This is the essential guide for HR practitioners who want to gain the statistical and analytical knowledge to fully harness the potential of HR metrics and organizational people-related data. The ability to use and analyse data has become an invaluable skill for HR professionals to not only identify trends and patterns, but also make well-informed business decisions. The third edition of Predictive HR Analytics provides a clear, accessible framework for understanding people data, working with people analytics and advanced statistical techniques. Readers will be taken step-by-step through…mehr
This is the essential guide for HR practitioners who want to gain the statistical and analytical knowledge to fully harness the potential of HR metrics and organizational people-related data. The ability to use and analyse data has become an invaluable skill for HR professionals to not only identify trends and patterns, but also make well-informed business decisions. The third edition of Predictive HR Analytics provides a clear, accessible framework for understanding people data, working with people analytics and advanced statistical techniques. Readers will be taken step-by-step through worked examples, showing them how to carry out analyses and interpret HR data in areas such as employee engagement, performance and turnover. Learn how to make effective business decision with this updated edition that includes the latest materials on predicting attrition with machine learning, biased algorithms and data protection, supported by online resources consisting of R and Excel data sets.
Martin R Edwards is a Professor in Management at UQ Business School, University Queensland, Australia and has been teaching HR and Statistics for over 20 years. Kirsten Edwards is the Global Head of People Data and Analytics at Rio Tinto. With over two decades of international experience in Analytics, HR and Management Consulting, she has supported various organisations across multiple sectors, empowering them to utilise people data and analytics more effectively. Daisung Jang Daisung Jang is an Assistant Professor at Melbourne Business School. He has over a decade of experience in data visualization and analysis using R. He has conducted workshops for PhD students and academic staff on statistical analyses using R.
Inhaltsangabe
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 Scenario modelling and business cases; Chapter 11: More advanced HR analytic techniques; Chapter 12: Reflection on HR analytics Usage, ethics and limitations; Chapter 13: Appendix
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 Scenario modelling and business cases; Chapter 11: More advanced HR analytic techniques; Chapter 12: Reflection on HR analytics Usage, ethics and limitations; Chapter 13: Appendix
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