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Modern Statistical Methods for HCI

  • Book
  • © 2016

Overview

Editors:
  1. Judy Robertson

    1. Moray School of Education, Edinburgh University, Edinburgh, United Kingdom

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    You can also search for this editor in PubMed   Google Scholar

  2. Maurits Kaptein

    1. Donders Centre for Cognition, Radboud University Nijmegen, Tilburg, The Netherlands

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    You can also search for this editor in PubMed   Google Scholar

  • Critically examines statistical methodologies used in human-computer interaction
  • Provides hands-on explanations of how to apply and interpret modern statistical methods using HCI examples
  • Provides [R] code for conducting statistical analysis
  • Includes supplementary material: sn.pub/extras

Part of the book series: Human–Computer Interaction Series (HCIS)

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Table of contents (14 chapters)

  1. Front Matter

    Pages i-xx
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  2. An Introduction to Modern Statistical Methods in HCI

    • Judy Robertson, Maurits Kaptein
    Pages 1-14

  3. Getting Started With Data Analysis

    1. Front Matter

      Pages 15-18
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    2. Getting Started with [R]; a Brief Introduction

      • Lianne Ippel
      Pages 19-35

    3. Descriptive Statistics, Graphs, and Visualisation

      • Joanna Young, Jan Wessnitzer
      Pages 37-56

    4. Handling Missing Data

      • Thom Baguley, Mark Andrews
      Pages 57-82

  4. Classical Null Hypothesis Significance Testing Done Properly

    1. Front Matter

      Pages 83-85
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    2. Effect Sizes and Power Analysis in HCI

      • Koji Yatani
      Pages 87-110

    3. Using R for Repeated and Time-Series Observations

      • Deborah Fry, Kerri Wazny, Niall Anderson
      Pages 111-133

    4. Nonparametric Statistics in Human–Computer Interaction

      • Jacob O. Wobbrock, Matthew Kay
      Pages 135-170

  5. Bayesian Inference

    1. Front Matter

      Pages 171-172
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    2. Bayesian Inference

      • Michail Tsikerdekis
      Pages 173-197

    3. Bayesian Testing of Constrained Hypotheses

      • Joris Mulder
      Pages 199-227

  6. Advanced Modeling in HCI

    1. Front Matter

      Pages 229-231
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    2. Latent Variable Models

      • A. Alexander Beaujean, Grant B. Morgan
      Pages 233-250

    3. Using Generalized Linear (Mixed) Models in HCI

      • Maurits Kaptein
      Pages 251-274

    4. Mixture Models: Latent Profile and Latent Class Analysis

      • Daniel Oberski
      Pages 275-287

  7. Improving Statistical Practice in HCI

    1. Front Matter

      Pages 289-289
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    2. Fair Statistical Communication in HCI

      • Pierre Dragicevic
      Pages 291-330

    3. Improving Statistical Practice in HCI

      • Judy Robertson, Maurits Kaptein
      Pages 331-348
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Keywords

About this book

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.

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)

Editors and Affiliations

  • Moray School of Education, Edinburgh University, Edinburgh, United Kingdom

    Judy Robertson

  • Donders Centre for Cognition, Radboud University Nijmegen, Tilburg, The Netherlands

    Maurits Kaptein

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