专著推荐 | 将R语言用于语言量化研究与数据可视化呈现

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★ 推荐语：本期主题是“使用R语言做语言量化研究与可视化呈现”，推荐两本书1是Data Visualization and Analysis in Second Language Research这本书447元，仅余下5本现货。2是Multimodal Theory and Methodology：For the Analysis of (Inter)action and Identity524元，有10本现货。现货售罄后，将转为期货（国际物流8周）

第一本

二语研究中的数据可视化与（量化）分析

Data Visualization and Analysis in Second Language Research

ISBN 9780367469610

By Guilherme D. Garcia

Published May 31, 2021 by Routledge

286 Pages 30 B/W Illustrations

本书特惠价格447元，下单即可发货，扫码即可购买（价格涵盖书费、国际物流、关税、报关费用、税票等一揽子在内）开具电子发票，支持对公转账，联系人王老师13501892122

现货5本，先购先得，售罄后则转为期货，国际物流8周左右

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.

本书目录

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 

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

第二本

使用R语言处理语篇数据

Textual Data Science with R

By Mónica Bécue-Bertaut

Copyright Year 2018

ISBN 9781032093659

Published June 30, 2021 by Chapman and Hall/CRC

212 Pages 50 B/W Illustrations

本书特惠价格447元，下单即可发货，扫码即可购买（价格涵盖书费、国际物流、关税、报关费用、税票等一揽子在内）开具电子发票，支持对公转账，联系人王老师13501892122

现货10本，先购先得，售罄后则转为期货，国际物流8周左右

Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method is illuminated by applications. The book is aimed at researchers and students in statistics, social sciences, hiistory, literature and linguistics. The book will be of interest to anyone from practitioners needing to extract information from texts to students in the field of massive data, where the ability to process textual data is becoming essential.

本书目录

1. Encoding: from a corpus to statistical tables

Textual and contextual data

Textual data

Contextual data

Documents and aggregate documents

Examples and notation

Choosing textual units

Graphical forms

Lemmas

Stems

Repeated segments

In practice

Preprocessing

Unique spellings

Partially-automated preprocessing

Word selection

Word and segment indexes

The Life UK corpus: preliminary results

Verbal content through word and repeated segment indexes

Univariate description of contextual variables

A note on the frequency range

Implementation with the Xplortext package

In summary

2. Correspondence analysis of textual data

Data and goals

Correspondence analysis: a tool for linguistic data analysis

Data: a small example

Objectives

Associations between documents and words

Profile comparisons

Independence of documents and words

The X2 test

Association rates between columns and words

Active row and column clouds

Row and column pro_le spaces

Distributional equivalence and the X2 distance

Inertia of a cloud

Fitting document and word clouds

Factorial axes

Visualizing rows and columns

Category representation

Word representation

Transition formulas

Superimposed representation of rows and columns

Interpretation aids

Eigenvalues and representation quality of the clouds

Contribution of documents and words to axis inertia

Representation quality of a point

Supplementary rows and columns

Supplementary tables

Supplementary frequency rows and columns

Supplementary quantitative and qualitative variables

Validating the visualization

Interpretation scheme for textual CA results

Implementation with Xplortext

Summary of the CA approach

3. Applications of correspondence analysis

Choosing the level of detail for analyses

Correspondence analysis on aggregate free text answers

Data and objectives

Word selection

CA on the aggregate table

Document representation

Word representation

Simultaneous interpretation of the plots

Supplementary elements

Supplementary words

Supplementary repeated segments

Supplementary categories

Implementation with Xplortext

Direct analysis

Data and objectives

The main features of direct analysis

Direct analysis of the culture question

Implementation with Xplortext

4. Clustering in textual analysis

Clustering documents

Dissimilarity measures between documents

Measuring partition quality

Document clusters in the factorial space

Partition quality

Dissimilarity measures between document clusters

The single-linkage method

The complete-linkage method

Ward's method

Agglomerative hierarchical clustering

Hierarchical tree construction algorithm

Selecting the final partition

Interpreting clusters

Direct partitioning

Combining clustering methods

Consolidating partitions

Direct partitioning followed by AHC

A procedure for combining CA and clustering

Example: joint use of CA and AHC

Data and objectives

Data preprocessing using CA

Constructing the hierarchical tree

Choosing the final partition

Contiguity-constrained hierarchical clustering

Principles and algorithm

AHC of age groups with a chronological constraint

Implementation with Xplortext

Example: clustering free text answers

Data and objectives

Data preprocessing

CA: eigenvalues and total inertia

Interpreting the first axes

AHC: building the tree and choosing the final partition

Describing cluster features

Lexical features of clusters

Describing clusters in terms of characteristic words

Describing clusters in terms of characteristic documents

Describing clusters using contextual variables

Describing clusters using contextual qualitative variables

Describing clusters using quantitative contextual variables

Implementation with Xplortext

Summary of the use of AHC on factorial coordinates coming from CA

5. Lexical characterization of parts of a corpus

Characteristic words

Characteristic words and CA

Characteristic words and clustering

Clustering based on verbal content

Clustering based on contextual variables

Hierarchical words

Characteristic documents

Example: characteristic elements and CA

Characteristic words for the categories

Characteristic words and factorial planes

Documents that characterize categories

Characteristic words in addition to clustering

Implementation with Xplortext

6. Multiple factor analysis for textual analysis

Multiple tables in textual analysis

Data and objectives

Data preprocessing

Problems posed by lemmatization

Description of the corpora data

Indexes of the most frequent words

Notation

Objectives

Introduction to MFACT

The limits of CA on multiple contingency tables

How MFACT works

Integrating contextual variables

Analysis of multilingual free text answers

MFACT: eigenvalues of the global analysis

Representation of documents and words

Superimposed representation of the global and partial configurations

Links between the axes of the global analysis and the separate analyses

Representation of the groups of words

Implementation with Xplortext

Simultaneous analysis of two open-ended questions: impact of lemmatization

Objectives

Preliminary steps

MFACT on the left and right: lemmatized or nonlemmatized

Implementation with Xplortext

Other applications of MFACT in textual analysis

MFACT summary

7. Applications and analysis workflows

General rules for presenting results

Analyzing bibliographic databases

Introduction to the lupus data

The corpus

Exploratory analysis of the corpus

CA of the documents _ words table

The eigenvalues

Meta-keys and doc-keys

Analysis of the year-aggregate table

Eigenvalues and CA of the lexical table

Chronological study of drug names

Implementation with Xplortext

Conclusions from the study

Badinter's speech: a discursive strategy Methods

Breaking up the corpus into documents

The speech trajectory unveiled by CA

Results

Argument flow

Conclusions on the study of Badinter's speech

Implementation with Xplortext

Political speeches

Data and objectives

Methodology

Results

Data preprocessing

Lexicometric characteristics of the speeches and lexical table coding

Eigenvalues and Cramér's V

Speech trajectory

Word representation

Remarks

Hierarchical structure of the corpus

Conclusions

Implementation with Xplortext

Corpus of sensory descriptions

Introduction

Data

Eight Catalan wines

Jury

Verbal categorization

Encoding the data

Objectives

Statistical methodology

MFACT and constructing the mean configuration

Determining consensual words

Results

Data preprocessing

Some initial results

Individual configurations

MFACT: directions of inertia common to the majority of groups

MFACT: representing words and documents on the first plane

Word contributions

MFACT: group representation

Consensual words

Conclusion

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