MGT6480
Predictive Human Resource Analytics
In this course, students will learn how to use predictive human resources (HR) analytic techniques to improve upon an organization’s ability to find, screen, recruit, train, on understanding engage, and retrain new employees. The class will start by defining HR analytics with special emphasis on understanding the need for utilizing HR analytic techniques, information systems, data, and analysis strategies. Additional topics include applied statistical modeling in the areas of diversity analytics, employee attitude surveys, predicting employee turnover, predicting employee performance, selection analytics, and HR policy evaluation. Upon completion, students will be able to determine the appropriate quantitative method given the different types of HR data available to a firm with the ultimate goal of improving upon HR operations. Finally, students will learn how to utilize a statistical processing software package in a lab-like setting to construct and interpret predictive HR analytics.
UPON COMPLETION OF THE COURSE, THE STUDENT WILL BE COMPETENT IN:
- Managing HR information sources including HR databases, employee attitude survey data, sales performance data, and HR operational performance data.
- Describing the HR analytics process from data collection to project completion.
- Examining procedures centered on the collection, management, analysis, and result interpretation of HR data.
- Summarizing HR data using descriptive statistics and graphical techniques.
- Examining HR data for missing information, outliers, normality, homoskedasticity, and linearity.
- Specifying HR models in consideration of data characteristics, functional forms, omitted variables, irrelevant variables, and measurement error.
- Developing an awareness of analysis software options including Stata, SPSS, Minitab, SAS, R, Python, and other statistical processing software packages.
- Predicting employee performance and loyalty using binary, stepwise, and multiple linear regression analysis.
- Modeling employee persistence using discriminant analysis, multiple regression analysis, and logistic regression.
- Monitoring the impact of HR interventions, programs, policies, and procedures.
- Conducting diversity analytics to evaluate firm performance in the areas of diversity, equity, and inclusion.
- Using inferential statistical methods to assess differences in and model employee engagement levels.
- Reflecting on HR analytics usage, ethics, and limitations.