The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences
Open accessAbstract
In recent years, Mechanical Turk (MTurk) has revolutionized social science by providing a way to collect behavioral data with unprecedented speed and efficiency. However, MTurk was not intended to be a research tool, and many common research tasks are difficult and time-consuming to implement as a result. TurkPrime was designed as a research platform that integrates with MTurk and supports tasks that are common to the social and behavioral sciences. Like MTurk, TurkPrime is an Internet-based platform that runs on any browser and does not require any downloads or installation. Tasks that can be implemented with TurkPrime include: excluding participants on the basis of previous participation, longitudinal studies, making changes to a study while it is running, automating the approval process, increasing the speed of data collection, sending bulk e-mails and bonuses, enhancing communication with participants, monitoring dropout and engagement rates, providing enhanced sampling options, and many others. This article describes how TurkPrime saves time and resources, improves data quality, and allows researchers to design and implement studies that were previously very difficult or impossible to carry out on MTurk. TurkPrime is designed as a research tool whose aim is to improve the quality of the crowdsourcing data collection process. Various features have been and continue to be implemented on the basis of feedback from the research community. TurkPrime is a free research platform.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The weirdest people in the world?
Henrich, J., Heine, S. J., & Norenzayan, A. (2010) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0999152x · Cited by 12,641
Eysenbach, G. (2004) · Journal of Medical Internet Research · doi:10.2196/jmir.6.3.e34 · Cited by 6,480
Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk
Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012) · Political Analysis · doi:10.1093/pan/mpr057 · Cited by 4,008
Running experiments on Amazon Mechanical Turk
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500002205 · Cited by 3,767
Conducting behavioral research on Amazon’s Mechanical Turk
Mason, W., & Suri, S. (2012) · Behavior Research Methods · doi:10.3758/s13428-011-0124-6 · Cited by 3,172
Reputation as a sufficient condition for data quality on Amazon Mechanical Turk
Peer, E., Vosgerau, J., & Acquisti, A. (2014) · Behavior Research Methods · doi:10.3758/s13428-013-0434-y · Cited by 1,618 · cited 1×
“Selecting workers with high approval ratings has been shown to improve data quality (Peer, Vosgerau, & Acquisti, 2014).”
BackgroundUsing Nonnaive Participants Can Reduce Effect Sizes
Chandler, J., Paolacci, G., Peer, E., Mueller, P., & Ratliff, K. A. (2015) · Psychological Science · doi:10.1177/0956797615585115 · Cited by 185
Cited by
Prolific.ac—A subject pool for online experiments
Palan, S., & Schitter, C. (2018) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2017.12.004 · Cited by 3,450
Determinants of COVID-19 vaccine acceptance in the US
Malik, A. A., McFadden, S. M., Elharake, J., & Omer, S. B. (2020) · EClinicalMedicine · doi:10.1016/j.eclinm.2020.100495 · Cited by 1,540
Brysbaert, M. (2019) · Journal of Cognition · doi:10.5334/joc.72 · Cited by 996
MTurk Character Misrepresentation: Assessment and Solutions
Sharpe Wessling, K., Huber, J., & Netzer, O. (2017) · Journal of Consumer Research · doi:10.1093/jcr/ucx053 · Cited by 330
Keith, M. G., Tay, L., & Harms, P. D. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01359 · Cited by 319
Metadata
- Authors
- Litman, L., Robinson, J., & Abberbock, T.
- Venue
- Behavior Research Methods
- Published
- 2016-04-12
- Volume
- 49
- Issue
- 2
- Pages
- 433-442
- ISSN
- 1554-351X (ISSN-L)
- 1554-3528
- OpenAlex
- W2342281051
- Cited by
- 1,982
- References
- 7
- FWCI
- 118.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. https://app.cochat.ai/papers/10.3758/s13428-016-0727-z
- The paper
- Litman, L., Robinson, J., & Abberbock, T. (2017). TurkPrime.com: A versatile crowdsourcing data acquisition platform for the behavioral sciences. Behavior Research Methods. https://doi.org/10.3758/s13428-016-0727-z
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.