This book critically reflects on current statistical methods used in Human-Computer Interaction (HCI) and introduces a number of novel methods to the reader. Covering many techniques and approaches for exploratory data analysis including effect and power calculations, experimental design, event history analysis, non-parametric testing and Bayesian inference; the research contained in this book discusses how to communicate statistical results fairly, as well as presenting a general set of recommendations for authors and reviewers to improve the quality of statistical analysis in HCI. Each chapter presents [R] code for running analyses on HCI examples and explains how the results can be interpreted. Modern Statistical Methods for HCI is aimed at researchers and graduate students who have some knowledge of “traditional” null hypothesis significance testing, but who wish to improve their practice by using techniques which have recently emerged from statistics and related fields. This book critically evaluates current practices within the field and supports a less rigid, procedural view of statistics in favour of fair statistical communication.

Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics. This book aims to help readers navigate this morass: to understand the debates, to be able to read and assess other people's statistical reports, and make appropriate choices when designing and analysing their own experiments, empirical studies, and other forms of quantitative data gathering.

Many people find statistics confusing, and perhaps even more confusing given recent publicity about problems with traditional p-values and alternative statistical techniques including confidence intervals and Bayesian statistics. This book aims to help readers navigate this morass: to understand the debates, to be able to read and assess other people's statistical reports, and make appropriate choices when designing and analysing their own experiments, empirical studies, and other forms of quantitative data gathering.

This book addresses common questions from HCI researchers when trying to do statistical analysis on their data.

In der Vergangenheit war die Mensch-Computer-Interaktion (Human-Computer Interaction) das Privileg einiger weniger. Heute ist Computertechnologie weit verbreitet, allgegenwärtig und global. Arbeiten und Lernen erfolgen über den Computer. Private und kommerzielle Systeme arbeiten computergestützt. Das Gesundheitswesen wird neu erfunden. Navigation erfolgt interaktiv. Unterhaltung kommt aus dem Computer. Als Antwort auf immer leistungsfähigere Systeme sind im Bereich der Mensch-Computer-Interaktion immer ausgeklügeltere Theorien und Methodiken entstanden. The Wiley Handbook of Human-Computer Interaction bietet einen Überblick über all diese Entwicklungen und untersucht die vielen verschiedenen Aspekte der Mensch-Computer-Interaktion und hat den Wert menschlicher Erfahrungen, die über Technologie stehen, ganzheitlich im Blick.

Takes the human-computer interaction researcher through the complete experimental process, from identifying a research question, to conducting an experiment and analysing the results.

Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns.