How to do Linguistics with R
Data exploration and statistical analysis
Author
This book provides a linguist with a statistical toolkit for exploration and analysis of linguistic data. It employs R, a free software environment for statistical computing, which is increasingly popular among linguists. How to do Linguistics with R: Data exploration and statistical analysis is unique in its scope, as it covers a wide range of classical and cutting-edge statistical methods, including different flavours of regression analysis and ANOVA, random forests and conditional inference trees, as well as specific linguistic approaches, among which are Behavioural Profiles, Vector Space Models and various measures of association between words and constructions. The statistical topics are presented comprehensively, but without too much technical detail, and illustrated with linguistic case studies that answer non-trivial research questions. The book also demonstrates how to visualize linguistic data with the help of attractive informative graphs, including the popular ggplot2 system and Google visualization tools.
This book has a companion website: http://doi.org/10.1075/z.195.website
[Not in series, 195] 2015. xi, 443 pp.
Publishing status: Available
© John Benjamins
Table of Contents
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Acknowledgements | pp. xi–xii
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Introduction | pp. 1–6
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Chapter 1. What is statistics? Main statistical notions and principles | pp. 7–20
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Chapter 2. Introduction to R | pp. 21–40
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Chapter 3. Descriptive statistics for quantitative variables | pp. 41–68
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Chapter 4. How to explore qualitative variables: proportions and their visualizations | pp. 69–86
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Chapter 5. Comparing two groups: t-test and Wilcoxon and Mann-Whitney tests for independent and dependent samples | pp. 87–114
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Chapter 6. Relationships between two quantitative variables: correlation analysis with elements of linear regression modelling | pp. 115–138
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Chapter 7. More on frequencies and reaction times: Linear regression | pp. 139–170
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Chapter 8. Finding differences between several groups: Sign language, linguistic relativity and ANOVA | pp. 171–198
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Chapter 9. Measuring associations between two categorical variables: Conceptual metaphors and tests of independence | pp. 199–222
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Chapter 10. Association measures: Collocations and collostructions | pp. 223–240
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Chapter 11. Geographic variation of quite: Distinctive collexeme analysis | pp. 241–252
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Chapter 12. Probabilistic multifactorial grammar and lexicology: Binomial logistic regression | pp. 253–276
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Chapter 13. Multinomial (polytomous) logistic regression models of three and more near synonyms | pp. 277–290
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Chapter 14. Conditional inference trees and random forests | pp. 291–300
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Chapter 15. Behavioural profiles, distance metrics and cluster analysis | pp. 301–322
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Chapter 16. Introduction to Semantic Vector Spaces: Cosine as a measure of semantic similarity | pp. 323–332
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Chapter 17. Language and space: Dialects, maps and Multidimensional Scaling | pp. 333–350
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Chapter 18. Multidimensional analysis of register variation: Principal Components Analysis and Factor Analysis | pp. 351–366
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Chapter 19. Exemplars, categories, prototypes: Simple and multiple correspondence analysis | pp. 367–386
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Chapter 20. Constructional change and motion charts | pp. 387–394
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Epilogue | pp. 395–396
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The most important R objects and basic operations with them
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Appendix 1. The most important R objects and basic operations with them | pp. 397–408
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Apeendix 2. Main plotting functions and graphical parameters in R | pp. 409–424
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References | pp. 425–432
“Levshina’s book achieves something few other books on doing linguistics with R have achieved. She has written a book that makes sense even for novice users of R and for linguists not accustomed to statistical computing. Levshina writes in a pedagogically sensitive style, in friendly language, and with just the right amount of explanatory prose to lead the reader to insightful analyses. Taken together, the chapters introduce the reader to a sparkling variety of statistical methods. Best of all, from the point of view of linguists, real *linguistic *problems take centre stage throughout the book– the statistical methods are the means to answer intriguing linguistic questions, not an end in themselves.”
John Newman, University of Alberta
“This is a fantastic textbook: extremely comprehensive (the book surveys almost all major analysis technique used in the linguistic literature – from descriptive statistics over regression analysis to semantic vector space modeling), well-written, and with a much appreciated emphasis on good data visualization. Both beginning and more experienced quantitative linguists will find this book an invaluable resource.”
Benedikt Szmrecsanyi, University of Leuven
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Subjects
Main BIC Subject
CFX: Computational linguistics
Main BISAC Subject
LAN009000: LANGUAGE ARTS & DISCIPLINES / Linguistics / General