Analyzing Spatial Models of Choice and Judgment

"This book will have broad appeal across the social sciences, but especially in political science and psychology. An obvious audience is scholars doing work in attitudinal scaling, or psychometrics. However, the application of spatial models of the sort addressed in this text is certainly not limited to survey data or other types of data for which people are the units of analysis. These methods can be used to assess and describe the structure of relationships between variables or units wherever such relationships can be conceptualized as distances in some abstract space. I expect that this book will be used mostly as a reference guide, but only because courses in spatial models of this sort are (unfortunately) fairly limited. However, more advanced courses in multivariate analysis, latent variable modeling, dimensional analysis, and measurement across the social sciences would likely find this text extremely useful. (Adam Enders, University of Louisville)

"This book provides excellent coverage of spatial models of choice and judgment...Overall the manuscript is technically correct and clearly written. The biggest strength of the book is the deliberately informal and applied nature of the approach of the book, where both code and output are shown. This makes it very easy for researchers to quickly get these models running on their own data quickly." (James Lo, USC)

"I find the manuscript technically sound, clearly written, and at an appropriate level of difficulty for quantitative social scientists. It has several strengths. First, it is a comprehensive and up-to-date survey of spatial models for scaling preferential and perceptual data (including dyadic data measuring similarities/distances). Second, it is replete with interesting examples from political science, which greatly increases the readability of the material. Third, by including many chunks of R code for data analysis and visualization, it greatly reduces barriers to implementing these methods for practitioners." (Xiang Zhou, Harvard University)