on the Internet. CAWI is a synonym for web survey.Cross‐sectional survey: A survey that observes a sample from the target population at one point in time. Objective is to describe the state of the population on that moment in time.Internet survey:A general term for various forms of data collection via the Internet. Examples are a web survey and e‐mail surveys. Included are also forms of data collection that use the Internet just to transport the questionnaire and the collected data.Longitudinal survey:A survey that observes the same sample from the target population at several points in time. Objective is to describe the changes of the population over time.Paradata:Data about the process by which the survey data are collected.Qualitative interview:An in‐person in‐depth interview with respondents that have completed a survey questionnaire. Such an interview aims at uncovering usability problems like difficult questions or cumbersome tasks.Self‐selection survey:A survey for which the sample has been recruited by means of self‐selection. It is left to the persons themselves to decide to participate in a survey.Usability testing: Conducting an experiment to check whether respondents find it easy to complete the web survey questionnaire. Aspects tested include the speed with which the survey task is carried, the number of errors made, and familiarity with the user interface.Web panel:A survey in which the same individuals are interviewed via the web at different points in time.Web survey:A form of data collection via the Internet in which respondents complete the questionnaires on the World Wide Web. The questionnaire is accessed by means of a link to a web page.
EXERCISES
1 Exercise 2.1 Which of the following statements does not apply to web surveys?The survey can be conducted faster.The survey can be conducted cheaper.The response rate is high.The survey collects a large number of interviews.
2 Exercise 2.2 In what respect does a web survey resemble a mail survey?They both rely on visual information transmission.They both rely on oral information transmission.They both use computer‐assisted interviewing techniques.They both cost the same amount of time to conduct.
3 Exercise 2.3 Which of the following phenomena is not a problem of self‐selection web surveys?A respondent can complete a questionnaire more than once.Persons not belonging to the target population can complete the questionnaire.The survey results show a lack of representativity.It is difficult to get a large number of respondents.
4 Exercise 2.4 What is the difference between a cross‐sectional survey and a longitudinal survey?A cross‐sectional survey measures changes over time, and a longitudinal survey measures the state of the population at one point in time.A cross‐sectional survey measures the state of a population at one moment in time, and a longitudinal survey measures time changes over time.A cross‐sectional survey mainly measures facts and behavior, and a longitudinal survey measures attitudes and opinions.For cross‐sectional surveys, any mode of data collection is adequate, whereas in longitudinal surveys only Internet is possible.
5 Exercise 2.5 What is offline data collection?Any form of data collection that does not use the Internet.A form of Internet data collection for which the questionnaire is not written in HTML.A survey that uses e‐mail to transfer information.A survey that uses the Internet to transfer the electronic questionnaire to the respondents.
6 Exercise 2.6 What is the main reason national statistical institutes consider using web surveys?It shows government also uses modern ICT.It reduces nonresponse in surveys.It improves the quality of the collected data.It reduces data collection costs.
REFERENCES
1 Ajzen, I. (1991), The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50, pp. 179–211.
2 Biffignandi, S. (2010a), Modeling Non‐sampling Errors and Participation in Web Surveys. Proceedings of the 45th SIS Scientific Meeting, Padova, Italy.
3 Biffignandi, S. (2010b), Internet Survey Methodology—Recent Trends and Developments. In: Lovric, M. (ed.), International Encyclopedia of Statistical Science. Springer, Heidelberg, Germany.
4 Biffignandi, S. & Pratesi, M. (2002), Internet Surveys: The Role of Time in Italian Firms Response Behaviour. Research in Official Statistics, 5, pp. 19–33.
5 Brown, J. A., Serrato, C. A., Hugh, M., Kanter, M. H., Spritzer, K. L., & Hays, R. D. (2016), Effect of a Post‐paid Incentive on Response Rates to a Web‐Based Survey. Survey Practice, 9, 1. https://doi.org/10.29115/SP‐2016‐0001.
6 Crawford, S. D., Couper, M. P., & Lamias, M. J. (2001), Web Surveys. Perceptions of Burden. Social Science Computer Review, 19, pp. 146–162.
7 Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014), Internet, Mail and Mixed‐Mode Surveys. The Tailored Design Methods. 4th Edition, Wiley, Hoboken, NJ.
8 DiSogra, C. & Callegaro, M. (2009), Computing Response Rates for Probability‐Based Web Panels. Proceeding of Section on Survey Research of the American Statistical Association, Washington, DC.
9 Göritz, A. (2006), Incentives in Web Studies: Methodological Issues and a Review. International Journal of Internet Science, 1, pp. 58–70.
10 Göritz, A. (2010), Using Lotteries, Loyalty Points, and Other Incentives to Increase Participant Response and Completion. In: Gosling, S. & Johnson, J. (eds.), Advanced Methods for Conducting Online Behavioral Research, pp. 219–233. American Psychological Association, Washington, DC. doi: https://doi.org/10.1037/12076‐014.
11 Göritz, A. (2015), Incentive Effects. In: Engel, U., Jann, B., Lynn, P., Scherpenzeel, A., & Sturgis, P. (eds.), Improving Survey Methods: Lessons from Recent Research, pp. 339–350. Routledge, London.
12 Lozar Manfreda, K. & Vehovar, V. (2008), Internet Surveys. In: de Leeuw, E., Hox, J. J., & Dillman, D. A. (eds.), International Handbook of Survey Methodology. Lawrence Erlbaum Associates, New York.
13 Singer, E. & Ye, C. (2013), The Use of Incentives in Surveys. Annals of the American Academy of Political and Social Sciences, 645 (1), pp. 112–141.
14 Steinmetz, S., Bianchi, A., Tijdens, K., & Biffignandi, S. (2014), Improving Web Survey Quality—Potentials and Constraints of Propensity Score Weighting. In: Callegaro, M., Baker, R., Bethlehem, J., Göritz, A., Krosnick, J.A., & Lavrakas, P. J. (eds.), Online Panel Research: A Data Quality Perspective. Wiley, Chichester. pp. 273–298.
Chapter Three A Framework for Steps and Errors in Web Surveys
3.1 Introduction
The framework proposed in this chapter is a collection of interrelated ideas and concepts that provide guidance for researchers undertaking a web survey. To carry out a survey, one must conduct a process that involves many steps and decisions. The process‐oriented approach divides the survey into steps that define the flow of the survey process, and this approach must be applied to web surveys as well. Steps differ in some respects from the traditional modes. Thus, the flow of the web survey process must be explicitly defined. Bethlehem and Biffignandi (2012) and Tourangeau, Conrad, and Couper (2013) pointed out the specifics of the web procedures without focusing on the whole process. Thorsdottir and Biffignandi1 presented a flowchart for the steps of a probability‐based web survey. This chapter follows the flowchart
