Web Survey Bibliography
Many surveys today are affected by high nonresponse. This can be a serious problem to survey quality since nonresponse causes systematic error (bias) in the survey estimates. Given the decreasing trend in response rates and the corresponding increasing resources needed to achieve preset response rates, taking measures only at the estimation stage is no more sufficient to overcome this problem, nor efficient. Measures need to be taken also at the data collection stage. In this direction, different forms of responsive design have recently been proposed. The general objectives of responsive design have been formulated in Groves and Heeringa (2006). The main idea underlying this method is to intervene in the data collection process, in order to achieve an ultimate set of responding units that is “better balanced” or “more representative” than if no special effort is made. Interventions are settled by evaluating the sample properly as the data collection unfolds. To this purpose different indicators have been proposed, such as the balance and representativity indicators of the set of respondents and the distance between respondents and nonrespondents (Särndal, 2011, Schouten et al., 2009, Schouten et al., 2011, and Lundquist and
Särndal, 2012). These indicators are computable from selected auxiliary variables, which are known for the responding units as well as for the non-responding ones. By monitoring the indicators during the data collection process, it is possible to modify the original design during the course of the data collection, in order to obtain a better balanced ultimate response set. The recent existing literature presents many progresses in the development of this methodology. However, further investigations are needed in order to apply it in practice, also to different contexts. The aim of this paper is to evaluate the potentials of responsive design in the framework of mixed mode panels, where one mode is Web. The empirical application uses data from the on-going probability-based PAADEL panel. The PAADEL-Producer panel is an Italian regional panel of businesses in the agro-food sector managed at the CASI centre of Bergamo University. The recruitment of the panel was conducted in 2012 and lasted approximately three months. The first step recruitment was based on phone mode (maximum number of contact attempts five); the second step recruitment was based on the mixed mode approach (Web, phone, mail, fax). Using the
database of data collection of this research, first the progression of the estimates of a few variables is studied as the data collection unfolds. Next the balance and representativity of the panel are investigated at different steps of the recruitment. Finally, a set of experimental responsive designs based on alternative interventions in the data collection is artificially reproduced. Results are analyzed in a comparative way to evaluate the impact of this approach on the final estimates. Special attention is devoted to the bias reduction issue. Some thoughts on the consequences on the variability of the estimates are also proposed. The results obtained are promising. By way of example, Table 1 shows some of the results that will be described in the study.
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Web survey bibliography - Marketing/business (336)
- Response Burden in Official Business Surveys: Measurement and Reduction Practices of National Statistical...; 2013; Giesen, D., Bavdaz, M., Loefgren, T., Raymond-Blaess, V.
- How Mobile Stacks Up to Traditional Online: A Comparison of Studies; 2013; Knowles, R.
- Book Review: Brand Together: How Co-creation Generates Innovation and Re-energizes Brands, by Nicholas...; 2013; Wilson, Al.
- Digging deeper: using implicit tests to define consumers' semantic network; 2013; Riviere, P., Cuny, C., Allain, G., Vereijken, C.
- Conceptualising and evaluating experiences with brands on Facebook; 2013; Smith, S.
- How incentives affect web-based survey response rates of athletic program donors; 2013; Alvarado, G., Callison, C.
- Permission email messages significantly increase gambler retention; 2013; Jolley, W., Lee, A., Mizerski, R., Sadeque, S.
- How virtual corporate social responsibility dialogs generate value: A framework and propositions; 2013; Korschun, D., Du, S.
- Customer loyalty to a commercial website: Descriptive meta-analysis of the empirical literature and...; 2013; Toufaily, E., Ricard, L., Perrien, J.
- Discovering interest groups for marketing in virtual communities: An integrated approach; 2013; Wang, K.-Y., Wu, H.-J., Ting, I.-H.
- The Gamification of Marketing Research; 2013; Donato, P., Link, M. W.
- Measuring Up: Impact of mobile and segmentation on respondent behaviour; 2013; Luck, K.
