Handbook of Latent Variable and Related Models Book

Handbook of Latent Variable and Related Models

  • Author : Anonim
  • Publisher : Elsevier
  • Release Date : 2011-08-11
  • Genre: Mathematics
  • Pages : 458
  • ISBN 10 : 0080471269

Handbook of Latent Variable and Related Models 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.

Latent Variable Models and Factor Analysis Book

Latent Variable Models and Factor Analysis

  • Author : David J. Bartholomew
  • Publisher : John Wiley & Sons
  • Release Date : 2011-06-28
  • Genre: Mathematics
  • Pages : 296
  • ISBN 10 : 9781119973706

Latent Variable Models and Factor Analysis Excerpt :

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. This book: Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family. Presents new material on ordered manifest variables, MCMC methods, non-linear models as well as a new chapter on related techniques for investigating dependency. Includes new sections on structural equation models (SEM) and Markov Chain Monte Carlo methods for parameter estimation, along with new illustrative examples. Looks at recent developments on goodness-of-fit test statistics and on non-linear models and models with mixed latent variables, both categorical and continuous. No prior acquaintance with latent variable modelling is pre-supposed but a broad understanding of statistical theory will make it easier to see the approach in its proper perspective. Applied statisticians, psychometricians, medical statisticians, biostatisticians, economists and social science researchers will benefit from this book.

Random Effect and Latent Variable Model Selection Book

Random Effect and Latent Variable Model Selection

  • Author : David Dunson
  • Publisher : Springer Science & Business Media
  • Release Date : 2010-03-18
  • Genre: Mathematics
  • Pages : 170
  • ISBN 10 : 0387767215

Random Effect and Latent Variable Model Selection Excerpt :

Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.

Handbook of Psychology  Research Methods in Psychology Book

Handbook of Psychology Research Methods in Psychology

  • Author : Irving B. Weiner
  • Publisher : John Wiley & Sons
  • Release Date : 2012-10-16
  • Genre: Psychology
  • Pages : 800
  • ISBN 10 : 9780470890646

Handbook of Psychology Research Methods in Psychology Excerpt :

Psychology is of interest to academics from many fields, as well as to the thousands of academic and clinical psychologists and general public who can't help but be interested in learning more about why humans think and behave as they do. This award-winning twelve-volume reference covers every aspect of the ever-fascinating discipline of psychology and represents the most current knowledge in the field. This ten-year revision now covers discoveries based in neuroscience, clinical psychology's new interest in evidence-based practice and mindfulness, and new findings in social, developmental, and forensic psychology.

Latent Variable and Latent Structure Models Book

Latent Variable and Latent Structure Models

  • Author : George A. Marcoulides
  • Publisher : Psychology Press
  • Release Date : 2014-04-04
  • Genre: Psychology
  • Pages : 296
  • ISBN 10 : 9781135640651

Latent Variable and Latent Structure Models Excerpt :

This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.

Bayesian Hierarchical Models Book

Bayesian Hierarchical Models

  • Author : Peter D. Congdon
  • Publisher : CRC Press
  • Release Date : 2019-09-16
  • Genre: Mathematics
  • Pages : 580
  • ISBN 10 : 9781498785914

Bayesian Hierarchical Models Excerpt :

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

Latent Variable Modeling with R Book

Latent Variable Modeling with R

  • Author : W. Holmes Finch
  • Publisher : Routledge
  • Release Date : 2015-06-26
  • Genre: Psychology
  • Pages : 340
  • ISBN 10 : 9781317970750

Latent Variable Modeling with R Excerpt :

This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details to help readers better understand the models and interpret the results. Every R command necessary for conducting the analyses is described along with the resulting output which provides readers with a template to follow when they apply the methods to their own data. The basic information pertinent to each model, the newest developments in these areas, and the relevant R code to use them are reviewed. Each chapter also features an introduction, summary, and suggested readings. A glossary of the text’s boldfaced key terms and key R commands serve as helpful resources. The book is accompanied by a website with exercises, an answer key, and the in-text example data sets. Latent Variable Modeling with R: -Provides some examples that use messy data providing a more realistic situation readers will encounter with their own data. -Reviews a wide range of LVMs including factor analysis, structural equation modeling, item response theory, and mixture models and advanced topics such as fitting nonlinear structural equation models, nonparametric item response theory models, and mixture regression models. -Demonstrates how data simulation can help researchers better understand statistical methods and assist in selecting the necessary sample size prior to collecting data. -www.routledge.com/9780415832458 provides exercises that apply the models along with annotated R output answer keys and the data that corresponds to the in-text examples so readers can replicate the results and check their work. The book opens with basic instructions in how to use R to read data, download functions, and conduct b

The SAGE Handbook of Quantitative Methodology for the Social Sciences Book

The SAGE Handbook of Quantitative Methodology for the Social Sciences

  • Author : David Kaplan
  • Publisher : SAGE
  • Release Date : 2004-06-21
  • Genre: Social Science
  • Pages : 511
  • ISBN 10 : 0761923594

The SAGE Handbook of Quantitative Methodology for the Social Sciences Excerpt :

Quantitative methodology is a highly specialized field. This handbook is intended to introduce applied statisticians, empirical researchers & graduate students to the broad array of state-of-the-art quantitative methodologies in the social sciences.

