The Importance of Predictive and Face Validity in Employee Selection and Ways of Maximizing Them: An Assessment of Three Selection Methods
The current exigencies and fluidity of the business environment engendered largely by demographic changes,
technological advances and globalisation have made it imperative for organisations to posses the brightest talents
as a source of competitive advantage, if they hope to survive. The continuing ‘talent war’ and fierce competition
in the global market place; and issues concerning employee branding and candidate attraction, means that
organisations and their managers have to carefully review their recruitment and selection processes, ensuring that
employee selection methods not only contributes towards enhancing organisational image, but also predicts
future job performance to a reasonable extent. There is therefore, the need for chosen methods to be high in both
Predictive and Face validities.
This article critically examines the importance of the concepts of Predictive and Face validities to employee
selection in a wider context as an HR strategy and as an integral part of organisations’ general strategy,
suggesting ways of improving both concepts. The central argument of this article, is that for selection methods to
be effective, reliable, valid and minimise costs associated with loosing top talents, poor employee performance
and turnover, it must possess high predictive and face value. The article assesses three major selection methods
(interviews, work sampling and assessment centres) with a view of maximising their predictive and face
validities, arguing that the design, contents and the manner of administrating these methods are major issues. The
paper concludes that there is no one best way of selecting new employees, but a combination of carefully chosen
methods and well-trained HR professionals will undoubtedly improve face and predictive validities and by
extension, the selection method.
This work is licensed under a Creative Commons Attribution 3.0 License.
International Journal of Business and Management ISSN 1833-3850 (Print) ISSN 1833-8119 (Online)
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