LabintheWild: Conducting Large-Scale Online Experiments With Uncompensated Samples. Reinecke, K. & Gajos, K. Z. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, of CSCW '15, pages 1364–1378, New York, NY, USA, February, 2015. Association for Computing Machinery. ![LabintheWild: Conducting Large-Scale Online Experiments With Uncompensated Samples [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Web-based experimentation with uncompensated and unsupervised samples has the potential to support the replication, verification, extension and generation of new results with larger and more diverse sample populations than previously seen. We introduce the experimental online platform LabintheWild, which provides participants with personalized feedback in exchange for participation in behavioral studies. In comparison to conventional in-lab studies, LabintheWild enables the recruitment of participants at larger scale and from more diverse demographic and geographic backgrounds. We analyze Google Analytics data, participants' comments, and tweets to discuss how participants hear about the platform, and why they might choose to participate. Analyzing three example experiments, we additionally show that these experiments replicate previous in-lab study results with comparable data quality.
@inproceedings{reinecke_labinthewild_2015,
address = {New York, NY, USA},
series = {{CSCW} '15},
title = {{LabintheWild}: {Conducting} {Large}-{Scale} {Online} {Experiments} {With} {Uncompensated} {Samples}},
isbn = {978-1-4503-2922-4},
shorttitle = {{LabintheWild}},
url = {https://doi.org/10.1145/2675133.2675246},
doi = {10.1145/2675133.2675246},
abstract = {Web-based experimentation with uncompensated and unsupervised samples has the potential to support the replication, verification, extension and generation of new results with larger and more diverse sample populations than previously seen. We introduce the experimental online platform LabintheWild, which provides participants with personalized feedback in exchange for participation in behavioral studies. In comparison to conventional in-lab studies, LabintheWild enables the recruitment of participants at larger scale and from more diverse demographic and geographic backgrounds. We analyze Google Analytics data, participants' comments, and tweets to discuss how participants hear about the platform, and why they might choose to participate. Analyzing three example experiments, we additionally show that these experiments replicate previous in-lab study results with comparable data quality.},
urldate = {2022-01-11},
booktitle = {Proceedings of the 18th {ACM} {Conference} on {Computer} {Supported} {Cooperative} {Work} \& {Social} {Computing}},
publisher = {Association for Computing Machinery},
author = {Reinecke, Katharina and Gajos, Krzysztof Z.},
month = feb,
year = {2015},
keywords = {crowdsourcing, online experimentation, replication, social comparison, uncompensated samples, weird},
pages = {1364--1378},
}
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