About Spencer Stang

As founder & owner of SDS, Spencer specializes in designing and implementing customized employee selection systems, with an emphasis on optimized algorithms to improve the decision making process. Throughout his 20-year career, Spencer has worked with many of the world’s largest business and sports organizations to help them identify, develop, and deploy the talent they need to gain a competitive advantage.

Does Your Hiring Process Resemble Election Day? Two Reasons That’s a Positive Comparison (and Two It’s a Negative)

Typically, in an election year at the four-year mark of a president’s term—where there’s no certainty the administration will change—many US employers tend to delay their decision-making processes. This year, however, much like the election itself, the hiring process has already had to adapt to several changes. 

That got us thinking about the many other ways your hiring process might resemble the upcoming election. Some are positive and reflect the true beauty that is selecting the right person for the job. Others are not so positive, and demonstrate the potential for confusion and disarray if you don’t carefully structure your decision.

How Does Your Hiring Process Mirror the Election?

See if your current hiring decisions are putting you on the positive or the negative side of our comparison: 

  • Negative—You have far too many candidates. Each year, it seems as if political parties without an incumbent in office produce more and more candidates for the American public to choose from. In 2020, there were a staggering 27 major Democratic candidates—far exceeding the previous record of 17—making it difficult for voters to remember the name of each candidate, let alone the platform. When it comes to hiring, while it’s essential to widen your pool initially, when you get to the interview/selection stage, too many finalists can breed confusion and lead to hiring the wrong candidate. 
  • Positive—You know which skills are important for your potential candidates. In a presidential election, there’s a clearly defined set of skills we all expect a potential candidate to have—character, integrity, excellent communication skills, crisis management abilities, great with foreign policy, business acumen, and the list goes on. It’s a basic expectation that our president can utilize the necessary skills to further our country’s goals. Similarly, if you have clearly outlined a set of skills necessary for your potential hires to help your company further its goals, you’re more likely to hire talent that can accomplish them.
  • Negative—You risk missing the most qualified candidates for the job. There are very few individuals with each of the skills necessary to achieve excellence as an American president, and they may not be the ones who apply. We’ve all weathered a few election terms where it seems like none of the finalists were qualified for the job. If this is consistently the case with your hiring process, you are likely neglecting the tools necessary to find and select qualified individuals.
  • Positive—Technology is improving the process. Today, we know more about each presidential candidate and their respective platforms than any group of voters in history. In a year marred by COVID-19, many of the traditional accouterments of a presidential election would not have been possible in the first place without the use of technology. Similarly, we live in an age where you have the necessary technological tools at your disposal to screen applicants, identify candidates with your required skills, and hire the best-fit talent for your organization. 

If you find yourself without a clearly defined skill-set in mind or lacking the tools to help you identify the candidates that are most likely to become a lasting, positive addition to your team, contact Stang Decision Systems. Our innovative HireScore platform can ensure that you elect to hire the right candidate for your organization. 







By |2020-09-28T12:13:37-04:00October 1st, 2020|Uncategorized|0 Comments

6 Signs Your Employee Selection Process is Broken

In this blog series, Spencer Stang, PhD, discusses why organizations fail to hire better employees, and gives insight into what companies can do to make better hiring decisions.

Occasionally, conflicts arise between being honest and making somebody upset, or not being honest and keeping people happy. My rule of thumb in these cases is that truth trumps tact. I would rather have somebody tell me the truth rudely than to have him or her pass along a polite lie.

I’ll try not to be rude, but the truth is that your employee selection process is almost surely broken. You may have put a great deal of time and effort into your process, and you also, most likely, know that it still isn’t where it should be. Here are six of the most common reasons your process is broken, and what you can do to correct them. As with all generalizations, some exceptions apply.

1. You Require Resumes

For most openings your best candidates aren’t in job hunt mode. Many of the people you would want to hire don’t have a resume and won’t bother making a resume based on a job posting–no matter how enticing. The first step of the selection process needs to be so easy that the curious candidate just falls into it. People always tell us, “I wasn’t really looking for a job when I saw the posting.” These are the people you want to attract!

