Coming soon · Book Profile
Using R in HR Analytics A practical guide to analysing people data
A practical, hands-on guide to applying inferential and predictive statistical techniques to human resources data using the open-source R programming language.
A profile of this book is on the way.
What it’s about
Using R in HR Analytics bridges the gap between data science and human resources by teaching HR professionals, students, and management-information teams how to move beyond descriptive reporting toward rigorous predictive analytics. Built on the foundation of the authors' earlier SPSS-based text, this R edition walks readers through the entire analytic journey: understanding HR information systems and data types, importing and manipulating data in R, choosing the correct statistical test, and applying techniques such as chi-square, t-tests, ANOVA, multiple and logistic regression, factor and reliability analysis, and survival analysis. Through six detailed case studies — diversity, engagement, turnover, performance, recruitment/selection, and intervention monitoring — plus chapters on scenario modelling, advanced methods (mediation, moderation, multilevel models, machine learning), and ethics, the book equips readers to diagnose causal drivers of key HR outcomes, predict future behaviour, build evidence-based business cases, and persuade leadership with 'hard' evidence while remaining alert to the limitations and ethical responsibilities of working with people data.
The through-line
- Who it’s for
- An HR professional, student, or management-information analyst who wants to become a credible, data-literate, high-performing contributor able to predict and influence key people outcomes.
- The problem
- They process vast amounts of people-related data but lack the statistical skills to move beyond descriptive reports to predictive, causal insight. They feel intimidated by statistics, fear being asked 'So what?' in the boardroom, and worry their analysis will be dismissed as coincidence.
- The plan
- Understand your organization's data sources, systems and data types.
- Learn R and how to import, manipulate and merge people data.
- Use the analysis-strategy reference to select the correct statistical test.
- Work through the six case studies applying tests to real-style HR data.
- Translate significant findings into predictive scenarios and business cases.
- The payoff
- The reader becomes a Master of the HR Metric, able to diagnose drivers of performance, turnover, engagement and diversity. · Their HR function becomes more credible and persuasive, presenting robust 'hard' evidence to leadership. · They build evidence-based business cases and 'what if' scenario models that help the organization invest wisely and prosper.
See our guide
Related profiles we’ve built
- Beyond Hr Boudreau Ramstad →
- Compensating Your Employees Fairly →
- Handbook of Regression Modeling in People Analytics →
- Transformative HR: How Great Companies Use Evidence-Based Change for Sustainable Advantage →
- Work Rules! →
- The basic principle of people analytics learn how to use hr data to drive better outcomes for your business and employees →
- Applied Multivariate Stats Social Sciences Stevens →
- Bayesian Multilevel Models for Repeated Measures dаta A Conceptual and Practical Introduction in R →
Additional reading
- Jack: Straight from the Gut · Jack Welch
The book critiques the widespread, unthinking adoption of GE's '20-70-10' performance ranking system as a prime example of management fad-following, which talentship aims to replace with context-specific, logical analysis.
- Moneyball: The Art of Winning an Unfair Game · Michael Lewis
Used as a key analogy for talentship. It demonstrates how a decision-science approach can identify undervalued, pivotal capabilities to create a competitive advantage, just as talentship aims to do for organizations.
- Work Rules! · Laszlo Bock
Written by Google's former head of People Operations, it provides detailed insights into how Google uses a data-driven approach for hiring and management, a core theme of this book.
- Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things · Bernard Marr
The author's own book, recommended for readers who want more detailed guidance on creating the data strategy that is presented as the foundational first step for data-driven HR.
- High Output Management · Andy Grove
Referenced in the book as an example of how to think analytically and build a business case for a management decision, specifically regarding the ROI of a manager training their own team.
- The Power of People: Learn How Successful Organizations Use Workforce Analytics to Improve Business Performance · Guenole, N., Ferrar, J., & Feinzig, S.
Cited in the book, this is a foundational text that aligns with the book's core theme of using workforce analytics to drive tangible business improvements.
- Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset · Pease, G., Byerly, B., & Fitz-enz, J.
Cited in the book and written by a pioneer in the field, this work provides a comprehensive view on human capital analytics, complementing this book's hands-on manual approach.
- Handbook of regression modeling in people analytics · Keith McNulty
The author's previous book, likely providing foundational quantitative skills for readers who are new to programming or data analysis in R.
- ggplot2: Elegant graphics for data analysis · Hadley Wickham
The definitive guide to the `ggplot2` package in R, which is the foundation for the `ggraph` network visualization package used extensively in the book.
- Stanford Large Network Dataset Collection (SNAP) · Stanford Network Analysis Project
A key public resource with a wide range of large network datasets, recommended by the author for further practice and exploration beyond the book's examples.