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Cited by 317Mobile Crowdsensing and CrowdsourcingSurvey Methodology and NonresponseExpert finding and Q&A systems

The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.

Comparing the Similarity of Responses Received from Studies in Amazon’s Mechanical Turk to Studies Conducted Online and with Direct Recruitment

Open access
Bartneck, Christoph, Duenser, Andreas, Moltchanova, Elena, Zawieska, Karolina·2015·PLOS ONE
DOI 10.1371/journal.pone.0121595PMC PMC4397064
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Abstract

Computer and internet based questionnaires have become a standard tool in Human-Computer Interaction research and other related fields, such as psychology and sociology. Amazon's Mechanical Turk (AMT) service is a new method of recruiting participants and conducting certain types of experiments. This study compares whether participants recruited through AMT give different responses than participants recruited through an online forum or recruited directly on a university campus. Moreover, we compare whether a study conducted within AMT results in different responses compared to a study for which participants are recruited through AMT but which is conducted using an external online questionnaire service. The results of this study show that there is a statistical difference between results obtained from participants recruited through AMT compared to the results from the participant recruited on campus or through online forums. We do, however, argue that this difference is so small that it has no practical consequence. There was no significant difference between running the study within AMT compared to running it with an online questionnaire service. There was no significant difference between results obtained directly from within AMT compared to results obtained in the campus and online forum condition. This may suggest that AMT is a viable and economical option for recruiting participants and for conducting studies as setting up and running a study with AMT generally requires less effort and time compared to other frequently used methods. We discuss our findings as well as limitations of using AMT for empirical studies.

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Figures

Fig 1 A LEGO Minifigure.
Fig 1 A LEGO Minifigure.CC BY · PubMed Central
Fig 2 Questionnaire layout a) MT b) Qualtrics.
Fig 2 Questionnaire layout a) MT b) Qualtrics.CC BY · PubMed Central
Fig 3 Plate Diagram for the simplified Bayesian multinomial logistic random effects model.
Fig 3 Plate Diagram for the simplified Bayesian multinomial logistic random effects model.CC BY · PubMed Central
Fig 4 Estimated average distribution of emotional assignment by method (campus, online, MT, MT Batch).
Fig 4 Estimated average distribution of emotional assignment by method (campus, online, MT, MT Batch).CC BY · PubMed Central
Fig 5 Frequencies of emotional categories for two Minifigures a) Minifigure 03753 b) Minifigure 00948
Fig 5 Frequencies of emotional categories for two Minifigures a) Minifigure 03753 b) Minifigure 00948CC BY · PubMed Central

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Research works printed but not linked: 1

  1. Bartneck C, Obaid M, Zawieska K (2013) Agents with faces - what can we learn from lego minfigures. In: 1st International Conference on Human-Agent Interaction. URL http://bartneck.de/publications/2013/agentsWithFaces/bartneckLEGOAgent.pdf.

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Metadata

Authors
Bartneck, C., Duenser, A., Moltchanova, E., & Zawieska, K.
Affiliations
  • University of Canterbury (NZ)
  • Commonwealth Scientific and Industrial Research Organisation (AU)
  • Łukasiewicz Research Network - Industrial Research Institute for Automation and Measurements (PL)
Corresponding author
Bartneck, Christoph
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2015-04-14
Volume
10
Issue
4
Pages
e0121595
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0121595
PMC
PMC4397064
OpenAlex
W2138247806
Cited by
317
References
49
Citations by year
2026: 5 · 2025: 19 · 2024: 22 · 2023: 21 · 2022: 31 · 2021: 43 · 2020: 59 · 2019: 33 · 2018: 28 · 2017: 29 · 2016: 19 · 2015: 7
FWCI
41.51
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UC Research Repository (University of Canterbury) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
  • Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
  • Physical Sciences › Computer Science › Information Systems › Expert finding and Q&A systems
Keywords
Amazon Mechanical Turk, participant recruitment, online surveys, response similarity, online forums, crowdsourcing
MeSH terms
Culture, Emotions, Employment, Facial Expression, Humans, Judgment, New Zealand, Personnel Selection, Surveys and Questionnaires, Research Design, Universities, Internet, Remuneration, Crowdsourcing

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