# Data Analysis with Mplus

Author: Christian Geiser

Publisher: Guilford Press

ISBN: 1462502458

Category: Social Science

Page: 305

View: 6275

A practical introduction to using Mplus for the analysis of multivariate data, this volume provides step-by-step guidance, complete with real data examples, numerous screen shots, and output excerpts. The author shows how to prepare a data set for import in Mplus using SPSS. He explains how to specify different types of models in Mplus syntax and address typical caveats--for example, assessing measurement invariance in longitudinal SEMs. Coverage includes path and factor analytic models as well as mediational, longitudinal, multilevel, and latent class models. Specific programming tips and solution strategies are presented in boxes in each chapter. The companion website (http://crmda.ku.edu/guilford/geiser) features data sets, annotated syntax files, and output for all of the examples. Of special utility to instructors and students, many of the examples can be run with the free demo version of Mplus.

# Advanced Multivariate Data Analysis with Mplus

Author: Christian Geiser,Claudia Crayen,Craig Enders

Publisher: Springer VS

ISBN: 9783531166049

Category: Education

Page: 300

View: 9397

Der zweite Band von "Datenanalyse mit Mplus" wendet sich an fortgeschrittene Anwender, die über solides statistisches Hintergrundwissen und erste Mplus-Kenntnisse verfügen. Wie geht man mit ordinalen oder dichtomen Variablen um? Wie mit einer Verletzung der Nomalverteilungsannahme? In vielen Forschungskontexten steht die Betrachtung mehrerer Gruppen im Vordergrund, andernorts sucht man nach Modellen zur Kombination von Strukturgleichungs-, Mehrebenen- und Latent-Class-Modellen. Darüber hinaus setzen Forscher verstärkt moderne Methoden zum Umgang mit fehlenden Daten sowie Stichproben- und Teststärkeplanung ein. Diese und weitere Fragen werden praxisnah und Schritt für Schritt erläutert.

# Structural Equation Modeling with Mplus

Basic Concepts, Applications, and Programming

Author: Barbara M. Byrne

Publisher: Routledge

ISBN: 1136663452

Category: Psychology

Page: 432

View: 9723

Modeled after Barbara Byrne’s other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using Mplus Versions 5 & 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed illustrated and annotated testing of the hypothesized and post hoc models explanation and interpretation of all Mplus input and output files important caveats pertinent to the SEM application under study a description of the data and reference upon which the model was based the corresponding data and syntax files available at http://www.psypress.com/sem-with-mplus/datasets . The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models. Intended for researchers, practitioners, and students who use SEM and Mplus, this book is an ideal resource for graduate level courses on SEM taught in psychology, education, business, and other social and health sciences and/or as a supplement for courses on applied statistics, multivariate statistics, intermediate or advanced statistics, and/or research design. Appropriate for those with limited exposure to SEM or Mplus, a prerequisite of basic statistics through regression analysis is recommended.

# Applied Quantitative Analysis in Education and the Social Sciences

Author: Yaacov M. Petscher,Christopher Schatschneider

Publisher: Routledge

ISBN: 0415893488

Category: Education

Page: 376

View: 2355

To say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model and interpret results. The mission of this book is to expose the reader to advanced quantitative methods as it pertains to individual level analysis, multilevel analysis, item-level analysis, and covariance structure analysis. Each chapter is self-contained and follows a common format so that readers can run the analysis and correctly interpret the output for reporting.

# Datenanalyse mit Mplus

Eine anwendungsorientierte Einführung

Author: Christian Geiser

Publisher: Springer-Verlag

ISBN: 3531920421

Category: Psychology

Page: 291

View: 2258

Strukturgleichungsmodelle gehören mittlerweile zu den etablierten statistischen Methoden in den Sozialwissenschaften und eignen sich für die Beantwortung einer Vielzahl von Fragestellungen. Das Analyseprogramm Mplus erfreut sich als eines der aktuellsten, flexibelsten und anwenderfreundlichsten Statistikprogramme zunehmender Beliebtheit. Praxisnah, mit zahlreichen Beispielen, Probedatensätzen und Abbildungen führt der Autor Schritt für Schritt in die Grundlagen der Handhabung von Mplus (Version 5) ein, und beschreibt die Anwendung grundlegender Analyseverfahren. Dabei werden nicht nur konventionelle Strukturgleichungsmodelle, sondern auch Strukturgleichungsmodelle der Veränderungsmessung sowie Mehrebenenregressionsmodelle und Latent-Class-Analysen besprochen.

