Title Predictive HR Analytics, 2/e
Subtitle Mastering the HR Metric
Author Martin R. Edwards, Kirsten Edwards
ISBN 9780749484446
List price GBP 34.99
Price outside India Available on Request
Original price
Binding Paperback
No of pages 536
Book size 171 x 241 mm
Publishing year 2019
Original publisher Kogan Page Limited
Published in India by .
Exclusive distributors Viva Books Private Limited
Sales territory India, Sri Lanka, Bangladesh, Pakistan, Nepal, .
Status New Arrival
About the book


 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

“Edwards and Edwards provide an essential roadmap to professionalize how HR analytics is applied in organizations. This book should be required reading for data scientists and HR generalists who need to learn the ropes of applying statistics and social scientific analysis to HR data.”

Alec Levenson, Senior Research Scientist at the Center for Effective Organizations, University of Southern California and author of Strategic Analytics and Employee Surveys That Work

Predictive HR Analytics is a practical guide for anyone working or interested in the burgeoning field of people analytics. What sets it apart from other books on the topic is that the authors demonstrate how to actually perform the analyses with a variety of HR-related data as well as providing insights on the statistical models that support them. Every people analytics team should own a copy of this book.”

David Green, Executive Director at Insight222, Global speaker, writer and consultant on people analytics

“An essential read for HR leaders and practitioners wishing to deepen their knowledge and capability in predictive analytics. The authors balance the need to present technical concepts and theory with clear and simple solutions to apply within the workplace. This book is full of wisdom for anyone wishing to embrace analytics as a fundamental HR capability.”

Caroline van der Feltz, HR Director, Danske Bank UK

“Finally, a book that students and HR professionals can use to learn how to do HR analytics! Not only do Edwards & Edwards show how to conduct analyses with clear, step by step examples, they show the critical insights that HR analytics can provide.”

Fritz Drasgow, Dean of the School of Labor and Employment Relations and Professor of Psychology, University of Illinois at Urbana-Champaign

“Detailed, precise and clearly written, this book isn’t just about statistics; it’s about the future of HR. Essential reading for everyone in HR.”

Professor Binna Kandola OBE, Business Psychologist, Senior Partner and Co-founder, Pearn Kandola

“Anyone reading and learning from this book and applying its lessons will be a more effective HR professional. It should certainly become compulsory reading for all HRM students. The authors are to be congratulated on providing a service to the HR community by writing this much needed and timely book.”

David Guest, Professor of Organizational Psychology and Human Resource Management, King’s College, London

“At a time when the HR function is facing huge changes and undergoing massive challenges, it is good to see the second edition of this noteworthy book. Whilst many in HR are still coping with reporting on metrics, Edwards and Edwards have updated and expanded their first edition by embracing the latest developments and staying ahead of the curve. This will make a welcome contribution to the knowledge base of academics and practitioners alike.”

David Simmonds, Chairman, Hr Analytics Ltd.

“This book is essential reading for anyone interested in how to actually apply statistical methods to HR data. The book fills an important gap between those that are too technical, making them impenetrable to all but the most experienced statisticians, and books that focus on organizational capability aspect of HR analytics. If you’re doing HR analytics you need a copy of this book.”

Dr. Nigel Guenole, Exectuive Consultant, IBM, Co-author of The Power of People: Learn how successful organizations use workforce analytics to improve business performance

“This book is a much-needed, well-researched, informative, and accessible treatment of HR/People Analytics. I highly recommend it for instructors who teach a course on this topic or HR professionals who want to learn on their own. It contains valuable guidance on how to collect and analyze human resource management and operational data to support data driven decision-making.”

Dr. Janet Marler, Professor of Management, University at Albany, State University of New York


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.




Chapter 1: Understanding HR analytics • Predictive HR analytics defined • Understanding the need (and business case) for mastering and utilizing predictive HR analytic techniques • Human capital data storage and ‘big (HR) data’ manipulation • Predictors, prediction and predictive modelling • Current state of HR analytic capabilities and professional or academic training • Business applications of modelling • HR analytics and HR people strategy • Becoming a persuasive HR function • References • Further reading

Chapter 2: HR information systems and data • Information sources • Analysis software options • Using SPSS • Preparing the data • Big data • References

Chapter 3: Analysis strategies • From descriptive reports to predictive analytics • Statistical significance • Examples of key HR analytic metrics/measures often used by analytics teams • Data integrity • Types of data • Categorical variable types • Continuous variable types • Using group/team-level or individual-level data • Dependent variables and independent variables • Your toolkit: types of statistical tests • Statistical tests for categorical data (binary, nominal, ordinal) • Statistical tests for continuous/interval-level data • Factor analysis and reliability analysis • What you will need • Summary • References

Chapter 4: Case study 1: Diversity analytics • Equality, diversity and inclusion • Approaches to measuring and managing D&I • Example 1: gender and job grade analysis using frequency tables and chi square • Example 2a: exploring ethnic diversity across teams using descriptive statistics • Example 2b: comparing ethnicity and gender across two functions in an organization using the independent samples t-test • Example 3: using multiple linear regression to model and predict ethnic diversity variation across teams • Testing the impact of diversity: interacting diversity categories in predictive modelling • A final note • References

