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 - 2016 (264)
- Web Health Monitoring Survey: A New Approach to Enhance the Effectiveness of Telemedicine Systems; 2017; Romano, M. F.; Sardella, M. V.; Alboni, F.
- 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.