While other departments in an organization deal with profits, sales growth, and strategic planning, Human Resources (HR) is responsible for employee well-being, engagement, and staff motivation. Even though it may not be immediately obvious, the management of these duties often requires a great deal of measurement and technical skill. Predictive HR Analytics provides a clear and accessible framework to understanding and learning to work with HR analytics at an advanced level, using examples of particular predictive models, such as diversity analysis, predicting turnover, evaluating interventions, and predicting performance.
When dealing with metrics, management information, and analytics, HR practitioners rarely use any advanced statistical techniques or go beyond describing the characteristics of the workforce. Authors Martin Edwards and Kirsten Edwards explain the business applications of HR predictive models; the ethics and limitations of HR analytics; how to carry out an analysis; predict turnover, performance, recruiting, and selection outcomes; and monitor the impact of interventions.
Table of Contents
Chapter 01 Understanding HR analytics
Chapter 02 HR information systems and data
Chapter 03 Analysis strategies
Chapter 04 Case study 1: Diversity analytics
Chapter 05 Case study 2: Employee attitude surveys – engagement and workforce perceptions
Chapter 06 Case study 3: Predicting employee turnover
Chapter 07 Case study 4: Predicting employee performance
Chapter 08 Case study 5: Recruitment and selection analytics
Chapter 09 Case study 6: Monitoring the impact of interventions
Chapter 10 Business applications
Chapter 11 More advanced HR analytic techniques
Chapter 12 Reflection on HR analytics