Showing posts with label research methods. Show all posts
Showing posts with label research methods. Show all posts

Contemporary Behavior Therapy Review

Contemporary Behavior Therapy
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Contemporary Behavior Therapy by Michael D. Spiegler and Dave C. Guevremont is a thorough overview of one of the most effective therapies known to modern psychology. It is a very well written, well organized, and easy to understand book. It breaks down and clearly demarcates all concepts related to the theoretical constructs defining this ever-important behavioral paradigm. This book is also an excellent read for anyone desiring to learn more about the cutting edge concepts operating in modern psychology today. If you are a serious student of psychology, you will not want to sell this one back.

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This respected up-to-date survey of contemporary behavior therapy synthesizes the clinical, research, theoretical, and ethical facets of behavior therapy. It is simultaneously an introduction for beginning students and a scholarly review and resource for advanced students. The book is comprehensive, covering all the major behavioral and cognitive therapies. The wealth of case studies illustrate the application of behavior therapy techniques to a wide array of problems and clinical populations. The text's multidisciplinary approach includes applications to diverse fields, including psychology, education, social work, nursing, and rehabilitation.

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Handbook of Advanced Multilevel Analysis (European Association of Methodology Series) Review

Handbook of Advanced Multilevel Analysis (European Association of Methodology Series)
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There are very few pieces of published literature where you can honestly say that they helped shape and transform entire cultures. In the recorded history of mankind, we look to the first printing of the Bible, the complete works of Shakespeare, To Kill a Mockingbird, War and Peace...written pieces of art that inspire, that provoke great thought and action, that literally change the human race and mold society. This, unfortunately, is not one of those times.
As I scrolled through various search results listed in the Amazon Books section, I came across this particular book. Of course, the title drew me in. I had to see what this was all about. Words like "Advanced" and "Multilevel" are words you can't ignore. They call out to you like a lighthouse safely leading ships back to port. I had to see for myself. I had to have a part of this in my life.
Little did I know that the brief synopsis alone would literally lull me into a coma-like state. After the first 2 sentences my vision had become blurred, my speech was slurred and I couldn't put together complete coherent sentences. I think I may have even wet myself a little. I believe I was suffering a stroke or isolated seizure.
This "Handbook" did nothing but cause me great discomfort. I would have to agree that it is "Advanced", as the cover tells. It was so advanced that my simple mind couldn't make sense of the brief overview. After recovering enough to continue typing, I quickly clicked through to one of the "recommended items" based on my previous Amazon purchases. This allowed me to avoid any further medical issues or further sensory damage.
Fortunately, the suggestion I clicked on hastily ended up being exactly what I needed. Thank you Amazon for knowing what I was capable of reading and what would encourage and inspire me to become more than I am today. ISBN-10: 1580080111 saved the day.


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Introduction to Structural Equation Modelling Using SPSS and Amos Review

Introduction to Structural Equation Modelling Using SPSS and Amos
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The quality of this book is low. It has many errors making it hard to follow. I purchased the book based on the fact that the author was making the files associated with the analysis in the book available. Well the data files do not match what is in the book. Second, there is very little help in showing how to use SPSS or AMOS properly. Your better choice are the texts from Barbara Byrne.

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New software (Lisrel and AMOS) has made the techniques of Structural Equation Modelling (SEM) increasingly available to students and researchers, while the recent adoption of AMOS as part of the SPSS suite has improved access still further.





As an alternative to existing books on the subject, which are customarily very long, very high-level and very mathematical, not to mention expensive, Niels Blunch's introduction has been designed for advanced undergraduates and Masters students who are new to SEM and still relatively new to statistics.



Illustrated with screenshots, cases and exercises and accompanied by a companion website containing datasets that can be easily uploaded onto SPSS and AMOS, this handy introduction keeps maths to a minimum and contains an appendix covering basic forms of statistical analysis.


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Principles and Practice of Structural Equation Modeling Review

Principles and Practice of Structural Equation Modeling
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Rex Kline easy writing style will take you through step-by-step in one of the most comprehesive yet accessible introductions to multivariate analysis and structural equation modeling. After outlining the building blocks of SEM (multiple regression, path analysis, and factor analysis), Kline gets the reader ready to tackle popular SEM software with examples for AMOS, LISREL and EQS. There is also a great chapter on what NOT to do with this often misused technique. For a preview, see Kline's article in the Journal of Clinical Psychology (1991), Latent variable path analysis: A beginner's tour guide. This book is an excellent read, and a must have for researchers and statisticians.

