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Latent Variable Modeling Using R: A Step-by-Step Guide Format Kindle


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Descriptions du produit

Présentation de l'éditeur

This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the lavaan package, this book is designed to help readers quickly understand LVMs and their analysis in R. The author reviews the reasoning behind the syntax selected and provides examples that demonstrate how to analyze data for a variety of LVMs.  Featuring examples applicable to psychology, education, business, and other social and health sciences, minimal text is devoted to theoretical underpinnings. The material is presented without the use of matrix algebra. As a whole the book prepares readers to write about and interpret LVM results they obtain in R.



Each chapter features background information, boldfaced key  terms defined in the glossary, detailed interpretations of R output, descriptions of how to write the analysis of results for publication, a summary, R based practice exercises (with solutions included in the back of the book), and references and related readings. Margin notes help readers better understand LVMs and write their own R syntax. Examples using data from published work across a variety of disciplines demonstrate how to use R syntax for analyzing and interpreting results. R functions, syntax, and the corresponding results appear in gray boxes to help readers quickly locate this material. A unique index helps readers quickly locate R functions, packages, and datasets. The book and accompanying website at http://blogs.baylor.edu/rlatentvariable/ provides all of the data for the book’s examples and exercises as well as R syntax so readers can replicate the analyses. The book reviews how to enter the data into R, specify the LVMs, and obtain and interpret the estimated parameter values.



The book opens with the fundamentals of using R including how to download the program, use functions, and enter and manipulate data. Chapters 2 and 3 introduce and then extend path models to include latent variables. Chapter 4 shows readers how to analyze a latent variable model with data from more than one group, while Chapter 5 shows how to analyze a latent variable model with data from more than one time period. Chapter 6 demonstrates the analysis of dichotomous variables, while Chapter 7 demonstrates how to analyze LVMs with missing data. Chapter 8 focuses on sample size determination using Monte Carlo methods, which can be used with a wide range of statistical models and account for missing data. The final chapter examines hierarchical LVMs, demonstrating both higher-order and bi-factor approaches. The book concludes with three Appendices: a review of common measures of model fit including their formulae and interpretation; syntax for other R latent variable models packages; and solutions for each chapter’s exercises.



Intended as a supplementary text for graduate and/or advanced undergraduate courses on latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, business, economics, and social and health sciences, this book also appeals to researchers in these fields. Prerequisites include familiarity with basic statistical concepts, but knowledge of R is not assumed.

Biographie de l'auteur

A. Alexander Beaujean is an Associate Professor in Educational Psychology at Baylor University.


Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 7696 KB
  • Nombre de pages de l'édition imprimée : 218 pages
  • Utilisation simultanée de l'appareil : Jusqu'à 4 appareils simultanés, selon les limites de l'éditeur
  • Editeur : Routledge; Édition : 1 (9 mai 2014)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
  • ASIN: B00K8230RU
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Amazon.com: HASH(0x99d7f86c) étoiles sur 5 3 commentaires
16 internautes sur 16 ont trouvé ce commentaire utile 
HASH(0x997eec00) étoiles sur 5 It would be a great textbook selection for a graduate-level introductory SEM class 6 août 2014
Par John Sakaluk - Publié sur Amazon.com
Format: Broché Achat vérifié
In my opinion, as a comprehensive beginner's guide to SEM in R, this book is without peer. It would be a great textbook selection for n graduate-level introductory SEM class, as it covers the gamut of 'essential' SEM topics, such as model identification, scale setting, indexes of model fit, basic measurement and structural models, missing data, multiple groups (focused on invariance testing), longitudinal models, power, and categorical indicators. In fact, it's so affordable, that you could probably use it as an R-specific supplement to a book that provides a more thorough and conceptual introduction, such as Brown (2006), Kline (2010), or Hoyle (2012).

For those who are already well-versed in SEM and lavaan, you probably already know much of what is in this book, although there are very useful tidbits here and there (e.g., how to manually free a specific parameter estimate).

I only have two minor complaints:

1) I'm not crazy about the order of the chapters. The chapters on missing data (Chapter 7) and power (Chapter 8), for example, are two of the last three of the chapters, when it is likely the case that these are more foundational topics compared to some of the advanced topics presented earlier in the book (e.g., multiple groups [Chapter 4], longitudinal models [Chapter 5], or categorical indicators [Chapter 6])

2) Related to point 1), although some advanced topics--like multiple groups models--are introduced very effectively, I was less enthusiastic about the coverage of others. The chapter on longitudinal SEMs (Chapter 5), in particular, seemed much weaker than the other chapters in the book. For example, whereas group measurement invariance is covered extensively in the multiple groups chapter, the concept of longitudinal measurement invariance is not covered at all. Further, the chapter exclusively caters to the latent growth curve approach to longitudinal data analysis, and ignores other legitimate (and for a beginning, perhaps intuitive) longitudinal models, such as latent panel models. For these reasons, as mentioned in point 1), Beujean's book might be best used as a supplement to books covering specialized applications of SEM, such as Little's (2013) book on longitudinal SEM, when modeling needs are more complicated.

These complaints aside, the book is a solid resource, with many good examples of code for lavaan and lavaan-affiliated packages (e.g., simsem, MICE, etc.,). If you're looking to learn or teach how to use SEM with freely available software (i.e., R), and want a book that covers most of the basics with examples of code that are relatively easy to follow, this is the book for you.

PS: there is a typo in the effects-coding example on page 48. The actual code for effects-coding is correct ("a+b+c+d+e==5"), but the preceding comment ("# constrain the loadings to sum to one") is inaccurate: effects-coding constrains the loadings to AVERAGE to one (in this case, by summing to 5 across 5 indicators).
0 internautes sur 1 ont trouvé ce commentaire utile 
HASH(0x997f2a80) étoiles sur 5 This book is great for anyone interested in overcoming the learning curve for ... 1 octobre 2015
Par Brandon Klinedinst - Publié sur Amazon.com
Format: Broché Achat vérifié
This book is great for anyone interested in overcoming the learning curve for using R, and for anyone interested in Latent Variable Modeling and Latent Curve Modeling.
1 internautes sur 3 ont trouvé ce commentaire utile 
HASH(0x997f2a44) étoiles sur 5 great companion to lavaan 6 avril 2015
Par Ernest Hobson - Publié sur Amazon.com
Format: Format Kindle Achat vérifié
Very useful companion to learning the latent variable technique hands on with good examples. R syntax unfortunately very small in the kindle version.
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