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SPSS Categories Data Analysis
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 assistants, but assistants do not differ from senior managers. You
also might find that heavy smoking is associated with junior managers whereas light smoking is associated with assistants.
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Researchers studied the images of six brands of iced coffee sold in South Australia. Brands are
denoted AA to FF and are characterized by various attributes. The Correspondence procedure in SPSS Categories 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
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Key words: spss, data, set, spss, file, spss, data, files, spss, data, sets, spss, files, spss, data, file, spss
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