Robertson J. Modern Statistical Methods for HCI 2016 torrent

Textbook in PDF format

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.
Front Matter
An Introduction to Modern Statistical Methods in HCI
Front Matter
Getting Started with [R]; a Brief Introduction
Descriptive Statistics, Graphs, and Visualisation
Handling Missing Data
Front Matter
Effect Sizes and Power Analysis in HCI
Using R for Repeated and Time-Series Observations
Nonparametric Statistics in Human–Computer Interaction
Front Matter
Bayesian Inference
Bayesian Testing of Constrained Hypotheses
Front Matter
Latent Variable Models
Using Generalized Linear (Mixed) Models in HCI
Mixture Models: Latent Profile and Latent Class Analysis
Front Matter
Fair Statistical Communication in HCI
Improving Statistical Practice in HCI