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Data Visualization and Analysis in Second Language Research

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. Les mer
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Innbundet
Vår pris: 2025,-

(Innbundet) Fri frakt!
Leveringstid: Ikke i salg

Om boka

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.

Fakta

Innholdsfortegnelse

Contents


List of figures


List of tables


List of code blocks


Acknowledgments


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-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


Glossary


References


Subject index


Function Index








Om forfatteren

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