7/31/2011

Mediation Analysis (Quantitative Applications in the Social Sciences) Review

Mediation Analysis (Quantitative Applications in the Social Sciences)
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This is a very readable, brief, and accurate introduction to mediation testing.
The book's notable strengths include: (1) integration of recent methodological developments beyond the now-dated Baron-Kenny approach, (2) one of the most readable introductions to the rather complex and often-misunderstood notions of moderated-mediation and mediated-moderation, and (3) frequent examples of how to conduct particular tests in structural equation modeling packages such as LISREL. The book also discusses how different types of variables should be dealt with (dichotomous, continuous, etc) in using each of the described procedures. All of these make 69 "real" pages of the book densely packed with contemporary methodological details.
The strengths of the book readily outweigh its weaknesses, which are two-fold.
First, the book's discussion of moderated-mediation and mediated-moderation models is rather simplistic. In particular, there are different classes of models (Type 1 through Type 5) that are not touched on here. Since you are reading this, the missing details can be found in the following two papers. (Please recognize that one of these papers might have appeared after the book was finished, and the author is unlikely to have a crystal ball specialized in forthcoming papers.)
Edwards, J., and Lambert, L. 2007. Methods for Integrating Moderation and Mediation: A General Analytical Framework Using Moderated Path Analysis. Psychological Methods. 12(1) 1-22.
Muller, D., Judd, C.M., and Yzerbyt, V. 2005. When Moderation Is Mediated and Mediation Is Moderated. Journal of Personality and Social Psychology. 89(6) 852-863.
Second, the author emphasizes SEM to a fault while discouraging the use of OLS regression. She also implies (page 49) that averaged construct scores are preferable for creating product terms in tests. This is a strong statement since this issue is still being debated in 2008.
Overall, I would highly recommend this book to anyone who uses mediation tests in their research and wants to break out of the 1980s mode of thinking that mediation testing stopped with Baron Kenny (1986). Notwithstanding its _minor_ weaknesses, this is well worth the seventeen bucks.

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This book covers mediation analysis-the examination of whether an effect of one variable on another is direct or indirect or both. Author Dawn Iacobucci offers thorough coverage of introductory and advanced material as well as conceptual and statistical information. The book begins by introducing arguments of causality, and proceeds to examine current options for analyzing data patterns purported to exhibit meditational structures. Iacobucci shows direct and indirect paths via causal paths, regression, and structural equations models. She also grounds readers in a popular structural equations modeling approach so they can implement the statistical methods discussed in testing for evidence of mediation in a variety of empirical contexts.



Key Features



· Explores even the fundamental assumptions underlying mediation analysis

· Describes options for analyzing mediation data

· Provides syntax for a widely available and popular computer program so users can begin implementing mediation ideas immediately on their data



Intended Audience



This book is appropriate for any course in regression and correlation, sociological research methods, quantitative research methods, quantitative techniques in Business & Management, Psychology, Political Science, or Public Policy departments.


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7/30/2011

System Identification: Theory for the User (2nd Edition) Review

System Identification: Theory for the User (2nd Edition)
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This book is very through in presenting methods of system identification...mathematically. However as with most good mathematical oriented books there are not a lot of practical design type problems. One other mark aginst the book is that similar notation is used for different topics, which can sometimes be a bit confusing. One thing I might recommend, to a potential buyer is that you take a course in random process, and possess a through understanding of signals and transforms. All in all though if a little more meat in terms of practicality were added I'd rate this book a five.

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A complete, coherent description of the theory, methodology, & practice of System Identification. This completely revised second edition introduces subspace methods that utilize frequency domain data, & general non-linear black box methods, including neural networks & neuro-fuzzy modeling.

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7/29/2011

Applying Regression and Correlation: A Guide for Students and Researchers Review

Applying Regression and Correlation: A Guide for Students and Researchers
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The authors make regression not only interesting but fairly painless. This is the clearest stats book ever. It even explained concepts from lower level stats classes so that they made more sense. Examples actually clarified material as well! All stats books should be written like this.

