Web Survey Bibliography
Title Impulsiveness, Speed and Reliability in Online Questionnaire
Author Harms, C.
Year 2016
Access date 29.04.2016
Presentation PDF (527KB)
Abstract
Relevance & Research Question: Online questionnaires offer a way to collect paradata, such as response time or mouse movements. As they require participants' behavior, personality is expected to have an influence on how the questions are answered. The present study investigates how impulsiveness influences response times in an online questionnaire. A hypothesis that found mixed evidence in the past (Moltó, Segarra & Avila, 1993; Malle & Neubauer, 1991). Further we were interested in the effect of response speed on the reliability of questionnaires. Montag & Reuter (2008) found no such link. We extend their study by using more precise client-side response times.
Methods & Data: A convenience sample of N=572 participants was recruited, mainly under-graduate students at the universities Bonn and Ulm. Participants studying psychology could receive course credit for participation. Participants completed an online questionnaire that included demographic data and two personality questionnaires (NEO-FFI, 60 items, and Barrett Impulsiveness Scale, 30 items, each in German translation). Personality items were presented in randomized order for each participant. This enabled us to measure reaction time for each item independently.
Bayesian regression analysis (Rouder & Morey, 2012; Morey & Rouder, 2015) was used to test the relationship between impulsiveness and overall completion time against a model including only age and education (Yan & Tourangeau, 2008). Reliability was measured in terms of internal consistency using Cronbach's Alpha (Cronbach, 1951).
Results:Bayesian regression analysis yielded substantial evidence for an effect of age and education on completion time against no effect (BF_10 = 7.11). Testing our hypothesized model including impulsiveness against the model including only age and educational level yielded some little evidence against our model (BF_10 = 0.18).No notable differences in internal consistency were observed in any of the scales for neither very fast nor very slow subjects. All alpha scores ranged between 0.66 and 0.84 showing acceptable to good reliability.
Added Value: Our results show that (a) impulsiveness has no impact on the time a subject needs to complete a questionnaire and that (b) the speed of completion does not impact the internal consistency of self-reports. Further studies should strengthen this evidence.
Methods & Data: A convenience sample of N=572 participants was recruited, mainly under-graduate students at the universities Bonn and Ulm. Participants studying psychology could receive course credit for participation. Participants completed an online questionnaire that included demographic data and two personality questionnaires (NEO-FFI, 60 items, and Barrett Impulsiveness Scale, 30 items, each in German translation). Personality items were presented in randomized order for each participant. This enabled us to measure reaction time for each item independently.
Bayesian regression analysis (Rouder & Morey, 2012; Morey & Rouder, 2015) was used to test the relationship between impulsiveness and overall completion time against a model including only age and education (Yan & Tourangeau, 2008). Reliability was measured in terms of internal consistency using Cronbach's Alpha (Cronbach, 1951).
Results:Bayesian regression analysis yielded substantial evidence for an effect of age and education on completion time against no effect (BF_10 = 7.11). Testing our hypothesized model including impulsiveness against the model including only age and educational level yielded some little evidence against our model (BF_10 = 0.18).No notable differences in internal consistency were observed in any of the scales for neither very fast nor very slow subjects. All alpha scores ranged between 0.66 and 0.84 showing acceptable to good reliability.
Added Value: Our results show that (a) impulsiveness has no impact on the time a subject needs to complete a questionnaire and that (b) the speed of completion does not impact the internal consistency of self-reports. Further studies should strengthen this evidence.
Access/Direct link Conference Homepage (presentation)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography - 2016 (264)
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- Socially Desirable Responding in Web-Based Questionnaires: A Meta-Analytic Review of the Candor Hypothesis...; 2016; Gnambs, T.; Kaspar, K.
- Dynamic Question Ordering in Online Surveys; 2016; Early, K.; Mankoff, J.; Fienberg, S. E.
- How to use online surveys to understand human behaviour concerning window opening in terms of building...; 2016; Fabbri, K.
- Impact of satisficing behavior in online surveys on consumer preference and welfare estimates; 2016; Gao, Z.; House, L. A.; Bi, X.
- Comparing Twitter and Online Panels for Survey Recruitment of E-Cigarette Users and Smokers; 2016; Guillory, J.; Kim, A.; Murphy, J.; Bradfield, B.; Nonnemaker, J.; Hsieh, Y. P.
- Influence of Importance Statements and Box Size on Response Rate and Response Quality of Open-Ended...; 2016; Kumar Chaudhary, A.; Israel, G. D.
- Web based health surveys: Using a Two Step Heckman model to examine their potential for population health...; 2016; Morrissey, K.; Kinderman, P.; Pontin, E.; Tai, S.; Schwannauer, M.
- “Better do not touch” and other superstitions concerning melanoma: the cross-sectional web...; 2016; Gajda, M.; Kamiñska-Winciorek, G.; Wydmañski, J.; Tukiendorf, A.
- Methods for Evaluating Respondent Attrition in Web-Based Surveys; 2016; Hochheimer, C. J.; Sabo, R. T.; Krist, A. H.; Day, T.; Cyrus, J.; Woolf, S. H.
- The Low Response Score (LRS): A Metric to Locate, Predict, and Manage Hard-to-Survey Populations; 2016; Erdman, C.; Bates, N.
- Targeted Appeals for Participation in Letters to Panel Survey Members; 2016; Lynn, P.
- Can we assess representativeness of cross-national surveys using the education variable?; 2016; Ortmanns, V.; Schneider, S.
