MODELING EMPLOYEE RESIGNATION USING A SEMIPARAMETRIC APPROACH COX PROPORTIONAL HAZARD

Survival analysis is a research method that studies the duration individuals or experimental units endure against events like death, disease, recovery, or other experiences. This study employs a semi-parametric survival analysis model using the Cox proportional hazards regression method to identify factors such as age, gender, marital status, and education influencing how long employees stay with a company before resigning. The aim is to describe and interpret significant factors affecting employee resignation using the Cox Regression method. The results indicate that age significantly influences employee tenure. The average tenure is eight years. The probability of an employee still working at age 32 for up to eight years is 0.0057, while the likelihood for an employee who has worked more than eight years at age 32 is 0.9943. The study uses secondary data on the tenure of 521 employees, analyzed with the Cox proportional hazards regression method. The data, however, has limitations due to type III censoring, where some subjects leave observation, resulting in incomplete data. The study concludes that age significantly impacts employee tenure. Younger employees tend to explore career opportunities, while older employees seek stability, pension benefits, and a comfortable work environment.

Authors

Institutions

Publication Details

Journal
BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN
Published
2024-10-11
DOI
https://doi.org/10.30598/barekengvol18iss4pp2471-2478
Citations
1
Primary Topic
Management and Optimization Techniques
Type
article
Field-Weighted Citation Impact
0.55
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

MODELING EMPLOYEE RESIGNATION USING A SEMIPARAMETRIC APPROACH COX PROPORTIONAL HAZARD

Elly Ana, Ardi Kurniawan, Ni Wayan Widya Septia Sari
1 citations
BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPAN
Management and Optimization Techniques
0.55
article

MODELING EMPLOYEE RESIGNATION USING A SEMIPARAMETRIC APPROACH COX PROPORTIONAL HAZARD

Elly Ana, Ardi Kurniawan, Ni Wayan Widya Septia Sari
article en
1 citations

Abstract

Survival analysis is a research method that studies the duration individuals or experimental units endure against events like death, disease, recovery, or other experiences. This study employs a semi-parametric survival analysis model using the Cox proportional hazards regression method to identify factors such as age, gender, marital status, and education influencing how long employees stay with a company before resigning. The aim is to describe and interpret significant factors affecting employee resignation using the Cox Regression method. The results indicate that age significantly influences employee tenure. The average tenure is eight years. The probability of an employee still working at age 32 for up to eight years is 0.0057, while the likelihood for an employee who has worked more than eight years at age 32 is 0.9943. The study uses secondary data on the tenure of 521 employees, analyzed with the Cox proportional hazards regression method. The data, however, has limitations due to type III censoring, where some subjects leave observation, resulting in incomplete data. The study concludes that age significantly impacts employee tenure. Younger employees tend to explore career opportunities, while older employees seek stability, pension benefits, and a comfortable work environment.

BAREKENG JURNAL ILMU MATEMATIKA DAN TERAPANVol. 18(4)
Universitas Airlangga (ID)
Gender equality
Openalex Percentile: Top 21%
Management and Optimization Techniques
0.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.