Can We Accurately Evaluate Leadership Before Someone Has a Chance to Lead?

In general, our personalities are pretty stable over our adulthood. Yes, we mature and big life events can alter us, but the building blocks of who we are as people are closer in stability to our eye color than our hair color.

This stability has been important to the science of employee selection. The underlying idea of giving any type of pre-employment or promotional test is that the knowledge, skill, ability, or characteristic being measured is stable in that person for a given period of time so that it can be used to predict future performance. With skills, we assume that they will improve over time, so we look for those that a person has right now. For personality and cognitive abilities, we assume that a person will have those at a consistent level for many years and that these aptitudes can be used to develop specific skills, such as leadership.

When I conduct leadership workshops, I typically ask participants if leaders are born (e.g., do some people just have what it takes) or made (e.g., pretty much anyone can be an effective leader if given the right opportunities to develop). The conversation gets people thinking about what behaviors are necessary to lead (good communication, willingness to direct others, attention to details, etc.), which of those can be taught, and which cannot. Put another way, to become a professional basketball player, I can improve how well I shoot, but I cannot do too much about how tall I am.

But, what if we have the direction of trait to leadership wrong? What if the traits to become a leader don’t blossom until someone is given the chance to lead?

This study suggests that being promoted into a leadership position does change the conscientiousness factor of personality. Conscientiousness has been found to be a significant predictor of overall manager effectiveness. It’s an interesting idea in that it suggests that, for some people, we do not know if they have a sufficient amount of a trait that contribute to leadership success until after they become leaders.

As with all good research, it poses as many new questions as answers. For instance, were there increases in conscientiousness across the spectrum or only among certain groups (e.g., were there gains for those who already showed relatively high levels of conscientiousness, so the rich got richer)? Or, does it take a leadership experience to bring out conscientiousness in people who typically do not show it? Or, is leadership a tide that raises everyone’s conscientiousness?

Practically speaking, this is where the study has me thinking about assessing leadership:

1)  Putting a re-emphasis on using performance on temporary assignments that involve leadership as part of the selection process in promoting people into supervisory positions. 

2)  Validating responses on personality tests that are taken after a person goes through a leadership role-play exercise or situational judgment test.

3)  Re-thinking what aspects of personality indicate leadership potential (e.g., willingness to direct others and resilience) and broaden our list of things that are leadership skills to include some other aspects of personality (e.g., conscientiousness). We can then focus on selecting based on the former and training on the latter.

Some people have the right mix of attributes that allow leadership to come easily to them. As it turns out, some of those things become more apparent after a person has a chance to lead. This should encourage us to think about how we choose to evaluate leadership potential.

Training Hiring AI Not to be Biased

Artificial Intelligence (AI) and Machine Learning (ML) play integral roles in our lives.  In fact, many of you probably came across this blog post due to a type of one of these systems.  AI is the idea that machines should be taught to do tasks (everything from search engines to driving cars).  ML is an application of AI where machines get to learn for themselves based on available data.

ML is gaining popularity in the evaluation of job candidates because, given large enough datasets, the process can find small, but predictive, bits of data and maximize their use.  This idea of letting the data guide decisions is not new.  I/O psychologists used this kind of process when developing work/life inventories (biodata) and examining response patterns of test items (item response theory—IRT).  The approaches have their advantages (being atheoretical, they are free from pre-conceptions) and problems (the number of people participating need to be very large so that results are not subject to peculiarities about the sample).  ML accelerated the ideas behind both biodata and IRT, which I think has led to solutions that don’t generalize well.  But, that’s for another blog post.

What is important here is the data made available and whether that data is biased.  For instance, if your hiring algorithm includes zipcodes or a classification of college/university attended, it has race baked in.  This article has several examples of how ML systems get well trained on only the data that goes in, leading to all kinds of biases (and not just human ones).  So, if your company wants to avoid bias based on race, sex, and age, it needs to dig into each element the ML is looking at to see if it is a proxy for something else (for instance, many hobbies are sex specific).  You then have to ask yourself whether the predictive value of that bit is worth the bias it has.

Systemic bias in hiring is insidious and we need to hunt it down.  It is not enough to say, “We have a data driven system” and presume that it is not discriminatory.  If the ML driving it was based on inadvertent bias, it will perpetuate it.  We need to check the elements that go into these systems to ensure that they are valid and fair to candidates.

I’d like to thank Dennis Adsit for recommending the article from The Economist to me.

Blacks Welcome to Apply

The aftermath of George Floyd’s murder has many of us asking, “What can I do better?” when it comes to ending racism.  This is critical in that racial bias in hiring have changed little in 30 years.  HR and I/O psychology play a unique role in that we create the processes that allow for equal employment.

