At 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.
Highlights include:
* 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, Win BUGS, 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.
Inhoudsopgave
Preface.
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. Mc Cullough).
9. Spatial Regression Models (James P. Le Sage).
10. Convergence Problems in Logistic Regression (Paul Allison).
11. Recommendations for Replication and Accurate Analysis.
Bibliography.
Author Index.
Subject Index.
Over de auteur
MICAH ALTMAN is Associate Director of the Harvard-MIT Data
Center in Cambridge, Massachusetts.
JEFF GILL is Associate Professor of Political Science at
the University of California, Davis.
MICHAEL P. Mc DONALD is Assistant Professor of Government
and Politics at George Mason University in Fairfax, Virginia.
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