Partial 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.

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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.

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Additional reading

Discovering Statistics Using IBM SPSS Statistics · Andy Field
The book frequently recommends this text as a go-to resource for readers who want a deeper, more comprehensive understanding of the statistical theories and tests being applied.
Beyond Baron and Kenny: Statistical mediation analysis in the new millennium · Andrew F. Hayes
Highlighted as the modern approach to testing for mediation, moving beyond the classic but limited Baron and Kenny method. The book points to Hayes's work for more robust analysis of indirect effects.
The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings · F. Schmidt and J. Hunter
Referenced as a foundational source for understanding the reliability and validity of different employee selection methods, a core concept in recruitment analytics.

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