Modern Statistical Methods for HCI

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 hist...

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Bibliographic Details
Main Authors Robertson, Judy, Kaptein, Maurits
Format eBook Book
LanguageEnglish
Published Cham Springer Nature 2016
Springer
Springer International Publishing AG
Springer International Publishing
Edition1
SeriesHuman–Computer Interaction Series
Subjects
Computer programming, programs, data
Computer Science
Human-computer interaction
Human-computer interaction > Statistical methods
Statistics
Statistics for Social Sciences, Humanities, Law
User Interfaces and Human Computer Interaction
Online AccessGet full text
ISBN9783319266336
3319266330
3319266314
9783319266312
ISSN1571-5035
DOI10.1007/978-3-319-26633-6

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Table of Contents:
  • 6.8 Statistical Model for Event History Analysis -- 6.9 Calculating Event History Analysis Using R -- 6.10 Interpreting and Presenting the Event History Analysis Findings -- References -- 7 Nonparametric Statistics in Human--Computer Interaction -- 7.1 Introduction -- 7.2 When to Use Nonparametric Analyses -- 7.2.1 Assumptions of Analysis of Variance (ANOVA) -- 7.2.2 Table of Analogous Parametric and Nonparametric Tests -- 7.3 Tests of Proportions -- 7.3.1 One-Sample Tests of Proportions -- 7.3.2 N-Sample Tests of Proportions -- 7.4 Single Factor Tests -- 7.4.1 Testing ANOVA Assumptions -- 7.4.2 Single-Factor Between-Subjects Tests -- 7.4.3 Single-Factor Within-Subjects Tests -- 7.4.4 Multifactor Tests -- 7.4.5 N-Way Analysis of Variance -- 7.4.6 Aligned Rank Transform (ART) -- 7.4.7 Generalized Linear Models (GLM) -- 7.4.8 Generalized Linear Mixed Models (GLMM) -- 7.4.9 Generalized Estimating Equations (GEE) -- 7.5 Summary -- References -- Part III Bayesian Inference -- 8 Bayesian Inference -- 8.1 Introduction -- 8.2 Introduction to Bayes' Theorem -- 8.3 Computing Bayesian Statistics -- 8.4 Bayesian Two Group Comparison for Binary Variables -- 8.5 Bayesian Two Group Comparison for Numeric Variables -- 8.6 Bayesian Regression with Numeric Predicted Variable -- 8.7 Do Not Reinvent the Wheel -- References -- 9 Bayesian Testing of Constrained Hypotheses -- 9.1 Introduction -- 9.1.1 Multiple Hypothesis Testing in HCI -- 9.1.2 Limitations of Classical Methods -- 9.1.3 Bayes Factors for Multiple Hypothesis Testing -- 9.1.4 Outline -- 9.2 Bayesian Estimation in the HCI Application -- 9.2.1 The Multivariate Normal Model -- 9.2.2 The Prior: A Formalization of Prior Beliefs -- 9.2.3 The Posterior: Our Belief After Observing the Data -- 9.2.4 HCI Data Example -- 9.3 Bayes Factors and Posterior Model Probabilities -- 9.3.1 Definition of the Bayes Factor
