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 (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.
- Telephone versus Online Survey Modes for Election Studies: Comparing Canadian Public Opinion and Vote...; 2017; Breton, C.; Cutler, F.; Lachance, S.; Mierke-Zatwarnicki, A.
- Examining Factors Impacting Online Survey Response Ratesin Educational Research: Perceptions of Graduate...; 2017; Saleh, A.; Bista, K.
- 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.