Planning, Construction, and Statistical Analysis of Comparative Experiments
from “I don’t want to hear about this,” and “Oh my God!” to “I’m confused.” Colleagues of a certain age, those who remember punching Fortran code onto cards or tape, were not surprised at all. Another colleague said “These issues are completely unknown to us.” The text provides thorough coverage of the numerical issues that a social scientist will face. The context provided by a good number of specific applications is particularly helpful in not only emphasizing that the consideration of numerical issues is important, but also in motivating the reader to think about numerical issues in his or her own applications. The writing is generally clear (despite somewhat frequent typos), and the focus on estimation problems is appropriate for most fields in the social sciences. My major disappointment with this book is that it misses its target audience by a wide mark. As the authors outline in the Preface, this book will be useful primarily for graduate students and practitioners in quantitative fields. Indeed, it fills a hard-felt gap between our statistical training and our daily research activities. But quantitative social scientists are a very small minority among social scientists. Most social scientists have not taken any formal statistics courses more advanced than analysis of variance, and many of those courses are offered by their own departments, so issues of concern to statisticians are completely foreign to them. For example, many users of ANOVA techniques were never taught that ANOVA actually fits a model to their data. What these users are looking for is a p value, and they will ignore much of the output provided by SAS or SPSS. These are the social scientists who most need to know about the issues raised in this text, and they will not be able to read past page 7, where the authors present “a simple Poisson generalized linear model with a log link function.” It is perhaps not the authors’ goal to reach this audience, the title of their text notwithstanding. However, the treatment of technical information is sufficiently uneven to make this a rough read for even a quantitative audience. Section 5.3, for example, presents Markov chain theory at a very advanced level, which I think is completely unnecessary and detracts from the goals of the chapter. A text aimed at the typical social scientist, even the quantitative social scientist, does not need to present probability in topological terms. In my final analysis, Numerical Issues in Statistical Computing for the Social Scientist this is an excellent text. It has the potential to be enormously influential across the social sciences, but at present only fairly sophisticated quantitative modelers will be able to appreciate it. For those modelers, this text will be invaluable. It should be required reading for everyone who performs statistical computing at an advanced level. Chapters 1 and 2 should be required reading for anyone who intends to publish the results of a statistical analysis using a commercial statistics package.