Guilherme D. Garcia is Assistant Professor of Linguistics at Ball State University, USA.

Highly recommended as an accessible introduction to the use of R for analysis of second language data. Readers will come away with an understanding of why and how to use statistical models and data visualization techniques in their research.Lydia White, McGill University, Canada.Curious where the field’s quantitative methods are headed? The answer is in your hands right now! Whether we knew it or not, this is the book that many of us have been waiting for. From scatter plots to standard errors and from beta values to Bayes theorem, Garcia provides us with all the tools we need—both conceptual and practical—to statistically and visually model the complexities of L2 development.Luke Plonsky, Northern Arizona University, USA.This volume is a timely and must-have addition to any quantitative SLA researcher’s data analysis arsenal, whether you are downloading R for the first time or a seasoned user ready to dive into Bayesian analysis. Guilherme Garcia’s accessible, conversational writing style and uncanny ability to provide answers to questions right as you’re about to ask them will give new users the confidence to make the move to R and will serve as an invaluable resource for students and instructors alike for years to come. Jennifer Cabrelli, University of Illinois at Chicago, USA.

• ContentsList of figuresList of tablesList of code blocksAcknowledgments Preface Part I Getting ready 1 Introduction 1.1 Main objectives of this book 1.2 A logical series of steps 1.2.1 Why focus on data visualization techniques? 1.2.2 Why focus on full-fledged statistical models? 1.3 Statistical concepts 1.3.1 p-values1.3.2 Effect sizes 1.3.3 Confidence intervals 1.3.4 Standard errors1.3.5 Further reading2 R basics 232.1 Why R?2.2 Fundamentals 2.2.1 Installing R and RStudio2.2.2 Interface 2.2.3 R basics 2.3 Data frames 2.4 Reading your data2.4.1 Is your data file ready? 2.4.2 R Projects2.4.3 Importing your data2.5 The tidyverse package2.5.1 Wide-to-long transformation2.5.2 Grouping, filtering, changing, and summarizing data 2.6 Figures 2.6.1 Using ggplot2 2.6.2 General guidelines for data visualization 2.7 Basic statistics in R 2.7.1 What’s your research question?2.7.2 t-tests and ANOVAs in R2.7.3 A post-hoc test in R 2.8 More packages 2.9 Additional readings on R 2.10 Summary2.11 Exercises Part II Visualizing the data 3 Continuous data 3.1 Importing your data 3.2 Preparing your data 3.3 Histograms 3.4 Scatter plots3.5 Box plots3.6 Bar plots and error bars3.7 Line plots 3.8 Additional readings on data visualization 3.9 Summary3.10 Exercises 4 Categorical data 4.1 Binary data 4.2 Ordinal data4.3 Summary 4.4 Exercises 5 Aesthetics: optimizing your figures 5.1 More on aesthetics 5.2 Exercises Part III Analyzing the data 1276 Linear regression 1296.1 Introduction6.2 Examples and interpretation6.2.1 Does Hours affect scores?6.2.2 Does Feedback affect scores? 6.2.3 Do Feedback and Hours affect scores?6.2.4 Do Feedback and Hours interact?6.3 Beyond the basics6.3.1 Comparing models and plotting estimates 6.3.2 Scaling variables 6.4 Summary 6.5 Exercises 7 Logistic regression 7.1 Introduction7.1.1 Defining the best curve in a logistic model 7.1.2 A family of models 7.2 Examples and interpretation 7.2.1 Can reaction time differentiate learners and native speakers? 7.2.2 Does Condition affect responses? 7.2.3 Do Proficiency and Condition affect responses? 7.2.4 Do Proficiency and Condition interact? 7.3 Summary7.4 Exercises 8 Ordinal regression 8.1 Introduction8.2 Examples and interpretation 8.2.1 Does Condition affect participants’ certainty? 8.2.2 Do Condition and L1 interact?8.3 Summary 8.4 Exercises 9 Hierarchical models 9.1 Introduction 9.2 Examples and interpretation 9.2.1 Random-intercept model 9.2.2 Random-slope and random-intercept model 9.3 Additional readings on regression models 9.4 Summary9.5 Exercises 10 Going Bayesian 10.1 Introduction to Bayesian data analysis 10.1.1 Sampling from the posterior 10.2 The RData format10.3 Getting ready10.4 Bayesian models: linear and logistic examples 10.4.1 Bayesian model A: Feedback 10.4.2 Bayesian model B: Relative clauses with prior specifications 10.5 Additional readings on Bayesian inference 10.6 Summary 10.7 Exercises11 Final remarks Appendix A: TroubleshootingAppendix B: RStudio shortcuts Appendix C: Symbols and acronyms Appendix D: Files used in this book Appendix E: Contrast coding Appendix F: Models and nested dataGlossary ReferencesSubject index Function Index

Guilherme D. Garcia is Assistant Professor of Linguistics at Ball State University, USA.

