Learning Agility: How Expert Manager Coaching Can Increase Performance

In the world of talent management, learning agility, a concept that clarifies how individuals learn in organizations, has received a good deal of attention in the last five to ten years. Some researchers have questioned the existence of learning agility as a construct, and have intimated that it may only be a passing fad. Counter to these claims, recent studies conducted as part of my research team at Teachers College, Columbia University, show that learning agility explains a significant proportion of variance in performance scores of executives, demonstrating that learning agility holds much promise as a topic for future study.

To date, our work has focused mainly on how high-performing leaders can develop their learning agility capacity to distinguish themselves from leaders with less potential. However, I believe that since learning agility research is in its infancy there are many other applications. Specifically, learning agility may well be a construct, that, when developed, can be used to differentiate high performers from other employees - not just leaders.

Long before our work started in the realm of learning agility, leadership success has been measured in terms of follower and team success.¹ Other important skills for leadership success are delegation, mentoring, supporting and consultation to their followers.² These sets of skills for successful leadership beg the question: How can leaders enhance the success of their followers? Does providing consultative support help? Additionally, an important question that is the focus of my research is: Does learning agility play a role?

Part of the answer to this question lies in another vein of research whose authors argue that coaching is a key skill of any leader in order to successfully lead their followers.³ Current research, at this point in time, has focused on general contextual constraints needed for a leader to effectively support their followers, however, the emphasis on specific behaviors leaders should engage in to help their followers succeed has been minimal.

Psychological safety may be a necessary link in this process and may help to explain how leader coaching can be translated into follower learning agility. Put simply, psychological safety defines the extent to which a follower feels comfortable taking risks and being authentic on a team or with his or her leader in a work context. Psychological safety is an important concept to consider because it can help push learning agility to a higher level.^4,5

 

The extent to which followers have a mindset that values learning instead of feeling threatened to demonstrate good performance - learning goal orientation - is also important to consider in the context of learning agility and ultimately, performance.⁶ Studies show that when individuals value learning for the sake of it, rather than performance as a means to an end, their performance actually increases.⁷ Recent studies⁸ have also demonstrated that individuals who have high learning goal orientation are more likely to be highly learning agile.

Over the next few months, the research I will conduct for my dissertation hypothesizes that both contextual elements in one’s work environment (leader coaching behavior to create a psychologically safe work experience) and follower personality variables (learning goal orientation) have a critical impact on follower learning agility and in turn, on follower performance. I am looking forward to conducting this work because it may demonstrate that learning agility has many applications in organizations that can be used to predict the success of all employees, rather than just the success of leaders. Furthermore, I hope to show that the excitement about learning agility in the talent management world is not just a fad that will dissipate anytime soon.

 

References

1. Hogan, R., Curphy, G. J., & Hogan, J. (1994). What we know about leadership: Effectiveness and personality. American Psychologist, 49(6), 493.

2. Yukl, G. A., Wall, S., & Lepsinger, R. (1990). Preliminary report on the validation of the management practices survey. In K. E. Clark & M. B. Clark (Eds.), Measures of leadership (pp. 223-238). West Orange, NJ: Leadership Library of America.

3. Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350-383.

4. Argyris, C. (1993) Knowledge for Action: Changing the Status Quo. San Francisco: Jossey-Bass.

5. DeRue, D. S., Ashford, S. J., & Myers, C. G. (2012). Learning agility: In search of conceptual clarity and theoretical grounding. Industrial and Organizational Psychology, 5(3), 258-279.

6. VandeWalle, D. (1997). Development and validation of a work domain goal orientation instrument. Educational and Psychological Measurement, 57(6), 995-1015.

7. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040.

8. W. Warner Burke research team, 2012-2016

Paradigm Shift: Using Social Media to Predict Personality Traits for Employee Selection & Development

Technology has had a substantial impact on assessment methods. While research in this area is in early phases, some work has found that using data like Facebook “Likes” can predict personality. Data mined from other social media networks has been linked to other facets of personality. In fact, new technologies have the potential to completely change the structure of the assessments market because of enhanced user experience and crowd sourcing as a method for employee selection. There are, however, several ethical issues with new assessment methods. One relates to the purported validity of such new tools. The other issue relates to transparency and anonymity concerns. While some predictive technologies are in place to revolutionize the assessments market, using data without explicit user consent may undermine the process from an ethical point of view. Our aim in this post is to examine changes in the assessments field in light of new technologies and to provide information on the emerging use of big data-based assessments for organizations.

