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Predict outcomes and reveal relationships in categorical data

IBM® SPSS® Categories makes it easy to visualize and explore relationships in your data and predict outcomes based on your findings. Using advanced techniques, such as predictive analysis, statistical learning, perceptual mapping and preference scaling, you can understand which characteristics consumers relate most closely to your product or brand, and learn how they perceive your products in relation to others.

SPSS Categories includes advanced analytical techniques to help you:

  • Easily analyze and interpret multivariate data and its relationships more completely.
  • Turn qualitative variables into quantitative ones by performing additional statistical operations on categorical data.
  • Graphically display underlying relationships in whatever types of categories you study, including market segments, medical diagnoses, political parties or biological species.

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.
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