Download or read book Bayesian Estimation of Factor Analysis Models with Incomplete Data written by Edgar C. Merkle and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Missing data are problematic for many statistical analyses, factor analysis included. Because factor analysis is widely used by applied social scientists, it is of interest to develop accurate, general-purpose methods for the handling of missing data in factor analysis. While a number of such missing data methods have been proposed, each individual method has its weaknesses. For example, difficulty in obtaining test statistics of overall model fit and reliance on asymptotic results for standard errors of parameter estimates are two weaknesses of previously-proposed methods. As an alternative to other general-purpose missing data methods, I develop Bayesian missing data methods specific to factor analysis. Novel to the social sciences, these Bayesian methods resolve many of the other missing data methods' weaknesses and yield accurate results in a variety of contexts. This dissertation details Bayesian factor analysis, the proposed Bayesian missing data methods, and the computation required for these methods. Data examples are also provided.

Download or read book Bayesian Factor Analysis written by Shin-ichi Mayekawa and published by . This book was released on 1985 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new Bayesian procedure for factor analysis is developed in which factor scores as well as factor loadings and error variances are treated as parameters of interest. The presentation is fully Bayesian in the sense that all the parameters have prior distributions and the posterior mode of a subset of the parameters is used as the point estimate. The model is a standard one where the observations are expressed as the sum of the linear combination of factor scores, with factor loadings being the weights, and a normal error term. As the prior distribution the following exchangeable form is assumed: A factor score vector for each observation has a common normal distribution. A factor loading vector for each variable has a common normal distribution. A error variance for each variable has a common inverted chi square distribution. When the exchangeability of all the observations/variables is in question observations/variables may be divided into several subsets and the observations/variables within each subset may be treated as exchangeable. Since the posterior marginal distribution of factor loadings and error variances can be expressed as the product of the covariance-based likelihood and the prior distributions of factor loadings and error variances the proposed method includes both the random and the fixed factor analysis models. Keywords include: Factor analysis, Bayesian estimation, and EM algorithm.

Download or read book Handbook of Latent Variable and Related Models written by and published by Elsevier. This book was released on 2011-08-11 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.

Download or read book Bayesian Structural Equation Modeling written by Sarah Depaoli and published by Guilford Publications. This book was released on 2021-08-16 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.

Download or read book Bayesian Psychometric Modeling written by Roy Levy and published by CRC Press. This book was released on 2017-07-28 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Bayesian Analysis of Random Coefficient Dynamic Factor Models written by Hairong Song and published by . This book was released on 2009 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Comparison of Frequentist and Bayesian Approaches for Confirmatory Factor Analysis written by Menglin Xu and published by . This book was released on 2019 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model fit indices within the framework of structural equation models are crucial in evaluating and selecting the most appropriate model to fit data. The performance of fit indices under varying suboptimal conditions requires further investigation. Moreover, with the increasing interest in applying Bayesian method to social sciences data, the comparison of Bayesian estimation and robust maximum likelihood (MLR) estimation methods in evaluating models and estimating parameters is of vital importance. This study aims 1 ) to investigate the performance of MLR associated model fit indices under various conditions of model misfit, data distribution, and sample sizes; 2) to compare the performance of Bayesian and MLR methods in model fit and parameter estimation based on a confirmatory factor analysis (CFA) model. Data were simulated based on a population CFA model consistent with Curran, West and Finch’s (1996) study using R 3.4.0. Simulation conditions include 3 sample sizes (N = 200, 500, 1000), 3 degrees of model misfit (none: RMSEA = 0; mild: RMSEA = .05; moderate: RMSEA = .10), and 3 degrees of data nonnormality (normal: skewness = 0, kurtosis = 0; mild: skewness = 1, kurtosis = 3; moderate: skewness = 2, kurtosis = 7). Model misfit was introduced using Cudeck and Browne’s (1992) method through the R package MBESS. Data were fit using the R package lavaan for MLR method and blavaan for Bayesian method. Results show that scaled CFI and scaled TLI are the most robust model fit indices to variousiii suboptimal conditions; compared to p values associated with MLR, PP p values associated with the Bayesian method are robust to small sample size and data nonnormality under correctly specified models, less sensitive to models with ignorable degree of misfit, and have sufficient statistical power to reject moderately misspecified models; Bayesian and MLR methods have similar performance in point estimation; MLR method produces more robust standard error estimations. Implications and suggestions for future students are discussed.

