Statistics Books

Internet, Mail, and Mixed-Mode Surveys: The Tailored Design Method

Don A. Dillman, Jolene D. Smyth, Leah Melani Christian

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Customer Reviews:
Total reviews: 17 Average rating: 4.5 of 5

Excellent Reference 5 out of 5 stars.
3 of 3 people found this review helpful.

Overall there appears to be some confusion over the purpose of the book, but if you are trying to improve responses and design of the actual instrument, this book is excellent. If you need help figuring out sampling and the such, you aren't ready for the book.

There are many people who do still use paper surveys and even mail them and given it's in the title, it's a bit surprising reviewers found this absurd.

Perhaps more absurd are the complaints relating to how unscientific the work is. Dillman has published a great deal in the scientific literature on how to improve the quality of the data through better visual design. That is not saying he does not understand the importance of sampling. It does indicate the book he is going to write is going to assume you have very basic understanding of survey methodology and you are looking to improve the usability of a survey with the assumption in mind.

Dillman's research provides excellent guidelines for creating visual surveys that are likely to improve the accuracy and reliability of the responses. If that's what you are looking for, it's the best book on the subject and is grounded in the literature.

If you need a book on sampling, come back to this book later.

Editorial Review:

For over two decades, Dillman's classic text on survey design has aided both students and professionals in effectively planning and conducting mail, telephone, and, more recently, internet surveys. The Third Edition presents thoroughly updated and revised information about all aspects of survey research and features expanded coverage of online surveys. Dillman's unique Tailored Design Method, thoroughly explained and illustrated in the book, is the preferred model for practice. This invaluable resource is crucial for any researcher seeking to increase response rates and obtain high-quality feedback from survey questions.

Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence

Judith D. Singer, John B. Willett

Amazon Price: $58.36 List Price: $72.95 Usually ships in 24 hours By: Oxford University Press, USA Amazon Marketplace: 41 new & used starting at $55.80

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Total reviews: 10 Average rating: 5.0 of 5

Editorial Review:

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

Using Multivariate Statistics (5th Edition)

Barbara G. Tabachnick, Linda S. Fidell

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Customer Reviews:
Total reviews: 14 Average rating: 4.5 of 5

nicely written applied book 4 out of 5 stars.
35 of 35 people found this review helpful.

This is a very nicely written and popular text on applied multivariate analysis. It does not provide theory but teaches how to use and interpret results from software packages. It has a good set of examples.

Editorial Review:

This text takes a practical approach to multivariate data analysis, with an introduction to the most commonly encountered statistical and multivariate techniques. Using Multivariate Statistics provides practical guidelines for conducting numerous types of multivariate statistical analyses. It gives syntax and output for accomplishing many analyses through the most recent releases of SAS and SPSS. The book maintains its practical approach, still focusing on the benefits and limitations of applications of a technique to a data set - when, why, and how to do it. Overall, it provides advanced students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics.

Reading and Understanding Multivariate Statistics

Laurence G. Grimm

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Customer Reviews:
Total reviews: 8 Average rating: 4.5 of 5

I read it - and I understood it! 5 out of 5 stars.
38 of 39 people found this review helpful.

"Reading and Understanding 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 some of the most common multivariate analyses. Each chapter focuses on a different analysis and presents its conceptual underpinnings, underlying assumptions, and basic procedures with a minimum of equations and many concrete examples. It does 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.

Editorial Review:

This text aims to help researchers and students to understand the purpose and presentation of multivariate statistical techniques. The most commonly used techniques are described in detail, such as multiple regression and correlation and path analysis

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Third Edition

Patricia Cohen, Jacob Cohen, Stephen G. West, Leona S. Aiken

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Customer Reviews:
Total reviews: 4 Average rating: 4.5 of 5

Editorial Review:

This classic text on multiple regression is noted for its non-mathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for a solid understanding of the rest of the text.

The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures and an accompanying CD with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT.

Applied Multiple Regression serves asboth a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters. The book is an ideal text for courses on multiple regression and correlational methods.

Introduction to Statistical Mediation Analysis (Multivariate Applications Series)

David MacKinnon, David P. MacKinnon

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Total reviews: 2 Average rating: 5.0 of 5

Editorial Review:

This volume introduces the statistical, methodological, and conceptual aspects of mediation analysis. Applications from health, social, and developmental psychology, sociology, communication, exercise science, and epidemiology are emphasized throughout. Single-mediator, multilevel, and longitudinal models are reviewed. The author's goal is to help the reader apply mediation analysis to their own data and understand its limitations.

Each chapter features an overview, numerous worked examples, a summary, and exercises (with answers to the odd numbered questions). The accompanying CD contains outputs described in the book from SAS, SPSS, LISREL, EQS, MPLUS, and CALIS, and a program to simulate the model. The notation used is consistent with existing literature on mediation in psychology.

The book opens with a review of the types of research questions the mediation model addresses. Part II describes the estimation of mediation effects including assumptions, statistical tests, and the construction of confidence limits. Advanced models including mediation in path analysis, longitudinal models, multilevel data, categorical variables, and mediation in the context of moderation are then described. The book closes with a discussion of the limits of mediation analysis, additional approaches to identifying mediating variables, and future directions.