Chapter 5: Case study 2: Employee attitude surveys – engagement and workforce perceptions • What is employee engagement? • How do we measure employee engagement? • Interrogating the measures • Conceptual explanation of factor analysis • Example 1: two constructs – exploratory factor analysis • Reliability analysis • Example 2: reliability analysis on a four-item engagement scale •
Example 3: reliability and factor testing with group-level engagement data • Analysis and outcomes • Example 4: using the independent samples t-test to determine differences in engagement levels • Example 5: using multiple regression to predict team-level engagement • Actions and business context • References

Chapter 6: Case study 3: Predicting employee turnover • Employee turnover and why it is such an important part of HR management information • Descriptive turnover analysis as a day-to-day activity • Measuring turnover at individual or team level • Exploring differences in both individual and team-level turnover • Example 1a: using frequency tables to explore regional differences in staff turnover • Example 1b: using chi-square analysis to explore regional differences in individual staff turnover • Example 2: using one-way ANOVA to analyse team-level turnover by country • Example 3: predicting individual turnover • Example 4: comparing expected length of service for men vs women using the Kaplan-Meier survival analysis technique • Example 5: predicting team turnover • Modelling the costs of turnover and the business case for action • Summary • References

Chapter 7: Case study 4: Predicting employee performance • What can we measure to indicate performance? • What methods might we use? • Practical examples using multiple linear regression to predict performance • Example 1a: using multiple linear regression to predict customer loyalty in a financial services organization • Example 1b: using multiple linear regression to predict customer reinvestment in a financial services organization • Example 2: using multiple linear regression to predict customer loyalty • Example 3: using multiple linear regression to predict individual performance • Example 4: using stepwise multiple linear regression to model performance • Example 5: using stepwise multiple linear regression to model change in performance over time • Example 6: using multiple regression to predict sickness absence • Example 7: exploring patterns in performance linked to employee profile data • Example 8: exploring patterns in supermarket checkout scan rates linked to employee demographic data • Example 9: determining the presence or otherwise of high-performing age groups • Ethical considerations caveat in performance data analysis • Considering the possible range of performance analytic models • References

Chapter 8: Case study 5: Recruitment and selection analytics • Reliability and validity of selection methods • Human bias in recruitment selection • Example 1: consistency of gender and BAME proportions in the applicant pool • Example 2: investigating the influence of gender and BAME on shortlisting and offers made • Validating selection techniques as predictors of performance • Example 3: predicting performance from selection data using multiple linear regression • Example 4: predicting turnover from selection data – validating selection techniques by predicting turnover • Further considerations • Reference

Chapter 9: Case study 6: Monitoring the impact of interventions • Tracking the impact of interventions • Example 1: stress before and after intervention • Example 2: stress before and after intervention by gender • Example 3: value-change initiative • Example 4: value-change initiative by department • Example 5: supermarket checkout training intervention • Example 6: supermarket checkout training course – Redux • Evidence-based practice and responsible investment • Reference

Chapter 10: Business applications: Scenario modelling and business cases • Predictive modelling scenarios • Example 1: customer reinvestment • Example 2: modelling the potential impact of a training programme • Obtaining individual values for the outcomes of our predictive models • Example 3: predicting the likelihood of leaving • Making graduate selection decisions with evidence obtained from previous performance data • Example 4: constructing the business case for investment in an induction day • Example 5: using predictive models to help make a selection decision in graduate recruitment • Example 6: which candidate might be a ‘flight risk’? • Further consideration on the use of evidence-based recommendations in selection • References

Chapter 11: More advanced HR analytic techniques • Mediation processes • Moderation and interaction analysis • Multi-level linear modelling • Curvilinear relationships • Structural equation models • Growth models • Latent class analysis • Response surface methodology and polynomial regression analysis • The SPSS syntax interface • Machine learning • References

Chapter 12: Reflection on HR analytics: Usage, ethics and limitations • HR analytics as a scientific discipline • The metric becomes the behaviour driver: Institutionalized Metric-Oriented Behaviour (IMOB) • Balanced scorecard of metrics • What is the analytic sample? • The missing group • The missing factor • Carving time and space to be rigorous and thorough • Be sceptical and interrogate the results • The importance of quality data and measures • Taking ethical considerations seriously • Ethical standards for the HR analytics team • The General Data Protection Regulation (GDPR) • The metric and the data are linked to human beings • References

Appendix R


About the Authors:

Dr 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 level students for over 15 years, and also teaches HR analytics to MSc HRM students. He has worked for a number of years as an HR consultant and has run many HR analytic workshops with FTSE-100 companies. His publications have featured in some of the world’s top HR academic journals.

Kirsten Edwards is Head of Analytics at Empathix Ltd and has over 20 years broad, international experience in analytics, HR and management consulting. She has close academic links with both Kent Business School and King’s Business School, King’s College London, where she is a visiting lecturer and has periodic involvement in research.

Target Audience:

Useful for HR leaders and practitioners, data scientists, HR generalists and students.


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