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This book takes a fresh look at applying regression analysis in the behavioural sciences by introducing the reader to regression analysis through a simple model building approach.The authors start with the basics and begin by re-visiting the mean, and the standard deviation, with which most readers will already be familiar, and show that they can be thought of a least squares model.The book then shows that this least squares model is actually a special case of a regression analysis and can be extended to deal with first one, and then more than one independent variable.Extending the model from the mean to a regression analysis provides a powerful, but simple, way of thinking about what students believe are the more complex aspects of regression analysis.The authors gradually extend the model to include aspects of regression analysis such as non-linear regression, logistic regression, and moderator and mediator analysis. These approaches are often presented in terms that are too mathematical for non-statistically inclined students to deal with. Throughout the book maintains a conceptual, non-mathematical focus.Most equations are placed in an appendix, where a detailed explanation is given, to avoid disrupting the flow of the main text. Indispensable for anyone using regression and correlation from undergraduates doing projects to postgraduate and researchers.

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7/28/2011

Environmental and Ecological Statistics with R (Chapman & Hall/CRC Applied Environmental Statistics) Review

Environmental and Ecological Statistics with R (Chapman and Hall/CRC Applied Environmental Statistics)
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Overpriced book for Stats. Not too simple to understand. actually did not need it much.

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Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R connects applied statistics to the environmental and ecological fields. It follows the general approach to solving a statistical modeling problem, covering model specification, parameter estimation, and model evaluation. The author uses many examples to illustrate the statistical models and presents R implementations of the models.The book first builds a foundation for conducting a simple data analysis task, such as exploratory data analysis and fitting linear regression models. It then focuses on statistical modeling, including linear and nonlinear models, classification and regression tree, and the generalized linear model. The text also discusses the use of simulation for model checking, provides tools for a critical assessment of the developed model, and explores multilevel regression models, which are a class of models that can have a broad impact in environmental and ecological data analysis.Based on courses taught by the author at Duke University, this book focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the processes of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.

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7/27/2011

Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses Review

Introduction to WinBUGS for Ecologists: Bayesian approach to regression, ANOVA, mixed models and related analyses
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This is a great book for using WinBUGS through R with the R library R2WinBUGS. It is actually also a pretty good book for performing classical linear modeling in R. All the analyses are performed in both R and WinBUGS. Much of the data is simulated but realistic, and the author shows you how he generated the data, which is also useful. Solutions to the exercises are available at the book's web site.
There is no reason to be an ecologist to use this book. The examples translate very well to other fields.
Despite this book being very useful to me, I gave it 4 instead of 5 stars for a few reasons. Very little attempt is made to explain the theory (to be fair, he says this at the outset, and it is a book about WinBUGS, not Bayesian statistics). The expected understanding of statistics and R is somewhat uneven throughout. For example, the author in one chapter shows you how to load libraries in R and other basic housekeeping tasks, but a few chapters later he shows more advanced model specification code in R's lm function without explaining it. Expect to spend some time in the R manual if you want to understand it all. Similarly, he repeatedly says that much of the statistics behind the code is too advanced for most ecologists, which might annoy me if I were an ecologist, but then he tends to assume a lot of the theoretical statistics is already well understood by the reader.
There is a quick introduction to Generalized Linear Models which I found helpful. Basically, this is a great practical book but you will need to look elsewhere for mathematical understanding. I like Peter Hoff's "A First Course in Bayesian Statistical Methods."

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Bayesian statistics has exploded into biology and its sub-disciplines such as ecology over the past decade. The free software program WinBUGS and its open-source sister OpenBugs is currently the only flexible and general-purpose program available with which the average ecologist can conduct their own standard and non-standard Bayesian statistics. Introduction to WINBUGS for Ecologists goes right to the heart of the matter by providing ecologists with a comprehensive, yet concise, guide to applying WinBUGS to the types of models that they use most often: linear (LM), generalized linear (GLM), linear mixed (LMM) and generalized linear mixed models (GLMM).



Introduction to WinBUGS for Ecologists combines the use of simulated data sets "paired" analyses using WinBUGS (in a Bayesian framework for analysis) and in R (in a frequentist mode of inference) and uses a very detailed step-by-step tutorial presentation style that really lets the reader repeat every step of the application of a given mode in their own research.