Highly recommended as an accessible introduction to the use of R for analysis of second language data. Readers will come away with an understanding of why and how to use statistical models and data visualization techniques in their research.Lydia White, McGill University, Canada.Curious where the field’s quantitative methods are headed? The answer is in your hands right now! Whether we knew it or not, this is the book that many of us have been waiting for. From scatter plots to standard errors and from beta values to Bayes theorem, Garcia provides us with all the tools we need—both conceptual and practical—to statistically and visually model the complexities of L2 development.Luke Plonsky, Northern Arizona University, USA.This volume is a timely and must-have addition to any quantitative SLA researcher’s data analysis arsenal, whether you are downloading R for the first time or a seasoned user ready to dive into Bayesian analysis. Guilherme Garcia’s accessible, conversational writing style and uncanny ability to provide answers to questions right as you’re about to ask them will give new users the confidence to make the move to R and will serve as an invaluable resource for students and instructors alike for years to come. Jennifer Cabrelli, University of Illinois at Chicago, USA.

• ContentsList of figuresList of tablesList of code blocksAcknowledgments Preface Part I Getting ready 1 Introduction 1.1 Main objectives of this book 1.2 A logical series of steps 1.2.1 Why focus on data visualization techniques? 1.2.2 Why focus on full-fledged statistical models? 1.3 Statistical concepts 1.3.1 p-values1.3.2 Effect sizes 1.3.3 Confidence intervals 1.3.4 Standard errors1.3.5 Further reading2 R basics 232.1 Why R?2.2 Fundamentals 2.2.1 Installing R and RStudio2.2.2 Interface 2.2.3 R basics 2.3 Data frames 2.4 Reading your data2.4.1 Is your data file ready? 2.4.2 R Projects2.4.3 Importing your data2.5 The tidyverse package2.5.1 Wide-to-long transformation2.5.2 Grouping, filtering, changing, and summarizing data 2.6 Figures 2.6.1 Using ggplot2 2.6.2 General guidelines for data visualization 2.7 Basic statistics in R 2.7.1 What’s your research question?2.7.2 t-tests and ANOVAs in R2.7.3 A post-hoc test in R 2.8 More packages 2.9 Additional readings on R 2.10 Summary2.11 Exercises Part II Visualizing the data 3 Continuous data 3.1 Importing your data 3.2 Preparing your data 3.3 Histograms 3.4 Scatter plots3.5 Box plots3.6 Bar plots and error bars3.7 Line plots 3.8 Additional readings on data visualization 3.9 Summary3.10 Exercises 4 Categorical data 4.1 Binary data 4.2 Ordinal data4.3 Summary 4.4 Exercises 5 Aesthetics: optimizing your figures 5.1 More on aesthetics 5.2 Exercises Part III Analyzing the data 1276 Linear regression 1296.1 Introduction6.2 Examples and interpretation6.2.1 Does Hours affect scores?6.2.2 Does Feedback affect scores? 6.2.3 Do Feedback and Hours affect scores?6.2.4 Do Feedback and Hours interact?6.3 Beyond the basics6.3.1 Comparing models and plotting estimates 6.3.2 Scaling variables 6.4 Summary 6.5 Exercises 7 Logistic regression 7.1 Introduction7.1.1 Defining the best curve in a logistic model 7.1.2 A family of models 7.2 Examples and interpretation 7.2.1 Can reaction time differentiate learners and native speakers? 7.2.2 Does Condition affect responses? 7.2.3 Do Proficiency and Condition affect responses? 7.2.4 Do Proficiency and Condition interact? 7.3 Summary7.4 Exercises 8 Ordinal regression 8.1 Introduction8.2 Examples and interpretation 8.2.1 Does Condition affect participants’ certainty? 8.2.2 Do Condition and L1 interact?8.3 Summary 8.4 Exercises 9 Hierarchical models 9.1 Introduction 9.2 Examples and interpretation 9.2.1 Random-intercept model 9.2.2 Random-slope and random-intercept model 9.3 Additional readings on regression models 9.4 Summary9.5 Exercises 10 Going Bayesian 10.1 Introduction to Bayesian data analysis 10.1.1 Sampling from the posterior 10.2 The RData format10.3 Getting ready10.4 Bayesian models: linear and logistic examples 10.4.1 Bayesian model A: Feedback 10.4.2 Bayesian model B: Relative clauses with prior specifications 10.5 Additional readings on Bayesian inference 10.6 Summary 10.7 Exercises11 Final remarks Appendix A: TroubleshootingAppendix B: RStudio shortcuts Appendix C: Symbols and acronyms Appendix D: Files used in this book Appendix E: Contrast coding Appendix F: Models and nested dataGlossary ReferencesSubject index Function Index

Guilherme D. Garcia is Assistant Professor of Linguistics at Ball State University, USA.