# The SAGE Handbook of Quantitative Methodology for the Social Sciences

Author: David Kaplan

Publisher: SAGE

ISBN: 0761923594

Category: Social Science

Page: 511

View: 2889

The SAGE Handbook of Quantitative Methodology for the Social Sciences is the definitive reference for teachers, students, and researchers of quantitative methods in the social sciences, as it provides a comprehensive overview of the major techniques used in the field. The contributors, top methodologists and researchers, have written about their areas of expertise in ways that convey the utility of their respective techniques, but, where appropriate, they also offer a fair critique of these techniques. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter and makes this an invaluable resource.

# Cross-Cultural Analysis

Methods and Applications

Publisher: Routledge

ISBN: 1135389918

Category: Psychology

Page: 528

View: 3388

Intended to bridge the gap between the latest methodological developments and cross-cultural research, this interdisciplinary resource presents the latest strategies for analyzing cross-cultural data. Techniques are demonstrated through the use of applications that employ cross national data sets such as the latest European Social Survey. With an emphasis on the generalized latent variable approach, internationally–prominent researchers from a variety of fields explain how the methods work, how to apply them, and how they relate to other methods presented in the book. Syntax and graphical and verbal explanations of the techniques are included. A website features some of the data sets and syntax commands used in the book. Applications from the behavioral and social sciences that use real data-sets demonstrate: The use of samples from 17 countries to validate the resistance to change scale across these nations How to test the cross-national invariance properties of social trust The interplay between social structure, religiosity, values, and social attitudes A comparison of anti-immigrant attitudes and patterns of religious orientations across European countries. The book is divided into techniques for analyzing cross-cultural data within the generalized-latent-variable approach: multiple-group confirmatory factor analysis and multiple-group structural equation modeling; multi-level analysis; latent class analysis; and item-response theory. Since researchers from various disciplines often use different methodological approaches, a consistent framework for describing and applying each method is used so as to cross ‘methodological borders’ between disciplines. Some chapters describe the basic strategy and how it relates to other techniques presented in the book, others apply the techniques and address specific research questions, and a few combine the two. A table in the preface highlights for each chapter: a description of the contents, the statistical methods used, the goal(s) of the analysis, and the data set employed. This book is intended for researchers, practitioners, and advanced students interested in cross-cultural research. Because the applications span a variety of disciplines, the book will appeal to researchers and students in: psychology, political science, sociology, education, marketing and economics, geography, criminology, psychometrics, epidemiology, and public health, as well as those interested in methodology. It is also appropriate for an advanced methods course in cross-cultural analysis.

# Longitudinal Structural Equation Modeling

Author: Todd D. Little

Publisher: Guilford Press

ISBN: 1462510167

Category: Social Science

Page: 386

View: 8729

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 (http: //crmda.ku.edu/guilford/little) 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).

# The SAGE Handbook of Quantitative Methods in Psychology

Author: Roger E Millsap,Alberto Maydeu-Olivares

Publisher: SAGE

ISBN: 144620667X

Category: Psychology

Page: 800

View: 2725

`I often... wonder to myself whether the field needs another book, handbook, or encyclopedia on this topic. In this case I think that the answer is truly yes. The handbook is well focused on important issues in the field, and the chapters are written by recognized authorities in their fields. The book should appeal to anyone who wants an understanding of important topics that frequently go uncovered in graduate education in psychology' - David C Howell, Professor Emeritus, University of Vermont Quantitative psychology is arguably one of the oldest disciplines within the field of psychology and nearly all psychologists are exposed to quantitative psychology in some form. While textbooks in statistics, research methods and psychological measurement exist, none offer a unified treatment of quantitative psychology. The SAGE Handbook of Quantitative Methods in Psychology does just that. Each chapter covers a methodological topic with equal attention paid to established theory and the challenges facing methodologists as they address new research questions using that particular methodology. The reader will come away from each chapter with a greater understanding of the methodology being addressed as well as an understanding of the directions for future developments within that methodological area. Drawing on a global scholarship, the Handbook is divided into seven parts: Part One: Design and Inference: addresses issues in the inference of causal relations from experimental and non-experimental research, along with the design of true experiments and quasi-experiments, and the problem of missing data due to various influences such as attrition or non-compliance. Part Two: Measurement Theory: begins with a chapter on classical test theory, followed by the common factor analysis model as a model for psychological measurement. The models for continuous latent variables in item-response theory are covered next, followed by a chapter on discrete latent variable models as represented in latent class analysis. Part Three: Scaling Methods: covers metric and non-metric scaling methods as developed in multidimensional scaling, followed by consideration of the scaling of discrete measures as found in dual scaling and correspondence analysis. Models for preference data such as those found in random utility theory are covered next. Part Four: Data Analysis: includes chapters on regression models, categorical data analysis, multilevel or hierarchical models, resampling methods, robust data analysis, meta-analysis, Bayesian data analysis, and cluster analysis. Part Five: Structural Equation Models: addresses topics in general structural equation modeling, nonlinear structural equation models, mixture models, and multilevel structural equation models. Part Six: Longitudinal Models: covers the analysis of longitudinal data via mixed modeling, time series analysis and event history analysis. Part Seven: Specialized Models: covers specific topics including the analysis of neuro-imaging data and functional data-analysis.

