Choice: Current Reviews for Academic Libraries, V41 #10, June 2004. P. 1916. 41-5950 QA276 2003-53470 CIP Extensive literature exists on statistical computing in many area, but it has been noticeably lacking in the social sciences until now. This comprehensive research and guidbook b Altman (Harvard-MIT Data Center), Gill (political science, Univ. of California, Davic), and McDonald (government and politics, Geroge Mason Univ.) offers to social scientists modern tools and tricks previously lacking in other works. They focus primarily on problems arising in maximum likelihood estimation and nonlinear regression. A diversity of topics, such as ecological inference, spatial analysis, logistic regression, and Markov chain [sic] Monte Carlo are also addresses. Readers should have some background in programming, such as writing likelihood functions in Gauss, R, or Strata [sic]; coding solutions in WinBUGS, and so forth. In addition, readers should have reasonable knowledge in statistics, matrix algebra, and calculus. An extensive bibliography and helpful Web site are also provided. Summing Up: Highly recommended. Upper-division undergraduates through professionals. D.J. Gougeon, University of Scranton