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 relatedfields. This book critically evaluates current practices within the field and supports a less rigid, procedural view of statistics in favour of fair statistical communication.
Rezensionen / Stimmen
"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)
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Illustrationen
77 s/w Abbildungen
XX, 348 p. 77 illus.
Dateigröße
ISBN-13
978-3-319-26633-6 (9783319266336)
DOI
10.1007/978-3-319-26633-6
Schweitzer Klassifikation