- Responsive design for mixed-mode panel data; 2013; Bianchi, A., Biffignandi, S.
- Comparative analysis of data from web and face-to-face surveys. A case study on e-commerce in young...; 2013; Cappello, C., Pellegrino, D.
- Insights into Action Profiling shopping occasions for retailers through mobile and online research; 2013; Churkina, O., Morris, T.
- Mobilizing your Branded Panel: Panel data quality during the smartphone transition; 2013; Kugel, C., Brien, D., Blechman, J.
- Möglichkeiten zur impliziten Messung von Emotionen am Beispiel webcambasierter Gesichtsausdruckserkennung...; 2013; Wachenfeld, A., Moentmann, A., Bernet, F.
- The 2012 Confirmit Annual Market Research Software Survey; 2013; Macer, T., Wilson, S.
- Using Mixed-Mode Contacts in Client Surveys: Getting More Bang for Your Buck; 2013; Israel, G. D.
- ESSnet Data: Collection for Social Surveys using Multiple Modes; 2013; Sattelberger, S., Blanke, K.
- The Influence of Answer Box Format, Personal Topic Interest, and Respondent Characteristics on Response...; 2013; Keusch, F.
- Effects of Displaying Videos on Measurement in a Web Survey; 2013; Mendelson, J., Gibson, J. L., Romano Bergstrom, J. C.
- Can Google Consumer Surveys Help Pre-Test Alternative Versions of a Survey Question?: A Comparison of...; 2013; Stern, M. J., Welch, W. W.
- Tablets and Smartphones and Netbooks, Oh My! Effects of Device Type on Respondent Behavior; 2013; Ross, H., Mendelson, J., Lackey, M.
- Survey quality prediction system 2.0; 2013
- A nationwide web-based freight data collection; 2013; Samimi, A., Mohammadian, A., Kawamura, K.
- An approach to selecting online respondents; 2013; Terhanian, G.
- By the Numbers: Theory of adaptation or survival of the fittest?; 2013; Cavallaro, K.
- Measuring wages via a volunteer web survey – a cross-national analysis of item nonresponse; 2013; Steinmetz, S., Annmaria, B.
- Measuring working conditions in a volunteer web survey; 2013; de Pedraza, P., Villacampa, A.
- Research Design as an Influencing Factor for Reliability in Online Market Research; 2013; Wengrzik, J., Theuner, G.
- Questionnaire Design: How to Plan, Structure and Write Survey Material for Effective Market Research...; 2013; Brace, I.
- The association between online gaming, social phobia, and depression: an internet survey; 2012; Chen, M.-H., Huang, P.-C., Bai, Y.-M., Wei, H.-T.
- Worldwide online research spending; 2012
- Unintentional mobile respondents; 2012; Peterson, G.
- The integration of facebook into class management: an exploratory study; 2012; Chou, P. N.
- The cross platform report. Q2 -2012 - US; 2012
- Screenwise panel: Frequently Asked Questions; 2012
- Research company spotlight - Mobile surveys; 2012
- Quality in market research. From theory to practice. 2nd Edition; 2012; Harding, D., Jackson, P.
- Online survey statistics for the mobile future. Updated with Q3 2012 data; 2012
- Not just playing around; 2012; Ewing, T.
- MRS Guidelines for online reseach; 2012
- Mobile email opens report 2nd half 2011; 2012
- ISO 20252. Market, opinion and social research-Vocabulary and service requirements, 2nd Edition; 2012
- Global market research 2012; 2012
- The war against unengaged online respondents; 2012; Gittelman, S. H., Trimarchi, E.
- Qualitatively Speaking: The five absolute, no-excuse must-dos for online qualitative researchers; 2012; Rossow, A.
- By the Numbers: Lessons for using online panels in B2B research; 2012; Elsner, N.
- Measure the response burden in the Swedish Intrastat system; 2012; Weideskog, F.