SPSS Data Preparation

データの準備を改善し、より正確な結果を

IBM® SPSS® Data Preparation を使用すると、分析者は先進的な手法を活用して分析プロセスのデータ準備段階を合理化することができます。

調査を行う人であれば誰でも、分析に先立って、データを準備する必要があります。IBM SPSS Statistics Base にもデータ準備のための基本ツールが含まれていますが、より正確な分析と結果を得ることができるように、IBM SPSS Data Preparation はデータ準備に特化した手法を提供します。

  • 疑わしいあるいは無効なケース、変数、データ値を素早く特定します。
  • 欠損データのパターンを表示します。
  • 変数の分布を要約します。
  • 名義型データを最適カテゴリー化します。
  • データをより正確に分析用に準備します。
  • 自動データ準備 (ADP) を使用して、質的エラーの検出と修正、および欠損値の補完を 1 ステップで効率的に行います。
  • 推奨と視覚化を行って、どのデータを使用するか決定するのに役立てます。
  • 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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Click image for full-sized versionExpand your Data Preparation Techniques with IBM SPSS Data Preparation

Use the specialized data preparation techniques in IBM SPSS Data Preparation to facilitate data preparation in the analytical process. IBM SPSS Data Preparation easily plugs into IBM SPSS Statistics Base so you can seamlessly work in the IBM SPSS environment.

Perform Data Checks
Data validation has typically been a manual process. You might run a frequency on your data, print the frequencies, circle what needs to be fixed and check for case IDs. This approach is time consuming and prone to errors. And since every analyst in your organization could use a slightly different method, maintaining consistency from project to project may be a challenge.

To eliminate manual checks, use the IBM SPSS Data Preparation Validate Data procedure. This enables you to apply rules to perform data checks based on each variable's measure level (whether categorical or continuous).

For example, if you're analyzing data that has variables on a five-point Likert scale, use the Validate Data procedure to apply a rule for five-point scales and flag all cases that have values outside of the 1-5 range. You can receive reports of invalid cases as well as summaries of rule violations and the number of cases affected. You can specify validation rules for individual variables (such as range checks) and cross-variable checks (for example, "retired 30 year-olds").

With this knowledge you can determine data validity and remove or correct suspicious cases at your discretion before analysis.

Quickly Find Multivariate Outliers
Prevent outliers from skewing analyses when you use the IBM SPSS Data Preparation Anomaly Detection procedure. This searches for unusual cases based upon deviations from similar cases, and gives reasons for such deviations. You can flag outliers by creating a new variable. Once you have identified unusual cases, you can further examine them and determine if they should be included in your analyses.

Pre-process Data before Model Building
In order to use algorithms that are designed for nominal attributes (such as Naïve Bayes and logit models), you must bin your scale variables before model building. If scale variables aren't binned, algorithms such as multinomial logistic regression will take an extremely long time to process or they might not converge. This is especially true if you have a large dataset. In addition, the results you receive may be difficult to read or interpret.

IBM SPSS Data Preparation Optimal Binning, however, enables you to determine cutpoints to help you reach the best possible outcome for algorithms designed for nominal attributes.
With this procedure, you can select from three types of binning for pre-processing data:

  • Unsupervised -- create bins with equal counts

  • Supervised -- take the target variable into account to determine cutpoints. This method is more accurate than unsupervised; however, it is also more computationally intensive.

  • Hybrid approach -- combines the unsupervised and supervised approaches. This method is particularly useful if you have a large number of distinct values.

We recommend complementing the features of IBM SPSS Data Preparation with those found in IBM SPSS Statistics Base.

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SPSS Data Preparation