| Description |
1 online resource |
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text file |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
How to do Linguistics with R -- Title page -- LCC data -- Dedication page -- Table of contents -- Acknowledgements -- Introduction -- 1. Who is this book written for? -- 2. The quantitative turn in linguistics -- 3. How to use this textbook -- 1. What is statistics? -- What you will learn from this chapter: -- 1.1 Statistics and statistics -- 1.2 Formulating and testing your hypotheses -- 1.2.1 Null and alternative hypotheses -- 1.2.2 Those mysterious p-valuesâ#x80;¦ -- 1.2.3 Type I and Type II errors -- 1.2.4 One-tailed and two-tailed statistical tests -- 1.3 What statistics cannot do for you -- 1.4 Types of variables -- 1.5 Summary -- 2. Introduction to R -- What you will learn from this chapter: -- 2.1 Introduction -- 2.2 Installation of the basic distribution and add-on packages -- 2.3 First steps with R -- 2.3.1 Starting R -- 2.3.2 R syntax -- 2.3.3 Exiting from R or terminating a process -- 2.3.4 Getting help -- 2.4 Main types of R objects -- 2.5 RStudio -- 2.6 Importing and exporting your data and saving your graphs -- 2.6.1 Importing your data to R -- 2.6.2 Exporting your data from R -- 2.6.3 Saving your graphs -- 2.7 Summary -- 3. Descriptive statistics for quantitative variables -- What you will learn from this chapter: -- 3.1 Analysing the distribution of word lengths: Basic descriptive statistics -- 3.1.1 The data -- 3.1.2 Measures of central tendency -- 3.1.3 Measures of dispersion -- 3.2 Bad times, good times: Visualization of a distribution and finding outliers -- 3.3 Zipf's law and word frequency: Transformation of quantitative variables -- 3.4 Summary -- 4. How to explore qualitative variables -- What you will learn from this chapter: -- 4.1 Frequency tables, proportions and percentages -- 4.2 Visualization of categorical data -- 4.3 Basic Colour Terms: Deviations of Proportions in subcorpora -- 4.3.1 The data and hypothesis. |
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4.3.2 Deviation of proportions as a measure of dispersion -- 4.4 Summary -- 5. Comparing two groups -- What you will learn from this chapter: -- 5.1 Comparing group means (medians): An overview of the tests -- 5.2 Comparing the number of associations triggered by high- and low-frequency nouns with the help of an independent t-test -- 5.2.1 Data and hypothesis -- 5.2.2 Descriptive statistics and visualizations -- 5.2.3 Choosing an appropriate test to compare the measures of central tendency in two groups -- 5.2.4 Confidence intervals and standard errors -- 5.3 Comparing concreteness scores of high- and low-frequency nouns with the help of a two-tailed Wilcoxon test -- 5.3.1 Data and hypotheses -- 5.3.2 Descriptive statistics and visualizations: Strip charts and rug plots -- 5.3.3 Inferential statistics: The two-tailed Wilcoxon test -- 5.4. Comparing associations produced by native and non-native speakers: A paired one-tailed t-test -- 5.4.1 Creating simulation data -- 5.4.2 Performing the paired t-test -- 5.5 Summary -- 6. Relationships between two numerical variables -- What you will learn from this chapter: -- 6.1 What is correlation? -- 6.2 Word length and word recognition: The Pearson product-moment correlation coefficient -- 6.2.1 The data and hypothesis -- 6.2.2 Descriptive statistics and visualizations -- 6.2.3 Testing the significance of the correlation coefficient -- 6.3 Emergence of grammar from lexicon: Spearman's Ï#x81; and Kendall's Ï#x84;. -- 6.3.1 The data and hypothesis -- 6.3.2 Exploring the data and computing correlation coefficients -- 6.4 Visualization of correlations between more than two variables with the help of correlograms -- 6.5 Summary -- 7. More on frequencies and reaction times -- What you will learn from this chapter -- 7.1 The basic principles of linear regression analysis. |
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7.2 Putting several factors together: Predicting reaction times in a lexical decision task -- 7.2.1 Data and hypotheses -- 7.2.2 The lm() function and interpretation of its output -- 7.2.3 Selecting the explanatory variables -- 7.2.4 Checking for outliers and overly influential observations -- 7.2.5 Checking the regression assumptions -- 7.2.6 Testing and interpreting interactions -- 7.2.7 Checking for overfitting -- 7.2.8 Robust regression: Bootstrap -- 7.3 Summary -- 8. Finding differences between several groups -- What you will learn from this chapter: -- 8.1 What is ANOVA? -- 8.2 Motion events in Nicaraguan Sign Language: Independent one-way ANOVA -- 8.2.1 Theoretical background and data -- 8.2.2 Exploring the data -- 8.2.3 Assumptions of one-way parametric ANOVA -- 8.2.4 Performing parametric one-way ANOVA -- 8.2.5 Alternative tests -- 8.2.6 Post-hoc tests -- 8.3 Development of spatial modulations in Nicaraguan Sign Language: Independent factorial (two-way) ANOVA -- 8.3.1 The data and hypothesis -- 8.3.2 Descriptive statistics for different groups and interaction plot -- 8.3.3 Assumptions of parametric factorial ANOVA -- 8.3.4 ANOVA and orthogonal contrasts -- 8.3.5 Alternative tests -- 8.3.6 Post-hoc tests -- 8.4 Do native English and native Mandarin Chinese speakers conceptualize time differently? Repeated-measured and mixed-design ANOVA (mixed GLM method) -- 8.4.1 The data and hypothesis -- 8.4.2 Fitting a mixed-design ANOVA with the help of mixed GLM -- 8.4.3 Post-hoc tests -- 8.5 Summary -- 9. Measuring associations between two categorical variables -- What you will learn from this chapter: -- 9.1 Testing independence -- 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) -- 9.2.1 The data and hypothesis. |
