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Predictive HR Analytics: Mastering the HR Metric
HR metrics and organizational people-related data are an invaluable source of information from which to identify trends and patterns in order to make effective business decisions. But HR practitioners often lack the statistical and analytical know-how to fully harness the potential of this data. Predictive HR Analytics provides a clear, accessible framework for understanding and working with people analytics and advanced statistical techniques. Using the statistical package SPSS (with R syntax included), it takes readers step by step through worked examples, showing them how to carry out and interpret analyses of HR data in areas such as employee engagement, performance and turnover. Readers are shown how to use the results to enable them to develop effective evidence-based HR strategies.This second edition has been updated to include the latest material on machine learning, biased algorithms, data protection and GDPR considerations, a new example using survival analyses, and up-to-the-minute screenshots and examples with SPSS version 25. It is supported by a new appendix showing main R coding, and online resources consisting of SPSS and Excel data sets and R syntax with worked case study examples.
529 pages, Kindle Edition
Published March 3, 2019
52 people are currently reading
271 people want to read
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Displaying 1 - 2 of 2 reviews
April 30, 2016
This is a specialist book, for a specialist audience about a fairly important and fascinating subject, namely analysing the performance of a company’s human resources (HR) and its HR department.
The routine activities of HR are well known, yet recording their activities and the makeup of a company is less-so. Modern day activities such as diversity analysis, employee turnover and predictive employee turnover are, or should be, increasingly relevant, especially to the larger enterprise.
In the right hands, the reader should be able to get a better understanding of how to gather, analyse and interpret HR data for the benefit of the company-at-large. It is not a “Big Brother” approach, recording ever minor transgression such as timekeeping or comments made on social media. There is nothing here that an employee needs to fear.
This book seeks to provide a means to using statistical modelling and analytical techniques. It is a heavy-read, despite being fairly accessible and well-written, due to the nature of the information under discussion. Nothing is left to chance, the book starts with an introduction to predictive HR analytics and explains gathering HR data and converting it into a usable format. Use of the statistical analysis program SPSS is necessary and the authors explain how to use this powerful, yet often bewildering program, for the specific purpose of HR analytics. No doubt you could use other packages or try and analyse things by hand, yet you’d lose out on a large chunk of this book’s benefit in the process. Focus is then given to specific user case examples, providing a real hands-on opportunity to uses such as diversity, employee attitude and engagement, employee turnover, employee performance, recruitment and selection and even impact interventions. Sample data is provided to let the reader “try it for themselves” and see how everything works and goes together.
There is a lot of theory, this is unavoidable, but it is still fairly clear and easy to follow. You just need to assign time to really focus on this stuff, if you don’t already live and breathe the subject.
The benefits are clear, the pathway to getting them is fairly understandable. It just needs a fair bit of hard work and focus along the way. If you are in the need for a book like this, this is a potential Godsend. Even if you don’t, it can be strangely interesting and compelling… or this reviewer is just a bit too nerdy for his own good!
The routine activities of HR are well known, yet recording their activities and the makeup of a company is less-so. Modern day activities such as diversity analysis, employee turnover and predictive employee turnover are, or should be, increasingly relevant, especially to the larger enterprise.
In the right hands, the reader should be able to get a better understanding of how to gather, analyse and interpret HR data for the benefit of the company-at-large. It is not a “Big Brother” approach, recording ever minor transgression such as timekeeping or comments made on social media. There is nothing here that an employee needs to fear.
This book seeks to provide a means to using statistical modelling and analytical techniques. It is a heavy-read, despite being fairly accessible and well-written, due to the nature of the information under discussion. Nothing is left to chance, the book starts with an introduction to predictive HR analytics and explains gathering HR data and converting it into a usable format. Use of the statistical analysis program SPSS is necessary and the authors explain how to use this powerful, yet often bewildering program, for the specific purpose of HR analytics. No doubt you could use other packages or try and analyse things by hand, yet you’d lose out on a large chunk of this book’s benefit in the process. Focus is then given to specific user case examples, providing a real hands-on opportunity to uses such as diversity, employee attitude and engagement, employee turnover, employee performance, recruitment and selection and even impact interventions. Sample data is provided to let the reader “try it for themselves” and see how everything works and goes together.
There is a lot of theory, this is unavoidable, but it is still fairly clear and easy to follow. You just need to assign time to really focus on this stuff, if you don’t already live and breathe the subject.
The benefits are clear, the pathway to getting them is fairly understandable. It just needs a fair bit of hard work and focus along the way. If you are in the need for a book like this, this is a potential Godsend. Even if you don’t, it can be strangely interesting and compelling… or this reviewer is just a bit too nerdy for his own good!
March 9, 2023
Very detailed case studies with deep statistical approaches. Would be great to use when these specific situations present.
Displaying 1 - 2 of 2 reviews