SPSS & Statistics Product Review: Statistical Design
Book annotation not available for this title.
Title: Statistical Design
Author: Casella, George
Publisher: Springer Verlag
Publication Date: 2008/04/03
Number of Pages: 307
Binding Type: HARDCOVER
Library of Congress: oc2008004690
Top 10: Essential SPSS & Statistics Book Reading List
-Books and recommendations by Your SPSS Guide
We are often asked to recommend a book that would work best for someone interested in learning SPSS or statistical analysis. Ten years ago, this was an easy question to answer, since there were so few options at the time.
Here are some of our favorites, with some reflections on each; we (strongly) suggest that all SPSS and statistics advocates read all of them so that you can recommend them articulately and loan them to friends and colleagues.
- Hierarchical linear models: applications and data analysis methods
- Qualitative data analysis: an expanded sourcebook
- An introduction to categorical data analysis
- Data analysis: a Bayesian tutorial
- A beginner’s guide to structural equation modeling
- Advances in data analysis: proceedings of the 30th Annual …
- Designing an Efficient Management System: Modelling of Convergence …
- Statistics for people who (think they) hate statistics
- Introduction to Statistics and Data Analysis
- Multilevel and Longitudinal Modeling With Spss
#1 Hierarchical Linear Models: Applications and Data Analysis Methods (Advanced Quantitative Techniques in the Social Sciences)
Popular in the first edition for its rich, illustrative examples and lucid explanations of the theory and use of hierarchical linear models (HLM), the book has been reorganized into four parts with four completely new chapters. The first two parts, Part I on “The Logic of Hierarchical Linear Modeling” and Part II on “Basic Applications” closely parallel the first nine chapters of the previous edition with significant expansions and technical clarifications, such as:
-An intuitive introductory summary of the basic procedures for estimation and inference used with HLM models that only requires a minimal level of mathematical sophistication in Chapter 3
-New section on multivariate growth models in Chapter 6
-A discussion of research synthesis or meta-analysis applications in Chapter 7
-Data analytic advice on centering of level-1 predictors and new material on plausible value intervals and -robust standard estimators
#2 Qualitative Data Analysis: An Expanded Sourcebook
In 1984, the first edition of Qualitative Data Analysis addressed a critical need faced by researchers in all fields of the human sciences – how to draw valid meaning from qualitative data. It provided methods of analysis that were practical, credible and reliable. This groundbreaking book has now been revised to take up where the first edition left off and account for the phenomenal expansion of qualitative inquiry since then.
In this second edition, Miles and Huberman bring the art of qualitative data analysis up to date, adding hundreds of new techniques, ideas and references that draw on the experience of the authors and many colleagues in the design, testing and use of qualitative data analysis methods. Each method of data display and analysis is described and illustrated in detail, with practical suggestions for adaptation and use. The growth of computer use in qualitative analysis is reflected throughout this volume, which also includes an extensive appendix on criteria useful for choosing among the currently available analysis packages.
#3 An Introduction to Categorical Data Analysis
In recent years, the use of statistical methods for categorical data has increased dramatically in a variety of areas and applications. This book provides an applied introduction to the most important methods for analyzing categorical data. It summarizes methods that have long played a prominent role, such as chi-squared tests, but places special emphasis on logistic regression and loglinear modeling techniques.
Special features of the book include:
* Emphasis on logistic regression modeling of binary data and Poisson regression modeling of count data
* A unified perspective, based on generalized linear models, that connects these methods with standard regression methods for normally-distributed data
* An appendix showing the use of a new SAS procedure (GENMOD) for generalized linear modeling that can conduct nearly all methods presented in the book
* An entertaining historical perspective of the development of the methods
* Specialized methods for ordinal data, small samples, multicategory data, and matched pairs
* More than 100 examples of real data sets and more than 200 exercises
Writing in an applied, nontechnical style, Alan Agresti illustrates methods using a wide variety of real data, including alcohol, cigarette, and marijuana use by teenagers; AZT use and delay of AIDS; space shuttle launches and O-ring failure; passive smoking and lung cancer; and much more. An Introduction to Categorical Data Analysis is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.
#4 Data Analysis: A Bayesian Tutorial
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis.
This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagation, hypothesis testing, maximum entropy and experimental design.
