## Basic and Advanced Bayesian Structural Equation Modeling

Author: Sik-Yum Lee
Publisher: John Wiley & Sons
ISBN: 1118358872
Format: PDF

This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables. Basic and Advanced Bayesian Structural Equation Modeling introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well as some of their combinations. In addition, Bayesian semiparametric SEMs to capture the true distribution of explanatory latent variables are introduced, whilst SEM with a nonparametric structural equation to assess unspecified functional relationships among latent variables are also explored. Statistical methodologies are developed using the Bayesian approach giving reliable results for small samples and allowing the use of prior information leading to better statistical results. Estimates of the parameters and model comparison statistics are obtained via powerful Markov Chain Monte Carlo methods in statistical computing. Introduces the Bayesian approach to SEMs, including discussion on the selection of prior distributions, and data augmentation. Demonstrates how to utilize the recent powerful tools in statistical computing including, but not limited to, the Gibbs sampler, the Metropolis-Hasting algorithm, and path sampling for producing various statistical results such as Bayesian estimates and Bayesian model comparison statistics in the analysis of basic and advanced SEMs. Discusses the Bayes factor, Deviance Information Criterion (DIC), and $L_\nu$-measure for Bayesian model comparison. Introduces a number of important generalizations of SEMs, including multilevel and mixture SEMs, latent curve models and longitudinal SEMs, semiparametric SEMs and those with various types of discrete data, and nonparametric structural equations. Illustrates how to use the freely available software WinBUGS to produce the results. Provides numerous real examples for illustrating the theoretical concepts and computational procedures that are presented throughout the book. Researchers and advanced level students in statistics, biostatistics, public health, business, education, psychology and social science will benefit from this book.

## Bayesian Statistics for the Social Sciences

Author: David Kaplan
Publisher: Guilford Publications
ISBN: 1462516513
Format: PDF, Docs

Bridging the gap between traditional classical statistics and a Bayesian approach, David Kaplan provides readers with the concepts and practical skills they need to apply Bayesian methodologies to their data analysis problems. Part I addresses the elements of Bayesian inference, including exchangeability, likelihood, prior/posterior distributions, and the Bayesian central limit theorem. Part II covers Bayesian hypothesis testing, model building, and linear regression analysis, carefully explaining the differences between the Bayesian and frequentist approaches. Part III extends Bayesian statistics to multilevel modeling and modeling for continuous and categorical latent variables. Kaplan closes with a discussion of philosophical issues and argues for an "evidence-based" framework for the practice of Bayesian statistics. User-Friendly Features *Includes worked-through, substantive examples, using large-scale educational and social science databases, such as PISA (Program for International Student Assessment) and the LSAY (Longitudinal Study of American Youth). *Utilizes open-source R software programs available on CRAN (such as MCMCpack and rjags); readers do not have to master the R language and can easily adapt the example programs to fit individual needs. *Shows readers how to carefully warrant priors on the basis of empirical data. *Companion website features data and code for the book's examples, plus other resources.

## Handbook of Structural Equation Modeling

Author: Rick H. Hoyle
Publisher: Guilford Press
ISBN: 1606230778
Format: PDF, ePub

"This handbook offers comprehensive coverage of structural equation modeling (SEM), beginning with background issues, continuing through statistical underpinnings and steps in implementation, then moving into basic and advanced applications of SEM. In a single volume, it offers virtually complete coverage of SEM and its use"--

## Structural Equation Modeling

Author: Gregory R. Hancock
Publisher: IAP
ISBN: 1623962463
Format: PDF, ePub, Docs

Sponsored by the American Educational Research Association's Special Interest Group for Educational Statisticians This volume is the second edition of Hancock and Mueller’s highlysuccessful 2006 volume, with all of the original chapters updated as well as four new chapters. The second edition, like the first, is intended to serve as a didacticallyoriented resource for graduate students and research professionals, covering a broad range of advanced topics often not discussed in introductory courses on structural equation modeling (SEM). Such topics are important in furthering the understanding of foundations and assumptions underlying SEM as well as in exploring SEM, as a potential tool to address new types of research questions that might not have arisen during a first course. Chapters focus on the clear explanation and application of topics, rather than on analytical derivations, and contain materials from popular SEM software.