- Introduction to the essential theories of key models used by ecologists

- Complete juxtaposition of classical analyses in R and Bayesian Analysis of the same models in WinBUGS



- Provides every detail of R and WinBUGS code required to conduct all analyses

- Written with ecological language and ecological examples

- Companion Web Appendix that contains all code contained in the book, additional material (including more code and solutions to exercises)

- Tutorial approach shows ecologists how to implement Bayesian analysis in practical problems that they face


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7/26/2011

Self-Efficacy in Changing Societies Review

Self-Efficacy in Changing Societies
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This book is very clearly written. Bandura does a great job of addressing self-efficacy theory in different societal contexts (education, family, health). I highly recommend it both as an academic resource and as a personal read for anyone is interested in thinking about their perceived level of control shapes their life outcomes.

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Adolescents' beliefs in their personal control affects their psychological well-being and the direction of their lives. Self-Efficacy in Changing Societies analyzes the diverse ways in which beliefs of personal efficacy operate within a network of sociocultural influences to shape life paths. The chapters, written by internationally known experts, cover such concepts as infancy and personal agency, competency through the life span, the role of family, and cross-cultural factors.

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7/25/2011

Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (Statistics for Biology and Health) Review

Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (Statistics for Biology and Health)
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Great book! Simple and understandable language. Nice graphs to explain the theory. All the statements are referenced and or supported with examples. Overall the book has a lot of depth as well; so it will be useful for your introduction to prediction models, but also later on. I was getting lost in all the publications and statistical study books, until I found this book.

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This book aims to provide insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or only in a simplistic way, and updating of already available models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice.Clinical Prediction Models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and clinical usefulness; internal validation; and presentation format. The steps are illustrated with many small case studies and R computer code, with data sets made available in the public domain [http://www.clinicalpredictionmodels.org/]. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to modifying and extending a model, and comparisons of centers after case-mix adjustment by a prediction model.The text is primarily intended for epidemiologists and applied biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. The book is practical in nature. But it also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision-making.

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7/24/2011

Organizational Psychology: A Scientist-Practitioner Approach Review

Organizational Psychology: A Scientist-Practitioner Approach
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I ordered this book for my class in Organizational Psychology. It would be an excellent text for students interested in conducting research in the field of organizational psychology. However, the information is not presented in a manner that makes it easy to glean major points for those interested in the application of the data to an organization. From this perspective, there are nuggets of gold, but they must be searched out and reorganized for use. Because I am interested in information pertaining to what organizations can do to improve processes, the presentation makes learning more difficult. Information on what organizations should do is scattered throughout the various sections of the text. Sumamry sections discussing how organizations can apply this information would be helpful. I would also prefer that in addition to research results the text provide example stories to illustrate these results. This would provide more interest and make it easier to assimilate the information presented. In summary, this is an excellent text from the perspective of one conducting research, but difficult to follow for those interested in applying this research to organizational management.

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Thorough and up-to-date coverage of both the science and practice of organizational psychologyThis Second Edition reflects the latest developments and research in the field using a scientist-practitioner model that expertly integrates multicultural and international issues as it addresses the most current knowledge and topics in the practice of organizational psychology.Beginning with a foundation of research methodology, this text examines the behavior of individuals in organizational settings and shows readers how psychological models can be used to improve employee morale, productivity, and quality of service.Written in an accessible style that brings the material to life, author Steve Jex and new coauthor Thomas Britt use their experiences as consultants and educators to bring new features to the Second Edition, including:* Updated chapters, particularly those on job attitudes, teams, and leadership* New "People Behind the Research" and "Illuminating Examples" boxes* New coverage of workplace stress, teams, and multicultural socialization* More material on personal difference, personality, and considerations of diversity * Extended coverage of financial incentives and executive compensation* Using descriptive cases to illustrate workplace issues, Organizational Psychology, Second Edition thoroughly addresses the major motivational theories in organizational psychology and the mechanisms that organizations use to influence employees' behavior.

