ADHD in Adults: What the Science Says Review

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(More customer reviews)The previous reviewer, Jon, said it all. Adult ADHD is real. You will learn all you need to know about it by reading this amazing book, culminating decades of rigorous study and clinical expertise.
Researchers such as authors Russell Barkley, Mariellen Fischer, and Kevin Murphy are the reliable anchors in a storm-tossed sea of ignorance, lassitude, indifference, and outright chicanery and propagandizing regarding adult ADHD.
Their studies are stunning for their elegant design, careful execution, and solid results. The text is well-written and profound, even to non-clinicians. When it comes to interpreting certain human behaviors, it will turn all your paradigms upside down and then inside out.
I cannot imagine any physician or therapist in this country--no matter what the specialty--not reading this book very carefully. Because untreated ADHD cuts across too many issues for any healthcare provider to remain unaware. These issues include higher risk of traffic and on-the-job accidents, substance use, and poor health habits that can lead to the chronic diseases that so afflict this country, including obesity, diabetes, and hypertension.
ADHD is thought to affect from 10 to 30 million adults in the U.S. alone, depending on how broadly the criteria are applied. Yet only 10 percent of that lower figure is diagnosed--and even fewer in treatment.
Too often, ADHD is misdiagnosed as depression, anxiety, bi-polar disorder, borderline personality disorder, substance use disorder, attachment disorder, and the list of personality disorders goes on--most of which carry poor prognoses. Consequently, too many people suffer in frustration, piled on with moral judgments or plied with the wrong medications or therapeutic interventions that often make ADHD worse.
It's time to join the 21st Century regarding a brain condition that affects so many people, and this book, in my opinion, leads the way.
Gina Pera

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Providing a new perspective on ADHD in adults, this compelling book analyzes findings from two major studies directed by leading authority Russell A. Barkley. Groundbreaking information is presented on the significant impairments produced by the disorder across major functional domains and life activities, including educational outcomes, work, relationships, health behaviors, and mental health. Thoughtfully considering the treatment implications of these findings, the book also demonstrates that existing diagnostic criteria do not accurately reflect the way ADHD is experienced by adults, and points the way toward developing better criteria that center on executive function deficits. Accessible tables, figures, and sidebars encapsulate the study results and methods.

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Statistics Hacks: Tips & Tools for Measuring the World and Beating the Odds Review

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(More customer reviews)In a previous academic life, I was a marketing major. One of the things marketers have to learn is statistics - the art/science of describing the world in terms of numbers and proportions. So I have a background in statistics, however basic, and that helped me in reading this book.
The first two sections in Statistics Hacks aren't really hacks; they serve as a basic statistics textbook. If your eyes glaze over at terms like "standard deviation," "correlation coefficient," "Z-scores," etc., you will have a tough time getting through the first 100 pages or so. But don't skip them - they are the foundation that the rest of the book is built on. You won't understand why a lot of the hacks later on in the book work unless you read the first 100 pages, and understanding why is a key to hacking anything, whether it's a computer network, an RC car, or the laws of mathematics and probability. Even if you've got a rudimentary understanding of probability, you will be well-advised to read the first hundred pages, as a refresher course if nothing else.
Chapter 3 is where the application begins. Frey teaches us how to understand percentile scores in standardized tests, and how to use a "normal curve" (think bell curve) to predict the future. The normal curve is vital to the rest of the book, so pay close attention to Hack # 25. Frey also teaches us how to establish the reliability of a certain test, how to establish its validity, and what the difference actually is between reliable and valid. We get a hint of the goodies to come in Hack # 33 (Predicting the Length of a Lifetime) and # 34 (Make Wise Medical Decisions).
Chapter 4 and 5 will be the most popular chapters in the book, Beating the Odds and Playing Games. Texas Hold 'Em is covered in Hacks # 36 and 37, but Frey also covers roulette (#39) and blackjack (#40). You can even design your own bar bets using principles of statistics. But I think that the most useful hack in the book is Spotting Fake Data (#64). Frey goes into a lot of detail here, and invokes Benford's law quite a bit, but I think that with the amount of statistics that we're confronted with daily, we need to be able to spot fake data, and be able to show why it's fake.
Statistics Hacks is not an easy book to read. As I mentioned, if you've got an aversion to math you might want to skip this one. But statistics aren't that hard to understand, and there is a lot of valuable (and potentially profitable) information in this book. Frey set out to make the concepts behind statistical analysis accessible to the average reader, and for the most part I think he has succeeded.
I'll let you know more after I win the lottery (Hack #41).