## Latent Variable Models

Author: John C. Loehlin
Publisher: Taylor & Francis
ISBN: 131728528X
Format: PDF, Kindle

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing path diagrams to explain the relationships in the models. This approach helps less mathematically-inclined readers to grasp the underlying relations among path analysis, factor analysis, and structural equation modeling, and to set up and carry out such analyses. This revised and expanded fifth edition again contains key chapters on path analysis, structural equation models, and exploratory factor analysis. In addition, it contains new material on composite reliability, models with categorical data, the minimum average partial procedure, bi-factor models, and communicating about latent variable models. The informal writing style and the numerous illustrative examples make the book accessible to readers of varying backgrounds. Notes at the end of each chapter expand the discussion and provide additional technical detail and references. Moreover, most chapters contain an extended example in which the authors work through one of the chapter’s examples in detail to aid readers in conducting similar analyses with their own data. The book and accompanying website provide all of the data for the book’s examples as well as syntax from latent variable programs so readers can replicate the analyses. The book can be used with any of a variety of computer programs, but special attention is paid to LISREL and R. An important resource for advanced students and researchers in numerous disciplines in the behavioral sciences, education, business, and health sciences, Latent Variable Models is a practical and readable reference for those seeking to understand or conduct an analysis using latent variables.

## Structural Equation Modeling

Author: Sik-Yum Lee
Publisher: John Wiley & Sons
ISBN: 0470024240
Format: PDF, ePub, Docs

***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances. Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

## Structural Equation Modeling

Author: David Kaplan
Publisher: SAGE Publications
ISBN: 148334259X
Format: PDF, Mobi

Using detailed, empirical examples, Structural Equation Modeling, Second Edition, presents a thorough and sophisticated treatment of the foundations of structural equation modeling (SEM). It also demonstrates how SEM can provide a unique lens on the problems social and behavioral scientists face. Intended Audience While the book assumes some knowledge and background in statistics, it guides readers through the foundations and critical assumptions of SEM in an easy-to-understand manner.

## Basics of Structural Equation Modeling

Author: Geoffrey M. Maruyama
Publisher: SAGE Publications
ISBN: 150632035X
Format: PDF, Docs

With the availability of software programs, such as LISREL, EQS, and AMOS, modeling (SEM) techniques have become a popular tool for formalized presentation of the hypothesized relationships underlying correlational research and test for the plausibility of hypothesizing for a particular data set. Through the use of careful narrative explanation, Maruyama's text describes the logic underlying SEM approaches, describes how SEM approaches relate to techniques like regression and factor analysis, analyzes the strengths and shortcomings of SEM as compared to alternative methodologies, and explores the various methodologies for analyzing structural equation data. In addition, Maruyama provides carefully constructed exercises both within and

## Longitudinal Structural Equation Modeling

Author: Todd D. Little
Publisher: Guilford Press
ISBN: 1462510272
Format: PDF, ePub, Docs

Featuring actual datasets as illustrative examples, this book reveals numerous ways to apply structural equation modeling (SEM) to any repeated-measures study. Initial chapters lay the groundwork for modeling a longitudinal change process, from measurement, design, and specification issues to model evaluation and interpretation. Covering both big-picture ideas and technical "how-to-do-it" details, the author deftly walks through when and how to use longitudinal confirmatory factor analysis, longitudinal panel models (including the multiple-group case), multilevel models, growth curve models, and complex factor models, as well as models for mediation and moderation. User-friendly features include equation boxes that clearly explain the elements in every equation, end-of-chapter glossaries, and annotated suggestions for further reading. The companion website (www.guilford.com/little-materials) provides datasets for all of the examples--which include studies of bullying, adolescent students' emotions, and healthy aging--with syntax and output from LISREL, Mplus, and R (lavaan).

## Application of Structural Equation Modeling in Educational Research and Practice

Author: Myint Swe Khine
Publisher: Springer Science & Business Media
ISBN: 9462093326
Format: PDF, ePub, Docs

Structural Equation Modeling (SEM) is a statistical approach to testing hypothesis about the relationships among observed and latent variables. The use of SEM in research has increased in psychology, sociology, and economics in recent years. In particular educational researchers try to obtain the complete image of the process of education through the measurement of personality differences, learning environment, motivation levels and host of other variables that affect the teaching and learning process. With the use of survey instruments and interviews with students, teachers and other stakeholders as a lens, educators can assess and gain valuable information about the social ecology of the classrooms that could help in improving the instructional approach, classroom management and the learning organizations. A considerable number of research have been conducted to identify the factors and interactions between students’ characteristics, personal preferences, affective traits, study skills, and various other factors that could help in better educational performance. In recent years, educational researchers use Structural Equation Modeling (SEM) as a statistical technique to explore the complex and dynamic nature of interactions in educational research and practice. SEM is becoming a powerful analytical tool and making methodological advances in multivariate analysis. This book presents the collective works on concepts, methodologies and applications of SEM in educational research and practice. The anthology of current research described in this book will be a valuable resource for the next generation educational practitioners.