SPSS Categories

Predicción de resultados e identificación de relaciones
en datos categóricos

IBM SPSS Categories le proporciona todas las herramientas necesarias para obtener una idea clara de los datos numéricos y categóricos complejos, así como de los datos de grandes dimensiones.

Utilice IBM SPSS Categories para entender qué características relacionan los clientes con su marca, o para determinar la percepción que tiene el cliente de sus productos en comparación con otros productos que ofrece su empresa o la competencia.

• Descubra relaciones subyacentes a través de mapas perceptivos, diagramas bidimensionales y tridimensionales
• Trabaje y entienda datos nominales (por ejemplo, sexo) y ordinales (por ejemplo, nivel educativo) con procedimientos similares a la regresión convencional, los componentes principales y la correlación canónica para prever resultados e identificar relaciones
• Interprete visualmente conjuntos de datos y compruebe cómo se relacionan filas y columnas en grandes tablas de puntuaciones, recuentos, valoraciones, clasificaciones y similitudes
• Trate los residuos no normales en datos numéricos o relaciones no lineales entre las variables de predictor (por ejemplo, atributos de cliente o producto) y la variable de resultados (por ejemplo, compra/no compra)
• Utilice la regresión contraída, el lazo, la red elástica, la selección de variables y la selección de modelos para los datos numéricos y categóricos
• Sistemas operativos admitidos: Windows, Mac, Linux

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• 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 and triplots enable you to look at the relationships among cases, variables and categories. For example, you can define relationships between products, customers and demographic characteristics.

• Use optimal scaling procedures to assign units of measurement and zero-points to your categorical data

• Choose from state-of-the art procedures for model selection and regularization

• 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

• Quantify your ordinal and nominal variables with an optimal scaling correlation matrix

• Use nonlinear canonical correlation analysis to incorporate and analyze variables of different measurement levels

• Categorical regression that predicts the values of a nominal, ordinal or numerical outcome variable from a combination of categorical predictor variables. Optimal scaling techniques are used to quantify variables. Three regularization methods: Ridge regression, the Lasso and the Elastic Net, improve prediction accuracy by stabilizing the parameter estimates.

• Correspondence analysis that enables you to 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.

• Multiple correspondence analysis which is used to analyze multivariate categorical data by allowing the use of more than two variables in your analysis. With this procedure, all the variables are analyzed at the nominal level (unordered categories).

• Categorical principal components analysis uses optimal scaling to generalize the principal components analysis procedure so that it can accommodate variables of mixed measurement levels.

• Nonlinear canonical correlation analysis uses optimal scaling to generalize the canonical correlation analysis procedure so that it can accommodate variables of mixed measurement levels. This type of analysis enables you to compare multiple sets of variables to one another in the same graph, after removing the correlation within sets.

• Multidimensional scaling performs multidimensional scaling of one or more matrices with similarities or dissimilarities (proximities).

• Preference scaling visually examines relationships between two sets of objects, for example, consumers and products. Preference scaling performs multidimensional unfolding in order to find a map that represents the relationships between these two sets of objects as distances between two sets of points.