Bayesian Multilevel Models For Repeated Measures Data

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Bayesian Multilevel Models for Repeated Measures Data

Santiago Barreda,Noah Silbert
Bayesian Multilevel Models for Repeated Measures Data Book PDF

Author : Santiago Barreda,Noah Silbert
Publisher : Taylor & Francis
Page : 485 pages
File Size : 48,9 Mb
Release : 2023-05-18
Category : Psychology
ISBN : 9781000869781

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Bayesian Multilevel Models for Repeated Measures Data by Santiago Barreda,Noah Silbert Pdf

This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated measures data, the focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book. In this book, the authors offer an introduction to statistics entirely focused on repeated measures data beginning with very simple two-group comparisons and ending with multinomial regression models with many ‘random effects’. Across 13 well-structured chapters, readers are provided with all the code necessary to run all the analyses and make all the plots in the book, as well as useful examples of how to interpret and write up their own analyses. This book provides an accessible introduction for readers in any field, with any level of statistical background. Senior undergraduate students, graduate students, and experienced researchers looking to ‘translate’ their skills with more traditional models to a Bayesian framework will benefit greatly from the lessons in this text.

Bayesian Multilevel Models for Repeated Measures Data

Santiago Barreda
Bayesian Multilevel Models for Repeated Measures Data Book PDF

Author : Santiago Barreda
Publisher : Unknown
Page : 0 pages
File Size : 42,8 Mb
Release : 2023
Category : Bayesian statistical decision theory
ISBN : 1003285872

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Bayesian Multilevel Models for Repeated Measures Data by Santiago Barreda Pdf

"This comprehensive book is an introduction to multilevel Bayesian models in R using brms and the Stan programming language. Featuring a series of fully worked analyses of repeated-measures data, focus is placed on active learning through the analyses of the progressively more complicated models presented throughout the book"--

Multilevel Analysis

Tom A B Snijders,Roel J Bosker
Multilevel Analysis Book PDF

Author : Tom A B Snijders,Roel J Bosker
Publisher : SAGE
Page : 369 pages
File Size : 49,8 Mb
Release : 2011-10-30
Category : Reference
ISBN : 9781446254332

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

The Second Edition of this classic text introduces the main methods, techniques and issues involved in carrying out multilevel modeling and analysis. Snijders and Bosker′s book is an applied, authoritative and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis. This book provides step-by-step coverage of: • multilevel theories • ecological fallacies • the hierarchical linear model • testing and model specification • heteroscedasticity • study designs • longitudinal data • multivariate multilevel models • discrete dependent variables There are also new chapters on: • missing data • multilevel modeling and survey weights • Bayesian and MCMC estimation and latent-class models. This book has been comprehensively revised and updated since the last edition, and now discusses modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and SuperMix. This is a must-have text for any student, teacher or researcher with an interest in conducting or understanding multilevel analysis. Tom A.B. Snijders is Professor of Statistics in the Social Sciences at the University of Oxford and Professor of Statistics and Methodology at the University of Groningen. Roel J. Bosker is Professor of Education and Director of GION, Groningen Institute for Educational Research, at the University of Groningen.

Multilevel Modeling

Steven P. Reise,Naihua Duan
Multilevel Modeling Book PDF

Author : Steven P. Reise,Naihua Duan
Publisher : Psychology Press
Page : 276 pages
File Size : 40,9 Mb
Release : 2003-01-30
Category : Mathematics
ISBN : 9781135655365

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Multilevel Modeling by Steven P. Reise,Naihua Duan Pdf

This book appeals to researchers who work with nested data structures or repeated measures data, including biomed & health researchers, clinical/intervention researchers and developmental & educational psychologists. Also some potential as a grad lvl tex

Handbook of Advanced Multilevel Analysis

Joop Hox,J. Kyle Roberts
Handbook of Advanced Multilevel Analysis Book PDF

Author : Joop Hox,J. Kyle Roberts
Publisher : Psychology Press
Page : 408 pages
File Size : 40,9 Mb
Release : 2011-01-11
Category : Psychology
ISBN : 9781136951275

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Handbook of Advanced Multilevel Analysis by Joop Hox,J. Kyle Roberts Pdf

