One of the issues that can potentially affect the internal validity of interactive online experiments that recruit participants using crowdsourcing platforms is collusion: participants could act upon information shared through channels that are external to the experimental design. Using two experiments, I measure how prevalent collusion is among MTurk workers and whether collusion depends on experimental design choices. Despite having incentives to collude, I find no evidence that MTurk workers collude in the treatments that resembled the design of most other interactive online experiments. This suggests collusion is not a concern for data quality in typical interactive online experiments that recruit participants using crowdsourcing platforms. However, I find that approximately 3% of MTurk workers collude when the payoff of collusion is unusually high. Therefore, collusion should not be overlooked as a possible danger to data validity in interactive experiments that recruit participants using crowdsourcing platforms when participants have strong incentives to engage in such behavior.
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http://dx.doi.org/10.3758/s13428-023-02220-3 | DOI Listing |
J Med Internet Res
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Cancer Screening, American Cancer Society, Atlanta, GA, United States.
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Methods: We followed the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and conducted a search of MEDLINE, PsycINFO, Embase, CINAHL, and Web of Science databases from August 2010 to April 2023.
PLoS One
January 2025
Department of Interior Design, University of Petra, Amman, Jordan.
This study examines how exhibits at the Royal Tank Museum (RTM) engage visitors through bodily experiences. The goal is to understand RTM's visitor interaction and engagement. This research fills a gap in the literature on Jordanian museums by documenting some experiential characteristics of museum exhibits and establishing a framework for evaluating embodied museum encounters.
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January 2025
Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, 37099, Germany.
Motivation: Histone modifications play an important role in transcription regulation. Although the general importance of some histone modifications for transcription regulation has been previously established, the relevance of others and their interaction is subject to ongoing research. By training Machine Learning models to predict a gene's expression and explaining their decision making process, we can get hints on how histone modifications affect transcription.
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January 2025
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
Motivation: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning models primarily relied on primary or secondary protein structural and related properties, which have limitations in capturing the spatial interactions of neighboring amino acids. This study introduces local environmental features as a novel approach that incorporates three-dimensional spatial information, significantly improving model performance by considering the spatial context around the target site.
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Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-2, Kusunoki-cho, Chuo-ku, Kobe, 650-0017 Japan.
Unlabelled: Endoplasmic reticulum (ER) stress due to obesity or systemic insulin resistance is an important pathogenic factor that could lead to pancreatic β-cell failure. We have previously reported that CCAAT/enhancer-binding protein β (C/EBPβ) is highly induced by ER stress in pancreatic β cells. Moreover, its accumulation hampers the response of these cells to ER stress by inhibiting the induction of the molecular chaperone 78 kDa glucose-regulated protein (GRP78).
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