# Principles and Practice of Structural Equation Modeling, Fourth Edition

Author: Rex B. Kline

Publisher: Guilford Publications

ISBN: 146252334X

Category: Social Science

Page: 534

View: 5502

New to This Edition *Extensively revised to cover important new topics: Pearl' s graphing theory and SCM, causal inference frameworks, conditional process modeling, path models for longitudinal data, item response theory, and more. *Chapters on best practices in all stages of SEM, measurement invariance in confirmatory factor analysis, and significance testing issues and bootstrapping. *Expanded coverage of psychometrics. *Additional computer tools: online files for all detailed examples, previously provided in EQS, LISREL, and Mplus, are now also given in Amos, Stata, and R (lavaan). *Reorganized to cover the specification, identification, and analysis of observed variable models separately from latent variable models. Pedagogical Features *Exercises with answers, plus end-of-chapter annotated lists of further reading. *Real examplesof troublesome data, demonstrating how to handle typical problems in analyses.

# Growth Modeling

Structural Equation and Multilevel Modeling Approaches

Author: Kevin J. Grimm,Nilam Ram,Ryne Estabrook

Publisher: Guilford Publications

ISBN: 1462526071

Category: Social Science

Page: 537

View: 4267

Growth models are among the core methods for analyzing how and when people change. Discussing both structural equation and multilevel modeling approaches, this book leads readers step by step through applying each model to longitudinal data to answer particular research questions. It demonstrates cutting-edge ways to describe linear and nonlinear change patterns, examine within-person and between-person differences in change, study change in latent variables, identify leading and lagging indicators of change, evaluate co-occurring patterns of change across multiple variables, and more. User-friendly features include real data examples, code (for Mplus or NLMIXED in SAS, and OpenMx or nlme in R), discussion of the output, and interpretation of each model's results. User-Friendly Features *Real, worked-through longitudinal data examples serving as illustrations in each chapter. *Script boxes that provide code for fitting the models to example data and facilitate application to the reader's own data. *"Important Considerations" sections offering caveats, warnings, and recommendations for the use of specific models. *Companion website supplying datasets and syntax for the book's examples, along with additional code in SAS/R for linear mixed-effects modeling.

# Latent Class Analysis of Survey Error

Author: Paul P. Biemer

Publisher: John Wiley & Sons

ISBN: 9781118099575

Category: Mathematics

Page: 416

View: 8295

Combining theoretical, methodological, and practical aspects,Latent Class Analysis of Survey Error successfully guides readersthrough the accurate interpretation of survey results for qualityevaluation and improvement. This book is a comprehensive resourceon the key statistical tools and techniques employed during themodeling and estimation of classification errors, featuring aspecial focus on both latent class analysis (LCA) techniques andmodels for categorical data from complex sample surveys. Drawing from his extensive experience in the field of surveymethodology, the author examines early models for surveymeasurement error and identifies their similarities and differencesas well as their strengths and weaknesses. Subsequent chapterstreat topics related to modeling, estimating, and reducing errorsin surveys, including: Measurement error modeling forcategorical data The Hui-Walter model and othermethods for two indicators The EM algorithm and its role in latentclass model parameterestimation Latent class models for three ormore indicators Techniques for interpretation of modelparameter estimates Advanced topics in LCA, including sparse data, boundary values,unidentifiability, and local maxima Special considerations for analyzing datafrom clustered andunequal probability samples with nonresponse The current state of LCA and MLCA (multilevel latent classanalysis), and an insightful discussion on areas for furtherresearch Throughout the book, more than 100 real-world examples describethe presented methods in detail, and readers are guided through theuse of lEM software to replicate the presented analyses. Appendicessupply a primer on categorical data analysis, and a related Website houses the lEM software. Extensively class-tested to ensure an accessible presentation,Latent Class Analysis of Survey Error is an excellent book forcourses on measurement error and survey methodology at the graduatelevel. The book also serves as a valuable reference for researchersand practitioners working in business, government, and the socialsciences who develop, implement, or evaluate surveys.

