With SPSS Categories sophisticated procedures in your toolbox, you are no longer hampered by categorical or highly-dimensional data. These techniques ensure you have all the tools you need to easily analyze and interpret your multivariate data and its relationships more completely.
Rely on SPSS Categories whenever you need to:
Visualize how rows and columns of large tables of counts or means relate
Determine how closely customers perceive your products to others in your offering set or your competitors’.
Understand what characteristics consumers relate to most regarding product or brand.
Work with and understand ordinal and nominal data with procedures similar to conventional regression, principal components, and canonical correlation.
Perform regression analysis with a categorical dependent variable.
Unleash the full potential of your data through optimal scaling and dimension reduction techniques
Correspondence analysis (CORRESPONDENCE):
Describe the relationships between two nominal variables in a low-dimensional space, while simultaneously describing the relationships between categories for each variable.
Nonlinear canonical correlation (OVERALS):
Use alternating least squares to generalize canonical correlation analysis. It allows more than one set of variables to be compared to one another on the same graph.
Multiple correspondence analysis (MULTIPLE CORRESPONDENCE):
Analyze a categorical multivariate data matrix when all the variables are analyzed at the nominal level. Similar to correspondence analysis except it doesn’t limit you to only two variables.
Proximity scaling (PROXSCAL):
Takes a matrix of similarity and dissimilarity distances between observations in a high-dimensional space and assigns them to a position in a low-dimensional space in order for you to gain “spatial” understanding of how objects relate.
Categorical Principal Components Analysis (CATPCA):
Use alternating least squares to generalize principal components analysis to accommodate variables of mixed measurement levels. Specify a transformation type of nominal, ordinal, or numeric on a variable-by-variable basis.
Preference scaling (PREFSCAL):
Set up the Preference Scaling procedure (PREFSCAL) in syntax to perform multidimensional unfolding on two sets of objects in order to find a common quantitative scale..