Predictive HR Analytics

Mastering the HR Metric
3. Auflage 2024
ISBN: 978-1-3986-1590-8
Verlag: Kogan Page

Buch, Englisch, 528 Seiten, Format (B × H): 175 mm x 250 mm, Gewicht: 1080 g

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 biased algorithms and data protection, supported by online resources consisting of R and Excel data sets.

Autoren/Hrsg.

Edwards, Martin

Edwards, Kirsten

Jang, Daisung

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Personalwesen, Human Resource Management
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management

Inhaltsverzeichnis

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

Über Autor(innen)

Edwards, Martin
Martin R Edwards is Reader in HRM and Organizational Psychology at King's Business School, King's College London. He has taught statistics to undergraduate, postgraduate and PhD students for over 15 years and also teaches HR analytics to MSc students. As a consultant, he has delivered HR analytics workshops to FTSE-100 companies.

Edwards, Martin
Martin R Edwards is Reader in HRM and Organizational Psychology at King's Business School, King's College London. He has taught statistics to undergraduate, postgraduate and PhD students for over 15 years and also teaches HR analytics to MSc students. As a consultant, he has delivered HR analytics workshops to FTSE-100 companies.

Edwards, Kirsten
Kirsten Edwards is HR Lead for Advanced Analytics and Data Science at Rio Tinto and has over 20 years' broad international experience in analytics, HR and management consulting. She is a visiting lecturer at Kent Business School and at King's Business School.

Jang, Daisung
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.

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.