Clients will sometimes say they want the application process to be challenging (e.g. send resume to apply) because it eliminates people who are too lazy to make a resume. There is some truth in this notion, but it is misplaced at the beginning of the process. The employee selection process is a two-sided relationship, and before you ask anything significant of a candidate, you first need to prove yourself to him or her.

In other words, show the applicant that the job is real, the company is real, the opportunity is real, and demonstrate that he or she will be treated with respect. Once you have established yourself, then it’s okay to ask applicants to do any number of assessments as part of your due diligence, and the applicants will understand and respect the process. Bottom line, you don’t want to ask too much, too soon, and you always want to treat candidates as you would want to be treated yourself.

2. You Immediately Make Candidates Create a Username and Password

Imagine that I tell you that I hold the secret to success and happiness. I go on to say that by following three straightforward rules, you are statistically guaranteed to be more successful and happy than the average person. All you have to do to learn these rules is create an account with a username and password . . . and the username is your private email. You can imagine that only a small percentage of people are going to create the account, because it is likely to be a waste of time. What if instead of making an account, you only had to scroll down the page to read the rules to happiness and success?

In this case, most people would scroll down–if nothing else for the sake of curiosity. Furthermore, after you read the rules, if they actually made sense, and they had a basis in research, the credibility of the source would go up significantly. Essentially, the more you prove yourself, the more credibility you build, the more information, time, and money a person will trade with you.

Respect must be earned, and immediately asking a person to create an account is not a way to earn respect. To see this in your current process, look up the number of people who click on your job posting and compare it to the number of people who actually apply. Most companies get fewer than one in ten people, and half of that loss is due to the “create an account” initiation process.

3. You Don’t Communicate Consistently and Honestly 

Starting with the basics, if a person applies for a job with your organization, you should tell that person if he or she is no longer under consideration, and/or if the job is filled. If you don’t have the time to do this, then you don’t have the time to run a hiring process.

Note that in your communication process, it’s okay to tell candidates that the process is taking longer than expected, or that it has been put on pause for a time. Research suggests that when you don’t say anything, most people will actually assume that something is worse than the truth–so lean towards honest disclosure without getting into the gory details.

Finally, unless you work for the CIA, never lie. Don’t lie to make a candidate feel better or to cover for a mistake made by somebody on your staff. Don’t lie to keep your company from being sued. When you lie, it leads to a culture of lying that will ultimate hurt your HR team and organization.

4. You Collect Data on Candidates Who Have No Chance of Being Hired

In the era of Google and Facebook, data has never been more valuable. It’s so valuable that many organizations use morally questionable methods to get people to provide them with their personal information. Once you know that a person will not be hired, you should stop collecting data on that person as soon as it’s reasonably possible. Do NOT use the hiring process as an excuse to collect data that you or your applicant tracking system vendor thinks may be useful or valuable later. This is a creepy practice that is used by far too many organizations.

Most organizations with applicant tracking systems ask knockout-type questions as part of the process (e.g. are you at least 18 years old? Can you work in the U.S.? etc.). Some of these processes ask these questions after candidates have already provided an extensive amount of personal information (which is bad). Some ask these questions first, but then go on to collect personal information even if the applicant doesn’t meet the minimum qualifications (also bad).

By asking the knockout questions first and then eliminating people who don’t meet minimum requirements, we can respect each applicant’s time and personal information, and improve the legal defensibility of the entire process. The only thing that doesn’t happen by using a more respectful process is you don’t get to store gigabytes of personal data on unqualified applicants, that you are probably never going to use anyway (unless you actually are Google or Facebook).