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9.2.2 Visualizations, proportions and measures of effect size: Odds ratios, Cramér's V and the ø coefficient -- 9.2.3 Testing statistical significance: The Ï#x87;2 -test of independence -- 9.3 Metaphorical and non-metaphorical uses of see in four registers (analysis of a 4-by-2 table) -- 9.3.1 The data and hypothesis -- 9.3.2 Descriptive statistics and visualizations -- 9.3.3 Testing the statistical significance and analysing the residuals: The Ï#x87;2-test and mosaic and association plots -- 9.4 Summary -- 10. Association measures -- What will you learn from this chapter: -- 10.1 Measures of association: A brief typology -- 10.1.1 Frequencies that you will need in order to compute association measures -- 10.1.2 Unidirectional (asymmetric) vs. bidirectional (symmetric) measures -- 10.1.3 Contingency-based vs. non-contingency-based measures -- 10.2 Case study: The Russian ditransitive construction and its collexemes -- 10.2.1 Theoretical background and data -- 10.2.2 Computation of some popular association measures -- 10.3 Summary -- 11. Geographic variation of quite: Distinctive collexeme analysis -- What you will learn from this chapter: -- 11.1 Introduction to distinctive collexeme analysis -- 11.2 Distinctive collexeme analysis of quite + ADJ in different varieties of English -- 11.2.1 Theoretical background and data -- 11.2.2 Simple distinctive collexeme analysis of quite + ADJ in British and American English -- 11.2.3 Multiple distinctive collexeme analysis: Quite + ADJ in the British, American and Canadian varieties of English -- 11.3 Summary -- 12. Probabilistic multifactorial grammar and lexicology -- What you will learn from this chapter: -- 12.1 Introduction to logistic regression -- 12.2 Logistic regression model of Dutch causative auxiliaries doen and laten -- 12.2.1 Theoretical background and data. |
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12.2.2 Fitting a binary logistic regression model: Main functions -- 12.2.3 Selection of variables -- 12.2.4 Testing possible interactions -- 12.2.5 Identifying outliers and overly influential observations -- 12.2.6 Checking the regression assumptions -- 12.2.7 Testing for overfitting -- 12.2.8 Interpretation of the model -- 12.3 Summary -- 13. Multinomial (polytomous) logistic regression models of three and more near synonyms -- What you will learn from this chapter: -- 13.1 What is multinomial regression? -- 13.2 Multinomial models of English permissive constructions -- 13.2.1 Data and hypotheses -- 13.2.2 Contrasting allow and permit with let -- 13.2.3 'One vs. rest' approach -- 13.3 Summary -- 14. Conditional inference trees and random forests -- What you will learn from this chapter: -- 14.1 Conditional inference trees and random forests -- 14.2 Conditional inference trees and random forests of three English causative constructions -- 14.2.1 The data and hypotheses -- 14.2.2 Fitting a conditional inference tree model -- 14.2.3 Random forests -- 14.3 Summary -- 15. Behavioural profiles, distance metrics and cluster analysis -- What you will learn from this chapter: -- 15.1 What are Behavioural Profiles? -- 15.2 Behavioural Profiles of English analytic causatives -- 15.2.1 Data and theoretical background -- 15.2.2 Computation of numeric BP vectors from the categorical data -- 15.2.3 Distance matrix -- 15.2.4 Hierarchical cluster analysis -- 15.2.4.1 Identifying the clusters -- 15.2.4.2 Interpretation of the cluster solution: Snake plots and effect size measures -- 15.2.4.3 Validation of a cluster solution -- 15.2.5 Partitioning methods -- 15.2.5.1 General introduction -- 15.2.5.2 Partitioning around centroids (k-means) -- 15.2.5.3 Partitioning around medoids -- 15.3 Summary -- 16. Introduction to Semantic Vector Spaces. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Computational linguistics -- Methodology.
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Computational linguistics -- Methodology. |
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Computational linguistics -- Statistical methods.
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Computational linguistics -- Statistical methods. |
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Computational linguistics. |
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Linguistics -- Software.
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Linguistics. |
| Genre/Form |
Software.
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Electronic books.
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| Other Form: |
Print version: Levshina, Natalia. How to do linguistics with R Amsterdam ; Philadelphia : John Benjamins Publishing Company, [2015] 9789027212245 (DLC) 2015016708 |
| ISBN |
9789027268457 (pdf) |
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9027268452 (pdf) |
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9789027212245 (hardback) (alkaline paper) |
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