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Analyzing Employee Turnover – Predictive Methods

Source | LinkedIn : By Richard Rosenow

Why study turnover?

At first glance, ‘intent to leave’ seems like it should be pretty good predictor of turnover. If a coworker told me that they were going to quit, I feel like I’d have a pretty good sense of how likely they were to leave. However, many researchers havedeveloped constructs to measure this intention and the results are surprising.

For example, there was a meta-analytic study (i.e., study of studies) in 2000 by Rodger Griffeth and Peter Hom on turnover that found the construct ‘intent to leave’ had a shared variance with actually leaving of 12% across all studies (explains roughly 12% of why people leave). That’s pretty good for a study on human behavior, but it does leave a reader wondering what is going on. If an employee’s own stated intention to leave the organization is only right 12% of the time, we know we have lot more to understand about why people quit before we can start predicting it.

I covered some of the descriptive methods of analyzing turnover in my last post, but those measures are not enough to learn what else could be causing turnover. There’s a quote attributed to Yogi Berra that goes “It’s difficult to make predictions, especially about the future” and that holds doubly true for predictions about people. However, there are a number of sophisticated methods that have been developed to get us closer to predicting turnover.

Advanced Methods in Turnover Analysis

I call these advanced methods in turnover because of the statistical background needed to apply them. As much as I wish I could, I will not be able to teach you how to perform these methods by the end of this post. My goal here is to make sure these methods are on your radar and to point you to resources where you can learn more about them. I want this to be a starter resource for anyone looking to predict turnover.

In the sections below, I’m going to introduce logistic regression and survival analysis and then speak briefly to tree methods (decision trees and random forests). Lastly, I’ll overview IBM’s Watson as a tool for analyzing turnover and point you in the right direction of some predictive analytics vendors automating these methods for HR applications.

For the non-technical readers these measures start to apply some advanced statistical methods and statistical language. Another goal of mine since writing the HR analytics starter kits (pt 1 and pt 2) has been to keep these posts entry level where I can to ease anyone into understanding these topics and how to learn more.

Read On…

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