Web Survey Bibliography
Transportation is an important part of urban policy and precise knowledge about the population’s travel practices is needed in order to develop sustainable transport policies. However, the increasing ‐selection or a selection decision by the study managers. When mixed survey modes are used, individuals choose to belong to one group or another or only respond if the proposed medium suits them. The responses are therefore not comparable, because the sample is no longer random and the presence of respondents is determined by external factors which may also affect the variable of interest in the studied model. This type of selection bias has received considerable coverage in the literature, from both theoretical and empirical standpoints, but as yet little attention has been paid to it with regard to transport surveys. ‐to‐face household travel survey, that is to say individuals who refused to allow an interviewer into their home or who could not be contacted during the first wave of interviews. The results of this survey show that Internet respondents travelled less than the respondents to the face‐to‐face survey. This result subsists even when we limit the socio‐economic differences between the two samples. The comparative analysis is fairly complex as it had to attempt to distinguish between three effects. First, the effect of socioeconomic differences between the Internet respondents and the standard respondents (who answered the face‐to‐face household travel survey), second the effect of any differences in travel between Internet respondents and face‐to‐face respondents, and, third, the effect of differences that were merely due to the survey medium and that did not reflect behavioural differences. It is highly likely that the socioeconomic characteristics and the travel behaviours of the individuals who respond using the Internet are different from those of the individuals who respond to a face‐to‐face interview. To take analysis further, it is necessary to apply econometric techniques that are used for qualitative variables. The sample selection model, whose parameters must be estimated using the two‐stage procedure developed by Heckman provide a promising avenue as, for example, it allows us to isolate the effect on daily travel of socio‐economic differences from that of survey mode. The first stage consists of estimating the survey medium “choice” equation using a probit model. The second stage consists of explaining the differences in travel behaviour using a specific model.
difficulty of obtaining representative data for the target population and the growing complexity of the data that are needed to feed increasingly sophisticated models mean that it is generally not possible to collect all the necessary data in the course of a single survey or with a single method. Combining different data sources has become an extremely important way of increasing knowledge about behaviours and how they are changing as well as improving transport models.
But proposing several data collection modes or methods carries a risk. The collection of information from different sources may provide results that lack comparability. The danger when databases are merged is that a sample selection bias will be created that will compromise the accuracy of explanatory models of travel behaviours. In practice, the selection bias has two sources. It results either from respondent self
The Laboratoire d’Economie des Transports has conducted an Internet survey of no respondents to the 2006 Lyon face
The object of this article is to show that survey mode has an impact on the mobility pattern of respondents. We first present the explanatory variables available for the analysis and estimate an equation of mobility which does not take into account the selection bias. Theoretical developments relative to selection bias follow. Finally, we propose an econometric model that takes into account the selection bias, applied to Lyon household travel survey data.
Conference homepage (abstract)
Web survey bibliography (281)
- Overview: Online Surveys; 2017; Vehovar, V.; Lozar Manfreda, K.
- Standard Definitions Final Dispositions of Case Codes and Outcome Rates for Surveys; 2016
- Retrospective Measurement of Students’ Extracurricular Activities with a Self-administered Calendar...; 2016; Furthmueller, P.
- Pitfalls, Potentials, and Ethics of Online Survey Research: LGBTQ and Other Marginalized and Hard-to...; 2016; McInroy, L. B.
- Computer-assisted and online data collection in general population surveys; 2016; Skarupova, K.
- A Statistical Approach to Provide Individualized Privacy for Surveys; 2016; Esponda, F.; Huerta, K.; Guerrero, V. M.
- Social Media Analyses for Social Measurement; 2016; Schober, M. F.; Pasek, J.; Guggenheim, L.; Lampe, C.; Conrad, F. G.
- Doing Surveys Online ; 2016; Toepoel, V.
- An Overview of Mobile CATI Issues in Europe; 2015; Slavec, A.; Toninelli, D.
- Utilizing iPads in the Field; 2015; Kiser, P.