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Structural Equation Modeling: Concepts, Issues, and Applications Review

Structural Equation Modeling: Concepts, Issues, and Applications
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This book was my saving grace in writing my dissertation. The book provides an excellent review of SEM's concepts, issues, and applications in both EQS and Lisrel, in a manner which is understandable to a budding researcher.

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This largely nontechnical volume reviews some of the major issues facing researchers who wish to use structural equation modeling. Individual chapters present recent developments on specification, estimation and testing, statistical power, software comparisons and analyzing multitrait/multimethod data. Numerous examples of applications are given and attention is paid to the underlying philosophy of structural equation modeling and to writing up results from structural equation modeling analyses.


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Structural Equation Modeling with EQS and EQS/WINDOWS: Basic Concepts, Applications, and Programming Review

Structural Equation Modeling with EQS and EQS/WINDOWS: Basic Concepts, Applications, and Programming
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Barbara Byrne manages to make a very complicated topic seem manageable and understandable. This book is ideal for people familiar with the basics of psychology statistics, but relatively new at structural equation modeling. My only complaints are that the index is a bit sparse, so I found myself thumbing through the book frequently; and sometimes the details of how to apply the concepts directly to EQS commands were left a bit unclear. However, overall this was an excellent starter book for structural equation newbies!

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Designed to help beginners estimate and test structural equation modeling (SEM) using the EQS approach, this book demonstrates a variety of SEM//EQS applications that include both partial factor analytic and full latent variable models. Beginning with an overview of the basic concepts of SEM and the EQS program, the author works through applications starting with a single sample approach to more advanced applications, such as a multi-sample approach. The book concludes with a section on using EQS for modeling with Windows.


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Basics of Structural Equation Modeling Review

Basics of Structural Equation Modeling
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I'm working on my Ph.D. using SEM. This book was very, very helpful. In fact out of about 20 books that I used, this was the most simple and easy to understand.
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Structural Equations with Latent Variables Review

Structural Equations with Latent Variables
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The software Lisrel was developed to model and analyze data using structural equation models which involve the introduction of latent variables. Although this topic has historically been most commonly used in the social sciences including psychology and sociology, it is finding a wide range of applications as statisticians encounter more and more problems where it is appropriate to use latent variables.
Bollen provides a thorough treatment of the topic that has advanced some since the publication of the book . This is still the best source for a detailed account of the methods. Bengt Meuthen at UCLA was one of the pioneers of the methodology and his books and papers provide good additional sources for the reader who wants to understand the theory and the software tools.

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Analysis of Ordinal Categorical Data Alan Agresti Statistical Science Now has its first coordinated manual of methods for analyzing ordered categorical data. This book discusses specialized models that, unlike standard methods underlying nominal categorical data, efficiently use the information on ordering. It begins with an introduction to basic descriptive and inferential methods for categorical data, and then gives thorough coverage of the most current developments, such as loglinear and logit models for ordinal data. Special emphasis is placed on interpretation and application of methods and contains an integrated comparison of the available strategies for analyzing ordinal data. This is a case study work with illuminating examples taken from across the wide spectrum of ordinal categorical applications. 1984 (0 471-89055-3) 287 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch This book provides the practicing statistician and econometrician with new tools for assessing the quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are either unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data and help to identify the variables involved in each; and pinpoint the estimated coefficients that are potentially most adversely affected. The primary emphasis of these contributions is on diagnostics, but suggestions for remedial action are given and illustrated. 1980 (0 471-05856-4) 292 pp. Applied Regression Analysis Second Edition Norman Draper and Harry Smith Featuring a significant expansion of material reflecting recent advances, here is a complete and up-to-date introduction to the fundamentals of regression analysis, focusing on understanding the latest concepts and applications of these methods. The authors thoroughly explore the fitting and checking of both linear and nonlinear regression models, using small or large data sets and pocket or high-speed computing equipment. Features added to this Second Edition include the practical implications of linear regression; the Durbin-Watson test for serial correlation; families of transformations; inverse, ridge, latent root and robust regression; and nonlinear growth models. Includes many new exercises and worked examples. 1981 (0 471-02995-5) 709 pp.

