Unleash the full potential of your data through perceptual mapping, optimal scaling, preference scaling, and dimension reduction techniques. SPSS Categories provides you with all the tools you need to obtain clear insight into complex high-dimensional or categorical data.
You can visually interpret datasets and see how rows and columns relate in large tables of counts, ratings, or rankings. 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.
- Perform regression using nominal or ordinal categorical outcome variables
Turn qualitative variables into quantitative ones
The advanced procedures available in SPSS Categories enable you to perform additional statistical operations on categorical data.
- Use SPSS Categories’ optimal scaling procedures to assign units of measurement and zero-points to your categorical data
- 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
- Use nonlinear canonical correlation analysis to incorporate and analyze variables of different measurement levels
Graphically display underlying relationships
SPSS Categories’ dimension reduction techniques enable you to clarify relationships in your data by using perceptual maps and biplots.
- 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 enable you to look at the relationships among cases, variables, and categories. For example, you can define relationships between products, customers, and demographic characteristics.
The new preference scaling feature enables you to further visualize relationships among objects. The breakthrough algorithm on which this procedure is based enables you to perform non-metric analyses for ordinal data and obtain meaningful results.