Web Survey Bibliography
Title Computer-assisted longitudinal surveys
Author Capiluppi, C.
Year 2002
Access date 21.04.2004
Full text
Abstract In longitudinal surveys, respondents are followed across time detecting changes on their measured variables, in order to understand the dynamics of some process. The same respondents are contacted at possibly regular time intervals, and administered by means of a questionnaire that can be dependent from the wave of the survey and from answers collected at the previous times. Longitudinal surveys are likely to have very complex questionnaires, with a very large number of questions and with complicated paths that can be extremely difficult to administer by paper and pencil interviewing, in particular when sophisticated dependent interviewing is involved.
Dependent interviewing makes use of information available on subjects, coming from previous surveys or other data sources, in order to customize the current interview in different ways. Dependent interviewing is used to probe for changes, to avoid asking already known information, to remember previous answers, to give a feedback to respondent before asking new information. Dependent interviewing is one of the main focus in longitudinal surveys, but it is very difficult to implement in practice by paper and pencil interviewing because of the intrinsic logistical constraints of this method. Think only the need for the interviewer to access frequently “historical” information about the current respondent, on related papers, in order to follow the designed question flow: there is an enormous work to prepare materials for PAPI interviewing at each wave of the survey, and for all these efforts, we have an high risk of errors in question administration, producing non pertinent answers and missing responses.
The introduction of computer-assisted interviewing in longitudinal surveys promises to revolution this field of surveys, making really possible to carry out a longitudinal survey implementing advanced interviewing techniques using historical data on subjects, such as complex dependent interviewing, reactive probing techniques, proactive feedback techniques, and consistency checks across times.
Computer-assisted interviewing allows for handling the complexity of a longitudinal questionnaire, virtually without errors, even in self administered interviewing, through the programming logic built in a software system. By means of automated question flow administration and on-line consistency checks, this method guarantees better control on extra-sampling survey errors, providing a significant gain in survey data quality.
These great features are, we have to make it clear, quite theoretical, in the sense that an ideal CAI system could, in principle, implement in some proper way the techniques we are thinking to use in longitudinal surveys. Unfortunately, we have to say the main currently available CAI systems do not implement a satisfactory and effective support for longitudinal surveys, limiting what is really possible to put into effect among all we can think to do.
Dependent interviewing makes use of information available on subjects, coming from previous surveys or other data sources, in order to customize the current interview in different ways. Dependent interviewing is used to probe for changes, to avoid asking already known information, to remember previous answers, to give a feedback to respondent before asking new information. Dependent interviewing is one of the main focus in longitudinal surveys, but it is very difficult to implement in practice by paper and pencil interviewing because of the intrinsic logistical constraints of this method. Think only the need for the interviewer to access frequently “historical” information about the current respondent, on related papers, in order to follow the designed question flow: there is an enormous work to prepare materials for PAPI interviewing at each wave of the survey, and for all these efforts, we have an high risk of errors in question administration, producing non pertinent answers and missing responses.
The introduction of computer-assisted interviewing in longitudinal surveys promises to revolution this field of surveys, making really possible to carry out a longitudinal survey implementing advanced interviewing techniques using historical data on subjects, such as complex dependent interviewing, reactive probing techniques, proactive feedback techniques, and consistency checks across times.
Computer-assisted interviewing allows for handling the complexity of a longitudinal questionnaire, virtually without errors, even in self administered interviewing, through the programming logic built in a software system. By means of automated question flow administration and on-line consistency checks, this method guarantees better control on extra-sampling survey errors, providing a significant gain in survey data quality.
These great features are, we have to make it clear, quite theoretical, in the sense that an ideal CAI system could, in principle, implement in some proper way the techniques we are thinking to use in longitudinal surveys. Unfortunately, we have to say the main currently available CAI systems do not implement a satisfactory and effective support for longitudinal surveys, limiting what is really possible to put into effect among all we can think to do.
Access/Direct link Homepage - conference (full text)
Year of publication2002
Bibliographic typeConferences, workshops, tutorials, presentations
Web Survey Bibliography (6374)
- Effects of Lotteries on Response Behavior in Online Panels; 2013; Goeritz, A., Luthe, S. C.
- Lotteries and study results in market research online panels; 2013; Goeritz, A., Luthe, S. C.
- The Effects of Errors in Paradata on Weighting Class Adjustments: A Simulation Study; 2013; West, B. T.
- Using Paradata to Study Response to Within-Survey Requests; 2013; Sakshaug, J. W.
- Paradata for Coverage Research ; 2013; Eckman, S.
- Improving Surveys with Paradata: Analytic Uses of Process Information; 2013; Kreuter, F.
- Theory of adaptation or survival of the fittest?; 2013; Cavallaro, K.
- Online Fundraising Essentials, Second Edition; 2013; Stevenson, S. C.
- Tips for Evaluating Online Effectiveness; 2013; Stevenson, S. C.