New technologies have opened doors in order to use assessments for HR purposes. One study, published in 2013, demonstrated that correlations between personality traits, cognitive ability, and demograpics could be predicted with surprising accuracy simply based on what users “Liked” on Facebook. Two other studies have found differences in narcissism between Twitter & Facebook users. Yet another study has found links between words bloggers use and personality with significant accuracy. Additionally, metadata has the potential to predict personality. Clearly, there is incredible potential for individuals in HR roles in organizations to harness this data to assess and profile individuals.

Recent changes in assessment tools that utilize new data methodologies have the potential to change the marketplace significantly because of their potential to reduce costs. According to experts, the gap between organizations hiring people who do not fully fit their needs and professionals who want jobs that fit them may close. Analytics utilized for HR purposes have the potential to create a situation in which organizations can directly "crowd source" the talent they need. This has been referred to as the “Uberization” of talent – after the on-demand car service. Accordingly, these new methods could drastically cut costs for organizations in the hiring process. Put simply, it is significantly less expensive to configure and run an algorithm than it is to use an established test to find talent.

User experience may be enhanced in the future with new methodologies compared to tried-and-true assessments. Ten to 15 years ago, consumer experience did not matter as much with assessments for HR purposes. Now, it is more important to potential applicants to use tools that are enjoyable to complete rather than tedious and time consuming. The case of gamification has emphasized this point. Gamification has involved the creation of various “games” whose outcomes can be correlated with measures of personality or other factors. One example is Knack.com that provides a game called “Wasabi Waiter” that is a situational judgment-type task. When all potential talent need to do is “Like” something on Facebook or play a game, clearly their enjoyment with these types of methods will be greater than with previous assessment methods such as filling out an irksome job application online.

While these new advances have occurred in the field, the research to substantiate the validity of such assessments has not kept apace. Incremental validity (the extent to which a new measure is valid over and above previously established measures) of certain algorithms has yet to be measured. Incremental validity is important to assess because, for example, in the Facebook “Likes” study, we need to know if this type of assessment algorithm might offer something better over and beyond traditional assessment methods (as of this writing, the Facebook algorithm study and other types of big data-based methods only offer about 50% of the predictive validity of traditional assessments). While the validity is not yet there, does the fact that such algorithms are cheaper and easier to administer have an impact on decision-making from an HR point of view? Clearly, as this field opens up, researchers need to partner with those who are commercializing these tools in order to make sure they are actually valid and rigorous.

In addition to understanding incremental validity of various new tools, the fact that some assessments have not been substantiated in a rigorous way can create ethical issues for organizations and for those commercializing various measures. The potential exists for manipulation and poor quality of assessments. For example, one new tool, has faced criticism. In this particular case, Lumosity, a product utilizing “brain games” is targeted toward two groups: the elderly hoping to stave off Alzheimers and to individuals hoping to benefit their cognitive abilities in general. Many neuroscientists have criticized Lumosity and similar tools for making unsubstantiated and inaccurate claims about their effectiveness. While this assessment has not made it into the HR sphere, similar tests have the potential to. For the most part, the big data-based assessments industry has gone relatively unregulated and almost anyone can make claims about their tools. Corporations preying off the ignorance of the elderly or anyone wanting to “improve” their cognitive abilities pose substantial ethical concerns that need to be investigated further.