Essential Statistical Methods for Medical Statistics Book

Essential Statistical Methods for Medical Statistics

  • Author : J. Philip Miller
  • Publisher : Elsevier
  • Release Date : 2010-11-08
  • Genre: Mathematics
  • Pages : 368
  • ISBN 10 : 0444537384

Essential Statistical Methods for Medical Statistics Excerpt :

Essential Statistical Methods for Medical Statistics presents only key contributions which have been selected from the volume in the Handbook of Statistics: Medical Statistics, Volume 27 (2009). While the use of statistics in these fields has a long and rich history, the explosive growth of science in general, and of clinical and epidemiological sciences in particular, has led to the development of new methods and innovative adaptations of standard methods. This volume is appropriately focused for individuals working in these fields. Contributors are internationally renowned experts in their respective areas. · Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

Handbook of Structural Equation Modeling Book

Handbook of Structural Equation Modeling

  • Author : Rick H. Hoyle
  • Publisher : Guilford Press
  • Release Date : 2012-06-01
  • Genre: Social Science
  • Pages : 740
  • ISBN 10 : 9781606230770

Handbook of Structural Equation Modeling Excerpt :

"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"--

Statistics in the Social Sciences Book

Statistics in the Social Sciences

  • Author : Stanislav Kolenikov
  • Publisher : John Wiley & Sons
  • Release Date : 2010-02-22
  • Genre: Mathematics
  • Pages : 288
  • ISBN 10 : 0470583320

Statistics in the Social Sciences Excerpt :

A one-of-a-kind compilation of modern statistical methods designed to support and advance research across the social sciences Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth Annual Winemiller Conference: Methodological Developments of Statistics in the Social Sciences, an international meeting that focused on fostering collaboration among mathematical statisticians and social science researchers. The book provides an accessible and insightful look at modern approaches to identifying and describing current, effective methodologies that ultimately add value to various fields of social science research. With contributions from leading international experts on the topic, the book features in-depth coverage of modern quantitative social sciences topics, including: Correlation Structures Structural Equation Models and Recent Extensions Order-Constrained Proximity Matrix Representations Multi-objective and Multi-dimensional Scaling Differences in Bayesian and Non-Bayesian Inference Bootstrap Test of Shape Invariance across Distributions Statistical Software for the Social Sciences Statistics in the Social Sciences: Current Methodological Developments is an excellent supplement for graduate courses on social science statistics in both statistics departments and quantitative social sciences programs. It is also a valuable reference for researchers and practitioners in the fields of psychology, sociology, economics, and market research.

Generalized Latent Variable Modeling Book

Generalized Latent Variable Modeling

  • Author : Anders Skrondal
  • Publisher : CRC Press
  • Release Date : 2004-05-11
  • Genre: Mathematics
  • Pages : 528
  • ISBN 10 : 9780203489437

Generalized Latent Variable Modeling Excerpt :

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

Applied Missing Data Analysis  Second Edition Book

Applied Missing Data Analysis Second Edition

  • Author : Craig K. Enders
  • Publisher : Guilford Publications
  • Release Date : 2022-07-01
  • Genre: Social Science
  • Pages : 546
  • ISBN 10 : 9781462549993

Applied Missing Data Analysis Second Edition Excerpt :

The most user-friendly and authoritative resource on missing data has been completely revised to make room for the latest developments that make handling missing data more effective. The second edition includes new methods based on factored regressions, newer model-based imputation strategies, and innovations in Bayesian analysis. State-of-the-art technical literature on missing data is translated into accessible guidelines for applied researchers and graduate students. The second edition takes an even, three-pronged approach to maximum likelihood estimation (MLE), Bayesian estimation as an alternative to MLE, and multiple imputation. Consistently organized chapters explain the rationale and procedural details for each technique and illustrate the analyses with engaging worked-through examples on such topics as young adult smoking, employee turnover, and chronic pain. The companion website (www.appliedmissingdata.com) includes datasets and analysis examples from the book, up-to-date software information, and other resources. New to This Edition *Expanded coverage of Bayesian estimation, including a new chapter on incomplete categorical variables. *New chapters on factored regressions, model-based imputation strategies, multilevel missing data-handling methods, missing not at random analyses, and other timely topics. *Presents cutting-edge methods developed since the 2010 first edition; includes dozens of new data analysis examples. *Most of the book is entirely new.

The SAGE Handbook of Multilevel Modeling Book

The SAGE Handbook of Multilevel Modeling

  • Author : Marc A. Scott
  • Publisher : SAGE
  • Release Date : 2013-08-31
  • Genre: Reference
  • Pages : 696
  • ISBN 10 : 9781473971318

The SAGE Handbook of Multilevel Modeling Excerpt :

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.

Handbook of Advanced Multilevel Analysis Book

Handbook of Advanced Multilevel Analysis

  • Author : Joop Hox
  • Publisher : Psychology Press
  • Release Date : 2011-01-11
  • Genre: Psychology
  • Pages : 408
  • ISBN 10 : 9781136951275

Handbook of Advanced Multilevel Analysis 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.