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7/23/2011

Investing in People: Financial Impact of Human Resource Initiatives (2nd Edition) Review

Investing in People: Financial Impact of Human Resource Initiatives (2nd Edition)
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I think this is an important book for HR management. The authors show the way of moving from the odd notions of how well HR delivers its services to demonstrating hard number measurements of HR value added to the performance of the company. After presenting their argument for making HR Management as strategic as Finance and Marketing, they show you how to become more analytical in your approach to HRM.
They then go through ten subjects showing you how to analyze the financial impact of that topic and then offer you a case study to work through using the tools you were given in that chapter. Then ten topics are:
1)The Hidden Costs of Absenteeism
2)The High Cost of Employee Separations
3)Employee Health, Wellness, and Welfare
4)Employee Attitudes and Engagement
5)Financial Effects of Work-Life Programs
6)Staffing Utility - Concept and Measurement
7)The Economic Value of Job Performance
8)The Payoff from Enhanced Selection (hiring better people using tools)
9)Costs and Benefits of HR Development Programs
10)Talent-Investment Analysis: Catalyst for Change
I found this book to be smart, on point, easy to understand, and think it will be of real use to anyone serious about becoming more analytical about the HR Management function.
Reviewed by Craig Matteson, Ann Arbor, MI

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More than ever before, HR practitioners must empirically demonstrate a clear link between their practices and firm performance. In this book, Wayne F. Cascio and John W. Boudreau show exactly how to choose, implement, and use metrics to improve decision-making, optimize organizational effectiveness, and maximize the value of HR investments.

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7/22/2011

A Gentle Introduction to Stata, Second Edition Review

A Gentle Introduction to Stata, Second Edition
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This is the book I have been waiting for! It assumes no prior knowledge of STATA and goes step-by-step through practical exercises that a person handling data would need to do in everyday work. This book would have helped me a ton during my graduate courses, as I did not have any formal introduction to STATA and pieced together commands from various books and websites. Thank you, Alan Acock and STATA Press, for recognizing that not everyone is innately adept at programming. If you are a beginner or find yourself spinning your wheels with commands, this book is what you need.

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Providing the basic collection of statistical procedures used by social scientists, A Gentle Introduction to Stata, Second Edition presents the fundamental tools to learn Stata. The book begins with showing how to enter and manage data as well as perform basic descriptive statistics and graphical analysis. It then examines standard statistical procedures from a t test, nonparametric tests, measures of association, multiple regression, and logical regression. The book ends with guidelines for future work and advanced topics. This learning source is an excellent introduction for those with little statistical software experience while also a useful reference for more knowledgeable statisticians by offering a detailed index of commands.

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7/21/2011

An R and S Plus Companion to Applied Regression Review

An R and S Plus Companion to Applied Regression
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I found this book to be a good basic regression book that covers the basics and gives liberal examples with code, and points out differences between R, S-Plus, S3 and S4. The book does give some general background material on the basic systems, like working with arrays, matrices, writing programs and other basic details. Book also covers data transformation, linear models, generalized linear models, model diagnostics and graphing.
Fox is a sociologist and the examples come from his line of work, however this does not degrade the books ability to show regression modelling using R or S. Except for the specific examples the book plus the extra chapters available on the books website more or less covered what was covered in my graduate engineering regression class.
Topics such as nonlinear, robust, resampling, time series, nonparametric, while not covered in the book, chapters are however provided on the books website and appear to the same quality as what is in the rest of the book. Scripts for all the chapters, including the online-only chapters, are also available on the website.

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"This book fits right into a needed niche: rigorous enough to give full explanation of the power of the S language, yet accessible enough to assign to social science graduate students without fear of intimidation. It is a tremendous balance of applied statistical "firepower" and thoughtful explanation. It meets all of the important mechanical needs: each example is given in detail, code and data are freely available, and the nuances of models are given rather than just the bare essentials. It also meets some important theoretical needs: linear models, categorical data analysis, an introduction to applying GLMs, a discussion of model diagnostics, and useful instructions on writing customized functions. "

-JEFF GILL, University of Florida, Gainesville


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7/20/2011

Statistical Methods for Spatial Data Analysis (Chapman & Hall/CRC Texts in Statistical Science) Review

Statistical Methods for Spatial Data Analysis (Chapman and Hall/CRC Texts in Statistical Science)
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This book was recommended to me by Amazon. It looks very attractive as I would like to learn more about this area.
However, when I clicked on the sample pages online and read the first example, I realized there are typos already. First, the second covariance function should not have a conditional line, it is meant to illustrate the correlation or covariance between two observations in the same experimental unit, unconditionally. Second, I think the second covariance function is missing the tau_i which generates another variance component in the covariance function. This looks disappointing and I will not purchase the book now based on what I read.
Author, any thought?