5. Your Process is Not Customized to the Job

This is an easy one to spot. If you use the same selection process across your organization, then your process is definitely broken. To state the obvious, accountants, programmers, machinists, and programmers are all different. Asking applicants for different types of jobs to go through the same process either because it’s simpler, or because “corporate wants a standard process,” is not going to get you the accuracy rate your organization deserves.

It will also feel generic to applicants, who then may not take it as seriously. Your application process should ask different questions based on the job. If you use personality assessments (and you should), then you need to match the personality profile to the job. Additional assessments, including job knowledge testing and basic ability testing (math/reading) as well as the interview also need to match to the role.

Furthermore, the intensity of the process should vary based on the consequences tied to a bad hire. If you are hiring a maintenance supervisor at a refinery, the consequences of a bad hire could literally be life or death. In this case, it makes sense to put more time and effort into your process than you would if you were hiring a ticket taker at the local theater.

6. You Think Hiring is More an Art Than a Science

Over the course of my career, I’ve had many people tell me that hiring is “more art than science.” Typically, this happens when a person is about to hire somebody who has a relatively low probability of success on the job. In other words, it’s a catch-all excuse for not following decision science. I know this well because I’ve made the mistake myself. The very first person I ever assessed for a client interviewed extremely well, had a perfect background for the job, but tested very poorly (i.e., big red flag!). The client dismissed the test results and I went along with their assessment noting, “the tests aren’t perfect.”

Within six months, the new hire crashed and burned, and cost the organization a great deal of money. In hindsight, I should have known better, but I got caught up in the “art” of the process. My gut told me that this person was going to be great in spite of his test scores. I knew that statistically the tests were just as accurate as the interview process, and yet I put unwarranted weight on my own observations. Had I factored the test results appropriately, I would have made a different recommendation–the right recommendation.

This, of course, is just an anecdote, and anecdotes make for bad science. After having the opportunity to track hiring “exceptions” on a much larger scale, we have solid evidence that when it comes to predicting future job performance, science trumps art. When you hire someone who goes against the science, you are four times more likely to be hiring someone who is going to fail than if you hire someone recommended by the process (science).

In other words, you shouldn’t ignore your gut, but you also shouldn’t trust it. If you want to put probability on your side, your process should include properly validated and weighted measures of education, experience, soft skills, technical skills, aptitude, character, and personality fit. If you “feel lucky,” you can skip all that and opt for short cuts and trusting your gut. But I’m guessing that’s not a risk most organizations would knowingly want to take.

If your organization is already following the recommendations made in this post, then you are off to a great start. Next month we will focus on “Problem Seven” which gets into a more advanced diagnosis allowing you to optimize a process that is already working well.

By |2018-03-07T16:37:21-05:00June 14th, 2016|Careers, Research, Updates|0 Comments

Algorithms Beat Experts When It Comes to Hiring

The status quo is broken. Specifically, resumes, unstructured interviews, and combining predictive measures using professional judgment doesn’t work well. Over one hundred years of research in the area of industrial and organizational psychology supports this thesis, so I’m going to skip the basics and dive into the good stuff.

The good stuff, in my opinion, is more toward the cutting edge of decision science. We know that human beings, on their own, are imperfect decision makers. We know that well over 100 documented cognitive biases have an effect on our ability to make optimal decisions. We also know that many decision tools have demonstrated the ability to improve the accuracy of hiring decisions.

Job simulations, cognitive assessments, structured interviews, well mapped personality assessments, situational judgment tests, biodata, and physical ability assessments are all potentially valuable for predicting job performance. Moreover, when we combine the results from multiple job-related assessments in a statistically optimal, or even reasonable, fashion, the overall prediction is far more accurate, on average, than predictions made by experts who rely strictly on their “guts” to make these decisions.

To re-phrase the last sentence, algorithms beat experts at predicting future job performance. This general finding has been tested hundreds of ways with different types of predictions and different types of experts. The results are powerful, they are conclusive, and they are massively underutilized in the real world. We aim to help change that by including statistically optimal scoring generated by carefully derived algorithms with all of our assessment results.

