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By: Morgan Sonderegger
The first comprehensive textbook on regression modeling for linguistic data offers an incisive conceptual overview along with worked examples that teach practical skills for realistic data analysis.
In the first comprehensive textbook on regression modeling for linguistic data in a frequentist framework, Morgan Sonderegger provides graduate students and researchers with an incisive conceptual overview along with worked examples that teach practical skills for realistic data analysis. The book features extensive treatment of mixed-effects regression models, the most widely used statistical method for analyzing linguistic data.
Sonderegger begins with preliminaries to regression modeling: assumptions, inferential statistics, hypothesis testing, power, and other errors. He then covers regression models for non-clustered data: linear regression, model selection and validation, logistic regression, and applied topics such as contrast coding and nonlinear effects. The last three chapters discuss regression models for clustered data: linear and logistic mixed-effects models as well as model predictions, convergence, and model selection. The book’s focused scope and practical emphasis will equip readers to implement these methods and understand how they are used in current work.
The first comprehensive textbook on regression modeling for linguistic data offers an incisive conceptual overview along with worked examples that teach practical skills for realistic data analysis.
In the first comprehensive textbook on regression modeling for linguistic data in a frequentist framework, Morgan Sonderegger provides graduate students and researchers with an incisive conceptual overview along with worked examples that teach practical skills for realistic data analysis. The book features extensive treatment of mixed-effects regression models, the most widely used statistical method for analyzing linguistic data.
Sonderegger begins with preliminaries to regression modeling: assumptions, inferential statistics, hypothesis testing, power, and other errors. He then covers regression models for non-clustered data: linear regression, model selection and validation, logistic regression, and applied topics such as contrast coding and nonlinear effects. The last three chapters discuss regression models for clustered data: linear and logistic mixed-effects models as well as model predictions, convergence, and model selection. The book’s focused scope and practical emphasis will equip readers to implement these methods and understand how they are used in current work.
- The only advanced discussion of modeling for linguists
- Uses R throughout, in practical examples using real datasets
- Extensive treatment of mixed-effects regression models
- Contains detailed, clear guidance on reporting models
- Equal emphasis on observational data and data from controlled experiments
- Suitable for graduate students and researchers with computational interests across linguistics and cognitive science
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