  • 13.2.3 How Useful Is the Information Conveyed by P? -- 13.2.4 Usability Problems with p-Values -- 13.2.5 Conclusion -- 13.3 Null Hypothesis Significance Testing Versus Estimation -- 13.3.1 A Few More Reminders -- 13.3.2 How Useful Is the α Cut-Off? -- 13.3.3 More Usability Problems Brought by the α Cut-Off -- 13.3.4 Conclusion -- 13.4 Fair Statistical Communication Through Estimation -- 13.4.1 General Principles -- 13.4.2 Before Analyzing Data -- 13.4.3 Calculating Confidence Intervals -- 13.4.4 Plotting Confidence Intervals -- 13.4.5 Interpreting Confidence Intervals -- 13.5 Conclusion -- References -- 14 Improving Statistical Practice in HCI -- 14.1 Introduction -- 14.2 Case Studies from the HCI Literature -- 14.2.1 Misinterpretations of the p-Value -- 14.2.2 The Fallacy of the Transposed Conditional -- 14.2.3 A Lack of Power (Type II Errors) -- 14.2.4 Confusion of p-Values and Effect Size Estimates -- 14.2.5 Multiple Comparisons (Type I Errors) -- 14.2.6 Researcher Degrees of Freedom -- 14.3 What Do We Know Now? -- 14.4 Recommendations for Improving Statistical Methodology -- 14.5 Closing Remarks -- References
  • Intro -- Foreword -- Preface -- About the Authors -- Maurits -- Judy -- Who Is This Book For? -- The Structure of the Book -- The Sample Dataset: The Mango Watch -- Our Approach -- Acknowledgements -- Contents -- Contributors -- 1 An Introduction to Modern Statistical Methods in HCI -- 1.1 Introduction -- 1.2 Earlier Commentaries on Statistical Methods in HCI and Beyond -- 1.3 Some of the Very Basic Ideas of Our Common ``NHST'' Method -- 1.4 The Common, and Well-Acknowledged, Problems with Our Current NHST Methods -- 1.4.1 Misinterpretations of the P-Value -- 1.4.2 The Fallacy of the Transposed Conditional -- 1.4.3 A Lack of Power -- 1.4.4 Confusion of P-Values and Effect Size Estimates -- 1.4.5 Multiple Comparisons -- 1.4.6 Researcher Degrees of Freedom -- 1.4.7 What Is the Aim of Statistical Analysis? -- 1.5 Broadening the Scope of Statistical Analysis in HCI -- References -- Part I Getting Started With Data Analysis -- 2 Getting Started with [R] -- a Brief Introduction -- 2.1 Introduction -- 2.1.1 Data Types -- 2.1.2 Storage -- 2.1.3 Storage Descriptives -- 2.2 Working with [R] -- 2.2.1 Writing Functions -- 2.2.2 Data: Input and Output -- 2.2.3 For Loop -- 2.2.4 Apply Function -- 2.2.5 Common Used Functions -- 2.2.6 Packages -- 2.3 Mastering [R] -- 2.3.1 Further Reading -- References -- 3 Descriptive Statistics, Graphs, and Visualisation -- 3.1 Introduction -- 3.1.1 Why Do We Visualise Data? -- 3.2 Descriptive Statistics and Exploratory Data Analysis -- 3.3 Principles of Visualisation -- 3.4 ggplot2---A Grammar of Graphics -- 3.4.1 ggplot2 -- 3.5 Case Study -- 3.5.1 Team Size -- 3.5.2 SUS Scores -- 3.5.3 Response Times -- 3.5.4 Model Predictions -- 3.6 Summary -- References -- 4 Handling Missing Data -- 4.1 Introduction -- 4.2 Missing Data: Problems and Pitfalls -- 4.2.1 Mechanisms of Misssingness
  • 4.2.2 Comparing Three Missing Data Scenarios -- 4.3 Missing Data: Potential Solutions -- 4.3.1 Popular but Inadequate `Solutions' -- 4.3.2 Multiple Imputation -- 4.3.3 Pooling Imputations Using Rubin's Equations -- 4.3.4 Multiple Imputation in Practice -- 4.4 Maximum Likelihood and Bayesian in Approaches to Missing Data -- 4.4.1 Maximum Likelihood Estimation in Ignorable Models Using Expectation-Maximization -- 4.4.2 Bayesian Inference with Missing Data -- 4.5 Conclusion -- References -- Part II Classical Null Hypothesis Significance Testing Done Properly -- 5 