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Want to calculate the probability that an event will happen? Be able to spot fake data? Prove beyond doubt whether one thing causes another? Or learn to be a better gambler? You can do that and much more with 75 practical and fun hacks packed into Statistics Hacks. These cool tips, tricks, and mind-boggling solutions from the world of statistics, measurement, and research methods will not only amaze and entertain you, but will give you an advantage in several real-world situations-including business.

This book is ideal for anyone who likes puzzles, brainteasers, games, gambling, magic tricks, and those who want to apply math and science to everyday circumstances. Several hacks in the first chapter alone-such as the "central limit theorem,", which allows you to know everything by knowing just a little-serve as sound approaches for marketing and other business objectives. Using the tools of inferential statistics, you can understand the way probability works, discover relationships, predict events with uncanny accuracy, and even make a little money with a well-placed wager here and there.

Statistics Hacks presents useful techniques from statistics, educational and psychological measurement, and experimental research to help you solve a variety of problems in business, games, and life. You'll learn how to:

Play smart when you play Texas Hold 'Em, blackjack, roulette, dice games, or even the lottery
Design your own winnable bar bets to make money and amaze your friends
Predict the outcomes of baseball games, know when to "go for two" in football, and anticipate the winners of other sporting events with surprising accuracy
Demystify amazing coincidences and distinguish the truly random from the only seemingly random--even keep your iPod's "random" shuffle honest
Spot fraudulent data, detect plagiarism, and break codes
How to isolate the effects of observation on the thing observed

Whether you're a statistics enthusiast who does calculations in your sleep or a civilian who is entertained by clever solutions to interesting problems, Statistics Hacks has tools to give you an edge over the world's slim odds.

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Applied Statistical Genetics with R: For Population-based Association Studies (Use R) Review

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(More customer reviews)Applied Statistical Genetics with R For Population-based Association Studies is by Andrea S. Foulkes
of the University of Massachusetts and is meant for an audience with some understanding of both genetics and statistics, though the level of understanding in both areas need not be extensive. The statistical knowledge required would be covered in one or two undergraduate courses and an introductory genetics course would be helpful. Lacking this background, the first three chapters provide a well written review of the required knowledge and also provide extensive references for further reading. Indeed, the entire book provides plenty of references for further study of all of the topics covered.
For genetic studies, this book covers several basic topics, including linkage disequilibrium, Hardy-Weinberg equilibrium, and haplotypic phase as well as methods for identifying associations between single genetic polymorphisms and a trait. Subjects that are not covered include family studies, population genetics or gene expression analysis.
One of the great strengths of this book is the presentation of topics that while relevant to genetics studies are also relevant to the general statistical reader. There are very good chapters and sections on missing data, multiple comparisons, cross-validation, the EM algorithm, classification and regression trees [CART] and random forests as well as several Bayesian techniques. While some statistical notation and formulas are used throughout, each topic is presented in clear fashion that is understandable to the less mathematically inclined. Algorithms are laid out in a step-by-step fashion that makes topics such as the EM algorithm and Gibbs sampling understandable. Indeed, this is one of the few statistical texts, beyond the most basic introductory texts, that can be read cover-to-cover. If not for the extensive use of examples with a genetics focus, I would recommend this as a general text on advanced statistics.
This book makes extensive use of the freely available R programming language and publicly available data sets, with many worked-out examples throughout the text. A web site provides download-able data sets and code. While there is an appendix that introduces the R language, some working familiarity with R beyond this text will be necessary for most readers.
In all, I found this to be a very readable introduction to the use of statistics in genetics. It would make a very good text for an introductory course on statistical genetics. I also recommend this book to the general statistics reader because of its very readable presentation of some complex statistical topics.

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Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

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The Reviewer's Guide to Quantitative Methods in the Social Sciences Review

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(More customer reviews)This is a tremendously helpful book for reviewers (and producers) of quantitative research in the social sciences. The strengths of this book include the authors and the content, but perhaps more importantly, the book's format. Each chapter concerns a particular methodology or topic, authored by experts in their respective fields. Written in brief, digestible segments, each chapter presents the "state of the art" in a particular statistical methodology or concept (e.g., Osbourne's chapter on correlations describes why Spearman's rank correlation is no longer needed, and the importance of checking for outliers and fringeliers). Yet, the incredibly user-friendly format of each chapter makes this content all the more accessible and useful. Chapters begin with a brief description of the topic, followed by a table listing the key criteria used to evaluate whether the particular statistical technique or concept was applied reasonably. Then, a brief but clear discussion of each criterion is presented. The modular nature of the book, and the chapters themselves, allow reviewers to quickly find needed information regarding techniques, assumptions, and potential confounds. This book cannot take the place of formal training in a particular statistical technique, but for those of us who, years ago, took classes in techniques we have not subsequently used very often, this book is an invaluable reminder of what criteria to attend to, and a good update regarding current thinking in the field. While I think this book is essential for reviewers of research, it would behoove producers of research to use this book as well when they determine what information needs to be included in their manuscripts to clearly convey the adequacy of their methodology.