Highly recommended as an accessible introduction to the use of R for analysis of second language data. Readers will come away with an understanding of why and how to use statistical models and data visualization techniques in their research.Lydia White, McGill University, Canada.Curious where the field’s quantitative methods are headed? The answer is in your hands right now! Whether we knew it or not, this is the book that many of us have been waiting for. From scatter plots to standard errors and from beta values to Bayes theorem, Garcia provides us with all the tools we need—both conceptual and practical—to statistically and visually model the complexities of L2 development.Luke Plonsky, Northern Arizona University, USA.This volume is a timely and must-have addition to any quantitative SLA researcher’s data analysis arsenal, whether you are downloading R for the first time or a seasoned user ready to dive into Bayesian analysis. Guilherme Garcia’s accessible, conversational writing style and uncanny ability to provide answers to questions right as you’re about to ask them will give new users the confidence to make the move to R and will serve as an invaluable resource for students and instructors alike for years to come. Jennifer Cabrelli, University of Illinois at Chicago, USA.

• ContentsList of figuresList of tablesList of code blocksAcknowledgments Preface Part I Getting ready 1 Introduction 1.1 Main objectives of this book 1.2 A logical series of steps 1.2.1 Why focus on data visualization techniques? 1.2.2 Why focus on full-fledged statistical models? 1.3 Statistical concepts 1.3.1 p-values1.3.2 Effect sizes 1.3.3 Confidence intervals 1.3.4 Standard errors1.3.5 Further reading2 R basics 232.1 Why R?2.2 Fundamentals 2.2.1 Installing R and RStudio2.2.2 Interface 2.2.3 R basics 2.3 Data frames 2.4 Reading your data2.4.1 Is your data file ready? 2.4.2 R Projects2.4.3 Importing your data2.5 The tidyverse package2.5.1 Wide-to-long transformation2.5.2 Grouping, filtering, changing, and summarizing data 2.6 Figures 2.6.1 Using ggplot2 2.6.2 General guidelines for data visualization 2.7 Basic statistics in R 2.7.1 What’s your research question?2.7.2 t-tests and ANOVAs in R2.7.3 A post-hoc test in R 2.8 More packages 2.9 Additional readings on R 2.10 Summary2.11 Exercises Part II Visualizing the data 3 Continuous data 3.1 Importing your data 3.2 Preparing your data 3.3 Histograms 3.4 Scatter plots3.5 Box plots3.6 Bar plots and error bars3.7 Line plots 3.8 Additional readings on data visualization 3.9 Summary3.10 Exercises 4 Categorical data 4.1 Binary data 4.2 Ordinal data4.3 Summary 4.4 Exercises 5 Aesthetics: optimizing your figures 5.1 More on aesthetics 5.2 Exercises Part III Analyzing the data 1276 Linear regression 1296.1 Introduction6.2 Examples and interpretation6.2.1 Does Hours affect scores?6.2.2 Does Feedback affect scores? 6.2.3 Do Feedback and Hours affect scores?6.2.4 Do Feedback and Hours interact?6.3 Beyond the basics6.3.1 Comparing models and plotting estimates 6.3.2 Scaling variables 6.4 Summary 6.5 Exercises 7 Logistic regression 7.1 Introduction7.1.1 Defining the best curve in a logistic model 7.1.2 A family of models 7.2 Examples and interpretation 7.2.1 Can reaction time differentiate learners and native speakers? 7.2.2 Does Condition affect responses? 7.2.3 Do Proficiency and Condition affect responses? 7.2.4 Do Proficiency and Condition interact? 7.3 Summary7.4 Exercises 8 Ordinal regression 8.1 Introduction8.2 Examples and interpretation 8.2.1 Does Condition affect participants’ certainty? 8.2.2 Do Condition and L1 interact?8.3 Summary 8.4 Exercises 9 Hierarchical models 9.1 Introduction 9.2 Examples and interpretation 9.2.1 Random-intercept model 9.2.2 Random-slope and random-intercept model 9.3 Additional readings on regression models 9.4 Summary9.5 Exercises 10 Going Bayesian 10.1 Introduction to Bayesian data analysis 10.1.1 Sampling from the posterior 10.2 The RData format10.3 Getting ready10.4 Bayesian models: linear and logistic examples 10.4.1 Bayesian model A: Feedback 10.4.2 Bayesian model B: Relative clauses with prior specifications 10.5 Additional readings on Bayesian inference 10.6 Summary 10.7 Exercises11 Final remarks Appendix A: TroubleshootingAppendix B: RStudio shortcuts Appendix C: Symbols and acronyms Appendix D: Files used in this book Appendix E: Contrast coding Appendix F: Models and nested dataGlossary ReferencesSubject index Function Index