This book presents a set of closely-related techniques that facilitate the visual exploration and display of a wide variety of multivariate data, both categorical and continuous. Three methods of metric scaling - correspondence analysis, principal components analysis and multiple dimensional preference scaling - are explored in detail for their strengths and weaknesses over a wide range of data types and research situations. The book focuses upon representing the relations among two or more sets of variables, and upon applications that are exploratory in nature rather than predictive.