How to do linguistics with R: data exploration and statistical analysis
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam ; Philadelphia
John Benjamins Publishing Company
[2015]
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | X, 443 Seiten Diagramme |
| ISBN: | 9789027212245 9789027212252 |
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Datensatz im Suchindex
| _version_ | 1804174773024980992 |
|---|---|
| adam_text | Table of contents
Acknowledgements xi
Introduction i
Chapter 1. What is statistics? Main statistical notions and principles 7
1.1 Statistics and statistics 7
1.2 How to formulate and test your hypotheses 8
1.3 What statistics cannot do for you 15
1.4 Types of variables 16
1.5 Summary 18
Chapter 2. Introduction to R 21
2.1 Why use R? 21
2.2 Installation of the basic distribution and add-on packages 22
2.3 First steps with R 24
2.4 Main types of R objects 30
2.5 RStudio 32
2.6 Importing and exporting your data and saving your graphs 32
2.7 Summary 40
Chapter 3. Descriptive statistics for quantitative variables 41
3.1 Analysing the distribution of word lengths: Basic descriptive statistics 41
3.2 Bad times, good times: Visualization of a distribution and detection of
outliers 50
3.3 Zipfs law and word frequency: Transformation of quantitative variables 62
3.4 Summary 68
Chapter 4. How to explore qualitative variables: Proportions and their visualizations 69
4.1 Frequency tables, proportions and percentages 69
4.2 Visualization of categorical data 72
4.3 Basic Colour Terms: Deviations of Proportions in subcorpora 80
4.4 Summary 85
Chapter 5. Comparing two groups: f-test and Wilcoxon and Mann-Whitney tests
for independent and dependent samples 87
5.1 Comparing group means or medians: An overview of the tests 87
5.2 Comparing the number of associations triggered by high- and low-frequency
nouns with the help of the independent ¿-test 89
5.3 Comparing concreteness scores of high- and low-frequency nouns with the help
of a two-tailed Wilcoxon test 104
viii
How to do Linguistics with R
5.4 Comparing associations produced by native and non-native speakers: ľhe
dependent one-tailed r-test no
5.5 Summary 113
Chapter 6. Relationships between two quantitative variables: Correlation analysis
with elements of linear regression modelling 1 yt
6.1 What is correlation? 115
6.2 Word length and word recognition: The Pearson product-moment correlation
coefficient 116
6.3 Emergence of grammar from lexicon: Spearmans p and Kendalls r 130
6.4 Visualization of correlations between more than two variables with the help ot
correlograms 134
6.5 Summary 137
Chapter 7. More on frequencies and reaction times: Linear regression 139
7.1 The basic principles of linear regression analysis 139
7.2 Putting several explanatory variables together: Predicting reaction times in a
lexical decision task 142
7.3 Summary 169
Chapter 8. Finding differences between several groups: Sign language, linguistic
relativity and AN OVA 171
8.1 WhatisANOVA? 171
8.2 Motion events in Nicaraguan Sign Language: Independent one-way ANOVA 173
8.3 Development of spatial modulations in Nicaraguan Sign Language: Independent
factorial (two-way) ANOVA 182
8.4 Do native English and native Mandarin Chinese speakers conceptualize time
differently? Repeated-measures and mixed ANOVA (mixed GLM method) 189
8.5 Summary 196
Chapter 9. Measuring associations between two categorical variables: Conceptual
metaphors and tests of independence ^
9.1 Testing independence 199
9.2 The story of over is not over: Metaphoric and non-metaphoric uses in two registers
(analysis of a 2֊by֊2 contingency table) 200
9.3 Metaphorical and non-metaphorical uses of see in four registers (analysis of a
4-by~2 table) 215
9.4 Summary 221
Chapter 10. Association measures: Collocations and collostructions 22:
10.1 Measures of association: A brief typology 223
10.2 Case study: The Russian ditransitive construction and its collexemes 226
10.3 Summary 238
Table of contents ix
Chapter 11. Geographic variation of quite: Distinctive collexeme analysis 241
11.1 Introduction to distinctive collexeme analysis 241
1L2 Distinctive collexeme analysis of quite + ADJ in different varieties of English:
A unified approach 242
11.3 Summary 251
Chapter 12. Probabilistic multifactorial grammar and lexicology: Binomial logistic
regression 253
12.1 Introduction to logistic regression 253
12.2 Logistic regression model of Dutch causative auxiliaries doen and laten 254
12.3 Summary 275
Chapter 13. Multinomial (polytomous) logistic regression models of three and more
near synonyms 277
