Visualization of Categorical DataA unique and timely monograph, Visualization of Categorical Data contains a useful balance of theoretical and practical material on this important new area. Top researchers in the field present the books four main topics: visualization, correspondence analysis, biplots and multidimensional scaling, and contingency table models. This volume discusses how surveys, which are employed in many different research areas, generate categorical data. It will be of great interest to anyone involved in collecting or analyzing categorical data. * Correspondence Analysis * Homogeneity Analysis * Loglinear and Association Models * Latent Class Analysis * Multidimensional Scaling * Cluster Analysis * Ideal Point Discriminant Analysis * CHAID * Formal Concept Analysis * Graphical Models |
Contents
| 1 | |
| 13 | |
Correspondence Analysis | 107 |
Multidimensional Scaling and Biplot | 325 |
Visualization and Modeling | 421 |
| 541 | |
About the Authors | 575 |
| 587 | |
Color Plate Section | 595 |
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Common terms and phrases
Agalev analyzed applied approximation art world associated axes axis biplot cantons categorical data categorical variables category points cell centroids Chapter chi-squared CLPs cluster color column points concept lattice conditional independence conditional probabilities contingency table contribution correlation correspondence analysis data analysis data set defined dependence dimensions distances distribution E-mail election estimates example facet Figure frequencies given graph graphical display Greenacre groups ideal point independence indicator individuals inertia interaction interpretation latent budget latent class analysis latent class model line diagram log-linear log-linear model marginal markers matrix measure methods modalities multidimensional multivariate node NSCA observed obtained odds ratios parameters party patterns plot political positions prediction regions predictor question representation represented respectively response categories response variable risk rows and columns sample scores shows similar similarity matrices social solution space split statistical structure three-way two-way vectors visualization votes