Multilevel And Longitudinal Modeling Using Stata Volumes I And Ii

Author : S. Rabe-Hesketh,Sophia (University of California Rabe-Hesketh, Berkeley USA),Anders (London School of Economics Skrondal, UK),Anders Skrondal
Publisher : Unknown
Page : 1098 pages
File Size : 55,7 Mb
Release : 2021-10-22
Category : Latent structure analysis
ISBN : 1597181366

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Multilevel and Longitudinal Modeling Using Stata, Volumes I and II by S. Rabe-Hesketh,Sophia (University of California Rabe-Hesketh, Berkeley USA),Anders (London School of Economics Skrondal, UK),Anders Skrondal Pdf

"Multilevel and Longitudinal Modeling Using Stata, Fourth Edition discusses regression modeling of clustered or hierarchical data, such as data on students nested in schools, patients in hospitals, or employees in firms. Longitudinal data are also clustered with, for instance, repeated measurements on patients or several panel waves per survey respondent. Multilevel and longitudinal modeling can exploit the richness of such data and can disentangle processes operating at different levels. Assuming some knowledge of linear regression, this bestseller explains models and their assumptions, applies methods to real data using Stata, and shows how to interpret the results. Across volumes, the 16 chapters, over 140 exercises, and over 110 datasets span a wide range of disciplines, making the book suitable for courses in the medical, social, and behavioral sciences and in applied statistics. This first volume is dedicated to models for continuous responses and is a prerequisite for the second volume on models for other response types. It has been thoroughly revised and updated for Stata 16. New material includes the Kenward-Roger degree-of-freedom correction for improved inference with a small number of clusters, difference-in-differences estimation for natural experiments, and instrumental-variable estimation to handle level-1 endogeneity"--

Author : Sophia Rabe-Hesketh,Anders Skrondal
Publisher : Stata Press
Page : 598 pages
File Size : 47,8 Mb
Release : 2008-02-07
Category : Computers
ISBN : 9781597180405

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Multilevel and Longitudinal Modeling Using Stata, Second Edition by Sophia Rabe-Hesketh,Anders Skrondal Pdf

This textbook looks specifically at Stata’s treatment of generalized linear mixed models, also known as multilevel or hierarchical models. These models are "mixed" because they allow fixed and random effects, and they are "generalized" because they are appropriate for continuous Gaussian responses as well as binary, count, and other types of limited dependent variables.

Author : S. Rabe-Hesketh,Anders Skrondal
Publisher : Unknown
Page : 974 pages
File Size : 47,7 Mb
Release : 2022
Category : Latent structure analysis
ISBN : 1597183091

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Multilevel and Longitudinal Modeling Using Stata by S. Rabe-Hesketh,Anders Skrondal Pdf

Author : Sophia Rabe-Hesketh,Anders Skrondal
Publisher : Stata Press
Page : 484 pages
File Size : 55,8 Mb
Release : 2012-04-02
Category : Mathematics
ISBN : 1597181048

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Multilevel and Longitudinal Modeling Using Stata by Sophia Rabe-Hesketh,Anders Skrondal Pdf

Volume II is devoted to generalized linear mixed models for binary, categorical, count, and survival outcomes. The second volume has seven chapters also organized in four parts. The first three parts in volume II cover models for categorical responses, including binary, ordinal, and nominal (a new chapter); models for count data; and models for survival data, including discrete-time and continuous-time (a new chapter) survival responses. The final part in volume II describes models with nested and crossed-random effects with an emphasis on binary outcomes.

Author : Sophia Rabe-Hesketh,Anders Skrondal
Publisher : Stata Press
Page : 514 pages
File Size : 49,6 Mb
Release : 2012-04-02
Category : Mathematics
ISBN : 159718103X

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Multilevel and Longitudinal Modeling Using Stata by Sophia Rabe-Hesketh,Anders Skrondal Pdf

Volume I is devoted to continuous Gaussian linear mixed models and has nine chapters. The chapters are organized in four parts. The first part provides a review of the methods of linear regression. The second part provides an in-depth coverage of the two-level models, the simplest extensions of a linear regression model. The mixed-model foundation and the in-depth coverage of the mixed-model principles provided in volume I for continuous outcomes, make it straightforward to transition to generalized linear mixed models for noncontinuous outcomes described in volume II.