This new handbook is the definitive resource on advanced topics related to multilevel analysis. The editors assembled the top minds in the field to address the latest applications of multilevel modeling as well as the specific difficulties and methodological problems that are becoming more common as more complicated models are developed. Each chapter features examples that use actual datasets. These datasets, as well as the code to run the models, are available on the book’s website http://www.hlm-online.com . Each chapter includes an introduction that sets the stage for the material to come and a conclusion. Divided into five sections, the first provides a broad introduction to the field that serves as a framework for understanding the latter chapters. Part 2 focuses on multilevel latent variable modeling including item response theory and mixture modeling. Section 3 addresses models used for longitudinal data including growth curve and structural equation modeling. Special estimation problems are examined in section 4 including the difficulties involved in estimating survival analysis, Bayesian estimation, bootstrapping, multiple imputation, and complicated models, including generalized linear models, optimal design in multilevel models, and more. The book’s concluding section focuses on statistical design issues encountered when doing multilevel modeling including nested designs, analyzing cross-classified models, and dyadic data analysis. Intended for methodologists, statisticians, and researchers in a variety of fields including psychology, education, and the social and health sciences, this handbook also serves as an excellent text for graduate and PhD level courses in multilevel modeling. A basic knowledge of multilevel modeling is assumed.

Multilevel Analysis

Joop J. Hox,Mirjam Moerbeek,Rens van de Schoot
Multilevel Analysis Book PDF

Author : Joop J. Hox,Mirjam Moerbeek,Rens van de Schoot
Publisher : Routledge
Page : 393 pages
File Size : 42,7 Mb
Release : 2010-09-13
Category : Education
ISBN : 9781136975356

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

This practical introduction helps readers apply multilevel techniques to their research. Noted as an accessible introduction, the book also includes advanced extensions, making it useful as both an introduction and as a reference to students, researchers, and methodologists. Basic models and examples are discussed in non-technical 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. For example, readers will find data sets on stress in hospitals, GPA scores, survey responses, street safety, epilepsy, divorce, and sociometric scores, to name a few. The data sets are available on the website in SPSS, HLM, MLwiN, LISREL and/or Mplus files. Readers are introduced to both the multilevel regression model and multilevel structural models. Highlights of the second edition include: Two new chapters—one on multilevel models for ordinal and count data (Ch. 7) and another on multilevel survival analysis (Ch. 8). Thoroughly updated chapters on multilevel structural equation modeling that reflect the enormous technical progress of the last few years. The addition of some simpler examples to help the novice, whilst the more complex examples that combine more than one problem have been retained. A new section on multivariate meta-analysis (Ch. 11). Expanded discussions of covariance structures across time and analyzing longitudinal data where no trend is expected. Expanded chapter on the logistic model for dichotomous data and proportions with new estimation methods. An updated website at http://www.joophox.net/ with data sets for all the text examples and up-to-date screen shots and PowerPoint slides for instructors. Ideal for introductory courses on multilevel modeling and/or ones that introduce this topic in some detail taught in a variety of disciplines including: psychology, education, sociology, the health sciences, and business. The advanced extensions also make this a favorite resource for researchers and methodologists in these disciplines. A basic understanding of ANOVA and multiple regression is assumed. The section on multilevel structural equation models assumes a basic understanding of SEM.

Multilevel Statistical Models

Harvey Goldstein
Multilevel Statistical Models Book PDF

Author : Harvey Goldstein
Publisher : John Wiley & Sons
Page : 306 pages
File Size : 47,6 Mb
Release : 2011-07-08
Category : Mathematics
ISBN : 9781119956822

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Multilevel Statistical Models by Harvey Goldstein Pdf

Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models. Key Features: Provides a clear introduction and a comprehensive account of multilevel models. New methodological developments and applications are explored. Written by a leading expert in the field of multilevel methodology. Illustrated throughout with real-life examples, explaining theoretical concepts. This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial.

The SAGE Handbook of Multilevel Modeling

Marc A. Scott,Jeffrey S. Simonoff,Brian D. Marx
The SAGE Handbook of Multilevel Modeling Book PDF

Author : Marc A. Scott,Jeffrey S. Simonoff,Brian D. Marx
Publisher : SAGE
Page : 745 pages
File Size : 54,7 Mb
Release : 2013-08-31
Category : Social Science
ISBN : 9781473971318

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The SAGE Handbook of Multilevel Modeling by Marc A. Scott,Jeffrey S. Simonoff,Brian D. Marx Pdf

In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.

Bayesian Hierarchical Models

Peter D. Congdon
Bayesian Hierarchical Models Book PDF

Author : Peter D. Congdon
Publisher : CRC Press
Page : 506 pages
File Size : 50,8 Mb
Release : 2019-09-16
Category : Mathematics
ISBN : 9780429532900

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Bayesian Hierarchical Models by Peter D. Congdon Pdf

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website

Hierarchical Linear Models

Stephen W. Raudenbush,Anthony S. Bryk
Hierarchical Linear Models Book PDF

Author : Stephen W. Raudenbush,Anthony S. Bryk
Publisher : SAGE
Page : 520 pages
File Size : 55,6 Mb
Release : 2002
Category : Social Science
ISBN : 076191904X

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Hierarchical Linear Models by Stephen W. Raudenbush,Anthony S. Bryk Pdf

New edition of a text in which Raudenbush (U. of Michigan) and Bryk (sociology, U. of Chicago) provide examples, explanations, and illustrations of the theory and use of hierarchical linear models (HLM). New material in Part I (Logic) includes information on multivariate growth models and other topics.

