A survival analysis approach to employee turnover: its application and advantages

Current investigations of employee tumover have focused on the ability of researchers to predict tumover through measuring variables such as employee and job satisfaction, organizational commitment, and job involvement. The analysis technique typically used in these studies, logistic regression, does not incorporate time as a variable of interest and also may not be appropriate for use in studies where the dependent variable is binary. Survival analysis, on the other hand, allows time to be included as a variable of interest and is specifically designed for use with a binary dependent variable such as employee tumover.

Using employee information from three separate corporations, this study demonstrates the benefits of applying survival analysis techniques to tumover data and examines the differences in the type of information gained from survival analysis versus logistic regression. Results showed that, unlike logistic regression, survival analysis is capable of indicating differences between active employees, early leavers(those with a longevity of zero to four years), middle leavers (those with a longevity of four to eight years), and later leavers (those with a longevity of more than eight years) based on predictor variables collected from employee records from each company.

Corporation One logistic regression results indicated that salary, payrate, gender, age, absences, and average number of previous jobs were significant predictors of whether hourly employees remained with the company or left. In contrast, the discriminant function analysis based on the survival groups for that sample showed that active employees or late leavers were distinguished from early and middle leavers on the variables of payrate, salary, and absences for canonical one. Further, canonical two and the corresponding class means showed that middle and late leavers were separated from early leavers and active employees based on absences, number of partial days worked, and employee title. Results for corporations two and three showed a similar pattem of results. All results showed that logistic regression only indicates the relationship of predictor variables to the occurrence of tumover, while survival analysis produces information on both the timing and occurrence of tumover, as well as the differences between active employees, early leavers, and late leavers on selected predictor variables.