Statistics for Linguists: An Introduction Using R

Cover
Half Title
Title
Copyright
Contents
Acknowledgments
0. Preface: Approach and How to Use This Book
	0.1. Strategy of the Book
	0.2. Why R?
	0.3. Why the Tidyverse?
	0.4. R Packages Required for This Book
	0.5. What This Book Is Not
	0.6. How to Use This Book
	0.7. Information for Teachers
1 Introduction to R
	1.1. Introduction
	1.2. Baby Steps: Simple Math with R
	1.3. Your First R Script
	1.4. Assigning Variables
	1.5. Numeric Vectors
	1.6. Indexing
	1.7. Logical Vectors
	1.8. Character Vectors
	1.9. Factor Vectors
	1.10. Data Frames
	1.11. Loading in Files
	1.12. Plotting
	1.13. Installing, Loading, and Citing Packages
	1.14. Seeking Help
	1.15. A Note on Keyboard Shortcuts
	1.16. Your R Journey: The Road Ahead
2 The Tidyverse and Reproducible R Workflows
	2.1. Introduction
	2.2. tibble and readr
	2.3. dplyr
	2.4. ggplot2
	2.5. Piping with magrittr
	2.6. A More Extensive Example: Iconicity and the Senses
	2.7. R Markdown
	2.8. Folder Structure for Analysis Projects
	2.9. Readme Files and More Markdown
	2.10. Open and Reproducible Research
3 Descriptive Statistics, Models, and Distributions
	3.1. Models
	3.2. Distributions
	3.3. The Normal Distribution
	3.4. Thinking of the Mean as a Model
	3.5. Other Summary Statistics: Median and Range
	3.6. Boxplots and the Interquartile Range
	3.7. Summary Statistics in R
	3.8. Exploring the Emotional Valence Ratings
	3.9. Chapter Conclusions
4 Introduction to the Linear Model: Simple Linear Regression
	4.1. Word Frequency Effects
	4.2. Intercepts and Slopes
	4.3. Fitted Values and Residuals
	4.4. Assumptions: Normality and Constant Variance
	4.5. Measuring Model Fit with R2
	4.6. A Simple Linear Model in R
	4.7. Linear Models with Tidyverse Functions
	4.8. Model Formula Notation: Intercept Placeholders
	4.9. Chapter Conclusions
5 Correlation, Linear, and Nonlinear Transformations
	5.1. Centering
	5.2. Standardizing
	5.3. Correlation
	5.4. Using Logarithms to Describe Magnitudes
	5.5. Example: Response Durations and Word Frequency
	5.6. Centering and Standardization in R
	5.7. Terminological Note on the Term ‘Normalizing’
	5.8. Chapter Conclusions
6 Multiple Regression
	6.1. Regression with More Than One Predictor
	6.2. Multiple Regression with Standardized Coefficients
	6.3. Assessing Assumptions
	6.4. Collinearity
	6.5. Adjusted R2
	6.6. Chapter Conclusions
7 Categorical Predictors
	7.1. Introduction
	7.2. Modeling the Emotional Valence of Taste and Smell Words
	7.3. Processing the Taste and Smell Data
	7.4. Treatment Coding in R
	7.5. Doing Dummy Coding ‘By Hand’
	7.6. Changing the Reference Level
	7.7. Sum-coding in R
	7.8. Categorical Predictors with More Than Two Levels
	7.9. Assumptions Again
	7.10. Other Coding Schemes
	7.11. Chapter Conclusions
8 Interactions and Nonlinear Effects
	8.1. Introduction
	8.2. Categorical * Continuous Interactions
	8.3. Categorical * Categorical Interactions
	8.4. Continuous * Continuous Interactions
	8.5. Nonlinear Effects
	8.6. Higher-Order Interactions
	8.7. Chapter Conclusions
9 Inferential Statistics 1: Significance Testing
	9.1. Introduction
	9.2. Effect Size: Cohen’s d
	9.3. Cohen’s d in R
	9.4. Standard Errors and Confidence Intervals
	9.5. Null Hypotheses
	9.6. Using t to Measure the Incompatibility with the Null Hypothesis
	9.7. Using the t-Distribution to Compute p-Values
	9.8. Chapter Conclusions
10 Inferential Statistics 2: Issues in Significance Testing
	10.1. Common Misinterpretations of p-Values
	10.2. Statistical Power and Type I, II, M, and S Errors
	10.3. Multiple Testing
	10.4. Stopping rules
	10.5. Chapter Conclusions
11 Inferential Statistics 3: Significance Testing in a Regression Context
	11.1. Introduction
	11.2. Standard Errors and Confidence Intervals for Regression Coefficients
	11.3. Significance Tests with Multilevel Categorical Predictors
	11.4. Another Example: The Absolute Valence of Taste and Smell Words
	11.5. Communicating Uncertainty for Categorical Predictors
	11.6. Communicating Uncertainty for Continuous Predictors
	11.7. Chapter Conclusions
12 Generalized Linear Models 1: Logistic Regression
	12.1. Motivating Generalized Linear Models
	12.2. Theoretical Background: Data-Generating Processes
	12.3. The Log Odds Function and Interpreting Logits
	12.4. Speech Errors and Blood Alcohol Concentration
	12.5. Predicting the Dative Alternation
	12.6. Analyzing Gesture Perception
	12.7. Chapter Conclusions
13 Generalized Linear Models 2: Poisson Regression
	13.1. Motivating Poisson Regression
	13.2. The Poisson Distribution
	13.3. Analyzing Linguistic Diversity Using Poisson Regression
	13.4. Adding Exposure Variables
	13.5. Negative Binomial Regression for Overdispersed Count Data
	13.6. Overview and Summary of the Generalized Linear Model Framework
	13.7. Chapter Conclusions
14 Mixed Models 1: Conceptual Introduction
	14.1. Introduction
	14.2. The Independence Assumption
	14.3. Dealing with Non-independence via Experimental Design and Averaging
	14.4. Mixed Models: Varying Intercepts and Varying Slopes
	14.5. More on Varying Intercepts and Varying Slopes
	14.6. Interpreting Random Effects and Random Effect Correlations
	14.7. Specifying Mixed Effects Models: lme4 syntax
	14.8. Reasoning About Your Mixed Model: The Importance of Varying Slopes
	14.9. Chapter Conclusions
15 Mixed Models 2: Extended Example, Significance Testing, Convergence Issues
	15.1. Introduction
	15.2. Simulating Vowel Durations for a Mixed Model Analysis
	15.3. Analyzing the Simulated Vowel Durations with Mixed Models
	15.4. Extracting Information out of lme4 Objects
	15.5. Messing up the Model
	15.6. Likelihood Ratio Tests
	15.7. Remaining Issues
	15.8. Mixed Logistic Regression: Ugly Selfies
	15.9. Shrinkage and Individual Differences
	15.10. Chapter Conclusions
16 Outlook and Strategies for Model Building
	16.1. What You Have Learned So Far
	16.2. Model Choice
	16.3. The Cookbook Approach
	16.4. Stepwise Regression
	16.5. A Plea for Subjective and Theory-Driven Statistical Modeling
	16.6. Reproducible Research
	16.7. Closing Words
References
Appendix A. Correspondences Between Significance Tests and Linear Models
	A1. t-Tests
	A2. Tests for Categorical Data
	A3. Other Tests
Appendix B. Reading Recommendations
	B1. Book Recommendations
	B2. Article Recommendations
	B3. Staying Up-to-Date
Index
Index of R Functions