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Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields,non-stationary covariance, and spatio-temporal processes.Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.

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7/19/2011

Semiparametric Regression (Cambridge Series in Statistical and Probabilistic Mathematics) Review

Semiparametric Regression (Cambridge Series in Statistical and Probabilistic Mathematics)
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David Ruppert and Ray Carroll have been a research team for over 25 years. They have published many articles and books on regression analysis. These articles are always very clearly written and are great at showing the big picture and not just the nitty gritty details of the theorems that they prove. Two of my favorite books that they published are "Transformations and Weighting in Regression" published by Chapman & Hall in 1988 and "Measurement Error in Nonlinear Models " with Stefanski in 1995 and also published by Chapman & Hall.
This book is no exception. It is lucid in expostion and paints a general picture summarizing the area of nonparametric regression models and incorporating them with parametric regression both linear and nonlinear.
Their work has also been motivated by the desire to extend the theory of regression models to practical problems where the standard theory with assumptions such as linearity, normality, and homogeneity of variance don't hold.
In the first chapter, they motivate their methods through a number of examples in the areas of health science and environmental pollution problems. Chapter two goes through the standard linear regression models and the diagnostic checks for those models. They also cover other practical issues including model selection, use of transformations and extensions to nonlinear models. The special case of polynomial regression (a particular example of linear regression) is presented in detail.
Chapter 3 on scatterplot smoothing introduces many of the key ideas to their approach to semiparametric regression. Their approach in its most general form is based on mixed models which are introduced in chapter 4. Chapter 5 deals with automated methods for implementing the scatterplot smoothing techniques. The remaining chapters cover for example, simple semiparametric models, additive models, semiparametric mixed models, and generalized parametric models which include the very useful generalized linear models that they have extended to cover mixed effects.
The generalized additive models of Tibshirani and Hastie are covered in chapter 11, Other important issues including variance function estimation, accounting for measurement error, Bayesian approaches and more are covered in the latter chapters (12-17), Finally in chapter 18 seven examples are introduced to illustrate applications of the various methods. An epilogue, chapter 19, was written to motivate further research.
Many of these chapter are the subjects of whole monographs including some that Ruppert and Carroll have co-authored. In the preface they say that the book is intended for three potential audiences. The first audience is the students and scientists with interest in applying the techniques or learning about them but possess at most a moderate background in regression. The second audience (the group I would put myself in)are the biostatisticians ,econometricians and scientists in other disciplines who have a good working knowledge of regression and want to add the flexibility of semiparametric methods to their arsenal of techniques. The third group is the researchers in nonparametric regression who may not yet know about some of the new advances of Carroll, Ruppert and Wand that are included in this text.
I find in general that their books are masterpieces. As a statistician who has done both applied and theoretical work, I know what it takes to write books that summarize a body of theory or connect the theory and applications, or incorporate new results. These authors do both of these things in this book. They have the rare talent to find a way to unify and simplify existing theory and that is another great feature you will find in this book.
I haven't been able to do that and only a few others that I know can. One example that comes to my mind is the book on extremes by Leadbetter, Lindgren and Rootzen. At the time if of publication in 1978, they provided a unified theory for extremes combining the theory for independent and dependent cases. They also provide some examples in the bppk. But even that landmark book is heavily theoretical. Ruppert, Carroll and Wand emphasize applications and provide a number of examples throughout.

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Science abounds with problems where the data are noisy and the answer is not a straight line. Semiparametric regression analysis helps make sense of such data in application areas that include engineering, finance, medicine and public health. The book is geared towards researchers and professionals with little background in regression as well as statistically oriented scientists (biostatisticians, econometricians, quantitative social scientists, and epidemiologists) with knowledge of regression and the desire to begin using more flexible semiparametric models.

