Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-23T19:36:52.189Z Has data issue: false hasContentIssue false
  • English
  • Français

How Can Big Data Science Transform the Psychological Sciences?

Part of: Current Debate in Psychology

Published online by Cambridge University Press:  05 November 2020

Betsy H. Albritton Open the ORCID record for Betsy H. Albritton [Opens in a new window]  and
Scott Tonidandel Open the ORCID record for Scott Tonidandel [Opens in a new window]
Betsy H. Albritton*
Affiliation:
University of North Carolina at Charlotte (USA)
Scott Tonidandel
Affiliation:
University of North Carolina at Charlotte (USA)
*
Correspondence concerning this article should be addressed to Betsy H. Albritton. University of North Carolina at Charlotte. 28223–0001 Charlotte, North Carolina (USA). E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Big data and related technologies are radically altering our society. In a similar way, these approaches can transform the psychological sciences. The goal of this commentary is to motivate psychologists to embrace big data science for the betterment of the field. Big data sources, algorithmic methods, and a culture that embraces prediction has the potential to advance our science, improve the robustness and replicability of our research, and allow us to focus more centrally on actual behaviors. We highlight these key transformations, acknowledge criticisms of big data approaches, and emphasize specific ways psychologists can contribute to the big data science revolution.

Keywords

algorithmsbig datadata sciencedeep learningmachine learning
Type
Review Article
Information
The Spanish Journal of Psychology , Volume 23 , 2020 , e44
Copyright
© Universidad Complutense de Madrid and Colegio Oficial de Psicólogos de Madrid 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Conflicts of Interest: None