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The Reviewer’s Guide to Quantitative Methods in the Social Sciences is designed for evaluators of research manuscripts and proposals in the social and behavioral sciences, and beyond. Itsthirty-one uniquely structured chapters cover both traditional and emerging methods of quantitative data analysis, which neither junior nor veteran reviewers can be expected to know in detail. The book updates readers on each technique’s key principles, appropriate usage, underlying assumptions, and limitations. It thereby assists reviewers to offer constructive commentary on works they evaluate, and also serves as an indispensable author’s reference for preparing sound research manuscripts and proposals. Key features include:Thechapters cover virtually all of the popular classic and emerging quantitative techniques, thus helping reviewers to evaluate a manuscript’s methodological approach and its data analysis. In addition, the volume serves as an indispensable reference tool for those designing their own research.For ease of use, all chapters follow the same structure:the opening page of each chapter defines and explains the purpose of that statistical methodthe next one or two pages provide a table listing various criteria that should be considered when evaluating and applying that methodological approach to data analysisthe remainder of each chapter contains numbered sections corresponding to the numbered criteria listed in the opening table. Each section explains the role and importance of that particular criterion.Chapters are written by methodological and applied scholars who are expert in the particular quantitative method being reviewed.

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The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies (New Edition) Review

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(More customer reviews)Viva la Difference! Scott Page takes us on a really enagaging tour that examines why diversity matters. You thought the subject was simple? Well it is if you think in terms of the usual ways we pigeon-hole each other (gender, race) but as Page quickly shows us - diversity can be framed in many, many more ways: and more than that - he can show proof after proof for why it makes a positive difference.
What I love is his entertaining command of language, his knowledge of the subject and his clear enthusiasm for the topic. This is one of those books that, alongside the likes of Blink, Wisdom of Crowds and Tipping Point, deserves wide reading and social influence. In many ways it is an unabashed (and welcome) celebration of human-ness. Recommended.

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Why Americans Hate Welfare: Race, Media, and the Politics of Antipoverty Policy (Studies in Communication, Media, and Public Opinion) Review

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(More customer reviews)The finding that welfare policies are not popular is not new, but Marty Gilens carefully analyzes the reasons people give for disliking welfare. By embedding experiments within surveys, he is able to gain insight into topics which would otherwise remain obscured. In effect, he is able to trick participants into revealing their true beliefs on race and welfare. The conclusions he reaches are new, convincing, and thought-provoking. In short, this is an excellent book for anyone interested in either public opinion research methodology or welfare politics.

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Building Positive Behavior Support Systems in Schools: Functional Behavioral Assessment Review

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(More customer reviews)The book was used for a graduate level (Doctoral and Masters) education class. The focus of the class was to learn about and develop schoolwide positive behavior supports (PBS) within schools and school districts. The book was helpful in outlining procedures consistent with a PBS model. It presented relevant information to the topic and was an easy read. However, the book oversimplified the Functional Behavior Assessment (FBA) process and did not provide enough information to allow an individual to become an "expert" in the process of an FBA. Of specific concern was the lack of direction concerning data based decisions (i.e. collecting data appropriately, visual analysis of data, how to determine when to make adjustments within a support plan, etc.). There are other concerns with this book; the main point however, is that the book cannot be used as a stand alone book. It either expects a very large amount of prerequisite skills in the area of Applied Behavior Analysis and data or these skills must be acquired along with the book to truly make the book useful.

