XQ: How it could determine your next job
A recent cover story in Time Magazine highlights the latest trend in employment — optimized hiring, in which often laborious personality and preference quizzes are given to potential hires, with the stated goals of offering employers deeper evaluations than traditional criteria: a resume, performance reviews, or self-defined experience.
Time asserts that employers rely on the data to determine people’s XQ, a nebulous, and seemingly indefinable trait, which differs from IQ (intelligence quotient) and EQ (emotional intelligence) in that it identifies potential for success in the position based on preferences and behavior. Advocates of optimized data feel that these tests are accurate indicators of people’s more fundamental nature: their motivators, strengths or weaknesses, and whether they will be successful in the role sought.
Behind this push for more information is corporations’ fascination with two business trends. According to Time, “the new rage for personality testing is being driven by a collision of two of the business world’s hottest trends. The first is Big Data … the second is analytics, a broad term that describes looking for patterns in data that can be used to optimize performance.”
The types of prompts which employees have to answer to reveal their XQ range from the highly personal to the offbeat and creepy. Those sampled by the article include:
I never get angry.
When I was young I felt like leaving home.
Opera music annoys me.
Would you rather read or watch TV?
I am shy.
I am impulsive.
While the goal of “optimizing performance” and finding the right hires for positions are reasonable ones for any employer, this use of XQ data seems highly flawed, because it depends on personal preference variables that are not truly quantifiable. The underlying inquiry in each question is completely malleable. For example, feeling shy or impulsive is often situational, making reliable conclusions about work aptitude unlikely.
The XQ testing also creates spurious correlations. Because someone prefers to watch TV to reading a book does not mean that they are less thoughtful, erudite or intellectual. The TV watcher may be watching NOVA and the reader might be enjoying a summer trashy beach read.
Or perhaps the romance novel lover needed a break from an all-consuming job, and the TV watcher was educating himself about foreign policy. When you consider the exponential possibilities in response to that question alone, data analysis on preferences seems highly undependable.
At their root, many of these questions have internal cultural and behavioral biases that seem to create screening tools that are contrary to corporate diversity goals (negatively affecting race, gender, sexuality and even socio-economic diversity efforts). Factoring in this partiality, XQ data reliance does not just seem defective but repugnant.
Many of the employers utilizing this type of programming claim that it is reliable because they are comparing prospective employees answers to their most productive present employees’ results. While that may in some instances create some uniformity of skill sets, it reduces workers to an Industrial Age mentality — increasing production by treating humans almost as machines, demanding that they replicate each other’s work.
There is no doubt that Big Data in the age of the internet and all its attendant available information mining is here to stay, but when it comes to achieving the most productive, effective and creative work force, XQ testing may not be the most ethical, responsible or successful way to get there because it seems to mistake preferences for competence.
Lisa Perez Tighe has been an attorney, writer and a professor. She attended the University of Notre Dame and New York University School of Law. A native of the Bronx, Lisa currently resides outside of Boston with her husband and four children.