The Second Edition of this successful tutorial book contains a new chapter on extensions to the ubiquitous least-squares procedure, allowing for the straightforward handling of outliers and unknown correlated noise, and a cutting-edge contribution from John Skilling on a novel numerical technique for Bayesian computation called ‘nested sampling’.
#5 A Beginner-s Guide to Structural Equation Modeling, Second Edition
This book’s strength is that it carefully walks the reader through all steps required to conduct an SEM analysis. Checklists are provided for each step, from model specification through validation. The accompanying CD provides all of the programs needed to do the analysis, with accompanying screen shots and directions in the book to allow the reader to self-teach the software application. This book would serve well as either a reference text or a required text for a course.
#6 Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation e.V., Freie Universität Berlin, March 8-10, … Data Analysis, and Knowledge Organization)
The book focuses on exploratory data analysis, learning of latent structures in datasets, and unscrambling of knowledge. It covers a broad range of methods from multivariate statistics, clustering and classification, visualization and scaling as well as from data and time series analysis. It provides new approaches for information retrieval and data mining. Furthermore, the book reports challenging applications in marketing and management science, banking and finance, bio- and health sciences, linguistics and text analysis, statistical musicology and sound classification, as well as archaeology. Special emphasis is put on interdisciplinary research and the interaction between theory and practice.
#7 Designing an Efficient Management System: Modeling of Convergence Factors Exemplified by the Case of Japanese Businesses in Thailand (Contributions to Management Science)
This book makes a significant and valuable contribution to the literature in the fields of organisational behaviour and design, performance analysis and structural equation modelling. The subject of this book is the development of an efficient and effective management system in the globalised world in order to improve overall organizational performance to achieve good corporate governance by reducing agency costs in a cross-cultural environment. Based on an empirical case study of Japanese management practices in Thailand, it examines factors that help to adapt management practices to the work culture of the host country and motivate local employees to adapt and implement unfamiliar management practices. The book provides a new methodological approach by applying structural equation modeling to psychosocial and motivational constructs of organizational performance, and thus presents an innovative behavioral framework of organizations in a contemporary cross-cultural setting.
#8 Statistics for People Who (Think They) Hate Statistics: Excel
Utilizing the personable and clear approach that made Statistics for People Who (Think They) Hate Statistics a bestseller, Neil J. Salkind focuses on the use of Excel as the primary analytic tool in the Second Edition of Statistics for People Who Think They Hate Statistics â Excel 2007 Edition. Salkind walks readers through various statistical procedures, beginning with correlations and graphical representation of data and ending with inferential techniques and analysis of variance. Throughout the book, he reveals the full capabilities of Excel, beginning with Part I, which is an introduction to Excel to the end, where he presents an extensive overview of Excel functionality.
#9 An Introduction to Statistical Methods and Data Analysis
Ott and Longnecker’s AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Sixth Edition, provides a broad overview of statistical methods for readers who have little or no prior experience in statistics. The authors teach readers to solve problems encountered in research projects, to make decisions based on data in general settings, and to become critical readers of statistical analyses in research papers and in news reports. The first eleven chapters present material typically covered in a college-level introductory statistics course, as well as interesting case studies and examples. The remaining chapters cover regression modeling and design of experiments.
#10 Multilevel and Longitudinal Modeling with IBM SPSS (Quantitative Methodology Series)
This is the first book to demonstrate how to use the multilevel and longitudinal modeling techniques available inãIBM SPSS Version 18. The authors tap the power ofãSPSSâsãMixed Models routine to provide an elegant and accessible approach to these models. Readers who have learned statistics using this software will no longer have to adapt to a new program to conduct quality multilevel and longitudinal analyses. Annotated screen shots with all of the key output provide readers with a step-by-step understanding of each technique as they are shown how to navigate through the program. Diagnostic tools, data management issues, and related graphics are introduced throughout.ãSPSSãcommands show the flow of the menu structure and how to facilitate model building. Annotated syntax is also available for those who prefer this approach. Most chapters feature an extended example illustrating the logic of model development. These examples show readers the context and rationale of the research questions and the steps around which the analyses are structured.ãThe data used in the text and syntax examples are available atãwww.psypress.com/multilevel-modeling-techniques.
There is, of course, quite a bit of overlap among these books. The overall theme, I think, is that there is a statistics book for everyone.
Happy reading!