Handbook of Latent Variable and Related Models

Handbook of Latent Variable and Related Models
Author: Anonim
Publsiher: Elsevier
Total Pages: 458
Release: 2011-08-11
ISBN 10: 9780080471266
ISBN 13: 0080471269
Language: EN, FR, DE, ES & NL

Handbook of Latent Variable and Related Models Book Review:

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

Latent Variable Models and Factor Analysis
Author: David J. Bartholomew,Martin Knott,Irini Moustaki
Publsiher: John Wiley & Sons
Total Pages: 296
Release: 2011-06-28
ISBN 10: 1119973708
ISBN 13: 9781119973706
Language: EN, FR, DE, ES & NL

Latent Variable Models and Factor Analysis Book Review:

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

Random Effect and Latent Variable Model Selection
Author: David Dunson
Publsiher: Springer Science & Business Media
Total Pages: 170
Release: 2010-03-18
ISBN 10: 9780387767215
ISBN 13: 0387767215
Language: EN, FR, DE, ES & NL

Random Effect and Latent Variable Model Selection Book Review:

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.

Generalized Latent Variable Modeling

Generalized Latent Variable Modeling
Author: Anders Skrondal,Sophia Rabe-Hesketh
Publsiher: CRC Press
Total Pages: 528
Release: 2004-05-11
ISBN 10: 0203489438
ISBN 13: 9780203489437
Language: EN, FR, DE, ES & NL

Generalized Latent Variable Modeling Book Review:

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

Handbook of Psychology Research Methods in Psychology

Handbook of Psychology  Research Methods in Psychology
Author: Irving B. Weiner,John A. Schinka,Wayne F. Velicer
Publsiher: John Wiley & Sons
Total Pages: 800
Release: 2012-10-16
ISBN 10: 0470890649
ISBN 13: 9780470890646
Language: EN, FR, DE, ES & NL

Handbook of Psychology Research Methods in Psychology Book Review:

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 Models

Latent Variable Models
Author: John C. Loehlin
Publsiher: Psychology Press
Total Pages: 336
Release: 2004-05-20
ISBN 10: 1135614334
ISBN 13: 9781135614331
Language: EN, FR, DE, ES & NL

Latent Variable Models Book Review:

This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx. New in the fourth edition of Latent Variable Models: *a data CD that features the correlation and covariance matrices used in the exercises; *new sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; and *reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching. Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful.

Bayesian Hierarchical Models

Bayesian Hierarchical Models
Author: Peter D. Congdon
Publsiher: CRC Press
Total Pages: 580
Release: 2019-09-16
ISBN 10: 1498785913
ISBN 13: 9781498785914
Language: EN, FR, DE, ES & NL

Bayesian Hierarchical Models Book Review:

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

Essential Statistical Methods for Medical Statistics

Essential Statistical Methods for Medical Statistics
Author: J. Philip Miller
Publsiher: Elsevier
Total Pages: 368
Release: 2010-11-08
ISBN 10: 9780444537386
ISBN 13: 0444537384
Language: EN, FR, DE, ES & NL

Essential Statistical Methods for Medical Statistics Book Review:

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

Statistics in the Social Sciences

Statistics in the Social Sciences
Author: Stanislav Kolenikov,Lori Thombs,Douglas Steinley
Publsiher: John Wiley & Sons
Total Pages: 288
Release: 2010-02-22
ISBN 10: 9780470583326
ISBN 13: 0470583320
Language: EN, FR, DE, ES & NL

Statistics in the Social Sciences Book Review:

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.

The SAGE Handbook of Multilevel Modeling

The SAGE Handbook of Multilevel Modeling
Author: Marc A. Scott,Jeffrey S. Simonoff,Brian D. Marx
Publsiher: SAGE
Total Pages: 696
Release: 2013-08-31
ISBN 10: 1473971314
ISBN 13: 9781473971318
Language: EN, FR, DE, ES & NL

The SAGE Handbook of Multilevel Modeling Book Review:

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 Structural Equation Modeling

Handbook of Structural Equation Modeling
Author: Rick H. Hoyle
Publsiher: Guilford Publications
Total Pages: 740
Release: 2014-01-01
ISBN 10: 1462516793
ISBN 13: 9781462516797
Language: EN, FR, DE, ES & NL

Handbook of Structural Equation Modeling Book Review:

The first comprehensive structural equation modeling (SEM) handbook, this accessible volume presents both the mechanics of SEM and specific SEM strategies and applications. The editor, contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results.

