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

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(More customer reviews)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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Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building Review

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(More customer reviews)All of the reviews on this book are generally consistent in their praise for the book and the authors. I do not have any points to add to the discussion other than this:
It is a credit to this version of Statistics for Experimenters that it has remained relevant throughout the years as a classic introductory text that has kept selling consistently since it was released in the 1970's. Nevertheless, unless you have a particular reason for purchasing this version, you should purchase the updated version(also available through Amazon).
The full title of the newer edition is:
Statistics for Experimenters: Design, Innovation, and Discovery, 2nd Edition
The 2nd edition, written in the same engaging and readable style as the 1st, contains virtually all of the content of the 1st edition plus advances in design of experiments that have happened since the 1st edition was published.

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Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for ``statistical thinking'' by using examples drawn from actual case studies.

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Using LISREL for Structural Equation Modeling: A Researcher's Guide Review

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(More customer reviews)In teaching structural equation modeling in a doctoral-level graduate class, I found Kelloway's book one of the most useful resources. Despite its title, the book is not only about LISREL. The initial chapters provide an excellent overview of the science (and art) of structural equation modeling. Kelloway provides probably a better summary than most other books on the topic.

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DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling Review

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(More customer reviews)This book tries to combine a practical and theoretical point of view concering microarray expermiments and the data analyis thereof. This is a very honourable goal. Unfortunatelly, it fails. An indicator for this can already be seen in the low number of pages. This book has less than 140 pages (I exclude the last chapter and the appendix). It is clear, that it is impossible to discuss in detail this topic in this limited number of pages. Hence, during reading the chapters one gets the feeling, that one reads short essays which are stringed together. At no point the authors go into detail but give only a short idea and references.
I see no reason, why I should recommend this book to anyone. It is in its current form just immature. My prediction: There will be no second edition because even its basic substance is very weak.
Some words to the last chapter (systems biology). This is indeed the most interesting and best chapter of the book (35 pages) without going into details as the rest of the book. I think according to this chapter one realize under which premise this book was written. Unfortunatelly, combining buzz worlds in short essays is not enough for a good book. Sorry guys, I think you can do better!

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Massive data acquisition technologies--such as genome sequencing, high-throughput drug screening, and DNA arrays--are in the process of revolutionizing biology and medicine. This concise, user-friendly and interdisciplinary guide to DNA microarray technology is an introduction and a reference for both biologists and computational scientists. The authors describe the underlying technologies and offer an awareness of the "noise" and pitfalls present in the data generated. They also provide an idea of the different data mining techniques and algorithms that are available to interpret data, and the advantages and disadvantages of each in differing situations.

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Modeling Survival Data: Extending the Cox Model (Statistics for Biology and Health) Review

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(More customer reviews)Terry Therneau is a research statistician at the Mayo Clinic and Patricia Grambsch is a Professor of Biostatistics at the University of Minnesota. The Cox proportional hazards model has been one of the key methods for analyzing survival data with covariates for the last 25 years. Proportionality is a key assumption that limits its use. There has long been a need to find methods which diagnose when the hazard rates are not proportional and provide alternative methods in such situations. Using the theory of counting processes the authors are able to extend the Cox model to more general situations including multiple/correlated event data using either marginal models or random effects (frailty) models. Time dependent covariates are also covered. Some of the theory of martigales and counting processes is included to make the book self-contained. Generalized residuals are used to identify outlying and influential observations (analogous to ordinary regression) and also to assess the proportional hazards assumption.
Although the topics are advanced and the mathematical level is high the book is designed for practitioners, emphasizing applications and providing numerous examples, many from the authors' experience. Statistical analyses are done in SAS and SPlus. The authors tend to use SAS for data management and analysis and SPlus for diagnostics and other plots. Therneau is an expert programmer who has written much of the necessary software in both systems.
Therneau gave an excellent short course that I attended a couple of years ago at the Joint Statistical Meetings based on a draft of the text. The finished product is as good as I expected.
The appendices include SAS and S-Plus tutorials on survival analysis and provide SAS Macros and S functions to apply the new methodology.
The book is now (December 2008) in its 6th printing which is another testament to its value and popularity and a nice deal at amazon's current price of $87. But O'Quigley's book is out now too. So maybe Terry and Patricia should be thinking about doing a revision if they don't already have one in the works.


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This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets.

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Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine) Review

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(More customer reviews)I used this book as the text for a biostatistics class that used STATA as the statistitical package. I found the organization, problems, and the STATA output the book provides, all very helpful. In addition, as I moved systematically through the book, the tips regarding using the STATA features were key to my learning many of the practical aspects of the STATA program.

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For biomedical researchers, the new edition of this standard text guides readers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is used to perform the analyses, in this edition employing the intuitive version 10. Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University.

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Ordinal Data Modeling (Statistics for Social and Behavioral Sciences) Review

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(More customer reviews)This book provides both the Bayesian and classical approaches to ordinal data analysis but is unique in emphasizing the Bayesian approach and the latest advances. The authors are academic statisticians and the text is designed for a graduate level course for statistics or social science majors. It includes some very well written introductory material on these two forms fo statistical inference.
The mathematical level is intermediate but is written in a clear way to be accessible to social science students. This is also a good reference book for statisticians especially those involved in educational testing.
Markov chain Monte Carlo methods are provided along with some programmed algorithms for doing Gibbs sampling. A website is available to help the reader get access to datasets and software to implement the procedures.
Although the offer of software is nice, the authors neglect to mention the BUGS software that has been developed in the UK to handle MCMC problems. BUGS or the new window based WinBUGS is easily accessible to the reader and provides a lot of additional modeling aids including diagnostics.
The book covers a lot of interesting and applications oriented topics including logistic regression, ordinal regression, item response models, graded response models and the analysis of ROC curves. Concepts are illustrated and techniques demonstrated through real problems.


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Ordinal Data Modeling is a comprehensive treatment of ordinal data models from both likelihood and Bayesian perspectives. A unique feature of this text is its emphasis on applications. All models developed in the book are motivated by real datasets, and considerable attention is devoted to the description of diagnostic plots and residual analyses. Software and datasets used for all analyses described in the text are available on websites listed in the preface.

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Constructing Measures: An Item Response Modeling Approach Review

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(More customer reviews)The book was required for a class I'm taking, it serves it purpose and got here within a reasonable amount of time.

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