Bayesian Generalized Structured Component Analysis
DOWNLOAD
Download Bayesian Generalized Structured Component Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Bayesian Generalized Structured Component Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Bayesian Generalized Structured Component Analysis
DOWNLOAD
Author : Ji Yeh Choi
language : en
Publisher:
Release Date : 2017
Bayesian Generalized Structured Component Analysis written by Ji Yeh Choi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
"Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling (SEM) that postulates and examines various directional relationships among latent and observed variables. GSCA constructs components or weighted composites of observed variables as proxies for latent variables. It combines three sub-models, such as measurement, structural, and weighted relation models, into a unified formulation, and estimates all model parameters simultaneously via least squares. Over the past decade, GSCA has been extended to deal with a wider range of data types including discrete, multilevel, or intensive longitudinal data, as well as to accommodate a more variety of complex analyses such as latent moderation analysis, the capturing of cluster-level heterogeneity, and regularized analysis. To date, nonetheless, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this dissertation, a novel extension of GSCA, called Bayesian GSCA, is proposed that estimates parameters within the Bayesian framework. Bayesian GSCA can be more attractive than GSCA in numerous respects. Firstly, it infers the probability distributions of parameters, treating the parameters as random variables, which in turn facilitates the interpretation of the parameters. Secondly, it permits specifying various structures of error terms in the measurement model, which are left unspecified in GSCA. Thirdly, it provides additional fit measures for model assessment and comparison from the Bayesian perspectives. Lastly, it allows directly incorporating external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters, as the form of prior distributions in the modelling process. Bayesian GSCA adopts a Markov Chain Monte Carlo method, i.e., Gibbs Sampler, to update the posterior distributions for parameters. The dissertation begins by describing two building blocks of Bayesian GSCA - GSCA and Bayesian inference, and subsequently discusses the technical underpinnings of Bayesian GSCA. It also demonstrates the usefulness of Bayesian GSCA based on the analyses of both simulated and real data. " --
Generalized Structured Component Analysis
DOWNLOAD
Author : Heungsun Hwang
language : en
Publisher: CRC Press
Release Date : 2014-12-11
Generalized Structured Component Analysis written by Heungsun Hwang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-11 with Mathematics categories.
Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling. Generalized structured component analysis allows researchers to evaluate the adequacy of a model as a whole, compare a model to alternative specifications, and conduct complex analyses in a straightforward manner. Generalized Structured Component Analysis: A Component-Based Approach to Structural Equation Modeling provides a detailed account of this novel statistical methodology and its various extensions. The authors present the theoretical underpinnings of generalized structured component analysis and demonstrate how it can be applied to various empirical examples. The book enables quantitative methodologists, applied researchers, and practitioners to grasp the basic concepts behind this new approach and apply it to their own research. The book emphasizes conceptual discussions throughout while relegating more technical intricacies to the chapter appendices. Most chapters compare generalized structured component analysis to partial least squares path modeling to show how the two component-based approaches differ when addressing an identical issue. The authors also offer a free, online software program (GeSCA) and an Excel-based software program (XLSTAT) for implementing the basic features of generalized structured component analysis.
Bayesian Psychometric Modeling
DOWNLOAD
Author : Roy Levy
language : en
Publisher: CRC Press
Release Date : 2017-07-28
Bayesian Psychometric Modeling written by Roy Levy and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Mathematics categories.
A Single Cohesive Framework of Tools and Procedures for Psychometrics and Assessment Bayesian Psychometric Modeling presents a unified Bayesian approach across traditionally separate families of psychometric models. It shows that Bayesian techniques, as alternatives to conventional approaches, offer distinct and profound advantages in achieving many goals of psychometrics. Adopting a Bayesian approach can aid in unifying seemingly disparate—and sometimes conflicting—ideas and activities in psychometrics. This book explains both how to perform psychometrics using Bayesian methods and why many of the activities in psychometrics align with Bayesian thinking. The first part of the book introduces foundational principles and statistical models, including conceptual issues, normal distribution models, Markov chain Monte Carlo estimation, and regression. Focusing more directly on psychometrics, the second part covers popular psychometric models, including classical test theory, factor analysis, item response theory, latent class analysis, and Bayesian networks. Throughout the book, procedures are illustrated using examples primarily from educational assessments. A supplementary website provides the datasets, WinBUGS code, R code, and Netica files used in the examples.