# Lineare Strukturgleichungsmodelle

Eine Einführung mit R

Author: Holger Steinmetz

Publisher: Rainer Hampp Verlag

ISBN: 3957101492

Category: Social Science

Page: 189

View: 7169

Lineare Strukturgleichungsmodelle sind aus der verhaltenswissenschaftlichen Forschung nicht mehr wegzudenken. Sie sind ein nützliches Werkzeug, um Hypothesen über Beziehungen zwischen Variablen zu prüfen und - mehr noch - Implikationen kausaler Strukturen zu testen. In diesem Buch werden Grundlagen, Hintergründe und die Vorgehensweisen bei der Spezifizierung von Kausalmodellen diskutiert. Dies wird an einem empirischen Beispiel und unter Verwendung des lavaan-Pakets innerhalb der Software R illustriert. Der Schwerpunkt des Buchs liegt hierbei weniger auf methodischen und statistischen Aspekten eines Modells, sondern auf der adäquaten Übersetzung der theoretischen Vorstellungen in ein Modell. Ein besonderer Stellenwert wird dem Verständnis der kausalen Implikationen von Modellen, der theoretische Bedeutung von Variablen, der Diagnostik einer Fehlanpassung an die Daten und modernen Konzepten wie dem Instrumentalvariablen-Ansatz aus der Ökonometrie und dem d-separation-Konzept, eingeräumt. Somit bietet das Buch sowohl einen Einstieg für Anfänger/innen als auch interessante Inhalte für Fortgeschrittene. Holger Steinmetz ist wissenschaftlicher Mitarbeiter am Lehrstuhl für International Business Studies der Universität Paderborn.

# Historische Sozialforschung

Einführung und Überblick

Author: N.A

Publisher: Springer-Verlag

ISBN: 3663012646

Category: Technology & Engineering

Page: 225

View: 9659

# Applications of Research Methodology

Author: Thomas E. Scruggs,Margo A. Mastropieri

Publisher: Emerald Group Publishing

ISBN: 0762312955

Category: Education

Page: 348

View: 2158

Research in learning and behavioral disabilities, employing a variety of methods and techniques, has provided information relevant to practitioners. This volume discusses, applies and evaluates different methodological approaches to learning and behavioral disorder research; and serves as a reference to educators, researchers, and others.

# Longitudinal Data Analysis

A Practical Guide for Researchers in Aging, Health, and Social Sciences

Author: Jason Newsom,Richard N. Jones,Scott M. Hofer

Publisher: Routledge

ISBN: 1136705473

Category: Psychology

Page: 405

View: 9788

First Published in 2012. Routledge is an imprint of Taylor & Francis, an informa company.

# Confirmatory Factor Analysis for Applied Research

Author: Timothy A. Brown

Publisher: Guilford Press

ISBN: 1593852746

Category: Social Science

Page: 475

View: 5645

The first and only complete resource on the details of using confirmatory factor analysis (CFA) as an analytic tool, this book emphasizes the practical and conceptual aspects of CFA over math and formulas. Rich examples are derived from actual research in psychology, management, and sociology.

# Confirmatory Factor Analysis for Applied Research, Second Edition

Author: Timothy A. Brown

Publisher: Guilford Publications

ISBN: 146251779X

Category: Science

Page: 462

View: 807

This accessible book has established itself as the go-to resource on confirmatory factor analysis (CFA) for its emphasis on practical and conceptual aspects rather than mathematics or formulas. 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 ...

# Principles and Practice of Structural Equation Modeling

Author: Rex B. Kline

Publisher: Guilford Press

ISBN: 1606238760

Category: Psychology

Page: 427

View: 2998

"Kline provides a text that is accessible for graduate students, practitioners, and researchers who are not intimately familiar with SEM techniques. In addition, he effortlessly summarizes current information that researchers who already use SEM should have. The reorganization of the material, new topic boxes, new Web page, and updated technical information enhance an already great resource."---James B. Schreiber, Center for Advancing the Study of Teaching and Learning, Duquesne University "In the third edition, Kline not only has updated the material, but has substantially improved it. He adds more depth to certain topics---such as estimation, in Chapter 7---and covers some intermediate-to-advanced topics not described in the previous edition, all at a level appropriate for beginners."---Noel A. Card, Division of Family Studies and Human Development, University of Arizona "Of all the introductory SEM texts, this one is the most interesting to read. Anyone who has taken a course in basic algebra or introductory statistics will be able to understand the ideas and work through the exercises, and those who work their way through the book will have a good foundation in SEM and will be able to use it effectively."---David F. Gillespie, George Warren Brown School of Social Work, Washington University in St. Louis This Bestselling Text Provides a Balance Between the Technical and Practical Aspects of structural equation modeling (SEM). Using clear and accessible language, Rex B. Kline covers core techniques, potential pitfalls, and applications across the behavioral and social sciences. Some more advanced topics are also covered, including estimation of interactive effects of latent variables and multilevel SEM.

# Basic and Advanced Bayesian Structural Equation Modeling

With Applications in the Medical and Behavioral Sciences

Author: Sik-Yum Lee,Xin-Yuan Song

Publisher: John Wiley & Sons

ISBN: 1118358872

Category: Mathematics

Page: 400

View: 6400

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.