Latent Variable Modeling with R, by W. Holmes Finch, Brian F. French
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Latent Variable Modeling with R, by W. Holmes Finch, Brian F. French
Best Ebook PDF Online Latent Variable Modeling with R, by W. Holmes Finch, Brian F. French
This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details to help readers better understand the models and interpret the results. Every R command necessary for conducting the analyses is described along with the resulting output which provides readers with a template to follow when they apply the methods to their own data. The basic information pertinent to each model, the newest developments in these areas, and the relevant R code to use them are reviewed. Each chapter also features an introduction, summary, and suggested readings. A glossary of the text’s boldfaced key terms and key R commands serve as helpful resources. The book is accompanied by a website with exercises, an answer key, and the in-text example data sets.
Latent Variable Modeling with R:
-Provides some examples that use messy data providing a more realistic situation readers will encounter with their own data.
-Reviews a wide range of LVMs including factor analysis, structural equation modeling, item response theory, and mixture models and advanced topics such as fitting nonlinear structural equation models, nonparametric item response theory models, and mixture regression models.
-Demonstrates how data simulation can help researchers better understand statistical methods and assist in selecting the necessary sample size prior to collecting data.
-www.routledge.com/9780415832458 provides exercises that apply the models along with annotated R output answer keys and the data that corresponds to the in-text examples so readers can replicate the results and check their work.
The book opens with basic instructions in how to use R to read data, download functions, and conduct basic analyses. From there, each chapter is dedicated to a different latent variable model including exploratory and confirmatory factor analysis (CFA), structural equation modeling (SEM), multiple groups CFA/SEM, least squares estimation, growth curve models, mixture models, item response theory (both dichotomous and polytomous items), differential item functioning (DIF), and correspondance analysis. The book concludes with a discussion of how data simulation can be used to better understand the workings of a statistical method and assist researchers in deciding on the necessary sample size prior to collecting data. A mixture of independently developed R code along with available libraries for simulating latent models in R are provided so readers can use these simulations to analyze data using the methods introduced in the previous chapters.
Intended for use in graduate or advanced undergraduate courses in latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, and social and health sciences, researchers in these fields also appreciate this book’s practical approach. The book provides sufficient conceptual background information to serve as a standalone text. Familiarity with basic statistical concepts is assumed but basic knowledge of R is not.
Latent Variable Modeling with R, by W. Holmes Finch, Brian F. French - Amazon Sales Rank: #595930 in Books
- Published on: 2015-06-18
- Original language: English
- Number of items: 1
- Dimensions: 10.00" h x 1.10" w x 7.00" l, .0 pounds
- Binding: Paperback
- 340 pages
Latent Variable Modeling with R, by W. Holmes Finch, Brian F. French Review
"Finch and French provide a timely, accessible, and integrated resource on using R to fit a broad range of latent variable models. It will be a valuable reference for researchers as well as students taking SEM, IRT, Factor Analysis, or Mixture Modeling courses. Coverage of simulation methods and advanced topics in IRT and SEM are particular assets." – Sonya Sterba, Vanderbilt University, USA
"With this highly accessible, easy-to-follow, step-by-step guide for analyzing both simple and complex latent variable models in the increasingly popular R software program, Finch and French provide an extremely valuable service to researchers in various fields." – Christian Geiser, Utah State University, USA
"A major characteristic of this book is the user-friendly presentation of content and easy to follow examples. It provides excellent instruction to gain basic proficiency in the methods and reaches a large audience." – Karl Schweizer, Goethe University, Germany
"The book integrates technical details and examples in a way that is friendly to beginner users of structural equation modeling and item response theory, and helps readers assimilate the concepts and transfer to their own research needs." – Walter L. Leite, University of Florida, USA
"A cohesive and accessible resource for applying the R language to analyze data in a latent variable framework. ... I found what was written to be easy to understand. ... I would use it, recommend it to others, and likely adopt it as a supplemental text." – Natalie D. Eggum, Arizona State University, USA
"This book is truly unique. ...The writing style is clear and comprehensible. ... [It] will ... serve as a ... supplementary text in ... measurement, latent variable modeling, and IRT classes." – D. Betsy McCoach, University of Connecticut, USA
"This ... book covers all the latent variable models commonly used in social sciences. ... [It] can be used as a text ... for ... courses ... in ... structural equation modelling, psychological measurement, item response theory, and mixture model or latent class analysis. ... [It] makes a significant contribution to the field. ... I will use [it] as a supplement ... and recommend it to my colleagues who ... teach Psychological Measurement and Item Response Theory."- Ke-Hai Yuan, University of Notre Dame, USA
"The topic has the potential ... to be of broad interest in social, prevention, and public health sciences. ... [It] could be of great interest to ... my students in courses on latent variable modeling." – Patrick S. Malone, University of South Carolina, USA
About the Author
Brian F. French is a Professor of Measurement, Statistics, and Research Methods at Washington State University.
W. Holmes Finch is the George and Frances Ball Distinguished Professor of Educational Psychology and Professor of Statistics and Psychometrics at Ball State University.
