Modern Statistical Methods for HCI

“The book is structured in five parts and 14 chapters/papers within. Each chapter presents R language codes, and explains the results obtained. … Each chapter presents multiple references and numerical illustrations for practical guide to writing codes in R. … The book can serve to students and practitioners in various fields where applied statistics is used so understanding hypotheses testing is needed for analysis and meaningful decision making.” (Stan Lipovetsky, Technometrics, Vol. 59 (2), April, 2017)

• Preface.- An Introduction to Modern Statistical Methods for HCI.- Part I: Getting Started With Data Analysis.- Getting started with [R]: A Brief Introduction.- Descriptive Statistics, Graphs, and Visualization.- Handling Missing Data.- Part II: Classical Null Hypothesis Significance Testing Done Properly.- Effect sizes and Power in HCI.- Using R for Repeated and Time-Series Observations.- Non-Parametric Statistics in Human-Computer Interaction.- Part III : Bayesian Inference.- Bayesian Inference.- Bayesian Testing of Constrained Hypothesis.- Part IV: Advanced Modeling in HCI.- Latent Variable Models.- Using Generalized Linear (Mixed) Models in HCI.- Mixture Models: Latent Profile and Latent Class Analysis.- Part V: Improving Statistical Practice in HCI.- Fair Statistical Communication in HCI.- Improving Statistical Practice in HCI.