1st Edition

Data Visualization and Analysis in Second Language Research

By Guilherme D. Garcia Copyright 2021
    286 Pages 30 B/W Illustrations
    by Routledge

    286 Pages 30 B/W Illustrations
    by Routledge

    This introduction to visualization techniques and statistical models for second language research focuses on three types of data (continuous, binary, and scalar), helping readers to understand regression models fully and to apply them in their work. Garcia offers advanced coverage of Bayesian analysis, simulated data, exercises, implementable script code, and practical guidance on the latest R software packages. The book, also demonstrating the benefits to the L2 field of this type of statistical work, is a resource for graduate students and researchers in second language acquisition, applied linguistics, and corpus linguistics who are interested in quantitative data analysis.


    List of figures

    List of tables

    List of code blocks



    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-values

    1.3.2 Effect sizes

    1.3.3 Confidence intervals

    1.3.4 Standard errors

    1.3.5 Further reading

    2 R basics 23

    2.1 Why R?

    2.2 Fundamentals

    2.2.1 Installing R and RStudio

    2.2.2 Interface

    2.2.3 R basics

    2.3 Data frames

    2.4 Reading your data

    2.4.1 Is your data file ready?

    2.4.2 R Projects

    2.4.3 Importing your data

    2.5 The tidyverse package

    2.5.1 Wide-to-long transformation

    2.5.2 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 R

    2.7.3 A post-hoc test in R

    2.8 More packages

    2.9 Additional readings on R

    2.10 Summary

    2.11 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 plots

    3.5 Box plots

    3.6 Bar plots and error bars

    3.7 Line plots

    3.8 Additional readings on data visualization

    3.9 Summary

    3.10 Exercises

    4 Categorical data

    4.1 Binary data

    4.2 Ordinal data

    4.3 Summary

    4.4 Exercises

    5 Aesthetics: optimizing your figures

    5.1 More on aesthetics

    5.2 Exercises

    Part III Analyzing the data 127

    6 Linear regression 129

    6.1 Introduction

    6.2 Examples and interpretation

    6.2.1 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 basics

    6.3.1 Comparing models and plotting estimates

    6.3.2 Scaling variables

    6.4 Summary

    6.5 Exercises

    7 Logistic regression

    7.1 Introduction

    7.1.1 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 Summary

    7.4 Exercises

    8 Ordinal regression

    8.1 Introduction

    8.2 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 Summary

    9.5 Exercises

    10 Going Bayesian

    10.1 Introduction to Bayesian data analysis

    10.1.1 Sampling from the posterior

    10.2 The RData format

    10.3 Getting ready

    10.4 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 Exercises

    11 Final remarks

    Appendix A: Troubleshooting

    Appendix B: RStudio shortcuts

    Appendix C: Symbols and acronyms

    Appendix D: Files used in this book

    Appendix E: Contrast coding

    Appendix F: Models and nested data



    Subject 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