Handbook of Regression Modeling in People Analytics

Despite the recent rapid growth in machine learning, AI and predictive analytics, many of the statistical questions that are faced by researchers and practitioners still involve explaining why something is happening. Regression analysis is the best 'Swiss army knife' we have for answering these kinds of questions. This book is a learning resource on inferential statistics and regression analysis. It teaches how to do a wide range of statistical analyses in both R and Python, ranging from simple hypothesis testing to advanced multivariable modelling. Although it is primarily focused on examples related to the analysis of people and talent, the methods easily transfer to any discipline. The book hits a 'sweet spot' where there is just enough mathematical theory to support a strong understanding of the methods, but with a step-by-step guide and easily reproducible examples and code, so that the methods can be put into practice immediately. This makes the book accessible to a wide readership, from public and private sector analysts and practitioners to undergraduate and postgraduate students and researchers. The second edition of this book substantially expands the range of methods taught. Bayesian approaches to regression modelling are now included, as well as an in-depth chapter on causal inference theory and methods. Key Features: - 19 accompanying datasets across a wide range of contexts (e.g., academic, corporate, sports, marketing) - Clear step-by-step instructions on executing the analysis. - Clear guidance on how to interpret results. - Primary instruction in R but added sections for Python coders. - Discussion and data exercises for each of the main chapters. - Final chapter of practice material and datasets ideal for class homework or project work.

Keith McNulty, PhD is a leading practitioner of applied statistics, psychometrics and people analytics. He has spent over 25 years developing data-driven frameworks to solve critical talent and organizational challenges in major organizations. He is currently Head of Talent Assessment at Citadel.

Foreword by Alexis Fink Introduction 1 The Importance of Regression in People Analytics 2 The Basics of the R Programming Language 3 Statistics Foundations 4 Linear Regression for Continuous Outcomes 5 Binomial Logistic Regression for Binary Outcomes 6 Multinomial Logistic Regression for Nominal Category Out-comes 7 Proportional Odds Logistic Regression for Ordered Category Outcomes 8 Poisson, Quasi-Poisson and Negative Binomial Regression for Count Outcomes 9 Modeling Explicit and Latent Hierarchy in Data 10 Survival Analysis for Modeling Singular Events Over Time 11 Power Analysis for Estimating Required Sample Sizes for Modeling 12 Bayesian Inference - A Modern Alternative to Classical Statistical Methods 13 Linear Regression Using Bayesian Inference 14 Fitting Other Regression Models Using Bayesian Inference 15 Causal Inference - Moving From Association to Causation 16 Further Exercises for Practice References Glossary Index