Statistics for linguists: an introduction using R
"Statistics for Linguists: An introduction using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematic...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York ; London
Routledge, Taylor & Francis Group
2020
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| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | "Statistics for Linguists: An introduction using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields including Psychology, Cognitive Science, and Data Science"-- |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xvi, 310 Seiten Diagramme |
| ISBN: | 9781138056091 9781138056084 |
Internformat
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| 500 | |a Includes bibliographical references and index | ||
| 520 | 3 | |a "Statistics for Linguists: An introduction using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields including Psychology, Cognitive Science, and Data Science"-- | |
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Datensatz im Suchindex
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| adam_text | Contents A cknowledgments 0. Preface: Approach and How to Use This Book 0.1. Strategy ofthe Book xii 0.2. WhyR? xiii 0.3. Why the Tidyverse? xiv 0.4. R Packages Requiredfor This Book xv 0.5. What This Book Is Not xv 0.6. How to Use This Book xv 0.7. Informationfor Teachers xvi 1 Introduction to R 1.1. Introduction 1 1.2. Baby Steps: Simple Math with R 2 1.3. Your First R Script 4 1.4. Assigning Variables 5 1.5. Numeric Vectors 7 1.6. Indexing 9 1.7. Logical Vectors 10 1.8. Character Vectors 11 1.9. Factor Vectors 12 1.10. Data Frames 13 1.11. Loading in Files 16 1.12. Plotting 19 1.13. Installing, Loading, and Citing Packages 20 1.14. Seeking Help 21 1.15. A Note on Keyboard Shortcuts 22 1.16. Your R Journey: The Road Ahead 23 2 The Tidyverse and Reproducible R Workflows 2.1. Introduction 27 2.2. tibble and readr 28 2.3. dplyr 30 2.4. ggplot2 34 2.5. Piping with magr ittr 36 2.6. A More Extensive Example: Iconicity and the Senses 37
vi Contents 2.7. 2.8. 2.9. 2.10. R Markdown 44 Folder Structure for Analysis Projects 45 Readme Files and More Markdown 46 Open and Reproducible Research 47 3 Descriptive Statistics, Modeis, and Distributions 3.1. Models 53 3.2. Distributions 53 3.3. The Normal Distribution 54 3.4. Thinking ofthe Mean as a Model 57 3.5. Other Summary Statistics: Median and Range 58 3.6. Boxplots and the Interquartile Range 59 3.7. Summary Statistics in R 60 3.8. Exploring the Emotional Valence Ratings 64 3.9. Chapter Conclusions 67 4 Introduction to the Linear Model: Simple Linear Regression 4.1. Word Frequency Effects 69 4.2. Intercepts and Slopes 71 4.3. Fitted Values and Residuals 72 4.4. Assumptions: Normality and Constant Variance 74 4.5. Measuring Model Fit with R2 74 4.6. A Simple Linear Model in R 77 4.7. Linear Models with Tidyverse Functions 82 4.8. Model Formula Notation: Intercept Placeholders 83 4.9. Chapter Conclusions 84 5 Correlation, Linear, and Nonlinear Transformations 5.1. Centering 86 5.2. Standardizing 87 5.3. Correlation 89 5.4. Using Logarithms to Describe Magnitudes 90 5.5. Example: Response Durations and Word Frequency 94 5.6. Centering and Standardization in R 98 5.7. Terminological Note on the Term ‘Normalizing’ 101 5.8. Chapter Conclusions 101 6 Multiple Regression 6.1. Regression with More Than One Predictor 103 6.2. Multiple Regression with Standardized Coefficients 105 6.3. Assessing Assumptions 109 6.4. Collinearity 112 6.5. Adjusted К2 115 6.6. Chapter Conclusions 116
Contents vii 7 Categorical Predictors 7.1. Introduction 117 7.2. Modeling the Emotional Valence of Taste and Smell Words 117 7.3. Processing the Taste and Smell Data 119 7.4. Treatment Coding in R 122 7.5. Doing Dummy Coding ‘By Hand’ 123 7.6. Changing the Reference Level 124 7.7. Sum-coding in R 125 7.8. Categorical Predictors with More Than Two Levels 127 7.9. Assumptions Again 129 7.10. Other Coding Schemes 130 7.11. Chapter Conclusions 131 117 8 Interactions and Nonlinear Effects 8.1. Introduction 133 8.2. Categorical * Continuous Interactions 134 8.3. Categorical * Categorical Interactions 139 8.4. Continuous * Continuous Interactions 146 8.5. Nonlinear Effects 150 8.6. Higher-Order Interactions 155 8.7. Chapter Conclusions 156 133 9 Inferential Statistics 1: Significance Testing 157 9.1. Introduction 157 9.2. Effect Size: Cohen’s d 159 9.3. Cohen’s d inR 161 9.4. Standard Errors and Confidence Intervals 162 9.5. Null Hypotheses 165 9.6. Using t to Measure the Incompatibility with the Null Hypothesis 166 9.7. Using the t-Distribution to Compute p-Values 167 9.8. Chapter Conclusions 169 10 Inferential Statistics 2: Issues in Significance Testing 10.1. Common Misinterpretations ofp- Values 171 10.2. Statistical Power and Type I, II, M, and S Errors 171 10.3. Multiple Testing 175 10.4. Stopping rules 177 10.5. Chapter Conclusions 178 171 11 Inferential Statistics 3: Significance Testing in a Regression Context 11.1. Introduction 180 11.2. Standard Errors and Confidence Intervals for Regression Coefficients 180 11.3. Significance Tests with Multilevel Categorical
