SPSS Categories 17.0

Categories 17.0
Predict outcomes and reveal relationships in categorical data
Unleash the full potential of your data through predictive analysis, statistical learning, perceptual mapping, preference scaling, and dimension reduction techniques—including optimal scaling of your variables. SPSS Categories provides you with all the tools you need to obtain clear insight into complex categorical and numeric data, as well as high-dimensional data. For example, use SPSS Categories to understand which characteristics consumers relate most closely to your brand, or to determine customer perception of your products compared to other products you or your competitors offer.

You can visually interpret datasets and see how rows and columns relate in large tables of scores, counts, ratings, rankings, or similarities. This gives you the ability to:

• Work with and understand nominal and ordinal data with procedures similar to conventional regression, principal components, and canonical correlation
• Deal with non-normal residuals in numeric data or nonlinear relationships between predictor variables and the outcome variable. You can now use Ridge Regression, the Lasso, the Elastic Net, variable selection, and model selection for both numeric and categorical data.

• Use SPSS Categories’ optimal scaling procedures to assign units of measurement and zero-points to your categorical data
• Choose from state-of-the art procedures for model selection and regularization
• Perform correspondence and multiple correspondence analyses to numerically evaluate similarities between two or more nominal variables in your dataset
• Summarize your data according to important components by using principal components analysis
• Quantify your ordinal and nominal variables with an optimal scaling correlation matrix
• Use nonlinear canonical correlation analysis to incorporate and analyze variables of different measurement levels

• Perceptual maps are high-resolution summary charts that graphically display similar variables or categories close to each other. They provide you with unique insight into relationships between more than two categorical variables.
• Biplots and triplots enable you to look at the relationships among cases, variables, and categories. For example, you can define relationships between products, customers, and demographic characteristics.