So, for example, if you have a candidate who has taken three assessments, we will provide you with four scores:

Works Hard = 9.1
Works Smart = 6.4
Works Safe = 8.5
Baseline = 8.4

The first three scores indicate the individual assessment results and the final score indicates the overall score, appropriately weighted, across the assessments. We call this weighted composite score a Baseline score.

The Baseline score is the single best indication of a candidate’s probability of success on the job and Baseline scores are directly comparable across candidates. So, in a situation where different candidates have different strengths, the Baseline score can be used to quickly and accurately rank order the candidates in terms of their probability of success on the job.

Think about employee selection decisions that you have observed. Most hiring decisions come down to a person or group of people trying to compare candidates in an apples and oranges fashion. Candidate Joe has the most appropriate college degree for the position, candidate Sue has better job experience, candidate Pat had the best energy level in the interview, and candidate Kyle scored highest on the math test.

Who should you pick? How much weight do you put on each of these factors and how do you combine them to look at the “whole person” and compare that person to the needs of the job? Does a college degree really matter for this job? Is job experience at one organization easily transferable to another? Does “energy level” in a 30-minute interview suggest high energy on a day to day basis? Does “high energy” really matter if the person isn’t smart enough to be trainable?

People who have been involved in hiring decisions readily see that the complexity of most decisions quickly goes beyond our abilities. Fortunately, the human brain has wonderful mechanisms for dealing with complexity. One of these mechanisms is the use of simplifying strategies commonly referred by decision making researchers to as “heuristics.”

Our brain knows that, on average, a loud noise is more important than a soft noise. Things that smell good are more likely to be edible than things that smell nasty. A restaurant with many cars in the parking lot is more likely to be a good option versus a restaurant with few cars. In all of these cases the rule of thumb has some merit, but it is also likely to lead us astray at times.

Imagine that you are in charge of picking players for an NBA basketball team. There are lots of players from all over the world to choose from so you decide that you aren’t going to look at anybody who’s height is under 6’. You know that there are some great players who are less than 6’ tall, but you also know that you don’t have time to evaluate every player and by cutting out all people under 6’ your pool of candidates seems much more manageable.

Cutting part of the pool allows you to focus your time on players with the highest probability of success at the expense of a tiny proportion of great players who are under 6’ tall. This same scenario applies for any type of minimum requirement when hiring. Requiring 5 years of work experience, a 3.0 GPA, or a college diploma will simplify your hiring decision . . . at the expense of precision. What if we could simplify our options without losing precision . . . wouldn’t that be a better option?



Source: http://www.nytimes.com/interactive/2013/11/03/sunday-review/so-you-want-to-play-pro-basketball.html?_r=0


As decision makers we face a dilemma. We need lots of data to make an accurate decision and we need to simplify that data to make sense of it. Simplifying is smart, which is why our brain does it automatically using heuristics. Over simplifying, however, is not smart and leads to many preventable mistakes, which is why we need a better way to make important decisions.

Algorithms provide the better way. An algorithm can efficiently combine data from a wide variety of measures in a fashion that minimizes data loss. An algorithm can be used to quickly and accurately rank order 7000 candidates in seconds and the results are far more accurate than if a team of people scour the same information for weeks. Algorithms can also be tracked over time and updated with improved algorithms.

Oddly enough we can model the decision making of experts using policy capturing studies, and the resulting algorithms are more accurate than the people they were modeled after (crazy, but true). In fact, we have modelled the decisions of NFL teams and created algorithms that rank order NFL draft prospects. On average, these algorithms are 36% more accurate than the selections made by actual NFL teams. We’ll get into the details of why this is true in future blog posts, but the simple explanation is that algorithms are extremely consistent whereas humans, even expert humans, are not.

