This anthology focuses on the behavioral study of political ideology and its connection to public policy formulation. The public policy implications of ideology, largely ignored by textbooks on ideology, have never been the purview of a single scholarly community. Instead, this area encompasses several specialties of political science, public administration, and economics. Across these disciplines the behavioral study of political ideology appears fragmented. This book is intended to pull these pieces together by showing the role of ideology in policy formulation and demonstrating methods by which the ideology-public policy relationship can be studied.

Author William G. Jacoby explores a variety of graphical displays that are useful for visualizing multivariate data. The basic problem involves representing information that varies along several dimensions when the display medium (a computer screen or printed page) is inherently two-dimensional. In order to address this problem, Jacoby introduces the concepts of a "data space." He then explains several methods for coding information directly into the plotting symbols used to represent the observations. He next describes pictorial representations of three-dimensional space followed by a discussion of the scatterplot matrix as a way of "flattening out" the multiple dimensions of a multivariate data space. In addition, he examines conditioning plots (which are strategies for "looking into subregions" of the multidimensional data space), and presents the biplot as a technique for showing observations and variables together in a single display. He concludes with a discussion of some general ideas about data visualization. Statistical Graphics for Visualizing Multivariate Data will enable researchers to better explore the contents of a dataset, find the structure in their data, check the underlying assumptions of the statistical model they used, and better communicate the results of their analysis.

Author William G. Jacoby focuses on graphical displays that researchers can employ as an integral part of the data analysis process. Such visual depictions are frequently more revealing than traditional, numerical summary statistics. Accessibly written, this book contains chapters on univariate and bivariate methods. The former covers histograms, smoothed histograms, univariate scatterplots, quantile plots, box plots, and dot plots. The latter covers scatterplot construction guidelines, jittering for overplotted points, marginal box plots, scatterplot slicing, the Loess procedure for nonparametric scatterplot smoothing, and banking to 45 degrees for enhanced visual perception. This book provides strategies for examining data more effectively. The resultant insights help researchers avoid the problem of forcing an inaccurate model onto uncooperative data and guide analysts to model specifications that provide accurate representations of empirical information.

For many readers, data theory is probably unfamiliar. Data isn't usually the subject matter of theory in and of itself. However, in this volume, William Jacoby introduces a theory of data idea. It examines how real world observations are transformed into something to be analyzed that is, data. Jacoby explores some of the basic ideas of data theory, and considers their implications for research strategies in the social sciences. "Like others in the series, it is reassuringly slim. It is intended for a general social science readership and is a worthwhile read even for experienced data analysts. since it draws attention not only to often overlooked assumptions, but also to often ignored analysis possibilities." --Telephone Surveys "On the whole, this book contains a lot of useful information." --Journal of Classification