Statistics for Compensation
John H. Davis
A practical guide teaching compensation and HR professionals the descriptive statistical and modeling techniques needed to analyze pay data and make sound organizational decisions.
Statistics for Compensation demystifies the numbers behind pay decisions, giving compensation and human resources professionals a hands-on toolkit of descriptive statistics and model-building techniques grounded in real-world case studies from a fictitious company, BPD. Author John H. Davis draws on decades of practitioner, consulting, and teaching experience to walk readers from basic notions of percent and compound interest through frequency distributions, measures of location and variability, and into powerful regression-based market models—linear, exponential, maturity curve, power, and multiple linear regression. The book's central message is that statistics do not simply answer questions; they raise issues, challenge assumptions, and require aggressive inquisitiveness because behind every data point there is a story. With worked examples, practice problems, and a disciplined five-step model-building framework, the book equips professionals to identify market positions, build salary structures, set salary increase budgets, and defend recommendations with data—all in service of helping organizations attract, retain, motivate, and align the people they need.
The model it argues
A framework in which analytical design levers (statistical techniques, model building, market analysis) applied to compensation data produce psychological and behavioral states (understanding, comprehension, confidence, aggressive inquisitiveness) that lead to better compensation decisions and organizational outcomes such as sound market positioning and the ability to attract, retain, motivate, and align employees.
Key ideas
- Application of Descriptive Statistical Techniques
- The deliberate and appropriate use of descriptive statistical methods to obtain, organize, summarize, present, interpret, and analyze compensation and HR data.
- Disciplined Model-Building Process
- A structured application of the scientific method to build a model relating a problem variable to critical factors through five explicit steps.
- Market Analysis and Salary Survey Process
- The applied end-to-end process of turning salary survey data into a market position, salary structure, and salary increase budget.
- Aggressive Inquisitiveness
- An inner drive and persistent curiosity to explain why data are the way they are, including anomalies, outliers, and the story behind each data point.
- Data Comprehension and Understanding
- The analyst's grasp of the meaning of a data set—its shape, central tendency, variability, and relationships—after summarization.
- Assumption Validation
- The practice of verifying the assumptions underlying an analysis before drawing conclusions.
- Analyst Decision Confidence
- The practitioner's comfort level in a model and its conclusions, enabling them to recommend and defend decisions.
- Quality of Compensation Decisions
- The soundness, defensibility, and appropriateness of compensation decisions and recommendations derived from analysis.