HR metrics and organizational people-related data are an invaluable source of information from which to identify key trends and patterns in order to make effective business decisions. HR practitioners often, however, lack the statistical and analytical know-how to fully harness their potential. Predictive HR Analytics provides a clear, accessible framework with which to understand and work with people analytics and advanced statistical techniques. Step-by-step and by using worked examples, this book shows readers how to carry out and interpret analyses of various forms of HR data, such as employee engagement, performance and turnover, using the statistical packages SPSS (with R syntax provided), and, importantly, how to use the results to enable practitioners to develop effective evidence-based HR strategies.
This second edition of Predictive HR Analytics has been updated to include new material on machine learning, biased algorithms, data protection and GDPR considerations, a new example using Kaplan Meier Survival analyses for tenure/turnover modelling and updated screenshots and examples with SPSS version 25. It is supported by a new appendix showing main R coding for the focal analyses approaches in the book, and online resources consisting of SPSS and Excel data sets and R syntax with worked case study examples.
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!