Download or read book Bayesian Inference in the Social Sciences written by Ivan Jeliazkov and published by John Wiley & Sons. This book was released on 2014-11-04 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

Download or read book Confirmatory Factor Analysis for Applied Research written by Timothy A. Brown and published by Guilford Publications. This book was released on 2014-12-29 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: With its emphasis on practical and conceptual aspects, rather than mathematics or formulas, this accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA). Detailed, worked-through examples drawn from psychology, management, and sociology studies illustrate the procedures, pitfalls, and extensions of CFA methodology. The text shows how to formulate, program, and interpret CFA models using popular latent variable software packages (LISREL, Mplus, EQS, SAS/CALIS); understand the similarities and differences between CFA and exploratory factor analysis (EFA); and report results from a CFA study. It is filled with useful advice and tables that outline the procedures. The companion website (www.guilford.com/brown3-materials) offers data and program syntax files for most of the research examples, as well as links to CFA-related resources. New to This Edition *Updated throughout to incorporate important developments in latent variable modeling. *Chapter on Bayesian CFA and multilevel measurement models. *Addresses new topics (with examples): exploratory structural equation modeling, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables. *Utilizes the latest versions of major latent variable software packages.

Download or read book The Prevention and Treatment of Missing Data in Clinical Trials written by National Research Council and published by National Academies Press. This book was released on 2010-12-21 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomized clinical trials are the primary tool for evaluating new medical interventions. Randomization provides for a fair comparison between treatment and control groups, balancing out, on average, distributions of known and unknown factors among the participants. Unfortunately, these studies often lack a substantial percentage of data. This missing data reduces the benefit provided by the randomization and introduces potential biases in the comparison of the treatment groups. Missing data can arise for a variety of reasons, including the inability or unwillingness of participants to meet appointments for evaluation. And in some studies, some or all of data collection ceases when participants discontinue study treatment. Existing guidelines for the design and conduct of clinical trials, and the analysis of the resulting data, provide only limited advice on how to handle missing data. Thus, approaches to the analysis of data with an appreciable amount of missing values tend to be ad hoc and variable. The Prevention and Treatment of Missing Data in Clinical Trials concludes that a more principled approach to design and analysis in the presence of missing data is both needed and possible. Such an approach needs to focus on two critical elements: (1) careful design and conduct to limit the amount and impact of missing data and (2) analysis that makes full use of information on all randomized participants and is based on careful attention to the assumptions about the nature of the missing data underlying estimates of treatment effects. In addition to the highest priority recommendations, the book offers more detailed recommendations on the conduct of clinical trials and techniques for analysis of trial data.

Download or read book Statistical Analysis with Missing Data written by Roderick J. A. Little and published by John Wiley & Sons. This book was released on 2014-08-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: * Emphasizes the latest trends in the field. * Includes a new chapter on evolving methods. * Provides updated or revised material in most of the chapters.

Download or read book Robustness of Bayesian Factor Analysis Estimates written by Sang Eun Lee and published by . This book was released on 1994 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bayesian Data Analysis Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-10-22 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.

Download or read book Correlated Bayesian Factor Analysis written by Daniel Bryant Rowe and published by . This book was released on 1998 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Advanced Multilevel Analysis written by Joop Hox and published by Psychology Press. This book was released on 2011-01-11 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives written by Andrew Gelman and published by John Wiley & Sons. This book was released on 2004-09-03 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together a collection of articles on statistical methods relating to missing data analysis, including multiple imputation, propensity scores, instrumental variables, and Bayesian inference. Covering new research topics and real-world examples which do not feature in many standard texts. The book is dedicated to Professor Don Rubin (Harvard). Don Rubin has made fundamental contributions to the study of missing data. Key features of the book include: Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.