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, WinBUGS, and MATLAB (R))
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.
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