Predictive HR Analytics

Mastering the HR Metric
2. Auflage 2019
ISBN: 978-0-7494-8444-6
Verlag: Kogan Page

Buch, Englisch, 504 Seiten, Format (B × H): 169 mm x 238 mm, Gewicht: 946 g

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.

Autoren/Hrsg.

Edwards, Martin

Edwards, Kirsten

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Wirtschaftswissenschaften Betriebswirtschaft Bereichsspezifisches Management Personalwesen, Human Resource Management
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik

Inhaltsverzeichnis

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; Chapter - 13: Appendix R

Über Autor(innen)

Edwards, Kirsten
Kirsten Edwards is HR Lead for Advanced Analytics and Data Science at Rio Tinto and has over 20 years' broad international experience in analytics, HR and management consulting. She is a visiting lecturer at Kent Business School and at King's Business School.

Edwards, Martin
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.

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 HR Lead for Advanced Analytics and Data Science at Rio Tinto and has over 20 years' broad international experience in analytics, HR and management consulting. She is a visiting lecturer at Kent Business School and at King's Business School.