Handbook of Advanced Multilevel Analysis

Handbook of Advanced Multilevel Analysis
Author: Joop Hox,J. Kyle Roberts
Publsiher: Psychology Press
Total Pages: 408
Release: 2011-01-11
ISBN 10: 113695127X
ISBN 13: 9781136951275
Language: EN, FR, DE, ES & NL

Handbook of Advanced Multilevel Analysis Book Review:

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.

Measurement Error in Longitudinal Data

Measurement Error in Longitudinal Data
Author: Alexandru Cernat,Joseph W. Sakshaug
Publsiher: Oxford University Press
Total Pages: 304
Release: 2021-03-18
ISBN 10: 0192603981
ISBN 13: 9780192603982
Language: EN, FR, DE, ES & NL

Measurement Error in Longitudinal Data Book Review:

Longitudinal data is essential for understanding how the world around us changes. Most theories in the social sciences and elsewhere have a focus on change, be it of individuals, of countries, of organizations, or of systems, and this is reflected in the myriad of longitudinal data that are being collected using large panel surveys. This type of data collection has been made easier in the age of Big Data and with the rise of social media. Yet our measurements of the world are often imperfect, and longitudinal data is vulnerable to measurement errors which can lead to flawed and misleading conclusions. Measurement Error in Longitudinal Data tackles the important issue of how to investigate change in the context of imperfect data. It compiles the latest advances in estimating change in the presence of measurement error from several fields and covers the entire process, from the best ways of collecting longitudinal data, to statistical models to estimate change under uncertainty, to examples of researchers applying these methods in the real world. This book introduces the essential issues of longitudinal data collection, such as memory effects, panel conditioning (or mere measurement effects), the use of administrative data, and the collection of multi-mode longitudinal data. It also presents some of the most important models used in this area, including quasi-simplex models, latent growth models, latent Markov chains, and equivalence/DIF testing. Finally, the use of vignettes in the context of longitudinal data and estimation methods for multilevel models of change in the presence of measurement error are also discussed.

Bayesian Theory and Applications

Bayesian Theory and Applications
Author: David A. Stephens
Publsiher: Oxford University Press
Total Pages: 702
Release: 2013-01-24
ISBN 10: 0199695601
ISBN 13: 9780199695607
Language: EN, FR, DE, ES & NL

Bayesian Theory and Applications Book Review:

This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.

Handbook of Markov Chain Monte Carlo

Handbook of Markov Chain Monte Carlo
Author: Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng
Publsiher: CRC Press
Total Pages: 619
Release: 2011-05-10
ISBN 10: 1420079425
ISBN 13: 9781420079425
Language: EN, FR, DE, ES & NL

Handbook of Markov Chain Monte Carlo Book Review:

Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie

Meta Analysis

Meta Analysis
Author: Mike W.-L. Cheung
Publsiher: John Wiley & Sons
Total Pages: 400
Release: 2015-04-07
ISBN 10: 1118957822
ISBN 13: 9781118957820
Language: EN, FR, DE, ES & NL

Meta Analysis Book Review:

Presents a novel approach to conducting meta-analysis using structural equation modeling. Structural equation modeling (SEM) and meta-analysis are two powerful statistical methods in the educational, social, behavioral, and medical sciences. They are often treated as two unrelated topics in the literature. This book presents a unified framework on analyzing meta-analytic data within the SEM framework, and illustrates how to conduct meta-analysis using the metaSEM package in the R statistical environment. Meta-Analysis: A Structural Equation Modeling Approach begins by introducing the importance of SEM and meta-analysis in answering research questions. Key ideas in meta-analysis and SEM are briefly reviewed, and various meta-analytic models are then introduced and linked to the SEM framework. Fixed-, random-, and mixed-effects models in univariate and multivariate meta-analyses, three-level meta-analysis, and meta-analytic structural equation modeling, are introduced. Advanced topics, such as using restricted maximum likelihood estimation method and handling missing covariates, are also covered. Readers will learn a single framework to apply both meta-analysis and SEM. Examples in R and in Mplus are included. This book will be a valuable resource for statistical and academic researchers and graduate students carrying out meta-analyses, and will also be useful to researchers and statisticians using SEM in biostatistics. Basic knowledge of either SEM or meta-analysis will be helpful in understanding the materials in this book.