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7/18/2011

Latent Growth Curve Modeling (Quantitative Applications in the Social Sciences) Review

Latent Growth Curve Modeling (Quantitative Applications in the Social Sciences)
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I'm a PhD student who is interested in the subject and wanted to do some background reading when I had a chance. I purchased a Kindle ebook because it seemed perfect -- cheap, portable reading. After receiving the book, I am very, very disappointed. The text is illegible. This is not the first time I have had problems with Amazon regarding their electronic book offerings... but it will be the last.
The results are horrible. The publisher (Sage) apparently just converted this to the Amazon book format without any concern for legibility or quality. The result is that all of the equations are blurry, and the subscripts and superscripts are completely illegible.
There are a number of problems with the Kindle edition that neither Amazon, nor the original publisher, seem to care about.
First, the equations in the text are too small to read. Additionally, they are contained in the text as images, so even if one tries to zoom in on an equation, you simply get a larger blurry image. (Hint: the publisher should use SVG - Scalable Vector Graphics and not a lossy image file format that they chose to use.) The size of the equations, which are inserted in the text as images, are the smallest font size possible, so even when you increase the font size, the equations remain at the smaller size (since they are captured as images).
In terms of the text, well, here too the Sage book falls flat. While Sage books are generally high quality it is often hit and miss as to whether the text of the booklet is a pleasurable read (whether or not it is an easy read is another matter!). The text overall is uninspiring but informative... however, given the problems with the equations just mentioned, there is no point in purchasing the book.
Do yourself a favour and take out any Sage books from the library. Don't bother with buying this.
By the way -- the sample version of this book does not contain any equations. It would have been nice if it did!

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Latent growth curve modeling (LGM)-a special case of confirmatory factor analysis designed to model change over time-is an indispensable and increasingly ubiquitous approach for modeling longitudinal data. This volume introduces LGM techniques to researchers, provides easy-to-follow, didactic examples of several common growth modeling approaches, and highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit. The book covers the basic linear LGM, and builds from there to describe more complex functional forms (e.g., polynomial latent curves), multivariate latent growth curves used to model simultaneous change in multiple variables, the inclusion of time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models. The authors also highlight approaches to dealing with missing data, different estimation methods, and incorporate discussion of model evaluation and comparison within the context of LGM. The models demonstrate how they may be applied to longitudinal data derived from the NICHD Study of Early Child Care and Youth Development (SECCYD).. Key Features

· Provides easy-to-follow, didactic examples of several common growth modeling approaches

· Highlights recent advancements regarding the treatment of missing data, parameter estimation, and model fit

· Explains the commonalities and differences between latent growth model and multilevel modeling of repeated measures data

· Covers the basic linear latent growth model, and builds from there to describe more complex functional forms such as polynomial latent curves, multivariate latent growth curves, time-varying covariates, predictors of aspects of change, cohort-sequential designs, and multiple-group models


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7/17/2011

Crossing the Finish Line: Completing College at America's Public Universities Review

