Overview
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.
Full Product Details
Author: Judy Robertson ,
Maurits Kaptein
Publisher: Springer International Publishing AG
Imprint: Springer International Publishing AG
Edition: Softcover reprint of the original 1st ed. 2016
Volume: 0
Dimensions:
Width: 15.50cm
, Height: 2.00cm
, Length: 23.50cm
Weight: 0.563kg
ISBN: 9783319799841
ISBN 10: 3319799843
Pages: 348
Publication Date: 24 April 2018
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: Manufactured on demand
We will order this item for you from a manufactured on demand supplier.
Reviews
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)
“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)
