Using R in HR Analytics

Understand how to use R and R Studio to analyse HR data and deliver insights that drive workforce and business performance.

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.

Covers data security and the ethical considerations of using HR analytics

Auteur
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.

Contenu

• • 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;