SPSS Statistics - Categories

Easily analyze and interpret multivariate data

• Use categorical regression procedures to predict the values of a nominal, ordinal or numerical outcome variable from a combination of numeric and (un)ordered categorical predictor variables.
• Quantify the variables to maximize the Multiple R with optimal scaling techniques.
• Clearly see relationships in your data using dimension reduction techniques such as perceptual maps and biplots.
• Gain insight into relationships among more than two variables with summary charts that display similar variables or categories.

Turn qualitative variables into quantitative ones

• Predict the values of a nominal, ordinal or numerical outcome variable from a combination of categorical predictor variables.
• Analyze two-way tables that contain some measurement of correspondence between rows and columns, as well as display rows and columns as points in a map. Also analyze multivariate categorical data by allowing the use of more than two variables in your analysis.
• Use optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels.
• Compare multiple sets of variables to one another in the same graph after removing the correlation within sets, and visually examine relationships between two sets of objects; for example, consumers and products.
• Perform multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities).

Graphically display underlying relationships

• Place the relationships among your variables in a larger frame of reference with optical scaling.
• Create perceptual maps that graphically display similar variables or categories close to each other for unique insights into relationships between more than two categorical variables.
• Use biplots and triplots to look at the relationships among cases, variables and categories; for example, to define relationships between products, customers and demographic characteristics.
• Further visualize relationships among objects using preference scaling, which helps you perform non-metric analyses for ordinal data and obtain more meaningful results.
• Analyze similarities between objects and incorporate characteristics for objects in the same analysis.