Implications of the big data movement for the advancement of I-O science and practice

Big Data at Work: The Data Science Revolution and Organizational Psychology

Volumn , Issue , 2016, Pages 181-212

  Putka, Dan J ^a   Oswald, Frederick L ^b  

^a HUMAN RESOURCES RESEARCH ORGANIZATION   (United States)

^b RICE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIG DATA;

EID: 84960540530     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9781315780504     Document Type: Chapter

References (57)

1. Aguinis, H., Bradley, K. J., & Brodersen, A. (2014). Industrial-organizational psychologists in business schools:Brain drain or eye opener? Industrial and Organizational Psychology:Perspectives on Science and Practice, 7, 284-303.
2. Aguinis, H., Pierce, C. A., Bosco, F. A., & Muslin, I. S. (2009). First decade of Organizational Research Methods:Trends in design, measurement, and data-analysis topics. Organizational Research Methods, 12, 69-112
3. Aiken, L. S., West, S. G., & Millsap (2008). Graduate training in statistics, measurement, and methodology in psychology:Replication and extension of Aiken, West, Sechrest, and Reno’s (1990) survey of PhD programs in North America. American Psychologist, 63, 32-50.
4. Allen, T. A. (2014). Opening presidential address. Presented at the 29th Annual Society for Industrial and Organizational Psychology Conference, Honolulu, HI.
5. Austin, J. T., Scherbaum, C. A., & Mahlman, R. A. (2002). History of research methods in industrial and organizational psychology:Measurement, design, analysis. In S. G. Rogelberg (Ed.), Handbook of research methods in industrial and organizational psychology (pp. 3-33). Malden, MA:Blackwell Publishing.
6. Berk, R. A. (2006). An introduction to ensemble methods for data analysis. Sociological Methods & Research, 34, 263-295.
7. Bersin, J. (2013, October 7). Big data in human resources:A world of haves and have- nots. Forbes. Retrieved from:http://www.forbes.com/sites/joshbersin/2013/10/07/big-data-in-human-resources-a-world-of-haves-and-have-nots/
8. Biga, A. (2014). Using big and small data to make better people decisions. Presented to the Personnel Testing Council of Metropolitan Washington, Washington, DC.
9. Binning, J. F., & Barrett, G. V. (1989). Validity of personnel decisions:A conceptual analysis of the inferential and evidential bases. Journal of Applied Psychology, 74, 478-494.
10. Bobko, P., Roth, P. L., & Buster, M. A. (2005). A systematic approach for assessing the recency ofjob-analytic information. Presented at the 29th Annual International Public Management Association Assessment Council Conference, Orlando, FL.
11. Borsboom, D. (2006). The attack of the psychometricians. Psychometrika, 71, 425-440.
12. Breiman, L. (1996). Bagging predictors. Machine Learning, 26, 123-140.
13. Breiman, L. (2001a). Random forests. Machine Learning, 45, 5-32.
14. Breiman, L. (2001b). Statistical modeling:The two cultures. Statistical Science, 16, 199-231.
15. Brown, S. H. (1994). Validating biodata. In G. S. Stokes, M. D. Mumford & W.A. Owens (Eds.), Biodata handbook:Theory, research, and use of biographical information in selection and performance prediction (pp. 199-236). Palo Alto, CA:Consulting Psychological Press.
16. Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York, NY:Springer.
17. Cascio, W. F., & Aguinis, H. (2008). Research in industrial and organizational psychology from 1963 to 2007:Changes, choices, and trends. Journal of Applied Psychology, 93, 1062-1081.
18. Chatfield, C. (1995). Model uncertainty, data mining, and statistical inference. Journal of the Royal Statistical Society A, 158, 419-466.
19. Cucina, J. M., Caputo, P. M., Thibodeaux, H. F., & MacLane, C. N. (2012). Unlocking the key to biodata scoring:A comparison of empirical, rational, and hybrid approaches at different sample sizes. Personnel Psychology, 65, 385-428.
20. DeMars, C. (2004). Detection of item parameter drift over multiple test administrations. Applied Measurement in Education, 17, 265-300.
21. Gibby, R. E., McCloy, R., & Putka, D. J. (2013). Viewing linkage research through the lenses of current practice and cutting edge advances. Presented at the 28th Annual Society for Industrial and Organizational Psychology Conference, Houston, TX.
22. Griffeth, R. W., & Hom, P. W. (2001). Retaining valued employees. Thousand Oaks, CA:Sage Publications.
23. Griffith, R. W., Hom, P. W., & Gaertner, S. (2001). A meta-analysis of antecedents and correlates of employee turnover:Update, moderator tests, and research implications for the next millennium. Journal of Management, 26, 463-488
24. Guion, R. M. (1998). Assessment, measurement, and prediction for personnel decisions. Mah- wah, NJ:Lawrence Erlbaum.
25. Hambrick, D. C. (2007). The field of management’s devotion to theory:Too much of a good thing? Academy of Management Journal, 50, 1346-1352.
26. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning:Data mining, inference, and prediction (2nd ed.). New York, NY:Springer.
27. Helfat, C. E. (2007). Stylized facts, empirical research and theory development in organizations. Strategic Organization, 5, 185-192.
28. Hoeting, J. A., Madigan, D., Raferty, A. E., & Volinksy, C. T. (1999). Bayesian model averaging:A tutorial. Statistical Science, 14, 382-417.
