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Beyond Empirical Equivalence

Published online by Cambridge University Press:  22 November 2017

Carter Gibson ,
Daly Vaughn  and
Mike Hudy
Carter Gibson*
Affiliation:
Shaker
Daly Vaughn
Affiliation:
Shaker
Mike Hudy
Affiliation:
Shaker
*
Correspondence concerning this article should be addressed to Carter Gibson, Shaker, 3201 Enterprise Parkway, Suite 360, Cleveland, OH 44122. E-mail: [email protected]
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Extract

We wonder whether theory alone can solve problems and answer questions faced by practitioners working on the front lines of assessment innovation. Stated another way, to what degree can current theories influence the application of our work to new technology when it comes available? We are speaking as practitioners working in selection, the area in which technology has been studied most commonly in industrial-organizational (I-O) psychology (e.g., King, Ryan, Kantrowitz, Grelle, & Dainis, 2015). More specifically, we focus on the impact of mobile technology on our selection systems. We are excited for the focal article (Morelli, Potosky, Arthur, & Tippins, 2017) on theory development relative to technological advancement because much of the work we do in this area has not been discussed significantly in the literature. Our goal in this commentary is to review what we have learned about the implications of technology from our experience building and validating innovative prehire assessments.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 10 , Issue 4 , December 2017 , pp. 676 - 680
Copyright
Copyright © Society for Industrial and Organizational Psychology 2017 

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References

Hewitt, Aon, (2016). Selection testing: A look at trends in mobile device usage. Retrieved from http://www.aon.com/unitedkingdom/attachments/trp/Mobile_Testing_Whitepaper_2016.pdf Google Scholar
Arthur, W. Jr., Doverspike, D., Munoz, G. J., Taylor, J. E., & Carr, A. E. (2014). The use of mobile devices in high-stakes remotely delivered assessments and testing. International Journal of Selection and Assessment, 22 (2), 113–223.Google Scholar
King, D. D., Ryan, A. M., Kantrowitz, T., Grelle, D., & Dainis, A. (2015). Mobile Internet testing: An analysis of equivalence, individual differences, and reactions. International Journal of Selection and Assessment, 23 (4), 382–394.Google Scholar
Morelli, N. A., Mahan, R. P., & Illingworth, A. J. (2014). Establishing the measurement equivalence of online selection assessments delivered on mobile verses nonmobile devices. International Journal of Selection and Assessment, 22 (2), 124–138.Google Scholar
Morelli, N., Potosky, D., Arthur, W. Jr., & Tippins, N. (2017). A call for conceptual models of technology in I-O psychology: An example from technology-based talent assessment. Industrial and Organizational Psychology: Perspectives on Science and Practice, 10 (4), 634–653.Google Scholar