents to stay in the panel and the variables of interest. As a result, propensity score weights which are designed to correct for this type of nonresponse frequently yield mixed results.
This paper addresses the question whether paradata can successfully be applied to improve the prediction of attrition in panel Web surveys. Their main advantage is that they are collected as a byproduct of the survey process. However, it is still an open question which paradata can be used to model attrition and to what extent these paradata are correlated with variables of interest (Kreuter and Olson 2013).
Methods & Data: We use logistic regressions to model attrition in a 7-wave panel Web survey and to compute propensity score weights. The models are fitted with sets of socio-demographic, substantial, survey evaluation, and paradata variables. The latter include measures of response times, user agent strings to determine the device used by the respondent, as well as indicators of the respondents’ response behavior. Finally, we use supplemental cross-sectional Web surveys to assess the effectiveness of propensity score weights based on different sets of variables.
Results: Our results show that including paradata significantly improves the prediction of panel attrition. However, the paradata variables do not supersede socio-demographic, survey evaluation and substantial variables, but they complement them. Yet, the paradata are only moderately correlated with variables of interest at best. As a result, including paradata does not significantly improve the effectiveness of propensity score weights.
Added Value: This paper enhances the existing knowledge in several ways: It presents a set of paradata variables and provides empirical tests of their capability to explain attrition. We show that these paradata can successfully be used to create auxiliary data in a cost-efficient way. At the same time, we demonstrate that they do not ultimately help to correct for panel attrition. Thus, we conclude that further research on paradata, panel attrition and its correction is needed.
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Web survey bibliography (4086)
- Measuring Nonresponse Bias in Web Surveys: The Role of Health Status; 2014; Zhang, M.
- Providing a Deadline for Response: Results from Two Recent Experiments; 2014; Kaiser, A., Walston, J. T., Medway, R., Ye, C., Tourangeau, R.
- Respondents Playing Fast and Loose?: Antecedents and Consequences of Respondent Speed of Completion; 2014; Thomas, R. K., Barlas, F. M.
- Does Self-Selection Affect Samples' Representativeness in Online Surveys? An Investigation in Online...; 2014; Khazaal, Y., van Singer, M., Chatton, A., Achab, S., Zullino, D., Rothen, S., Khan, R., Billieux, J.,...
- Awareness and Treatment of Alcohol Dependence in Japan: Results from Internet-Based Surveys in Persons...; 2014; Taguchi, Y., Takei, Y., Sasai, R., Murteira, S.
- Internet-based survey on current practice for evaluation, prevention, and treatment of scars, hypertrophic...; 2014; Lumenta, D. B., Siepmann, E., Kamolz, L. P.
- Awareness and correlates of the role of physical activity in breast cancer prevention among Japanese...; 2014; Miyawaki, R., Shibata, A., Ishii, K., Oka, K.
- Barriers and facilitators for participation in a preventive pelvic floor muscle training program from...; 2014; Albers-Heitner, P., Moossdorff-Steinhauser, H., Weemhoff, M., Nieman, F., Berghmans, B.
- Leading Edge Insights: Foundations of Quality 2.0; 2014; Fuguitt, G.
- Current Practices in Management of Hepatocellular Carcinoma in India: Results of an Online Survey; 2014; Kumar, A.
- Inside the Turk Understanding Mechanical Turk as a Participant Pool; 2014; Paolacci, G., Chandler, J.
- Differences in intrapersonal and interactional empowerment between lurkers and posters in health-related...; 2014; Petrovcic, A., Petric, G.
- Test-retest reliability of the International Study of Asthma and Allergies in Childhood questionnaire...; 2014; Yoshida, K., Adachi, Y., Sasaki, M., et al.
- Web-based Emergency Department Patient Satisfaction Surveys May Introduce Potential for Bias; 2014; Broadwater-Hollifield, C., Fair, J., Podolsky, S., et al.
- Process Planning for CNC Machining of Swedish Subcontractors – A Web Survey; 2014; Anderberg, S., Beno, T., Pejryd, L.
- Perceptions on developing clinical practice guidelines for traditional medicine in Korea: Results of...; 2014; Choi, J., Choi, T.-Y., A.; A., Yun, K.-J., A., Lim, H.-J., Lee, J. A., Lee, M. S.
