Regression Modeling for Linguistic Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression Modeling for Linguistic Data write by Morgan Sonderegger. This book was released on 2023-06-06. Regression Modeling for Linguistic Data available in PDF, EPUB and Kindle. 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
Mixed-Effects Regression Models in Linguistics
Mixed-Effects Regression Models in Linguistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Mixed-Effects Regression Models in Linguistics write by Dirk Speelman. This book was released on 2018-02-07. Mixed-Effects Regression Models in Linguistics available in PDF, EPUB and Kindle. When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed. In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addresses a number of common complications, misunderstandings, and pitfalls. Topics that are covered include the use of huge datasets, dealing with non-linear relations, issues of cross-validation, and issues of model selection and complex random structures. The volume features examples from various subfields in linguistics. The book also provides R code for a wide range of analyses.
Analyzing Linguistic Data
Analyzing Linguistic Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Analyzing Linguistic Data write by R. H. Baayen. This book was released on 2008-03-06. Analyzing Linguistic Data available in PDF, EPUB and Kindle. Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.
Regression Modeling for Linguistic Data
Regression Modeling for Linguistic Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Regression Modeling for Linguistic Data write by Morgan Sonderegger. This book was released on 2023-06-06. Regression Modeling for Linguistic Data available in PDF, EPUB and Kindle. 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
How to do Linguistics with R
How to do Linguistics with R - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook How to do Linguistics with R write by Natalia Levshina. This book was released on 2015-11-25. How to do Linguistics with R available in PDF, EPUB and Kindle. This book provides a linguist with a statistical toolkit for exploration and analysis of linguistic data. It employs R, a free software environment for statistical computing, which is increasingly popular among linguists. How to do Linguistics with R: Data exploration and statistical analysis is unique in its scope, as it covers a wide range of classical and cutting-edge statistical methods, including different flavours of regression analysis and ANOVA, random forests and conditional inference trees, as well as specific linguistic approaches, among which are Behavioural Profiles, Vector Space Models and various measures of association between words and constructions. The statistical topics are presented comprehensively, but without too much technical detail, and illustrated with linguistic case studies that answer non-trivial research questions. The book also demonstrates how to visualize linguistic data with the help of attractive informative graphs, including the popular ggplot2 system and Google visualization tools. This book has a companion website: http://doi.org/10.1075/z.195.website