The emerging field of big data-based assessments may also have other ethical issues. According to an article in The Atlantic, “By one estimate, more than 98% of the world’s information is now stored digitally, and the volume of that data has quadrupled since 2007”. With such a vast amount of data at our disposal, how do we use it in an ethical way? Much of this data has been collected outside of our conscious awareness – web browsing, meta-data collected from when, where, and to whom we make telephone calls. Using social media has unwittingly helped to unleash this new database of information. Of course, after the Edward Snowden revelations, we now know that there is a database of such information that the government is actively collecting. After Snowden revealed the NSA’s data collection practices, the public was outraged and clearly felt violated. A quarter of US citizens changed their patterns of social media platforms. Additionally, 52% of US citizens surveyed said they were either “very concerned” or “concerned” about US government surveillance. Given the reach of current dissatisfaction among US citizens, large-scale programs like the NSA clearly make individuals feel uncomfortable with the fact that their data can be stored and recalled, and used for unknown purposes at any point in time. Similarly, using data on a smaller scale within the realm of a single organization for assessment purposes might leave participants feeling violated if their data is being used without their consent. Therefore, when using data to profile individuals, these data need to be used in a transparent way.

The use of large-scale big data-based assessments for hiring and development practices might have another downside although organization change efforts might be able to reduce ethical concerns. As discussed previously, monitoring people at work electronically is dubious from an ethical point of view. However, when people do know they are being monitored at work, their engagement from work-related tasks can suffer, which may have an impact on performance – although at this point, more research needs to be done to substantiate these claims. Other research has shed light on this potential point as an ethical violation. If people in a given workplace are being electronically monitored, negative outcomes can be reduced by providing them with a compelling reason for using their data and giving them input in the process. When these conditions are met, they feel more at ease and may be more in line with a sense of procedural justice. This could have practical implications for ethics in using assessment tools. If companies were to scrape data from emails, for example, and try to make links between the content of those emails and individual performance, individuals might be more likely to buy into this process if they have had some input on the process to understand why the policies are in effect. Granted, our explanation here is simplistic as such a change in organizational policies would likely involve massive organizational change efforts, but there still is evidence that some of the effects of using big data in a way that invades privacy could be negated if employees participate in the process.

New advances in psychological assessments that are open to HR professionals and organizations are exciting and changing quickly. Suddenly, we live in a world in which the algorithms from social networking sites and metadata from our smartphones and Internet usage can predict things like personality, cognitive ability, and demographics. Due to the shifting nature of assessments, the market is also shifting. Finding talent through assessments has the potential to become on-demand and crowd sourced like Uber. Furthermore, user experience will likely be enhanced through gamification. These exciting new directions in the assessments field are not without their problems, however. Several concerns exist regarding the use of such data. Major issues still remain regarding validity. Products have the potential to be sold (and are) without being properly scientifically vetted. Furthermore, gaining explicit user consent with the myriad sources of data available to researchers, data scientists, and employers alike, is important. If explicit consent is not obtained and employees find out their data is being used, they may lash out against the company in destructive ways and organization-wide performance could suffer. One way around this may be to instill major organizational change to gain buy-in from employees so they do not feel uncomfortable that their data is being used. Clearly, the direction big data-based assessments are currently moving in is exciting, but it also implies many shifts – in assessments themselves, in the marketplace, in ethics, and in how we work with organizations and accept the use of our data in a major way. These changes are not minor – instead, they are radical and like the revelations of Edward Snowden, are likely to involve impassioned large-scale societal discussions on all sides of the issue.

