Web Survey Bibliography
Survey data are unavoidably contaminated with measurement error. We focus on formatting error, the increase in con dential intervals of survey estimates, making it more difficult to detect existing dierences. Formatting error happens when respondents do not fi nd an option on a rating scale that perfectly reflects their true value. The dierence between the true value and the chosen response option is formatting error.
From a theoretical point of view, continuous visual analogue scales (VASs) have on the individual as well as on the aggregated level an expected formatting error of zero, because there is a perfectly tting option for every graduation of the true value. An empirical determination of formatting error with VASs is pending and it is unclear, if populations with a low formal education are able to use VASs in a meaningful way.
Formatting error with categorical scale is dierent for single individual variables and for aggregate data. In the first case, it depends on the number of categories only. In the second case it is additionally in uenced by the actual distribution of values in the sample. We simulated differently distributed data (e.g. from uniform distributions, narrow and wide normal distributions, chi-square and exponential distributions) to determine the expected formatting error with categorical scales consisting of 3 to 21 categories. Overall (N = 1909), we found a very low mean empirical formatting error of M = -1.24 percentage points (SD = 3.05). Ratings on plain VASs without any marker (n = 167) were worse (M = -2.48, SD = 2.64) and formal education (below college: M = -2.87, SD = 2.93; at least college: M = -1.71, SD = 1.61) made a statistically signi cant dierence: F(1, 166) = 7.56, p < .01, eta2 = .04. VASs with ten markers (n = 181) lead to the smallest formatting error (M = -0.41, SD = 1.55) for respondents with a low education (M = -0.47, SD = 1.63) and respondents with a high formal education (M = -0.30, SD = 1.39), F(1, 180) < 1. For individual variables, the empirical formatting error for VASs with ten markers is even with respondents with a low formal education lower than the expected formatting error for categorical scales up to 50 options. Overall, the authors strongly recommend considering VASs in computer-based self-administered questionnaires for the sake of more con dent survey estimates.
Web Survey Bibliography - European survey research associaton conference 2009, ESRA, Warsaw (36)
- An experimental mixed mode design on a general population survey ; 2009; Eva, G.
- Presentation of a Single Item versus a Grid: Effects on the Vitality and Mental Health Scales of the...; 2009; Callegaro, M., Shand-Lubbers, J., Dennis, J. M.
- Survey Research in Virtual Worlds: Second Life R as a Research Platform; 2009; Hill, C., Dean, E.
- Elderly in an Internet panel, the quality of the data; 2009; Vis, C.
- Computer-Assisted Audio Recording (CARI): Repurposing a Tool for Evaluating Comparative Instrument Design...; 2009; Edwards, B., Hicks, W., Tourangeau, K., Harris-Kojetin, L., Moss, A.
- Do online translated questionnaires result in higher response rates for patient surveys?; 2009; Boyd, J., Davis, A.
- A comparison of two mixed mode designs: cati-capi and web-cati-capi; 2009; Beukenhorst, D., Wetzels, W.
- Comparison between Liss panel (web) and ESS data (face to face); 2009; Revilla, M., Saris, W. E.
- Is a cell phone really a personal device? Results from the first wave of a mobile phone panel on sharing...; 2009; Fuchs, M., Busse, B.
- Mobile Phone Surveys in Germany – Response rates and response behaviour; 2009; Hader, S., Schneiderat, G.
- Ethical Considerations in the Use of Paradata in Web Surveys; 2009; Couper, M. P., Singer, E.
- Interviewer voice characteristics and productivity in telephone surveys; 2009; Best, H., Bauer, G., Steinkopf, L.
- Standardized recall aids for online life course surveys; 2009; Glasner, T.
- The impact of forgiving wording and question context on social desirability bias in sensitive surveys...; 2009; Naher, A.- F., Krumpal, I.
- Interactive feedback can improve accuracy of responses in web surveys; 2009; Conrad, F. G., Couper, M. P., Tourangeau, R., Galesic, M.
- Increasing Confidence in Survey Estimates with Visual Analogue Scales; 2009; Funke, F., Reips, U. -D., Thomas, R. K.
- Effectiveness of incentives in mixed-mode systems: An evaluation of errors & costs; 2009; Lozar Manfreda, K., Berzelak, N., Vehovar, V.
- The influence of the field time on data quality in list-based Web surveys; 2009; Goeritz, A., Stieger, S.
- Twisting Rating Scales: Horizontal versus Vertical Visual Analogue Scales versus Categorical Scales...; 2009; Funke, F., Reips, U. -D.
- Online Analysis and Programmed Disclosure Risk Protection: New Access to Restricted-use Microdata; 2009; McFarland O’Rourke, J., Rush, S. H., Maxwell, C.
- Using the Available On-line Secondary Data in Education and Research Practice; 2009; Perek-Bialas, J.
- Nice portal! But where is the data . . . ? - Experiences of a data archive with offering online access...; 2009; Mauer, R.
- Making Use of Online Survey Documentation & Analysis; 2009; Terwey, M.
- Access to Survey Data on the Internet; 2009; Kolsrud, K.
- Individual Follow-up of the Target Population: the Plural Strategies of a Web Survey; 2009; Markou, E., de Cledat, B., Razafindratsima, N., Laurent, R., Issenhuth, P.
- The influence of selective nonresponse in the analysis of levels of annoyance and sleep disturbance...; 2009; Breugelmans, O.
- Motivating different groups: questionnaire topic and participation rates; 2009; Marchand, M.
- How to cover the general public by Internet interviewing; 2009; Das, M.
- The Internet sample; 2009; Getka-Wilczynska, E.
- Comparing different weighting procedures for volunteer online panels - Lessons to be learned from German...; 2009; Steinmetz, S., Tijdens, K., de Pedraza, P.
- Selection bias in Internet panels: challenge or dead blow?; 2009; Lensvelt-Mulders, G. J.
- Presentation of WEBSURVNET; 2009; de Pedraza, P., Steinmetz, S., Tijdens, K.
- Telephone Survey and political behaviour estimates in 22 European countries: Evaluating the need for...; 2009; Hufken, V.
- Self-Selected Samples in Customer Satisfaction Surveys; 2009; Nicolini, G., Dalla Valle, L.
- What to do if Probability Sampling is Impossible in a Web Survey?; 2009; Markou, E., Razafindratsima, N., de Cledat, B., Issenhuth, P., Laurent, R.
- New Challenges in Sampling: Introduction; 2009; Laaksonen, S.