Web Survey Bibliography
Title Changing the scoring procedure and the response format to get the most out of multiple-choice tests conducted online
Author Papenberg, M.; Diedenhofen, B.; Musch, J.
Year 2016
Access date 29.04.2016
Abstract
Relevance & Research Question: Traditional multiple-choice (MC) tests are often administered using paper and pencil, and typically rely on calculating the number of correctly solved questions to measure a test-taker’s knowledge. A major disadvantage of MC tests is their inability to capture partial knowledge and to adequately control for guessing and for testwiseness. We investigated whether conducting multiple choice tests online can help to address these problems by changing either the scoring procedure or the response format.
Methods & Data: We conducted a series of experimental investigations involving several hundred participants each. To investigate whether the traditional number-right scoring procedure can be improved, we computed option weights for MC tests both empirically and by querying experts. We also used two alternative response formats that can better be employed online than offline: (1) Discrete-option multiple choice (DOMC) testing that employs a sequential, rather than simultaneous presentation of answer alternatives; this response format presumably provides a better control of testwiseness because it does not allow test-takers to compare all available answers. (2) Answer-until-correct (AUC) testing, a response format that allows test takers to answer repeatedly until they identify the correct answer. By determining how many attempts a test taker needs to successfully solve an item, AUC allows to capture partial knowledge and to provide test takers with a direct feedback on their performance.
Results: We found that DOMC tests allowed for a better control of testwiseness than traditional MC tests while achieving the same level of reliability and validity. We also found that both, answer-until-correct testing and empirical option weighting allowed to improve the validity of a knowledge test. However, option weights were useful only if they were determined automatically on an empirical basis, rather than by querying experts.
Added Value: We compare the advantages and disadvantages of the various methods, and give recommendations on when the different response formats and scoring procedures should best be used, and when they should better be avoided.
Methods & Data: We conducted a series of experimental investigations involving several hundred participants each. To investigate whether the traditional number-right scoring procedure can be improved, we computed option weights for MC tests both empirically and by querying experts. We also used two alternative response formats that can better be employed online than offline: (1) Discrete-option multiple choice (DOMC) testing that employs a sequential, rather than simultaneous presentation of answer alternatives; this response format presumably provides a better control of testwiseness because it does not allow test-takers to compare all available answers. (2) Answer-until-correct (AUC) testing, a response format that allows test takers to answer repeatedly until they identify the correct answer. By determining how many attempts a test taker needs to successfully solve an item, AUC allows to capture partial knowledge and to provide test takers with a direct feedback on their performance.
Results: We found that DOMC tests allowed for a better control of testwiseness than traditional MC tests while achieving the same level of reliability and validity. We also found that both, answer-until-correct testing and empirical option weighting allowed to improve the validity of a knowledge test. However, option weights were useful only if they were determined automatically on an empirical basis, rather than by querying experts.
Added Value: We compare the advantages and disadvantages of the various methods, and give recommendations on when the different response formats and scoring procedures should best be used, and when they should better be avoided.
Access/Direct link Conference Homepage (presentation)
Year of publication2016
Bibliographic typeConferences, workshops, tutorials, presentations
Web survey bibliography (4086)
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- Answering Without Reading: IMCs and Strong Satisficing in Online Surveys; 2017; Anduiza, E.; Galais, C.
- Ideal and maximum length for a web survey; 2017; Revilla, M.; Ochoa, C.
- Social desirability bias in self-reported well-being measures: evidence from an online survey; 2017; Caputo, A.
- Web-Based Survey Methodology; 2017; Wright, K. B.
- Handbook of Research Methods in Health Social Sciences; 2017; Liamputtong, P.
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- Web Survey Gamification - Increasing Data Quality in Web Surveys by Using Game Design Elements; 2017; Schacht, S.; Keusch, F.; Bergmann, N.; Morana, S.
- Effects of sampling procedure on data quality in a web survey; 2017; Rimac, I.; Ogresta, J.
- Comparability of web and telephone surveys for the measurement of subjective well-being; 2017; Sarracino, F.; Riillo, C. F. A.; Mikucka, M.
- Achieving Strong Privacy in Online Survey; 2017; Zhou, Yo.; Zhou, Yi.; Chen, S.; Wu, S. S.
- A Meta-Analysis of the Effects of Incentives on Response Rate in Online Survey Studies; 2017; Mohammad Asire, A.
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- Usability Testing for Survey Research; 2017; Geisen, E.; Romano Bergstrom, J. C.