So, when you think about humans being beat by algorithms, how do you react? Anger . . . “they” can’t be better than us! Fear . . . what are people going to do if the algorithms start making all the decisions? Disbelief . . . what does this guy know, he’s just a guy writing a blog. Or amazement . . . imagine the great things people could do if they improved their decision making when making life changing decisions! Many years ago I was a bit irritated that computers were beating Chess champions and, much later, Jeopardy champions. Today I have come full circle and I am simply amazed that people can build machines, computers, and algorithms that make people better at doing things they love to do.

Just as a bulldozer amplifies the power of a person and allows her to move more dirt faster than a hundred people digging with their hands, algorithms allow us to make faster, more accurate decisions in a less biased manner. The initial fear and pushback against using algorithms to improve our decision making is both understandable and irrational.

Let’s get over it and start making better decisions.

By |2018-03-07T16:37:21-05:00February 8th, 2016|Research, Updates|2 Comments

“He works on computers”

At a family function, a close relative once introduced me to a friend of his and said, “Spence works on computers, so if you have any computer issues he’s the one to talk to.”

It hit me then that this person who knew me well, didn’t really have any idea what I did for a living. When he said, “He works on computers,” he meant that I literally fix broken computers for a living. This also explained the unusual number of requests I received for helping family members solve computer problems (close/open the program, reboot the computer, no I can’t help you remember your password).

It also reminded me how often people will hear that I’m a psychologist and say, “Are you analyzing me?” or worse, “I have this boyfriend who gets very angry…” I half-smile and say, “I’m not that kind of psychologist,” and they generally look a bit confused and let it go.

If I don’t feel like I’m imposing, I’ll go on to tell the person that I’m an industrial psychologist and I own a company that helps organizations make better hiring decisions. From that they will say, “So, you’re a recruiter?” I say, “Recruiting is small part of what we do, but our real purpose is to help organizations design, validate, and implement hiring systems that may include online assessments, multiple-choice testing, interviews, and hands-on assessments.”

At that point the topic usually turns to the weather or local happenings, but occasionally folks will be intrigued because they’ve never heard of industrial and organizational psychology, or because they have just experienced the devastating effects of a poor hire, or because they are more curious than most. To me, this is where the conversation gets interesting because I can talk about my true reason for doing what I do…

I like to help people make better decisions.

I like to understand . . .

– Why was a specific decision made?

– What variables went into the equation (there’s always an equation)?

– Which factors are known (conscious, System II) versus unknown (unconscious, System I) to the decision maker?

– Can the decision making process be mathematically modeled (e.g., is it a repeated decision such as the decision to hire/not-hire a salesperson)?

– How will the results of the decision be evaluated (i.e., criteria)?

– How can we improve the process to help future decision makers?

– How can we make those improvements user-friendly/“sticky” so they don’t get lost in the archives of unimplemented good ideas?

Wanting to help people make better decisions isn’t an altruistic goal to try to make the world a better place. I’d like to see all homeless people safe and sheltered, but I don’t go far out of my way to make it happen. I’d like all people to be able to see well, but I don’t work as an optometrist.

In other words, I think I would be interested in how people make decisions and how to improve that process even if it didn’t have practical value. Fortunately for me, it does have value. I am intensely curious about why people seem to be hard-wired to make some decisions amazingly well, while systematically making mistakes on seemingly easier decisions (aka, why do smart people do such stupid things!?).

Over a hundred cognitive biases have been shown to have systematic effects on people’s decision making, and many of these affect the judgment of experts when making life changing decisions. A related line of inquiry demonstrates that utilizing mathematical models (i.e., algorithms) to combine variables into an overall score consistently improves upon the decision making of experts.

This has been shown across a wide variety of domains including medical diagnosis, weather forecasting, yield forecasting in agriculture, and predicting the future performance of people who are put into specific jobs. As an organization, SDS tends to focus on the last example, predicting or improving job performance. As a person, I’m interested in everything related to the science and practice of better decision making.

Unfortunately, I really can’t help you with your computer problems.

By |2018-03-07T16:37:21-05:00November 20th, 2015|Updates|0 Comments
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