New-Hire Enthusiasm Makes Liars of Us All

Posted on by Stuart Murray
game-time-by-michael-neel-v3
Game Time, courtesy of Michael Neel.

This interesting blog post by Mike West from One Model describes a data anomaly in “Best Employer” awards.  Many of these awards are based on employee engagement surveys, which are consistent and scientific, but susceptible to a subtle sampling bias.

The issue is that engagement is highest for new employees.  I have seen this phenomenon in other surveys, and I have pondered why this would be true.  It will make sense when you consider your personal experience.  When you are first employed, you have recently chosen to work for that employer, you have just been chosen by the manager, and you get the greatest concentration of training and personal attention.

By contrast, years later you might wish you could work elsewhere, even if you have not made an effort to move.  You may have changed managers, breaking the personal sense of loyalty and trust.  Even under a favorable scenario you will be deemed “fully-performing” …and be neglected as a result.  Negative career events occur over the years, and with greater length of service you will have more opportunity for annoyances, defeats, and betrayals.  You might leave, and lo and behold the cycle starts all over again!

Mike West notes that growing companies hire more staff into brand new positions.  This means a larger fraction of their workforce have less than one year of job tenure, which means a larger fraction of the survey sample will have high engagement.  Yes, it is nice to work for a growing company, but growth itself is not what makes people happy.

If you were the only new hire in a company that is stable in size and has low turnover, you might be just as excited as a peer who joined a growing company.  But the growing company would get a better score.  The article references the constantly-growing Google, often rated the very best employer.  Google tends to lose the top spot when they hire fewer people.

So, how do you game the awards?  Make email addresses for new staff more readily available.  How to correct this anomaly?  Companies conducting surveys should report the data on a stratified basis, adjusting for length of service.  Or, run a multivariate model which isolates employee culture and adjusts for the length-of-service effect.

But hey, it’s math.  It’s all fun and games until someone loses an award.

Mercer’s 2015 Survey, “Inside Employees Minds”

Posted on by Stuart Murray
brains-by-annabellaphoto
“Brains.”  Attributed to Annabellaphoto.

In September 2015, Graham Dodd from the Canadian offices of Mercer released the results of a large survey of employees across Canada.  Amongst their findings, was that plenty of employees with high job satisfaction are still considering leaving their current employer.  It makes sense; those who are driven and talented will be both engaged and also looking for their next adventure.  Why would we presume that employees who strive are those that are easily satisfied?

Millennial Turnover Similar to Prior Generations

Posted on by Stuart Murray
hipster-attributed-to-rodger-evans
Hipster.  Attributed to Rodger Evans.

It is my pleasure to draw your attention to a great paper produced by three students at the University of British Columbia.  Grace Hsu, Geoff Roeder, and Andrew Lee produced a paper for their Statistics 450 course with Dr. Gabriela Cohen Freue which was put in for a student research contest.  The paper, Analysis of Factors Affecting Resignations of University Employees won an honourable mention for the contest.

The paper identifies that “Millennials do not exhibit a practically significant different length of employment compared to other generational groups.”  That is, that although those born after 1975 have a high quit rate right now, they are passing through a high-turnover age group.  Prior generations that passed through the 25-34 year old age group in years past, themselves had high quit rates.

Getting more to the point… “This finding disrupts stereotyped representations of generational factors in the workforce and suggests that younger employees resigning sooner can be better explained as a feature of their age rather than their generational group.” My guess is that age 25-34 is when people figure out their career, partners, and housing, with some things changing a few times before getting stable.

Working with twenty years of data covering 7000 staff who quit, their data model chose “years of service” as the variable that would be explained by other data points.  If we could predict the number of years a new hire would stay, this might be something an employer could improve.  That is, assuming it was not illegal to pre-judge.  Thankfully, their findings suggest we should not pre-judge.

Years of service prior to quitting averaged 1.2 to 1.9 years for 25-to-34 year olds, and 4.3 to 5.5 years amongst 35-to-44 year olds.  There were small differences between generations, but not in a manner that strengthened a stereotype.  For example Generation X quit more quickly when they were younger, but stuck around for longer once they were 35-44.  Baby Boomers were not always big on job loyalty, being the quickest to quit in the 35-to-44 age bracket.

One more thing… men and women do not have a big difference in their length of service.  When sizing-up job candidates for staying power, it is not just unfair and illegal to favour men; it is wrong on the facts.  Keep that in your back pocket next time you help with hiring.

The Mathematics of Love

Posted on by Stuart Murray
coffee-hearts-photo-attributed-to-peter-burka
Photo by Peter Burka.

In this fascinating TED talk by Hannah Fry, the speaker describes three mathematical algorithms that explain love.  One of her findings was that the number of romantic advances someone receives is improved if there are polarized views of whether they are attractive.  That is, you will approach someone if you think that many people other than you will think they are unattractive.  How would we apply this to human resources?  I would point out that the Oakland Athletics baseball team was successful at snapping-up rookies that they knew were great and everyone else had passed over.  Using better metrics, the team found high-performers who seemed wrong, but were factually very good.  In many cases the Oakland A’s got first dibs on diamonds-in-the-rough.  Can you apply this insight to your own workplace?

The Math Behind Basketball

Posted on by Stuart Murray
Kobe Bryant, Photo by Alexandra Walt (public domain).jpg
Kobe Bryant.  Photo by Alexandra Walt.

In this TED talk by Rajiv Maheswaran, the speaker describes the translation of basketball moves into a series of moving dots, looking at games played by professional basketball players.  Using machine learning they were able to identify the difference between the baseline probability that a shot would be successful, and an individual player’s personal odds that they would make a shot. This distinction allows us to tell the difference between those who have more opportunities (or create more opportunities), as opposed to those who perform well based on what is in front of them.

The speaker invites us to consider other applications for the analysis of moving dots.  In my opinion, this means we can think of activity levels, workplace layout, and injury statistics alongside other workplace wellbeing indicators.  It might be that physical movements have a substantial impact on non-physical workplace performance.

Rainer Strack TED Talk

Posted on by Stuart Murray

 

stack-of-rainier-no-rights-claimed
A Stack of Rainier, no relation.

This TED Talk is one of the most compelling explanations of why workforce planning is so important to the business success of major employers.  He takes it global and forecasts into 2030.  With jokes.