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Regression Modeling for Linguistic Data

Regression Modeling for Linguistic Data

by Morgan Sonderegger

$60.00 Paperback

454 pp., 7 x 10 in, 6 color illus., 90 b&w illus.

Paperback
9780262045483
Published: June 6, 2023
Publisher: The MIT Press

$60.00

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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

Morgan Sonderegger is Associate Professor of Linguistics at McGill University.

This textbook is a fantastic and much-needed resource, perfect for a graduate-level statistics course for linguists. Sonderegger emphasizes that statistical analysis requires making a series of well-informed choices and prepares students to do so through expert guidance on best practices and nuanced discussion of relevant considerations.
Zuzanna Fuchs, University of Southern California

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Regression Modeling for Linguistic Data

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