- Paradata as an aide to questionnaire design: Improving quality and reducing burden; 2017; Timm, E.; Stewart, J.; Sidney, I.
- Fieldwork monitoring and managing with time-related paradata; 2017; Vandenplas, C.
- Interviewer effects on onliner and offliner participation in the German Internet Panel; 2017; Herzing, J. M. E.; Blom, A. G.; Meuleman, B.
- Interviewer Gender and Survey Responses: The Effects of Humanizing Cues Variations; 2017; Jablonski, W.; Krzewinska, A.; Grzeszkiewicz-Radulska, K.
- Millennials and emojis in Spain and Mexico.; 2017; Bosch Jover, O.; Revilla, M.
- Where, When, How and with What Do Panel Interviews Take Place and Is the Quality of Answers Affected...; 2017; Niebruegge, S.
- Comparing the same Questionnaire between five Online Panels: A Study of the Effect of Recruitment Strategy...; 2017; Schnell, R.; Panreck, L.
- Nonresponses as context-sensitive response behaviour of participants in online-surveys and their relevance...; 2017; Wetzlehuetter, D.
- Do distractions during web survey completion affect data quality? Findings from a laboratory experiment...; 2017; Wenz, A.
- Predicting Breakoffs in Web Surveys; 2017; Mittereder, F.; West, B. T.
- Measuring Subjective Health and Life Satisfaction with U.S. Hispanics; 2017; Lee, S.; Davis, R.
- Humanizing Cues in Internet Surveys: Investigating Respondent Cognitive Processes; 2017; Jablonski, W.; Grzeszkiewicz-Radulska, K.; Krzewinska, A.
- A Comparison of Emerging Pretesting Methods for Evaluating “Modern” Surveys; 2017; Geisen, E., Murphy, J.
- The Effect of Respondent Commitment on Response Quality in Two Online Surveys; 2017; Cibelli Hibben, K.
- Pushing to web in the ISSP; 2017; Jonsdottir, G. A.; Dofradottir, A. G.; Einarsson, H. B.
- The 2016 Canadian Census: An Innovative Wave Collection Methodology to Maximize Self-Response and Internet...; 2017; Mathieu, P.
- Push2web or less is more? Experimental evidence from a mixed-mode population survey at the community...; 2017; Neumann, R.; Haeder, M.; Brust, O.; Dittrich, E.; von Hermanni, H.
- In search of best practices; 2017; Kappelhof, J. W. S.; Steijn, S.
- Redirected Inbound Call Sampling (RICS); A New Methodology ; 2017; Krotki, K.; Bobashev, G.; Levine, B.; Richards, S.
- An Empirical Process for Using Non-probability Survey for Inference; 2017; Tortora, R.; Iachan, R.
- The perils of non-probability sampling; 2017; Bethlehem, J.
- A Comparison of Two Nonprobability Samples with Probability Samples; 2017; Zack, E. S.; Kennedy, J. M.
- Rates, Delays, and Completeness of General Practitioners’ Responses to a Postal Versus Web-Based...; 2017; Sebo, P.; Maisonneuve, H.; Cerutti, B.; Pascal Fournier, J.; Haller, D. M.
- Necessary but Insufficient: Why Measurement Invariance Tests Need Online Probing as a Complementary...; 2017; Meitinger, K.
- Nonresponse in Organizational Surveying: Attitudinal Distribution Form and Conditional Response Probabilities...; 2017; Kulas, J. T.; Robinson, D. H.; Kellar, D. Z.; Smith, J. A.
- Theory and Practice in Nonprobability Surveys: Parallels between Causal Inference and Survey Inference...; 2017; Mercer, A. W.; Kreuter, F.; Keeter, S.; Stuart, E. A.
- Is There a Future for Surveys; 2017; Miller, P. V.
- Reducing speeding in web surveys by providing immediate feedback; 2017; Conrad, F.; Tourangeau, R.; Couper, M. P.; Zhang, C.
- Social Desirability and Undesirability Effects on Survey Response latencies; 2017; Andersen, H.; Mayerl, J.
- A Working Example of How to Use Artificial Intelligence To Automate and Transform Surveys Into Customer...; 2017; Neve, S.
- A Case Study on Evaluating the Relevance of Some Rules for Writing Requirements through an Online Survey...; 2017; Warnier, M.; Condamines, A.
- Estimating the Impact of Measurement Differences Introduced by Efforts to Reach a Balanced Response...; 2017; Kappelhof, J. W. S.; De Leeuw, E. D.
- Targeted letters: Effects on sample composition and item non-response; 2017; Bianchi, A.; Biffignandi, S.