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Structural Equation Modeling With Eqs: Basic Concepts, Applications, And Programming (Multivariate Applications) (Multivariate Applications Series) Review

Structural Equation Modeling With Eqs: Basic Concepts, Applications, And Programming (Multivariate Applications) (Multivariate Applications Series)
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Byrne offers a more readable alternative to the hard core approach of learning EQS from its official manual. The latter presents a heavily mathematical framework that might be offputting to some readers who lack a strong statistical background.
Instead, her book goes through much of what EQS can do for you. But at a gentler pace. Giving a brief walkthrough of multivariate statistical modelling. The book starts off explaining the format of the EQS input file. It uses a graphical approach similar to what electrical engineers have for laying out circuits. More intuitive for the user. Where you can draw regressions paths from one component in the diagram to other components, for example. This is reminiscent of how SPICE went from a text input file that described a circuit to a graphical approach that was far easier to understand.
Much of the book then goes into how you can test for causality in your model. EQS has formidable abilities to do so, and you need to master how to control these.

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Researchers and students who want a less mathematical alternative to the EQS manual will find exactly what they're looking for in this practical text. Written specifically for those with little to no knowledge of structural equation modeling (SEM) or EQS, the author's goal is to provide a non-mathematical introduction to the basic concepts of SEM by applying these principles to EQS, Version 6.1. The book clearly demonstrates a wide variety of SEM/EQS applications that include confirmatory factor analytic and full latent variable models. Analyses are based on a wide variety of data representing single and multiple-group models; these include data that are normal/non-normal, complete/incomplete, and continuous/categorical. Written in a "user-friendly" style, the author "walks" the reader through the varied steps involved in the process of testing SEM models. These include model specification and estimation, assessment of model fit, description of EQS output, and interpretation of findings. Each of the book's applications is accompanied by: a statement of the hypothesis being tested, a schematic representation of the model, explanations and interpretations of the related EQS input and output files, tips on how to use the associated pull-down menus and icons, and the data file upon which the application is based. Beginning with an overview of the basic concepts of SEM and the EQS program, the book carefully works through applications starting with relatively simple single group analyses, through to more advanced applications, such as a multi-group, latent growth curve, and multilevel modeling.The new edition features: •many new applications that include a latent growth curve model, a multilevel model, a second-order model based on categorical data, a missing data multigroup model based on the EM algorithm, and the testing for latent mean differences related to a higher-order model; •a CD enclosed with the book that includes all application data; •vignettes illustrating procedural and/or data management tasks using a Windows interface; •description of how to build models both interactively using the BUILDULEQ interface and graphically using the EQS Diagrammer.

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A First Course in Structural Equation Modeling Review

A First Course in Structural Equation Modeling
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This book does a good job of explaining the basic concepts involved in SEM, path analysis, and CFA in easy to understand terms. Also, it lays out syntax and output for running popular SEM programs, such as LISREL, EQS, and Mplus. This book is perfect for the person who is trying to learn SEM on their own. I think it gives you enough to be able to know how to run a SEM, but it isn't enough to really make you a pro at it.

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A Beginner's Guide to Structural Equation Modeling: Third Edition Review

A Beginner's Guide to Structural Equation Modeling: Third Edition
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No one expects statistics to be easy reading, especially when it concerns complicated models such as structural equation modeling (SEM). Nevertheless, this book manages to do just that.
Schumacker and Lomax have successfully put together a guide that explains to beginners (like myself) in simple terms how the whole thing works. As with most books that treat complex models, some basic knowledge of statistics is preferable before you begin to read it. But if your statistics is rusty and you have only vague impressions of probability sketches in your memory, fear not! Schumacker and Lomax are kind to us poor souls, and begin by introducing some basics in chapter 1 to prod your memory: terminology, variable scales, how to treat missing data, outliers and normality. And in chapter 2, they discuss correlation and covariance.
Before talking about structural equation models, Schumacker and Lomax dedicate chapter 3 to a number of statistical methods on which SEM is built. This chapter gives a basic overview of regression, path analysis and factor analysis. The review of these methods helps you to understand SEM better later on. They also provide an excellent understanding of the methods, in case you have not used them before or it's been a while ...
The rest of the guide covers SEM: how to develop and measure a model (chapters 4 and 5), how the model parameters are estimated and how you can check for reliability and validity (chapter 6), and checking for goodness of fit of your model (chapter 7).
In chapter 8, you are shown some examples of computer outputs by two software packages that can conduct SEM, EQS5 and LISREL8-SIMPLIS.
Chapter 9 goes into more detail on models and diagrams (regression, analysis of covariance, path, measurement and structural models). For those that feel by this point that they've gained enough experience, advanced topics such as cross validation, simulation, bootstrap and jacknife methods as well as multiple same and interaction models are covered in chapter 10. And for the super-keen, the technical bits are covered in chapter 11 (health warning: you better be up to speed on matrix algebra).
The great thing about this book is that you most likely will be able to run models and interpret results by chapter 7, and you don't need to go into the nitty-gritty if you don't want to. On the other hand, the details are there if you need them. In essence, the authors start at the beginning, building up slowly until you are able to handle a basic model, before going into more complex issues.
One drawback, I have found, is that this book was published in 1996. That's nearly a decade ago, and (fortunately) computer power and statistical modeling has come a long way since then. The authors, for example, are convinced that WordPerfect is the software of choice for word processing and that at some point in the future it would be possible to copy and paste diagrams into a word processing program. We've come a long way since then. Today, many SEM packages exist that are much more user friendly than some of the older stodgy packages that require you to enter data in a very specific way and interpret results by going through reams of data output. As such, the authors (or publishers) probably ought to update it with a second edition.
Nevertheless, the strength of the book lies in its guidance and explanatory power. And even if you use a different package, you can skim through the data outputs they use, and focus on your model, how to construct it, and of what pitfalls to beware.
I highly recommend this for anyone starting on SEM - your modeling days will be much happier with this guide.

