SPSS Categories

カテゴリー・データの結果を予測し、関係性を解明

IBM® SPSS® Categories には、複雑なカテゴリー・データと数値データ、そして高次元データを明確に把握するのに必要なツールがすべて含まれています。

IBM SPSS Categories を使用すると、消費者がブランドにどのような特徴を最も深く関連付けているのかを理解できます。あるいは、競合製品と比較して製品がどのように認識されているのかを把握できます。

• 知覚マップ、バイプロットおよびトリプロットを使用して、隠された関係性を提示します。
• 従来の回帰分析、主成分分析、正準相関分析に類するプロシージャーで名義型 (例えば給料) および順序型 (例えば教育レベル) のデータを扱い、理解して、結果を予測し関係性を解明します。
• スコア、度数、格付け、順位、または類似性を示した大きなテーブルで行と列の関係性を見ることができ、データ・セットを視覚的に把握できます。
• 数値データの非標準残差、または予測変数 (例えばお客様または製品の属性) と目的変数 (例えば購入する/購入しない) の間の非線形関係を扱います。
• Ridge 回帰、Lasso、Elastic Net、変数選択、そして数値データとカテゴリー・データの両方のモデル選択を使用できます。
• サポートされるオペレーティング・システム: 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.