Multilevel Modeling Using R

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Multilevel Modeling Using R

Author : W. Holmes Finch,Jocelyn E. Bolin,Ken Kelley
Publisher : CRC Press
Page : 242 pages
File Size : 47,5 Mb
Release : 2019-07-16
Category : Mathematics
ISBN : 9781351062251

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Multilevel Modeling Using R by W. Holmes Finch,Jocelyn E. Bolin,Ken Kelley Pdf

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

Author : W. Holmes Finch,Jocelyn E. Bolin,Ken Kelley
Publisher : CRC Press
Page : 225 pages
File Size : 50,7 Mb
Release : 2016-03-09
Category : Mathematics
ISBN : 9781466515864

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Multilevel Modeling Using R by W. Holmes Finch,Jocelyn E. Bolin,Ken Kelley Pdf

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/

Categorical Data Analysis and Multilevel Modeling Using R

Author : Xing Liu
Publisher : SAGE Publications
Page : 624 pages
File Size : 55,9 Mb
Release : 2022-02-25
Category : Social Science
ISBN : 9781544324883

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Categorical Data Analysis and Multilevel Modeling Using R by Xing Liu Pdf

Categorical Data Analysis and Multilevel Modeling Using R provides a practical guide to regression techniques for analyzing binary, ordinal, nominal, and count response variables using the R software. Author Xing Liu offers a unified framework for both single-level and multilevel modeling of categorical and count response variables with both frequentist and Bayesian approaches. Each chapter demonstrates how to conduct the analysis using R, how to interpret the models, and how to present the results for publication. A companion website for this book contains datasets and R commands used in the book for students, and solutions for the end-of-chapter exercises on the instructor site.

Multilevel Modeling

Author : G. David Garson
Publisher : SAGE Publications
Page : 910 pages
File Size : 52,5 Mb
Release : 2019-07-31
Category : Education
ISBN : 9781544319285

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Multilevel Modeling by G. David Garson Pdf

Multilevel Modeling: Applications in STATA®, IBM® SPSS®, SAS®, R & HLMTM provides a gentle, hands-on illustration of the most common types of multilevel modeling software, offering instructors multiple software resources for their students and an applications-based foundation for teaching multilevel modeling in the social sciences. Author G. David Garson’s step-by-step instructions for software walk readers through each package. The instructions for the different platforms allow students to get a running start using the package with which they are most familiar while the instructor can start teaching the concepts of multilevel modeling right away. Instructors will find this text serves as both a comprehensive resource for their students and a foundation for their teaching alike.

Data Analysis Using Regression and Multilevel/Hierarchical Models

Author : Andrew Gelman,Jennifer Hill
Publisher : Cambridge University Press
Page : 654 pages
File Size : 40,8 Mb
Release : 2007
Category : Mathematics
ISBN : 052168689X

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Data Analysis Using Regression and Multilevel/Hierarchical Models by Andrew Gelman,Jennifer Hill Pdf

This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.

Beyond Multiple Linear Regression

Author : Paul Roback,Julie Legler
Publisher : CRC Press
Page : 436 pages
File Size : 48,5 Mb
Release : 2021-01-14
Category : Mathematics
ISBN : 9781439885406

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Beyond Multiple Linear Regression by Paul Roback,Julie Legler Pdf

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)

Multilevel Analysis

Author : Tom A. B. Snijders,Roel J. Bosker
Publisher : SAGE
Page : 282 pages
File Size : 41,7 Mb
Release : 1999
Category : Mathematics
ISBN : 0761958908

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Multilevel Analysis by Tom A. B. Snijders,Roel J. Bosker Pdf

Multilevel analysis covers all the main methods, techniques and issues for carrying out multilevel modeling and analysis. The approach is applied, and less mathematical than many other textbooks.

Multilevel Modeling in Plain Language

Author : Karen Robson,David Pevalin
Publisher : SAGE
Page : 166 pages
File Size : 50,5 Mb
Release : 2015-11-02
Category : Social Science
ISBN : 9781473934306

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Multilevel Modeling in Plain Language by Karen Robson,David Pevalin Pdf

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.

An Introduction to Multilevel Modeling Techniques

Author : Ronald H. Heck,Scott L. Thomas
Publisher : Psychology Press
Page : 283 pages
File Size : 40,9 Mb
Release : 1999-11-01
Category : Computers
ISBN : 9781135678319

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An Introduction to Multilevel Modeling Techniques by Ronald H. Heck,Scott L. Thomas Pdf

This book provides a broad overview of basic multilevel modeling issues and illustrates techniques building analyses around several organizational data sets. Although the focus is primarily on educational and organizational settings, the examples will help the reader discover other applications for these techniques. Two basic classes of multilevel models are developed: multilevel regression models and multilevel models for covariance structures--are used to develop the rationale behind these models and provide an introduction to the design and analysis of research studies using two multilevel analytic techniques--hierarchical linear modeling and structural equation modeling.

