Unveiling Employee Turnover Insights Through Survival Analysis
In today’s fast-paced business world, employee turnover is a critical issue that organizations strive to manage. With advanced analytics, understanding and predicting employee attrition has become more sophisticated. In this article, I will explore how Survival Analysis, specifically the Kaplan-Meier estimator and the Cox proportional hazards model, can offer invaluable insights into turnover trends and risk factors.
Survival Analysis is a statistical method designed to analyze the time until a specific event occurs, such as an employee leaving a company. This technique helps me understand the duration an employee stays with the organization and can reveal patterns and risk factors associated with turnover.
The Kaplan-Meier estimator is a non-parametric statistic used in Survival Analysis to estimate the survival function. It provides a visual representation of the probability of employees remaining with the company over time, typically illustrated as a survival curve. This curve helps in understanding the proportion of employees who stay with the organization at various time points.
The Cox proportional hazards model is a regression technique used in Survival Analysis to explore the relationship between the time until an event (like employee departure) and one or more predictor variables. This model assumes that the effect of these predictors is multiplicative and proportional over time, allowing me to identify which factors significantly influence employee turnover.