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This indispensable volume presents an up-to-date conceptual model and practical tools for meeting the challenges of severe problem behavior in elementary and middle-school settings. The focus is on developing and implementing team-based support plans for the 1-5% of students who require intensive, individualized behavioral assessment and intervention. Outlined are field-tested procedures that enable the practitioner to evaluate student needs using functional behavioral assessment; transform assessment results into practical, function-based interventions; develop school-wide systems that support the assessment and intervention process; establish clear roles for leaders and members of behavior support teams; and work collaboratively with administrators, teachers, students, and parents. A much-needed resource and text, the book features illustrative case examples, figures, and charts. Also included are reproducible assessment instruments, parent and teacher forms, sample meeting agendas, and other useful materials. Note: A less intensive, prevention-oriented intervention for students with moderate problem behavior is presented in the complementary volume, Responding to Problem Behavior in Schools.

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Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)Believe it or not this important topic in applied statistics was developed by Hastie and Tibshirani when they were graduate students, a sure sign of greatness to come. After their graduation this highly acclaimed book came out explaining both the theory and pratice of generalized additive models in a clear and concise way. Generalized additive models are similar to generalized linear models in their additive structure but the form of the additive functions is much more general.
Both Hastie and Tibshirani are now Stanford professors in the Statistics Department and both have written other excellent books including their joint publication with Jerry Friedman "The Elements of Statistical Learning" and Tibshirani along with Efron wrote an excellent monograph on bootstrap.

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Volume 43 of the series entitled, Monographs on Statistics and Applied Probability. Describes a new array of power tools for data analysis, based on nonparametric regression or smoothing techniques.

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Dyadic Data Analysis (Methodology In The Social Sciences) Review

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(More customer reviews)This book is the gold standard for any social scientist learning about dyadic statistical analysis techniques.

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Dyadic Data Analysis--This text refers to the Kindle Edition edition.

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Reading and Understanding More Multivariate Statistics Review

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(More customer reviews)Like its predecessor, "Reading and Understanding MORE Multivariate Statistics" achieves exactly what its title implies. Geared toward non-statisticians in behavioral and social science fields, this book provides clear and reasonably simple explanations of common multivariate analyses. This book includes special attention to scales of measurement, reliability and generalizability theory, item response theory, and assessing the validity of measurement. In addition, it covers cluster analysis, Q-technique factor analysis, structural equation modeling, canonical correlation analysis, repeated measures analysis, and survival analysis. The authors present the conceptual underpinnings, underlying assumptions, and basic procedures for each analysis with a minimum of equations and many concrete examples. The book not teach you how to perform the analyses but does provide references for those who wish to get more detailed information. As a research scientist who doesn't always remember everything I learned in graduate statistics class, I find this book an invaluable aid keeping up with the current literature in my field and in making the most of statistical consultations. This book is ideal for anyone whose job requires them to be a "consumer" of research; for researchers who wish to further their understanding of data analysis; and as a companion text for graduate statistics classes.

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A new companion volume to one of APA's most successful books. Provide s easy-to-understand explanations of more multivariate statistics not covered in Reading and Understanding Multivariate Statistics. The aut hors have kept the same popular format and writing style of the first book.

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Discovering Statistics Using SAS Review

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(More customer reviews)I am returning this book. I began with the chapter on ANCOVA and found that syntax for the sample program the authors provide produces the wrong sum of squares, because the variables are entered into the model statement incorrectly. Also, the Sage publications website promises a companion website, but check this out before you consider buying. All that is offered is a poorly constructed, difficult to search through .doc file. False advertising. A doc file is not a website. The construction of the book is flimsy, even more flimsy than the SPSS version of this book. Also the pages are not glossy or color like Field's SPSS book, they are dull, low quality paper and eyesore to look at. Save your money.
I do recommend the SPSS version of this book, which is very helpful. I would recommend purchasing the SPSS book instead, and searching online to find the syntax for the types of programs you want to run using SAS.
Hope the next edition will be better!

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Hot on the heels of Andy Field’s best-selling Discovering Statistics Using SPSS, Third Edition (2009), the author has teamed up with a co-author, Jeremy Miles, to adapt this textbook for SAS® using the most up-to-date commands and programming language available in latest release 9.2. As with its sister textbook, Discovering Statistics Using SAS® takes the entry level student from first principles right the way through to advanced level statistical concepts all the while grounding knowledge through the use of SAS®. Its approach is to teach statistical concepts as well as the computational principles, commands and language of the SAS® software package in one textbook, and given this comprehensive coverage this textbook should be enthusiastically adopted on a wide variety of statistics courses.

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Time Series: Theory and Methods (Springer Series in Statistics) Review

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(More customer reviews)Of course, this an advanced textbook on Time Series. The reader is supposed to have been introduced to the subject, and certainly is looking for a more theoretical treatment.
If you want to learn time series for the first time, this is not the book.
If you want a friendly book, do not see springer's publications.
However, if you want a fair rigourous book, you have found it.
I think the exercises are illustrative, but sometimes long.