None of the suggestions below require lowering of standards.  Rather, it provides a framework for applying standards in an equitable way.  Science and good sense points us in this direction with these actions:

  1. Widen your recruitment net.  If you recruit from the same places, your workforce will always look the same.  There is talent everywhere—go find it.  Whether from a high school in a different part of town or a historically black college/university.
  2. Make Resumes Anonymous.  The science is very clear that anonymous resumes reduce racial and gender bias.  It is not an expensive process to implement and works for all kinds of business.
  3. Examine minimum qualifications carefully.  Whether based on job experience or education, these can serve as barriers to black job candidates.  The ground breaking employment discrimination lawsuit, Griggs v. Duke Power, was based on an invalid requirement that supervisors needed a high school diploma.  Don’t get me wrong—I want my surgeon to be an M.D. But, do your entry level positions really need a college degree?  Do your managers really need to be MBAs?  If you analyze the relationships between education/experience and job performance, you are likely to find that they are not as strong as you think.
  4. Use validated pre-employment and promotional tests.  As a rule, validated pre-employment tests do not adversely affect blacks and are certainly less biased than interviews (see below).  This is particularly true for work sample tests (show me what you can do) and personality tests.  However, cognitive ability tests, especially speeded ones, may lead to discrimination.  If you use them, analyze your cutting score to ensure that it is not set so high that qualified candidates are being screened out.
  5. Reduce reliance on interviews.  Interviews can be biased by race and ethnicity.  And, more often than not, they are far less valid than tests.  We need to convince hiring managers that they are not good judges of talent—very few people are.  Remember, interviewing someone to see if s/he is a “good fit” is another way of saying, “this person is like me.” 

  6. Make your interviews more structured.  This can be achieved by asking candidates the same questions and using an objective scoring methodology.   Adding structure to the interview process can reduce bias (and improve validity).

You may already be doing some of the above.  I would encourage you to do all of them.  The outcome is fairness AND better hires.  What could be better than that?

What Are Your Company’s Selection Myths?

For North American sports fans, there is not a more public selection process than that National Football League (NFL) preparing for the annual talent draft.  This is the process for allocating new players (rookies) who have finished their college careers to the teams.  Players cannot sign a contract with any team they choose until after they complete their rookie contract with the team that drafts them.  Players not chosen in the draft can sign with any team.

Besides evaluating players based on their college games, the NFL teams also invite the top players to be evaluated at what they call a combine.  At the combine, players get interviewed by teams and are put through a variety of physical and medical tests.  Teams use all of this information to compare players against each other (by position) so they can make the best choices during the draft.

Of course, in reality, the top draft choices are made mostly based on the players performance in college.  Players at the best schools compete with and against other players who are likely to be drafted, so watching them perform in a game tells teams pretty much what they need to know.  And, as I wrote about last week, there is a big bias towards players who went to the “best” schools.  But, the teams do use information at the combine to inform them about players who they don’t feel they have good data on.  For instance, those who are recovering from injuries or played at schools that don’t compete against the top schools.

There’s only one problem:  There is very little data that supports that the “tests” given at the combine of predictive of success in the NFL.  This article about the problems in measuring hand size in quarterbacks provides just one example of that.

One can see how this all got started.  Quarterbacks need to be able to throw a ball well (with a lot of speed and accuracy) and to be able to hold on to it under pressure and having a large hand (as measured from the tip of the thumb to tip of the pinkie) would seemingly be related to both of those.  But, it’s not.  All quarterbacks grip the ball a little bit differently, regardless of hand size, to get the best results.  The article suggests that hand strength is the better predictor of quarterback performance and that it is unrelated to size.  But, those who evaluate quarterbacks just cannot let the size measurement go.

I am guessing that most of your organizations have an unproven selection myth, such as, “Our best managers have gotten their MBAs from one of 10 schools” or “Good supervisors are those who have worked their way up in our industry” or “Our most successful programmers had previous experience at specific companies.”  I used to hear, “Our best call center agents have previous experience before coming here” all of the time.  But, when I conducted validation studies in contact centers, it was rare that previous experience was a good predictor of future performance. These myths are easy to evaluate, but changing HR practices is harder.  It often requires good data and a shift in culture to change thinking.  However, moving on from myths is often required to make better talent decisions.

Adjusting Your HR Strategy When Your Company Decides to Train For Basic Job Skills

There is a presumption that the US education system will provide employers with workers that possess requisite job skills.  Companies are then responsible for providing more advanced ones through apprenticeships, job training, and leadership development.  But, what if job seekers do not possess the skills for tech jobs?

This article describes what lengths some employers are going to get people in their talent pipeline.  In many ways, there is nothing new here.  It comes down to searching for talent where they previously hadn’t and providing training rather than expecting people to come with skills.  It’s the latter that I find most interesting.

