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Teach an I-O To Fish: Integrating Data Science Into I-O Graduate Education

Published online by Cambridge University Press:  17 December 2015

Juliet R. Aiken*
Affiliation:
Department of Psychology, University of Maryland
Paul J. Hanges
Affiliation:
Department of Psychology, University of Maryland
*
Correspondence concerning this article should be addressed to Juliet R. Aiken, Department of Psychology, University of Maryland, College Park, MD 20742. E-mail: [email protected]

Extract

Big data is becoming a buzzword in today's corporate language and lay discussions. From individually targeting advertising based on previous consumer behavior or Internet searches to debates by Congress concerning National Security Agency (NSA) access to phone metadata, the era of big data has arrived. Thus, the Guzzo, Fink, King, Tonidandel, and Landis (2015) discussion of the challenges (e.g., confidentiality, informed consent) that big data projects present to industrial and organizational (I-O) psychologists is timely. If the hype associated with these techniques is warranted, then our field has a clear imperative to debate the ethics and best practices surrounding use of these techniques. We believe that Guzzo et al. have done our field a service by starting this discussion.

Type
Commentaries
Copyright
Copyright © Society for Industrial and Organizational Psychology 2015 

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References

Collins, J. M., & Clark, M. R. (1993). An application of the theory of neural computation to the prediction of workplace behavior: An illustration and assessment of network analysis. Personnel Psychology, 46, 503524.Google Scholar
Friedenberg, J. (2009). Dynamical psychology: Complexity, self-organizational, and mind. Litchfield Park, AZ: ICSE.Google 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: Perspectives on Science and Practice, 8 (4), 491508.Google Scholar
Hanges, P. J., Lord, R. G., Godfrey, E. G., & Raver, J. L. (2002). Modeling nonlinear relationships: Neural networks and catastrophe analysis. In Rogelberg, S. (Ed.), Handbook of research methods in industrial and organizational psychology (pp. 431455). Malden, MA: Blackwell.Google Scholar
Lord, R. G., Hanges, P. J., & Godfrey, E. G. (2003). Integrating neural networks into decision making and motivational theory: Rethinking VIE theory. Canadian Psychologist, 44, 2138.Google Scholar
McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 60, 6066.Google Scholar
Minbashian, A., Bright, J. E. H., & Bird, K. D. (2010). A comparison of artificial neural networks and multiple regression in the context of research on personality and work performance. Organizational Research Methods, 13, 540561.Google Scholar
Payne, S. C., Morgan, W. B., & Bryan, L. K. (2015). Revision of SIOP's guidelines for education and training at the doctoral and master's level in I-O psychology. Executive Board Special Session at the 30th Annual Conference of the Society for Industrial and Organizational Psychology, Philadelphia, PA.Google Scholar
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. Sebastopol, CA: O'Reilly Media.Google Scholar
Society for Industrial and Organizational Psychology. (1994). Guidelines for education and training at the master's level in industrial–organizational psychology. Arlington Heights, IL: Author.Google Scholar
Society for Industrial and Organizational Psychology. (1999). Guidelines for education and training at the doctoral level in industrial/organizational psychology. Bowling Green, OH: Author.Google Scholar
Somers, M. J. (1999). Application of two neural network paradigms to the study of volunteer employee turnover. Journal of Applied Psychology, 84, 177185.Google Scholar
Stanton, J. M. (2014). Data mining: A practical recommendations for organizational researchers. In Cortina, J. M. & Landis, R. S. (Eds.), Modern research methods for the study of behavior in organizations (pp. 199230). New York, NY: Routledge.Google Scholar