专著推荐 | 《二语研究中的数据可视化与分析》Routledge新书

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专著推荐

通讯君与Routledge出版社合作推广最新、最好的语言研究著作。本期"专著推荐"（本栏目不区分books与edited books）强烈种草美国学者Guilherme Duarte Garcia的最新著作Data Visualization and Analysis in Second Language Research。传统二语习得的数据可视化主要是通过SPSS软件实现的，但是囿于软件功能已经不能满足国际学术研究的需求。使用R做二语研究数据分析与可视化呈现不仅能满足更多研究需求，更是一种国际学术研究的当下潮流。

当下的数据可视化主要是借助于图形化手段，清晰有效地传达与沟通信息。但是，这并不就意味着，数据可视化就一定因为要实现其功能用途而令人感到枯燥乏味，或者是为了看上去绚丽多彩而显得极端复杂。主要是为了有效地传达思想概念，美学形式与信息揭示需要齐头并进，通过直观地传达关键的方面与特征，从而实现对于相当稀疏而又复杂的数据集的深入洞察。

（注：本图为精装版封面）

本期书目 

ISBN 9780367469610

Published May 31, 2021 by Routledge

286 Pages 30 B/W Illustrations

作者：Guilherme Duarte Garcia

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内容简介

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.

作者简介

 

Dr. Garcia is an Assistant Professor in the Department of English at Ball State University. He has a PhD in Linguistics from McGill University, where he was also a member of the Centre for Research on Brain, Language, and Music (CRBLM). His research interests are Phonology, (Second) Language Acquisition, and quantitative data analysis. He's especially interested in using data analysis to uncover patterns that help us better assess representational and theoretical assumptions in phonology.

本书目录

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 

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 ggplot

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 

6 Linear regression 

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

编者按

本文编辑：吉林大学 王峰

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