When designing selection programs, particularly for entry level positions, we tend to focus on what knowledge or skills the candidates needs on the first day.  Those expectations are higher if we expect someone to come with experience than if we are going to be providing a lot of training.  This has important impacts on how we select candidates, including:

  1. Use of aptitude tests rather than knowledge tests.  Aptitude tests are terrific measures of basic skills and are quite valid.  However, speeded ones can lead to adverse impact, so they require good validation studies, meaningful passing scores, and adverse impact analyses.
  2. Alter interview questions so that a wide variety of experiences can be used to answer them.  If you are hiring people who don’t have experiences in your industry, you should be asking valid questions that people with little or no job experience can answer.  For instance, instead of, “Tell me about a time when you led a team project at work and…” use “Tell me about a time when you had to influence a group of friends and…”
  3. Focus on reducing turnover.  Training is EXPENSIVE, so hiring mistakes in a boot camp environment are very costly.  Take special care in developing realistic job previews and other ways that allow candidates to decide if they are not a good fit.  Collect information (previous experiences, referral sources, school majors, etc.) that may be indicative of future turnover and validate them.  These can be part of very useful pre-employment processes.

What this approach really presents is a change in HR strategy from one that relies on people to be able to start on day one to taking time to get them up to speed.  By having recruitment, selection, and development leaders involved in the execution, organizations can adapt their tactics for identifying and selecting talent and have a smoother transition.

Who’s Next?

My process improvement friends like to say, “Improving the work is the work.”  There is some truth to that in HR as well.  But, I think that it is also fair to say that “Keeping the talent pipeline full is the work.”  I’ll admit that it’s less catchy.

Succession planning is a topic as old as business, so I will cut to the chase:  This may be an area where companies are getting better.  The data is interesting as well in that it shows (at least in this sample) that public companies are better at it than private ones.  I would be curious as to whether there is an additional split between family owned and other types of ownership among the private companies.

I think that transparency (welcomed or not) has a lot to do with boards (and, hopefully, HR) being more concerned about high level succession planning.  Part of what big investors are buying is the leadership team and the more focus there is on CEOs and their impact, the more concern investors will have in the less-than-famous leaders.

Good succession planning does not stop at the C-Suite.  It should be considered part of talent development for every position in the company.  Whether it is at the entry (where are we going to find new employees?) or management (how can we identify leadership potential?) levels, the work is ensuring that there is a strategy for identifying talent.

This process involves both valid assessment (who is interested and capable of doing what we need?) and development (what are the experiences that a person needs to be prepared for the next move?).  Keeping the pipeline full means focusing on both so that when a position comes open the question, “Who’s next?” can be answered quickly and reliably.

Putting Too Fine a Point On It

I will admit that I am more of a big picture person than a perfectionist.  Going through old blog posts would likely lead to the finding of some spelling errors and grammatical mistakes.  That does not bother me as long as I am getting my point across.  I also have a pretty good sense that I am in the minority of people who are willing to admit that I lack a big attention to detail.

At the same time, I also advise clients to use pre-employment tests that measure attention to detail and conscientiousness for those jobs that require it.  So, like other personality traits, it certainly has its place.  I’m just not the person you want looking for needles in haystacks.

So, this article definitely caught my attention.  Here’s the most important takeaway (at least to me) from the authors, “…the answer to the question ‘is perfect good?’ is that in total, perfectionism is likely not constructive at work.”  Given this, what are we really getting when a job candidate tells us that he is a perfectionist?

The data shows that we will get someone who will work long hours, but is not more likely to be engaged in the work.  Rather, perfectionists tend to burn out more than those who can let the little things slide.  This is particularly true of those whose perfectionism comes from a place of avoiding failure than those striving to be excellent.

Most importantly, when compared to supervisor ratings of job performance, y’know, the people the perfectionist is trying to impress, levels of perfectionism are not related.  That’s right—managers feel that the job performance of those who feel that good is good enough is the same as for those who choose to gild the lily.

From a selection perspective, a more subtle approach is called for.  There are some jobs (brain surgeon and quality inspector, to name two) where perfectionism is probably important and should be part of your assessment process.  However, it should not be considered a good universal predictor of performance.  One can easily imagine some jobs where being a perfectionist would be a negative predictor, such as creative jobs like marketing or app design. Also, when interviewing, if a candidate brags about her perfectionism, I would not get too excited.  She may be confusing activity for productivity.

The organizational implications here are straightforward: Having a culture of perfectionism will get you more hours, but not better performance, out of your team.  While not explicitly tested in the article, it is likely to also get you more turnover.  This is a reminder that we should be clear about quality expectations and work-life balance.

Selecting Managers Who Understand the Value of Praise

When I do leadership/management workshops, the first topic is always motivation.  While I am a big believer that motivation must come from within, managers can impact performance, in the short term, by effectively using rewards.

