Regression Modeling for Linguistic Data

<b>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.</b><br><br>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<i>.</i> The book features extensive treatment of mixed-effects regression models, the most widely used statistical method for analyzing linguistic data. <br><br>Sonderegger begins with preliminaries to regression modelin 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 nonline

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 Sondereggerprovides 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 modelin 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 linguistsUses R throughout, in practical examples using real datasetsExtensive treatment of mixed-effects regression modelsContains detailed, clear guidance on reporting modelsEqual emphasis on observational data and data from controlled experimentsSuitable forgraduate students and researchers with computational interestsacross linguistics and cognitive science