Crossing the Finish Line: Completing College at America's Public Universities
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The most interesting implication of this book to me is one the authors do not state: If the United States succeeds in increasing the % of citizens who attend AND graduate from college, the wage premium for college graduates will decline. The wage premium is rising largely because high-end universities are not growing enrollment, ensuring a supply/demand imbalance. High achievers capture a disparate share of the degree-wage premium. And the bulk of those high achievers come from a short list of "name" universities that get first pick of students. Do America's top 100 universities have any interest in growing the number of educated Americans? Hardly. Their pace of enrollment growth has been glacial for many decades.
The authors briefly argue against diminishing returns, citing anecdotal studies but not confronting the larger numbers that would be involved if they got their wish. This book carries on the attitude of the more, the better, because it's all about trying to understand why the % of students who finish college is not going up. It's stuck at 50%--45% for public schools, 55% for private.
The authors don't want that diminishing-returns debate, for they are fixated on the persistence of social and economic inequality. Their massive statistical analysis of the incoming class of 1999 shows that if your parents were poor and didn't attend college, you have a low chance of finishing college in six years regardless of your high school record. People in the top quartile of incomes have a better than 50/50 chance of getting a bachelor's degree by age 26; only about one in 10 from the poorest 25% of US families did so.
Inequality is reinforced by state flagship universities' increasingly selective admissions. The book samples UCLA, North Carolina and Virginia from 1974 to 2006; over that span the % of new students with >3.5 high school GPA rose from 55% to 90%. A similar move appeared in three other not-so-elitist flagship schools. Students from households in the top 25% of incomes are greatly overrepresented at these schools.
The authors miss the obvious explanation. Take UCLA. From '74 to '06 its undergraduate enrollment grew at a CAGR of 0.6%, less than half the rate of US population growth.
Our economy is fixated on hiring the best graduates of the same old list of schools, where the best faculty are hoarded. A smaller and smaller % of college students get to see them.
College participation is rising, rising fast in fact, but the completion rate isn't. It likely will fall due to a spike in the % of high school grads entering college, influenced by the recession's destruction of entry-level jobs.
This book is focused on the freshman class of 1999, which entered school half a year before the top of the Internet bubble. At this time, 55% of the age 18-19 population was employed, and their official unemployment rate was 12%. From the high school class of '99, the BLS tells us 63% immediately went to college (two or four-year). This was a generational low, equaled in fall 2000. Not coincidentally, the US unemployment rate dipped below 4% in 2000. Jobs were easy to get.
In fall 2009, 37-38% of the age 18-19 population was employed, and their official unemployment rate was as high as 26%. A full 70% of '99 high school grads went to college, highest on record. With a jobless rate of 10%, the labor market was ugly.
The book studies the graduation rate of the '99 class over a six-year period, on the veiled assumption that six years is long enough. Over a recession, of course, six years isn't long enough for people of limited economic means. And all they can tell is whether these students graduated from these schools--not how many had to transfer to lower-cost schools and finished there. Not how many finished in more than six years.
The Student Clearinghouse recently reported that 49% of 2005-06 college graduates finished in no more than four academic years, with about one-quarter taking more than five years, 10% more than seven. NCES reports that students who begin at two-year schools and finish at four-year schools take almost two academic years longer to finish than those who start four-year schools as freshmen. Economic strain forces people to save on college costs, which prolongs their time in college. The longer college is stretched out, the more likely one won't finish. Kids who go full-time without a worry about how to pay for it are at a great advantage.
This book's data supports the well-established fact that high schools in low-income areas do not prepare graduates well enough for college. But not as much as you might think.
The authors analyzed the quality of high schools that supplied the '99 college freshmen by college prep measures (SAT, ACT, AP). The performance spread between high/low income, high/low test scores, ethnic mixes isn't great. Surprisingly consistent. The spread between the elite state universities and other state schools, however, is remarkably wide across any subgroup. Selective admissions appears to improve grad rate by 220-300 basis points.
Further, the authors note the Chicago Consortium's conclusions that less than half of Chicago high school grads studied enrolled in colleges good enough to match their academic achievements. The authors studied their North Carolina data set and found more than 40% of '99 high school grads with excellent SAT scores + GPAs did not attend exclusive universities. They suggest these students have low expectations too early in the process, most not even applying to better schools. I ask: If they had gotten in, who would have been bumped?
Inevitably, good students will be forced into lesser schools, and many of them will be bored or shamed into dropping out. It doesn't take reams of data to tell you that.
If you read this study, you may conclude that the subsequent question is how to improve college opportunities for people who weren't excellent in high school, weren't raised by college grads, aren't made of money. This no longer is the problem of the state universities. Their capacity constraints lead them to price out and test out more and more applicants. Our country has only one source of new capacity for educating adults, the for-profit schools that the Obama administration seems to abhor. The problem of controlling for-profit schools, ensuring they teach well and award meaningful degrees, seems more manageable than finding the dollars to persuade not-for-profit schools to grow again.

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The United States has long been a model for accessible, affordable education, as exemplified by the country's public universities. And yet less than 60 percent of the students entering American universities today are graduating. Why is this happening, and what can be done? Crossing the Finish Line provides the most detailed exploration ever of college completion at America's public universities. This groundbreaking book sheds light on such serious issues as dropout rates linked to race, gender, and socioeconomic status.

