DescriptionAt last—a social scientist's guide through the pitfalls of modern statistical computing
Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing.
Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field.
- A focus on problems occurring in maximum likelihood estimation
- Integrated examples of statistical computing (using software packages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS, WinBUGS, and MATLAB®)
- A guide to choosing accurate statistical packages
- Discussions of a multitude of computationally intensive statistical approaches such as ecological inference, Markov chain Monte Carlo, and spatial regression analysis
- Emphasis on specific numerical problems, statistical procedures, and their applications in the field
- Replications and re-analysis of published social science research, using innovative numerical methods
- Key numerical estimation issues along with the means of avoiding common pitfalls
- A related Web site includes test data for use in demonstrating numerical problems, code for applying the original methods described in the book, and an online bibliography of Web resources for the statistical computation
Designed as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field.
1. Introduction: Consequences of Numerical Inaccuracy.
2. Sources of Inaccuracy in Statistical Computation.
3. Evaluating Statistical Software.
4. Robust Inference.
5. Numerical Issues in Markov Chain Monte Carlo Estimation.
6. Numerical Issues Involved in Hessian Matrices (Jeff Gill & Gary King).
7. Numerical Behavior of King's EI Method.
8. Some Details of Nonlinear Estimation (B. D. McCullough).
9. Spatial Regression Models (James P. LeSage).
10. Convergence Problems in Logistic Regression (Paul Allison).
11. Recommendations for Replication and Accurate Analysis.
"Clarity of presentations is excellent. Applied statisticians and computer scientists will like this book and find it very useful." (Journal of Statistical Computation and Simulation, November 2005)
"[The authors] …have succeeded in providing...a good understanding of the potential pitfalls involved in the implementation of methodology computationally, and...good advice on dealing with the problems that can arise." (Statistics in Medical Research, June 2005)
“This book provides the researcher with an overview of the issues involved in the implementation and computation of common statistical procedures….” (Statistical Methods in Medical Research, Vol. 14, 2005)
"…this book is a good reference for social scientists that are involved in computational statistics." (Journal of Statistical Software, April 2005)
"…timely and interesting, and on the whole provides a good balance of theory, application, and computation." (Technometrics, May 2005)
"…an excellent text. It has the potential to be enormously influential across the social sciences…It should be required reading for everyone who performs statistical computing at the advanced level…" (Journal of the American Statistical Association, June 2005)
“…a compact guide to the voluminous literature on optimisation, numerical analysis, and computational statistics. This is no small achievement.” (Statistical Software Newsletter in Computational Statistics and Data Analysis)
"…a very important one for researchers, social scientists, and…graduate and post-graduate students in various disciplines..." (Computing Reviews.com, July 6, 2004)
"This comprehensive research and guidebook by Altman, Gill, and McDonald offers to social scientists modern tools and tricks previously lacking in other works.” (Choice, June 2004, Vol. 41 No. 10)