Web Survey Bibliography
The share of households without a landline phone, but aving at least one cell phone, is steadily growing, and so is evidence of corresponding non-coverage bias in landline surveys. Consequently, surveys increasingly use dual frame sampling, typically with a landline share of 60-80%. The aim of this paper is to find the optimal value of this share (i.e. mixture parameter), while considering survey error and costs.
For this purpose, the target population is divided into five strata, according to possession and availability of landlines and cell phones. Next, by optimizing the product of costs and the mean squared error across strata, we get the analytical solution (a 4th order polynomial). Finally, this approach is illustrated in eight countries with a 2008 Flash Eurobarometer dual frame survey. The questions cover various socioeconomic and political issues: the support of the EU and its currency; attitudes toward economic reforms; and evaluations of household income fluctuations. The optimal mixture parameter is estimated for different variables, countries, sources of population data, and cost options. The solution depends on the landline-cell phone cost ratio. However, it generally lies in a relatively flat optimal area, from 30-70%. Surprisingly, the results are almost invariant to variables, while linear regression (using strata weights and cost ratio) gives excellent prediction of the analytical solution. In countries where the share of the cell-only segment is not very high (less than 25%), it is optimal to have more landline units, and vice versa: where the cell-only segment is larger a predominately cell sample is optimal. However, this connection can be affected by changes in cost ratio, which is the most important factor in determining the optimal design allocation. When the cost of cell interviews is very high in relation to landline phone interviews, taking more landline units is optimal in all countries.
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Web Survey Bibliography - Costs (460)
- Methodological Considerations of Qualitative Email Interviews; 2013; Nehls, K.
- Compared to a small, supervised lab experiment, a large, unsupervised web-based experiment on a previously...; 2013; Ryan, R. S., Wilde, M., Crist, S.
- Managing mobile research: How it's different and why it matters; 2013; Kachhi-Jiwani, D., Tucker, J., Wilding-Brown, L.
- A standard for test reliability in group research; 2013; Ellis, J. L.
- The General Survey System Initiative at RTI International: An Integrated System for the Collection and...; 2013; Thalji, L., Mitchell, S., Hill, C. A., Suresh, R., Speizer, H., Pratt, D.
- Innovation in Data Collection: the Responsive Design Approach; 2013; Bianchi, A., Biffignandi, S.
- Measurement effects in mixed-mode panel surveys; 2013; Lugtig, P. J.
- The ONS Beyond 2011 Programme & possible implications for social surveys; 2013; Morris, L.
- Issues of Coverage and Sampling in Web Surveys for the General Population: An Overview; 2013; Lynn, P.
- Use of a Social Networking Web Site for Recruiting Canadian Youth for Medical Research; 2013; Chu, J. L., Snider, C. E.
- Measurement invariance and quality of composite scores in a face-to-face and a web survey; 2013; Revilla, M.
- Convenient yet not a convenience sample: Jury pools as experimental subject pools; 2013; Murray, G. R., Rugeley, C. R., Mitchell, D.-G., Mondak, J. J.
- Online Survey Software; 2013; Baker, J. D.
- Incentives for college student participation in web-based substance use surveys; 2013; Patrick, M. E., Singer, E., Boyd, C. J., Cranford, J. A., McCabe, S. E.
- Screenwise panel: Frequently Asked Questions; 2012
- Social media as a data collection tool: the impact of Facebook in behavioural research; 2012; Zoppos, E.
- Social research in online context: methodological reflections on web surveys from a case study; 2012; Pandolfini, V.
- Can Microtargeting Improve Survey Sampling? An Assessment of Accuracy and Bias in Consumer File Marketing...; 2012; Pasek, J.
- Optimizing Response Rates; 2012; Brick, J. M.
- Modes of Data Collection; 2012; Tourangeau, R.
- The re-engineering of the Structural Earnings survey process: Mixed - Mode data collection and new E...; 2012; Cardinaleschi, S., De Santis, S., Rocci, F., Spinelli, V.
- Between demand and reality: Ensuring efficiency and quality in pretesting questionnaires; 2012; Sattelberger, S., Blanke, K.