Bayesian Methods
DOWNLOAD
Author : Jeff Gill
language : en
Publisher: CRC Press
Release Date : 2014-12-11
Bayesian Methods written by Jeff Gill and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-11 with Mathematics categories.
An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of th
Comparison Of Maximum Likelihood Bayesian Partial Least Squares And Generalized Structured Component Analysis Methods For Estimation Of Structural Equation Models With Small Samples
DOWNLOAD
Author : Frances L. Chumney
language : en
Publisher:
Release Date : 2012
Comparison Of Maximum Likelihood Bayesian Partial Least Squares And Generalized Structured Component Analysis Methods For Estimation Of Structural Equation Models With Small Samples written by Frances L. Chumney and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Handbook Of Item Response Theory
DOWNLOAD
Author : Wim J. van der Linden
language : en
Publisher: CRC Press
Release Date : 2017-12-15
Handbook Of Item Response Theory written by Wim J. van der Linden and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-15 with Mathematics categories.
Drawing on the work of internationally acclaimed experts in the field, Handbook of Item Response Theory, Volume 3: Applications presents applications of item response theory to practical testing problems. While item response theory may be known primarily for its advances in theoretical modeling of responses to test items, equal progress has been made in its providing innovative solutions to daily testing problems. This third volume in a three-volume set highlights the major applications. Specifically, this volume covers applications to test item calibration, item analysis, model fit checking, test-score interpretation, optimal test design, adaptive testing, standard setting, and forensic analyses of response data. It describes advances in testing in areas such as large-scale educational assessment, psychological testing, health measurement, and measurement of change. In addition, it extensively reviews computer programs available to run any of the models and applications in Volume One and Three. Features Includes contributions from internationally acclaimed experts with a history of advancing applications of item response theory Provides extensive cross-referencing and common notation across all chapters in this three-volume set Underscores the importance of treating each application in a statistically rigorous way Reviews major computer programs for item response theory analyses and applications. Wim J. van der Linden is a distinguished scientist and director of research and innovation at Pacific Metrics Corporation. Dr. van der Linden is also a professor emeritus of measurement and data analysis at the University of Twente. His research interests include test theory, adaptive testing, optimal test assembly, parameter linking, test equating, and response-time modeling as well as decision theory and its applications to problems of educational decision making.
Multilevel Modeling Using Mplus
DOWNLOAD
Author : Holmes Finch
language : en
Publisher: CRC Press
Release Date : 2017-02-03
Multilevel Modeling Using Mplus written by Holmes Finch and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-03 with Mathematics categories.
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.
Bayesian Hierarchical Models
DOWNLOAD
Author : Peter D. Congdon
language : en
Publisher: CRC Press
Release Date : 2019-09-16
Bayesian Hierarchical Models written by Peter D. Congdon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-16 with Mathematics categories.
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
Basic And Advanced Bayesian Structural Equation Modeling
DOWNLOAD
Author : Sik-Yum Lee
language : en
Publisher: John Wiley & Sons
Release Date : 2012-07-05
Basic And Advanced Bayesian Structural Equation Modeling written by Sik-Yum Lee and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-07-05 with Mathematics categories.
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.
Adaptive Survey Design
DOWNLOAD
Author : Barry Schouten
language : en
Publisher: CRC Press
Release Date : 2017-07-28
Adaptive Survey Design written by Barry Schouten and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-28 with Mathematics categories.
Adaptive survey designs (ASDs) provide a framework for data-driven tailoring of data collection procedures to different sample members, often for cost and bias reduction. People vary in how likely they are to respond and in how they respond. This variation leads to opportunities to selectively deploy design features in order to control both nonresponse and measurement errors. ASD aims at the optimal matching of design features and the characteristics of respondents given the survey budget. Such a goal is sensible, but ASD requires investment in more advanced technical systems and management infrastructure and asks for the collection of relevant auxiliary data. So what are current best practices in ASD? And is ASD worthwhile when the same auxiliary data are employed in the estimation afterwards? In this book, the authors provide answers to these questions, and much more.