References

• Alge, B. (2001). Effects of computer surveillance on perceptions of privacy and procedural justice. Journal of Applied Psychology, 86(4), 797-804.
• Asman, M., & Essex, P. (2001). Electronic monitoring of employees at work. Ohio CPA Journal, 60(1), 25-28.
• Chamorro-Premuzic, T. (2014a, March 21a). The Uberisation of talent: Can the job market really be optimised? Retrieved April 5, 2015, from http://www.forbes.com/sites/tomaspremuzic/2014/03/21/the-uberisation-of-talent-can-the-job-market-really-be-optimised/
• Chamorro-Premuzic, T. (2014b, July 16b). Should we get excited about new assessment tools? Retrieved April 5, 2015, from http://www.hrexaminer.com/should-we-get-excited-about-new-assessment-tools/
• Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802-5805.
• McKinney, B., Kelly, L., & Duran, R. (2012). Narcissism or openness?: College students’ use of Facebook and Twitter. Communication Research Reports, 29(2), 108-118.
• Nerney, C. (2014, March 28). How HR analytics can transform the workplace. Retrieved April 5, 2015, from http://www.citeworld.com/article/2137364/big-data-analytics/how-hr-analytics-can-transform-the-workplace.html
• Panek, E., Nardis, Y., & Konrath, S. (2013). Mirror or Megaphone?: How relationships between narcissism and social networking site use differ on Facebook and Twitter. Computers in Human Behavior, 29, 2004-2012.
• Peck, D. (2013, November 20). They're watching you at work. Retrieved April 6, 2015, from http://www.theatlantic.com/magazine/archive/2013/12/theyre-watching-you-at-work/354681/
• Quarter of US citizens changed technology use after Snowden revelations. (2015, March 16). Retrieved April 7, 2015, from http://sputniknews.com/us/20150316/1019572149.html
• Tucker, P. (2014, March 28). How the NSA can use metadata to predict your personality. Retrieved April 7, 2015, from http://www.defenseone.com/technology/2014/03/how-nsa-can-use-metadata-predict-your-personality/81538/
• Underwood, E. (2014, October 22). Neuroscientists speak out against brain game hype. Retrieved April 6, 2015, from http://news.sciencemag.org/brain-behavior/2014/10/neuroscientists-speak-out-against-brain-game-hype
• Yarkoni, T. (2010). Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. Journal of Research in Personality, 44, 363-373

Learning Agility - A New Tool Under Development

Our team of researchers at Columbia University is eager to bring a new, cutting-edge tool designed to enhance Learning Agility to potential clients and leaders.

Learning Agility, of burgeoning interest in the field of organizational development, may be used to predict and understand how some leaders learn and develop more quickly than others. In 2012, Dr. Warner Burke, along with several of his doctoral students, published a white paper with the Center for Creative Leadership (CCL) outlining their theory on Learning Agility. Since that time, many organizations have come forward and sought out Dr. Burke and his students and asked to use their Learning Agility tool, which is still under development.  Dr. Burke hopes to use the finished Learning Agility tool for leadership development with organizations while also allowing researchers access to the tool in order to continually improve our collective understanding of the construct in the future.  

To contact Dr. Burke’s research team about the state of the tool as well as potential future collaboration, please contact me at learning.agility.research@gmail.com. For more updates on the construct and tool as it emerges, please visit my professional blog at http://blogs.cuit.columbia.edu/gad2125/ 

Using Assessments for Leader Selection

Organizations vary in the extent to which they use personality tests to select and develop their leaders. Certain personality traits may help best predict who will be a leader (also referred to as leader emergence) while other traits can be linked to leader effectiveness. Just because someone emerges as a leader does not mean he or she will necessarily be a good leader. Furthermore, destructive traits can be challenging and time-consuming to deal with because personality traits are difficult to fundamentally change. Therefore, thinking about how to develop emergent leaders is important to think about - how can we get our leadership to the next level? Researchers have found that some traits are more likely to lead to positive change within developing leaders than others. Coaching can take advantage of these traits to lead to successful leadership.

Researchers have studied the phenomena of leader emergence, which we can think of as the likelihood of someone being selected for a leadership position at an organization. In several studies, particular traits have been shown to predict leader emergence. A seminal study found that leader emergence was best predicted by conscientiousness and extraversion, which are important traits of the Five-Factor Model (FFM), the leading model of personality psychologists use (to put this in context, the FFM is composed of openness to new experience, conscientiousness, extraversion, agreeableness and emotional stability). While these personality characteristics may be important in selecting leaders, another classic study noted that intelligence and masculinity were seen as important traits predicting leadership perceptions. Therefore, a leader might be more likely to get selected (leadership emergence) if he (or she) is seen as stereotypically masculine, dominant, and intelligent. Furthermore, a longitudinal study found that intelligence, dominance, self-efficacy and self-monitoring predict leader emergence which is important to think about in selection contexts. A study noted that these traits are related to the FFM because self-monitoring is similar to agreeableness and emotional stability for leader emergence. Therefore, several personality-related constructs such as extraversion, intelligence, dominance, masculinity, self-monitoring and agreeableness may be important to understand leader emergence.

