1. Introduction -- Some sample results -- Some background comments -- The central unifying theme of the monograph -- 2. The basic structure of a data matrix -- Basic structure of a matrix -- Transformations -- 3. Principal components analysis -- Single factor example -- Multifactor example -- 4. Multidimensional preference scaling -- 5. Correspondence analysis of contingency tables -- The mechanics of correspondence analysis -- The reconstruction of expected and observed data -- Another perspective: The case approach with indicator variables -- 6. Correspondence analysis of nonfrequency data -- Correspondence analysis of rank-order data -- Correspondence analysis of proximities -- 7. Ordination, seriation, and Guttman scaling -- The horseshoe effect -- Guttman scaling as a special case of correspondence analysis -- An invariance property of multiple-way indicator matrices -- 8. Multiple correspondence analysis -- Multiple comparisons using "stacked" matrices -- A few final words
