Factors of Employee Attrition: A Logistic Regression Approach

Research Article
Open access

Factors of Employee Attrition: A Logistic Regression Approach

Bingzhe Chen 1*
  • 1 Tsinghua University    
  • *corresponding author chenbz20@mails.tsinghua.edu.cn
Published on 13 September 2023 | https://doi.org/10.54254/2754-1169/20/20230198
AEMPS Vol.20
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-915371-83-6
ISBN (Online): 978-1-915371-84-3

Abstract

The problem of employee turnover has gradually become a common problem faced by companies around the world. Because it has several major negative impacts on the company's cultural and economic levels, it's urgent to solve this problem. In this paper, IBM's employee data is visualized to get the factors that affect employee attrition. At the same time, a predictive model based on logistic regression analysis was constructed to provide quantitative data for the impact of various factors on employee turnover. After multiple model evaluations, the goodness of fit and accuracy of the forecasting model were judged to be good. According to the data visualization and prediction model, the company's employee turnover factors were summarized, including both human resource factors and contextual factors such as age, salary, marital status. These influencing factors and the analysis of the relationship between them can be used as the theoretical basis for the company's future employee policies, providing direction and solutions for its work on reducing employee attrition.

Keywords:

employee attrition, logistic regression model, data visualization

Chen,B. (2023). Factors of Employee Attrition: A Logistic Regression Approach. Advances in Economics, Management and Political Sciences,20,214-225.
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References

[1]. Qutub A, Al-Mehmadi A, Al-Hssan M, Aljohani R, Alghamdi HS 2021 Prediction of employee attrition using machine learning and ensemble methods Int. J. Mach. Learn. Comput. 11(2) 110-4.

[2]. Here’s What Your Turnover and Retention Rates Should Look Like. 15 June 2021. Available online: https://www.ceridian.com/blog/turnover-and-retention-rates-benchmark (accessed on 2 Feb 2023).

[3]. Barpanda S, Athira S 2022 Cause of Attrition in an Information Technology-Enabled Services Company: A Triangulation Approach International Journal of Human Capital and Information Technology Professionals (IJHCITP) 13(1) 1-22.

[4]. Lee Liu J 2014 Main causes of voluntary employee turnover a study of factors and their relationship with expectations and preferences PhD thesis (Chile: Univ. Chile).

[5]. Sridhar GV, Venugopal S, Vetrivel S 2018 Employee Attrition and Employee Retention-Challenges & Suggestions Conf. on Economic Transformation with Inclusive Growth-2018 (Chennai) vol 1 p 16.

[6]. Jain PK, Jain M, Pamula R 2020 Explaining and predicting employees’ attrition: a machine learning approach SN Appl. Sci. 2 1-11.

[7]. Raza A, Munir K, Almutairi M, Younas F, Fareed MM 2022 Predicting Employee Attrition Using Machine Learning Approaches. Appl. Sci. 12(13) 6424.

[8]. Kaggle. Employee-Attrition-Rate. Available online: https://www.kaggle.com/datasets/prachi13/employeeattritionrate

[9]. Zhang SQ, Lv JN, Jiang Z, Zhang L 2009 Study of the Correlation Coefficients in Mathematical Statistics Mathematics in Practice and Theory 39(19) 102-7.

[10]. Wang QQ, Yu SC, Qi X, Hu YH, Zheng WJ, Shi JX, Yao HY 2019 Overview of logistic regression model analysis and application Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] 53(9) 955-60.

[11]. Yazici B, Alpu Ö, Yang Y 2007 Comparison of goodness-of-fit measures in probit regression model Communications in Statistics—Simulation and Computation®. 36(5) 1061-73.

Cite this article

Chen,B. (2023). Factors of Employee Attrition: A Logistic Regression Approach. Advances in Economics, Management and Political Sciences,20,214-225.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2023 International Conference on Management Research and Economic Development

ISBN：978-1-915371-83-6(Print) / 978-1-915371-84-3(Online)
Editor：Canh Thien Dang, Javier Cifuentes-Faura
Conference website: https://2023.icmred.org/
Conference date: 28 April 2023
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.20
ISSN：2754-1169(Print) / 2754-1177(Online)

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References

[1]. Qutub A, Al-Mehmadi A, Al-Hssan M, Aljohani R, Alghamdi HS 2021 Prediction of employee attrition using machine learning and ensemble methods Int. J. Mach. Learn. Comput. 11(2) 110-4.

[2]. Here’s What Your Turnover and Retention Rates Should Look Like. 15 June 2021. Available online: https://www.ceridian.com/blog/turnover-and-retention-rates-benchmark (accessed on 2 Feb 2023).

[3]. Barpanda S, Athira S 2022 Cause of Attrition in an Information Technology-Enabled Services Company: A Triangulation Approach International Journal of Human Capital and Information Technology Professionals (IJHCITP) 13(1) 1-22.

[4]. Lee Liu J 2014 Main causes of voluntary employee turnover a study of factors and their relationship with expectations and preferences PhD thesis (Chile: Univ. Chile).

[5]. Sridhar GV, Venugopal S, Vetrivel S 2018 Employee Attrition and Employee Retention-Challenges & Suggestions Conf. on Economic Transformation with Inclusive Growth-2018 (Chennai) vol 1 p 16.

[6]. Jain PK, Jain M, Pamula R 2020 Explaining and predicting employees’ attrition: a machine learning approach SN Appl. Sci. 2 1-11.

[7]. Raza A, Munir K, Almutairi M, Younas F, Fareed MM 2022 Predicting Employee Attrition Using Machine Learning Approaches. Appl. Sci. 12(13) 6424.

[8]. Kaggle. Employee-Attrition-Rate. Available online: https://www.kaggle.com/datasets/prachi13/employeeattritionrate

[9]. Zhang SQ, Lv JN, Jiang Z, Zhang L 2009 Study of the Correlation Coefficients in Mathematical Statistics Mathematics in Practice and Theory 39(19) 102-7.

[10]. Wang QQ, Yu SC, Qi X, Hu YH, Zheng WJ, Shi JX, Yao HY 2019 Overview of logistic regression model analysis and application Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] 53(9) 955-60.

[11]. Yazici B, Alpu Ö, Yang Y 2007 Comparison of goodness-of-fit measures in probit regression model Communications in Statistics—Simulation and Computation®. 36(5) 1061-73.

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