Web Survey Bibliography
Response latencies answering to attitude questions can be used as a measure of chronic attitude accessibility. But depending on the theoretical interest, several determinants of response latencies have to be treated as bias effects and should be statistically controlled to adequately interpret response latencies. Main bias factors are the individual baseline speed of respondents reflecting a constant individual characteristic of mental speed of information processing, effects of the measurement instrument and situational effects. In this study using response latency data of a nation-wide German survey (CATI), four statistical transformation methods to control the individual baseline speed are empirically evaluated and compared: Z-Score, Difference Score, Ratio Index, and Rate-Amount Index. The empirical findings support the assumption of an increased data quality transforming "raw" reaction times into indices controlling the baseline speed. Additionally, the data quality increases if additional systematic bias effects are controlled (here: question order, effect of extremity). 1 INTRODUCTION In attitude theory, response latencies answering to attitude questions are regarded as an indicator of attitude strength. Defining an attitude as the association between an object and its evaluation (Fazio 1986, 1989, 1990b), the strength of an attitude is the strength of this association. Response latencies are often used to measure the chronic accessibility of attitudes. This accessibility points directly to the mental process during the activation of an attitude and is regarded as a measure of the associative strength. Therefore, an attitude is assumed to be stronger if it is easily accessible, measured by a shorter response latency, and to be weaker if it is less or not accessible, measured by a longer response latency. In recent decades, the development of modern techniques of computer assisted interviewing has made it possible to measure response latencies to attitude questions in the context of large scale survey studies (Bassili/Fletcher 1991, Bassili 1993, 1996b). In contrast to the laboratory context, the measurement of mental information processing in a relatively uncontrolled survey context is much more biased. For example, different interviewers may measure the reaction time with different accuracy (raw response latencies implicate the latency of the interviewer to press the appropriate key), or the respondent may be distracted by the presence of others or unforeseen events. Additional problems appear if the respondent fails to answer 'correctly' if he or she has difficulties to understand the question or to generate an answer and translate it into the given categories or scale (see next chapter).
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Web survey bibliography (81)
- Data chunking for mobile web: effects on data quality; 2017; Lugtig, P. J.; Toepoel, V.
- Do Initial Respondents Differ From Callback Respondents? Lessons From a Mobile CATI Survey; 2016; Vicente, P.; Marques, C.
- Mobile Device Use in Web Surveys Among College Students: Predictors and Consequences for Data Quality...; 2016; Beach, S.; Musa, D.; Strotmeyer, S.; Schlarb, J.
- Web surveys for offline rural communities ; 2016; Gichohi, B. W.
- An experiment comparing grids and item-by-item formats in web surveys completed through PCs and smartphones...; 2016; Revilla, M.; Toninelli, D.; Ochoa, C.
- Pre-Survey Text Messages (SMS) Improve Participation Rate in an Australian Mobile Telephone Survey:...; 2016; Dal Grande, E.; Chittleborough, C. R.; Campostrini, S.; Dollard, M.; Taylor, A. W.
- When Should I Call You? An Analysis of Differences in Demographics and Responses According to Respondents...; 2016; Vicente, P.; Lopes, I.
- Online Surveys are Mixed-Device Surveys. Issues Associated with the Use of Different (Mobile) Devices...; 2016; Toepoel, V.; Lugtig, P. J.
- Mobile Research Methods: Opportunities and challenges of mobile research methodologies. ; 2015; Toninelli, D. (Ed.); Pinter, R.; de Pedraza, P.
- Collecting Health Research Data - Comparing Mobile Phone-assisted Personal Interviewing to Paper-and...; 2015; van Heerden, A. C.; Norris, S. A.; Tollman, S. M.; Richter, L. M.
- The Effects of Questionnaire Completion Using Mobile Devices on Data Quality. Evidence from a Probability...; 2015; Bosnjak, M.; Struminskaya, B.; Weyandt, K.
- Designing web surveys for the multi-device internet; 2015; de Bruijne, M.
- Mobility Enabled: Effects of Mobile Devices on Survey Response and Substantive Measures; 2015; Barlas, F. M.; Randall, T. K.
- Innovations in Email Invitation Design for Today’s Digital World; 2015; Saunders, T.; Kessler, A.
- Emerging Technologies: The Rise of Mobile Devices: From Smartphones to Smart Surveys; 2015; Buskirk, T. D.