29. Hogan, J. B. (1994). Empirical keying of background data measures. In G. S. Stokes, M. D. Mumford & W.A. Owens (Eds.), Biodata handbook:Theory, research, and use of biographical information in selection and performance prediction (pp. 69-107). Palo Alto, CA:Consulting Psychological Press.
30. Johnson, J. W., & LeBreton, J. M. (2004). History and use of relative importance indices in organizational research. Organizational Research Methods, 7, 238-257.
31. Kantrowitz, T. M. (2014). 2014 global assessment trends report. Alpharetta, GA:SHL.
32. Kemp, M. (2014). Starting the analytics engine:Practical approaches and lessons learned. Presented at the 29th Annual Society for Industrial and Organizational Psychology Conference, Honolulu, HI.
33. Kim, Y., & Ployhart, R. E. (2014). The effects of staffing and training on firm productivity and profit growth before, during, and after the Great Recession. Journal of Applied Psychology, 99, 361-389.
34. Koppes, L. L. (Ed.) (2007). Historical perspectives in industrial-organizational psychology. Mahwah, NJ:Lawrence Erlbaum Associates.
35. Laney, D. (2001). 3D data management:Controlling data volume, velocity, and variety. Application Delivery Strategies. Stamford, CT:Meta Group, Inc. Retrieved from http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
36. Lievens, F., De Corte, W., & Westerveld, L. (2015). Understanding the building blocks of selection procedures:Effects of response fidelity on performance and validity. Journal of Management, 41, 1604-1627.
37. Locke, E. A. (2007). The case for inductive theory building. Journal of Management, 33, 867-890.
38. Lohr, S. (2013, April 20). Big data, trying to build better workers. New York Times. Retrieved from:http://www.nytimes.com/2013/04/21/technology/big-data-trying-to-build- better-workers.html?pagewanted=all
39. Mael, F. A. (1991). A conceptual rationale for the domain and attributes of biodata items. Personnel Psychology, 44, 763-792.
40. Merenda, P. F. (2007). Psychometrics and psychometricians in the 20th and 21st centuries:How it was in the 20th century and how it is now. Perceptual and Motor Skills, 104, 3-20.
41. Mumford, M. D., & Owens, W. A. (1987). Methodology review:Principles, procedures, and findings in the application of background data measures. Applied Psychological Measurement, 11, 1-31.
42. Olshen, R. (2001). A conversation with Leo Breiman. Statistical Science, 16, 184-198.
43. Overby, S. (2013, August 27). HR departments invaded by data scientists. CIO.com. Retrieved from:http://www.cio.com/article/2383195/business-intelligence/hr-departments- invaded-by-data-scientists.html
44. Owens, W. A. (1976). Background data. In M. D. Dunnette (Ed.), Handbook of industrial and organizational psychology (pp. 609-644). Chicago, IL:Rand-McNally.
45. Ployhart, R. E., Schneider, B., & Schmitt, N. (2006). Staffing organizations:Contemporary practice and theory (3rd ed.). Mahwah, NJ:Lawrence Erlbaum Associates.
46. Ployhart, R. E., & Vandenberg, R. J. (2010). Longitudinal research:The theory, design, and analysis of change. Journal of Management, 36, 94-120.
47. Putka, D. J. (2013). HEY! We’ve been doing this for 100 years! Personnel Testing Council of Metropolitan Washington Quarterly Newsletter, 9, 2-9.
48. Roberts, S. (2014). Pivot tables to p-values:Creating an internal HR analytics function. Presented at the 29th Annual Society for Industrial and Organizational Psychology Conference, Honolulu, HI.
49. Scherbaum, C., Putka, D. J., Naidoo, L., & Youssefina, D. (2010). Key driver analyses:Current trends, problems, and alternative approaches. In S. Albrecht (Ed.), Handbook of employee engagement:Models, measures, and practice (pp. 182-196). Cheltenham, UK:Edward-Elgar Publishing House.
50. Schmidt, F. L., & Hunter, J. E. (1977). Development of a general solution to the problem of validity generalization. Journal of Applied Psychology, 62, 529-540.
51. Shapiro, D. L., Kirkman, B. L., & Courtney, H. G. (2007). Perceived causes and solutions of the translation problem in management research. Academy of Management Journal, 50, 249-266
52. Shen, W., Kiger, T. B., Davies, S. E., Rasch, R. L., Simon, K. M., & Ones, D. S. (2011). Samples in applied psychology:Over a decade of research in review. Journal of Applied Psychology, 96, 1055-1064.
53. Silzer, R., & Cober, R. (2010). The science-practice gap in I-O psychology:A fish bowl exercise. The Industrial-Organizational Psychologist, 48, 95-103. Retrieved from:http://www.siop.org/tip/july10/14silzer.aspx
54. Silzer, R., & Parson, C. (2012). Industrial-organizational psychology journals and the science-practice gap. The Industrial-Organizational Psychologist, 49, 97-117. Retrieved from:http://www.siop.org/tip/apr12/15silzer.aspx
55. Society for Industrial and Organizational Psychology. (1999). Guidelines for education and training at the doctoral level in industrial/organizational psychology. Bowling Green, OH:Society for Industrial and Organizational Psychology. Retrieved from:http://www.siop.org/PhDGuidelines98.aspx
56. Walls, T. A., & Schafer, J. L. (Eds.) (2006). Models for intensive longitudinal data. New York, NY:Oxford University Press.
57. Wiley, J. W. (1996). Linking survey data to the bottom line. In A. I. Kraut (Ed.), Organizational surveys:Tools for assessment and change (pp. 330-350). San Francisco, CA:Jossey-Bass.