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With the Right Map, Survey Key Driver Analysis Can Help Get Organizations to the Right Destination

Published online by Cambridge University Press:  29 June 2017

Charles A. Scherbaum ,
Justin Black  and
Sara P. Weiner
Charles A. Scherbaum*
Affiliation:
Department of Psychology, Baruch College, City University of New York
Justin Black
Affiliation:
Organizational Development Science, Glint
Sara P. Weiner
Affiliation:
Organizational Development Science, Glint
*
Correspondence concerning this article should be addressed to Charles A. Scherbaum, Associate Professor, Department of Psychology, Baruch College, City University of New York, Box B 8–215, One Bernard Baruch Way, New York, NY 10010. E-mail: Charles.Scherbaum@baruch.cuny.edu
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Extract

Cucina, Walmsley, Gast, Martin, and Curtin (2017) raise an important issue in evaluating whether our current approaches for key driver analysis on employee opinion survey data are indeed best practices. As has been argued elsewhere (Putka & Oswald, 2016; Scherbaum, Putka, Naidoo, & Youssefnia, 2010), there is and can be misalignment between current and best practices. We agree with Cucina et al. that our field should engage in larger discussion of these issues. That discussion is critical, as industrial and organizational (I-O) psychologists are competing with those outside our field who have either little knowledge of best practices in data analysis (but who have been empowered by technology that automates the analysis) or little knowledge of psychology (but a great deal of knowledge in big data analytical techniques). I-O psychologists are in the vanguard of survey data analysis (Ducey et al., 2015), and we have a responsibility to maintain the standards of our field as well as to wield our influence to guide other practitioners outside our field on sound theoretical and analytical approaches.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 10 , Issue 2 , June 2017 , pp. 290 - 298
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

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References

Cucina, J. M., Walmsley, P. T., Gast, I. F., Martin, N. R., & Curtin, P. (2017). Survey key driver analysis: Are we driving down the right road? Industrial and Organizational Psychology: Perspectives on Science and Practice, 10 (2), 234–257.Google Scholar
Ducey, A. J., Guenole, N., Weiner, S. P., Herleman, H. A., Gibby, R. E., & Delany, T. (2015, December). I-Os in the vanguard of big data analytics and privacy. Industrial and Organizational Psychology, 8 (4), 555–563.CrossRefGoogle Scholar
Gerstner, C. R., & Day, D. V. (1997). Meta-analytic review of leader–member exchange theory: Correlates and construct issues. Journal of Applied Psychology, 82 (6), 827–844.Google Scholar
King, E. B., Tonidandel, S., Cortina, J. M., & Fink, A. A. (2016). Building understanding of the data science revolution and IO psychology. In Tonidandel, S., King, E. B., & Cortina, J. M. (Eds.), Big data at work: The data science revolution and organizational psychology. New York: Routledge.Google Scholar
Oswald, F. L., & Putka, D. J. (2016). Statistical methods for big data: A scenic tour. In Tonidandel, S., King, E., & Cortina, J. (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 43–63). New York: Routledge.Google Scholar
Petty, R., & Cacioppo, J. T. (2012). Communication and persuasion: Central and peripheral routes to attitude change. New York: Springer Science & Business Media.Google Scholar
Porras, J. I., & Robertson, P. J. (1993). Organization development: Theory, practice, and research. In Dunnette, M. D. & Hough, L. M. (Eds.), The handbook of industrial and organizational psychology (Vol. 3, pp. 719–822). Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Putka, D. J., & Oswald, F. L. (2016). Implications of the big data movement for the advancement of I-O psychology and practice. In Tonidandel, S., King, E., & Cortina, J. (Eds.), Big data at work: The data science revolution and organizational psychology (pp. 43–63). New York: Routledge.Google Scholar
Saari, L. M., & Judge, T. A. (2004). Employee attitudes and job satisfaction. Human Resource Management, 43 (4), 395–407.CrossRefGoogle Scholar
Scherbaum, C. A., Putka, D. J., Naidoo, L. J., & Youssefnia, D. (2010). Key driver analyses: Current trends, problems, and alternative approaches. In Albrecht, S. (Ed.), Handbook of employee engagement. Camberley, UK: Edward-Elgar Publishing House.Google Scholar
Schmidt, F. L., & Hunter, J. E. (1977). Development of a general solution to the problem of validity generalization. Journal of Applied Psychology, 62 (5), 529–540.Google Scholar
Staw, B. M., & Ross, J. (1985). Stability in the midst of change: A dispositional approach to job attitudes. Journal of Applied Psychology, 70 (3), 469–480.Google Scholar
Wood, W. (2000). Attitude change: Persuasion and social influence. Annual Review of Psychology, 51 (1), 539–570.Google Scholar