Handbook of Advanced Multilevel Analysis

Handbook of Advanced Multilevel Analysis
Author: Joop Hox,J. Kyle Roberts
Publsiher: Routledge
Total Pages: 408
Release: 2011-01-11
ISBN 10: 1136951261
ISBN 13: 9781136951268
Language: EN, FR, DE, ES & NL

Handbook of Advanced Multilevel Analysis Book Review:

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.

Handbook of Partial Least Squares

Handbook of Partial Least Squares
Author: Vincenzo Esposito Vinzi,Wynne W. Chin,Jörg Henseler,Huiwen Wang
Publsiher: Springer Science & Business Media
Total Pages: 798
Release: 2010-03-10
ISBN 10: 3540328270
ISBN 13: 9783540328278
Language: EN, FR, DE, ES & NL

Handbook of Partial Least Squares Book Review:

This handbook provides a comprehensive overview of Partial Least Squares (PLS) methods with specific reference to their use in marketing and with a discussion of the directions of current research and perspectives. It covers the broad area of PLS methods, from regression to structural equation modeling applications, software and interpretation of results. The handbook serves both as an introduction for those without prior knowledge of PLS and as a comprehensive reference for researchers and practitioners interested in the most recent advances in PLS methodology.

Introduction to Statistical Mediation Analysis

Introduction to Statistical Mediation Analysis
Author: David MacKinnon
Publsiher: Routledge
Total Pages: 488
Release: 2012-10-02
ISBN 10: 1136676139
ISBN 13: 9781136676130
Language: EN, FR, DE, ES & NL

Introduction to Statistical Mediation Analysis Book Review:

This volume introduces the statistical, methodological, and conceptual aspects of mediation analysis. Applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout. Single-mediator, multilevel, and longitudinal models are reviewed. The author's goal is to help the reader apply mediation analysis to their own data and understand its limitations. Each chapter features an overview, numerous worked examples, a summary, and exercises (with answers to the odd numbered questions). The accompanying CD contains outputs described in the book from SAS, SPSS, LISREL, EQS, MPLUS, and CALIS, and a program to simulate the model. The notation used is consistent with existing literature on mediation in psychology. The book opens with a review of the types of research questions the mediation model addresses. Part II describes the estimation of mediation effects including assumptions, statistical tests, and the construction of confidence limits. Advanced models including mediation in path analysis, longitudinal models, multilevel data, categorical variables, and mediation in the context of moderation are then described. The book closes with a discussion of the limits of mediation analysis, additional approaches to identifying mediating variables, and future directions. Introduction to Statistical Mediation Analysis is intended for researchers and advanced students in health, social, clinical, and developmental psychology as well as communication, public health, nursing, epidemiology, and sociology. Some exposure to a graduate level research methods or statistics course is assumed. The overview of mediation analysis and the guidelines for conducting a mediation analysis will be appreciated by all readers.

Handbook of Developmental Research Methods

Handbook of Developmental Research Methods
Author: Brett Laursen,Todd D. Little,Noel A. Card
Publsiher: Guilford Press
Total Pages: 788
Release: 2012-02-01
ISBN 10: 1609189515
ISBN 13: 9781609189518
Language: EN, FR, DE, ES & NL

Handbook of Developmental Research Methods Book Review:

Appropriate for use in developmental research methods or analysis of change courses, this is the first methods handbook specifically designed to meet the needs of those studying development. Leading developmental methodologists present cutting-edge analytic tools and describe how and when to use them, in accessible, nontechnical language. They also provide valuable guidance for strengthening developmental research with designs that anticipate potential sources of bias. Throughout the chapters, research examples demonstrate the procedures in action and give readers a better understanding of how to match research questions to developmental methods. The companion website (www.guilford.com/laursen-materials) supplies data and program syntax files for many of the chapter examples.