Presenters

Graham NashFollow

Disciplines

Analysis | Applied Statistics | Biostatistics | Business Analytics | Categorical Data Analysis | Data Science | Health and Physical Education | Multivariate Analysis | Public Health Education and Promotion | Statistical Methodology | Statistical Models

Abstract (300 words maximum)

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a dataset generated by IBM data scientists exploring employee attrition within their company, assessing variables like overall job satisfaction, performance rating, education, monthly income, travel distance from home to work, and work-life balance. The research question for this project is whether there is a significant relationship between the two primary variables of interest: job satisfaction and performance rating. Job satisfaction will be the independent variable and performance rating the dependent variable. The relevant hypothesis for this project is that there is a positive relationship, meaning that an increase in an employee’s performance rating directly leads to an increase in job satisfaction. This project will utilize four supplementary variables to reinforce the results of this study. The data was collected from Kaggle, a well-renowned data collection and machine learning website, with no missing variables and few errors with variable categorization. This project will also conduct an exploratory analysis by assessing the descriptive statistics for each of the variables, interpreting the graphs for each variable, and discovering potential correlations between the variables. After that, a discussion of the results of the analysis will determine whether the initial research question was answered or if there is no relationship between the variables of interest.

Academic department under which the project should be listed

WCHHS - Health Promotion and Physical Education

Primary Investigator (PI) Name

Kevin Gittner

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Employee Attrition: Analyzing Factors Influencing Job Satisfaction of IBM Data Scientists

Employee attrition is a relevant issue that every business employer must consider when gauging the effectiveness of their employees. Whether or not an employee chooses to leave their job can come from a multitude of factors. As a result, employers need to develop methods in which they can measure attrition by calculating the several qualities of their employees. Factors like their age, years with the company, which department they work in, their level of education, their job role, and even their marital status are all considered by employers to assist in predicting employee attrition. This project will be analyzing a dataset generated by IBM data scientists exploring employee attrition within their company, assessing variables like overall job satisfaction, performance rating, education, monthly income, travel distance from home to work, and work-life balance. The research question for this project is whether there is a significant relationship between the two primary variables of interest: job satisfaction and performance rating. Job satisfaction will be the independent variable and performance rating the dependent variable. The relevant hypothesis for this project is that there is a positive relationship, meaning that an increase in an employee’s performance rating directly leads to an increase in job satisfaction. This project will utilize four supplementary variables to reinforce the results of this study. The data was collected from Kaggle, a well-renowned data collection and machine learning website, with no missing variables and few errors with variable categorization. This project will also conduct an exploratory analysis by assessing the descriptive statistics for each of the variables, interpreting the graphs for each variable, and discovering potential correlations between the variables. After that, a discussion of the results of the analysis will determine whether the initial research question was answered or if there is no relationship between the variables of interest.