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Multilevel Modeling Using R [Print Replica] [Format Kindle]

W. Holmes Finch , Jocelyn E. Bolin , Ken Kelley

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  • ISBN-10 : 1466515856
  • ISBN-13 : 978-1466515857
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Descriptions du produit

Présentation de l'éditeur

A powerful tool for analyzing nested designs in a variety of fields, multilevel/hierarchical modeling allows researchers to account for data collected at multiple levels. Multilevel Modeling Using R provides you with a helpful guide to conducting multilevel data modeling using the R software environment.



After reviewing standard linear models, the authors present the basics of multilevel models and explain how to fit these models using R. They then show how to employ multilevel modeling with longitudinal data and demonstrate the valuable graphical options in R. The book also describes models for categorical dependent variables in both single level and multilevel data. The book concludes with Bayesian fitting of multilevel models. For those new to R, the appendix provides an introduction to this system that covers basic R knowledge necessary to run the models in the book.



Through the R code and detailed explanations provided, this book gives you the tools to launch your own investigations in multilevel modeling and gain insight into your research.

Biographie de l'auteur

W. Holmes Finch is a professor in the Department of Educational Psychology at Ball State University, where he teaches courses on factor analysis, structural equation modeling, categorical data analysis, regression, multivariate statistics, and measurement to graduate students in psychology and education. Dr. Finch is also an Accredited Professional Statistician (PStat®). He earned a PhD from the University of South Carolina. His research interests include multilevel models, latent variable modeling, methods of prediction and classification, and nonparametric multivariate statistics.

Jocelyn E. Bolin is an assistant professor in the Department of Educational Psychology at Ball State University, where she teaches courses on introductory and intermediate statistics, multiple regression analysis, and multilevel modeling to graduate students in social science disciplines. Dr. Bolin is a member of the American Psychological Association, the American Educational Research Association, and the American Statistical Association and is an Accredited Professional Statistician (PStat®). She earned a PhD in educational psychology from Indiana University Bloomington. Her research interests include statistical methods for classification and clustering and use of multilevel modeling in the social sciences.

Ken Kelley is the Viola D. Hank Associate Professor of Management in the Mendoza College of Business at the University of Notre Dame. Dr. Kelley is also an Accredited Professional Statistician (PStat®) and associate editor of Psychological Methods. His research involves the development, improvement, and evaluation of quantitative methods, especially as they relate to statistical and measurement issues in applied research. He is the developer of the MBESS package for the R statistical language and environment.


Détails sur le produit

  • Format : Format Kindle
  • Taille du fichier : 6542 KB
  • Nombre de pages de l'édition imprimée : 230 pages
  • Editeur : CRC Press (9 mars 2016)
  • Vendu par : Amazon Media EU S.à r.l.
  • Langue : Anglais
  • ASIN: B00L2EBEOO
  • Synthèse vocale : Non activée
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  • Composition améliorée: Non activé
  • Classement des meilleures ventes d'Amazon: n°12.699 dans la Boutique Kindle (Voir le Top 100 dans la Boutique Kindle)

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Amazon.com: 4.3 étoiles sur 5  3 commentaires
20 internautes sur 20 ont trouvé ce commentaire utile 
5.0 étoiles sur 5 Loved it--exceptional clarity for stats book, one of the best I've read 18 juillet 2014
Par littlejorge - Publié sur Amazon.com
Format:Broché|Achat vérifié
I bought this to better understand multilevel models (aka nested, random-effects, or mixed models) having been exposed to (but inadequately learned the topic during a few days "overview" during a statistics course. Therefore, I approached this book having heard many of the terms mentioned in the book but had not achieved understanding of the principles.

Summary: Really quite excellent book. Already wrote course instructors for prior course suggesting they look at it. Was able to read it cover-to-cover (almost) on the train, which requires more fortitude than I can muster for most statistics books but was both painless and fruitful in this case.

More detail: It is superbly written and structured. The organization is exceptionally clear--each chapter builds on the one before, and there is plenty of referring forward ("this does not yet address xx, which will be covered in chapter xx") and backward ("as was described in the prior chapter on xx") which facilitates understanding. Furthermore, for most topics authors are to be commended for including the full trifecta of points: a) a mathematical description, b) clear description supported by examples, and c) a commentary on what a topic does and does not cover, or where the weak points are in potential analyses, to place it into context. This makes it easy to understand and apply, no matter your approach to the material.

In terms of integration with R, this is also superb. In due course, they use detailed and parallel examples from both lme4 and nmle packages, with comments on how they differ or supplement each other and areas that neither covers well. There is also an entire chapter on graphical exploration of multilevel models in R (mostly how to make plots for assumption testing and exploring data). It uses base package and lattice-package-based examples, but for those of us who are ggplot2 users it is not difficult to adapt. I am only of moderate R experience but I found this quite easy to follow.

Note that this covers nested/multilevel linear models for a continuous outcome, with clear explanations of how to extend to generalized linear models for other outcomes (logistic regression, Poisson, categorical or ordinal). There is a chapter on longitudinal data, but this is focused on dealing with the clustering involved in repeated measures over time on the same subjects. It is still limited to the outcomes types above. There is not a direct exploration of multilevel survival data where the primary interest is a hazard ratio (time-to-event, censored data)-- e.g. frailty models. However, even for those who are often dealing with survival models (as I am) this book is very worthwhile for its exploration of the general principles underlying all the multilevel models. There is also a chapter on Bayesian methods which I have not yet read.

No connection to authors, etc. etc.--bought this book on a whim since I buy a lot of R books (all the senior researchers use SAS here but the institution is transitioning to R, so a lot has to be self-taught). Very glad I did.
3 internautes sur 3 ont trouvé ce commentaire utile 
3.0 étoiles sur 5 Practitioner's guide without enough background 26 novembre 2015
Par Terran - Publié sur Amazon.com
Format:Broché|Achat vérifié
This brief book is designed in the model of a practitioner's guide with just enough theory to understand how to call and interpret the R functions. Unfortunately, it partially fails in this; the mathematical background it provides is too thin to explain several key concepts and they are glossed over.

For example, when explaining why to use multilevel models, the book compares them to a strawman of not including the groupings at all; a more meaningful comparison would have been to a linear model which included the grouping variable as a factor. A better explanation is provided for free in one paragraph in section 2.2 of the lmer vignette documentation.

As another example, in describing the lme4 syntax, the book explains how to specify that random slopes are correlated or uncorrelated, but does not explain what that actually means, what it translates to in equations, or how it actually impacts the model fit. The term "shrinkage" is never mentioned.

You will be able to learn multilevel modeling with this book as a guide, but you will also need to refer to other references to fill in the gaps.
5.0 étoiles sur 5 Five Stars 25 février 2016
Par J. M. SINGH - Publié sur Amazon.com
Format:Broché|Achat vérifié
I enjoyed reading the book, and found it to be very useful for applied research.
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