000			02743nam a2200229Ia 4500
003			NULRC
005			20250520102820.0
008			250520s9999 xx 000 0 und d
020			_a9783319266312
040			_cNULRC
050			_aQA 76.9 .R63 2016
245		0	_aModern statistical methods for HCI / _cedited by Judy Robertson and Maurits Kaptein.
260			_aSwitzerland : _bCham Springer International Publishing Springer , _cc2016
300			_axx, 348 pages : _billustrations ; _c24 cm.
365			_bUSD129
504			_aIncludes bibliographical references and index.
505			_aPreface.- 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.
520			_aThis 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.
650			_aCOMPUTER SCIENCE
700			_aRobertson, Judy;Kaptein, Maurits _eeditor;co-editor
942			_2lcc _cBK
999			_c16026 _d16026