- The Digital Divide: The internet and social inequality in international perspective; 2013; Ragnedda, M., Muschert, G.
- Ten questions to ask your online survey provider; 2013; Williams, D.
- Survey quality prediction system 2.0; 2013
- Practical tools for designing and weighting survey samples; 2013; Valliant, R. L., Daver, J. A., Kreuter, F.
- Paradata in web surveys; 2013; Callegaro, M.
- Report Of The AAPOR Task Force On Non-probability sampling; 2013; Baker, R. P., Brick, J. M., Bates, N., Battaglia, M. P., Couper, M. P., Dever, J. A., Gile, K. J., Tourangeau...
- Incentive effects; 2013; Goeritz, A.
- A nationwide web-based freight data collection; 2013; Samimi, A., Mohammadian, A., Kawamura, K.
- Mode Matters: Evaluating Response Comparability in a Mixed-Mode Survey; 2013; Bowyer, B. T., Rogowski, J. C.
- Comparing Survey Results Obtained via Mobile Devices and Computers: An Experiment With a Mobile Web...; 2013; de Bruijne, M., Wijnant, A.
- Cognitive Probes in Web Surveys: On the Effect of Different Text Box Size and Probing Exposure on Response...; 2013; Behr, D., Bandilla, W., Kaczmirek, L., Braun, M.
- The E-Interview in Qualitative Research; 2013; Bampton, R., Cowton, C., Downs, Y.
- Methodological Considerations of Qualitative Email Interviews; 2013; Nehls, K.
- Best Practice in Online Survey Research with Sensitive Topics; 2013; Kays, K., Keith, T. L., Broughal, M. T.
- Research Intentions are Nothing without Technology: Mixed-Method Web Surveys and the Coberen Wall of...; 2013; Ganassali, S., Rodriguez-Santos, C.
- Reducing Response Burden for Enterprises Combining Methods for Data Collection on the Internet; 2013; Vik, T.
- Measuring Wages Worldwide: Exploring the Potentials and Constraints of Volunteer Web Surveys; 2013; Steinmetz, S., Raess, D., Tijdens, K., de Pedraza, P.
- Using Web Surveys for Psychology Experiments: A Case Study in New Media Technology for Research; 2013; Peden, B. F., Tiry , A. M.
- The Distinctiveness of Online Research: Descriptive Assemblages, Unobtrusiveness, and Novel Kinds of...; 2013; Lanfrey, D.
- Sampling, Channels, and Contact Strategies in Internet Survey; 2013; Macrì, E., Tessitore, C.
- Advancing Research Methods with New Technologies; 2013; Sappleton, N.
- Data Quality in PC and Mobile Web Surveys; 2013; Mavletova, A. M.
- PDAs in socio-economic surveys: instrument bias, surveyor bias or both?; 2013; Escobal, J., Benites, S.
- Virtual research assistants: Replacing human interviewers by automated avatars in virtual worlds; 2013; Hasler, B. S., Tuchman, P., Friedman, D.
- Compared to a small, supervised lab experiment, a large, unsupervised web-based experiment on a previously...; 2013; Ryan, R. S., Wilde, M., Crist, S.
- From mixed-mode to multiple devices. Web surveys, smartphone surveys and apps: has the respondent gone...; 2013; Callegaro, M.
- Moving an established survey online – or not?; 2013; Barber, T., Chilvers, D., Kaul, S.
- Using mobile devices to access the realities of youth: How identification with society influences political...; 2013; Smith, M.
- On the Use of Latent Variable Models to Detect Differences in the Interpretation of Vague Quantifiers...; 2013; Griffin, J.
- Managing mobile research: How it's different and why it matters; 2013; Kachhi-Jiwani, D., Tucker, J., Wilding-Brown, L.
- An approach to selecting online respondents; 2013; Terhanian, G.
- By the Numbers: Theory of adaptation or survival of the fittest?; 2013; Cavallaro, K.
- Designing and conducting business surveys; 2013; Snijkers, G.,Araldsen, G., , Willimack, D. K.Jones, J.
- Battle of the Scales: Understanding Respondent Scale Usage in the US and Abroad; 2013; Courtright, M., Pashupati, K., Pettit, F. A.
- Modular Survey Design: A Bite Size Proposal; 2013; Kelly, F., Stevens, S., Johnson, A.
- Cyborgs vs. Monsters: Assembling Modular Surveys to Create Complete Datasets; 2013; Johnson, E. P., Siluk, L., Tarraf, S.
- Do I Have Your Full Attention?; 2013; Cape, P. J.
- Does Sample Size Still Matter?; 2013; Bakken, D. G., Bond, M.
- Optimizing Surveys for Smartphones: Maximizing Response Rates While Minimizing Bias; 2013; Lattery, K., Park Bartolone, G., Saunders, T.
- Shorter Isn't Always Better; 2013; Burdein, I.
- Solving the Unintentional Mobile Challenge; 2013; Peterson, G., Mechling, J., LaFrance, J., Ham, G.