Personality assessments can also be used to predict leadership effectiveness, also known as the “bright side” of personality. Some of these constructs are mentioned previously – extraversion, conscientiousness and self-monitoring are some traits that are linked to the “bright side” of personality. Only through understanding the “bright side” of personality can we understand the “dark side” which is linked to leader development. These methods come with several caveats, which I will explain in more detail later. Like the literature on leader emergence, leader effectiveness and the “bright side” can be predicted by conscientiousness and extraversion. Other research similarly found that predicting leadership effectiveness can be done through the use of leader self-efficacy (LSE) – that is, a special kind of self efficacy that pertains to leaders and task efficacy. Furthermore, they found that LSE is mediated by emotional stability, conscientiousness and extraversion from the FFM. Self-efficacy can be thought of as a personality characteristic because it is a trait that is generally stable. Leadership effectiveness was also significantly related to self-efficacy noted in previous research. They found that self-efficacy was correlated with conscientiousness most highly followed by emotional stability and extraversion. Further linked to prediction of leader effectiveness, researchers behind several seminal studies found that transformational leadership, which is more likely than other traits to be developed, is linked to several FFM traits. Agreeableness emerged as the trait most highly linked to transformational leadership (e.g., charisma, empathy, working well with people) and extraversion was also a predictor of transformational leadership that the authors note can be used in development.

The FFM traits are part of the “bright side” of personality. Therefore, successful executives and leaders may have higher levels of these traits (such as agreeableness and extraversion) than leaders who are not as successful. So, how do we develop leaders to make them more successful, especially leaders who have been selected yet exhibit behaviors that could potentially derail their career? In order to answer this question, we need to shift our discussion to the “dark side” of personality traits. Noted researchers behind the "gold standard" of workplace personality assessments discuss “dark side” traits like arrogance, emotional volatility and untrustworthiness. These types of traits are often not evident until the leader is already in his or her position.

In order to further understand leadership development we also need to understand that changing personality through development is typically very difficult. In fact these researchers noted that most leadership development programs fail. The exception to the rule is long-term coaching programs (typically lasting a year or more) in which leaders and executives raise their self-awareness to focus and manage their “dark side” traits more effectively. While not all traits are easy to develop, perhaps agreeableness and extraversion are two FFM traits practitioners can focus on with respect to leadership development to become transformational leaders. Agreeableness and extraversion are linked to emotional intelligence – also known as EQ – which has been noted as being somewhat malleable. Another study found that leaders high on Dutiful and Diligent HDS scales, for example, were more likely to experience greater efficacy in leadership development programs over time compared to peers with other high HDS traits. The authors hypothesized that the Dutiful and Diligent scales are closely linked to conscientiousness, which is related to self-awareness. Therefore, while changing one’s “dark side” personality characteristics might prove to be very difficult, leaders who have enough EQ (and, specifically the self-awareness facet of EQ) might be able to change more effectively than leaders who do not have as much self-awareness.

Another problem with “dark side” traits is that they are hard to detect using assessments. One reason is that potential leaders can fake their results. Faking is an issue with regard to personality testing and cannot be avoided. Additionally, the very method most often used to assess personality – self assessment – is likely to be inaccurate because leaders tend to self inflate their scores. While we may be able to debate the difference between faking and self-inflation, the point here is that self-assessments may not always be accurate because leaders have a vested interest in their outcome. Instead, understanding leader effectiveness may be best when done at the group level – through the use of multi-rater feedback tools in which peers, managers and subordinates can rate the leader in question.

In sum, personality tools can be valuable to leadership selection and development but practitioners have to be mindful of their pitfalls and limitations. FFM models can be used to predict leaders for selection purposes to a degree, but models in which constructs are more precise may provide higher degrees of predictive validity. As a practitioner, what can you do?