13.1 What is multinomial regression? 277
13.2 Multinomial models of English permissive constructions 277
13.3 Summary 288
Chapter 14. Conditional inference trees and random forests 291
14.1 Conditional inference trees and random forests 291
14.2 Conditional inference trees and random forests of three English causative
constructions 292
14.3 Summary 299
Chapter 15. Behavioural profiles, distance metrics and cluster analysis 301
15.1 What are Behavioural Profiles? 301
15.2 Behavioural Profiles of English analytic causatives 302
15.3 Summary 320
Chapter 16. Introduction to Semantic Vector Spaces: Cosine as a measure of semantic
similarity 3^3
16.1 Distributional models of semantics and Semantic Vector Space models 323
16.2 A Semantic Vector Space model of English verbs of cooking 324
16.3 Summary 331
Chapter 17. Language and space: Dialects, maps and Multidimensional Scaling 333
17.1 Making maps with R 333
17.2 What is Multidimensional Scaling? 336
17.3 Computation and representation of geographical distances 338
17.4 Computation and representation of linguistic distances: The Kruskal non-metric
MDS 342
17.5 The Mantel test for distance matrices 348
17.6 Summary 349
How to do Linguistics with R
Chapter 18. Multidimensional analysis of register variation: Principal Components
Analysis and Factor Analysis
18.1 Multidimensional analysis of register variation 351
18.2 Case study: Register variation in the British National Corpus 352
18.3 Summary 365
Chapter 19. Exemplars, categories, prototypes: Simple and multiple correspondence
analysis
19.1 Register variation of Basic Colour Terms: Simple Correspondence Analysis 367
19.2 Visualization of exemplars and prototypes of lexical categories: Multiple
Correspondence Analysis of Stuhl and S ess el 373
19.3 Summary 384
Chapter 20. Constructional change and motion charts
20.1 The past and present of the future: Diachronic motion charts
of be going to and will 387
20.2 Summary 393
Epilogue
Appendix 1. The most important R objects and basic operations with them
Appendix 2. Main plotting functions and graphical parameters in R
References
Subject Index
Index of R functions and packages
The companion website can be found at:
http://dx.doi.org/10.1075/z. 195. website
35i
3*7
3«7
395
397
409
425
433
441
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.
Howto 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.
|
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| author | Levshina, Natalia 1978- |
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| dewey-hundreds | 400 - Language |
| dewey-ones | 410 - Linguistics |
| dewey-raw | 410.285 |
| dewey-search | 410.285 |
| dewey-sort | 3410.285 |
| dewey-tens | 410 - Linguistics |
| discipline | Sprachwissenschaft Soziologie Literaturwissenschaft |
| format | Book |
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| spelling | Levshina, Natalia 1978- Verfasser (DE-588)107982409X aut How to do linguistics with R data exploration and statistical analysis Natalia Levshina, Université catholique de Louvain Amsterdam ; Philadelphia John Benjamins Publishing Company [2015] X, 443 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Sprachstatistik (DE-588)4182534-2 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Sprachstatistik (DE-588)4182534-2 s R Programm (DE-588)4705956-4 s DE-604 Erscheint auch als Online-Ausgabe 978-90-272-6845-7 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028037273&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028037273&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
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| title | How to do linguistics with R data exploration and statistical analysis |
| title_auth | How to do linguistics with R data exploration and statistical analysis |
| title_exact_search | How to do linguistics with R data exploration and statistical analysis |
| title_full | How to do linguistics with R data exploration and statistical analysis Natalia Levshina, Université catholique de Louvain |
| title_fullStr | How to do linguistics with R data exploration and statistical analysis Natalia Levshina, Université catholique de Louvain |
| title_full_unstemmed | How to do linguistics with R data exploration and statistical analysis Natalia Levshina, Université catholique de Louvain |
| title_short | How to do linguistics with R |
| title_sort | how to do linguistics with r data exploration and statistical analysis |
| title_sub | data exploration and statistical analysis |
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