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Predictive HR Analytics: Mastering the HR Metric 3rd Revised edition [Hardback]

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  • Formāts: Hardback, 528 pages, height x width: 240x170 mm
  • Izdošanas datums: 03-Jun-2024
  • Izdevniecība: Kogan Page Ltd
  • ISBN-10: 1398615900
  • ISBN-13: 9781398615908
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  • Hardback
  • Cena: 167,86 €
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Predictive HR Analytics: Mastering the HR Metric 3rd Revised editionPalielināt
  • Formāts: Hardback, 528 pages, height x width: 240x170 mm
  • Izdošanas datums: 03-Jun-2024
  • Izdevniecība: Kogan Page Ltd
  • ISBN-10: 1398615900
  • ISBN-13: 9781398615908
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781398615908.html
Keywords:
Confidently use predictive analytic and statistical techniques to identify key relationships and trends in HR-related data to aid strategic organizational decision-making.

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.

Recenzijas

"Developing a clear understanding how talent creates customer and economic value and translating this knowledge into action is a key driver of success in any organization. But most firms lack both the data that they need and the requisite analytical capabilities required to drive real change. In Predictive HR Analytics, Edwards and Edwards demonstrate how to make this happen, using evidenced based examples data from a wide variety of organizational settings. This book should be studied carefully by any serious workforce analyst. A tremendous resource." * Mark Huselid, Ph.D, Distinguished Professor of Workforce Analytics and Chair, DAmore-McKim School of Business, Northeastern University., author of The HR Scorecard, The Workforce Scorecard, and The Differentiated Workforce. * "Predictive HR Analytics is a comprehensive and detailed guide for any professional interested in this exciting new field. The book will help you understand what data to analyze, how to interpret and analyze the data and how different types of models work. Highly recommended for people analytics specialists!" * Josh Bersin, Global Industry Analyst and Founder, Bersin by Deloitte *

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
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.