Funding Statement: This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

References

Aghajanzadeh, S., Jebb, A. T., Li, Y., Lu, Y.-H., & Thiruvathukal, G. K. (2020). Observing human behavior through worldwide network cameras. In Woo, S. E., Tay, L., & Proctor, R. W. (Eds.), Big data in psychological research (pp. 109–123). American Psychological Association. https://doi.org/10.1037/0000193-006CrossRefGoogle Scholar
Anderson, C. (2008, June 23). The end of theory: The data deluge makes the scientific method obsolete. Wired. https://www.wired.com/2008/06/pb-theory/Google Scholar
Banks, G. C., Woznjy, H. M., & Mansfield, C. (2020). Where is "behavior" in theories of organizational behavior? A call for a behavioral revolution [Manuscript submitted for publication]. Belk College of Business, University of North Carolina at Charlotte.Google Scholar
Banks, G. C., Rogelberg, S. G., Woznyj, H. M., Landis, R. S., & Rupp, D. E. (2016). Editorial: Evidence on questionable research practices: The good, the bad, and the ugly. Journal of Business and Psychology, 31(3), 323–338. https://doi.org/10.1007/s10869-016-9456-7CrossRefGoogle Scholar
Baumeister, R. F., Vohs, K. D., & Funder, D. C. (2007). Psychology as the science of self- reports and finger movements: Whatever happened to actual behavior? Perspectives on Psychological Science, 2(4), 396–403. https://doi.org/10.1111/j.1745-6916.2007.00051.xCrossRefGoogle ScholarPubMed
Blake, A. B., Lee, D. I., De La Rosa, R., & Sherman, R. A. (2020). Wearable cameras, machine vision, and big data analytics: Insights into people and the places they go. In Woo, S. E., Tay, L., & Proctor, R. W. (Eds.), Big data in psychological research (pp. 125–143). American Psychological Association. https://doi.org/10.1037/0000193-007CrossRefGoogle Scholar
Breiman, L. (2001). Statistical modeling: The two cultures. Statistical Science, 16(3), 199–231. https://doi.org/10.1214/ss/1009213726CrossRefGoogle Scholar
Carpenter, S. (2012). Psychology’s bold initiative. Science, 335, 1558–1560. https://doi.org/10.1126/science.335.6076.1558CrossRefGoogle ScholarPubMed
Dastin, J. (2018, October 9). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08GGoogle Scholar
Day, D. V. (2014). The future of leadership: Challenges and prospects. In Day, D. V. (Ed.), Oxford Library of Psychology. The Oxford handbook of leadership and organizations (p. 859–867). Oxford University Press.Google Scholar
de Rooij, M., & Weeda, W. (2020). Cross-validation: A method every psychologist should know. Advances in Methods and Practices in Psychological Science, 3(2), 248–263. https://doi.org/10.1177/2515245919898466CrossRefGoogle Scholar
Grand, J. A., Rogelberg, S. G., Allen, T. D., Landis, R. S., Reynolds, D. H., Scott, J. C., Tonidandel, S., & Truxillo, D. M. (2018). A systems-based approach to fostering robust science in industrial-organizational psychology. Industrial and Organizational Psychology, 11, 4–42. https://doi.org/10.1017/iop.2017.55CrossRefGoogle Scholar
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial–organizational psychology. Industrial and Organizational Psychology, 8, 491–508. https://doi.org/10.1017/iop.2015.40CrossRefGoogle Scholar
Haig, B. D. (2020). Big data science: A philosophy of science perspective. In Woo, S. E., Tay, L., & Proctor, R. W. (Eds.), Big data in psychological research (pp. 15–33). American Psychological Association. https://doi.org/10.1037/0000193-002CrossRefGoogle Scholar
Harlow, L. L., & Oswald, F. L. (2016). Big data in psychology: Introduction to the special issue. Psychological Methods, 21(4), 447–457. https://doi.org/10.1037/met0000120CrossRefGoogle ScholarPubMed
Hartshorne, J., & Schachner, A. (2012). Tracking replicability as a method of post-publication open evaluation. Frontiers in Computational Neuroscience, 6, Article 8. https://doi.org/10.3389/fncom.2012.00008CrossRefGoogle ScholarPubMed
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), Article e124. https://doi.org/10.1371/journal.pmed.0020124CrossRefGoogle ScholarPubMed
Jacobucci, R., & Grimm, K. J. (2020). Machine learning and psychological research: The unexplored effect of measurement. Perspectives on Psychological Science, 15(3), 809–816. https://doi.org/10.1177/1745691620902467CrossRefGoogle ScholarPubMed
Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196–217. https://doi.org/10.1207/s15327957pspr0203_4CrossRefGoogle ScholarPubMed
Knight, W. (2020, July 19). Even the best AI models are no match for the coronavirus. Wired. https://www.wired.com/story/best-ai-models-no-match-coronavirus/Google Scholar
Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6(70), 1.Google Scholar
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203–1205. https://doi.org/10.1126/science.1248506CrossRefGoogle ScholarPubMed
Matusik, J. G., Heidl, R., Hollenbeck, J. R., Yu, A., Lee, H. W., & Howe, M. (2019). Wearable bluetooth sensors for capturing relational variables and temporal variability in relationships: A construct validation study. Journal of Applied Psychology, 104(3), 357–387. https://doi.org/10.1037/apl0000334CrossRefGoogle ScholarPubMed
National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research . (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. U.S. Department of Health and Human Services. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.htmlGoogle Scholar
O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.Google Scholar
Paxton, A. (2020). The Belmont Report in the age of big data: Ethics at the intersection of psychological science and data science. In Woo, S. E., Tay, L., & Proctor, R. W. (Eds.), Big data in psychological research (pp. 347–372). American Psychological Association. https://doi.org/10.1037/0000193-016CrossRefGoogle Scholar
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778CrossRefGoogle Scholar
Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310. https://doi.org/10.1214/10-STS330CrossRefGoogle Scholar
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359-1366. https://doi.org/10.1177/0956797611417632CrossRefGoogle ScholarPubMed
Thompson, I., Koenig, N., Mracek, D., & Tonidandel, S. (2020). Integrating deep learning and measurement science: Automating the subject matter expertise used to evaluate candidate work samples [Manuscript submitted for publication]. Belk College of Business, University of North Carolina at Charlotte.Google Scholar
Tonidandel, S., King, E. B., & Cortina, J. M. (Eds.). (2015). Big data at work: The data science revolution and organizational psychology. Routledge. https://doi.org/10.4324/9781315780504CrossRefGoogle Scholar
Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21(3), 525–547. https://doi.org/10.1177/1094428116677299CrossRefGoogle Scholar
Urban, C., & Gates, K. (2019). Deep learning: A primer for psychologists. PsyArXiv. https://doi.org/10.31234/OSF.IO/4Q8NACrossRefGoogle Scholar
Woo, S. E., Tay, L., Jebb, A. T., Ford, M. T., & Kern, M. L. (2020). Big data for enhancing measurement quality. In Woo, S. E., Tay, L., & Proctor, R. W. (Eds.), Big data in psychological research (pp. 59–85). American Psychological Association. https://doi.org/10.1037/0000193-004CrossRefGoogle Scholar
Woo, S. E., Tay, L., & Proctor, R. W. (Eds.). (2020). Big data in psychological research. American Psychological Association. https://doi.org/10.1037/0000193-000CrossRefGoogle Scholar