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This best-seller introduces readers to structural equation modeling (SEM) so they can conduct their own analysis and critique related research. Noted for its accessible, applied approach, chapters cover basic concepts and practices and computer input/output from the free student version of Lisrel 8.8 in the examples. Each chapter features an outline, key concepts, a summary, numerous examples from a variety of disciplines, tables, and figures, including path diagrams, to assist with conceptual understanding.The book first reviews the basics of SEM, data entry/editing, and correlation. Next the authors highlight the basic steps of SEM: model specification, identification, estimation, testing, and modification, followed by issues related to model fit and power and sample size. Chapters 6 through 10 follow the steps of modeling using regression, path, confirmatory factor, and structural equation models. Next readers find a chapter on reporting SEM research including a checklist to guide decision-making, followed by one on model validation. Chapters 13 through 16 provide examples of various SEM model applications. The book concludes with the matrix approach to SEM using examples from previous chapters.Highlights of the new edition include:A website with raw data sets for the book's examples and exercises so they can be used with any SEM program, all of the book's exercises, hotlinks to related websites, and answers to all of the exercises for Instructor's onlyNew troubleshooting tips on how to address the most frequently encountered problemsExamples now reference the free student version of Lisrel 8.8Expanded coverage of advanced models with more on multiple-group, multi-level, & mixture modeling (Chs. 13 & 15), second-order and dynamic factor models (Ch. 14), and Monte Carlo methods (Ch. 16)Increased coverage of sample size and power (Ch. 5) and reporting research (Ch. 11)New journal article references help readers better understand published research (Chs. 13 - 17) and 25 % new exercises with answers to half in the book for student review.Designed for introductory graduate level courses in structural equation modeling or factor analysis taught in psychology, education, business, and the social and healthcare sciences, this practical book also appeals to researchers in these disciplines. An understanding of correlation is assumed. To access the website visit the book page or the Textbook Resource page at http://www.psypress.com/textbook-resources/ for more details.

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Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Second Edition (Multivariate Applications Series) Review

Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming, Second Edition (Multivariate Applications Series)
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For a book that presumes to describe the use of a particular software (AMOS) for conducting Structural Equation Modeling, this book misses the mark by quite a bit. Many of the terms and windows figures the author uses do not correspond well to those actually used in AMOS. Also, the description of what the author is doing is often not as clear as one would hope, and I found myself often at a lost as to what the intent or purpose was for performing each step and why one may want to perform each procedure discussed. Lastly, this book spends the vast majority of its page space discussing factor analyses (confirmatory factor analysis) and the use of modification indices. While this may be of some importance to some researchers, the more theorectically and empirically meaningful discussion of assessing and comparing various causal models of association are tersely covered and and the rationale behind such analytical tools poorly explained. Also, the author seems to advocate for some practices in SEM that many other experts may not condone.

