Confidently analyse your organization's HR data using R and R Studio to gain insights that improve people strategy and business decision-making.

Effective use of HR data has the power to transform a business. However, this is only possible if HR practitioners have the knowledge, skills and confidence to analyse the data and to draw evidence-based insights from it. This book is the practical guide that HR professionals need. Through worked examples, this book shows readers how to carry out and interpret analyses of HR data in areas such as recruitment, performance, employee engagement and diversity. People professionals are then shown how to use the results to develop robust people strategies and to support more effective evidence-based decision-making.

Using R in HR Analytics provides a thorough grounding in the differences between descriptive reporting and predictive analytics as well as the methods and measures used to identify the validity of results. There is also expert guidance on the role of artificial intelligence, machine learning and large language modelling on HR analytics. Written for HR professionals at any level, there is essential coverage of data privacy and the ethical considerations of using people data. Online resources include sample datasets to allow readers to practice analysing HR data.

About the Authors

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.

Kirsten Edwards is Global Head of People Data and Insights at Rio Tinto. She has over 20 years' broad international experience in analytics, HR and management consulting. She is based in Queensland.

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.

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