Understand groupings using perceptual maps to predict categorical outcomes

Analyse your data more completely using SPSS Categories®. This add-on module for SPSS gives you what's needed to enhance your research and learn more about your categorical data. Whatever types of categories you study — market segments, subcultures, political parties or biological species — SPSS Categories' optimal scaling procedures free you from the restrictions of two-way tables, placing the relationships among your variables in a larger frame of reference. You can see a map of your data — not just a statistical report. SPSS Categories' high-resolution summary charts give you unique insight into relationships between more than two variables.

The data are a 2x5x6 table containing information on two genders, five age groups and six products. This plot shows the results of a two-dimensional multiple Correspondence Analysis of the table. Notice that products such as "A" and "B" are chosen at younger ages and by males, while products such as "G" and "C" are preferred at older ages.

SPSS Categories has the procedures you need get the most from your multivariate data analysis. These procedures include:

• Categorical Regression:  quantify categorical data by assigning numerical values to categories, resulting in an optimal linear regression equation for transformed variables

• Correspondence Analysis:  analyse two-way contingency tables or data that can be expressed as a two-way table, such as brand preferences or sociometric choice data

• And more

More statistics for data analysis

Expand SPSS® Base's capabilities for the data analysis stage in the analytical process. Using SPSS Categories® with SPSS Base gives you an even wider range of statistics so you can get the most accurate response for predicting categorical outcomes. It easily plugs into SPSS Base so you can seamlessly work in the SPSS environment.

Statistical highlights for SPSS Categories:

Categorical Regression:  quantify categorical data by assigning numerical values to categories, resulting in an optimal linear regression equation for transformed variables. You could use Categorical Regression to describe how customer satisfaction depends on ease of purchase, price and quality. The resulting equation can be used to predict customer satisfaction for any combination of the three independent variables.

Correspondence Analysis:  analyze two-way contingency tables or data that can be expressed as a two-way table, such as brand preferences or sociometric choice data. Correspondence analysis describes the relationship between two nominal variables in a low-dimensional space, while simultaneously describing the relationship between categories for each variable. For example, you can use Correspondence Analysis to graphically display the relationship between staff category and smoking habits. You might find, with regard to smoking, junior managers differ from secretaries, but secretaries do not differ from senior managers. You also might find that heavy smoking is associated with junior managers, whereas light smoking is associated with secretaries.

Researchers studied the images of six brands of iced coffee sold in South Australia. Brands are denoted AA to FF and characterized by various attributes. SPSS' Correspondence procedure produced the correspondence map shown in this figure. Brand AA, the market leader, is near the "popular" attribute. CC and DD target consumers interested in health and low-fat products. FF is perceived as a rich, sweet premium brand. (Source for data and example: Kennedy, R., Riquier, C. and Sharp Byron. 1996. "Practical Applications of Correspondence Analysis to Categorical Data in Market Research," Journal of Targeting, Measurement and Analysis for Marketing, Vol. 5, No. 1, pp. 56-70.)

SPSS Categories gives you:

• Multi-Dimensional Scaling of Proximity Data

• Principal Components Analysis

• Correspondence Analysis

• Categorical Regression Analysis via optimal scaling

• Homogeneity Analysis via alternating least squares (also known as Multiple Correspondence Analysis)

• Canonical Correlation Analysis of two or more sets of variables via alternating least squares

SPSS Categories® system requirements

• SPSS® Base 

• 1MB hard drive space

• Other system requirements vary according to platform