Years of research tells us that cash and other extrinsic rewards can be effective motivators for tasks where individual effort leads to individual results.  However, the bigger the distance between effort and results, the less value these incentives have.  Oh, and they also lose their effect over time.

The wise manager knows that recognition, praise, and other behaviors that lead to intrinsic rewards are much more powerful. This article provides a good synopsis on how to use a combination of intrinsic and extrinsic rewards.

While there tends to be a strong focus on rewards, something that gets overlooked is how to select managers who already have this insight.  Sure, most can learn it. But, I would think that there are traits that predict how well a person rewards employees.  Three of these would include:

  • A person with a high level of agreeableness is usually warm, friendly, and tactful. They generally have an optimistic view of human nature and get along well with others.  People high on this trait are likely to want to make others feel engaged in their work.
  • Generous people are the ones who give more than is expected of them.  Giving a reward to another person is an act that provides praise or a reward to another person when it could be kept to oneself.
  • View of Employees. Managers who have a “your paycheck is your reward” mentality are not likely to give out a lot of praise.  Those who recognize people as individuals, and learn what their needs are, will be much more likely to provide meaningful motivators.

By making motivational skills part of the valid selection process, we are more likely to hire managers who will seek out opportunities to reward results.  Appropriate use of such techniques will lead to more engaged and productive employees.  They are less likely to turnover, which is critical in our current low unemployment economy.

Reducing Bias Through Structure

Finding examples of racial or gender bias in hiring or job evaluations is not hard.  The latest comes from a survey of lawyers.  My sense is that the results did not come from a random sample of attorneys, so I would not quote the group differences as gospel.  The authors recommended some specific ways that law firms and companies that hire lawyers can correct the bias in their HR processes.  There were two things I took from the study:

  • Many, but not all, of the recommendations came from a solid research base. It was good to see that their hiring suggestions included behaviorally based interviews, skills based assessments, and using behavioral definitions of culture.  Each of these suggestions introduces objectively and structure into the hiring process.
  • Given that attorneys have either brought employment lawsuits or have had to defend companies against them since 1964, did it really take this long to come up with some hiring process recommendations?

My consulting experience tells me that people who hire for professional jobs seem to think there is more magic and intuition in selection than those who staff for other types of positions.  This is especially true when hiring for a job they used to have.  They could not be more wrong.  Every job has a set of critical skills and abilities required to do it well.  It is possible to objectively measure these in candidates.  Doing so will likely reduce bias.

Who Should Identify and Develop the Non-College Workforce?

On some occasions I have mentioned that companies that need blue-collar workers are in a tough spot. Their jobs are not very sexy to the millennial or Gen Z workforces who prefer tech jobs.  Also, because lifetime wages are significantly higher for jobs that require a bachelor’s degree, parents and high school students tend to have a much more favorable attitude towards going to college than training in a vocation, which is reflected in college application statistics.  We are currently in the midst of low unemployment which makes recruitment for blue collar jobs even more difficult.

Companies should think about this as a long-term, rather than an immediate, issue.  This article talks about how some firms are dipping into high schools to begin identifying students who might not desire (or be qualified for) 4 additional years of school and providing them with what used to be called vocational training.

Of course, if a specific company or industry designs the career education programs (read: vocational), there is a danger of the training being too narrow.  However, no public school in its right mind would ever turn down private money that helps kids get jobs.  And we don’t seem to have a problem with it at the college level where business schools take money (and input) from big employers and provide the students with internships.

The economy goes in cycles and it is not a matter of if, but when, the economy slows and there won’t be the same worker shortage.  However, the trend towards more interest in college and tech jobs will continue for the foreseeable future.  This means that employers of skilled, but not college educated, workers will have to find more ways to create a larger labor pool to find the talent they need.  They can do this by:

  • Aligning with local high schools and community colleges to create curriculum that is broad enough that provides students with career options, but specific enough to allow for an easy transfer from school to the employer.
  • Gauge the interests of students as they enter the program. Interest inventories are an under-utilized selection tool.  This is especially true for entry level employees.  If I’m not interested in social activities, I probably should not be on the wait staff at a restaurant, even if I need the money.  But, if I’d rather work with things than people, then becoming a welder might be up my alley. Validating these types of tests can be a good way to predict potential success by placing students in areas where they are more likely to do well.
  • Provide lifetime learning programs. One thing we know about millennials and Gen Z is that rewarding them for learning is a powerful incentive. Companies should show new recruits all of the opportunities they could potentially have, not just the ones in their trade.

Companies that need skilled blue-collar workers can no longer passively expect a deep talent pool to be available.  Rather, they should take action to identify and develop potential employees.  This will require partnerships, better pre-employment screening, and having developmental programs.  It may not solve the immediate problem, but it will ensure that they have the necessary talent in the future.

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