1. Be aware of social desirability cues that self-assessment tools may bring about, especially in the selection process. Leaders might be able to be selected using “bright side” personality characteristics but then derailing behaviors, or “dark side” characteristics can start to show. Leaders may be able to avoid development by managing up and suppressing “dark side” characteristics with superiors but unleash these unwanted characteristics on subordinates.
2. Use feedback whenever possible. Multirater feedback assessments may be especially useful in detecting “dark side” traits in order to assess leader effectiveness. When this occurs, leaders need to be developed. Once “dark side” traits have been detected, developing and changing a leader can be difficult. Personality traits are typically not malleable.
3. Help leaders tap into their self-awareness. Individuals with adequate levels of self-awareness (linked to EQ) and motivation to change may be able to overcome these difficulties. Specifically, these leaders may be most likely to reign in certain “dark side” traits. Agreeableness and extraversion, also linked to EQ, can be developed for leaders to embody a transformational style.

Therefore, it appears that transformational leadership – a type of leadership that many leaders aspire to embody – can be developed through self-awareness and hard work in a rigorous development programs like coaching.



References

• Foti, R., & Hauenstein, N. (2007). Pattern and variable approaches in leadership emergence and effectiveness. Journal of Applied Psychology, 92(2), 347-355.
• Harms, P., Spain, S., & Hannah, S. (2011). Leader development and the dark side of personality. The Leadership Quarterly, 22, 495-509.
• Hogan, J., & Holland, B. (2003). Using theory to evaluate personality and job-performance relations: A socioanalytic perspective. Journal of Applied Psychology, 88(1), 100-112.
• Hogan, R., Curphy, G., & Hogan, J. (1994). What we know about leadership: Effectiveness and personality. American Psychologist, 493-504.
• Judge, T., & Bono, J. (2000). Five-factor model of personality and transformational leadership. Journal of Applied Psychology, 85(5), 751-765.
• Judge, T., Bono, J., Ilies, R., & Gerhardt, M. (2002). Personality and leadership: A qualitative and quantitative review. Journal of Applied Psychology, 87(4), 765-780.
• Judge, T., & Ilies, R. (2002). Relationship of personality to performance motivation: A meta-analytic review. Journal of Applied Psychology, 87(4), 797-807.
• Lord, R., Vader, C., & Alliger, G. (1986). A meta-analysis of the relation between personality traits and leadership perceptions: An application of validity generalization procedures. Journal of Applied Psychology, 71(3), 402-410.
• Morgeson, F., Campion, M., Dipboye, R., Hollenbeck, J., Murphy, K., & Schmitt, N. (2007). Reconsidering the use of personality tests in personnel selection contexts. Personnel Psychology, 60, 683-729.
• Ng, K., Ang, S., & Chan, K. (2008). Personality and leader effectiveness: A moderated mediation model of leadership self-efficacy, job demands, and job autonomy. Journal of Applied Psychology, 93(4), 733-743.

The Advantages and Disadvantages of Using Cognitive Ability Tests for Workplace Selection

Cognitive ability tests that seek to measure intelligence, like the SAT, have been commonly used in organizations to successfully select personnel. But like any selection tool, there are practical advantages and disadvantages and this blog post examines the costs and benefits of using such assessments for selection purposes.

Several organizations have implemented assessments that tap into General Mental Ability (GMA) into the workplace to help predict job performance. Related to several sub-types of intelligence, GMA has become one of the more powerful constructs to measure for selection. Tests that measure GMA are low cost and easy to administer. A popular and commonly used GMA test is the Wonderlic Personnel Test (WPT), because it only takes applicants 12 minutes to complete and costs roughly five dollars per person.

While GMA tests are simple and cost effective in measuring intelligence, in some contexts, they do not work as well, especially when hiring for leadership positions. While leadership and intelligence are certainly correlated, sometimes intelligence may not be the single most important factor to predict job performance. Leadership positions tend to require skills beyond intelligence, like management experience and interpersonal skills.

Many people do not think that taking a test actually predicts future job performance. While cognitive ability tests might be helpful hiring tools for organizations, some participants may perceive cognitive ability tests as having less validity which can subsequently make them feel less motivated and ultimately perform poorly.

Another disadvantage to using GMA tests is that it could potentially be racially discriminating. In a 30-year study comparing intelligence between racial groups in America, the results found that there are differences in GMA between some minorities, compared to white Americans (more on this later about how to interpret and implement, as well as make sense of this research that personally upsets us).