- Methodological Aspects of Central Left-Right Scale Placement in a Cross-national Perspective; 2016; Scholz, E.; Zuell, C.
- Fieldwork Effort, Response Rate, and the Distribution of Survey Outcomes: A Multilevel Meta-analysis; 2016; Sturgis, P.; Williams, Jo.; Brunton-Smith, I.; Moore, J.
- Mobile-only web survey respondents; 2016; Lugtig, P. J.; Toepoel, V.; Amin, A.
- Comparison of Face-to-Face and Web Surveys on the Topic of Homosexual Rights; 2016; Liu, M.; Wang, Yic.
- Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated...; 2016; Lee, S.; McClain, C.; Webster, N.; Han, S.
- Web-Based Statistical Sampling and Analysis; 2016; Quinn, A.; Larson, K.
- Standard Definitions: Final Dispositions of Case Codes and Outcome Rates for Surveys 2016; 2016
- Using Visual Analogue Scales in eHealth: Non-Response Effects in a Lifestyle Intervention; 2016; Kuhlmann, T.; Reips, U.-D.; Wienert, J.; Lippke, S.
- Development and Pilot Test of a Mobile Application for Field Data Collection; 2016; Chiappetta, L.; Kerr, M. M.
- Statistical Design for Online Experiments Across Desktops, Tablets, Smartphones (and Maybe Wearable...; 2016; Qian, P.; Sadeghi, S.; Arora, N. K.
- A Case Study on the Use of Propensity Score Adjustments with Web Survey Data; 2016; Parsons, V.
- Motivated Misreporting in Web Panels; 2016; Bach, R.; Eckman, S.
- Are Initial Respondents Different from the Nonresponse Follow-Up Cases? A Study of Probability-Based...; 2016; Zeng, W.; Dennis, J. M.
- Using official surveys to reduce bias of estimates from nonrandom samples collected by web surveys; 2016; Beresovsky, V.; Dorfman, A.; Rumcheva, P.
- Predicting and Preventing Break-Offs in Web Surveys; 2016; Mittereder, F.
- A Feasibility Study of Recruiting and Maintaining a Web Panel of People with Disabilities; 2016; Chandler, J.
- Exploration of Methods for Blending Unconventional Samples with Traditional Probability Samples; 2016; Gellar, J.; Zhou, H.; D.; Sinclair, M. D.
- Ratio of Vector Lengths as an Indicator of Sample Representativeness ; 2016; Shin, H. C.
- Design of Sample Surveys That Complement Observational Data to Achieve Population Coverage; 2016; Slud, E.; Ashmead, R.
- Inferences from Internet Panel Studies and Comparisons with Probability Samples; 2016; Lachan, R.; Boyle, J.; Harding, R.
- Exploring the Gig Economy Using a Web-Based Survey: Measuring the Online 'and' Offline Side...; 2016; Robles, B. J.; McGee, M.
- Comparing data quality between online panel and intercept samples; 2016; Liu, M.
- Effect of a Pre-Paid Incentive on Response Rates to an Address-Based Sampling (ABS) Web-Mail Survey; 2016; Suzer-Gurtekin, Z.; Elkasabi, M.; Liu, Me.; Lepkowski, J. M.; Curtin, R.; McBee, R.
- Response Behavior in a Video-Web Survey: A Mode Comparison Study; 2016; Haan, M.; Ongena, Y. P.; Vannieuwenhuyze, J. T. A.; de Glopper, K.
- Standard Definitions Final Dispositions of Case Codes and Outcome Rates for Surveys; 2016
- Integration of a phone-based household travel survey and a web-based student travel survey; 2016; Verreault, H.; Morency, C.
- Evaluation of mode equivalence of the MSKCC Bowel Function Instrument, LASA Quality of Life, and Subjective...; 2016; Bennett, A. V.; Keenoy, K.; Shouery, M.; Basch, E.; Temple, L. K.
- Making use of Internet interactivity to propose a dynamic presentation of web questionnaires; 2016; Revilla, M.; Ochoa, C.; Turbina, A.
- A streamlined approach to online linguistic surveys; 2016; Erlewine, M. Y.; Kotek, H.
- Du kommst hier nicht rein: Türsteherfragen identifizieren nachlässige Teilnehmer in Online-Umfragen; 2016; Merkle, B.; Kaczmirek, L.; Hellwig, O.
- Incorporating eye tracking into cognitive interviewing to pretest survey questions; 2016; Neuert, C.; Lenzner, T.
- Population Survey Features and Response Rates: A Randomized Experiment; 2016; Guo, Y.; Kopec, J.; Cibere, J.; Li, L. C.; Goldsmith, C. H.
- Mode Effect and Response Rate Issues in Mixed-Mode Survey Research: Implications for Recreational Fisheries...; 2016; Wallen, K. E.; Landon, A. C.; Kyle, G. T.; Schuett, M. A.; Leitz, J.; Kurzawski, K.
- A measure of survey mode differences; 2016; Homola, J.; Jackson, N. M.; Gill, Je.
- Web Health Monitoring Survey: A New Approach to Enhance the Effectiveness of Telemedicine Systems ; 2016; Romano, M. F.; Sardella, M. V.; Alboni, F.
- Smartphones vs PCs: Does the Device Affect the Web Survey Experience and the Measurement Error for...; 2016; Toninelli, D.; Revilla, M.
- Question order sensitivity of subjective well-being measures: focus on life satisfaction, self-rated...; 2016; Lee, S.; McClain, C.; Webster, N.; Han, S.