An Introduction to Multilevel Modeling Techniques: Second Edition (Quantitative Methodology Series) Review

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(More customer reviews)This isn't the most "introductory" introduction to multilevel modeling, but it's an excellent book. Heck and Thomas present multilevel models from an integrated structural equation modeling perspective, in the vein of Muthen and the Mplus software or Skrondal & Rabe-Hesketh and the GLLAM software.
The authors illustrate how traditional multilevel models, such as those estimated with the HLM software, can be extended to models with latent predictors and latent outcomes, and they describe advanced extensions (e.g., finite-mixture models and models with categorical indicators) as well as similarities with other methods (e.g., latent growth curve models). It is nice to find a book that is both conceptually integrative and practical.
If you plan to use Mplus for your multilevel analyses, this is the single best book to buy. If you're new to multilevel models, you should start with other books (such as Multilevel Modeling (Quantitative Applications in the Social Sciences)) before digging into this one.

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This comprehensive, applied approach to multilevel analysis is distinguished by its wide range of applications relevant to the behavioral, educational, organizational, and social sciences. Univariate and multivariate models are used to understand how to design studies and analyze data. Readers are encouraged to consider what they are investigating, their data, and the strengths and limitations of each technique before selecting their approach. Numerous examples and exercises allow readers to test their understanding of the techniques. Input programs from HLM and Mplus demonstrate how to set up and run the models.A latent variable conceptual framework is emphasized to show the commonality of the approaches and to make each technique more accessible. The first section is devoted to conceptual issues underlying multilevel modeling, while the second section develops several types of multilevel analyses including univariate regression, structural equation, growth curve and latent change, and latent variable mixture modeling. The new edition features:New chapters on multilevel longitudinal and categorical models80% new exercises and exampleswebsite at http://www.psypress.com/multilevel-modeling-techniques/ providing datasets and program setups in HLM, SPSS, Mplus, and LISRELIncreased emphasis on how multilevel techniques are used to examine changes in individuals and organizations over time.Ideal for introductory graduate level courses on multilevel and/or latent variable modeling, this book is intended for students and researchers in psychology, business, education, health, and sociology interested in understanding multilevel modeling. Prerequisites include an introduction to data analysis and univariate statistics.

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Multilevel Modeling (Quantitative Applications in the Social Sciences) Review

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(More customer reviews)Sage university papers on quantitative applications are presented as brief and inexpensive treatments of specialized topics in statistics and data analysis. Some are well worth the price, while some leave you wishing you used the money towards acquiring a full-length treatise or textbook. If you need to learn about multilevel modeling on your own, Douglas Luke's Multilevel Modeling is worth much more than its price, especially if you buy it from Amazon.com, because it is a model of compositional economy in addressing a complex idea, and of what a truly introductory textbook should be. Luke maintains focus, precision, and masterful clarity in a fashion that is rarely encountered among books which claim to be "An Introduction to ... " a topic as specialized, intricate, and novel as is multilevel statistical modeling. Luke defines the terms more lucidly than some of the most popular full-sized books which aim to introduce multilevel analysis (and which still leave the reader mired in ambiguity). The author does not attempt to impart any gratuitous complexity to his exposition and manages to integrate textual clarity with statistical notation and equations, figures, and tables which are equally clear for someone who, while familiar with concepts beyond one-variable statistics and simple linear regression and ANOVA, has never studied or engaged in this type of data analysis or research design before. You may need to proceed to thicker treatises to make a thorough analysis and find out how to use your favorite software, but if you begin with one or more of those and find the topic still unclear in its elements - either the big picture or the basic details - you will find Luke's 78 pages (including reference to data online) enlightening.

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Taking a practical, hands-on approach to multilevel modeling, this book provides readers with an accessible and concise introduction to HLM and how to use the technique to build models for hierarchical and longitudinal data. Each section of the book answers a basic question about multilevel modeling, such as, "How do you determine how well the model fits the data?" After reading this book, readers will understand research design issues associated with multilevel models, be able to accurately interpret the results of multilevel analyses, and build simple cross-sectional and longitudinal multilevel models.