Author : Anders Skrondal,Sophia Rabe-Hesketh
Publisher : CRC Press
Page : 528 pages
File Size : 40,6 Mb
Release : 2004-05-11
Category : Mathematics
ISBN : 9780203489437

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Generalized Latent Variable Modeling by Anders Skrondal,Sophia Rabe-Hesketh Pdf

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wi

Author : Holmes Finch,Jocelyn Bolin
Publisher : CRC Press
Page : 266 pages
File Size : 50,8 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.

Author : Lang Wu
Publisher : CRC Press
Page : 431 pages
File Size : 44,9 Mb
Release : 2009-11-11
Category : Mathematics
ISBN : 1420074083

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Mixed Effects Models for Complex Data by Lang Wu Pdf

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.

Author : Brian S. Everitt,Sophia Rabe-Hesketh
Publisher : CRC Press
Page : 354 pages
File Size : 53,6 Mb
Release : 2006-11-15
Category : Mathematics
ISBN : 9781466580572

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Handbook of Statistical Analyses Using Stata by Brian S. Everitt,Sophia Rabe-Hesketh Pdf

With each new release of Stata, a comprehensive resource is needed to highlight the improvements as well as discuss the fundamentals of the software. Fulfilling this need, AHandbook of Statistical Analyses Using Stata, Fourth Edition has been fully updated to provide an introduction to Stata version 9. This edition covers many

Author : Ronald H. Heck,Scott L. Thomas,Lynn N. Tabata
Publisher : Routledge
Page : 835 pages
File Size : 54,9 Mb
Release : 2013-08-22
Category : Psychology
ISBN : 9781135074241

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Multilevel and Longitudinal Modeling with IBM SPSS by Ronald H. Heck,Scott L. Thomas,Lynn N. Tabata Pdf

This book demonstrates how to use multilevel and longitudinal modeling techniques available in the IBM SPSS mixed-effects program (MIXED). Annotated screen shots provide readers with a step-by-step understanding of each technique and navigating the program. Readers learn how to set up, run, and interpret a variety of models. Diagnostic tools, data management issues, and related graphics are introduced throughout. Annotated syntax is also available for those who prefer this approach. Extended examples illustrate the logic of model development to show readers the rationale of the research questions and the steps around which the analyses are structured. The data used in the text and syntax examples are available at www.routledge.com/9780415817110. Highlights of the new edition include: Updated throughout to reflect IBM SPSS Version 21. Further coverage of growth trajectories, coding time-related variables, covariance structures, individual change and longitudinal experimental designs (Ch.5). Extended discussion of other types of research designs for examining change (e.g., regression discontinuity, quasi-experimental) over time (Ch.6). New examples specifying multiple latent constructs and parallel growth processes (Ch. 7). Discussion of alternatives for dealing with missing data and the use of sample weights within multilevel data structures (Ch.1). The book opens with the conceptual and methodological issues associated with multilevel and longitudinal modeling, followed by a discussion of SPSS data management techniques which facilitate working with multilevel, longitudinal, and cross-classified data sets. Chapters 3 and 4 introduce the basics of multilevel modeling: developing a multilevel model, interpreting output, and trouble-shooting common programming and modeling problems. Models for investigating individual and organizational change are presented in chapters 5 and 6, followed by models with multivariate outcomes in chapter 7. Chapter 8 provides an illustration of multilevel models with cross-classified data structures. The book concludes with ways to expand on the various multilevel and longitudinal modeling techniques and issues when conducting multilevel analyses. It's ideal for courses on multilevel and longitudinal modeling, multivariate statistics, and research design taught in education, psychology, business, and sociology.

Author : Ding-Geng (Din) Chen,Jenny K. Chen
Publisher : Springer Nature
Page : 239 pages
File Size : 52,5 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.

Author : Karen Robson,David Pevalin
Publisher : SAGE
Page : 166 pages
File Size : 44,6 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.

Author : Brady T. West,Kathleen B. Welch,Andrzej T Galecki
Publisher : CRC Press
Page : 442 pages
File Size : 53,9 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.

Author : Marc A. Scott,Jeffrey S. Simonoff,Brian D. Marx
Publisher : SAGE
Page : 745 pages
File Size : 44,9 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.

Author : Daniel Powers,Yu Xie
Publisher : Emerald Group Publishing
Page : 296 pages
File Size : 52,8 Mb
Release : 2008-11-13
Category : Psychology
ISBN : 1781906599

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Statistical Methods for Categorical Data Analysis by Daniel Powers,Yu Xie Pdf

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. Companion website also available, at https://webspace.utexas.edu/dpowers/www/