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Online panels in social science research: Expanding sampling methods beyond Mechanical Turk
Amazon Mechanical Turk (MTurk) is widely used by behavioral scientists to recruit research participants. MTurk offers advantages over traditional student subject pools, but it also has important limitations. In particular, the MTurk population is small and potentially overused, and some groups of in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797699/ https://www.ncbi.nlm.nih.gov/pubmed/31512174 http://dx.doi.org/10.3758/s13428-019-01273-7 |
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author | Chandler, Jesse Rosenzweig, Cheskie Moss, Aaron J. Robinson, Jonathan Litman, Leib |
author_facet | Chandler, Jesse Rosenzweig, Cheskie Moss, Aaron J. Robinson, Jonathan Litman, Leib |
author_sort | Chandler, Jesse |
collection | PubMed |
description | Amazon Mechanical Turk (MTurk) is widely used by behavioral scientists to recruit research participants. MTurk offers advantages over traditional student subject pools, but it also has important limitations. In particular, the MTurk population is small and potentially overused, and some groups of interest to behavioral scientists are underrepresented and difficult to recruit. Here we examined whether online research panels can avoid these limitations. Specifically, we compared sample composition, data quality (measured by effect sizes, internal reliability, and attention checks), and the non-naivete of participants recruited from MTurk and Prime Panels—an aggregate of online research panels. Prime Panels participants were more diverse in age, family composition, religiosity, education, and political attitudes. Prime Panels participants also reported less exposure to classic protocols and produced larger effect sizes, but only after screening out several participants who failed a screening task. We conclude that online research panels offer a unique opportunity for research, yet one with some important trade-offs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.3758/s13428-019-01273-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6797699 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-67976992019-11-01 Online panels in social science research: Expanding sampling methods beyond Mechanical Turk Chandler, Jesse Rosenzweig, Cheskie Moss, Aaron J. Robinson, Jonathan Litman, Leib Behav Res Methods Article Amazon Mechanical Turk (MTurk) is widely used by behavioral scientists to recruit research participants. MTurk offers advantages over traditional student subject pools, but it also has important limitations. In particular, the MTurk population is small and potentially overused, and some groups of interest to behavioral scientists are underrepresented and difficult to recruit. Here we examined whether online research panels can avoid these limitations. Specifically, we compared sample composition, data quality (measured by effect sizes, internal reliability, and attention checks), and the non-naivete of participants recruited from MTurk and Prime Panels—an aggregate of online research panels. Prime Panels participants were more diverse in age, family composition, religiosity, education, and political attitudes. Prime Panels participants also reported less exposure to classic protocols and produced larger effect sizes, but only after screening out several participants who failed a screening task. We conclude that online research panels offer a unique opportunity for research, yet one with some important trade-offs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.3758/s13428-019-01273-7) contains supplementary material, which is available to authorized users. Springer US 2019-09-11 2019 /pmc/articles/PMC6797699/ /pubmed/31512174 http://dx.doi.org/10.3758/s13428-019-01273-7 Text en © The Author(s) 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Article Chandler, Jesse Rosenzweig, Cheskie Moss, Aaron J. Robinson, Jonathan Litman, Leib Online panels in social science research: Expanding sampling methods beyond Mechanical Turk |
title | Online panels in social science research: Expanding sampling methods beyond Mechanical Turk |
title_full | Online panels in social science research: Expanding sampling methods beyond Mechanical Turk |
title_fullStr | Online panels in social science research: Expanding sampling methods beyond Mechanical Turk |
title_full_unstemmed | Online panels in social science research: Expanding sampling methods beyond Mechanical Turk |
title_short | Online panels in social science research: Expanding sampling methods beyond Mechanical Turk |
title_sort | online panels in social science research: expanding sampling methods beyond mechanical turk |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797699/ https://www.ncbi.nlm.nih.gov/pubmed/31512174 http://dx.doi.org/10.3758/s13428-019-01273-7 |
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