Web Survey Bibliography
Title Human vs. artificial intelligence: Are software solutions already able to replace human beings?
Author Koch, M.
Year 2016
Access date 29.04.2016
Full text PDF (537MB)
Abstract
Relevance & Research Question: Human-based coding of open-ended answers is a time-consuming and tedious procedure – especially in terms of large sample sizes. Therefore, in recent years, several software solutions have been developed in order to enable an automatic process of coding. The objective of this study is to investigate if these software solutions can already replace human-based coding or if they should only be used as supportive tools.
Methods & Data: In the first step, verbatims were coded by two different people in order to calculate a basis value for inter-rater reliability: therefore, the coders independently assigned the answers to 14 predefined categories. In the next step, three software solutions (one freeware and two commercial software) were used for coding the open-ended answers: All three software programs were applied to cluster the open-ended answers based on the semantics (no predefined categories), while one of the two commercial software was also able to automatically allocate the answers to the 14 predefined categories. For analysis, the different types of coding were compared with each other (human – human, human-machine and machine-machine).
Results: Human-human: The human-based coding achieved the highest matching rate (M=86%; average Cohen’s kappa к=0,8). Human-machine: The clustering of the open-ended answers by their semantics delivered different thematic codes. However, one code was evident with all three software solutions and the human-based coding. Yet, the level of average agreement was rather low (M= 25%; Kappa к=0,3). In contrast to that, the machine-based allocation to the predefined categories performed better: the average inter-rater reliability was к=0,4 with 38% percent of agreement. Machine-machine: In terms of the code that was evident with all three software, an average agreement of 71% could be achieved between the three different programs (average Kappa к=0,6).
Added Value: In consideration of the results, it seems that human-based coding is much more precise than machine-based coding. Yet, software solutions can facilitate the tedious procedure of coding: They can be applied before coding in order to get an overview of important keywords and thematic aspects within seconds. Besides, the categorization via predefined categories is also promising.
Methods & Data: In the first step, verbatims were coded by two different people in order to calculate a basis value for inter-rater reliability: therefore, the coders independently assigned the answers to 14 predefined categories. In the next step, three software solutions (one freeware and two commercial software) were used for coding the open-ended answers: All three software programs were applied to cluster the open-ended answers based on the semantics (no predefined categories), while one of the two commercial software was also able to automatically allocate the answers to the 14 predefined categories. For analysis, the different types of coding were compared with each other (human – human, human-machine and machine-machine).
Results: Human-human: The human-based coding achieved the highest matching rate (M=86%; average Cohen’s kappa к=0,8). Human-machine: The clustering of the open-ended answers by their semantics delivered different thematic codes. However, one code was evident with all three software solutions and the human-based coding. Yet, the level of average agreement was rather low (M= 25%; Kappa к=0,3). In contrast to that, the machine-based allocation to the predefined categories performed better: the average inter-rater reliability was к=0,4 with 38% percent of agreement. Machine-machine: In terms of the code that was evident with all three software, an average agreement of 71% could be achieved between the three different programs (average Kappa к=0,6).
Added Value: In consideration of the results, it seems that human-based coding is much more precise than machine-based coding. Yet, software solutions can facilitate the tedious procedure of coding: They can be applied before coding in order to get an overview of important keywords and thematic aspects within seconds. Besides, the categorization via predefined categories is also promising.
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.