Linear Mixed Models

Brady T. West,Kathleen B. Welch,Andrzej T Galecki
Linear Mixed Models Book PDF

Author : Brady T. West,Kathleen B. Welch,Andrzej T Galecki
Publisher : CRC Press
Page : 442 pages
File Size : 52,7 Mb
Release : 2014-07-17
Category : Mathematics
ISBN : 9781466560994

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Linear Mixed Models by Brady T. West,Kathleen B. Welch,Andrzej T Galecki Pdf

Highly recommended by JASA, Technometrics, and other journals, the first edition of this bestseller showed how to easily perform complex linear mixed model (LMM) analyses via a variety of software programs. Linear Mixed Models: A Practical Guide Using Statistical Software, Second Edition continues to lead readers step by step through the process of fitting LMMs. This second edition covers additional topics on the application of LMMs that are valuable for data analysts in all fields. It also updates the case studies using the latest versions of the software procedures and provides up-to-date information on the options and features of the software procedures available for fitting LMMs in SAS, SPSS, Stata, R/S-plus, and HLM. New to the Second Edition A new chapter on models with crossed random effects that uses a case study to illustrate software procedures capable of fitting these models Power analysis methods for longitudinal and clustered study designs, including software options for power analyses and suggested approaches to writing simulations Use of the lmer() function in the lme4 R package New sections on fitting LMMs to complex sample survey data and Bayesian approaches to making inferences based on LMMs Updated graphical procedures in the software packages Substantially revised index to enable more efficient reading and easier location of material on selected topics or software options More practical recommendations on using the software for analysis A new R package (WWGbook) that contains all of the data sets used in the examples Ideal for anyone who uses software for statistical modeling, this book eliminates the need to read multiple software-specific texts by covering the most popular software programs for fitting LMMs in one handy guide. The authors illustrate the models and methods through real-world examples that enable comparisons of model-fitting options and results across the software procedures.

Handbook of Multilevel Analysis

Jan Deleeuw,Erik Meijer
Handbook of Multilevel Analysis Book PDF

Author : Jan Deleeuw,Erik Meijer
Publisher : Springer Science & Business Media
Page : 498 pages
File Size : 41,8 Mb
Release : 2007-12-26
Category : Mathematics
ISBN : 9780387731865

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Handbook of Multilevel Analysis by Jan Deleeuw,Erik Meijer Pdf

This book presents the state of the art in multilevel analysis, with an emphasis on more advanced topics. These topics are discussed conceptually, analyzed mathematically, and illustrated by empirical examples. Multilevel analysis is the statistical analysis of hierarchically and non-hierarchically nested data. The simplest example is clustered data, such as a sample of students clustered within schools. Multilevel data are especially prevalent in the social and behavioral sciences and in the biomedical sciences. The chapter authors are all leading experts in the field. Given the omnipresence of multilevel data in the social, behavioral, and biomedical sciences, this book is essential for empirical researchers in these fields.

Multilevel Modeling

Douglas A. Luke
Multilevel Modeling Book PDF

Author : Douglas A. Luke
Publisher : SAGE Publications
Page : 114 pages
File Size : 47,6 Mb
Release : 2019-12-24
Category : Social Science
ISBN : 9781544310299

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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.

Bayesian Data Analysis, Third Edition

Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Bayesian Data Analysis Third Edition Book PDF

Author : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin
Publisher : CRC Press
Page : 677 pages
File Size : 47,7 Mb
Release : 2013-11-01
Category : Mathematics
ISBN : 9781439840955

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Bayesian Data Analysis, Third Edition by Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin Pdf

Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Bayesian Modeling Using WinBUGS

Ioannis Ntzoufras
Bayesian Modeling Using WinBUGS Book PDF

Author : Ioannis Ntzoufras
Publisher : John Wiley & Sons
Page : 477 pages
File Size : 55,8 Mb
Release : 2011-09-20
Category : Mathematics
ISBN : 9781118210352

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Bayesian Modeling Using WinBUGS by Ioannis Ntzoufras Pdf

A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all data sets and code are available on the book's related Web site. Requiring only a working knowledge of probability theory and statistics, Bayesian Modeling Using WinBUGS serves as an excellent book for courses on Bayesian statistics at the upper-undergraduate and graduate levels. It is also a valuable reference for researchers and practitioners in the fields of statistics, actuarial science, medicine, and the social sciences who use WinBUGS in their everyday work.