Probing graduation rates at twenty-one flagship public universities and four statewide systems of public higher education, the authors focus on the progress of students in the entering class of 1999--from entry to graduation, transfer, or withdrawal. They examine the effects of parental education, family income, race and gender, high school grades, test scores, financial aid, and characteristics of universities attended (especially their selectivity). The conclusions are compelling: minority students and students from poor families have markedly lower graduation rates--and take longer to earn degrees--even when other variables are taken into account. Noting the strong performance of transfer students and the effects of financial constraints on student retention, the authors call for improved transfer and financial aid policies, and suggest ways of improving the sorting processes that match students to institutions.

An outstanding combination of evidence and analysis, Crossing the Finish Line should be read by everyone who cares about the nation's higher education system.


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7/16/2011

Legacies: The Story of the Immigrant Second Generation Review

Legacies: The Story of the Immigrant Second Generation
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This book is a detailed and well-written analysis of the most important demographic shift in America today. In addition to testimonies and explanation, the book focuses on the Children of Immigrants Longitudinal Study - which allows significant insight into the current state of the immigrant second generation, and implications for the future. I am a journalist, and this book sparked an entire project for me (see "Breaking Through: Second Generation Immigrants in California and Social Mobility" at [...]). A worthy read for anyone interested in the future of the country, education and social studies.

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One out of five Americans, more than 55 million people, are first-or second-generation immigrants. This landmark study, the most comprehensive to date, probes all aspects of the new immigrant second generation's lives, exploring their immense potential to transform American society for better or worse. Whether this new generation reinvigorates the nation or deepens its social problems depends on the social and economic trajectories of this still young population. In Legacies, Alejandro Portes and Rubén G. Rumbaut--two of the leading figures in the field--provide a close look at this rising second generation, including their patterns of acculturation, family and school life, language, identity, experiences of discrimination, self-esteem, ambition, and achievement.Based on the largest research study of its kind, Legacies combines vivid vignettes with a wealth of survey and school data. Accessible, engaging, and indispensable for any consideration of the changing face of American society, this book presents a wide range of real-life stories of immigrant families--from Mexico, Cuba, Nicaragua, Colombia, the Dominican Republic, Haiti, Jamaica, Trinidad, the Philippines, China, Laos, Cambodia, and Vietnam--now living in Miami and San Diego, two of the areas most heavily affected by the new immigration. The authors explore the world of second-generation youth, looking at patterns of parent-child conflict and cohesion within immigrant families, the role of peer groups and school subcultures, the factors that affect the children's academic achievement, and much more. A companion volume to Legacies, entitled Ethnicities: Children of Immigrants in America, was published by California in Fall 2001. Edited by the authors of Legacies, this book will bring together some of the country's leading scholars of immigration and ethnicity to provide a close look at this rising second generation.A Copublication with the Russell Sage Foundation

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7/15/2011

Business Analysis Using Regression: A Casebook Review

Business Analysis Using Regression: A Casebook
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This book has an interesting premise of focusing statistics for business at the mid-level using the JMP software. The concepts addressed are the right ones, even the order is good.
However, on the downside, it is very difficult to read. The cases are not well explained. This book is used at Wharton's 1st year core course in statistics and the general agreement is that it is very hard to comprehend it. I recommend an editor to the authors.

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Providing an introduction to modern data analysis techniques, these casebooks can be used as the primary or secondary text for elementary business statistics courses. Statistics has the reputation of being a boring, complicated, and confusing mix of mathematical formulas accompanied by computers used to do something. This casebook and its companion volume Business Analysis Usuing Regression change that impression by showing how statistics gives insights and ansswers interesting business questions.The material is organized into classes of related case studies that develop a single key idea of statistics. The authors begin by discussing an application that motivates the key idea. Students are then shown how to analyze a data set. The emphasis of the analysis is to answer important business questions with statistics rather than talk about statistics. Basic Business Statistics introduces ideas often not emphasized in elementary texts such as issues of robustness, the use of transformations to simplify problems, sampling bias, confounding, kernel density, quality control, and scatterplot matrices. Business Analysis with Regression includes a discussion of scatterplot smoothing, prediction intervals for new observations, collinearity, logistic regression, nonlinear models, and multiple comparisons in regression.The text includes directions for data analyses with JMP and an appendix with Minitab commands. Professors Dean P. Foster, Robert A. Stine, and Richard P. Waterman are members of the Department of Statistics at the Wharton School, University of Pennsylvania.

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