Book Review: The SAGE Handbook of Quantitative Methodology for the Social Sciences

As the saying by Abraham Maslow goes, ‘‘If the only tool you have is a hammer, you tend to see every problem as a nail. ’’ With this in mind, a book that characterizes the heterogeneity in both problems (i.e., research questions and data structure) and the available tools to solve these problems (i.e., quantitative methodologies) is the sort of resource that should sit on the bookshelf of any social science researcher. Yet mapping the terrain of quantitative methodology in the social sciences is not so straightforward. We might suspect that if a panel of the top methodologians across social science disciplines were convened for this task, finding consensus would be elusive, with each panel member looking for validation of his or her own areas of specialization. What makes the task so challenging is the necessity, on one hand, of providing a comprehensive and accessible introduction to a broad range of methodological approaches while, on the other hand, integrating the collection of approaches within a larger taxonomy. David Kaplan has in fact convened a panel of top-notch methodologians, who take on the aforementioned challenge in the writing of The SAGE Handbook of Quantitative Methodology for the Social Sciences (SHQM). The result is an engrossing collection of chapters that are sure to add screwdrivers, wrenches, and the occasional buzzsaw to your toolbox. That the book falls a bit short of developing an organizing structure for the toolbox may not be so much an indictment of the editor and the authors but a reflection of the state of the field. The SHQM consists of 24 chapters grouped into six sections. For a compact summary of the structure of the SHQM, see Table 1. The rows of Table 1 correspond to different statistical models and topics presented in the book, as organized into sections. The first section (chapters 1-3) is on the topic of scaling. These three chapters present dual scaling (Nishisato), multidimensional scaling (Heiser and Busing), and extensions to principal components analysis (Meulman, Van der Kooij, and Heiser) as fundamental methods for the quantification of qualitative observations. The second section (chapters 4-7) is on the topic of testing and measurement. These four chapters present an overview of both classical test theory and item response theory within the context of reliability and validity theory (Zumbo and Rupp), an algorithmic approach to the choice of a taxonomy of unidimensional or multidimensional item response models (Nandakumar and Ackerman), an overview of differential item functioning analysis (Roussos and Stout), and a survey of one of the unique advantages of item response modeling, computerized adaptive testing (Chang). The third section (chapters 8-11) deals with models for categorical data. These four chapters include an overview of trends in categorical data analysis (Rindskopf), followed by presentations on ordinal regression models (Johnson and Albert), latent class models (Magidson and Vermunt), and survival analysis (Willett and Singer). The fourth section (chapters 12-15) deals with models for multilevel data. These four chapters present applications of hierarchical linear models in the context of growth modeling (Hedeker), school effectiveness research