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Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence Review

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(More customer reviews)This book is, bar none, the best book on longitudinal analysis in social sciences.
The book has three outstanding features that make it the must-have for researchers who conduct longitudinal studies. First, the book has numerous examples that use data from real studies, collected by prominent scholars in this area. With the help of the accompanying website at UCLA, you will learn how to set up data files, which is crucial in longitudinal analysis. The sample codes and data files in SAS, SPSS, Stata, MLwiN, Mplus, HLM, and Splus will allow you to replicate the analyses. The authors use every effort to explain the results in plain, understandable language. They use a lot of graphs and tables to compare different nested models and help you to choose the one that best describes your data. It feels like you have an excellent tutor by your side when you are reading this book.
Second, the coverage of this book is comprehensive. Part I covers the regular growth curve modeling and multilevel modeling, with a few chapters dealing with time-varying covariates, discontinuous and nonlinear change. Part II covers discrete-time and continuous-time survival analysis. If you are conducting a longitudinal study, chances are you will find a technique in this book that suits you just right.
Third, the book is quite deep. Although it gears toward applications of different longitudinal analyses, it is no cakewalk. You need at least some background in multiple regression and multivariate statistics. I think the treatment of mathematics (both concepts and formulas) is just right. In some sections you may need to revisit them often in order to fully understand the subject.


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Change is constant in everyday life.Infants crawl and then walk, children learn to read and write, teenagers mature in myriad ways, the elderly become frail and forgetful.Beyond these natural processes and events, external forces and interventions instigate and disrupt change:test scores may rise after a coaching course, drug abusers may remain abstinent after residential treatment. By charting changes over time and investigating whether and when events occur, researchers reveal the temporal rhythms of our lives. Applied Longitudinal Data Analysis is a much-needed professional book for empirical researchers and graduate students in the behavioral, social, and biomedical sciences.It offers the first accessible in-depth presentation of two of today's most popular statistical methods: multilevel models for individual change and hazard/survival models for event occurrence (in both discrete- and continuous-time). Using clear, concise prose and real data sets from published studies, the authors take you step by step through complete analyses, from simple exploratory displays that reveal underlying patterns through sophisticated specifications of complex statistical models. Applied Longitudinal Data Analysis offers readers a private consultation session with internationally recognized experts and represents a unique contribution to the literature on quantitative empirical methods. Visit http://www.ats.ucla.edu/stat/examples/alda.htm for: Downloadable data sets Library of computer programs in SAS, SPSS, Stata, HLM, MLwiN, and more Additional material for data analysis

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Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling Review

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(More customer reviews)Snijders and Bosker's Multilevel Analysis is mathematically demanding but more readable than Raudenbush and Bryk's Hierarchical Linear Models. Snijders and Bosker's text would be much more useful for the less-experienced reader if it contained a directory identifying essential material. As is often the case with books of this kind, it is difficult to distinguish between really important topics, those crucial to understanding multilevel models, and ancillary topics which can be treated as non-essential, at least for the first pass through this dense and difficult material.
It would also help if the authors located multilevel modeling within a statistical context likely to contain material that readers already understand. As it is, multilevel analysis is treated as a separate and new topic, and is readily accessible only to those with generalized mathematical maturity.
The authors do, however, cover a broad range of pertinent material. Thus, while it is not a good choice for beginners looking for a self-instructional tool, the statistically sophisticated reader will find it to be an excellent reference. While just as difficult to understand as the rest of the book, Snijders and Bosker's develop some really informative and interesting examples of three-level models.
This text has been in print for a decade, and is still widely used. I've found that as I become more familiar with multilevel modeling, the book becomes more valuable.

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The Second Edition of this classic text introduces the main methods, techniques, and issues involved in carrying out multilevel modeling and analysis. Snijders and Boskers' book is an applied, authoritative, and accessible introduction to the topic, providing readers with a clear conceptual and practical understanding of all the main issues involved in designing multilevel studies and conducting multilevel analysis. This book has been comprehensively revised and updated since the last edition, and now includes guides to modeling using HLM, MLwiN, SAS, Stata including GLLAMM, R, SPSS, Mplus, WinBugs, Latent Gold, and Mix.

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