An illustrative example of the Wonderlic Personnel Test (WPT) administered by the National Football League (NFL) brings depth to understanding this phenomena. The NFL administers the WPT to all players who seek to be drafted into the League, along with other tests of physical ability, strength and agility. Some have questioned the League’s use of the WPT, as it does not predict future success in the NFL, but it does measure two important forms of intelligence: crystallized and fluid intelligence. Crystallized intelligence can be thought of as “permanent” intelligence that builds with age and experience, while fluid intelligence relates more to processing speed and typically decreases with age. A current example a WPT scores for NFL quarterbacks: Tom Brady scored a 33, and Tim Tebow scored a 22. The average score for quarterbacks in the NFL is 24, so some may argue that Brady's increased success is a result of his superior IQ.

The NFL’s use of the WPT has not predicted that Black players perform more poorly than White players, which may be in part because black players represent the majority in the NFL, which is atypical from most job-application settings. Some have speculated that the NFL may not use the WPT in order to predict success in the NFL but instead, they might use it as a baseline measure of intelligence and then examine players on a case-by-case basis. For example, if a player passes tests of physical ability with flying colors but scores in the 10th percentile on the WPT, teams may want to proceed with caution in drafting this player.

Clearly, it is a complex issue. In the real world, no choice for selection is perfect and oftentimes must be used in conjunction with multiple methods. Using tests of cognitive ability have high levels of success in predicting future job performance, but their use in organizations depends on many factors:

1. HR professionals administering the assessment need to be clear on how and why they are using the assessment.  They need to understand that the test is ideally used for a predictive purposes, rather than just to measure intelligence for the sake of it. 
2. Recognize that there may be differences in GMA between whites and some minorities, but keep in mind our American cultural frame that all people are created equal.  Holding these two polarities can be complex and uncomfortable.
3. Using these instruments could save organizations considerable amounts of money because of their substantial predictive validity, but might result in adversely impacting some minority groups compared to white Americans.

Ultimately, these tests are very effective but can also be racially discriminating. Morally and legally, organizations refrain from using these selection tools, however, this contradicts what many organizational psychologists subscribe to: predicting future performance using scientific instruments. Moving forward, HR professionals need to be firmly aware of the inherent risks and benefits with using these instruments and be prepared to take a stand on one side of the issue or the other. Is the priority to maintain accuracy and hire the best talent, which may racially discriminate? Or is the priority to enforce equal opportunity for all, but potentially hire candidates that may not be the most qualified? An organization might throw away its ability to predict top performance in order to satisfy equally daunting issues with regards to disparate impact. On both sides of the debate, the risks are substantial but it’s important to understand the nuances in order to objectively evaluate both sides to make an informed decision.


References 

• Chan, D. (1997). Racial subgroup differences in predictive validity perceptions on personality and cognitive ability tests. Journal of Applied Psychology, 82(2), 311-320.
• Gill, A., & Brajer, V. (2012). Wonderlic, race, and the NFL draft. Journal of Sports Economics, 13, 642-653. Gottfredson, L. (2005). What if the hereditarian hypothesis is true? Psychology, Public Policy, and Law, 11(2), 311-319.
• Judge, T., Colbert, A., & Ilies, R. (2004). Intelligence and leadership: A quantitative review and test of theoretical propositions. Journal of Applied Psychology, 89(3), 542-552.
• Kuncel, N., Ones, D., & Sackett, P. (2010). Individual differences as predictors of work, educational, and broad life outcomes. Personality and Individual Differences, 49(4), 331-336.
• Leverett, J. P., Matthews, T. D., Lassiter, K. S., & Bell, N. L. (2001). Validity comparison of the general ability measure for adults with the Wonderlic Personnel Test. North American Journal of Psychology, 3, 172-182.
• Lyons, B. D., Hoffman, B.J., & Michel, J. W. (2009). Not much more than g? An examination of the impact of intelligence on NFL performance. Human Performance, 22, 225–245.
• Rushton, J. P., & Jensen, A. R. (2005). Thirty years of research on race differences in cognitive ability. Psychology, Public Policy, and Law, 11, 235–294.
• Schmidt, F., & Hunter, J. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262-274.