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This is an incomparably useful examination of statistical methods for comparison. The nature of doing science, be it natural or social, inevitably calls for comparison. Statistical methods are at the heart of such comparison, for they not only help us gain understanding of the world around us but often define how our research is to be carried out. The need to compare between groups is best exemplified by experiments, which have clearly defined statistical methods. However, true experiments are not always possible. What complicates the matter more is a great deal of diversity in factors that are not independent of the outcome. "Statistical Group Comparison" brings together a broad range of statistical methods for comparison developed over recent years. The book covers a wide spectrum of topics from the simplest comparison of two means or rates to more recently developed statistics including double generalized linear models and Bayesian as well as hierarchical methods. The coverage includes: testing parameter equality in linear regression and other generalized linear models (GLMs), in order of increasing complexity; likelihood ratio, Wald, and Lagrange multiplier statistics examined where applicable; group comparisons involving latent variables in structural equation modelling; models of comparison for categorical latent variables; examples are drawn from the social, political, economic, and biomedical sciences; and many examples can be implemented using widely available software. Because of the range and the generality of the statistical methods covered, researchers across many disciplines - beyond the social, political, economic, and biomedical sciences - will find the book a convenient reference for many a research situation where comparisons may come naturally.