Effect Sizes and Power Analysis in HCI -- 5.1 Introduction -- 5.2 Myths of the p Value -- 5.2.1 Myth 1: Threshold of the P Value -- 5.2.2 Myth 2: Magnitude of the Effect -- 5.2.3 Myth 3: ``Significant'' is ``Important'' -- 5.2.4 Summary -- 5.3 Effect Size -- 5.3.1 Interpretation of Effect Sizes -- 5.3.2 Paired t-Test -- 5.3.3 Unpaired t-Test -- 5.3.4 ANOVA -- 5.4 One-Way ANOVA -- 5.5 One-Way Repeated-Measure ANOVA -- 5.6 Two-Way ANOVA -- 5.7 Non-parametric Tests -- 5.8 Pearson's Correlation -- 5.9 Power Analysis -- 5.9.1 Type I Error and Type II Error -- 5.9.2 Conducting Power Analysis -- 5.9.3 Sample Size Estimation -- 5.9.4 Retrospective Power Analysis (Caution: Not Recommended) -- 5.10 Conclusion -- References -- 6 Using R for Repeated and Time-Series Observations -- 6.1 Repeated Measures and Time Series in HCI -- 6.2 An Introduction to Within-Subjects Analysis of Variance (ANOVA) -- 6.2.1 Assumptions of Within-Subjects ANOVA -- 6.2.2 Sphericity -- 6.2.3 Correcting for Sphericity -- 6.3 Conceptual Model for Within-Subjects ANOVA -- 6.4 Statistical Inference for Within-Subjects ANOVA -- 6.5 Calculating Within-Subjects ANOVA Using R -- 6.6 Interpreting and Presenting the Within-Subjects ANOVA Findings -- 6.7 An Introduction to Event History Analysis -- 6.7.1 Censoring
  • 9.3.2 Interpreting Bayes Factors -- 9.3.3 Posterior Model Probabilities -- 9.3.4 Prior Specification When Computing Bayes Factors -- 9.4 A Bayesian T Test Using BIEMS -- 9.4.1 Hypotheses -- 9.4.2 Prior Specification -- 9.4.3 Bayes Factors and Posterior Model Probabilities -- 9.5 Evaluation of the HCI Application -- 9.6 Discussion -- References -- Part IV Advanced Modeling in HCI -- 10 Latent Variable Models -- 10.1 Introduction -- 10.1.1 Data -- 10.2 Path Models -- 10.2.1 Variables Types in a Path Model -- 10.2.2 Variables Relations in a Path Model -- 10.2.3 Tracing Rules -- 10.3 Latent Variable Models -- 10.3.1 Identification of Latent Variable Models -- 10.4 Latent Variable Models in R -- 10.4.1 Entering Data -- 10.4.2 Specifying Models in Lavaan -- 10.4.3 More Complex Models -- 10.5 Cautions and Considerations in Using Latent Variable Models -- 10.6 Conclusion -- References -- 11 Using Generalized Linear (Mixed) Models in HCI -- 11.1 Introduction -- 11.2 Linear Models -- 11.2.1 Fitting a Line -- 11.2.2 [R] Model Formulas -- 11.3 Regularization -- 11.4 Generalized Linear Models -- 11.5 Linear Mixed Models -- 11.5.1 Random Intercept Models -- 11.5.2 More Random Effect Specifications -- 11.5.3 Generalized Mixed Models -- 11.6 Conclusions and Recommendations -- References -- 12 Mixture Models: Latent Profile and Latent Class Analysis -- 12.1 Introduction -- 12.2 Mixtures of Continuous Variables: Latent Profile Analysis -- 12.3 Mixtures of Categorical Variables: Latent Class Analysis -- 12.4 Other Uses of Latent Class/Profile Analysis -- 12.5 Further References -- References -- Part V Improving Statistical Practice in HCI -- 13 Fair Statistical Communication in HCI -- 13.1 Introduction -- 13.2 p-Values, Effect Sizes and Confidence Intervals -- 13.2.1 A Minimalistic Example and Quick Reminders -- 13.2.2 Choosing a Pill