Hostname: page-component-848d4c4894-r5zm4 Total loading time: 0 Render date: 2024-06-22T10:15:20.619Z Has data issue: false hasContentIssue false
  • English
  • Français

Validity in a Jiffy: How Synthetic Validation Contributes to Personnel Selection

Published online by Cambridge University Press:  07 January 2015

Frederick L. Oswald  and
Leaetta M. Hough
Frederick L. Oswald*
Affiliation:
Rice University
Leaetta M. Hough
Affiliation:
The Dunnette Group, Ltd.
*
E-mail: foswald@rice.edu, Address: Department of Psychology, Rice University, 6100 Main Street MS25, Houston, TX 77005
Get access Rights & Permissions [Opens in a new window]

Extract

Conclusions about the effectiveness of selection systems require gathering, evaluating, weighting, and interpreting validity data, but these conclusions are obviously challenged to the extent that this process is suspect. Local validity information within the organization may be desirable but not available, and conducting a local validity study may be practically infeasible because of limited time, resources, and small sample sizes. Specific validity studies outside the organization may also be problematic if they are based on jobs or settings of questionable relevance, small sample sizes, range-restricted incumbent samples, and unreliable or content-deficient predictor and criterion measures. It is usually an understatement to say that sifting through a pile of such studies to make educated guesses about the validity of selection measures within of a specific organizational setting could be an idiosyncratic, time-consuming, and frustrating process, resulting in little confidence in any summary conclusions.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 3 , Issue 3 , September 2010 , pp. 329 - 334
Copyright
Copyright © Society for Industrial and Organizational Psychology 2010 

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

*

Department of Psychology, Rice University

**

The Dunnette Group, Ltd.

References

Beaty, J. C., Cleveland, J. N., & Murphy, K. R. (2001). The relation between personality and contextual performance in “strong” versus “weak” situations. Human Performance, 14, 125–148.Google Scholar
Converse, P. D., & Oswald, F. L. (2004). The effects of data type on job classification and its purposes. Psychology Science, 46, 99–127.Google Scholar
De Corte, W., Lievens, F., & Sackett, P. R. (2007). Combining predictors to achieve optimal trade-offs between selection quality and adverse impact. Journal of Applied Psychology, 92, 1380–1393.Google Scholar
Editorial. (1953). The Validity Information Exchange: Announcing a new Personnel Psychology feature. Personnel Psychology, 6, 265–270.Google Scholar
Hoffman, C. C., Holden, L. M., & Gale, K. (2000). So many jobs, so little “N”: Applying expanded validation models to support generalization of cognitive test validity. Personnel Psychology, 53, 955–990.Google Scholar
Hoffman, C. C., & McPhail, S. M. (1998). Exploring options for supporting test use in situations precluding local validation. Personnel Psychology, 51, 987–1003.Google Scholar
Hoffman, C. C., Rashovsky, B., & D’Egidio, E. (2006). Job component validity: Background, current research, and applications. In McPhail, S. M. (Ed.), Alternative validation strategies: Developing new and leveraging existing validity evidence (pp. 82–121). San Francisco: Jossey-Bass.Google Scholar
Hough, L. M., & Oswald, F. L. (2005). They’re right, well…mostly right: Research evidence and an agenda to rescue personality testing from 1960s' insights. Human Performance, 18, 373–387.Google Scholar
James, L. R., Demaree, R. G., Mulaik, S. A., & Ladd, R. T. (1992). Validity generalization in the context of situational models. Journal of Applied Psychology, 77, 3–14.Google Scholar
Jeanneret, P. R. (1992). Applications of job component/synthetic validity to construct validity. Human Performance, 5, 81–96.Google Scholar
Johnson, J. W., Carter, G. W., Davison, H. K., & Oliver, D. (2001). A synthetic validity approach to testing differential prediction hypotheses. Journal of Applied Psychology, 86, 774–780.Google Scholar
Johnson, J. W., Steel, P., Scherbaum, C. A., Hoffman, C. C., Jeanneret, P. R., & Foster, J. (2010). Validation is like motor oil: Synthetic is better. Industrial and Organizational Psychology: Perspectives on Science and Practice, 3, 305–328.Google Scholar
Johnson, J. W., & Carter, G. W. (in press). Validating synthetic validation: Comparing traditional and synthetic validity coefficients. Personnel Psychology, 34, 791–804.Google Scholar
LaPolice, C. C., Carter, G. W., & Johnson, J. W. (2008). Linking O*NET descriptors to occupational literacy requirements using job component validation. Personnel Psychology, 61, 405–441.Google Scholar
McCloy, R. A. (1994). Predicting job performance scores without performance data. In Green, B. F., & Mavor, A. S. (Eds.), Modeling cost and performance for military enlistment: Report of a workshop (pp. 61–100).Google Scholar
McDaniel, M. A., Rothstein, H. R., & Whetzel, D. L. (2006). Publication bias: A case study of four test vendors. Personnel Psychology, 59, 927–953.Google Scholar
Oswald, F. L., & Hough, L. M. (2010). Personality and its assessment in organizations: Theoretical and empirical developments. In Zedeck, S. (Ed.), APA handbook of industrial and organizational psychology: Vol. 2. Selecting and developing members for the organization (pp. 153–184). Washington, DC: American Psychological Association.Google Scholar
Pearlman, K. (1980). Job families: A review and discussion of their implications for personnel selection. Psychological Bulletin, 87, 1–28.Google Scholar
Robinson, W. S. (1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351–357.Google Scholar
Sackett, P. R., Cornelius, E. T. III, & Carron, T. J. (1981). A comparison of global judgment vs. task oriented approaches to job classification. Personnel Psychology, 34, 791–804.Google Scholar
Sackett, P. R., & Arvey, R. D. (1993). Selection in small N settings. In Schmitt, N., & Borman, W. (Eds.), Personnel selection in organizations (pp. 418–447). San Francisco: Jossey-Bass.Google Scholar
Scherbaum, C. A. (2005). Synthetic validity: Past, present, and future. Personnel Psychology, 58, 481–515.Google Scholar
Steel, P. D. G., Huffcutt, A. I., & Kammeyer-Mueller, J. (2006). From the work one knows the worker: A systematic review of the challenges, solutions and steps to creating synthetic validity. International Journal of Selection and Assessment, 14, 1–35.Google Scholar
Trattner, M. H. (1982). Synthetic validity and its application to the Uniform Guidelines validation requirements. Personnel Psychology, 35, 383–397.Google Scholar
Trattner, M. H., & O’Leary, B. S. (1984). Sample sizes for specified statistical power in testing for differential validity. Journal of Applied Psychology, 65, 127–134.Google Scholar