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2015; 2015
- The Web Survey Revolution ; 2015; Murray, D.
- Methodology of the RAND Mid-Term 2014 Election Panel; 2015; Carman, K. G; Pollack, S.
- 28 Questions to Help Buyers of Online Samples; 2015; Cape, P. J.; Phillips, A.; Baker, R.; Cooke, M.; Ribeiro, E.; Terhanian, G.
- Ethical decision-making and Internet research 2.0: Recommendations from the AoIR ethics working committee...; 2015; Markham, A.; Buchanan, E. A.
- Doing online research involving university students with disabilities: Methodological issues; 2015; De Cesarei, A.; Baldaro, B.
- Exploring ethical issues associated with using online surveys in educational research; 2015; Roberts, L. D.; Allen, P. J.
- An Introduction to Survey Research; 2015; Cowles, E. L.; Nelson, E.
- Ethical issues in online research; 2015; James, N.; Busher, H.
- Leading Edge Insights: Foundations of Quality 2.0; 2014; Fuguitt, G.
- Methods and systems for managing an online opinion survey service; 2014; Mcloughlin, M. H., Seton, N., Blesy, K.
- Recent Books and Journals in Public Opinion, Survey Methods, and Survey Statistics; 2014; Callegaro, M.
- Undisclosed Privacy: The Effect of Privacy Rights Design on Response Rates; 2014; Haer, R., Meidert, N.
- Tailoring mode of data collection in longitudinal studies; 2013; Kaminska, O., Lynn, P.
- How do we Know Cognitive Interviewing is Any Good?; 2013; Willis, G. B.
- Quality of Web surveys; 2013; Revilla, M.
- Experiments in Obtaining Data Linkage Consent in Web Surveys ; 2013; Sakshaug, J. W., Kreuter, F.
- Response Burden in Official Business Surveys: Measurement and Reduction Practices of National Statistical...; 2013; Giesen, D., Bavdaz, M., Loefgren, T., Raymond-Blaess, V.
- Internet as a new source of information for the production of official statistics. Experiences of Statistics...; 2013; Heerschap, N.
- A standard with quality indicators for web panel surveys: a Swedish example; 2013; Nyfjaell, M.
- How Mobile Stacks Up to Traditional Online: A Comparison of Studies; 2013; Knowles, R.
- How to make your questionnaire mobile-ready; 2013; Cape, P. J.
- Phish Rising: How Internet Criminals are Undermining the Viability of Online Survey Research…and...; 2013; Kunovic, K.
- Self-Reported Participation in Research Practices Among Survey Methodology Researchers; 2013; Perez-Vergara, K., Smith, C., Lowenstein, C., Ozonoff, A., Martins, Y.
- Ethics, privacy and data security in web-based course evaluation; 2013; Salaschek, M., Meese, C., Thielsch, M.
- Beyond methodology - some ethical implications of "doing research online"; 2013; Heise, N.
- Code Comparison; 2012
- Evaluation procedures for Survey questions; 2012; Saris, W. E.
- Transparency, Access and the Credibility of Survey Research; 2012; Lupia, A.
- Anonymity and Confidentiality; 2012; Tourangeau, R.
- Cognitive Evaluation of Survey Instruments: State of the Science (Art?) and Future Directions; 2012; Willis, G. B.
- How to provide high data quality in online-questionnaires: Setting guidelines in design; 2012; Tries, S., Nebel, S., Blanke, K.
- Comparability of Survey Measurements; 2012; Oberski, D.
- Classification of Surveys; 2012; Stoop, I., Harrison, E.
- Enhancing Web Surveys With New HTML5 Input Types; 2012; Funke, F.
- Why one should incorporate the design weights when adjusting for unit nonresponse using response homogeneity...; 2012; Kott, P. S.
- Assessing the Quality of Survey Data ; 2012; Blasius, J.
- Designing and Doing Survey Research; 2012; Andres, L.
- Using break-offs in web interviews for predicting web response in mixed mode surveys; 2011; Beukenhorst, D.
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2011; 2011