Introduction to Statistical Mediation Analysis is intended for researchers and advanced students in health, social, clinical, and developmental psychology as well as communication, public health, nursing, epidemiology, and sociology. Some exposure to a graduate level research methods or statistics course is assumed. The overview of mediation analysis and the guidelines for conducting a mediation analysis will be appreciated by all readers.

A Beginner's Guide to STRUCTURAL EQUATION MODELING, SECOND EDITION (The Inquiry and Pedagogy Across Diverse Contexts Series)

Randall E. Schumacker, Richard G. Lomax

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Customer Reviews:
Total reviews: 6 Average rating: 5.0 of 5

Easy to read and understand; needs a second edition 5 out of 5 stars.
21 of 21 people found this review helpful.

No one expects statistics to be easy reading, especially when it concerns complicated models such as structural equation modeling (SEM). Nevertheless, this book manages to do just that.

Schumacker and Lomax have successfully put together a guide that explains to beginners (like myself) in simple terms how the whole thing works. As with most books that treat complex models, some basic knowledge of statistics is preferable before you begin to read it. But if your statistics is rusty and you have only vague impressions of probability sketches in your memory, fear not! Schumacker and Lomax are kind to us poor souls, and begin by introducing some basics in chapter 1 to prod your memory: terminology, variable scales, how to treat missing data, outliers and normality. And in chapter 2, they discuss correlation and covariance.

Before talking about structural equation models, Schumacker and Lomax dedicate chapter 3 to a number of statistical methods on which SEM is built. This chapter gives a basic overview of regression, path analysis and factor analysis. The review of these methods helps you to understand SEM better later on. They also provide an excellent understanding of the methods, in case you have not used them before or it's been a while ...

The rest of the guide covers SEM: how to develop and measure a model (chapters 4 and 5), how the model parameters are estimated and how you can check for reliability and validity (chapter 6), and checking for goodness of fit of your model (chapter 7).

In chapter 8, you are shown some examples of computer outputs by two software packages that can conduct SEM, EQS5 and LISREL8-SIMPLIS.

Chapter 9 goes into more detail on models and diagrams (regression, analysis of covariance, path, measurement and structural models). For those that feel by this point that they've gained enough experience, advanced topics such as cross validation, simulation, bootstrap and jacknife methods as well as multiple same and interaction models are covered in chapter 10. And for the super-keen, the technical bits are covered in chapter 11 (health warning: you better be up to speed on matrix algebra).

The great thing about this book is that you most likely will be able to run models and interpret results by chapter 7, and you don't need to go into the nitty-gritty if you don't want to. On the other hand, the details are there if you need them. In essence, the authors start at the beginning, building up slowly until you are able to handle a basic model, before going into more complex issues.

One drawback, I have found, is that this book was published in 1996. That's nearly a decade ago, and (fortunately) computer power and statistical modeling has come a long way since then. The authors, for example, are convinced that WordPerfect is the software of choice for word processing and that at some point in the future it would be possible to copy and paste diagrams into a word processing program. We've come a long way since then. Today, many SEM packages exist that are much more user friendly than some of the older stodgy packages that require you to enter data in a very specific way and interpret results by going through reams of data output. As such, the authors (or publishers) probably ought to update it with a second edition.

Nevertheless, the strength of the book lies in its guidance and explanatory power. And even if you use a different package, you can skim through the data outputs they use, and focus on your model, how to construct it, and of what pitfalls to beware.

I highly recommend this for anyone starting on SEM - your modeling days will be much happier with this guide.

Editorial Review:

This revised edition is intended to give students and researchers a basic understanding of SEM, focusing on the conceptual steps one takes in analyzing theoretical models. SEM is useful in statistical methods courses in social science, education, busines

SPSS for Windows Step-by-Step: A Simple Guide and Reference 16.0 Update (9th Edition)

Darren George, Paul Mallery

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Principles and Practice of Structural Equation Modeling, Second Edition (Methodology In The Social Sciences)

Rex B. Kline

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Customer Reviews:
Total reviews: 8 Average rating: 4.5 of 5

great book. 5 out of 5 stars.
1 of 1 people found this review helpful.

Great book for those wanting to learn Structural Equation Modeling (SEM). From a beginners point-of-view I've found it helpful in integrating regression with SEM models.

Learning SEM. This is the book that you need. 5 out of 5 stars.
1 of 1 people found this review helpful.

Well...This book is as understandable as statistics can be. You will learn about the advantages of using mathematical models and how they represent reality way better than doing group comparisons. Highly recommended.

Editorial Review:

This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.

Reading and Understanding More Multivariate Statistics

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Total reviews: 3 Average rating: 5.0 of 5

Editorial Review:

Since 1995, over 13,000 graduate students and researchers have relied on Reading and Understanding Multivariate Statistics for a basic understanding of the most commonly used multivariate analyses in the research literature today. In Reading and Understanding More Multivariate Statistics, the editors provide the same accessible approach to a new group of multivariate techniques and related topics in measurement. Chapters demystify the use of cluster analysis, Q-technique factor analysis, structural equation modeling, canonical correlation analysis, repeated measures analyses, and survival analysis. Chapter authors describe the research questions for which the statistic is most appropriate, the underlying assumptions and rationale of the analysis, and the logic behind interpreting the results. Designed to clarify each statistic's logic and utility rather than teach hands-on application, the book emphasizes the real-world use of statistical methods with minimal reliance on complex mathematical formulas.