Using R in HR Analytics A practical guide to analysing people data
Martin Edwards, Kirsten Edwards .
A practical, hands-on guide to applying inferential and predictive statistical techniques to human resources data using the open-source R programming language.
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 model it argues
An inferred factor model expressing how organizational design levers and contextual conditions influence psychological and perceptual states and behavioural patterns, which in turn drive key HR outcome metrics such as engagement, turnover, performance and diversity representation. The book operationalizes this through statistical tests selected by variable type.
Key ideas
- HR Intervention and Design Levers
- Deliberate organizational programmes and actions designed to change employee states, behaviours or outcomes, such as training, induction, work-life balance programmes and value-change events.
- Contextual and Structural Conditions
- Stable structural and contextual features of the work environment that form the backdrop for employee states and outcomes and that can moderate relationships.
- Individual Attributes and Capabilities
- Demographic and capability characteristics of individual employees used as predictors of behavioural and outcome variables.
- Psychological and Perceptual States
- Employees' internal attitudes, perceptions and affective-cognitive states that mediate between design/context and behavioural/outcome variables.
- Behavioural and Discretionary Patterns
- Observable employee behaviours and discretionary effort that lie between internal psychological states and hard outcome metrics.
- Key HR Outcome Metrics
- The tangible performance and people indicators the organization seeks to understand, predict and influence.