Doing Meta-Analysis with R

Author : Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert
Publisher : CRC Press
Page : 500 pages
File Size : 44,5 Mb
Release : 2021-09-15
Category : Mathematics
ISBN : 9781000435634

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Doing Meta-Analysis with R by Mathias Harrer,Pim Cuijpers,Toshi A. Furukawa,David D. Ebert Pdf

Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book

Multilevel Modeling Using Mplus

Author : Holmes Finch,Jocelyn Bolin
Publisher : CRC Press
Page : 266 pages
File Size : 54,7 Mb
Release : 2017-02-03
Category : Mathematics
ISBN : 9781351678407

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Multilevel Modeling Using Mplus by Holmes Finch,Jocelyn Bolin Pdf

This book is designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. The focus is on presenting the theory and practice of major multilevel modelling techniques in a variety of contexts, using Mplus as the software tool, and demonstrating the various functions available for these analyses in Mplus, which is widely used by researchers in various fields, including most of the social sciences. In particular, Mplus offers users a wide array of tools for latent variable modelling, including for multilevel data.

Multilevel Analysis

Author : Joop J. Hox,Mirjam Moerbeek,Rens van de Schoot
Publisher : Routledge
Page : 348 pages
File Size : 55,8 Mb
Release : 2017-09-14
Category : Psychology
ISBN : 9781317308683

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Multilevel Analysis by Joop J. Hox,Mirjam Moerbeek,Rens van de Schoot Pdf

Applauded for its clarity, this accessible introduction helps readers apply multilevel techniques to their research. The book also includes advanced extensions, making it useful as both an introduction for students and as a reference for researchers. Basic models and examples are discussed in nontechnical terms with an emphasis on understanding the methodological and statistical issues involved in using these models. The estimation and interpretation of multilevel models is demonstrated using realistic examples from various disciplines including psychology, education, public health, and sociology. Readers are introduced to a general framework on multilevel modeling which covers both observed and latent variables in the same model, while most other books focus on observed variables. In addition, Bayesian estimation is introduced and applied using accessible software.

Statistical Regression Modeling with R

Author : Ding-Geng (Din) Chen,Jenny K. Chen
Publisher : Springer Nature
Page : 239 pages
File Size : 51,6 Mb
Release : 2021-04-08
Category : Mathematics
ISBN : 9783030675837

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Statistical Regression Modeling with R by Ding-Geng (Din) Chen,Jenny K. Chen Pdf

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear regression for normally distributed data, logistic regression for binomially distributed data, and Poisson regression and negative-binomial regression for count data. It then progresses to these regression models that work with longitudinal and multi-level data structures. The volume is designed to guide the transition from classical to more advanced regression modeling, as well as to contribute to the rapid development of statistics and data science. With data and computing programs available to facilitate readers' learning experience, Statistical Regression Modeling promotes the applications of R in linear, nonlinear, longitudinal and multi-level regression. All included datasets, as well as the associated R program in packages nlme and lme4 for multi-level regression, are detailed in Appendix A. This book will be valuable in graduate courses on applied regression, as well as for practitioners and researchers in the fields of data science, statistical analytics, public health, and related fields.

Multilevel Modeling

Author : Douglas A. Luke
Publisher : SAGE Publications
Page : 96 pages
File Size : 40,5 Mb
Release : 2019-12-13
Category : Social Science
ISBN : 9781544310282

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Multilevel Modeling by Douglas A. Luke Pdf

Multilevel Modeling is a concise, practical guide to building models for multilevel and longitudinal data. Author Douglas A. Luke begins by providing a rationale for multilevel models; outlines the basic approach to estimating and evaluating a two-level model; discusses the major extensions to mixed-effects models; and provides advice for where to go for instruction in more advanced techniques. Rich with examples, the Second Edition expands coverage of longitudinal methods, diagnostic procedures, models of counts (Poisson), power analysis, cross-classified models, and adds a new section added on presenting modeling results. A website for the book includes the data and the statistical code (both R and Stata) used for all of the presented analyses.

Growth Modeling

Author : Kevin J. Grimm,Nilam Ram,Ryne Estabrook
Publisher : Guilford Publications
Page : 558 pages
File Size : 51,7 Mb
Release : 2016-10-17
Category : Social Science
ISBN : 9781462526062

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Growth Modeling by Kevin J. Grimm,Nilam Ram,Ryne Estabrook Pdf

Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results. User-Friendly Features *Real, worked-through longitudinal data examples serving as illustrations in each chapter. *Script boxes that provide code for fitting the models to example data and facilitate application to the reader's own data. *"Important Considerations" sections offering caveats, warnings, and recommendations for the use of specific models. *Companion website supplying datasets and syntax for the book's examples, along with additional code in SAS/R for linear mixed-effects modeling.