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Procedures and Statistics for Analyzing Categorical Data
Using SPSS Categories with SPSS Base gives you a selection of statistical techniques for analyzing high-dimensional or categorical data.
- Categorical regression (CATREG) predicts the values of a nominal, ordinal, or numerical outcome variable from a combination of categorical predictor variables. Optimal scaling techniques are used to quantify variables.
- Correspondence analysis (CORRESPONDENCE) enables you to analyze two-way tables that contain some measurement of correspondence between the rows and columns. You can then visualize these relationships by using biplots and perceptual maps.
- Multiple correspondence analysis (MULTIPLE CORRESPONDENCE) is used to analyze multivariate categorical data. It differs from correspondence analysis in that it allows you to use more than two variables in your analysis. With this procedure, all the variables are analyzed at the nominal level (unordered categories).
- Categorical principal components analysis (CATPCA) uses optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels. It is similar to multiple correspondence analysis, except that you are able to specify an analysis level on a variable-by-variable basis.
- Nonlinear canonical correlation analysis (OVERALS) uses optimal scaling to generalize the canonical correlation analysis procedure so that it can accommodate variables of mixed measurement levels. This type of analysis enables you to compare multiple sets of variables to one another in the same graph, after removing the correlation within sets.
- Multidimensional scaling (PROXSCAL) performs multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities). Alternatively, you can compute distances between cases in multivariate data as input to PROXSCAL.
- Preference scaling (PREFSCAL) visually examines relationships between variables. Preference scaling performs multidimensional unfolding on two sets of objects in order to find a common quantitative scale.
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