- Recognition of and interventions for Mibyeong (subhealth) in South Korea: a national web-based survey...; 2014; Lee, J., Lee, Y., Dong, S. O., Kim, S.-H., Lee, S.
- Recruiting an Internet Panel Using Respondent-Driven Sampling; 2014; Schonlau, M., Weidmer, B., Kapteyn, A.
- Systematic Review of the Use of Online Questionnaires of Older Adults; 2014; Remillard, M. L., Mazor, K. M., Cutrona, S. L., Gurwitz, J. H., Tjia, J.
- Validating respondents' identity in online samples; 2014; Baker, R., Miller, C., Kachhi-Jiwani, D., Lange, K., Wilding-Brown, L., Tucker, J.
- The relationship between nonresponse strategies and measurement error; 2014; Malhotra, N., Miller, J. M., Wedeking, J.
- Nonresponse and measurement error in an online panel; 2014; Roberts, C., Allum, N., Sturgis, P.
- Estimating the effects of nonresponses in online panels through imputation; 2014; Zhang, W.
- An empirical test of the impact of smartphones on panel-based online data collection; 2014; Drewes, F.
- Professional respondents in nonprobability online panels; 2014; Hillygus, D. S., Jackson, N. M., Young, M.
- Informing panel members about study results; 2014; Scherpenzeel, A., Toepoel, V.
- Determinants of the starting rate and the completion rate in online panel studies; 2014; Goeritz, A.
- The untold story of multi-mode (online and mail) consumer panels; 2014; McCutcheon, A. L., Rao, K., Kaminska, O.
- Online panels and validity; 2014; Groenlund, K., Strandberg, K.
- Assessing representativeness of a probability-based online panel in Germany; 2014; Struminskaya, B., Kaczmirek, L., Schaurer, I., Bandilla, W.
- A critical review of studies investigating the quality of data obtained with online panels based on...; 2014; Callegaro, M., Villar, A., Yeager, D. S., Krosnick, J. A.
- Online panel research: History, concepts, applications and a look at the future; 2014; Callegaro, M., Baker, R., Bethlehem, J., Goeritz, A., Krosnick, J. A., Lavrakas, P. J.
- Motives for joining nonprobability online panels and their association with survey participation behavior...; 2014; Keusch, F., Batinic, B., Mayerhofer, W.
- Improving web survey quality; 2014; Steinmetz, S., Bianchi, S. M., Tijdens, K. G., Biffignandi, S.
- WebSM Study: Survey Software in 2014; 2014; Vehovar, V., Cehovin, G., Mocnik, A.
- Design and Implementation of an Online Questionnaire Tool; 2014; Schaniel, R.
- The Influence of the Answer Box Size on Item Nonresponse to Open-Ended Questions in a Web Survey; 2014; Zuell, C., Menold, N., Koerber, S.
- What are the Links in a Web Survey Among Response Time, Quality, and Auto-Evaluation of the Efforts...; 2014; Revilla, M., Ochoa, C.
- Does Age Matter? The Influence of Age on Response Rates in a Mixed-Mode Survey; 2014; Gigliotti, L. M., Dietsch, A.
- Does the Choice of Header Images influence Responses? Findings from a Web Survey on Students’...; 2014; Barth, A.
- Methods and systems for managing an online opinion survey service; 2014; Mcloughlin, M. H., Seton, N., Blesy, K.
- Comparison of the quality estimates in a mixed-mode and a unimode design: an experiment from the European...; 2014; Revilla, M.
- Forget gamification; try writing a humanized survey; 2014; Pettit, A.
- Using respondent tweets to fill in survey gaps; 2014; Murphy, J.
- Using Paradata to Predict and to Correct for Panel Attrition in a Web-based Panel Survey; 2014; Rossmann, J., Gummer, T.
- Targeting the bias – the impact of mass media attention on sample composition and representativeness...; 2014; Steinmetz, S., Oez, F., Tijdens, K. G.
- Offline Households in the German Internet Panel; 2014; Bossert, D., Holthausen, A., Krieger, U.
- Which fieldwork method for what target group? How to improve response rate and data quality; 2014; Wulfert, T., Woppmann, A.
- Exploring selection biases for developing countries - is the web a promising tool for data collection...; 2014; Tijdens, K. G., Steinmetz, S.
- Evaluating mixed-mode redesign strategies against benchmark surveys: the case of the Crime Victimization...; 2014; Klausch, L. T., Hox, J., Schouten, B.