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Most helpful customer reviews
0 of 0 people found the following review helpful. Helpful or unhelpful book? It largely depends on what you already know... By John Sakaluk I was very excited to buy this book--I got a deal on the hardcover version, and was looking forward to the authors' attempt to provide a one-stop-shop [book] for conducting and interpreting different types of latent variable analyses in R. Though I have a few generalizable impressions of the book as a whole, I was left with some VERY different feelings (some positive, some negative) about different portions of the book. Specifically, I felt like the book provided pretty unsatisfactory coverage of topics I was already knowledgeable about (in my case, the chapters on more "traditional" forms of latent variable analysis, like EFA, CFA, and SEM), but alternatively, provided much better coverage of the topics I knew little about (e.g., LCA, and IRT). Perhaps this is a problem faced by all books that strive to be broadly comprehensive, and yet accessible. If you want to know more specifics, my feelings about the book as a whole, and then the traditional and untraditional latent variable analysis chapters (separately) are below.tl;dr: consider buying this book if you want a very preliminary conceptual/practical introduction to any/all of these analytic methods, but if you know what your analytic needs are, I feel as though there are much more helpful books available (for both beginners and experts).Overall-I disliked the first chapter of the book; can authors of forthcoming R-based stats books stop pretending that new R users can be taught enough base R in 9 pages? It's not gonna happen. That's why books like R Cookbook (O'Reilly Cookbooks) exist. The space would be much better used to provide more depth elsewhere.-The omission of guidance for when to model latent continuous variables (as in EFA/CFA/SEM) vs. when to model latent categories (as in LCA) seems like a huge missed opportunity. The two approaches can often provide interpretable solutions for the same dataset, and new users will crave an answer to the very intuitive question of "...so how do I choose between a good-fitting interpretable categorical vs. continuous model?".-Equations make regular appearances throughout the book. Sometimes these are helpful (e.g., calculation of model fit indexes) but many other times they are not (e.g., what beginner is going to understand the maximum likelihood or diagonally weighted least squares criteria?)-The simulation chapter at the end of the book felt pretty unhelpful. Simulation is not something, in my opinion, that the authors' target market will want to do all that often, and at least for continuous latent variable simulations, there are MUCH more user-friendly means to carrying out simulations (check out the simsem package) than the approach taken by the authors.-A nit-picky point, but I wasn't a huge fan of how the authors formatted their R code within the chapters; aesthetically, it left much to be desired in terms of easy-reading, especially compared to some other books on this topic.-One big strength of the book, as a whole, is that I think it does a very good job of accessibly describing the different types of latent analyses one might consider; it's a pretty effective conceptual introduction.Traditional Latent Variable Analysis Chapters (Chapters 2-7 [overall dislike])-The EFA chapter (Chapter 2) is not great. Why try to teach beginning R users three separate functions (factanal, fa [in psych package], and princomp), when one of them (fa) possesses all of the functionality (and then some [err, a lot]) of the other two!? This needlessly complicates users' lives. And while some of the conceptual overview of EFA is okay, I'd still recommend Exploratory Factor Analysis (Understanding Statistics) for that purpose over this book any day of the week.-The CFA chapter (Chapter 3) is also, in my opinion, not great. First, zero coverage is given to the crucial conceptual matters of identification and scale-setting--I just don't see how you can attempt to teach new users CFA/SEM without covering these features of model specification at all (and they have hugely impactful downstream consequences for SEM...). There also are some mistakes (e.g.,(1) RMSEA, (2) SRMR, (3) CFI, and (4) TLI are all presented as "relative" indices of model fit, but the first two are "absolute" indices) that eventually lead to confusing organizational decisions (e.g., why teach those four indices in the order of (1), (3), (4), (2), when (1/2) and (3/4) are both computationally and interpretably similar?). I will say, though, that I very much appreciated the discussion and code for evaluating multivariate normality assumptions. But if I'm stuck recommending an R book for CFA/SEM, my money is still heavily on Latent Variable Modeling Using R: A Step-by-Step Guide.-The SEM for Multiple Groups chapter (Chapter 5) also could have benefited from some simpler code selection; the semTools package for R has a measurementInvariance function that fits each of the models described in their chapter with a singleton line of code, and automatically performs the subsequent nested model comparisons for the user.-The Growth Curve Modelling chapter (Chapter 7), like the one in the Beaujean book (link above), is a pretty sparse introduction to longitudinal SEM (e.g., no discussion of the importance of longitudinal measurement invariance). I'll simply leave it at this: if you are really serious about learning how to specify growth curves or other longitudinal models in SEM, get Longitudinal Structural Equation Modeling (Methodology in the Social Sciences)"Untraditional" Latent Variable Analysis Chapters (Chapters 8-10)-As someone with no training in LCA, and long-forgotten training in IRT/MIRT, these chapters were actually very helpful for (re)learning the conceptual and practical nuts and bolts of these types of models, and it was especially informative to learn of packages for fitting them in R. Although it wouldn't surprise me if these chapters suffered some of the same limitations as the previous section, for someone without that higher-level knowledge, these chapters seem totally helpful for at least gaining a sense of what these models involve, and pointing towards some helpful and more detailed/comprehensive resources.
0 of 1 people found the following review helpful. Five Stars By J. M. SINGH This is an excellent read and geared towards applied research, examples provided in R is very clear.
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