Predictors 184 180
viii Contents 11.4. A nother Example: The A bsolute Valence of Taste and Smell Words 188 11.5. Communicating Uncertaintyfor Categorical Predictors 190 11.6. Communicating Uncertaintyfor Continuous Predictors 194 11.7. Chapter Conclusions 197 12 Generalized Linear Models 1: Logistic Regression 12.1. Motivating Generalized Linear Models 198 12.2. Theoretical Background: Data-Generating Processes 198 12.3. The Log Odds Function and Interpreting Logits 202 12.4. Speech Errors and Blood A kohol Concentration 204 12.5. Predicting the Dative Alternation 207 12.6. Analyzing Gesture Perception 210 12.7. Chapter Conclusions 216 13 Generalized Linear Models 2: Poisson Regression 13.1. Motivating Poisson Regression 218 13.2. The Poisson Distribution 218 13.3. Analyzing Linguistic Diversity Using Poisson Regression 220 13.4. Adding Exposure Variables 225 13.5. Negative Binomial Regression for Overdispersed Count Data 227 13.6. Overview and Summary of the Generalized Linear Model Framework 229 13.7. Chapter Conclusions 230 14 Mixed Models 1: Conceptual Introduction 14.1. Introduction 232 14.2. The Independence Assumption 232 14.3. Dealing with Non-independence via Experimental Design and Averaging 233 14.4. Mixed Models: Varying Intercepts and Varying Slopes 234 14.5. More on Varying Intercepts and Varying Slopes 237 14.6. Interpreting Random Effects and Random Effect Correlations 238 14.7. Specifying Mixed Effects Models: Ime 4 syntax 240 14.8. Reasoning About Your Mixed Model: The Importance of Varying Slopes 241 14.9. Chapter Conclusions 244 15 Mixed Models 2: Extended Example,
Significance Testing, Convergence Issues 15.1. Introduction 245 15.2. Simulating Vowel Durationsfor a Mixed Model Analysis 245
Contents ix 15.3. Analyzing the Simulated Vowel Durations with Mixed Models 253 15.4. Extracting Information out of Ime 4 Objects 255 15.5. Messing up the Model 257 15.6. Likelihood Ratio Tests 260 15.7. Remaining Issues 264 15.8. Mixed Logistic Regression: Ugly Selfies 267 15.9. Shrinkage and Individual Differences 271 15.10. Chapter Conclusions 272 16 Outlook and Strategies for Model Building 16.1. 16.2. 16.3. 16.4. 16.5. 16.6. 16.7. 274 What You Have Learned So Far 274 Model Choice 275 The Cookbook Approach 275 Stepwise Regression 276 A Plea for Subjective and Theory-Driven Statistical Modeling 277 Reproducible Research 279 Closing Words 280 References Appendix A. Correspondences Between Significance Tests and Linear Models Al. t-Tests 290 A2. Tests for Categorical Data 295 A3. Other Tests 299 Appendix B. Reading Recommendations BÍ. Book Recommendations 301 B2. Article Recommendations 302 B3. Staying Up-էo-Date 303 Index Index ofR Functions 281 290 301 304 308
|
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| spelling | Winter, Bodo Verfasser (DE-588)1187771937 aut Statistics for linguists an introduction using R Bodo Winter New York ; London Routledge, Taylor & Francis Group 2020 xvi, 310 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index "Statistics for Linguists: An introduction using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields including Psychology, Cognitive Science, and Data Science"-- Mathematische Linguistik (DE-588)4037950-4 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Sprachstatistik (DE-588)4182534-2 gnd rswk-swf Linguistics / Statistical methods R (Computer program language) Mathematical linguistics (DE-588)4123623-3 Lehrbuch gnd-content Mathematische Linguistik (DE-588)4037950-4 s R Programm (DE-588)4705956-4 s Sprachstatistik (DE-588)4182534-2 s DE-604 Erscheint auch als Online-Ausgabe Winter, Bodo Statistics for linguists New York : Routledge, 2020 1 Online-Ressource (xvi, 310 Seiten) 9781315165547 9781351677431 9781351677417 9781351677424 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=031668356&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Winter, Bodo Statistics for linguists an introduction using R Mathematische Linguistik (DE-588)4037950-4 gnd R Programm (DE-588)4705956-4 gnd Sprachstatistik (DE-588)4182534-2 gnd |
| subject_GND | (DE-588)4037950-4 (DE-588)4705956-4 (DE-588)4182534-2 (DE-588)4123623-3 |
| title | Statistics for linguists an introduction using R |
| title_auth | Statistics for linguists an introduction using R |
| title_exact_search | Statistics for linguists an introduction using R |
| title_full | Statistics for linguists an introduction using R Bodo Winter |
| title_fullStr | Statistics for linguists an introduction using R Bodo Winter |
| title_full_unstemmed | Statistics for linguists an introduction using R Bodo Winter |
| title_short | Statistics for linguists |
| title_sort | statistics for linguists an introduction using r |
| title_sub | an introduction using R |
| topic | Mathematische Linguistik (DE-588)4037950-4 gnd R Programm (DE-588)4705956-4 gnd Sprachstatistik (DE-588)4182534-2 gnd |
| topic_facet | Mathematische Linguistik R Programm Sprachstatistik Lehrbuch |
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