Browsing by Author "Waka Olivia"

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Statistical Modelling of Staff Survival Time in Service at Chuka University
(Asian Journal of Probability and Statistics, 2025) Waka Olivia; Dennis K. Muriithi; Mark Okong’o a
Staff attrition is identified as a major challenge affecting the education sector globally and in Kenya. The main objective of this study is to develop a statistical model of staff survival time in service at Chuka University. The study investigated seven covariates (Gender, age group, Marital Status, Terms of employment, staff Category, staff Highest Qualifications and Job group) on the outcome variable which is staff survival time in Service at Chuka University. In this research, survival analysis methods that were used are the Kaplan Meier and log rank test, the Cox-proportional hazard model and the Accelerated Failure Time models (Weibull AFT Model). The cox proportional regression model was employed to study the effect of the covariates on the survival time of the staff at Chuka University. To compare the survival rate of different groups of staff, the study employed the use of Kaplan Meier Estimator. To test if a difference exists in survival time between two or more independent groups, this study applied log-rank test. The results on the Univariate Cox PH Model showed that the covariates, gender, age group, terms of employment, staff category, highest qualification and Job group were statistically significant at 5% level of significance except for the Marital status which was insignificant associated with the survival time of staff at Chuka University. The study was able to predict the probability of survival of staff using the covariates. The findings from the study showed that the male staff have high survival probability compared to their female counterparts: the younger employees between the age of 18-25 stay in service for fewer years compared to those between the age of 26-35 and 36-50. Further results indicated that, The Weibull AFT model give the best model fit with more precise estimates that had small standard errors compared to the Cox Proportional Hazard Model. These findings will be beneficial for the policy makers to review the policies related to staff retention at different Universities in Kenya and more especially to the Chuka University, Human Resource Management to make informed decisions and implement strategies to improve staff retention and more effective recruitment strategies. These policies include Professional Development Programs: Universities may offer ongoing training and development opportunities, such as workshops, certifications, and seminars, to help staff improve their skills and advance their careers. Also Universities can offer competitive salaries, performance- based bonuses, and comprehensive benefits packages (health insurance, retirement plans, tuition waivers) to retain staff.
Statistical modelling of staff survival time in service at Chuka University
(Chuka University, 2024) Waka Olivia
Statistical Modeling of Staff Survival Time in Service at Chuka University focuses on addressing the rate of staff attrition in public universities in Kenya, particularly in Chuka University. Staff attrition is identified as a major challenge affecting the education sector globally and in Kenya. Several studies have estimated the staff attrition using other statistical methods, however fewer studies have used the method of survival analysis. Understanding the survival time is crucial for the university's planning and management, particularly for future recruitment needs. By determining how long employees typically remain in their positions, the university can better anticipate when vacancies may occur and prepare accordingly. The objective of this study was to fit a statistical model of staff survival time in service at Chuka University. The data used in this study was extracted from the Chuka University Human Resource. The data comprised of all staff who had been employed at Chuka University from the period of 2012 to 2023 including those who had exited the service. In this study, methods of survival analysis that were used are the Kaplan Meier and log rank test, the Cox-proportional hazard model and the Accelerated Failure Time models (Weibull AFT Model). The cox proportional regression model was employed to study the effect of the covariates (age, gender, marital status, salary, level of education, level of experience, terms of service, motivating factors and job groups) on the survival time of the staff at Chuka University. To compare the survival rate of different groups of staff, the study employed the use of Kaplan Meier estimator that shows the median curves of these groups. To test if a difference exists in survival time between two or more independent groups, this study applied log-rank test. The R software was utilized to conduct all the data analysis. The results on the Univariate Cox PH Model showed that the covariates, gender, age group, terms of employment, staff category, highest qualification and Job group were statistically significant at 5% level of significance except for the Marital status which was insignificant associated with the survival time of staff at Chuka University. The study was able to predict the probability of survival of staff using the covariates. The findings from the study showed that the male staff have high survival probability compared to their female counterparts: the younger employees between the age of 18-25 stay in service for fewer years compared to those between the age of 26-35 and 36-50. Further results indicated that, The Weibull AFT model give the best model fit with more precise estimates that had small standard errors compared to the Cox Proportional Hazard Model. Chuka University’s Human Resource Management will use these findings to make informed decisions and implement strategies to improve staff retention and more effective recruitment strategies. Therefore, the University management should strengthen measures that can improve the employment security of all categories of employees with lower survival rates.