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This paperback edition is a reprint of the 1991 edition.Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time. The aim is to provide specific techniques for handling data and at the same time to provide a thorough understanding of the mathematical basis for the techniques. Both time and frequency domain methods are discussed, but the book is written in such a way that either approach could be emphasized. The book is intended to be a text for graduate students in statistics, mathematics, engineering, and the natural or social sciences. It contains substantial chapters on multivariate series and state-space models (including applications of the Kalman recursions to missing-value problems) and shorter accounts of special topics including long-range dependence, infinite variance processes, and nonlinear models.Most of the programs used in the book are available in the modeling package ITSM2000, the student version of which can be downloaded from http://www.stat.colostate.edu/~pjbrock/student06.

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Statistics Course Pack Set 1 Op: Statistics in Plain English, 2nd Edition Review

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(More customer reviews)This book is useful for those who use statistics while they donnot go deep into theory and advanced topics. It's got good interpretation for formulas and the main topics of statistics supported with nice examples. It's even good for Statistics undergraduate students who are victims of teachers who love theory without connection to the real life. This books aims to go through the main topics in statistics (non-advanced topics) and then it explains its formulas with nice interpretation and connection to the real life and with good examples as well. Although advanced topics like (logistic regression and many others) are not included, but it gives you a good basis for interpretation of what's going on in statistics.

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Statistics in Plain English, 2/e provides a brief, simple overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. It presents brief explanations of statistical concepts and techniques in simple, everyday language. Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included. New features in the second edition include: *An interactive CD with Power point presentations and problems for each chapter including an overview of the problem's solution; *New chapters on basic research concepts including sampling, definitions of different types of variables, and basic research designs and on nonparametric statistics including the chi square test of independence; *More graphs and more precise descriptions of each statistic; and *A discussion of confidence intervals in the chapter on statistical significance and effect size. This brief paperback is an ideal supplement for statistics, research methods, courses that use statistics, or as a handy reference tool to refresh one's memory about key concepts. The actual research examples are from psychology, education, and other social and behavioral sciences.

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Family Violence in the United States: Defining, Understanding, and Combating Abuse Review

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(More customer reviews)There are many textbooks that students sell back at the end of the quarter, but for me and many of my classmates this was not one of them. This book provided me with a much deeper understanding of domestic violence and helped me form a better understaning of how to deal with the issue professionally. This book is extremely informative and is a must-read for anyone in the mental health field.

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Family Violence in the United States: Defining, Understanding, and Combating Abuse examines all types of family aggression. The book is designed to provoke readers into questioning assumptions, evaluating information, formulating hypotheses, and designing solutions to problems of family violence in the United States. Using an ecological framework, authors Denise A. Hines and Kathleen Malley-Morrison provide a thought-provoking and informative discussion not only of the most well-recognized forms of maltreatment in families, but also of less understood and more controversial issues such as husband abuse, parent abuse, and gay/lesbian abuse.

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Model Selection and Multi-Model Inference: A Practical Information-Theoretic Approach Review

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(More customer reviews)Burnham and Anderson have put together a scholarly account of the developments in model selection techniques from the information theoretic viewpoint. This is an important practical subject. As computer algorithms become more and more available for fitting models and data mining and exploratory analysis become more popular and used more by novices, problems with overfitting models will again raise their ugly heads. This has been an issue for statisticians for decades. But the problems and the art of model selection has not been commonly covered in elementary courses on statistics and regression. George Box puts proper emphasis on the iterative nature of model selection and the importance of applying the principle of parismony in many of his books. Classic texts on regression like Draper and Smith point out the pitfalls of goodness of ift measures like R-square and explain Mallows Cp and adjusted R-square. There are now also a few good books devoted to model selection including the book by McQuarrie and Tsai (that I recently reviewed for Amazon) and the Chapman and Hall monograph by A. J. Miller.
Burnham and Anderson address all these issues and provide the best coverage to date on bootstrap and cross-validation approaches. They also are careful in their historical account and in putting together some coherence to the scattered literature. They are thorough in their references to the literature. Their theme is the information theoretic measures based on the Kullback-Liebler distance measure. The breakthrough in this theory came from Akaike in the 1970s and improvements and refinement came later. The authors provide the theory, but more importantly, they provide many real examples to illustrate the problems and show how the methods work.
They also refer to the recent work in Bayesian methods. Chapter 1 is a great introduction that everyone should read. Being a fan of the bootstrap I was interested in their coverage of it in chapters 4, 5 and 6 (much of which is the authors' own work).
Because the authors work in biological fields they cover survival models as well as the standard time series and regression models where most of the emphasis has been placed on model selection in the past.
It is a great reference source and an important book for learning about model selection as part of the inferential process. The pictures of the famous contributors inserted throughout the book is also nice to see. We have Akaike, Boltzmann, Shibata, Kullback, and Liebler brought to life in photographs or sketches.

