Exploring The Determinants of Married Employee Attrition: A Logistic Regression

This study investigates the determinants of employee attrition among married employees using logistic regression analysis. Employee attrition poses significant organizational productivity and growth challenges, particularly among married individuals who balance professional and personal responsibilities. The research aims to identify the determinants influencing attrition, including demographic, organizational, and job satisfaction elements, and to predict the likelihood of employee attrition based on these predictors. A dataset from the Kaggle website was used, which involved 366 married employees. The study applies logistic regression to evaluate the significance of variables such as gender, distance from home, work-life balance, and job satisfaction. Logistic regression results indicate that gender, poor work-life balance, and moderate job satisfaction contribute significantly to attrition, while factors such as age and income show minimal influence. These results were supported by p-values under the standard threshold of 0.05, highlighting their significance in affecting employees’ likelihood of departing from the organization. The predictive efficiency of the model was checked using measures such as Cox and Snell’s R2 and Nagelkerke’s R2, which showed the model was good at explaining a lot of the reasons for leaving. Overall, significant predictors like gender, distance from home, and work-life balance play a crucial role in attrition, while trivial factors such as age and monthly income have little impact. These findings emphasize prioritizing relevant variables to better understand and model attrition dynamics.