Checkout

Total items: 0

Subtotal excl delivery & tax: £
Menu
Search

Predictive HR Analytics

Mastering the HR Metric

Confidently use predictive analytic and statistical techniques to identify key relationships and trends in HR-related data to aid strategic organizational decision-making.
Temporarily Unavailable
EAN: 9781398615656
Edition: 3
Published:
Format: 240x170
528 pages

About the book

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.

About the authors

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.