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This bestselling text provides a practical guide to the basic concepts of structural equation modeling (SEM) and the AMOS program (Versions 17 & 18). The author reviews SEM applications based on actual data taken from her research. Noted for its non-mathematical language, this book is written for the novice SEM user. With each chapter, the author "walks" the reader through all steps involved in testing the SEM model including: an explanation of the issues addressed an illustration of the hypothesized and posthoc models tested AMOS input and output with accompanying interpretation and explanationThe function of the AMOS toolbar icons and their related pull-down menusThe data and published reference upon which the model was based.With over 50% new material, highlights of the new edition include:All new screen shots featuring Version 17 of the AMOS program  All data files now available at www.psypress.com/sem-with-amos Application of a multitrait-mulitimethod model, latent growth curve model, and second-order model based on categorical dataAll applications based on the most commonly used graphical interfaceThe automated multi-group approach to testing for equivalenceThe book opens with an introduction to the fundamental concepts of SEM and the basics of the AMOS program. The next 3 sections present applications that focus on single-group, multiple-group, and multitrait-mutimethod and latent growth curve models. The book concludes with a discussion about non-normal and missing (incomplete) data and  two applications capable of addressing these issues. Intended for researchers, practitioners, and students who use SEM and AMOS in their work, this book is an ideal resource for graduate level courses on SEM taught in departments of psychology, education, business, and other social and health sciences and/or as a supplement in courses on applied statistics, multivariate statistics, statistics II, intermediate or advanced statistics, and/or research design. Appropriate for those with limited or no previous exposure to SEM, a prerequisite of basic statistics through regression analysis is recommended.

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Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Science) Review

Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Science)
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This book fills an important niche, a sweet spot in between basic SEM texts such as Rex Kline's helpful beginner's book and more complex, advanced treatments of SEM such as Ken Bollen's classic 1989 Wiley text. Methodologists might argue that the latter text is intermediate rather than advanced, but practitioners and applied users of SEM who are not in the business of creating new methods but instead want to use SEM in a rigorous, productive way on applied analysis problems will find this text to be just the ticket to getting things done using SEM and tackling typical problems such as how to handle missing data and how to calculate power for goodness-of-fit tests and parameter estimates.
The editors have done a terrific job in working with the chapter authors to make all chapters accessible with helpful examples and consistent notation and terminology. This book, along with Loehlin's Latent Variable Models, is one I find myself pulling off my bookcase repeatedly to solve applied problems or to learn more about a particular SEM issue (modeling options for complex survey data, mixture modeling) quickly, yet comprehensively. Highly recommended.

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A volume in Quantitative Methods in Education and the Behavioral Sciences:Issues, Research, and Teaching
(sponsored by the American Educational Research Association's Special Interest Group:Educational Statisticians)
Series EditorRonald C. Serlin, University of Wisconsin-Madison
This volume is intended to serve as a didactically-oriented resource covering a broad range of advanced topics often not discussedin introductory courses on structural equation modeling (SEM). Such topics are important in furthering the understandingof foundations and assumptions underlying SEM as well as in exploring SEM as a potential tool to address new types ofresearch questions that might not have arisen during a first course. Chapters focus on the clear explanation and application oftopics, rather than on analytical derivations, and contain syntax and partial output files from popular SEM software.
CONTENTS: Introduction to Series, Ronald C. Serlin. Preface, Richard G. Lomax. Dedication. Acknowledgements. Introduction,Gregory R. Hancock & Ralph O. Mueller. Part I: Foundations. The Problem of Equivalent Structural Models, Scott L.Hershberger. Formative Measurement and Feedback Loops, Rex B. Kline. Power Analysis in Covariance Structure Modeling,Gregory R. Hancock. Part II: Extensions. Evaluating Between-Group Differences in Latent Variable Means, Marilyn S.Thompson & Samuel B. Green. Using Latent Growth Models to Evaluate Longitudinal Change, Gregory R. Hancock & FrankR. Lawrence. Mean and Covariance Structure Mixture Models, Phill Gagné. Structural Equation Models of Latent Interactionand Quadratic Effects, Herbert W. Marsh, Zhonglin Wen, & Kit-Tai Hau. Part III: Assumptions. Nonnormal and CategoricalData in Structural Equation Modeling, Sara J. Finney & Christine DiStefano. Analyzing Structural Equation Models withMissing Data, Craig K. Enders. Using Multilevel Structural Equation Modeling Techniques with Complex Sample Data,Laura M. Stapleton. The Use of Monte Carlo Studies in Structural Equation Modeling Research, Deborah L. Bandalos. Aboutthe Authors.

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