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Author: S. Rabe-Hesketh
Publisher:
ISBN: 9781597181365
Category : Latent structure analysis
Languages : en
Pages : 1098
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Book Description
"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: S. Rabe-Hesketh
Publisher:
ISBN: 9781597181365
Category : Latent structure analysis
Languages : en
Pages : 1098
Get Book Here
Book Description
"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
Publisher: Stata Press
ISBN: 1597180408
Category : Computers
Languages : en
Pages : 598
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Book Description
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: Anders Skrondal
Publisher: CRC Press
ISBN: 0203489438
Category : Mathematics
Languages : en
Pages : 523
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Book Description
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: Sophia Rabe-Hesketh
Publisher: Stata Press
ISBN: 9781597181037
Category : Mathematics
Languages : en
Pages : 514
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Book Description
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: Ronald H. Heck
Publisher: Psychology Press
ISBN: 1135678324
Category : Computers
Languages : en
Pages : 224
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Book Description
Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. -- Provided by Publisher.
Author: Lesa Hoffman
Publisher: Routledge
ISBN: 1317591097
Category : Psychology
Languages : en
Pages : 655
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Book Description
Longitudinal Analysis provides an accessible, application-oriented treatment of introductory and advanced linear models for within-person fluctuation and change. Organized by research design and data type, the text uses in-depth examples to provide a complete description of the model-building process. The core longitudinal models and their extensions are presented within a multilevel modeling framework, paying careful attention to the modeling concerns that are unique to longitudinal data. Written in a conversational style, the text provides verbal and visual interpretation of model equations to aid in their translation to empirical research results. Overviews and summaries, boldfaced key terms, and review questions will help readers synthesize the key concepts in each chapter. Written for non-mathematically-oriented readers, this text features: A description of the data manipulation steps required prior to model estimation so readers can more easily apply the steps to their own data An emphasis on how the terminology, interpretation, and estimation of familiar general linear models relates to those of more complex models for longitudinal data Integrated model comparisons, effect sizes, and statistical inference in each example to strengthen readers’ understanding of the overall model-building process Sample results sections for each example to provide useful templates for published reports Examples using both real and simulated data in the text, along with syntax and output for SPSS, SAS, STATA, and Mplus at www.PilesOfVariance.com to help readers apply the models to their own data The book opens with the building blocks of longitudinal analysis—general ideas, the general linear model for between-person analysis, and between- and within-person models for the variance and the options within repeated measures analysis of variance. Section 2 introduces unconditional longitudinal models including alternative covariance structure models to describe within-person fluctuation over time and random effects models for within-person change. Conditional longitudinal models are presented in section 3, including both time-invariant and time-varying predictors. Section 4 reviews advanced applications, including alternative metrics of time in accelerated longitudinal designs, three-level models for multiple dimensions of within-person time, the analysis of individuals in groups over time, and repeated measures designs not involving time. The book concludes with additional considerations and future directions, including an overview of sample size planning and other model extensions for non-normal outcomes and intensive longitudinal data. Class-tested at the University of Nebraska-Lincoln and in intensive summer workshops, this is an ideal text for graduate-level courses on longitudinal analysis or general multilevel modeling taught in psychology, human development and family studies, education, business, and other behavioral, social, and health sciences. The book’s accessible approach will also help those trying to learn on their own. Only familiarity with general linear models (regression, analysis of variance) is needed for this text.
Author: Karen Robson
Publisher: SAGE
ISBN: 1473934303
Category : Social Science
Languages : en
Pages : 153
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Book Description
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: Tom A. B. Snijders
Publisher: SAGE
ISBN: 9780761958901
Category : Mathematics
Languages : en
Pages : 282
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Book Description
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.
Author: G. David Garson
Publisher: SAGE
ISBN: 1412998859
Category : Mathematics
Languages : en
Pages : 393
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Book Description
This book provides a brief, easy-to-read guide to implementing hierarchical linear modeling using three leading software platforms, followed by a set of original how-to applications articles following a standardard instructional format. The "guide" portion consists of five chapters by the editor, providing an overview of HLM, discussion of methodological assumptions, and parallel worked model examples in SPSS, SAS, and HLM software. The "applications" portion consists of ten contributions in which authors provide step by step presentations of how HLM is implemented and reported for introductory to intermediate applications.
Author: Douglas A. Luke
Publisher: SAGE Publications
ISBN: 1544310285
Category : Social Science
Languages : en
Pages : 96
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Book Description
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.