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A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

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Nonlinear Regression with R (Use R) Review

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(More customer reviews)Nonlinear Regression with R (NRwR) offers an example driven tour of R's base nonlinear regression tool, nls(). As you can glean from the table of contents, NRwR covers nonlinear models, generalized linear models, self-starting functions and model diagnostics (tools for inference as well). It does so in ~113 pages. BE WARNED. The book looks short because it is. Most of the material is helpful, but you are paying close to 50 bucks for a relatively short book.
High points:
-Model diagnostics are handled fairly well. The book walks you through checking for heteroskedacity, writing a function to test a hypothesis and dealing with mis-specified errors.
-Examples abound. If you read this book as it was intended, you will download the nlrwr package and follow along as the authors walk through datasets. Each page has sample code allowing you to reproduce the graphs and results in the book.
-Length can be an advantage. If you aren't particularly interested in the theory behind nonlinear regression, you wont be encumbered by it here.
-The bibliography is broken down by theme and is genuinely helpful.
Low Points:
-As reliant as the book is on nls(), I would love to have seen more digging into the peculiarities of this function. Chapter 4 is devoted to nls(), but it is a mere 16 pages, one of which is basically the gist of ?nls. I know it is beyond the scope, but I would have been excited to see more detail on nls so we can use the output from a nonlinear least squares problem as a building block to a larger function.
- organization for the book is a bit odd. The introduction is comprised of three examples of nonlinear regression in the field. It is about six pages and seems to have been cobbled together. The book would have been better served by a more thorough introduction.
-The topics covered are relatively narrow.
-Length is a serious factor.
All in all this is a good effort which would be easily 4 or 5 stars at a lower price point. I'm happy I bought it, but if your budget is constrained and you are forced to choose between this and another book on R's internals, think hard about the pros and cons.

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- Coherent and unified treatment of nonlinear regression with R.- Example-based approach.- Wide area of application.

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Fixed Effects Regression Methods for Longitudinal Data Using SAS Review

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(More customer reviews)Recently, I was asked to do some analysis on longitudinal patient data using fixed effects regression. This book provided an excellent, step-by-step approach on how to tackle the problem.
The first two chapters cover an overview of fixed effects and random effects modeling in the context of ordinary least squares. Then, Prof. Allison devotes a chapter to each of several topics: modeling binary outcomes, modeling counting outcomes, and modeling time to event (I admit that I stopped at Chapter 5).
I am particularly impressed by the way he shows different approaches to solving problems. And, that he compares the performance of different routines. It turns out that for many of my problems, proc genmod with GEE approximation is a good solution -- and one that finished in a few minutes rather than a few hours.

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Fixed Effects Regression Methods for Longitudinal Data Using SAS is an invaluable resource for all researchers interested in adding fixed effects regression methods to their tool kit of statistical techniques. First introduced by economists, fixed effects methods are gaining widespread use throughout the social sciences. Designed to eliminate major biases from regression models with multiple observations (usually longitudinal) for each subject (usually a person), fixed effects methods essentially offer control for all stable characteristics of the subjects, even characteristics that are difficult or impossible to measure. This straightforward and thorough text shows you how to estimate fixed effects models with several SAS procedures that are appropriate for different kinds of outcome variables. The theoretical background of each model is explained, and the models are then illustrated with detailed examples using real data. The book contains thorough discussions of the following uses of SAS procedures: PROC GLM for estimating fixed effects linear models for quantitative outcomes, PROC LOGISTIC for estimating fixed effects logistic regression models, PROC PHREG for estimating fixed effects Cox regression models for repeated event data, PROC GENMOD for estimating fixed effects Poisson regression models for count data, PROC CALIS for estimating fixed effects structural equation models. To gain the most benefit from this book, readers should be familiar with multiple linear regression, have practical experience using multiple regression on real data, and be comfortable interpreting the output from a regression analysis. An understanding of logistic regression and Poisson regression is a plus. Some experience with SAS is helpful, but not required.

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