- Open narrative questions in PC and smartphones: is the device playing a role?; 2015; Revilla, M.; Ochoa, C.
- Mixed-method feasibility study comparing the outpatient assessment of burn patients using a tablet device...; 2015; Mitchell, S. S.
- Mobile Devices for the Collection of Sensitive Information; 2015; Maitland, A.; Mercer, A. W.; Tourangeau, K.; Williams, Do.
- The Impact of Mixing Modes on Reliability in Longitudinal Studies; 2014; Cernat, A.
- Global market research 2013; 2013
- Australia: building a 21st century readership survey; 2013; Green, A., White, H.
- The new swiss national readership survey: fit for the future ; 2013; Amschler, H., Hoffmann, J.
- Relative Mode Effects on Data Quality in Mixed-Mode Surveys by an Instrumental Variable; 2013; Vannieuwenhuyze, J. T. A., Revilla, M.
- Is the Sky Falling? New Technology, Changing Media, and the Future of Surveys; 2013; Couper, M. P.
- A report on the Confirmit Market Research Software Survey 2013; 2013; Macer, T., Wilson, S.
- Survey Breakoffs in a Computer-Assisted Telephone Interview; 2013; McGonagle, K.
- Impact of mode design on reliability in longitudinal data; 2013; Cernat, A.
- Mobility and Smartphones: a pilot study of travel data collection among experienced and inexperienced...; 2013; Douhou, S., Scherpenzeel, A.
- Mobile devices a way to recruit hard-to-reach groups? Results from a pilot study comparing desk top...; 2013; Toepoel, V., Lugtig, P. J.
- Mode effects in Labour Force Surveys - do they really matter?; 2013; Koerner, T.
- Comparison of quality of web survey and CATI data using unobtrusive response latencies; 2013; Mayerl, J.
- Comparability of substantive results between modes and incentive conditions in a probability-based telephone...; 2013; Pekari, N.
- Data Collection Method Comparisons for the 2011 Fishing, Hunting, and Wildlife-Associated Recreation...; 2013; Herbstritt, M., Hornick, D.
- The comparability of Don't Know answers between CATI and CAWI modes; 2013; Pohjanpaa, K., Jarvensivu, M.
- Measuring the same concepts in several modes in the "BIBB/BAuA-Employee-Survey 2011/12" ; 2013; Gensicke, M., Tschersich, N., Hartmann, J.
- Error Prevention through Interviewer Emulation? Introducing questionnaire dialogues in the Norwegian...; 2013; Gravem, D. F.
- Mode Effects in Mixed-Mode Surveys: Prevention, Diagnostics, and Adjustment 1; 2013; de Leeuw, E. D., Dillman, D. A., Schouten, B.
- Comparing Tablet, Computer, and Smartphone Survey Administrations; 2013; Wells, T., Bailey, J., Link, M. W.
- Cross-Platform Measurement: User Experience With a Smartphone and Web Self- Reported Data Collection...; 2013; Petras, A. P., Duan, S., Dan, O.
- Examining the Feasibility of SMS as a Contact Mode for a College Student Survey; 2013; Crawford, S. D., McClain, C., O'Brien, S., Nelson, T. F.
- Surveys on Mobile Devices: Opportunities and Challenges; 2013; Couper, M. P.
- Benefits of Modular Design for Mobile and Online Surveys; 2012; Kelly, F., Johnson, A., Stevens, S.
- Unintentional mobile respondents; 2012; Peterson, G.
- Understanding Mode Effects between Mobile Web and Mobile SMS Surveys; 2012; Poduska, B., Johnson, E. P.
- Measures of Data Quality Across the RDD Frames; 2012; Lavrakas, P. J.
- Mobile Survey Participation Rates in Commercial Market Research: A Meta-Analysis; 2012; Bosnjak, M., Poggio, T., Becker, K. R., Funke, F., Wachenfeld, A., Fischer, B.
- Qualitatively Speaking: Mobile qualitative finally hits its stride; 2012; Bryson, J.
- Modular Survey Design for Mobile Devices; 2012; Johnson, A., Kelly, F.
- Using SMS Text Messaging To Collect Time Use Data; 2012; Brenner, P., DeLamater, J.
- Telephone Status, Attitudes toward Participation in Future Surveys, and Willingness to Join a Local...; 2012; Beach, S., Musa, D.