- Choosing a Data Collection Approach: Mixed Mode Design Experiences in Statistics Finland; 2012; Taskinen, P., Kiianmaa, N.
- An experimental investigation of the effects of noncontingent and contingent incentives in recruiting...; 2012; Lavrakas, P. J., Dennis, J. M., Peugh, J., Shand-Lubbers, J., Lee, E., Peugh, J., Charlebois, O., Murakami...
- The Feasibility of Conducting a Web Survey Using Respondent Driven Sampling among Transgenders in the...; 2012; Kappelhof, J.
- WEBDATANET: web-based data-collection methodological challenges, solutions and implementations. Action...; 2012; de Pedraza, P.
- WebSM Study: Survey software features overview ; 2012; Vehovar, V.; Cehovin, G.; Kavcic, L.; Lenar, J.
- Web Panels; 2012; Bethlehem, J., Biffignandi, S.
- Weighting Adjustment Techniques; 2012; Bethlehem, J., Biffignandi, S.
- Mixed-Mode Surveys; 2012; Bethlehem, J., Biffignandi, S.
- Effects of E-Mailed Versus Mailed Invitations and Incentives on Response Rates, Data Quality, and Costs...; 2012; Dykema, J., Stevenson, J., Klein, L., Kim, Y., Day, B.
- Prenotification, Incentives, and Survey Modality: An Experimental Test of Methods to Increase Survey...; 2012; Tepper, J. R., Jacob, B.
- Costs and Errors in Fixed and Mobile Phone Surveys; 2012; Vehovar, V., Slavec, A., Berzelak, N.
- Paper-and-Pencil versus Web Administration of a Student Satisfaction Survey; 2012; Bowen, C.-C.
- An Initial Look at Non-Response and Attrition in Understanding Society; 2012; Lynn, P., Burton, J., Kaminska, O., Knies, G., Nandi, A.
- Evidence on the Comparison of Telephone and Internet Surveys for Respondent Recruitment.; 2012; Potoglou, D., Kanaroglou, P. S., Robinson, N.
- Comparison of response rates and cost-effectiveness for a community-based survey: postal, internet and...; 2012; Sinclair, M., O'Toole, J., Malawaraarachchi, M., Leder, K.
- Online Questionnaire Data Analysis System (OQDAS); 2012; Ali, A. Q.
- Solving the Mode Mystery The Cost, Coverage and Quality Tradeoffs of Picking (and Mixing) Online and...; 2012; Cape, P. J., Phillips, K.
- Modular Survey Design for Mobile Devices; 2012; Johnson, A., Kelly, F.
- Improving RDD Cell Phone Samples. Evaluation of Different Pre-call Validation Methods; 2012; Kunz, T., Fuchs, M.
- Influencing Mode Choice in a Mixed Mode Survey; 2012; Mooney, G., Lan, F., Lin, X., Hurwitz, A.
- Best Approaches to Mode Order and Non-response Prompting in a Multi-Mode Survey; 2012; Newsome, J., Levin, K., Schafer, B., Vigil, M., Liu, W.
- Better (Quality), Faster, Cheaper? Completing Web Surveys on Cell-Enabled iPads; 2012; Driscoll, H., Dayton, J. J., Pels, R.
- The Challenge of Going National: An Experimental Evaluation of the Effects of Local vs. Distant Survey...; 2012; Edwards, M. L., Dillman, D. A.
- What are the Odds? Lotteries versus Cash Incentives. Response Rates, Cost and Data Quality for a Web...; 2012; Stevenson, J., Dykema, J., Klein, L., Cyffka, K., Goldrick-Rab, S.
- Data Quality from Low Cost Data Collection Methodologies; 2012; Traugott, M. W.
- Using the iPad2 as a Prize-based Incentive to Boost Response Rates; 2012; McClendon, R., Jenson, E., Olsen, D.
- What Would You Do? Conducting Web-Based Factorial Vignette Surveys; 2012; Aviram, H.
- The “MediaLiveTracker” – A New Online-Tool for Real-Time-Response-Measurement; 2012; Kercher, J., Bachl, M., Voegele, C., Vohle, F.
