In this video, Chris at Kunze Analytics references thirty (30) years of research to understand the difference between status quo Assessment usefulness (validity) and Machine Learning usefulness. Next, Chris demonstrates how Machine Learning also reduces the risk of using Assessments in error.
How it Works
Posts about the science behind assessments
Today, Chris at Kunze Analytics simply asks some questions every company should know.
Chris at Kunze Analytics demonstrates how AI and Machine Learning are allowing custom performance modeling work with Selection Assessments to be done at smaller and smaller companies.
Chris at Kunze Analytics makes an analogy. When Michael Lewis wrote the book "Money Ball" fifteen years ago, he gave the world a successful example of applying predictive analytics to an iconic, sports industry. Today, these same algorithms are applied to quantifying the value of Pre-Hire Assessments. Are we hitting the strike zone?
Chris at Kunze Analytics discusses why our deliverables strive to be elegant. The threefold meaning entails scientific precision, neatness, and simplicity.
Chris at Kunze Analytics answers a question about the value of hiring using Assessments. He estimates the average, annual value per hire for three roles: Engineer, Long Haul Truck Driver, and Registered Nurse.
A disgruntled employee can be contagious, killing the enthusiasm of others.
A bad hire’s poor productivity lowers the bar for the rest of the team.
A bad apple is detrimental to the company culture as other’s pride and esprit de corps begin to erode.
When organizations focus on the growth and success of their people, organizational growth naturally follows. The July 10th, 2018 edition of HR TECH OUTLOOK contains an article about how Assessment Publisher TALEXES, LLC recently increased revenue by millions of dollars at an insurance claims company.
A question I frequently get asked by both new and experienced Partners in the assessment industry is why the number of 30 incumbents is recommended as a minimum sufficient sample size for creating custom concurrent performance models.
One of the purposes of the study was to provide the scientific community and the world with a more accurate understanding of what small, medium, and large effect sizes (correlations symbolized by the letter “r”) are in applied Psychology.