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Original Articles

Use of latent profile analysis to characterise patterns of participation in crowdsourcing

ORCID Icon, , , &
Pages 1487-1495 | Received 14 Jul 2021, Accepted 20 May 2022, Published online: 07 Jul 2022

References

  • Aristeidou, M., E. Scanlon, and M. Sharples. 2017. “Profiles of Engagement in Online Communities of Citizen Science Participation.” Computers in Human Behavior 74: 246–256. doi:10.1016/j.chb.2017.04.044
  • Berlin, K. S., G. R. Parra, and N. A. Williams. 2013. “An Introduction to Latent Variable Mixture Modeling: Longitudinal Latent Class Growth Analysis and Growth Mixture Models.” Journal of Pediatric Psychology 39: 188–203. doi:10.1093/jpepsy/jst085
  • Boakes, E. H., G. Gliozzo, V. Seymour, M. Harvey, C. Smith, D. B. Roy, and M. Haklay. 2016. “Patterns of Contribution to Citizen Science Biodiversity Projects Increase Understanding of Volunteers’ Recording Behaviour.” Scientific Reports 6: 33051. doi:10.1038/srep33051
  • Bowyer, A., V. Maidel, C. Lintott, A. Swanson, and G. Miller. 2015. “This Image Intentionally Left Blank: Mundane Images Increase Citizen Science Participation.” In Conference on Human Computation & Crowdsourcing. San Diego, CA. https://www.humancomputation.com/2015/papers/45_Paper.pdf.
  • Clare, J. D., P. A. Townsend, C. Anhalt-Depies, C. Locke, J. L. Stenglein, S. Frett, K. J. Martin, A. Singh, T. R. Van Deelen, and B. Zuckerberg. 2019. “Making Inference with Messy (Citizen Science) Data: When are Data Accurate Enough and How Can They be Improved?” Ecological Applications 29: e01849. doi:10.1002/eap.1849.
  • Fong, C. J., T. W. Acee, and C. E. Weinstein. 2018. “A Person-Centered Investigation of Achievement Motivation Goals and Correlates of Community College Student Achievement and Persistence.” Journal of College Student Retention: Research. Theory & Practice 20: 369–387.
  • Füchslin, T. 2019. “Science Communication Scholars use More and More Segmentation Analyses: Can we Take Them to the Next Level?” Public Understanding of Science 28: 854–864. doi:10.1177/0963662519850086
  • Hartigan, J. A., and M. A. Wong. 1979. “Algorithm AS 136: A K-Means Clustering Algorithm.” Journal of the Royal Statistical Society 28: 100–108. doi:10.2307/2346830.
  • Jackson, C., C. Østerlund, V. Maidel, K. Crowston, and G. Mugar. 2016. "Which Way Did They Go? Newcomer Movement Through the Zooniverse.” In Proceedings of the 19th ACM Conference on Computer-supported Cooperative Work & Social Computing. San Francisco, CA, 624–635. doi:10.1145/2818048.2835197
  • Jordan, R. C., H. L. Ballard, and T. B. Phillips. 2012. “Key Issues and new Approaches for Evaluating Citizen-Science Learning Outcomes.” Frontiers in Ecology and the Environment 10: 307–309. doi:10.1890/110280
  • Kittur, A., E. H. Chi, and B. Suh. 2008. “Crowdsourcing User Studies with Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. Florence, Italy. 453–456. doi:10.1145/1357054.1357127
  • Kodinariya, T. M., and P. R. Makwana. 2013. “Review on Determining Number of Cluster in K-Means Clustering.” International Journal of Advance Research in Computer Science and Management Studies 1: 90–95.
  • Lewandowski, E., and H. Specht. 2015. “Influence of Volunteer and Project Characteristics on Data Quality of Biological Surveys.” Conservation Biology 29: 713–723. doi:10.1111/cobi.12481
  • Locke, C., C. Anhalt-Depies, S. Frett, J. Stenglein, S. Cameron, V. Malleshappa, T. Peltier, B. Zuckerberg, and P. Townsend. 2019. “Managing a Large Citizen Science Project to Monitor Wildlife.” Wildlife Society Bulletin 43: 4–10. doi:10.1002/wsb.943
  • Muthén, L. K., and B. O. Muthén. 2010. MPLUS User's Guide. Sixth Edition. Los Angeles, CA: Muthén& Muthén.
  • Nielsen, J. 2006. Participation Inequality: Encouraging More Users to Participate. Fremont, USA: Nielsen Norman Group. http://www.useit.com/alertbox/participation_inequality.html
  • Oberski, D. 2016. “Mixture Models: Latent Profile and Latent Class Analysis.” In Human–Computer Interaction Series, edited by J. Robertson and M. Kaptein. Cham, Switzerland: Springer. doi:10.1007/978-3-319-26633-6_12.
  • Pastor, D. A., K. E. Barron, B. Miller, and S. L. Davis. 2007. “A Latent Profile Analysis of College Students’ Achievement Goal Orientation.” Contemporary Educational Psychology 32: 8–47. doi:10.1016/j.cedpsych.2006.10.003
  • Ponciano, L., and F. Brasileiro. 2015. “Finding Volunteers’ Engagement Profiles in Human Computation for Citizen Science Projects.” Human Computation 1: 245–264.
  • Ram, N., and K. J. Grimm. 2009. “Growth Mixture Modeling: A Method for Identifying Differences in Longitudinal Change among Unobserved Groups.” International Journal of Behavioral Development 33: 565–576. doi:10.1177/0165025409343765
  • R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
  • Rosenberg, J. M., P. N. Beymer, J. Anderson D, and J. A. Schmidt. 2018. “tidyLPA: An R Package to Easily Carry out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software.” Journal of Open Source Software 3: 978. doi:10.21105/joss.00978
  • Sauermann, H., and C. Franzoni. 2015. “Crowd Science User Contribution Patterns and Their Implications.” Proceedings of the National Academy of Sciences USA 112: 679–684. doi:10.1073/pnas.1408907112
  • Spurk, D., A. Hirschi, M. Wang, D. Valero, and S. Kauffeld. 2020. “Latent Profile Analysis: A Review and “how to” Guide of its Application Within Vocational Behavior Research.” Journal of Vocational Behavior 120: 103445. doi:10.1016/j.jvb.2020.103445
  • Stanley, L., F. W. Kellermanns, and T. M. Zellweger. 2017. “Latent Profile Analysis: Understanding Family Firm Profiles.” Family Business Review 30: 84–102. doi:10.1177/0894486516677426
  • Steinley, D., and M. J. Brusco. 2011. “Evaluating Mixture Modeling for Clustering: Recommendations and Cautions.” Psychological Methods 16: 63–79. doi:10.1037/a0022673
  • Stewart, O., D. Lubensky, and J. M. Huerta. 2010. "Crowdsourcing Participation Inequality: A SCOUT Model for the Enterprise Domain.” In Proceedings of the ACM SIGKDD Workshop on Human Computation, 30–33. Washington, DC. doi:10.1145/1837885.1837895
  • Straub, T., H. Gimpel, F. Teschner, and C. Weinhardt. 2015. “How (not) to Incent Crowd Workers.” Business & Information Systems Engineering 57: 167–179. doi:10.1007/s12599-015-0384-2
  • Tein, J.-Y., S. Coxe, and H. Cham. 2013. “Statistical Power to Detect the Correct Number of Classes in Latent Profile Analysis.” Structural Equation Modeling: A Multidisciplinary Journal 20: 640–657. doi:10.1080/10705511.2013.824781

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