Multilevel Modeling Using 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 Multilevel Modeling Using R write by W. Holmes Finch. This book was released on 2019-07-16. Multilevel Modeling Using R available in PDF, EPUB and Kindle. Like its bestselling predecessor, Multilevel Modeling Using R, Second Edition provides the reader with a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. New in the Second Edition: Features the use of lmer (instead of lme) and including the most up to date approaches for obtaining confidence intervals for the model parameters. Discusses measures of R2 (the squared multiple correlation coefficient) and overall model fit. Adds a chapter on nonparametric and robust approaches to estimating multilevel models, including rank based, heavy tailed distributions, and the multilevel lasso. Includes a new chapter on multivariate multilevel models. Presents new sections on micro-macro models and multilevel generalized additive models. This thoroughly updated revision gives the reader state-of-the-art tools to launch their own investigations in multilevel modeling and gain insight into their research. About the Authors: W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology at Ball State University. Jocelyn E. Bolin is a Professor in the Department of Educational Psychology at Ball State University. Ken Kelley is the Edward F. Sorin Society Professor of IT, Analytics and Operations and the Associate Dean for Faculty and Research for the Mendoza College of Business at the University of Notre Dame.
Multilevel Modeling Using R
Multilevel Modeling Using 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 Multilevel Modeling Using R write by W. Holmes Finch. This book was released on 2016-03-09. Multilevel Modeling Using R available in PDF, EPUB and Kindle. Multilevel Modelling using R provides a helpful guide to conducting multilevel data modeling using the R software environment. After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The book concludes with Bayesian fitting of multilevel models. Complete data sets for the book can be found on the book's website www.mlminr.com/
Data Analysis Using Regression and Multilevel/Hierarchical Models
Data Analysis Using Regression and Multilevel/Hierarchical Models - 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 Data Analysis Using Regression and Multilevel/Hierarchical Models write by Andrew Gelman. This book was released on 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models available in PDF, EPUB and Kindle. This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Multilevel Modeling in Plain Language
Multilevel Modeling in Plain Language - 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 Multilevel Modeling in Plain Language write by Karen Robson. This book was released on 2015-11-02. Multilevel Modeling in Plain Language available in PDF, EPUB and Kindle. Have you been told you need to do multilevel modeling, but you can′t get past the forest of equations? Do you need the techniques explained with words and practical examples so they make sense? Help is here! This book unpacks these statistical techniques in easy-to-understand language with fully annotated examples using the statistical software Stata. The techniques are explained without reliance on equations and algebra so that new users will understand when to use these approaches and how they are really just special applications of ordinary regression. Using real life data, the authors show you how to model random intercept models and random coefficient models for cross-sectional data in a way that makes sense and can be retained and repeated. This book is the perfect answer for anyone who needs a clear, accessible introduction to multilevel modeling.
Beyond Multiple Linear Regression
Beyond Multiple Linear Regression - 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 Beyond Multiple Linear Regression write by Paul Roback. This book was released on 2021-01-14. Beyond Multiple Linear Regression available in PDF, EPUB and Kindle. Beyond Multiple Linear Regression: Applied Generalized Linear Models and Multilevel Models in R is designed for undergraduate students who have successfully completed a multiple linear regression course, helping them develop an expanded modeling toolkit that includes non-normal responses and correlated structure. Even though there is no mathematical prerequisite, the authors still introduce fairly sophisticated topics such as likelihood theory, zero-inflated Poisson, and parametric bootstrapping in an intuitive and applied manner. The case studies and exercises feature real data and real research questions; thus, most of the data in the textbook comes from collaborative research conducted by the authors and their students, or from student projects. Every chapter features a variety of conceptual exercises, guided exercises, and open-ended exercises using real data. After working through this material, students will develop an expanded toolkit and a greater appreciation for the wider world of data and statistical modeling. A solutions manual for all exercises is available to qualified instructors at the book’s website at www.routledge.com, and data sets and Rmd files for all case studies and exercises are available at the authors’ GitHub repo (https://github.com/proback/BeyondMLR)
